Consumption Behaviour and Social Responsibility: A Consumer Research Approach [1st ed.] 9789811530043, 9789811530050

Table of contents :
Front Matter ....Pages i-xxv
Front Matter ....Pages 1-1
Background and Thought (Karnika Gupta, Narendra Singh)....Pages 3-44
Front Matter ....Pages 45-45
An Overview of Literature (Karnika Gupta, Narendra Singh)....Pages 47-113
Conceptual Framework and Research Model (Karnika Gupta, Narendra Singh)....Pages 115-148
Front Matter ....Pages 149-149
Methodological Procedures and Techniques (Karnika Gupta, Narendra Singh)....Pages 151-185
Front Matter ....Pages 187-187
Exploration and Validation of Behavioural–Attitudinal Dimensions (Karnika Gupta, Narendra Singh)....Pages 189-238
Model Specification and Theory Testing (Karnika Gupta, Narendra Singh)....Pages 239-299
Segmentation of Consumers and Identification of Responsibles (Karnika Gupta, Narendra Singh)....Pages 301-321
Characterizing and Profiling Responsible Consumer Segments (Karnika Gupta, Narendra Singh)....Pages 323-392
Front Matter ....Pages 393-393
Findings and Discussions (Karnika Gupta, Narendra Singh)....Pages 395-425
Implications and Research Directions (Karnika Gupta, Narendra Singh)....Pages 427-435
Back Matter ....Pages 437-450

Citation preview

Approaches to Global Sustainability, Markets, and Governance Series Editors: David Crowther · Shahla Seifi

Karnika Gupta Narendra Singh

Consumption Behaviour and Social Responsibility A Consumer Research Approach

Approaches to Global Sustainability, Markets, and Governance Series Editors David Crowther, Faculty of Business and Law, De Montfort University, Leicester, UK Shahla Seifi, University of Derby, Derby, UK

Approaches to Global Sustainability, Markets, and Governance takes a fresh and global approach to issues of corporate social responsibility, regulation, governance, and sustainability. It encompasses such issues as: environmental sustainability and managing the resources of the world; geopolitics and sustainability; global markets and their regulation; governance and the role of supranational bodies; sustainable production and resource acquisition; society and sustainability. Although primarily a business and management series, it is interdisciplinary and includes contributions from the social sciences, technology, engineering, politics, philosophy, and other disciplines. It focuses on the issues at a meta-level, and investigates the ideas, organisation, and infrastructure required to address them. The series is grounded in the belief that any global consideration of sustainability must include such issues as governance, regulation, geopolitics, the environment, and economic activity in combination to recognise the issues and develop solutions for the planet. At present such global meta-analysis is rare as current research assumes that the identification of local best practice will lead to solutions, and individual disciplines act in isolation rather than being combined to identify truly global issues and solutions.

More information about this series at http://www.springer.com/series/15778

Karnika Gupta Narendra Singh •

Consumption Behaviour and Social Responsibility A Consumer Research Approach

123

Karnika Gupta Department of Commerce Kurukshetra University Kurukshetra, Haryana, India

Narendra Singh Department of Commerce Kurukshetra University Kurukshetra, Haryana, India

SNRL Jairam Girls College Kurukshetra, Haryana, India

ISSN 2520-8772 ISSN 2520-8780 (electronic) Approaches to Global Sustainability, Markets, and Governance ISBN 978-981-15-3004-3 ISBN 978-981-15-3005-0 (eBook) https://doi.org/10.1007/978-981-15-3005-0 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

I devote this book to my adoring parents, my father Mr. Sudesh Kumar Gupta and mother Mrs. Geeta Gupta. Also, on behalf of my co-author and on my personal behalf, I dedicate this work to humanity and ecology.

Karnika Gupta

Preamble

With the advent of sustainable development goals (SDGs), the time in this modern century has become the occasion for considering many of the areas of human concern in general, and global environment in particular. The rationale for gaining this attention towards sustainable development is the deleterious social/ environmental consequences of both production and consumption. It has been established that sustainable development can be achieved with the ability of masses to meet the present needs without compromising the stake of future generations. This ability is popularized with the name of sustainability, and requires that society itself, within and among nations, becomes a steward of the planet. In this manner, it can be said that vision of sustainable development requires sustainability, which demands stewardship, and stewardship is about a special level of dedication and commitment that can convert the thought of sustainable development into a reality. This perspective motivated us to examine whether the masses are acting as, or has the potential to become so-called stewards. Therefore, a research is conducted which is fabricated in the shape of this book. Being concerned with the subject of Marketing, the emphasis is on the power of common man established as consumers in marketing parlance. It is considered that consumers, who operate by the process of consumption behaviour, can become stewards of the planet, if responsibility intermingles in their routine life. With these notions, it is justified to study their consumption behaviour together with the concept of social responsibility, as entitled Consumption Behaviour and Social Responsibility. To make the text reader-friendly, it is structured into five parts followed by Annexure. Part I—Introduction: This part comprises Chap. 1 Background and Thought which explores the backdrop and worth of the topic. Part II—Review of Literature: In this part, the conceptual standing of the study is provided by describing literature in Chap. 2 An Overview of Literature, and by contently analysing it in Chap. 3 Conceptual Framework and Research Model. This part also clarifies the objectives and hypotheses.

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Preamble

Part III—Research Methodology: This consists of Chap. 4 Methodological Procedures and Techniques which is based on the statistical means and mechanisms of attaining the objectives. Part IV—Analyses and Interpretations: Here, gathered data are analysed in distinct manners and the outcomes are described in four chapters. Chapter 5 Exploration and Validation of Behavioural–Attitudinal Dimensions. Chapter 6 Model Specification and Theory Testing. Chapter 7 Segmentation of Consumers and Identification of Responsibles. Chapter 8 Characterizing and Profiling Responsible Consumer Segments. Part V—Conclusions and Practicality: After analyses of data, this part concludes the results in Chap. 9 Findings and Discussions, and takes a practical stance in Chap. 10 Implications and Research Directions. Annexure: The Questionnaire is shown in annexure for familiarizing the reader with research instrument.

Acknowledgements

Firstly, I am grateful to Omnipresent God, the supreme natural power for showering upon me all the blessings, and for illuminating my path at each step in completing this book. I express my deep love and affection to my sweet–smart family; my respected grandparents (Mr. Ratan Lal Gupta and Mrs. Kaushalya Gupta); affectionate father and mother (Mr. Sudesh Kumar Gupta and Mrs. Geeta Gupta); loving brother (Mr. Rajesh Gupta); beloved husband (Mr. Ishu Garg); and all concerned uncles, aunts, cousins for sustaining me in my thick and thins. If I will not be backed by the power of their love and strength, I would be nothing and nobody in this world. Many gracious personalities helped me, motivated me, and shared with me their research experiences, knowledge, and insights. I know that only by uttering a few words of thanks, I cannot repay their debt; but, it is an opportunity for me to express my thankfulness to these admirable personalities. I would like to place on record the commendable guidance, enthusiasm, and inspiration provided to me by Dr. Narendra Singh, Professor, Department of Commerce, Kurukshetra University. Sir provided me with the rich benefits of his life’s experiences, always remained open to my views, and motivated me to perform the best of my abilities. Words cannot express even a little bit of my gratitude to him for each single contribution he made in this work. I feel proud to have him as my co-author in this book. My heartily regards to Dr. S. C. Davar, Professor (Retired), Department of Commerce, Kurukshetra University for always helping me with a smiling face, and giving me his valuable time whenever required. One cannot pay back the debt to one’s teachers. Narendra Sir and Davar sir both have remained my teachers, and beyond their academic support, I am grateful to both these legends for their entrenched blessings they extended towards me for my bright future. I appreciate Dr. C. R. Darolia, Professor, Department of Psychology and Dr. R. C. Dalal, Professor, University School of Management, Kurukshetra University for their valuable contributions.

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Acknowledgements

I extend my thanks to Prof. Neelam Dhanda, Prof. Ajay Suneja, Prof. Tejinder Sharma, Prof. Mahabir Narwal, and Prof. Subhash Chand of Department of Commerce, Kurukshetra University for being so nice, co-operative, and compassionate. I know it is not sufficient, but with deep heart, I show gratefulness to Dr. Rashmi Chaudhary, Assistant Professor, Department of Commerce for her interminable assistance in every manner which cannot be reimbursed. The role played by Prof. David Crowther, Faculty of Business and Law, De Montfort University, Leicester, United Kingdom is highly appreciable. Sir communicated his valuable suggestions through e-mails. I acknowledge my indebtedness to various authors and research academics, whose research papers, articles, and reports are being cited. All the respondents of this research work too need special thanks due to their time and valuable responses to the questionnaire. Last but not the least, I thank all other stakeholders who either directly or indirectly contributed to this work.

Karnika Gupta

Contents

Part I 1

Introduction

Background and Thought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Environmental Thought: Evolution and Escalation . . . . . . . 1.1.1 Issue of Environmental Sustainability—A Glance of Its Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Environment Concise and Awareness—A New Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Steps for Sustainable Development: Key Initiatives . . . . . . . 1.2.1 Towards Sustainability—International Landmarks . . 1.2.2 India Towards Sustainability—Some Evidence . . . . 1.3 Environment and Marketing: Integration and Relationship . . 1.3.1 Stages of Marketing Concepts—A Fleeting Look . . 1.3.2 Marketing—In Consideration with Environment . . . 1.4 The Twofold Role of Consumers: Destroyers or Developers 1.4.1 Consumers—Appearance as Destroyers . . . . . . . . . 1.4.2 Consumers—Expected Developers . . . . . . . . . . . . 1.5 Consumption Behaviour and Budding Environmental Challenges: A Factual Attempt . . . . . . . . . . . . . . . . . . . . . 1.6 Concepts: Defining and Measurements . . . . . . . . . . . . . . . . 1.6.1 Consumption Behaviour—Meaning and Definitions 1.6.2 Social Responsibility—Meaning and Definitions . . 1.7 Corporate Social Responsibility (CSR): An Operational Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 Consumer Social Responsibility (CnSR): A Social Change . 1.8.1 Consumer Social Responsiblity—Emergence . . . . . 1.8.2 Consumer Social Responsibility—Meaning . . . . . .

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1.9

Studying Consumer Social Responsibility: Theoretical Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9.1 Problems and Challenges for Corporations . . . . 1.9.2 CnSR—A Driver for CSR . . . . . . . . . . . . . . . 1.10 Relevance of Studying Social Responsibility/Consumer Social Responsibility in India: Empirical Evidence . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part II

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Review of Literature

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An Overview of Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Literature Review: Exploring What Has Been Done . . . . . 2.2 Literature Comprehension: Identification of Research Gaps References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Conceptual Framework and Research Model . . . . . . . . . . . 3.1 Integration of Consumption Behaviour and Social Responsibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Responsible Consumption . . . . . . . . . . . . . . . . 3.1.2 Socially Conscious Consumption Behaviour . . 3.1.3 Socially Responsible Consumption Behaviour . 3.1.4 Ecologically Concerned Behaviour . . . . . . . . . 3.1.5 Ecologically Conscious Behaviour . . . . . . . . . . 3.1.6 Ecological Behaviour . . . . . . . . . . . . . . . . . . . 3.1.7 Environmentally Significant Behaviour . . . . . . 3.1.8 Environmentally Supportive Behaviour . . . . . . 3.1.9 Environmentally Friendly Behaviour . . . . . . . . 3.1.10 Environmentally Related Behaviour . . . . . . . . . 3.1.11 Environmentally Responsible Behaviour . . . . . 3.1.12 Environmental Behaviour . . . . . . . . . . . . . . . . 3.1.13 Pro-environmental Behaviour . . . . . . . . . . . . . 3.1.14 Green Consumption Behaviour . . . . . . . . . . . . 3.1.15 Sustainable Consumption Behaviour . . . . . . . . 3.2 Formulation of Behavioural Construct: An Exploration . 3.2.1 Terminology of Responsible Behavioural Identities—A Comprehension . . . . . . . . . . . . . 3.2.2 Constituents of Responsible Consumption Behaviour (RCB)—An Elaboration . . . . . . . . . 3.3 Behavioural Antecedents—An Exploration . . . . . . . . . . 3.3.1 Environmental Knowledge (EK) . . . . . . . . . . . 3.3.2 Environmental Concern (EC) . . . . . . . . . . . . . 3.3.3 Environmental Attitude (EA) . . . . . . . . . . . . . . 3.3.4 Perceived Consumer Effectiveness (PCE) . . . . . 3.3.5 Willingness/Intentions/Commitment to Behave Responsibly . . . . . . . . . . . . . . . . . .

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3.4

Antecedents of Responsible Consumption Behaviour: Decision for the Present Context . . . . . . . . . . . . . . . 3.5 Research Objectives and Hypotheses . . . . . . . . . . . . 3.5.1 Objectives and the Reasoning . . . . . . . . . . . 3.5.2 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Methodological Procedures and Techniques . . . . . . . . . . . . . . . 4.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Designing Phases of Research . . . . . . . . . . . . . . . . 4.1.2 Construction of Data Collection Instrument . . . . . . 4.1.3 Sample Size and Sampling Process . . . . . . . . . . . . 4.1.4 Gathering Data—Distribution and Collection of Questionnaires . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.5 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . 4.1.6 Transcription and Survey Database Preparation . . . 4.1.7 Assessment of Reliability . . . . . . . . . . . . . . . . . . . 4.1.8 Plan for Analyses . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Sample Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Classification Based on Demographic Characteristics 4.2.2 Classification Based on Sociological Characteristics 4.2.3 Classification Based on Cultural Characteristics . . . 4.2.4 Classification Based on Geographic Characteristics . 4.2.5 Classification According to Economic Variables . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part III 4

Part IV 5

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Research Methodology

Analyses and Interpretations

Exploration and Validation of Behavioural–Attitudinal Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Domains of Behavioural Construct: Responsible Consumption Behaviour (RCB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Responsible Purchasing Domain . . . . . . . . . . . . . . . 5.1.2 Responsible Usage Domain . . . . . . . . . . . . . . . . . . . 5.1.3 Responsible Maintenance Domain . . . . . . . . . . . . . . 5.1.4 Responsible Disposal Domain . . . . . . . . . . . . . . . . . 5.1.5 Domain of Allied Socially Responsible Behaviours .

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5.2

Domains of Attitudinal Construct . . . . . . . . . . . . . . . . . 5.2.1 General Attitudinal Domain . . . . . . . . . . . . . . 5.2.2 Specific Attitudinal Domain—Attitude towards Sustainable Living (ASL) . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Model Specification and Theory Testing . . . . . . . . . . . . . . . . . 6.1 Behavioural Dimensions: A Descriptive Analysis . . . . . . . 6.2 Attitudinal Dimensions: A Descriptive Analysis . . . . . . . . 6.3 Investigation of C-A-C-B Model: An Empirical Approach 6.4 Responsible Purchasing Domain: Path Analysis . . . . . . . . 6.4.1 Hypotheses Testing for Direct Causal Effects . . . . 6.4.2 Hypotheses Testing for Mediation Effects . . . . . . 6.5 Responsible Usage Domain: Path Analysis . . . . . . . . . . . . 6.5.1 Hypotheses Testing for Direct Causal Effects . . . . 6.5.2 Hypotheses Testing for Mediation Effects . . . . . . 6.6 Responsible Maintenance Domain: Path Analysis . . . . . . . 6.6.1 Hypotheses Testing for Direct Causal Effects . . . . 6.6.2 Hypotheses Testing for Mediation Effects . . . . . . 6.7 Responsible Disposal Domain: Path Analysis . . . . . . . . . . 6.7.1 Hypotheses Testing for Direct Causal Effects . . . . 6.7.2 Hypotheses Testing for Mediation Effects . . . . . . 6.8 Environmentally Relevant Activities: Path Analysis . . . . . 6.8.1 Hypotheses Testing for Direct Causal Effects . . . . 6.8.2 Hypotheses Testing for Mediation Effects . . . . . . 6.9 Sustainable Societal Conduct: Path Analysis . . . . . . . . . . . 6.9.1 Hypotheses Testing for Direct Causal Effects . . . . 6.9.2 Hypotheses Testing for Mediation Effects . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Segmentation of Consumers and Identification of Responsibles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Determination of Variables . . . . . . . . . . . . . . . . . . 7.1.2 Selection of Number of Clusters . . . . . . . . . . . . . . 7.2 Non-hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Representation of Clusters According to Means . . . 7.2.2 Labelling the Clusters . . . . . . . . . . . . . . . . . . . . . . 7.3 Test of Significance and Validity of Cluster Solution . . . . . 7.3.1 Tests of Significance Between Means of Segments . 7.3.2 Discriminant Analysis and Validation of Segments .

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7.4

Interpretation of Segments . . . . . . . . . . . . . . . . . . . 7.4.1 Red Segment: Apathetics and Imprudents . 7.4.2 Yellow Segment: Aesthetics and Hopefuls . 7.4.3 Green Segment: Aspirants and Illuminators References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Characterizing and Profiling Responsible Consumer Segments . 8.1 Characterization and Categorization of Influencing Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Cross-Tabulation and Consumer Membership: Chi-Square and Proportional Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Demographic Determinants and Segmentation . . . . 8.2.2 Sociological Determinants and Segmentation . . . . . 8.2.3 Geographic Determinants and Segmentation . . . . . . 8.2.4 Economic Determinants and Segmentation . . . . . . . 8.2.5 Cultural Determinants and Segmentation . . . . . . . . 8.2.6 Personality Determinants and Segmentation . . . . . . 8.3 Identification of Consumer Membership . . . . . . . . . . . . . . . 8.3.1 Demographic Variables and Segmentation . . . . . . . 8.3.2 Sociological Variables and Segmentation . . . . . . . . 8.3.3 Geographical Variables and Segmentation . . . . . . . 8.3.4 Economic Variables and Segmentation . . . . . . . . . 8.3.5 Cultural Variables and Segmentation . . . . . . . . . . . 8.3.6 Personality Variables and Segmentation . . . . . . . . . 8.4 Profiling of Responsible Consumers . . . . . . . . . . . . . . . . . . 8.4.1 Integration of Consumers’ Attributes—Attempt of Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 Inter-comparison of Features of Segments . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part V 9

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Conclusions and Practicality

Findings and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 An Approach to Study’s Theme . . . . . . . . . . . . . . . . . . . . . . 9.1.1 Evolution of Thought . . . . . . . . . . . . . . . . . . . . . . . 9.1.2 Reasoning Behind Objectives . . . . . . . . . . . . . . . . . 9.2 Methodological Viewpoints . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Development of Research Instrument . . . . . . . . . . . . 9.2.2 Sampling and Sample Profile . . . . . . . . . . . . . . . . . 9.3 Main Findings: Summarization . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Objective 1: To Explore the Dimensions Underlying Attitudinal and Behavioural Constructs . . . . . . . . . . 9.3.2 Objective 2: To Examine the Extent to Which Consumers Adopt Each Behavioural Kind and to Affirm the Extent of Their Attitudinal Viewpoints . .

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9.3.3

9.3.4 9.3.5 9.3.6

Objective 3: To Investigate Theory of Responsible Behaviour Formation by Empirically Testing C-A-C-B Model . . . . . . . . . . . . . . . . . . . . . . . . . . Objective 4: To Identify Consumer Segments as Per Behavioural and Attitudinal Dimensions . . . Objective 5: To Anticipate the Proportion of Responsible Consumers in Indian Market . . . . . . . Objective 6: To Analyse the Characteristics of Identified Segments, Their Profiles, and Distinctiveness . . . . . . . . . . . . . . . . . . . . . . . . . . .

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. . . 413 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422

10 Implications and Research Directions . . . . . . . . . . . . . 10.1 Recommendations and Implications . . . . . . . . . . . 10.1.1 Recommendations to Marketers . . . . . . . . 10.1.2 Implications for Public Policy . . . . . . . . . 10.1.3 Suggestions to Different Societal Sections 10.2 Limitations and Further Research Directions . . . . . 10.2.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Directions for Future Research . . . . . . . .

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Annexure: Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437

List of Figures

Fig. 1.1 Fig. 1.2

Fig. 1.3

Fig. 1.4 Fig. 1.5 Fig. 1.6 Fig. 1.7 Fig. 1.8

Fig. 1.9 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 4.1 Fig. 4.2 Fig. 4.3

A countdown of ecological intimidation. Source Authors’ compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The root of ecological problems: population explosion. Source United States Census Bureau, International Data Base (2011) and (2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a A model of marketing process emphasizing on consumers. Source Kotler and Armstrong (2005: p. 5). b Marketing and consumers. Source Kotler and Armstrong (2005: p. 58) and Kotler and Keller (2005: p. 20) . . . . . . . . . . . . . . . . . . . . . . . The process of consumption. Source Authors’ compilation . . The totality of consumption behaviour. Source Hoyer et al. (2009: p. 4). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dimensions of responsibility. Source Authors’ compilation . . Domains of social responsibility. Source Authors’ compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Global ecological overshoot. Source http:// www.footprintnetwork.org/en/index. php/gfn/page/world_footprint/ . . . . . . . . . . . . . . . . . . . . . . . . . Projected water consumption in India. Source Ernst and Young (2011) . . . . . . . . . . . . . . . . . . . . . . . Evolution and improvement in research identities. Source Authors’ compilation . . . . . . . . . . . . . . . . . . . . . . . . . Hierarchy of behavioural ideologies. Source Authors’ compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A conceptual model for ‘theory of responsible behaviour formation’. Source Developed by authors . . . . . . . . . . . . . . . . A Schematic C-A-C-B Model of Integrated Conceptual Framework. Source Developed by authors . . . . . . . . . . . . . . . Research design: procedure and phases . . . . . . . . . . . . . . . . . Process of development of scales . . . . . . . . . . . . . . . . . . . . . . Survey database: SPSS worksheet (variable view) . . . . . . . . .

..

4

..

6

.. ..

15 19

.. ..

22 23

..

26

..

35

..

37

. . 126 . . 127 . . 137 . . . .

. . . .

139 152 154 169 xvii

xviii

Fig. 4.4 Fig. 4.5 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5 Fig. 5.6 Fig. 5.7 Fig. 5.8 Fig. 5.9 Fig. 5.10

Fig. 5.11 Fig. 5.12 Fig. 5.13 Fig. 5.14 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 6.5 Fig. 6.6 Fig. 6.7

List of Figures

Survey database: SPSS worksheet (data view) . . . . . . . . . . . . Summary of statistical techniques applied . . . . . . . . . . . . . . . . Responsible purchasing domain: scree plot for number of components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible purchasing domain: measurement model for two dimensional component structure . . . . . . . . . . . . . . . . Responsible usage domain: scree plot for number of components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible usage domain: measurement model for two dimensional component structure . . . . . . . . . . . . . . . . Responsible maintenance domain: scree plot for number of components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible maintenance domain: zero order confirmatory measurement model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible disposal domain: scree plot for number of components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible disposal domain: measurement model for two dimensional component structure . . . . . . . . . . . . . . . . Domain of allied socially responsible behaviours: scree plot for number of components . . . . . . . . . . . . . . . . . . . . . . . . . . . Domain of allied socially responsible behaviours: measurement model for two dimensional component structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concern for sustainable future: zero order confirmatory measurement model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Commitment to Initiate: zero order confirmatory measurement model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attitude towards sustainable living: scree plot for number of components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attitude towards sustainable living: One stage confirmatory measurement model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extended survey database for summated scores of behavioural dimensions as new variables . . . . . . . . . . . . . . . . . . . . . . . . . . Extended survey database for summated scores of attitudinal dimensions as new variables . . . . . . . . . . . . . . . . . . . . . . . . . . C-A-C-B model concerning attitudinal specificity in behavioural domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Free model: a path model for responsible purchasing domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of responsible purchasing domain: mediating effect of OGM . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of responsible purchasing domain: mediating effect of CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Free model: a path model for responsible usage domain . . . .

. . 169 . . 176 . . 192 . . 194 . . 201 . . 203 . . 205 . . 205 . . 208 . . 210 . . 215

. . 215 . . 219 . . 222 . . 227 . . 231 . . 241 . . 243 . . 245 . . 247 . . 253 . . 253 . . 255

List of Figures

Fig. 6.8 Fig. 6.9 Fig. 6.10 Fig. 6.11 Fig. 6.12 Fig. 6.13 Fig. 6.14 Fig. 6.15 Fig. 6.16 Fig. 6.17 Fig. 6.18 Fig. 6.19 Fig. 6.20 Fig. 6.21 Fig. Fig. Fig. Fig.

7.1 7.2 7.3 7.4

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

7.5 7.6 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11

Constrained model of responsible usage domain: mediating Effect of ACE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of responsible usage domain: mediating effect of CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Free model: a path model for responsible maintenance domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of responsible maintenance domain: mediating effect of AMW . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of responsible maintenance domain: mediating effect of CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Free model: a path model for responsible disposal domain . . . Constrained model of responsible disposal domain: mediating effect of NR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of responsible disposal domain: mediating effect of CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Free model: a path model for environmentally relevant activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of environmentally relevant activities: mediating effect of ET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of environmentally relevant activities: mediating effect of CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Free model: a path model for sustainable societal conduct . . . Constrained model of sustainable societal conduct: mediating effect of CA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained model of sustainable societal conduct: mediating effect of CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elbow plot for the number of clusters . . . . . . . . . . . . . . . . . . Representation of final cluster centres . . . . . . . . . . . . . . . . . . . Symbolizing consumer segments with Pie-chart . . . . . . . . . . . Extended survey database for cluster membership (clusters) as a new variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discriminant scores and predicted group membership. . . . . . . Sorting of misclassified cases and segment sizes . . . . . . . . . . Gender-wise segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . Age-wise segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Education-wise segmentation . . . . . . . . . . . . . . . . . . . . . . . . . Segmentation as per academic orientation . . . . . . . . . . . . . . . . Academic intelligence and segmentation . . . . . . . . . . . . . . . . . Marital status and segmentation . . . . . . . . . . . . . . . . . . . . . . . Parenthood and segmentation . . . . . . . . . . . . . . . . . . . . . . . . . Years of marriage and segmentation . . . . . . . . . . . . . . . . . . . . Segmentation as per profession . . . . . . . . . . . . . . . . . . . . . . . . Type of family and segmentation . . . . . . . . . . . . . . . . . . . . . . Family size and segmentation . . . . . . . . . . . . . . . . . . . . . . . . .

xix

. . 260 . . 261 . . 267 . . 267 . . 268 . . 271 . . 273 . . 274 . . 275 . . 277 . . 278 . . 282 . . 286 . . . .

. . . .

287 303 304 306

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

307 318 318 341 342 344 346 348 355 356 357 359 360 362

xx

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

List of Figures

8.12 8.13 8.14 8.15 8.16 8.17 8.18 8.19 8.20 8.21 8.22 8.23 8.24 8.25

Fig. 8.26 Fig. 10.1

Composition of family (gender-wise) and segmentation Composition of family (age-wise) and segmentation . . . Household support and segmentation . . . . . . . . . . . . . . Segmentation as per place of living . . . . . . . . . . . . . . . Commuting and segmentation . . . . . . . . . . . . . . . . . . . . Segmentation as per family income . . . . . . . . . . . . . . . Home ownership and segmentation. . . . . . . . . . . . . . . . Religion and segmentation . . . . . . . . . . . . . . . . . . . . . . Religiosity and segmentation . . . . . . . . . . . . . . . . . . . . Objective directed traits and segment membership . . . . Social directed traits and segment membership . . . . . . . Self-directed traits and segment membership . . . . . . . . Emotions directed traits and segment membership . . . . Extended survey database for personality variables (variable view) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extended survey database for personality variables (data view) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Unidentified segments: for future consideration . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

363 365 366 367 369 371 372 373 375 376 377 378 379

. . . . . . . 387 . . . . . . . 391 . . . . . . . 435

List of Tables

Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 1.5 Table 2.1 Table 3.1 Table Table Table Table Table Table Table

4.1 4.2 4.3 4.4 4.5 4.6 4.7

Table 4.8 Table 4.9 Table Table Table Table Table Table Table

4.10 4.11 4.12 4.13 4.14 4.15 4.16

International governance: global sustainable development reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . International governance: sustainable development conferences and workshops . . . . . . . . . . . . . . . . . . . . . . . . . Consumers and their roles in consumption behaviour . . . . . Sectoral consumption of commercial energy in India (in million tonnes of oil equivalent) . . . . . . . . . . . . . . . . . . . Consumption of commercial energy as per its components . Classification of literature based on the diversity of works Source Authors’ compilation . . . . . . . . . . . . . . . . . . . . . . . . Conceptualizations prevailing in literature Source Authors’ compilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pilot-test reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Divisions of 21 districts of Haryana . . . . . . . . . . . . . . . . . . . Expected gender composition of the sample . . . . . . . . . . . . . Questionnaire survey over selected geographical areas . . . . . Questionnaire survey compatible with gender quota . . . . . . . Rating of scale items of constructs . . . . . . . . . . . . . . . . . . . . Numerical coding of questions related to personal information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internal consistency: alpha method . . . . . . . . . . . . . . . . . . . . Item-total-statistics: behavioural, attitudinal, and personality measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Split-half reliability of all measures . . . . . . . . . . . . . . . . . . . Research objectives and allied techniques of analysis . . . . . Demographic classification of sample respondents . . . . . . . . Classification based on Sociological Attributes. . . . . . . . . . . Classification based on cultural features . . . . . . . . . . . . . . . . Geographic classification of sample respondents . . . . . . . . . Classification based on economic attributes . . . . . . . . . . . . .

..

9

.. ..

10 21

.. ..

35 36

. . 104 . . . . . . .

. . . . . . .

116 155 161 162 163 164 166

. . 167 . . 170 . . . . . . . .

. . . . . . . .

171 174 175 178 179 180 180 181

xxi

xxii

Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11 Table 5.12 Table 5.13 Table 5.14 Table 5.15 Table 5.16 Table 5.17 Table 5.18 Table 5.19 Table 5.20 Table 5.21 Table 5.22 Table 5.23 Table 5.24 Table 5.25 Table 6.1

List of Tables

Responsible purchasing domain: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible purchasing domain: results of principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible purchasing domain: model fit measures . . . . . . Responsible usage domain: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible usage domain: results of principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible usage domain: model fit measures . . . . . . . . . . Responsible maintenance domain: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible maintenance domain: results of principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible maintenance domain: model fit measures . . . . . Responsible disposal domain: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible disposal domain: results of principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible disposal domain: model fit measures . . . . . . . . Domain of allied socially responsible behaviours: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . Domain of allied socially responsible behaviours: results of principal component analysis . . . . . . . . . . . . . . . . . . . . . . Domain of allied socially responsible behaviours: model fit measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concern for sustainable future: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . Concern for sustainable future: model fit measures . . . . . . . Concern for sustainable future: results of confirmatory factor analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Commitment to Initiate: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Commitment to Initiate: model fit measures . . . . . . . . . . . . . Commitment to Initiate: results of confirmatory factor analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attitude towards sustainable living: descriptive statistics and bivariate correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . Attitude towards sustainable living: results of principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attitude towards sustainable living: model fit measures . . . . Summarization of behavioural and attitudinal dimensions . . Behavioural dimensions: descriptive statistics and significance testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . 191 . . 193 . . 196 . . 198 . . 200 . . 202 . . 204 . . 204 . . 207 . . 208 . . 209 . . 211 . . 213 . . 214 . . 216 . . 218 . . 220 . . 221 . . 221 . . 223 . . 223 . . 225 . . 228 . . 233 . . 237 . . 242

List of Tables

Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 6.11 Table 6.12 Table 6.13 Table 6.14 Table 6.15 Table 6.16 Table 6.17 Table 6.18 Table 6.19 Table 6.20 Table 6.21 Table 6.22 Table 6.23 Table 6.24 Table 6.25

xxiii

Attitudinal dimensions: descriptive statistics and significance testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible purchasing domain: results of path analysis . . . Responsible purchasing domain: summary results for mediation effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible usage domain: results of path analysis . . . . . . . Responsible usage domain: summary results for mediation effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible maintenance domain: results of path analysis . . Responsible maintenance domain: summary results for mediation effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible disposal domain: results of path analysis . . . . . Responsible disposal domain: summary results for mediation effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmentally relevant activities: results of path analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmentally relevant activities: summary results for mediation effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustainable societal conduct: results of path analysis . . . . . . Sustainable societal conduct: summary results for mediation effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration of hypotheses testing of direct effects: domain wise results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration of hypotheses testing of indirect/mediation effects: domain wise results . . . . . . . . . . . . . . . . . . . . . . . . . Responsible purchasing domain: significance testing of direct effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible purchasing domain: significance testing of indirect effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible usage domain: significance testing of direct effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible usage domain: significance testing of indirect effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible maintenance domain: significance testing of direct effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible maintenance domain: significance testing of indirect effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible disposal domain: significance testing of direct effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible disposal domain: significance testing of indirect effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmentally relevant activities: significance testing of direct effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . 244 . . 249 . . 250 . . 257 . . 259 . . 263 . . 264 . . 270 . . 272 . . 279 . . 280 . . 283 . . 285 . . 289 . . 291 . . 293 . . 293 . . 293 . . 294 . . 294 . . 295 . . 295 . . 296 . . 296

xxiv

List of Tables

Table 6.26 Table 6.27 Table 6.28 Table Table Table Table Table Table Table Table Table Table

7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 8.1 8.2

Table 8.3 Table 8.4 Table 8.5 Table 8.6 Table 8.7 Table 8.8 Table 8.9 Table 8.10 Table 8.11 Table 8.12 Table 8.13 Table 8.14 Table 8.15

Environmentally relevant activities: significance testing of indirect effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustainable societal conduct: significance testing of direct effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustainable societal conduct: significance testing of indirect effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agglomeration schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distances between final cluster centres . . . . . . . . . . . . . . . . . Descriptive statistics for consumer segments . . . . . . . . . . . . Tests of significance and association . . . . . . . . . . . . . . . . . . Pair-wise post hoc multiple comparisons . . . . . . . . . . . . . . . Pooled within-groups correlation matrix . . . . . . . . . . . . . . . . Variance and significance of discriminant functions . . . . . . . Prior probability and classification results . . . . . . . . . . . . . . Segmentation bases and their categories . . . . . . . . . . . . . . . . Demographic variables and segmentation: cross-tabulation and test of association/dependence . . . . . . . . . . . . . . . . . . . . Sociological variables and segmentation: cross-tabulation and test of association/dependence . . . . . . . . . . . . . . . . . . . . Geographic variables and segmentation: cross-tabulation and test of association/dependence . . . . . . . . . . . . . . . . . . . . Economic variables and segmentation: cross-tabulation and test of association/dependence . . . . . . . . . . . . . . . . . . . . Cultural variables and segmentation: cross-tabulation and test of association/dependence . . . . . . . . . . . . . . . . . . . . Personality variables and segmentation: cross-tabulation and test of association/dependence . . . . . . . . . . . . . . . . . . . . Gender-wise intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Age-wise intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Education and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Academic orientation and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . Academic intelligence and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . Marital status and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . Parenthood and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . Years of marriage and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . .

. . 297 . . 297 . . . . . . . . . .

. . . . . . . . . .

298 302 304 308 310 312 314 315 317 325

. . 327 . . 333 . . 335 . . 337 . . 338 . . 339 . . 341 . . 343 . . 345 . . 347 . . 349 . . 355 . . 356 . . 358

List of Tables

Table 8.16 Table 8.17 Table 8.18 Table 8.19 Table 8.20 Table 8.21 Table 8.22 Table 8.23 Table 8.24 Table 8.25 Table 8.26 Table 8.27 Table 8.28 Table 8.29 Table 8.30 Table Table Table Table

8.31 8.32 8.33 8.34

Table Table Table Table Table Table Table Table

8.35 9.1 9.2 9.3 9.4 9.5 9.6 9.7

xxv

Profession and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Type of family and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Family size and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Family composition and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Household support and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Place of living and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Commuting and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Family income and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Home ownership and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Religion and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Religiosity and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Objective directed traits and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . Social directed traits and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Self-directed traits and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . . . . . . . Emotions directed traits and intra-segment comparison: paired differences and test of relevance . . . . . . . . . . . . . . . . Profiling of segments: incorporating distinctive features . . . . Descriptive statistics for personality variables . . . . . . . . . . . Inter-item correlation matrix . . . . . . . . . . . . . . . . . . . . . . . . . Personality variables: results of principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Partition points: medians . . . . . . . . . . . . . . . . . . . . . . . . . . . Responsible purchasing behaviour and matching studies . . . Responsible usage behaviour and matching studies . . . . . . . Responsible maintenance behaviour and matching studies . . Responsible disposal behaviour and matching studies . . . . . Allied socially responsible activities and matching studies . . General attitudinal domain and matching studies . . . . . . . . . Specific attitudinal components and matching studies . . . . .

. . 360 . . 361 . . 363 . . 364 . . 366 . . 368 . . 370 . . 371 . . 372 . . 374 . . 375 . . 376 . . 377 . . 378 . . . .

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380 381 388 389

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390 391 399 400 401 402 403 404 404

Part I

Introduction

Part I incorporates Chap. 1: Background and Thought which introduces the diversity and development of the topic. Taking a broad perspective, it establishes theoretical foundation of this book.

Chapter 1

Background and Thought

Consumption Behaviour and Social Responsibility has a wide historical background based on the idea of sustainable development. Understanding these concepts and their integration in this form is an innovative work that is elaborated in this chapter spread into ten sections. The idea of sustainable development is of global relevance with the emergence of the issues of environment protection and sustainability. Accordingly, the first Sect. 1.1 enlighten upon the evolution of such issues. It is followed by subsequent sections that are allied with each for detailing on the subject matter.

1.1 Environmental Thought: Evolution and Escalation It can increasingly be recognized that human actions and contemporary ways of living are harnessing the natural environment, consequently, making it an environmental malady and a hurdle for a healthy and sustainable living (Nyberg and Sto 2001). The realities around us also demonstrate that the Earth is undergoing an ecological crisis. Over the last four decades, environmental issues are threatening mankind evolving a new answer to an old question of survival with a unique name of sustainable development,1 the key pillar of which is environmental sustainability2 , and can be achieved by attaining sustainable production and consumption patterns (Fuchs and 1 Sustainable

development is defined as that development which meets the needs of the present without compromising the ability of future generations to meet their own needs (Peattie 1995: p. 33; Bhattacharya 2007: p. 255). 2 Environmental sustainability is the ability to maintain the qualities that are valued in the physical environment. Environmental sustainability programmes include actions to reduce the use of physical resources, the adoption of a ‘recycle everything/buy recycled’ approach, the use of renewable rather than depletable resources, the redesign of production processes and products to eliminate the production of toxic materials, and the protection and restoration of natural habitats and environments valued for their livability or beauty (Sutton 2004). © Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_1

3

4

1 Background and Thought

Lorek 2004). The roots of the issue of sustainable development and environmental sustainability are entrenched in the past ecological problems that have its lineage in the early nineteenth century when some people realized that the wild aspects of United States were disappearing and the high destruction rates were noticed after the industrial revolution. Since then, environmental problems are spreading its roots all over the world and no part is spared. Over the decades, these problems have grown like flammable diseases that are threatening the sustainable and healthy living of human on Earth. Figure 1.1 enlists the major environmental predicament in a chronological order to establish the roots of ecological intimidation and Sect. 1.1.1 makes a detailed description of these. Fig. 1.1 A countdown of ecological intimidation. Source Authors’ compilation

1960s – Extinction of Species + Air Pollution 1970s – 1960s + Water Pollution + Loss of Aesthetic Values + Energy Crisis

1980s – 1970s + Major Predicament: Ozone Depletion

1990s – 1980s + Loss of Biodiversity + Forests’ Destruction

Beginning of 21st Century – 1990s + Global Warming and Climate Change

Present – How to achieve Environmental sustainability by handling all environmental predicaments

1.1 Environmental Thought: Evolution and Escalation

5

1.1.1 Issue of Environmental Sustainability—A Glance of Its Phases • 1960s: The era of 1960s was defined as the era of foremost environmental challenge. As modern humans started occupying the Earth, many species got extinct from our planet during 1960s. Ecologists realized that in a world where other animals could not live, man’s doom is also not far behind. In this regard, biologist Rachel Carson warned the future generations in his famous book Silent Spring (published on 27 September 1962) that considered the detrimental effects of pesticides on the environment, and presented a gloom scenario of future without songbirds along with other dire consequences (www.en.wikipedia.org)[A] . During the same period, air pollution and energy consumption also came out to be the major environmental hazards and since then these emerged as the prominent issues of environment (Straughan and Roberts 1999). • 1970s: According to Peattie (1995: p. 5), the counterculture of late 1960s and early 1970s challenged many of the prevailing values and assumptions within industrialized societies. It was the time when many of the effects of decades of environmental neglect began to mark themselves most dramatically in the form of river pollution. Predictions of an impending environmental crisis were widely debated and addressed as a significant item on business agenda for the first time. In this era, the major problems receiving attention were the air and water pollution, resource depletion, and energy crisis. Also, localized pollution, hazardous waste, and energy consumption were too manifesting themselves in the line of problems (Dunlap et al. 2000). The two worst crises of this period were the 1973 oil crisis, caused by the US production peak in 1971 and the 1979 energy crisis due to the Iranian revolution. In the light of these difficulties, the experts warned that there would be times when it would not be possible to meet the needs of everyone, and problems for the coming world would increase repeatedly. • 1980s: During this period, Antil (1984) noticed that the depletion and increasing scarcity of natural resources were in the line of problems. Stretched from 1970s, energy consumption and oil crisis were the biggest problems of this time. Even the solutions to past problems were not adequately retorted and new problems further joined the street. In this time period, a hole in Earth’s Ozone layer was discovered, which disturbed common living in the whole world. According to Peattie (1995: p. 75), no other environmental issue points up the catastrophe as clearly as this discovery of a hole in the sky. • 1990s: In 1990s, with some new facts, huge losses to the biodiversity and species’ extinction were again recognized. According to the report of Environment News Service3 for the period August 1999, the extinction rate was approaching 3 The

Environment News Service, referred to as ENS, is an environmental news agency which provides a press release distribution service World-Wire, in addition to original stories. ENS contributors around the world cover issues and events that affect the environment. Its official website is http://ens-newswire.com/[ C ] .

6

1 Background and Thought

1000 times the background rate. An estimate was also presented that this figure might climb to 10000 times the background rate during the next century (www. globalissues.org)[B] . Today also, these issues are of immense significance as biodiversity is clearly a fundamental component of life on Earth and creates complex ecosystems, if destroyed could never be reproduced by humans (Trowbridge 2001, updated 2008). • Present: As twenty-first century drew near, global warming and climate change were the names of two new environmental dilemmas mankind faced and confronting. The names are new but actually they are the consequences of those past problems which were troubling through all these decades. Recently, Budak et al. (2005) wrote that the major environmental problems of the world are deforestation, loss of biodiversity, the continuous ozone depletion, global climate change, pollution, and overconsumption of natural resources. Some others (Kaur 2006; Tan and Lau 2009) defined pollution of sea and river, polluted drinking water, desertification, cutting down trees, soil erosion, noise pollution, acid rain, and starvation as the biggest problems of the era. In reality, the list is endless and manifesting one after another. Actually, the root cause of all these problems is massive and extravagant growth in the world population (Fig. 1.2) which comes out to be more troubling and devastating trepidation for human beings. In Fig. 1.2, the horizontal axis shows the era beginning from 1950 and reaches towards anticipations of population increase up to the year 2050. The vertical axes are visible with population count in billion (Fig. 1.2a) and its growth rate (Fig. 1.2b). The figure at the left shows that the world population had increased from 3 billion in 1959 to 6 billion by 1999, a doubling that occurred over 40 years. The latest projections imply this increase from 6 billion in 1999 to 9 billion by 2044, an increase of 50%. The growth rates of the population show that rates of population expansion hiked from about 1.5% per year from 1950 to a peak of over 2% in the early 1960s; Figure (a) World Population 1950-2050

Figure (b) World Population Growth Rates 1950-2050

Fig. 1.2 The root of ecological problems: population explosion. Source United States Census Bureau, International Data Base (2011) and (2014)

1.1 Environmental Thought: Evolution and Escalation

7

thereafter, started to decline. However, the population increase in absolute numbers clearly highlights that we are going towards the toughest times. The world population is going to hype and the continuity makes it a matter of great concern. In response to troubling environmental melodies, societies started giving attention to these problems and the era of ecological revolution started. This phase of beginning and enlargement of environmental responsibility is revealed in the next section.

1.1.2 Environment Concise and Awareness—A New Transformation In response to the tribulations defined earlier, environmental thought developed along with increasing initiation of individual–societal concerns. The appearance of ecological consciousness began in the same year when the environmental predicaments stared. • 1960s-Ecology Movement: The consciousness and concern for environment that was seen in 1960s in response to tribulations of this era was named ecology movement (Straughan and Roberts 1999). • 1970s-Environmentalism: In 1970s, environmental issues achieved a prominent position for setting policies and agendas (Dunlap et al. 2000) and then environmentalism emerged as a global phenomenon (Chen et al. 2011). • Succeeding phase: According to Tuna (2003), environment has fully attracted attention and concerns in the second half of twentieth century especially after 1970s. The increased media coverage of issues and events such as detection of hole in ozone layer, medical waste that were washing up the shores of East-Coast beaches, media coverage of Exxon-Valdez oil spills, and environment destruction at the time of Gulf war woke up firstly the American’s interest in environment and placed it high in public policy, subsidizing various responses and legislative initiatives. The above initiatives can be a welcome sign for growing concern towards environment with time, but it is too important that this thought progresses both in developed as well as developing countries. Tuncer et al. (2005) monitored that the patterns of consumption and production are not sustainable both in developed as well as developing countries. In developed countries, the levels of pollution especially those causing global change are far too high and all trends are not moving in the desired directions. In developing countries, there is too much strain on the local resource base and this strain is increasing due to population increase and urbanization. India being a developing country is undergoing lifestyle changes and facing enormous environmental issues. It is a predicament that times ago, what was the epoch in the Western world is now each day’s problem in India challenging the country for its sustainable development. Hence, the next section will elaborate on international initiatives for sustainable development and also Indian preparedness for the same.

8

1 Background and Thought

1.2 Steps for Sustainable Development: Key Initiatives 1.2.1 Towards Sustainability—International Landmarks For the very first time, the United Nations World Commission on Environment and Development (UNCED 1987) in its publication, our common future established a systematic relationship between environment and development issues. Since then, many deliberations and discussions took place across the world regarding how environmental tribulations can be tackled. The foremost points emerged are as follows: • Earth Summits: In the midst of various initiatives on sustainable development, Earth Summit 1992, 2002, and 2012 cover up the cream of the crop. United Nations Conference on Environment and Development (UNCED): Earth Summit, 1992 is the leading amongst all and firmly established the concept of sustainable consumption by its Agenda 21. The fourth chapter of this Agenda called for the adoption of sustainable consumption patterns across the world. World Summit on Sustainable Development, 2002 (WSSD) also referred to as Earth Summit, 2002 was built on the earlier declarations made at the United Nations Conference on the Human Environment at Stockholm in 1972 and the Earth Summit in Rio de Janeiro in 1992. The Summit once again raised the issue of sustainable development in more general ways. Recently, Rio 2012 (Rio+20) or Earth Summit 2012 held as the third international convention organized by the United Nations Conference on Sustainable Development (UNCSD) and afresh called for the promotion of a sustainable future. The chief upshot of the conference is a 49-page work paper. The Future We Want. The document largely affirms previous action plans like Agenda 21. As an outcome, it is recognized that fundamental changes in the ways societies consume and produce are indispensable for achieving global sustainable development. Also, in 1997, Earth summit+5 was organized. Recently in 2015, sustainable development goals (SDGs) are being set by United Nations General Assembly to be achieved by 2030. • Various Reports: Various reports had also been issued from time to time by many International Government Organizations (IGOs) and Non-Governmental Organizations (NGOs). A list of these global reports is presented in Table 1.1. • Conferences and Workshops: In lieu of the Earth summits as defined above, a synopsis of other major conferences and workshops organized across the globe is accessible from Table 1.2. In this way, it can be concluded that many initiatives have been revealed throughout the world on the aspect of sustainable development. The next section highlights India’s stance on this aspect.

1.2 Steps for Sustainable Development: Key Initiatives

9

Table 1.1 International governance: global sustainable development reports Major reports issued by IGOs

Year

UN: Changing Consumption Patterns—Report of Secretary General

1995

OECD: Sustainable Consumption and Production: Clarifying the Concepts

1997

IIED: Unlocking Trade Opportunities: Changing Consumption and Production Patterns

1997

UN CHS: Changing Consumption Patterns in Human Settlements

1997

OECD: Sustainable Consumption Indicators

1998

IIED: Consumption in a Sustainable World (Kabelvag Report)

1998

UN DESA: Measuring Changes in Consumption and Production Patterns, A set of Indicators

1998

UNECE: ECE Governments on Encouraging Local Initiatives towards Sustainable Consumption

1998

OECD: Towards more Sustainable Household Consumption Patterns—Indicators to Measure Progress

1999

UN: Comprehensive review of changing Consumption and Production patterns

1999

UN DESA: Trends in Consumption and Production: Household Energy Consumption

1999

UNEP/CDG: Sustainable Consumption and Production—Creating Opportunities in a Changing World

2000

UNEP: Consumption Opportunities: Strategies for Change

2001

OECD: Information and Consumer Decision-Making for Sustainable Consumption

2002

OECD: Towards Sustainable Household Consumption? Trends and Policies in OECD Countries

2002

UNEP/CI: Tracking Progress: Implementing Sustainable Consumption Policies

2002

UN DESA: Survey of International Activities on Consumption and Production Patterns

2003

Major reports issued by NGOs

Year

Friends of the Earth Europe: Sustainable Europe

1995

WBCSD: Sustainable Consumption and Production: A Business Perspective

1996

Friends of the Earth International: Sustainable Consumption—A Global Perspective

1997

WBCSD: Sustainability Thought the Market

1999

Tools for Transition: Transitions to Sustainable Consumption and Production

2001

The World Federation of Advertisers and The European Association of Communications Agencies: Advertising Sector Report: Advertising a Better Quality of Life for all

2002

WBCSD: The Business Case for Sustainable Development

2002

International Coalition for Sustainable Consumption and Production (ICSPAC): Waiting for Delivery

2002

Source Fuchs and Lorek (2004)

10

1 Background and Thought

Table 1.2 International governance: sustainable development conferences and workshops Global conferences/Workshops on sustainable development

Year

Soria Moria Symposium (Oslo)

1994

UN Global Conference on the Sustainable Development of SIDS (BPOA)

1994

Oslo Ministerial Roundtable

1995

Clarifying the Concepts Workshop (Rosendal)

1995

Workshop on Policy Measures for Changing Consumption Patterns (Seoul)

1995

Workshop on Patterns and Policies (Brasilia)

1996

Inter-Regional Expert Group Meeting regarding UN Guidelines on Consumer Protection (Sao Paulo)

1998

Workshop on Indicators for Sustainable Production and Consumption (New York)

1998

Encouraging Local Initiatives towards Sustainable Consumption Patterns (Vienna)

1998

Consumption in a Sustainable World (Kabelvag)

1998

From Consumer Society to Sustainable Society (Soesterberg)

1999

Sustainable Consumption: Trends and Traditions in East Asis (Chejudo)

1999

Seventh Session of CSD (New York)

1999

Five-Year Review of Barbados Programme of Action (BPOA+5)

1999

Creating Opportunities in a Changing World (Berlin)

2000

Implementing Sustainable Consumption and Production Policies (Paris)

2002

Mauritius Strategy of Implementation (MSI)

2005

Five-Year Review of Mauritius Strategy of Implementation (MSI+5)

2005

First session of the High-level Political Forum on Sustainable Development

2013

Third International Conference on Small Island Developing States

2014

Source Fuchs and Lorek (2004) and UNESCO (2014)

1.2.2 India Towards Sustainability—Some Evidence The following notes highlight India’s legal provisions, policies, and other efforts made in the direction to protect and improve the environment. • Legal Acts: At first, India has included in its constitution the environment protection rights and related duties. Article 48(a) directs the States to take strong measures not only for its protection but also to work actively for its improvement. Article 51(a) incurs a corresponding duty on the citizens to do the same (Mahfooz-Nomani 2011). Various laws have also been passed to protect the natural environment. India has River Boards Act, 1956; Wildlife Protection Act, 1972; Water (Prevention and Control of Pollution) Act, 1974; Water (Prevention and Control of Pollution) Cess Act, 1977; Forests (Conservation) Act, 1980; The Air (Prevention and Control of Pollution) Act, 1981; The Environment Protection Act, 1986 (amended from time to time to control hazardous pollutants). There are also forest acts and the Ministry of Environment and Forests operating. State

1.2 Steps for Sustainable Development: Key Initiatives

11

and Central boards are also in commission for preservation of environment (Kaur 2006). Regarding the performance of environmental and social responsibility by corporations, the Ministry of Corporate Affairs, Government of India, has notified Section 135 in Chapter IX of Companies Act, 2013 and Schedule VII in the same act. Companies (Corporate Social Responsibility Policy) Rules, 2014 was also made. • Five-Year Plans: Each one of India’s five-year plans contained a chapter on safeguarding the environment. The planning commission of India had identified twelve strategy challenges for the twelfth five-year plan (2012–2017). Ganesmurthy (2011) mentioned that in this plan, management of environment and ecology is one of these challenges with the following five components: (1) (2) (3) (4) (5)

Land, mining and forest rights Mitigation and climate change Waste management and pollution abatement Degradation of forests and loss of biodiversity Issues of environment sustainability.

• National Policies and Abatements: India has a number of national policies governing environment management, including the National Policy on Pollution Abatement (NPPA 1992); The National Conservation Policy (NCS 1992); and Policy Statement on the Development Environment (PSED 1992). The NPPA seeks to encourage abatement of pollution at source, adopt the best technology available, take on the principle of polluter pays and public participation in decision-making. The NCS and PSED deal with framework on how to manage environment; but, these National policies are not judicially enforceable and serve only as guiding principles for central and state governments (Mahfooz-Nomani 2011). • Awareness Programmes: Government is also initiating programmes to make the general public informed and to create awareness. Bhagidari Programme has been launched by the government of Delhi to create awareness about environment issues amongst the citizens of Delhi. In it, campaigns like anti-littering, anti-plastic bag campaign, eco-care programme, keep city clean drive, anti-fire crackers campaign, khelo holi naturally campaign, and clean Yamuna campaign have been initiated. In 2004, the Supreme Court also lifted a ruling requiring all public transport vehicles to switch to Compressed Natural Gas (CNG) in the capital city (Kaur 2006). The Public Interest Litigations (PIL’s) are also working at their levels but are only at the beginning stage. At Hyderabad, promotions of programmes like—Do not Waste, Donate your Waste or Kachra Daan, Karo Kalyan are also encouraged to reduce waste through green volunteer clubs. • Other Associated Activities: The environment acts and regulations have not been met with expectations (Kaur 2006); thus, allied activities are established and promoted to protect environment. In 1991, the Ministry of Environment and Forests, government of India launched a voluntary environmental eco-labelling scheme known as ECOMARK for easy identification of eco-friendly products (Kaur 2006; Savita and Kumar 2010).

12

1 Background and Thought

• Clean India Mission: Clean India Mission (Swachh Bharat Abhiyan) originates as the largest programme on sanitation launched by Indian Government on 2 October 2014. The aim of this programme was to make India free from open defecation by the year 2019 (Pathak 2015; Kaul 2015). • Smart Cities Concept: The conceptualization of Smart Cities had been initiated in India on 25 June 2015. It aimed at enhancing the quality of urban life and providing a clean and sustainable environment with smart solutions to one hundred selected Indian cities (Jain 2015). It is a predicament that certain loopholes including shortage of clean drinking water, water contamination, ignorance of wastewater treatment, water logging and flooding, traffic congestion, air pollution, etc. are challenges in India for proper implementation of this concept, and calls for an urgent need for their eradication (Gupta and Garg 2017). Incidentally, this is an encouraging programme for upgrading the existing cities and paving the way for the creation of new vibrant cities in the light of the concept of sustainable development (Ahluwalia 2015). Now, as the work in this book is concerned with the discipline of Marketing at a fundamental level, it will be significant to mention how research within this field can be interlinked with the concepts of sustainable development and environmental sustainability as mentioned. Accordingly, the next section draws out picture regarding the assimilation of two premises marketing and environment, and how these concepts together can target the vision of sustainable development.

1.3 Environment and Marketing: Integration and Relationship As stated by Peattie (1995: p. 24), marketing has contributed to the current environmental crisis because of its central role as a driving force behind the unsustainable growth in consumption (or what could be termed as overconsumption). Hence, marketing as it will become, by contrast, a significant part of solution. One of the old definitions of marketing is ‘meeting needs profitably’ (Kotler and Keller 2005: p. 5) but today aligning with environment, marketing is ‘meeting needs sustainably’. Related to it, the relationship between environment and marketing has been assessed by Kilbourne and Beckman (1998). Long ago in his work, Fisk (1973) too described the Direct Ecological Impact of Marketing Inputs. Environment is simply a world in which we live, and marketing deals with identifying and meeting human and social needs. Therefore, the two subjects marketing and environment are interrelated. Marketing activities affect the environment, and here a relationship between marketing concepts and environmental problems is established. In the light of environment problems and development of concepts of marketing, it will be important to know these facts (Sect. 1.3.2). The beginning is done by defining the concepts of marketing in Sect. 1.3.1.

1.3 Environment and Marketing: Integration and Relationship

13

1.3.1 Stages of Marketing Concepts—A Fleeting Look • Production-Oriented Stage (1900–1930): The very first marketing orientation that is the production concept focuses on increasing the production as initially there was more demand than supply and no efforts were required to sell the products because of shortages; marketers assumed that what would be produced would be sold in the markets, and thus started producing at length. • Sales-Oriented Stage (1930–1950): Second concept: the selling concept emerged when selling of produced output became a problem for marketers. Therefore, the organizations took aggressive selling and promotional efforts. The purpose of marketing was to sell more stuff to more people more often for more money in order to make more profit (Kotler and Keller 2005: p. 15). • Customer-Oriented Stage (1950–1990): Subsequently, marketing concept became prominent by holding that consumer will favour such products and offerings that most match with their needs and wants. • Social-Oriented Stage (1990 to Update): Afterwards, time changed and environment considerations mixed up in marketing philosophy leading it to today’s socially responsible marketing concept (a component of holistic marketing concept). This concept incorporates social responsibility by understanding broader concerns for the ethical, environmental, legal, and social context of marketing activities and programmes.

1.3.2 Marketing—In Consideration with Environment The environment problems like resource depletion and pollution are the outrageous outcome of marketplace mentality of businessmen as shown in the first two concepts of marketing. It may also be a result of profit temptation without analysing the harmful effects of products that are being produced, marketed, and sold in the markets. The products’ qualities thus were ensured at the expense of environment and nature. By the recent development in the field of marketing (societal marketing concept), two themes, i.e. social marketing and green marketing came out in the limelight by which marketing gets coupled with the issue of environmental sustainability. This discourse is presented in the following phases. • 1970s: Finisterra do Paco and Raposo (2008) made the initial attempt to establish a relationship between marketing and environment in the early 1970s. In 1975, the first workshop was organized by American Marketing Association (AMA) on Ecological Marketing resulted into a book titled Ecological Marketing by Henion and Kinnear (1975) which co-related the two streams of environment and marketing. In this book, ecological marketing is defined as the study of positive and negative aspects of marketing activities on pollution, energy depletion, and monetary resource depletion. It has previously been mentioned that this (1970s)

14

1 Background and Thought

was the time when environment problems were originating and started threatening mankind. Ecological marketing in the present era is also famous by alternate names of green marketing or environmental marketing or sustainable marketing (Jaidev et al. 2018). • 1980s: Tantawi et al. (2009) mentioned that despite the environment attention in 1970s, it has really been in 1980s that the idea of green marketing has emerged. The period of mid 1980s seemed to have coincided with an increase in legislation and greater effort on the part of government aiming to protect the environment. In the late 1980s, consumers became a prominent part when a dramatic and inevitable shift of consumers’ consumption towards green products was noted. Prior to that, green marketing had spoken on the rapid increase in green consumerism but to the end of 1980s, it emerged green from the side of consumers’ concern for the environment (Finisterra do Paco and Raposo 2008). • 1990s: Throughout 1990s, policies such as labelling products on the basis of their environmental impacts and conducting large-scale mass media information campaigns to educate the public were enacted. All this resulted in two mass media initiatives: Going for Green and Are You Doing Your Bit to communicate a general green message to the public in hope that this would have a larger impact upon their decisions and will convince them rationally to adopt responsible behaviour towards environment. Marketers were also found understanding the consumers in that time period to adopt customer-oriented marketing philosophy (Hargreaves 2010). The 1990s had been identified as the decade of the environment and named as The Earth Decade (Finisterra do Paco and Raposo 2008). It was the time when attitude and actions of consumers and their expressed level of environmental concerns thrive throughout 1990s. However, researchers exploring on consumers recognized a significant gap between their levels of attitude and behaviour, seeing that the harmonious concerns were not able to target actions to be pleasing for the environment. • Present: In the present twenty-first century, the approaches being adopted towards healthy living remained remarkably familiar with new banners of sustainable living and sustainable development. Initially, it was conceptualized that people should find ways for their healthy living; but today, with a forward-looking approach, it should not only be healthy but sustainable too. Mankind is going to become accountable and answerable for what they did. Along these lines, the section provided guide that marketing at the outset came out as destructive, but seeking responsibility, emerged as protective as well. After understanding the relationship between Marketing and Environment, it is imperative to point out that consumers are the pillars of marketing activities and biggest role players. Now, it is significant to mention that throughout all marketing tasks, consumers play the best role of drivers of marketing activities. Appropriately, the next section pinpoints the twofold role of consumers and answers about why empirical work upon them in this direction will be fruitful.

1.4 The Twofold Role of Consumers: Destroyers or Developers

15

1.4 The Twofold Role of Consumers: Destroyers or Developers This section considers the role of consumers in propagating sustainable living. As the book is concerned with marketing as a subject, here human beings are termed as consumers; and individual, man, people, and similar words are interchangeably used leading to the same person: the consumer. Kotler and Armstrong (2005: p. 5) defined a marketing process in which all elements or stages target consumers (Fig. 1.3a), emphasizing on the importance of this person. Kotler and Keller (2005: pp. 19– 20) also associated four P’s of marketing with four C’s of consumers (Fig. 1.3b) by affirming that marketing is an activity which begins with consumers and also ends upon them. Therefore, the work in this book focuses on consumers. The word consumer is best suited for the purpose as Ozkan (2009) states that individuals view themselves and are viewed as consumers in more fields of life.

1.4.1 Consumers—Appearance as Destroyers Here, consumers are regarded as destroyers because of the experiences which have been noticed in the marketplace regarding them. As stated by Stern (2000), environmental impact has largely been a by-product of human desires for physical comfort, mobility, relief from labour, enjoyment, power, status, personal security, and maintenance of tradition-family, and even the organizations and technologies have been created to satisfy these needs of humans which were blamed for deterioration. Also,

a

b Understand Needs and Wants of Consumers Design a Customer-Driven Marketing Strategy

Four P’s (Marketer’s Aspect) Product Price Place Promotion

Construct a Marketing Program that Drivers Superior Value Build Profitable Relationship and Create Customer Delight

Capture Value from Customers to Create Profits and Customer Quality

Four C’s (Consumer’s Aspect) Customer Solution Customer Cost Convenience Communication

Fig. 1.3 a A model of marketing process emphasizing on consumers. Source Kotler and Armstrong (2005: p. 5). b Marketing and consumers. Source Kotler and Armstrong (2005: p. 58) and Kotler and Keller (2005: p. 20)

16

1 Background and Thought

as given by Mondejar-Jimenez et al. (2011), all prior economic activities originally aroused as a response to fully satisfying human needs. In Sect. 1.2.2, it has been described that there are enormous programmes that are shaped and developed in India for protecting the environment and maintaining its sustainability; but, the most consequential question is do these programmes get support and aid of consumers who must have a sense of duty for making all the endeavours of government a reality? Viewed from this pole, it is found that the coin has the other side also. As far as Indian consumers are concerned, less support was found from their side when CNG (Compressed Natural Gas) vehicles were started by the government, only because people felt inconvenience in daily handling. This created public protests, riots, and widespread consumer chaos. The people of India are adopting the means of sustainable lifestyles by using products like health foods or organic vegetables–fruits, natural textiles, vegetable dyes, herbal cosmetics, handmade stationary, less polluting products, and environment efficient fuels but more probably the changes are due to their own health concerns instead of environmental concern. A major role is placed by cost factor especially when it comes to fuel, electricity, and water consumption. People here generally conserve energy sources to save money rather than improving environmental tribulations. Although, in many regions of the country, there is a ban on the use of plastic bags but even then poly-bags are often used recklessly. In this way, many of the tasks and behaviour of consumers force them to be called as destroyers of environment.

1.4.2 Consumers—Expected Developers In no doubt, the point that humans enjoy a unique position in nature due to their exceptional ability to influence and mould the environment is of immense significance but it must not be forgotten that existence of clean and healthy environment is a prerequisite for their own survival on Earth (Tantawi et al. 2009). When humans have a unique position, the solutions will also be in their own hands (Kalantari et al. 2007). Also, Karp (1996) elaborated that consumers are important to study as they are not only expected to look out for their own welfare but also expected to consider what is best for the society as a whole. They are expected to give up their personal benefits for societal good outcomes. Using different words, Ajiboye and Silo (2008) describe that citizens will have to do well for common good means. They need to be willing to discuss about the nature of what is good for public and how to achieve it, so that they can sustainably develop their living world. The above discussion talked about the role of individuals both as destroyers and developers. The evidence of their role as destroyer is provided by Stern (2000). On the other hand, Karp (1996) and Ajiboye and Silo (2008) see them as developers, which is their expected role in society. Practically, consumers face the dilemma of their own well-being when it contradicts with the welfare of environment. For instances, environmentally responsible products may be costly and involve more time in searching, but the opposite alternatives may be cheaper and cost less. Driving

1.4 The Twofold Role of Consumers: Destroyers or Developers

17

own vehicle is more comfortable than sharing with others or using public transport. Irresponsible use of energy, water, and other natural resources bring no immediate pain, and unsustainable means of disposal are totally effortless instead enduring for recycling or reusing. Liegeois and Cornelissen (2006) quote similar views and remark that each choice confronts consumers with self conflicts (choice between an easy solution that harm the environment) and a sustainable substitute (a solution for which they themselves sacrifice or pay a price). Some consumers set aside their own benefits and select those options which can sustain environment or at least do not harm it. At the same time, some self-seekers pay no attention. Reading between the lines, one will become able to understand that why the discussion is concerned with the role of consumers and aligning with this part, the next section tries to get the answer to another foremost question, how a study on consumers can become possible?

1.5 Consumption Behaviour and Budding Environmental Challenges: A Factual Attempt Consumers operate through the process of consumption and their consumption behaviour has a profound influence on environment and the other people. According to Kaiser et al. (1999a, b), environment is a common property that is available to all. One’s individual consumption affects others, and abstinence from consumption is often at one’s own expense but improve the situation of others. Hence, in view of authors, a study on consumers is best possible by studying their consumption behaviour according to which they may operate as destroyers or developers. Several facts are gathered here which support this view. Decades ago, in 1970s Maloney and Ward (1973) called environmental crisis a consequence of ‘maladaptive human behaviour’. Kinnear et al. (1974) accepted that the role of personal consumption is significant in the deterioration of environment. Antil (1984) supplemented this view by saying that consumers are always likely to assume a key role in whatever direction national policy follows. Not only will consumers’ preferences be considered but most importantly, their consumption behaviour could determine the success or failure of whatever policies are implemented for environment or other purposes. In the words of Barnett et al. (2005), consumption is associated with the conspicuous and extravagant display of social position. The spread of consumptive lifestyles all over the world brought about the fastest fundamental changes in the lives of modern people. Only within a few generations, lifestyles of people have totally changed. They have turned into individuals, who are the addicts of shopping malls, drivers of automobiles, watchers of television, internet seekers converting into internet generation, and continuous buyers of almost everything that frequently goes into waste.

18

1 Background and Thought

The evidence provided by Ozkan (2009) clarifies the state and nature of consumers’ consumption behaviour and patterns. He mentioned that the era of 1920– 1960 was an era of having valuable goods, living in private houses and private cars. The concept of comfort became a prominent value to live life. As formerly stated, in this time period because of prevailing production concept of marketing, there were numerous offerings for consumers to enjoy. From 1960 to 1990, while making consumption decisions, people started paying attention to the issues such as inflation, pollution, and energy crisis. It was following 1990s that consumer behaviour started exhibiting efficient and responsible consumption patterns. Due to unsustainable lifestyles, consumers have been destroying the environment, yet not been able to lead successful and satisfactory lives. But now consumers must understand that they live in a global village and can ill afford the negative legacy of their consumption. In the words of Black and Cherrior (2010), anti-consumption is necessary to live a sustainable lifestyle. Thus, the above discourse clarifies that consumption habits affect the environment directly as well as indirectly. Directly, through consumption, and indirectly, as consumer’s demand influences production processes for which corporations consume resources to satisfy human needs. It is not the case that consumers should stop consumption; it is that reality which cannot be denied, but immediate changes in consumption behaviour are solely required to avoid superfluous deterioration of environment. Fuchs and Lorek (2004) emphasized that there will be no sustainable development without responsible consumption behaviour. Therefore, the task can be profoundly completed when the sense of social responsibility will get intermingled with consumption behaviour of consumers. These are the two concepts (Consumption Behaviour and Social Responsibility) on which this book is grounded. Accordingly, the next section defines these concepts.

1.6 Concepts: Defining and Measurements 1.6.1 Consumption Behaviour—Meaning and Definitions Before defining the meaning of consumption behaviour, it is imperative to define consumption. • Consumption According to Peattie (1995: p. 79), consumption is consumers’ actions of consuming, and consuming refers to the use of resources and creation of waste. Peattie (1995: p. 81) define consumption in several ways. He observes that marketers and economists view consumption relatively relationally in terms of satisfaction of wants and needs through the features and technical performance of products. On the other hand, sociologists tend to view consumption as a symbolic and social process in

1.6 Concepts: Defining and Measurements Fig. 1.4 The process of consumption. Source Authors’ compilation

19

Consumption Selection and Acquisition Usage/Storage Maintenance Disposition and Post Disposition

which products are not consumed for what they are and do, but for which they symbolize. Consumption as per Ozkan (2009) means to consume, waste, squander, or destroy, and the word has become synonymous with environmental destruction in most corners of the globe. It is defined as a process in which the substance of a thing is completely destroyed, used up, incorporated, or transformed into something else. In line with Nyberg and Sto (2001) and Hoyer et al. (2009: p. 5), to the practical level, consumption is not a single activity rather a process which includes several other activities including selection, purchase, use, disposal, and post-disposal evaluations of offerings. Different authors include different components of consumption. Hoyer et al. (2009: p. 5) include acquiring, using, and disposing. According to Peattie (1995: p. 81), it includes product and service selection, purchase, and use. As consumption is to be studied in the context of marketing, here it is referred to as a process which starts from selection and acquisition, and ends upon disposition and post disposition decisions. After disposition, the process again starts with selection. This process is evident from Fig. 1.4. • Consumption Behaviour Few authors have attempted to explain the meaning of consumption behaviour but many authors have defined the meaning of consumer behaviour which is ultimately a study of the process of consumption of consumers. Schiffman and Kanuk (1997: p. 6) describe consumer behaviour as the behaviour which consumers display in searching for, purchasing, using, evaluating, and disposing of products and services that they expect will satisfy their needs. According to Loudon and Della Bitta (2002: p. 5), it is a decision process and physical activity individuals engage in when evaluating, acquiring, using, and disposing of goods and services. With somewhat different words but the same theme, Solomon (2009: p. 7) asserts it as the study of processes involved when individuals or groups select, purchase, use, or dispose of products, services, ideas, or experiences to satisfy needs and desires. According to Hoyer et al. (2009: p. 4), consumer behaviour reflects the totality of consumers’ decisions with

20

1 Background and Thought

respect to acquisition, use, and disposition of goods, services, activities, and ideas by human decision-making units over time. An understanding of these definitions with the meaning of consumption comes out with an important point that consumer behaviour is actually a study of consumption behaviour of consumers. However, Glock and Francesco (1964) attempted to explain the difference between consumer behaviour and consumption behaviour. They mentioned two basic approaches of studying human behaviour. One approach is termed as micro behaviour which is related to the behaviour of single individual. Second approach is defined as macro behaviour referred to as the behaviour of the mass or aggregate of individuals. The authors defined the former approach as the study of consumer behaviour and later one is called as the study of consumption behaviour. They further elaborated that the study of consumer behaviour always focuses on the decisions and buying process (acts of choosing) of an individual consumer or consuming unit (unit implies a family or household) at a given time or over a period of time. On the other hand, the study of consumption behaviour is concerned with the description and explanation of behaviour of aggregate of consumers or consuming units again at a given time or over a period of time. In this way, the subject matters of the two concepts remain the same, but the distinction is that the subject matters of consumption behaviour parallels at the aggregate level that of consumer behaviour at the individual level. Thus, as derived, consumption behaviour comes out to be a study of selection and acquisition behaviour, usage behaviour, maintenance behaviour, disposition and post disposition behaviour, and also behaviour of evaluation by all human decisionmaking units over time with respect to marketing offerings. This definition has several implications. (1) Consumption behaviour involves distinct aspects. • Selection and Acquisition: For the purpose of selecting, consumers first start searching for information and try to gather knowledge. Selection may also involve evaluation of offering by comparing and anticipating the potential benefits. Acquisition can include many ways of obtaining products and services such as direct purchasing, leasing, trading, bartering, gift giving, finding, or borrowing (Hoyer et al. 2009: p. 5). However, it is represented by buying behaviour in the present context. • Usage: Usage is the act of using the acquired offering. It is an obvious behaviour, after consumers acquire, they typically use. • Maintenance: The work of keeping something in proper condition is taken as maintenance. • Disposition/Post Disposition: Disposition is the throwing away of used items. It refers to how consumers get rid of an offering they previously acquired (Hoyer et al. 2009: p. 5). • Evaluation: Evaluation is also stated as a part of consumption process. Schiffman and Kanuk (2002: p. 7) define evaluation as the third step of process in the model of consumer decision-making. However, Loudon and Della Bitta (2002: p. 5) mention that the process itself starts with evaluation. Accordingly, it may occur from several aspects in the stages of consumption, for

1.6 Concepts: Defining and Measurements

21

instance, before buying (actual need for the offering), while buying (evaluation of alternatives, evaluating advertising, the features of offerings and price, etc.), while using (expectations and requirements are being met or not), or disposing (consumers may evaluate second time purchase). (2) Consumption behaviour can have more than one decision-making unit. Generally, it is thought that consumer is an individual who identifies, purchase, use, and dispose of offerings during stages in consumption process (Solomon 2009: p. 8). Different actors can play their role as information gatherer (initiator), influencer, decider, purchaser, and user (Loudon and Della Bitta 2002: p. 6; Hoyer et al. 2009: p. 4; Solomon 2009: p. 8). One consumer may play all these roles or there can be different personalities involved. Loudon and Della Bitta (2002: p. 7) define that the word consumer is not specific. While defining consumer roles, they mention that a consumer can be an initiator, influencer, buyer, and user of the marketing offerings. The point given by Peattie (1995: p. 81) is also worth mentioning as he stated that the meaning of individual consumer can be misleading and this process can involve a variety of different people who can initiate the purchase or make the purchase decision. Then he/she can purchase the product and use it. The roles which a consumer plays are defined in Table 1.3. Now, coming to the concept of consumer in this book, every individual sustaining his or her life in the world can be seen as a consumer. The definition of consumer here involves all the roles as described in Table 1.3. Table 1.3 Consumers and their roles in consumption behaviour Roles

Description

Initiator

A consumer who determines that some need or want is not being met and authorizes a purchase to rectify the situation

Influencer

One who by some intentional or unintentional word or action influences the purchase decision; the actual purchase and/or the use and disposal of product and service

Decider

That person who decides about the real purchase of the offering and recognize the real need to have the offering

Buyer

Buyer is the purchaser who actually makes the purchase transaction

User

Users are ultimately engaged in the use of purchase

Disposer

Disposer may be the user or some other consumer who after use disposes the waste into dustbin

Recyclers

Some consumers may also play the role of recyclers by taking the waste to recycling amenities or by applying household recycling methods

Source Authors’ compilation; Format is taken from Loudon and Della Bitta (2002: p. 7)

22

1 Background and Thought

(3) There can be different times involved. The individual consumer or groups can operate in different time periods. It may be hours, days, weeks, months, and years (Hoyer et al. 2009: p. 4). (4) There can be different marketing offerings. Earlier, only goods and services were considered as marketing offerings but now, as the sense is expanding; offerings mean products, services, activities, and ideas (Hoyer et al. 2009: p. 4). Offerings are defined as ‘what is marketed’ by Kotler and Keller (2005: pp. 8–9) which are goods, services, events, experiences, persons, places, properties, organizations, information, and ideas. (5) Consumption behaviour involves many decisions. Hoyer et al. (2009: pp. 7–8) said that there can be different decisions involved in consumption behaviour. These decisions start with many questions such as whether, what, why, why not, how, when, where, how much, how often, and how long to consume an offering. (6) Consumption behaviour is a dynamic process. From the above part, it is recognized that consumption behaviour is in fact an ongoing process involving different steps specifically; selection, acquisition, usage, maintenance, disposition, and post disposition. Different parties are involved in it and this sequence can occur over a matter of hours, days, weeks, months, or even years. Also, it is a combination of different decisions. The totality of consumption behaviour is reflected in Fig. 1.5.

The decisions

About the Consumption

Whether What Why How When How much How often How Long

Selection Acquisition Usage/Storage Disposition PostDisposition

What is Marketed (Offerings) Products Services Events Experiences Persons Places Properties Information Ideas

Decision Makers

Over Time

Information -Gatherer Influencer Decider Purchaser User

Hours Days Weeks Months Years

The Totality of Consumption Behaviour Fig. 1.5 The totality of consumption behaviour. Source Hoyer et al. (2009: p. 4)

1.6 Concepts: Defining and Measurements

23

1.6.2 Social Responsibility—Meaning and Definitions • Responsibility Responsibility is defined as a duty or obligation to satisfactorily perform or complete the task that one must fulfil, and which has a consequent penalty for failure. The task may be assigned by someone or created by one’s own promise or circumstances (www.businessdictionary.com)[D] . According to Bexell (2005), responsibility may be positive and negative. Positive responsibility is the answer for what one actually does, and in the sense of negative responsibility, it is the bad situations, we fail to avert. He also distinguished between prospective and retrospective responsibility. Prospective responsibility for something is to have a duty or obligation in the virtue of some role that one fills. On the other hand, retrospective responsibility implies being responsible for something in the past. There can be different ways in which these responsibilities can be described. Rikner (2010) defines responsibility from a psychological perspective as a self-chosen restriction about whether or not to take action and partly depending on how these actions affect other people. One step further, with the concept of sustainable development as propounded by the World Business Council for Sustainable Development (WBCSD), here one more responsibility can be described that is responsibility for the future: for the upcoming generations. Figure 1.6 entails the dimensions of responsibility as pulled out by the above definitions. Prospective Responsibilities

Restrospective Responsibilities

Productive Responsibilities: Directed towards production of good outcomes. Preventive Responsibilities: Preventing another actor from producing bad outcomes. Protective Responsibilities: Avoiding doing harm

Being Blameworthy: Having failed to fulfill a duty Being Praiseworthy: Having done something good in the past.

Backward

Forward

Concepts such as accountability, answerability and liability deal with the backward looking sense of responsibility, thus implies restrospective responsibility.

Concepts such as obligations, duties, roles and tasks belong to forward looking sense of responsibility. Thus, prospective responsibilities are forward in sense.

Fig. 1.6 Dimensions of responsibility. Source Authors’ compilation

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1 Background and Thought

• Social Responsibility As per one definition of social responsibility, it is an ethical theory that an entity (organization or individual) has an obligation to act to benefit society at large and a duty every individual has to perform so as to maintain a balance between the economy and the ecosystems, and maintaining the equilibrium between the two. It is a moral binding on everyone to act in such a way that the people immediately around them are not adversely affected (www.en.wikipedia.org)[E] . Another definition of social responsibility stresses that both people and organizations must behave ethically and with sensitivity towards social, cultural, economic, and environmental issues (www. imasocialenterpreneur.com)[F] . Armstrong (1977) elaborated on it as a decision of not to accept an alternative that is thought by the decision-maker to be inferior to another alternative when the effects upon all parties are considered. Generally, this involves a gain by one party at the expense of the total system. This definition shows that social responsibility stands for a gain to the whole society at the expense of personal gains. In this way, there are distinct aspects of defining social responsibility and in order to fully understand this concept, the contents of these definitions are arranged here. (1) Social Responsibility is multidimensional: A scrutiny of the above definitions reveals that social responsibility involves multiple dimensions. It may involve responsibility for the moral, legal, and ethical norms of the society, responsibility towards cultural issues, responsibility to protect the environment in general, for the economic issues of the society, philanthropic responsibilities, and responsibility for sustainability. (2) Performance of social responsibilities: These responsibilities are to be performed by every entity whether individual or institution. Both have different social roles to perform. It pertains not only to business organizations but to everyone. (3) It may be negative or positive: Social responsibility can be negative in that it is a responsibility to refrain from acting (resistance) or it can be positive meaning there is a responsibility to act for the good (proactive stance). (4) It may be passive or active: Taken from the negative sense, social responsibility can be passive by avoiding engaging in socially harmful acts. Positive means active responsibility by performing activities which directly advances social goals. (5) It may include different time perspectives: Time perspective such a past, present, and future responsibilities. Considering the meaning of social responsibility from performance point of view, it becomes clear that institutions and individuals are the two pillars upon which our society is engaged. Ethics, environment, sustainability, and other similar issues are their shared responsibility which cannot be burdened upon any one of them. They can perform their social responsibility only by having a close and interdependent relationship with each other (Brinkmann 2004; Brinkmann and Peattie 2008; World

1.6 Concepts: Defining and Measurements

25

Business Council for Sustainable Development 2008). Thus, in marketing literature, on the basis of performance, social responsibility is seen as two aspects. First is Corporate Social Responsibility (CSR), which can be ascribed on corporations (institutions); second is Consumer Social Responsibility (CnSR or ConSR) which is about the responsibility of consumers (individuals). The next two sections broadly describe these concepts (CSR and CnSR).

1.7 Corporate Social Responsibility (CSR): An Operational Change As environmental problems were witnessed, firstly the discussions began for the responsibility of corporations. The corporate social responsibility has a long history which evolved with the development of businesses that has been meeting the emerging needs of the society. • 1950s: The 1950s can be called as the beginning of the era of CSR in which Bowen (1953) made an initiative to define the social responsibilities of a businessman to pursue those policies, decisions and to follow those lines of actions which were in line with the objectives and values of society at that time. • 1960s: In 1960s, the area of CSR expanded. It was thought that economy means of production and distribution should be employed in such a way that they must enhance the total economic and social welfare (Frederick 1960). • 1970s: The next decade, the period of 1970s worked as a boom in CSR area and added various dimensions to this concept. Friedman (1970) took it as a free competition without deception or fraud. Johnson (1971) supplemented the responsibilities towards all the stakeholders of businesses. These stakeholders may be employees, suppliers, dealers, local community, and the consumers who were ranked higher amongst all. Eilbert and Parket (1973) in this time conceptualized CSR as Good Neighbourliness. The term meant for not doing things that spoil the neighbourhood or voluntary obligation to solve neighbourhood problems which may be racial discrimination, pollution, transportation, or urban decay. Afterwards, Carroll (1979) encompassed four dimensions of CSR: economic, legal, ethical, and discretionary expectations from organizations. • 1980s: During 1980s, Freeman (1984) broadened the area of stakeholders’ responsibility previously given by Johnson (1971). He enriched the literature with his stakeholder’s theory. In this theory, stakeholders are defined as customers, competitors, trade associations, media, environmentalists, suppliers, consumer advocates, government, business, and local communities. • 1990s: There will be no over saying if the beginning of twentieth century is called as a revolution in the area of CSR. In this time period, Elkington (1997) introduced the famous Triple-Bottom line concept. The concept focused on three aspects—Responsibility towards people (social responsibility), responsibility towards planet and nature (environment responsibility), and responsibility for the survival or profit (economic responsibility).

26

1 Background and Thought

• Present: The academicians and scholars are continuously adding the literature of CSR in their studies that is vital with the change in time, views, and expectations of society. In the very beginning of twenty-first century, Lantos (2001) identified three kinds of CSR. The moral responsibility to prevent injuries and harm (ethical), true volunteering caring not enforced by law even at personal or organizational sacrifice (altruistic), and also caring corporate community service activities that accomplish strategic business goals (strategic). Recently the World Business Council for Sustainable Development (2008) added a responsibility of sustainable development and improvement of quality of life in the list of responsibilities. In India, under Companies Act, 2013 certain activities have been prescribed which may be included by companies in their CSR activities, and these activities relate to the social and environmental issues in the Indian context. Thus, in this present time social and environment both the aspects are integrating in the domain of the concept of social responsibility. In reality, determining what is good is controversial because behaviour consisted to be socially responsible by one group can cause other groups to complain (Ozkan 2009). However, it can be said that, for social responsibility, personal goals are at the bottom, and social and environmental goals are foremost and startling. In view of the above discourse, the philanthropic responsibilities of corporations for achieving sustainable development can be broadly classified into two: social and environmental, revealed in Fig. 1.7.

Social (The People) • • • • • •

Environmental (The Planet)

Resource Conservation Being responsible to all stakeholders and improving quality of life Economic development of the society Ethical and Moral activities Law Abiding Protection of Human Rights

• • • • • • • •

Resource Conservation Maximum Use Protection of neighbourhood environment Recycling No Pollution Biodegradability Improving Quality of Life Helpful in Ecological crisis

To Achieve Sustainability

Fig. 1.7 Domains of social responsibility. Source Authors’ compilation

1.8 Consumer Social Responsibility (CnSR): A Social Change

27

1.8 Consumer Social Responsibility (CnSR): A Social Change 1.8.1 Consumer Social Responsiblity—Emergence Ha-Brookshire and Hodges (2009) mentioned that research on the topic of social responsibility has primarily focused on firm’s strategies to meet growing consumer demands regarding societal issues; but, as more importance has been placed on understanding social responsibility for consumer behaviour, a consumer social responsibility study stream emerged and developed. Actually, it was in the early 1970s when Wood (1971) originated the concept of consumer responsibility on the notion that everybody in this world including the businessman is a consumer. Decades ago, Anderson and Cunningham (1972) obtained that the corporate responsibility issues were shifting to more conventional market segmentation problems as it is essential to know about consumers who constitute the market for products, services, or other corporate actions that promote social and/or environmental well-being. Thus, the question evolved for the responsibility of consumers and the task was shifted from socially responsible organizations towards socially responsible consumers. Also increasing importance of consumption behaviour and social–environmental problems caused by the human consumption patterns increased the importance of today’s concept of consumer responsibility (Ozkan 2009).

1.8.2 Consumer Social Responsibility—Meaning This concept may be new to Marketing Literature but it is as old as the golden rule that not to do the things to others which we do not expect to be done to us. As the consumer is an individual first, consumer responsibility is initially termed as Individual Social Responsibility (ISR). Individual Social Responsibility is taken as a common social responsibility from the viewpoint of Marketing (Ozkan 2009). Since here an individual is popularized as consumer who performs a number of roles (Table 1.3), their responsibility is referred to as Consumer Social Responsibility (CnSR). This social responsibility of consumers was propounded by the name of ‘other CSR’ by Devinney et al. (2006). As stated by them, in its broadest form, consumer responsibility could be defined as the conscious and deliberate choice of a consumer to make certain consumption decisions based on personal and moral beliefs. It includes two basic components. First is ethical which relates to the underlying importance of the social aspects of a company’s products and business processes. Second is consumerism which implies the preferences and desires of consumer segments as partially responsible for the increasing influence of ethical or social factors. The authors also stated that this responsibility can be shown in three ways. (1) As an expressed activity with respect to specific causes, such as donations or willingness to

28

1 Background and Thought

be involved in protests and boycotts. (2) As an expressed activity in terms of purchasing or non-purchasing behaviour. (3) As expressed opinions in surveys or other forms of market research. Taken from another perspective, consumer social responsibility too includes the component of environmentalism (Carrigan and Attalla 2001). From the overhead discussion, it can be noted that similar to corporate social responsibility, social responsibility of consumers may also operate in two ways. These ways are societal and environmental (Fig. 1.7). Societal aspects include the people, moral–ethical norms of society, and consumerism. On the other hand, environmental aspects cover environmentalism which is defined as attitude for and actions on behalf of the environment, its preservation and protection from destruction, pollution, and other human irresponsible acts (Dietz et al. 1998; Stern 2000; Tuna 2003; Zelezny et al. 2000). This book concentrates on the aspect of Social Responsibility of consumers. Now, it will be of prime importance to understand why to study responsibility of consumers instead of corporations. In the next section, the literature is contently analysed to understand the advantage of researching the concept of CnSR over CSR.

1.9 Studying Consumer Social Responsibility: Theoretical Relevance In the words of Rake and Grayson (2009), as a part of corporate social responsibility, efforts of corporations are growing towards sustainable development, yet these are not enough to tackle a big issue of sustainability. It may be because of two reasons which are explained in Sects. 1.9.1 and 1.9.2, and emphasize that studying consumer social responsibility is imperative.

1.9.1 Problems and Challenges for Corporations This section highlights that corporate responsibility and its dimensions are widely studied but the challenges or problems which corporations face because of consumers are little discussed. Thus, synthesizing the results from literature, problems and challenges are noted in the path of corporations when they prepare their responsible practices. These challenges and problems are related to consumer behaviour, and signify the importance of studying consumers for their social responsibilities so that corporations match their practices accordingly. (1) Segmentation Problems: The main challenge for marketers is to understand the dynamics of consumer behaviour. The structure of determinants that influence consumer behaviour is very complex. A range of determinants that are demographic (Laroche et al. 2001; Shanka and Gopalan 2005; Singh 2009; Singh and Gupta 2011), social (Dietz et al. 1998; Tilikidou and Delistavrou 2007; Alibeli

1.9 Studying Consumer Social Responsibility: Theoretical Relevance

29

and Johnson 2009), cultural (Dietz et al. 1998; Kim and Choi 2005; McCright 2010), geographic (Schwepker and Cornwell 1991; Hunter et al. 2004; Singh 2009), economic (Laroche et al. 2001; Tilikidou and Delistavrou 2007; Singh 2009; Singh and Gupta 2011), and psychological (Schwepker and Cornwell 1991; Berger and Corbin 1992; Mondejar-Jimenez et al. 2011) are generating the particular actions of consumers and become the bases of segmentation by the academics (Straughan and Roberts 1999; Laroche et al. 2001; Albayrak et al. 2010). In reality, the structure of these determinants is very much complex and choosing a particular base requires judgment and subjectivity. It is very risky for a marketer to take assumption of ceteris paribus; because all factors may operate simultaneously. While adopting one, marketers always have to bear the cost of another opportunity that may also be equally important. Therefore, a research for comprehending these determinants which drive consumer behaviour becomes relevant. (2) Cost and Benefits: In the present day competitive environment, businesses have a challenge to frame their green offerings keeping in mind how much it costs to consumers and what benefit they will get in return. Devinney et al. (2006) mention that some consumers are ready to pay more for products with positive social attributes, but ultimately settle satisfactorily only when the functional benefits of these products meet their needs. Further, Casey (2007) identified that consumers will not accept so-called green package that is not an improvement over the previous package, and in addition is costly. As identified by Ozkan (2009), products with an environmental and social appeal may have an edge for consumers only if they meet other competitive requirements. Moreover, it has also been observed that consumers as a whole do not want to pay extra for environmental benefits unless marketers add additional values to broaden green appeals. Ottman et al. (2006) supplemented by saying that these green appeals by companies are not likely to attract consumers unless they are offered with desirable benefits such as cost-savings or improved product performances. In this way, consumers’ own sense of cost–benefit analysis is also a challenge to meet this end and enforce a need for a study upon them. (3) Dump Psychology of Consumers: Most often consumers believe that products manufactured with recycled materials are of lower quality (Mohr 2000). According to Rikner (2010), if environmental behaviour is not profitable, people do not feel it is worth the effort. As per them, reduced consumption leads to lower standard of living. People’ conventional wisdom also takes non green products better than green ones (Ottman et al. 2006), and there has been found a lack of credibility amongst consumers in firm’s claim of greenness. As stated by Prakash (2002), consumers are also of the belief that individual actions alone have no impact and collective endeavours are impeded by free riding. These kinds of consumer attitudes are big challenges for corporations in going green and stress for studying consumers’ own sense of social responsibility. (4) Lifestyles of Consumers: Young et al. (2010) describe that consumers often stick to behaviours like driving own vehicle because of time, convenience, and prestige. Therefore, are not much encouraged for pooling a vehicle. Tiring to-do job lists hardly allow them to spare time for examination of buying behaviour

30

(5)

(6)

(7)

(8)

1 Background and Thought

and search for green products. Also, ‘being green’ needs time and space in people’ lives, whereas, busy schedules hardly permit such actions. Thus, habitual lifestyle of consumers is a big deal how marketers can align their practices with consumer behaviour towards green products. Problem of Communication: Lack of information on what persuades consumers to buy green products or what they feel after buying such products is one of the constraints that businesses encounter in adopting green practices. Green claims and eco-labels4 of corporations as are prevailing in market are distressing to many people because they are unable to rightly perceive them as green due to non-existence or ineffectiveness of communication systems (Shrum et al. 1995; Prakash 2002; Zaman et al. 2010). Hence, it can be understood that investment in a reliable and effective communication becomes a challenging job for marketers, and again puts a pressure for a study on consumers. Lack of Perfect Knowledge: According to Mohr et al. (2001), there is a great deal of debate about social responsibilities of businesses but little information is available on expectations of general public. So, businessmen lack a clear understanding of what the public wants from them, and how far they are expected to go towards helping their communities. One of green marketing challenge is the lack of standards or public consensus about what constitutes ‘green’. People are confused about environmental terminology. Environment friendly, recyclable, recycled, and biodegradable have different meanings to different consumers. A marketer always remains in ambiguity because of scarce knowledge. Also, today’s environment-friendly action may become unfriendly tomorrow. As according to Carbajal and Kanter (2009) initially HFC (Hydro Fluoro Carbon) is used as an alternative to CFC (Chloro Fluoro Carbon) when CFC is found to be causing environmental damage, but later it is observed that HFC too harms the environment and it is a growing threat to our climate (www. greenpeace.org)[H] . Thus, marketers are indistinct for consumer behaviour and about their own green practices because of scarce knowledge. Green Marketing Myopia and Green Wash: The objective of green marketing is to improve environmental quality and customer satisfaction; misjudging or overemphasizing the former at the expense of the latter is termed as green marketing myopia. Green Wash is the misuse of the principles of environmental marketing and means that consumers cannot trust the content of advertisements (Karna et al. 2001). Research indicates that many green products have failed because of more focus on greenness instead of expectations of consumers (Ottman et al. 2006). Also, some companies cheat consumers by making false environmental claims. These situations restrict other companies to go for green marketing and then it seems as ‘green wash’ rather than ‘green hope’ (Zaman et al. 2010). Thus, challenges are intact in the working path of corporations. Fake Outlook: Survey method has remained a prime method for research on consumers; but, surveys have not been reliable so far as consumers often act

4 Eco-labels are the tools for green marketing. These are the labelling systems for food and consumer

products. They are a form of sustainability measurement directed at consumers, intended to make it easy to take environmental concerns into account when shopping (www.en.wikipedia.org)[G] .

1.9 Studying Consumer Social Responsibility: Theoretical Relevance

31

differently from their aphorism. According to Ottman (2004), many consumers say that they will buy green products, but usually the good intentions are never turned into actions at store. Alwitt and Pitts (1996) have also mentioned that the gap between attitude and behaviour is not unexpected. Although consumers say they are concerned with the environment, they do not buy green products with overwhelming preference. As an instance quoted by Devinney et al. (2006), although consumer activism and pressure from NGOs led Starbucks to promote and sell fair-trade coffee, the sales levels have been much lower than expected and demand has remained relatively flat since it is introduced. Greenness amongst consumers is also both difficult to identify and measure as consumers do not act green in all circumstances, and all consumers are also not equally green (Kaur 2006). Also, they may not be green at all levels; as instance, those who are conservers may not be recyclers at times, and then marketers again have problems in locating their ready market to tap. (9) Retailers’ Pressure: Retailers, as intermediate consumers, are forcing consumer goods marketing companies into becoming green. They are setting strict guidelines as to what type of packaging they will accept (Casey 2007) but marketers are facing problems in gathering data on formats and materials to determine their strategies for the future. As a result, the manufacturers, instead of adopting real green marketing are falsely rushed to make green claims. This provides marketers with a real challenge of global survival with true honesty and reliability on their part. Comprehension of the above section shows that although corporations are held responsible for environmental deterioration and imposed with legislations for their social responsibility; but, it is not the case that corporations are neglecting environmental standards, and only their responsibility is crucial. Rather it can be said that they are working under the insistence of consumers to adjust their practices for environmental sustainability, and face a number of problems while performing their part. In the light of the facts, it will not be mistaken if we say that consumers are the drivers of corporations’ actions. Many companies have either altered their practices or are in the process. Facts regarding the same are highlighted next.

1.9.2 CnSR—A Driver for CSR In the present scenario, companies are making efforts to gradually transform their businesses compatible with environment. This change of corporate practices is a result of many factors from imposed regulations and stakeholders pressure to voluntary behaviour of economic green opportunities and ethical motivation. Consumers are also affecting the businesses in terms of inclining their loyalty with the emergence of a new class of green consumers. Here, points are arranged from content analysis of literature that responsibility of consumers is working as a cause and has a profound influence on the responsible working of corporations.

32

1 Background and Thought

(1) ISR leads to CSR: Consumer responsibility may be a new concept as now individuals view themselves and are viewed as consumers in most fields of life; but, it is as old as the concept of ISR. It has been observed that individual social responsibility (ISR) is all the roots of Corporate Social Responsibility (CSR) and is important to achieve it. A corporate comprises of individuals and hence determines the social responsibility culture it creates. This is the intermingled relationship between CSR and ISR. Individuals are becoming socially responsible and in response, corporations need to become more and more socially responsible from all aspects to meet consumer demand (www.slideshare.net)[I] . (2) Environmental Movement and Consumerism: Consumer movement is one of the causes for setting agenda for corporate practices as it asserts that all consumers have an inherent right to get such products which are safe in use, designed economically, have less or no harmful effects on environment, reliable, honestly labelled and advertised (Winsor 1999). According to Straughan and Roberts (1999), with increasing social and political pressure, companies have moved beyond simply addressing pollution and waste disposal to search for alternative package composition and design, alternative product formulations, and causerelated promotion in an effort to synchronize with the environmental movement. In line with Kaur (2006), much of the pressure for socially responsible actions on businesses has come from their customers in the form of product performance, environment protection, safety, and information disclosure. Companies have to get well with the stakeholders in order to attract the consumers; and thus, it will get a harmonious, stable, and sustainable commercial environment (Chen and Kong 2009). Thus, environmental movement and consumerism both induce green practices. (3) Consumer Responsible Behaviour: According to Wright and Klyn (1998), public environmental concern is reaching out at unprecedented levels, and manufacturers in many countries seek to take account of this concern in their product design and promotion. Chan (2001) suggests that if consumers exhibit a high degree of ecological consciousness and channel it to corresponding eco-friendly or green purchases it is likely that profit-driven enterprises will be strongly motivated to adopt the concept of green marketing in their operations. Consequently, it is an elaborated fact that consumer behaviour has a profound effect on businesses. The growing competition in the market makes it a compulsion for businesses to adopt such programmes and practices, designs, packages, and distribution processes that are in conformity with the responsible behaviour of consumers and society at large (NRC5 2008). Consistent with Webb et al. (2008), much of the American Public wants companies to be more environmentally responsible and a number of corporations are responding to these desires. According to Chen and Kong (2009), initially few Chinese companies were reluctant to disclose the 5 The

National Recycling Coalition, Inc., (NRC) is based in Washington D.C. and was founded in 1978 as a not-for-profit organization whose objective is to eliminate waste and promote a sustainable economy through advancement of sound management practices for natural resources. Its official website is www.nrcrecycles.org[J] .

1.9 Studying Consumer Social Responsibility: Theoretical Relevance

(4)

(5)

(6)

(7)

33

CSR information but with the improvement of consumer consciousness, companies disclose full CSR report voluntarily without any enforced regulations. This highlights that consumer responsibility is commanding green practices of corporations. Cohesive Consumer Behaviour: The point is well recognized in the market place that while introducing and promoting green products, marketers always have to follow consumers who are normally bounded by their past behaviour which they don’t want to change. General Motors and Ford encountered problems when they launched their electric vehicles. It was found that most drivers were not willing to drastically change driving habits and expectations to accommodate electric cars. Thus, the product ultimately was taken off from the market (Ottman et al. 2006). This again highlights that consumer behaviour acts as a cue for the green practices. Consumer Boycotts and Social Change: Consumer spending habits affect business practices of corporations. Consumer boycotts proved as an effective tool for social change. US consumers boycotted PepsiCo for doing business in Burma because of that country’s poor human records. Their boycott costs in US were more than their earnings in Burma. So, the company pulled out its business from Burma under the direct instructions of consumers (Sen et al. 2001). The decision of Taco Bell and McDonald to increase wages for tomato pickers did not come through government; it occurred because thousands of Americans protested and threatened to spend their money elsewhere (Einterz 2008). When consumers show that environmental degradation, racism, low wages, and manpower exploitation in any way will not be tolerated, the corporations have to change to meet consumer standards. Hence, behaviour of consumers must be studied as it is ever-changing and dynamic. Consumer Appreciations: Appreciation means positive consideration by consumers to corporations for their responsible practices. Mohr et al. (2001) attained that in consumers’ view, every purchase has implications for a larger society, both in terms of environmental impact and rewarding or punishing companies that are seen as more or less socially responsible. Based on the assumption that consumers will reward firms for their support of social programmes, many organizations have adopted social causes (Levy 1999). This confirms that organizations are induced by the rewarding behaviour of consumers and they work accordingly. Consumer Groups Alertness: In the wake of adverse business practices, consumers are becoming aware of its impact on environment, and are now exerting pressure on companies to behave in socially responsible manners. Leading by an NGO, Starbucks started displaying and selling fair-trade coffee (Devinney et al. 2006). Recycled paper, plastic goods, and dolphin-safe tuna are other examples of products positioned on the basis of environmental appeals issued by consumers (Mostafa 2007). Greenpeace took a stand on Tuna and Dolphins (www. greenpeace.org)[K] in US, and forced to change fishing techniques when consumers become aware that such techniques are resulting into killing of a large number of dolphins every year (www.swfsc.noaa.gov)[L] . It highlights that this

34

1 Background and Thought

form of consumers’ stance works as setting new standards for corporations and provides ways for their actions. (8) Rules Targeting Consumers: Ottman (2008) provided five rules of green marketing which have a focus on consumers and emphasize that any company can start taking advantage of new opportunities and can ignore pitfalls by relying on these simple rules. The rules circulate around consumers and work as a driver for taking decisions for corporate green practices. These rules are • Know your customer—Be sure that the customer is aware of the issue that your product attempts to address. • Empower consumers—Focus on empowerment of consumers or the belief that consumer individual efforts can make a difference. • Be Transparent—Provide legitimacy in product and in claims that are stressed. • Reassure the Buyer—Assurance of validity: environmental claims will not work over product performance and quality. • Consider pricing—Make a surety that consumer can afford premium price and they are willing to pay high for environment purposes. Thus, consumer is the pivot and all marketing activities cluster around him. Consumer behaviour is the factor that is superimposed on all other factors which influence the working of corporations. The obligation of profits to a company can provisionally be abandoned but if the offerings don’t meet the needs of consumers, then failure is unavoidable. Accordingly, by reading the above two sections, it can be understood that the area of CSR will bear fruits only if consumers will be studied for their behaviour and responsibility because then corporations can work smoothly and the concept of shared responsibility can be met out.

1.10 Relevance of Studying Social Responsibility/Consumer Social Responsibility in India: Empirical Evidence After explaining the above sections, it hardly needs any justification that most of the nations of the world have achieved high growth and decent standard of living at the expense of environment. This fact can also be explained on the ground of increasing global ecological overshoot as is shown in Fig. 1.8. Global ecological overshoot occurs when humanity’s demand exceeds the biosphere’s supply or regenerative capacity. Such overshoot results in depletion of Earth’s resources to a large extent which in turn generates a huge amount of waste. In Fig. 1.8, x-axis reveals the ‘years’ and y-axis embraces with the ‘number of Earth planets’. It reflects that as per estimates, today humanity is utilizing the equivalent of 1.5 planets for resource consumption and absorption of produced waste. Present scenario of Earth usage suggests that if the trend of population increase and growing consumption continues;

1.10 Relevance of Studying Social Responsibility/Consumer Social …

Number of Planets

Fig. 1.8 Global ecological overshoot. Source http:// www.footprintnetwork.org/ en/index.php/gfn/page/ world_footprint/[M]

35

Years

by the 2050s, there will be a need of the equivalent of three Earths to support us; certainly, we have only one. This trend of ecological overshoot is visible in India also. Unfortunately, in India, in terms of resource consumption, it is found that Earth’s resources are exploited and depleted recklessly and the facts and figures gathered (for resources like energy and water) substantiate upon the trend of ecological incompatibility taking place here. In this regard, the sectoral consumption of commercial energy6 and its various forms are highlighted in Tables 1.4 and 1.5. Trend of water consumption is revealed in Fig. 1.9. Table 1.4 reveals that the consumption of commercial energy in India is increased in all sectors over the years. The share of industry sector in energy consumption is highest and increasing in all the years which point towards setting the responsibility Table 1.4 Sectoral consumption of commercial energy in India (in million tonnes of oil equivalent) Sectors

Years 1980–81

Agriculture

1985–86

1990–91

1995–96

2000–01

2005–06

2010–11

1.6

2.4

4.9

8.4

15.2

15.1

23.14

Industry

36.9

49.2

62.9

77.5

77.4

96.2

137.98

Transport

17.4

21.7

28

37.2

33.5

36.5

55.34

5.6

8.9

12.6

15.3

24.1

32.6

43.43

Residential and commercial

Source TERI Energy Data Directory and Yearbook (2012/13: p. 3)

6 The

energy sources that are available in the market for a definite price are known as commercial energy. By far the most important forms of commercial energy are electricity, coal, and refined petroleum products. Commercial energy forms the basis of industrial, agricultural, transport, and commercial development in the modern world (Bureau of Energy Efficiency–Energy Scenario 2014: p. 2).

37,568

48,069

66,980

84,209

104,693

107,622

151,557

171,293

189,424

209,474

236,752

272,589

346,469

–

44.84

6.03

1980–81

1985–86

1990–91

1995–96

2000–01

2005–06

2006–07

2007–08

2008–09

2009–10

2010–11

2011–12

2012–13

Percentage distribution (2011–12)

Compound growth rate (1970–71 onwards)

8.43

17.30

–

133,660

131,967

120,209

109,610

104,182

99,023

90,292

84,729

85,732

50,321

23,422

14,489

8,721

4,470

9.44

22.01

–

170,034

169,326

146,080

131,720

120,918

111,002

100,090

75,629

51,733

31,982

17,258

9,246

5,821

3,840

8.16

8.97

–

69,266

67,289

60,600

54,189

46,685

40,220

35,965

22,545

16,996

11,181

7,290

4,682

3,507

2,573

Source Energy Statistics (2013), Published by Government of India

29,579

1975–76

Commercial

5.76

1.85

–

14,327

14,003

12,408

11,425

11,108

10,800

9,944

8,213

6,223

4,112

3,182

2,266

1,855

1,364

Traction and railways

7.45

5.03

–

38,847

39,218

36,595

37,577

29,660

23,411

24,039

17,862

11,652

8,552

4,967

3,615

2,774

1,898

Others

7.08

100.00

–

772,603

694,392

612,645

553,995

501,977

455,749

411,887

316,600

277,029

190,357

123,099

82,367

60,246

43,724

Total electricity consumed (GWH)

4.86

__

–

535.88

532.69

532.04

492.76

457.08

430.83

407.04

313.70

273.42

213.86

154.30

114.01

99.68

72.95

7.00

__

69.08

64.75

60.07

56.32

51.67

47.67

42.90

40.19

37.96

32.26

21.14

14.89

10.35

6.60

3.84

Diesel

5.70

__

15.74

14.99

14.19

12.82

11.26

10.33

9.29

8.65

6.61

4.68

3.55

2.28

1.52

1.28

1.45

Petrol

Coal

Domestic

Industry

Agriculture

Consumption (in million tonnes)

Electricity consumption (from utilities) by sectors (in Giga Watt Hour = 106 × Kilo Watt Hour)

1970–71

Years

Table 1.5 Consumption of commercial energy as per its components 36 1 Background and Thought

1.10 Relevance of Studying Social Responsibility/Consumer Social … Fig. 1.9 Projected water consumption in India. Source Ernst and Young (2011)

37

Italic figures are in billion cubic meters Agriculture 1.11 Times 2030 E

674 CGR 0.6 %

2010

606

Domestic 1.95 Times 2030 E

66.44 CGR 3.4%

2010

34.05

Industry 2.24 Times 2030 E

91.63 CGR 4.1%

2010

40.86

of corporations (CSR). However, energy consumption in transport sector and residential–commercial sector is also escalating over the years. Accordingly, emphasize on the aspect of responsibility of consumers (CnSR). Table 1.5 manifests the trends of the consumption of various components of commercial energy. The data for electricity consumption shows that total consumption of electricity has amplified. The fact is advocated by compound growth rate (CGR = 7.08%). Sector-wise, industry sector ranks first due to its highest percentage share that is 44.84%. This sector’s foremost consumption of electricity ultimately reflects a strong case for industrial responsibility namely CSR. Next proponents of electricity consumption are the domestic and agriculture sectors with 22.01 and 17.30% shares, respectively. The growth rate (CGR = 9.44%) of electricity consumption for domestic sector is highest in comparison to other sectors which justifies the point of responsibility for general consumers, to be exact, point towards CnSR. In addition to electricity, the other components of commercial energy are coal, diesel, and petrol. Estimated compound annual growth rates highlight that the consumption of these resources has substantially increased from a period of 1970–71 to 2012–13. As these resources are primarily consumed by industry, transport, agriculture, and household sectors, it can be said that they all have a corresponding duty for their revival. With the same notions, Fig. 1.9 gives estimated consumption of water in year 2030 as compared to the year 2010. In Fig. 1.9, consumption of water in agriculture,

38

1 Background and Thought

domestic, and industry sector is considered. In line with this figure, consumption of water is highest in agriculture sector. Compared to the year 2010, it is expected to rise to 674 billion cubic metres: an approximate, increase of 1.11 times. Water consumption of industry sector is expected to rise very speedily, specifically 2.24 times from the present rate of consumption. Compound growth rate for this sector infers that the consumption may escalate nearly 4%. Coming to domestic sector, anticipations reveal that from 2010, there may be estimated 2 times increase in water usage in 2030 with 3.4% growth rate. This emphasizes that both industrial and domestic sectors are consuming a substantial amount of water resources and anticipated future demand from both the sides is projected to rise. Hence, conservation of water should be regarded as the communal responsibility of both these societal groups. Once again, it emphasizes that consumers are equally responsible to be studied with corporations. The area of study of their responsibility is CnSR which is the ground or platform for the present book. Above analysis shows that India, home to the second largest and approximately 1/6th of world’s population, has to act fast and responsibly for the sustainability issues it is facing. Sustainability has always remained a core-component of Indian culture and sub cultures. But, here population size and density make the task cumbersome and demanding. Also, the consumption trends and behaviour is fast changing in India. The people here are passing through demonstration effect from developed world, the discussion upon which is outside the scope of this book. Whatsoever, the things cannot be changed unless the general public who act as consumers put efforts for the same and change their consumption behaviour towards sustainability. This can be possible only if they show their social responsibility and act as steward of the planet. In this sense, study of Indian consumers for their social responsibility has much significance and worth. All in all, this chapter introduced the concepts of ‘consumption behaviour’ and ‘social responsibility’. Consumption behaviour signifies the process of decisionmaking in various consumption stages, and social responsibility here means consumer social responsibility (CnSR) for environment and society. In this book, sample of Indian consumers will be studied for their consumer social responsibility. Now Part II of the book brings out the work that has already been carried out in literature on these two themes. It too visualizes the conceptual framework, elaborates upon research model, and gives specifications on what constitutes research model and why. Websites [A] [B] [C] [D] [E] [F] [G]

http://en.wikipedia.org/wiki/Silent_Spring. http://www.globalissues.org/article/171/loss-of-biodiversity-and-extinctions. http://ens-newswire.com/. http://www.businessdictionary.com/definition/responsibility.html. http://en.wikipedia.org/wiki/Social_responsibility. http://www.imasocialentrepreneur.com/social-responsibility/. http://en.wikipedia.org/wiki/ecolabels.

1.10 Relevance of Studying Social Responsibility/Consumer Social …

39

[H] http://www.greenpeace.org/usa/PageFiles/58801/hfcs-a-growing-threat.pdf. [I] http://www.slideshare.net/anuptiwari/isr-individual-social-responsibility? qid=2608715a-2e07-4621-9501-0c01c2ded237andv=qf1andb=andfrom_ search=1. [J] http://nrcrecycles.org/about/. [K] http://www.greenpeace.org/india/en/news/where-are-the-pacific-tuna/. [L] https://swfsc.noaa.gov/textblock.aspx?Division=PRDandParentMenuId= 228andid=1408. [M] http://www.footprintnetwork.org/en/index.php/gfn/page/world_footprint/.

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Part II

Review of Literature

This Section describes and analyses the literature through Chaps. 2 and 3. Chapter 2: An Overview of Literature portrays abstracts of literature work. Whereas, Chap. 3: Conceptual Framework and Research Model presents a comprehensive examination of literature, describes the variables to comprehend the theme, highlights the research model, and answers about the reasons of the formation of responsible consumption behaviour.

Chapter 2

An Overview of Literature

A detailed and comprehensive analysis of literature is necessary in order to understand the progress done so far in this field and discover the gaps need to be looked upon for the academic furtherance. For the reason, a number of research papers, articles, and a range of scholarly work are used for getting the insights into the trends of the studies on consumption behaviour and social responsibility. This chapter draws together the literature spread of over five decades and synthesizes that with the present research. For the effortless comparison, each study is underlined on the purpose, design, statistical approach, and conclusion.

2.1 Literature Review: Exploring What Has Been Done (1) Berkowitz and Lutterman (1968) talked about the traditional socially responsible personality. Purpose/Objectives: The aim was to extend the exploration of socially responsible personality and to report a number of attitudinal and behavioural correlates of Social Responsibility Scale (SRS) from literature. Design/Methods: On the basis of item and statistical analysis, eight statements were selected for including in interview. These items coupled with many other questions were administered to a state-wide probability sample of 766 Wiscovision adults. Age, gender, and education along with some other personality characteristics were studied as predictors. Statistical Approach: Percentages, Chi-Square, and correlation were used for getting the results. Findings/Conclusion: It was reported that SRS was positively correlated with educational level and age. The respondents in the urban working class were more likely than their rural counterparts to prefer democrats. Further, high scores in SRS generally were least inclined to deviate from the political traditions of respondent’s class and community. (2) Anderson and Cunningham (1972) initiated to search about consumers designated in their study as socially conscious consumers. Purpose/Objectives: They pro© Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_2

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ceeded with two hypotheses and intended to test the viability of both. These were consumers exhibiting a high degree of social consciousness differed significantly from consumers who did not, on selected demographic and socio-psychological attributes. Design/Methods: A self-administered questionnaire was developed with five-point continuum from ‘strongly agree’ to ‘strongly disagree’. It was projected to collect data from 1200 Austin Texas households, but 412 questionnaires were returned. Statistical Approach: Data were analysed through linear discriminant analysis. Findings/Conclusion: Findings indicated that demographic variables were able to differentiate between the high and low socially responsible consumers in which occupation of the household head, socio-economic status, and age of the household head yielded strong correlations with discriminant scores. The socio-psychological and demographic variables combined were not effective in differentiating high and low socially responsible groups. Also, results bring into being that the socially conscious consumers were more cosmopolitan, less dogmatic, less conservative, less status-conscious, less alienated, and less personally competent. (3) Kinnear et al. (1974) answered about ecologically concerned consumers and their profile. Purpose/Objectives: The purpose was to empirically explore the relationship between socio-economic and personality characteristics of consumers and the amount of ecological concern they might indicate. Design/Methods: Keeping an eye on the purpose, behavioural and attitudinal measures of ecological concern were combined into an index named Ecological Concern. Twenty independent variables were examined. Seven were socio-economic, twelve of the predictors were provided by scores on standard personality scales, and one other final predictor was perceived consumer effectiveness (PCE). Data was collected by means of a mail questionnaire returned by 500 members of the Canadian Family Opinion University of Western Ontario consumer panel. Statistical Approach: Regression and ANOVA (Analysis of Variance) were the research tools applied. Findings/Conclusion: It was interpreted that ecologically concerned consumers tended to score high in perceived consumer effectiveness against pollution, high in openness to new ideas, high in their need to understand the working of things, and their intellectual curiosity (understanding) was satisfied. Also, they were found moderately high in their need to obtain personal safety. (4) Webster (1975) determined the characteristics of socially conscious consumers by testing the social involvement model.1 Purpose/Objectives: He purposed to work on three dependent variables: SCC (Socially Conscious Consumer Index), SR (Socially Responsible Scale), and Recycling (R). Design/Methods: Social Responsibility Index (SR), Perceived Consumer Effectiveness (PCE), and Perceived Power of Big Businesses (PB) were the variables defined in an attempt to measure attitudes. 1 Webster

(1975) developed propositions from the literature review to guide research into the characteristics of the socially conscious consumer and called it social involvement model, a hypothesis for his study. According to this model, the socially conscious consumer was defined as a person who was in a good position in terms of income, education, and occupation to contribute to the community and his/her self concept allows him to take an active role. He/she acts in a manner consistent with his/her attitudes and plays an active role not only in organized activities but also in his individual behaviour as a consumer.

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Four personality scales were also considered namely, Dominance (Do), Responsibility (Re), Socialization (So), and Tolerance (To). These four subscales were the part of the California Psychological Inventory, shortly called CPI. To measure social activity variables, respondents were asked to list all community organizations to which they belonged to, or in which they had participated or volunteered services. A 60-item questionnaire was mailed to all household customers in one New England community of which 231 useable questionnaires were returned. Statistical Approach: A multiple regression package (MULTREG), a step-wise regression procedure (STEPREG), and multiple discriminant analysis package (MDA) were operated upon. Findings/Conclusion: Results showed that recycling behaviour was related both to the SCC and SR measure. The two variables significant in predicting both SCC and SR were PCE and To. In addition, the socially conscious consumers and the recyclers both showed openness to other person’s views. The SR respondents also felt that they could influence the environment and other people with their purchases. (5) Antil (1984) profiled socially responsible consumers and discussed implications for public policy. Purpose/Objectives: It was premeditated that consumers would be profiled based on demographic and psychographic variables. Design/Methods: A 40-item SRCB (Socially Responsible Consumption Behaviour) scale was developed as dependent variable and 16 independent variables were selected. Six of these were individual difference variables and ten others were demographic variables. This was the first study to utilize a national representative sample. The research instrument with dependent and independent variables were mailed to 1000 household members of a market Facts Inc., of which 690 useable questionnaires were processed. Statistical Approach: Bivariate and Multivariate techniques were exercised. Multiple Regression determined the combined effect of independent variables and product moment correlation, and t-test provided insights into the extent of relationships and significance of them. Findings/Conclusion: The findings indicated that population density was significantly related to SR consumption; however, conservatism was inversely related to it. The overall profile of these consumers highlighted that they were more knowledgeable, had elevated environmental concern, and tended to be more involved in community and service organizations. This group of consumers was also defined as more demanding and critical of the U.S. government and more concerned about health. It was a group of people who enjoyed physical activities like gardening, fixing up house working, reading, and painting. The people preferred magazines to television, classical to popular music, and like exercising in the outdoors. Further, they had less desire to own a big car, were satisfied with their lives and jobs, interested in culturally oriented activities, more confident in personal ability, take direct actions, and more likely to be brand loyal. (6) Hines et al. (1986/87) meta-analysed research on responsible environmental behaviour and synthesized the empirical results. Purpose/Objectives: The major goal was to analyse and synthesize the studies to identify the variables most significantly associated with responsible behaviour. Design/Methods: The primary methodology employed in accomplishing the goals involved the use of Schmidt– Hunter Meta-Analysis technique. Firstly, the studies were located and pertinent

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information was extracted. The findings were converted to Point Biserial Correlation. Moderator variables were also identified. The variables were categorized into three types specifically; cognitive, psychosocial, and demographic. Statistical Approach: Meta-analytical mean and standard deviation was calculated to accumulate empirical results. Findings/Conclusion: Meta-analysis resulted in a positive correlation between attitude and behaviour indicating individuals with more positive attitude were highly engaged in responsible environmental behaviours. Mode of behaviour assessment worked as a moderator in attitude–behaviour link. People having internal locus of control was found environmentally engaged than external locus of control people. Verbal commitment also highly predicted behaviour. The relationship of economic-orientation with behaviour, and the relationship between income and education were positive. Negative relationship of age indicated that youngest individuals were slightly more likely to engage in responsible behaviour; but because of larger standard deviation, they stated this relationship might be low. However, low relationship and high standard deviation explained no relationship between gender and responsible environmental behaviour. (7) Leigh et al. (1988) approached to consumers’ socially responsible consumption tendencies. Purpose/Objectives: One purpose of their research was to examine Social Responsibility Scale (SRS) from literature as they recognized that this scale remained much in use to assess socially responsible consumption in literature they sighted. At the main point, they purposed to develop and test a new scale with a focus on a range of product features and uses. Design/Methods: They developed their own product differentiation construct, and the questionnaire contained statements representing each differentiation dimension for the three selected product categories namely beverage containers, automobile engines, and hairstyling. However, general product category was also considered. The SRS and demographic information were too included in the questionnaire. A five-point agree–disagree response format was used both for product differentiation and SRS items. This instrument was mailed to representatives of three different populations explicitly marketing managers, consumerists, and FTC (Federal Trade Commission) professional staff members. In view of a nationally representative sample, four geographically dispersed cities New York, Chicago, Atlanta, and Los Angeles were selected. Statistical Approach: Factor analysis with varimax rotation, MANOVA (Multivariate Analysis of Variance), and ANOVA (Analysis of Variance) were the statistical tools applied. Findings/Conclusion: During analysis, SRS was suspected as a reliable and valid indicator of attitudes towards social responsibility due to the marginal internal consistency in-between the scale items. They emphasized on a need for consumer researchers to be responsible for scale development while assessing the degree of social concern amongst consumers. It was also advocated that product differentiation formulation as they adopted seemed to serve the need adequately but deserved further attention. (8) Schwepker and Cornwell (1991) examined ecologically concerned consumers. Purpose/Objectives: It was wished to find out the intentions of consumers to purchase ecologically packaged products. Design/Methods: They used a questionnaire for the purpose and included seven scales on a five-point measurement. In addition, they obtained demographic information on place of residence, public or private

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recreation area, and the number of hours spent on outdoor leisure activities. Number of community organizations to which one belongs, gender, race, marital status, age, education, and income were also taken. In total, 150 individuals were selected as sample from the metropolitan area of central southern United States but they proceeded on 146 questionnaires. Statistical Approach: Linear discriminant analysis was used to assess the effect of predictor variables on low and high purchase intentions with regard to ecologically packaged products. Seventy-two respondents were included in low purchase intentions group and seventy-four in high purchase intentions group. Findings/Conclusion: Results indicated that demographic variables were not as important as socio-psychological variables in understanding the ecologically concerned consumers. The individuals with internal locus of control, who were concerned about litter, who believed in pollution problems, and had favourable attitude towards ecologically conscious living were more inclined to purchase ecologically packaged products. Race and income were significant in discriminating between the groups, and all other demographic variables were found insignificant. (9) Berger and Corbin (1992) tested perceived consumer effectiveness and faith in others as moderators of environmentally responsible behaviour. Purpose/objectives: The purpose of their study was to examine the validity of two hypotheses. First, the relationship between environmental concern and personal behaviour get moderated by perceived consumer effectiveness. Second, the relationship between environmental concern and support behaviour get moderated by faith in others. Design/Methods: Three behavioural measures were used which were consumer behaviours, willing to pay behaviour, and regulatory support behaviours. Respondents were asked for their level of agreement/disagreement with 16 Likert-type statements on a seven-point scale. Angus Reid Group, a major Canadian polling company supplied data to them. A total of 1521 telephone interviews were conducted. Statistical Approach: Factor analysis, t-test, correlation, regression, and graphic techniques were used. Findings/Conclusion: Perceived Consumer Effectiveness was found a very influential moderator of attitude–behaviour relationship. Similarly, the variable measuring faith in the efficacy of others was found to moderate the degree and form of relationship between environmental attitude and support for regulatory actions. (10) Banerjee and McKeage (1994) explored the relationship between environmentalism and materialism. Purpose/Objectives: The purpose was to examine the dimensionality of materialism, environmentalism, and the relationship between materialism, pro-environmental intentions, and behaviours. Design/Methods: The instrument consisted of the measures of materialism, environmentalism, environmental intentions, environmental behaviours, and a scale of social desirability. Statistical Approach: Exploratory factor analysis and correlations were the main statistical tools. Findings/Conclusion: Initially four factors were obtained but due to the mixed loading of fourth factor, only three factors were interpreted. These factors were internal environmentalism, substantive environmentalism, and external environmentalism. Finally, a negative but significant correlation was obtained between environmentalism and materialism. (11) Benton and Funkhouser (1994) explored environmental attitude and made an international comparison amongst business students. Purpose/Objectives: The

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work aimed at testing environmental knowledge of the business students of United States and Singapore. Design/Methods: The questionnaire consisted of four subscales designed to tap present and past behaviour. The concern, willingness to act, actual behaviour subscales each consisted of ten true–false items. The knowledge subscale had fifteen, five-point multiple choice items. Two samples based on convenience were drawn in which one was from major private Midwestern University in the United States and one from the major University in Singapore. Findings/Conclusion: The results demonstrated that on the knowledge and concern subscales, the Singapore sample scored higher, but difference was not statistically significant. The mean score on willingness to act scale for the United States was also higher, but again the difference was not significant. Only on actual behaviour, the mean score of the United States was significantly higher. The findings upon gender gap stated that American males scored lower on the three attitudinal scales but again the difference was not significant. For the Singapore sample, scores of females were higher on all four subscales; but the difference was statistically significant only on willingness to act subscales. (12) Grob (1995) structurally modelled environmental attitude and ecological behaviour. Purpose/Objectives: The main questions investigated were whether, to what extent, and in which constellation personal belief systems affect environmental behaviour, and about the generalizability of their model. Design/Methods: In two different studies, a structural model linking environmental awareness, emotions, personal-philosophical values, perceived control, and behaviour was proposed and tested. New instruments were created to measure the constructs of the model. Findings/Conclusion: Study I—Here, the proposed model was confirmed. Personal-philosophical values and emotions had the strongest effect on environmental behaviour and factual knowledge had no effect. Thirty-nine percent of the variance in environmental behaviour was explained by the attitudinal components. Study II—This phase showed the extent to which persons differed in their environmental behaviour depending on their membership in a green driver’s association, compared with traditional drivers. (13) McMillan et al. (1995) studied social and demographic influences on environmental attitudes. Purpose/Objectives: Their research sought to determine what factors were related to environmental attitude. Design/Methods: They analysed data which was the part of a larger project about Albemarle-Pamlico Estuarine system, and the public’s perceptions of water quality in Coastal North Carolina. The research design was cross-sectional and for data collection, a random sample of households with telephones in 100 countries in North Caroline and 16 countries in Southeast Virginia was selected. The sample size was 1047 with the elimination of questionnaires with missing data. NEP (New Environmental Paradigm) scale was assessed on the sample with scoring ranging from 1 to 5. The demographic features studied were age, gender, race, education, income, and residence. Statistical Approach: Regression was the main technique of analysis. Findings/Conclusion: Their findings extended previous research by examining the relative influence of demographic variables on each subscale and on overall NEP. They concluded that younger people, women,

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whites, and people of higher education levels hold high environmental attitude, and income had a significant nonlinear effect. (14) Roberts (1995) investigated the levels of socially responsible consumer behaviour (SRCB). Purpose/Objectives: The study was progressed with three primary objectives. One was to segment the market for socially responsible products and services; second, to develop a demographic and attitudinal profile of each segment; and third, to discuss the implications for the marketers. Design/Methods: SRCB scale was studied with its Ecologically Conscious Consumer Behaviour (ECCB) and Socially Conscious Consumer Behaviour (SCCB) dimensions on five-point measurement. The questionnaires were sent to a random sample of 1503 U.S. consumers, but a response rate of 46% was found. Statistical Approach: The statistical tools applied were factor analysis, cluster analysis, and analysis of variance with Scheffe’s significance test. Findings/Conclusion: The results from factor analysis bifurcated socially responsible consumers into two parts: ecologically conscious and socially conscious. Cluster analysis further segmented these into four clusters: socially responsible, middle americans, greens, and browns. Further, the study found that demographic variables were as effective as attitudes in distinguishing between differing clusters of socially responsible consumers. (15) Shrum et al. (1995) examined the characteristics of green consumers in relation with green buying. Purpose/Objectives: The goal was to profile green consumers in a manner that could assist in the development of advertising strategies. Design/Methodology: The 1993 DDB Needham lifestyle2 study was the data source. The survey included a variety of questions on attitudes, interests, and opinion of respondents as well as their activities, product usage, media habits, and demographic information. The emphasis was placed on issues related to the buying process (impulse buying, price consciousness, brand loyalty) and the communication process (attitudes towards advertising, opinion leadership, opinions about, and use of media). Twenty-four statements were used as possible predictors of interest in buying environmentally friendly products on six-point Likert-type scale anchored by ‘I definitely disagree’ to ‘I definitely agree’. Two items measured the purchase of environmentally friendly products. Statistical approach: Analysis was completed using factor analysis and regression analysis. Findings/Conclusion: Five factors were found: impulse buying, opinion leadership, interest in products, brand loyalty, and care in shopping. Findings suggested that persons who made a special effort to buy green considered themselves to be opinion leaders, were interested in new products, exchanged product information, and were careful in their shopping habits especially in being price sensitive. (16) Alwitt and Pitts (1996) predicted purchase intentions for environmentally sensitive products. Purpose/Objectives: The main purpose was to assess the extent to which general ecological concern affects purchase intentions, and to test mediation by attitudes towards consumption and the importance of environment. Design/Methods: Data were collected through a survey mailed to 1500 women between 18 and 40 years of age in three states—California, Illinois, and Pennsylvania—that had experienced 2 DDB

Needham is an advertising agency and conducts a proprietary life study annually.

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significant ecological and policy issues related to solid waste disposal. General ecological concern (GEC), attitude about the environmentally sensitive product (EATT), and importance of the product’s attitude (EATIMP) were also measured. Statistical Approach: Path analysis and Chi-square were used for data analysis. Findings/Conclusion: The findings suggested that general environmental concern had only an indirect effect that was mediated by attitudes about specific environmentally relevant products. The results also suggested that environmental concern was related to the consequences of using the product. (17) Karp (1996) examined values and their effect on pro-environmental behaviour. Purpose/Objectives: The objective of the study was to clarify the role of values in predicting environmental behaviour. Design/Methods: For the purpose, questionnaires were distributed to 302 undergraduates enrolled in an introductory social course at the University of Washington. The Schwartz scale of values consisted of 56 items designed to measure values cross-culturally. The environmental behaviour scale consisted of 16 items measuring the frequency of participation in a variety of environmental activities. All measurements were done on a five-point scale. Statistical Approach: Principal-axis factoring, Chi-square, and regression were applied. Findings/Conclusion: Factor analysis divided environmental behaviour into three factors: good citizen, activist, and healthy consumer. The good citizen factor was descriptive of pro-environmental behaviours that were engaged in relatively frequently. The activist factor expressed the behaviours, engaged in infrequently by consumers, because of the higher investment required for such actions. The healthy consumer factor was distinguished not only by the level of participation but by an orientation to consumer behaviour. The main finding of the study was the effect of self-transcendence/openness to change and universalism/biospheric as positive predictors of all three types of environmental behaviours. (18) Steel (1996) examined environmental attitudes, behaviour, and activism. Purpose/Objectives: The purpose was to empirically investigate the link between attitudes and self-reported behaviours regarding the environment. Design/Methods: The data was gathered from 1992 National survey of American public. Findings/Conclusion: Findings suggested that attitude intensity was correlated with self-reported environmental behaviour and political activism in environmental issues when controlling for socio-demographic factors. Respondents with environmentally protective attitudes reported that they usually think globally and act locally. Additional findings suggested that women were significantly more likely than men to participate in environmentally protective behaviours and policy issues, and the gender difference in behaviour appeared to be greatest amongst older adults. (19) Roberts and Bacon (1997) explored the subtle relationships between environmental concern and ecologically conscious consumer behaviour (ECCB). Purpose/Objectives: They purposed to explore the dimensions of environmental concern and ecologically conscious consumer behaviour, and also to test the correlations between these dimensions. Design/Methods: Two scales, 12-item NEP, and 30-item ECCB were utilized to form a questionnaire and sent to 1503 adult US consumers. However, the utilized sample on which analysis was performed was of 572 respondents. Statistical Approach: The NEP and ECCB scales were factor analysed using

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CCAL clustering and LISREL. Findings/Conclusion: NEP and ECCB resulted in four and six factors, respectively. The factors were interpreted according to their loading and item descriptions. Significant correlations had also been found between attitudinal and behavioural factors suggesting relationship between environmental concern and ecological behaviour. (20) Kilbourne and Beckman (1998) critically assessed research on marketing and the environment. Purpose/Objectives: The purpose was to provide a review and categorization of environmentally related studies published in the major English language marketing journals over the periods from 1971 to 1997. Design/Methods: The paper traced the development from the early research which focused predominantly on the characterization of the green consumer and conceptualization of environmental consciousness, environmentally related behaviours such as recycling, and attitudes towards environmental problems such as pollution. This was followed by a period in which energy conservation, legislation, and public policy issues were added. Same issues were studied with in 1990s when the research agenda was expanded and broader issues were included in the literature such as environmental values and institution. Statistical Approach: Content analysis had been applied. Findings/Conclusion: It was concluded that the investigation of macro issues from an interdisciplinary perspective was necessary for effective and strong public policy regarding the relationship between environment and marketing. (21) Wright and Klyn (1998) studied attitude–behaviour correlations. Purpose/Objectives: They aimed to specify attitude–behaviour link across the globe and in different countries, thus 21 countries were selected. Design/Methods: Their research drew data from 1993 International Social Survey Programme: ISSP.3 Statistical Approach: Factor analysis with varimax solution and correlations were operated upon. Findings/Conclusion: The three factors were obtained as concern, consume, and activism. The results of correlation demonstrated that although globally the correlations were significant but were not particularly high. In fact, it was found that consume-activism correlation was largest instead of concern-consume and concernactivism relationship. Also, attitude–behaviour correlations were reasonably low and vary considerably between countries. English-speaking countries appeared to have relatively high correlations as do Western European countries. Poorer and less developed countries had weaker correlations, while in some countries (Russia, Bulgaria, Poland, Spain, and the Philippines), there were no significant correlations. (22) Kaiser et al. (1999a) combined ecological behaviour, environmental attitude, and feeling of responsibility for the environment. Purpose/Objectives: They proposed an extension of their earlier developed general model. The study was divided into two parts: first study explored the utility of the extended attitude model, and second study was conducted to tackle the generalizability issue.

3 The

ISSP Surveys are administered by leading academic institutions in each of the member countries and involve annual surveys of economic and social policy issues with the specific topics varying in a five-year cycle, and the survey data become freely available to ISSP participants. The main topic of 1993 survey was environment, totaling 26,552 respondents across countries.

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Study 1—Design/Methods: The sample involved 445 Swiss adults from two ideologically differentiated Swiss Transportation associations. The questionnaire consisted of a social desirability scale, a general ecological behaviour measure, three scales that represent environmental attitude, and a measure of responsibility feelings regarding the environment. Statistical Approach: Correlation and factor analysis were applied. Findings/Conclusion: They reported that ecological behaviour intention could be predicted more accurately by including responsibility feelings into the sort of conceptual structure that unifies most extent of environmental attitude approaches. Further, as participants might be inclined to adopt researcher’s expectations, they said that social desirability effects had to be assessed and controlled. Study 2—Design/Methods: The sample here consisted of 488 students who were either biology or social ecology majors at the University of California, Irvine. The measures were the same as used in study 1. Statistical Approach: Chi-square, correlations, and factor analysis were utilized to get results. Findings/Conclusion: The findings here confirmed and modified various aspects of the findings from study 1. The first major finding was that ecological behaviour intention could be predicted more accurately by adding responsibility feelings into the general environmental attitude approach. The second finding emphasized that environmental knowledge, values, and responsibility feelings were determinants of ecological behaviour; but, the relative influence of these concepts might vary across different groups of people. The third finding referred to the sole determination of General Ecological Behaviour (GEB) by ecological behaviour intention. (23) Kaiser and Shimoda (1999) tested responsibility as a predictor of ecological behaviour. Purpose/Objectives: The authors aimed to find out the extent to which a person feels responsible for the environment, and the power of this feeling as predictor of that person’s ecological behaviour. Design/Methods: A survey of 445 members of two Swiss transportation associations was done and the relative influence of the distinguishable responsibility concepts on ecological behaviour was assessed. Statistical Approach: Structural equation analyses were used to reach the target. Findings/Conclusion: Results highlighted that people’ feeling of guilt for what they did wrong or failed to do what might be right in reference to environment, developed the feelings of moral responsibility in them. This feeling promoted their self-ascription of responsibility and then predicted a considerable portion of ecological behaviour. (24) Siu and Cheung (1999) examined environmental attitude and behaviour. Purpose/Objectives: They proceeded with two aims. First, replicating the studies conducted at the beginning of the 1990s to a sample of University students in Hong Kong in order to arrive at a higher reliability of measures. Second was to establish a structural equation model relating affect, verbal commitment, and actual commitment. Design/Methods: The theoretical framework for the study was modelled from the theory of reasoned action. A self-administrated questionnaire survey was used to collect data. Three scales affect, verbal commitment, and actual commitment were

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used with items on a six-point scale. Statistical Approach: Mean, standard deviation, t-test, correlation, and structural equation modeling were considered. Findings/Conclusion: Findings indicated that there was only a gender difference in verbal commitment with female students scored significantly higher than male students. The mean of affect was significantly higher than verbal commitment, and that was statistically significantly higher than actual commitment. Correlation indicated that affect was highly and positively related to verbal commitment, and verbal commitment was highly and positively related to actual commitment. Affect was also positively related to actual commitment, but relatively this relationship was weaker than the other two. Next, structure equation analysis resulted in 60% of the variance of verbal commitment that was explained by affect, and 19% of actual commitment that was explained by verbal commitment. Thus, intentions were found as a mediator of attitude–behaviour relationship. (25) Straughan and Roberts (1999) segmented consumers according to specific characteristics and presented profile of green consumers. Purpose/Objectives: The work was carried on with an aim to extend the characteristics of green consumers discussed so far in literature. Design/Methods: The research instrument (questionnaire) was administered to a convenience sample of 235 students. The dependent measure was 30-item Ecologically Conscious Consumer Behaviour (ECCB) scale anchored by ‘5’ always true to ‘1’ never true. The independent measures were utilized from two broad categories: demographic and psychographic. Statistical Approach: The analysis was done in two phases. In the first phase, basic correlation was examined to determine the direction and significance of relationship. The second phase of the analysis used the multiple and step-wise regression to develop profile of ecologically conscious consumers. Findings/Conclusion: The result of preliminary analysis of correlation indicated that all demographic and psychographic variables were significantly correlated with ECCB. High and low correlations suggested the hierarchy of predictors to enter in regression model. Regression supported that PCE was most importantly correlated with ECCB and then altruism found the place. Third predictor was liberalization, and lastly environmental concern determined ecological behaviour. Afterwards, young, mid to high-income group, educated people, urban, women, altruistic, liberals, and people having good perceptions and attitudes were profiled as green consumers. (26) Tanner (1999) studied constraints on environmental behaviour. Purpose/Objectives: The study was aimed at identifying prevalent constraints inhibiting individuals from reducing their driving frequency. Here, behaviour regarding driving and frequency of driving was termed as environmental behaviour. Design/Methods: For data collection, a questionnaire was used with questions in the first section about personal and general problem awareness, perceived efficacy, sense of responsibility, and perceived behavioural barriers which were taken on a scale ranging from 1 ‘Not True’ to 5 ‘Very True’. The second section was about automobile ownership on the extent of automobile use and on perceived behavioural barriers. Statistical Approach: The analysis began with principal component analysis with varimax rotation. Further, multiple regression and ANOVA (Analysis of Variance) were

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also applied. Findings/Conclusion: Four factors were confirmed. Multiple regression indicated that subjective constraints explained a significant amount of variance in behavioural reports, and structural constraints (demographic conditions) were also contributing to the model. In demography, automobile ownership was found significantly related to driving frequency. ANOVA showed that three demographic variables: place of residence, income, and age were significantly related to driving frequency. Gender was not associated with driving frequency but when automobile owners as well as non owners were included, significant gender differences emerged, showing men used the car more frequently than women. (27) Dunlap et al. (2000) measured endorsement of the New Ecological Paradigm (NEP) and presented a revised NEP scale. Purpose/Objectives: They addressed the directionality imbalance in original 12-item NEP scale and worked on updating and broaden the scale’s contents. Design/Methods: A representative sample of Washington state residents were employed as was used in the original set of items. A questionnaire covering a wide range of environmental issues was used for data collection, and 676 completed questionnaires were received. Statistical Approach: Correlation and principal component analysis were applied, and predictive and construct validity were obtained. Findings/Conclusion: Initially, the result of correlations evidenced the internal consistency of revised NEP scale. Overall, the results reported that to measure New Ecological Paradigm, new NEP scale had slightly more internal consistency and good correlations, thus revised NEP appeared to be an improved measuring instrument compared to the original scale from three aspects: (1) provided more comprehensive coverage of the key facts of an ecological worldview, (2) avoided the unfortunate lack of balance in the item direction, (3) removed the outmoded terminology in some of the original scale items. (28) Follows and Jobber (2000) tested a consumer model regarding environmentally responsible purchase behaviour. Design/Methods: Model was tested using covariance structural analysis. Findings/Conclusion: The model successfully predicted the purchase of environmentally responsible and non-responsible product alternatives. A hierarchical relationship from values to product-specific attitudes towards purchase intentions and then to purchase behaviour was confirmed. Individual consequences which took the personal implications of consumption into account were found to be just as important in predicting intentions as the environmental consciousness. (29) Stern (2000) developed a conceptual framework for advancing theories of environmentally significant behaviour. Purpose/objectives: It was aimed to present some major propositions supported by available research and information about behavioural programmes on environmental protection. Design/Methods: Environmentally significant behaviour was categorized and studied into five parts. Findings/Conclusion: A value-belief-norm (VBN) theory of environmentalism was developed and he stated that environmentally significant behaviour was dependent on a broad range of causal factors, both general and behaviour specific. The causal variables included attitudinal, personal capabilities, contextual factors, habits, and routines. Also, different kinds of environmentally significant behaviours had different causes.

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(30) Zelezny et al. (2000) elaborated on gender differences in environmentalism. Purpose/Objectives: They purposed to work on three studies. Study 1 examined gender differences in environmental attitudes and behaviours. Study 2 examined gender differences in environmental attitude and behaviours across 14 countries. Study 3 explained the reasons for gender differences. Study 1—Design/Methods: Here, a 35-item questionnaire was designed to assess students’ environmental attitude, knowledge, feelings, recycling attitudes, intentions to participate in school recycling, and real participation in school recycling. The questionnaire was administered to a stratified sample of primary and secondary school students surveyed over a two-year period (1994 and 1995). Five-point scaling from agree to disagree was used. Statistical Approach: F-test and Meta-analytical correlation effect size was used to analyse the data. Findings/Conclusion: Girls reported significantly stronger overall concern and responsibility. They were concerned about trash, interested in recycling, and reported more participation in school recycling. Girls and boys both were least concerned about the wastage of energy. Study 2—Design/Methods: For the purpose of study 2, English- and Spanishspeaking students (N = 2160) were selected across 14 countries. A questionnaire was designed to assess students’ demographic characteristics, general environmental attitudes, value-based environmental attitudes, and pro-environmental behaviours. Responses were taken on a five-point Likert response scale. Statistical Approach: Descriptive analysis and F-test was used to interpret the data. Findings/Conclusion: Overall, analyses revealed that females in 14 countries did report greater participation in pro-environmental behaviour than males; however, significant gender differences in pro-environmental behaviour were found only in Paraguay and Venezuela. Study 3—Design/Methods: 119 participants were selected. A questionnaire was designed to measure general environmental attitude using NEP scale, feminine and masculine orientation scale, California Psychological Inventory (CPI), socialization scale, and ethic of care to take responsibility using Minnesota Multiphasic Personality Inventory (MMPI). Statistical Approach: t-test and post hoc analyses were applied. Findings/Conclusion: The findings supported gender socialization as an explanation for gender differences in environmentalism. The study found that compared to males, females had a stronger level of social responsibility. Overall, they supported the idea that females would play an active and positive role in this progress. (31) Kaplan (2000) examined human nature and environmentally responsible behaviour. Purpose/Objectives: The author aimed to constitute a search for a peopleoriented approach for encouraging environmentally responsible behaviour. An attempt was initiated to provide sources of motivation to reduce the corrosive sense of helplessness and generate solutions to environmental problems that do not undermine the quality of life of people who get affected. Design/Methods/Approach: Views and previous research were accumulated to reach at the objectives. Findings/Conclusion: It was obtained that an assumption was prevailing in past work that good motives lead to good behaviour. But some failures to this approach were also presented. The altruism centred approach currently popular in the academic literature, by contrast, was seen contributing to helplessness and focusing on sacrifice rather than quality of

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life enhancing solutions. An alternative, the reasonable person model, also offered an evolutionary/cognitive/motivational approach for understanding human nature. (32) Bickerstaff and Walker (2001) examined public understanding of air pollution. Purpose/Objectives: The purpose was to offer a current picture of the ways in which residents think about the problem of urban air pollution and also to evaluate the role of available air quality information. Design/Methods: A quantitative survey and a series of in-depth interviews were taken from a research project in Birmingham (UK) examining people perceptions of urban air pollution. Statistical Approach: Analysis was done through percentages and bar graphs. Findings/Conclusion: Findings indicated that over half of the respondents identified an awareness of negative conditions. The sources by which people became aware of poor quality of air were health-associated impacts, different types of sensory evidence, and weather conditions. The data indicated that for many people, air quality perception was spatially bound and corresponded to a source-direction distance-decay relationship with limited weight attached to more complex processes of environmental dispersion. Almost, half of respondents identified some form of health impact, mostly related to breathing allergic and irrigation problems. On the other, only a small number of people had a low level of concern about the health effects of air pollution. (33) Chan (2001) determined the Chinese consumer’s green purchase behaviour. Purpose/Objectives: The study aimed to grasp a better understanding of psychological factors which might affect green purchasing. Design/Methods: The questionnaire consisted of different constructs. Man–Nature orientation (MNO) with five statements, three statements for collectivism, three statements for attitude towards green purchasing, and three statements for green purchase intentions; all were anchored on a seven-point scale. The survey was carried out through door to door personal interviews in Beijing and Guangzhou, China. With two-stage area sampling, 300 households in each of two cities were randomly selected. Statistical Approach: Descriptive statistics and confirmatory factor analysis were techniques of data analysis. Findings/Conclusion: The findings indicated that in terms of man–nature orientation and collectivism, the respondents exhibited Chinese cultural characteristics as postulated. Although, the respondents in general, exhibited only little knowledge of ecological issues, they demonstrated a very strong emotional attachment to the issues. The findings were also encouraging for attitude towards green purchasing and green purchase intentions. Apparently, the encouraging attitudes and intentions were not effectively translated into green purchase behaviour. However, green purchase intention was also found a significant predictor of green purchase behaviour. (34) Chan and Lau (2001) explained green purchasing behaviour. Purpose/Objectives: The purpose of the study was to examine the applicability of Theory of Planned Behaviour to green purchasing behaviour in Chinese and American cultural settings. Design/Methods: Consumers from Shanghai were selected for explaining eco-friendly purchases of the Chinese and American consumers. Statistical Approach: Structural equation modeling was used for analysis. Findings/Conclusion: The cross group analysis highlighted that both subjective norm and perceived behavioural control exerted stronger influences on Chinese consumers’ behavioural intention than on American consumers. Moreover, the translation of

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green purchasing intention to corresponding behaviour was found to be more effective in the American sample. These cross group variances were likely to be attributed to cultural differences and discrepancies in the environmental development of the two nations. (35) Laroche et al. (2001) targeted consumers who were willing to pay more for environment-friendly products. Purpose/objectives: The main purpose was to prepare a profile of these consumers with their relevant features. Design/Methods: Using a non-disguised questionnaire, data were collected in selected municipality of North American city. The questionnaire was divided into five parts and measured various research questions in different forms. Statistical Approach: Factor analysis, cross-tabs, t-test, discriminant analysis, and Chi-square were applied. Findings/Conclusion: They concluded that gender, marital status, and number of children living at home differentiated the segments. Females who were married and had children living at home were more willing to pay for green products. Results from discriminant analysis and the student t-test indicated that attitudes were very good predictors of consumers’ willingness to spend more for green products. Values also affected green buying intentions and ecologically conscious consumers reported that collectivism and security were important principles guiding their lives. Also, consumers who considered environmental issues when making a purchase were more likely to spend more for green products. It was too obtained that eco-literacy was not a good predictor of consumers’ willingness to pay more for green products. (36) Mohr et al. (2001) explored consumer expectations from companies. Purpose/Objectives: The objective was to get answers about the impact of corporate social responsibility on buying behaviour. Design/Methods: Five people were appointed to conduct interviews for this project. The demographic features were age/cohort, gender, ethnicity, socio-economic status, income, education, children, marital status, political ideology, social ideology, and political party. Statistical Approach: The researchers analysed and interpreted the interview transcripts in three distinct ways to capture the richness of the data. They examined each case to determine why each participant did or did not use CSR as a consideration when purchasing. Finally, the researchers read all the transcripts together to identify common themes. Findings/Conclusion: The results summarized that most of the respondents were positive towards business in general and towards socially responsible companies, in particular. They found a small but articulate group of consumers who were actively practicizing SRCB. Consumers marked the views that every purchase had implications for the larger society, in terms of environmental impact and in terms of reward or punishment to companies which behaved more or less socially responsible. (37) Pornpitakpan (2001) examined environmental concern in Thailand. Design/Methods: A survey was presented based on 271 Thai adults, using SRCB scale. Statistical Approach: For the analysis part, mean and standard deviation for each item and for the whole scale were reported. Findings/Conclusion: The results indicated that respondents were moderate on environmental concern. There was no difference between males and females. Older respondents exhibited more concern than youngers. Lower educated showed more concern than higher educated but only on few items.

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(38) Usui (2001) surveyed Japanese consumers. Purpose/Objectives: It was purposed to analyse the effect of values on green consumer behaviour. By examining values, the author meant to replicate several models used in past international comparison survey. Design/Methods: The survey was carried out by the National Institute for Environmental Studies, Japan Environment Agency. Total 1530 interviews were completed by professional interviewers. The questionnaire contained topics concerning values, attitudes, and behaviour towards environment. Statistical Approach: Factor analysis, correlation, and ordinary least square regression were applied. Diagrams and pictorial presentation were also added to analyse each length of data. Findings/Conclusion: Value items resulted into three factors named biophysicstradition, altruistic, and egoistic. Economic progress versus environment resulted into two factors: preference for progress and preference for the environment. It was concluded that values seemed to play a significant role in determining behaviours and that behaviour sometimes differed by gender. Although, female consumers tended to be more environment oriented, they occasionally took progress-oriented actions. (39) Vaske and Kobrin (2001) studied environmentally responsible behaviour (ERB) in relation with place attachment. Purpose/Objectives: The paper elaborates upon how an attachment of an individual to local natural resource can influence environmentally responsible behaviour in his/her everyday life. Design/Methods: The study utilized some general (like talking with others about environmental issues) and specific (like sorting recyclable trash) behavioural indicators which reflected construct of environmentally responsible behaviour. Place attachment contained two concepts: first, place dependence which was defined as a functional attachment; and second, place identity which was delineated as an emotional attachment. Data were obtained from a sample of 182 respondents between 14 and 17 years of age, who participated in local natural resource work programmes. Statistical Approach: The influence of place attachment on environmentally responsible behaviour was examined using Structural Equation Modeling. Findings/Conclusion: Results supported the hypotheses. Place dependence influenced place identity, and place identity was significantly related to ERB. In this way, place identity mediated the relationship between place dependence and responsible behaviour. Overall, the model suggested that by encouraging an individual’s connection to a natural setting, general environmentally responsible behaviours can be facilitated. (40) Kurz (2002) considered four general psychological approaches (rationaleconomic, social dilemmas, attitude–behaviour, and applied behavioural analysis) of environmentally sustainable behaviour (ESB). Purpose/Objectives: The aim was to discuss these four approaches to the analysis of environmentally sustainable behaviour and to focus on the problems inherent in applying each approach. Design/Methods: Methodology adopted is the analysis of literature based on the theories. Findings/Conclusion: Findings indicated that in rational-economic models individuals would engage in the process of cost–benefit analysis when deciding an appropriate action; but, the criticism was that people did not necessarily function in rational-economic ways and price could also mean differently to different people. Social-dilemma models took into account the situations in which decisions were made but again the limitation was on the applicability of findings from laboratory to

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real life situations. In attitude models of environmental behaviour, the key issues in attempting to promote ESB became the extent to which pro-environmental attitudes influenced ESB. (41) Bamberg (2003) analysed about how does environmental concern influence specific environmentally related behaviours. Purpose/Objectives: The aim of the study was to analyse the impact of environmental concern on green energy buying (defined as a specific domain of environmentally related behaviours). Design/Methods: A total of 380 University students participated in the study only from natural science departments. Questionnaire’ measures were perceived likelihood of behavioural beliefs, evaluation of behavioural beliefs, normative and control beliefs, attitude towards behaviour, subjective norms, perceived behavioural control, intentions, actual behaviour, and environmental concern. Statistical Approach: Mean, standard deviation, percentage, chi-square, and correlations were applied in analysis phase. Findings/Conclusion: Highly environmentally concerned students showed not only a greater interest in obtaining information about green electricity products, but also more likely to associate this information with using the offered brochure. (42) Clark et al. (2003) investigated consumers’ participation in green electricity4 programme. Purpose/Objectives: The purpose was to examine the motives of participation of respondents, and the effect of internal and external influences. Design/Methods: The research instrument asked respondents to complete two scales: a nine item ‘altruism’ scale and a 10-item ‘modified NEP’ scale. The survey was sent to 281 Detroit Edison customers in the state of Michigan. Statistical Approach: Factor analysis, mean, standard deviation, regression, and percentages were utilized. Findings/Conclusion: Altruism and environmentalism appeared to be internal variables that independently influenced pro ecological behaviour. The motives came out as air quality benefits to Michigan residents, improvements in ecosystem health, warm-glow (intrinsic) satisfaction from programme participation, improvements in personal or family health, and addressing global warming. Amongst these, beliefs about ecosystem health had the highest mean rank, followed by beliefs about benefits to southeastern Michigan residents, personal and family health, and global warming. (43) Kaiser et al. (2003) measured ecological behaviour and its environmental consequences. Purpose/Objectives: The objective was to validate 52 ecological behaviours of the most recent version of General Ecological Behaviour (GEB) scale by contrasting each item’s environmental consequences with the environmental achievement of a reasonable alternative. Design/Methods: The most recent version of GEB scale was used consisted of 65 items derived from six domains: energy conservation, mobility and transportation, waste avoidance, consumerism, recycling, and social behaviours towards conservation. Statistical Approach: Kappa measure and percentages were used to derive the results. Findings/Conclusion: The study validated 46 out of 52 ecological behaviour items, and raised questions about six 4 According

to Clark et al. (2003), green electricity refers to electricity that is generated from solar, wind, or other renewable energy sources. Throughout the United States, green electricity is offered to households as a supplement to electricity derived from fossil fuels and nuclear power.

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behaviours that should not be used as ecological performance indicators. The data revealed that majority of mismatches occurred with either consumption or waste avoidance items. Thus, more precise behavioural definitions might readily resolve these mismatches. (44) Tanner and Kast (2003) examined green purchases, as it was termed that these could promote sustainable consumption. Purpose/Objectives: The study was designed to uncover personal and contextual factors that influence green food purchases by Swiss consumers. Design/Methods: The personal factors incorporated in this study were based upon previous research and on interviews with customers of a Swiss supermarket in an organic food store. Survey data for rural and urban areas in and around the city of Bern were collected from 6500 randomly selected households on the set of questions in the form of a questionnaire. Statistical Approach: Principal component analyses with promax rotation, correlation, and the technique of regression analysed the data. Findings/Conclusion: It was confirmed that personal attitudes and beliefs were powerful predictors of green purchases. Positive attitude towards environment protection, fair trade, and local production were major facilitators of green purchases. Action related knowledge was an additional predictor of green purchases. One striking result was that cost did not play an integral role in green purchases. The results also indicated that people with high environmental motivation were less sensitive to price. (45) Shaw and Shiu (2003) studied ethics in consumer choices. Purpose/objectives: The authors clarified that there was an increasing demand for ethical choices in the market, but very little had been published about the decisionmaking processes of these ethical consumers. So, consumers’ ethical marketplace choices were aimed to be studied. Design/Methods: A large-scale National survey of known ethical consumers was completed in UK. Statistical Approach: Structural equation modeling technique was used to explore the relationships in data. Findings/Conclusion: Using two data sets, a new model of consumer-decision-making was developed. Results highlighted the improved ability of this new model in the explanation of intention to purchase fair-traded products. (46) Tindall et al. (2003) studied the contradictory effects of gender on Activism5 and conservation behaviour. Purpose/Objectives: The authors wanted to compare women’s and men’s environmental activism and environmentally friendly behaviour (EFB). The authors were interested to see whether the effects of gender were independent of selected demographic and socio-economic characteristics or not. Accordingly, they extrapolated some hypotheses from literature and aimed to test those. Design/Methods: Their primary data source was a self-administered questionnaire tested on 381 respondents. As sampling, systematic random sampling procedure was employed. The two dependent variables (EFB and activism) were also constructed. Questions on gender, age, education, length of membership, income, parent, postmaterialist values, frequency of communication, and level of movement identification were asked upon. Statistical Approach: Percentages, t-test, and regression models 5 Activism

referred to specific movement-supporting activities that are promoted by environmental organizations (Tindall et al. 2003).

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were utilized to analyse the data. Findings/Conclusion: Findings showed no substantial gender differences in level of activism but revealed that women engaged in significantly higher rates of EFB. While level of activism was not a significant predictor of EFB for men, it was the strongest predictor amongst women. On the basis of findings, they argued that women might be more concerned about environmental issues and committed to environmentalism; but, their biographical unavailability6 constrained their activism. However, they also said that because much environmentally friendly behaviour (EFB) could be undertaken in the context of domestic labour and everyday routines, biographical unavailability does not constrained their EFB. (47) Tuna (2003) intended to study public environmental attitudes in relation with demographic correlates in Turkey. Design/Methods: The research population was all non-institutionalized Turkishs’ over 18 years of age. Sample size was 1028 assumed to represent population. The sampling model was multistage sampling by dividing the population into seven strata according to geographical region of turkey. Dimensions of environmental attitude, environmental worldview, environmental concern, and environmental commitment which were measured on the score of 1–5 Likert-type scale ranging strongly agree to strongly disagree. Statistical Approach: Exploratory factor analysis and regression model were the tools by which the task of analysis was completed. Findings/Conclusion: The results suggested that environmental world view had highest scores, environmental concern was modest, and environmental commitment scored lowest. The regression test indicated that only the variable residence significantly affected worldview, meaning that the likelihood of being focusable to worldview is higher to line in larger residence. The others had no significant effect on environmental worldview. On second dimension of environmental concern, no variable were found with significant effect. Moreover, the dimension environmental commitment indicated that education and occupation significantly affected it. (48) Usui et al. (2003) made an international comparison of pro-environmental attitudes and behaviours. Design/Methods: The survey was a part of an international comparative study entitled GOEs (Global Environmental Survey). This was carried out in Japan, Bangkok, Thailand, Manila, and Philippines. They utilized a modified version of Schwartz’s general value items and economy-versus-environment items to clarify the value basis of environmental attitudes and pro-environmental behaviour. Statistical Approach: Factor analysis was applied to understand the data structure and present results. Findings/Conclusion: In Japan, Netherlands, and George mason Group, three factors named Biosphere Traditional, Altruistic, and Egoistic were obtained. The altruistic factor was not statistically significant in the estimation of environmental values, but had a statistically negative indicator in progress values. In Japan: the Post-materialism values, the biospheric-tradition factor, altruistic values, household income, education, age, and gender had positive effects on the three types of behaviour; and only progressive values had a negative effect. In Netherlands, post-materialist values, biospheric–altruistic values, egoistic values, household income, and age had positive effect on political behaviour. Except for 6 Biographical

unavailability means personal constraints that present barriers to participation as entailed in demands of the ‘double-day’ of paid and domestic work (Tindall et al. 2003).

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tradition values almost all variables had a negative effect on energy saving and green consumer behaviours. Women in Netherlands were less likely to be positive than men for energy saving behaviours, and Japanese women were ahead in comparison to men in energy saving and green consumer behaviour. (49) Barken (2004) explained public support for environmental movement by applying civic voluntarism model.7 Purpose/Objectives: It was aimed to apply this model to public practical support for environmental movement. Design/Methods: The data for the assessment came from 2000 GSS.8 Predictor variables were derived from civic voluntarism model. Education, annual family income, marital status, and number of children (a form he called biographical availability) were taken as ‘resources’. Political interest and political efficacy were studied in ‘psychological engagement’. Labour force status and religious services attendance were grouped into ‘recruitment’. ‘Issue engagements’ were taken through some more predictors specifically: willingness to sacrifice for the environment, worry about the environment, perceptions of greater damage for environment, and distress in business. Next, gender, race, age, and place of residence came in the category of socio-demographical variables. Statistical Approach: The data was analysed through OLS regression analysis. Findings/Conclusion: The findings concluded that environmental citizenship was higher amongst women, whites, people born in 1941 or later, educated, living in urban areas, and for people having children at home. Psychological engagements of the model helped in explaining environmental citizenship and relevant general political and religious ideologies also add to the model’s explanatory ability. (50) Jain and Kaur (2004) explored environmentalism by stating that it had fastly emerged as a worldwide phenomenon. Purpose/Objectives: Due to the importance of the fact that green consumerism has played a significant role in ushering corporate environmentalism, and business firms are responding to environmental challenges by practicizing green marketing strategies, it was aimed to study the level of environmentalism amongst people. Design/Methods: Data were collected through field survey. Findings/Conclusion: The paper made an assessment of the extent of environmental awareness, attitudes, and behaviour for Indian consumers. Implications of the study findings were listed for the government and non-governmental organizations engaged in marketing of green ideas and products in the country. 7 Civic

voluntarism model was developed by Verba and associates, meant to apply it on electoral participation. Barken (2004) argued that it should also apply to social movement participation because of similar findings that socio-demographic variables predict both electoral and movement participation. The model has four components: resources, psychological engagement, recruitment, and issue engagements. Resources include time, money, communication, and organizational skills that provide the means to be active for a particular issue. Psychological engagements mean interest, efficacy, belief, feelings, and trust. Recruitment shows that people may have resources and psychological engagement for political activity but still remain inactive unless asked by their network members to take part. Thus, common network places of worship, voluntary organizations, and work settings come under this final component as issue engagement shows people engagement that may be based upon the view that it affects them personally or because it bears on moral and political values. 8 The GSS is a survey of adults, no institutionalized population of the United States. It is conducted regularly since 1972. A total of 2817 respondents were included in 2000 GSS (Barken 2004).

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(51) Walton et al. (2004) studied commuter’s concern for the environment and their knowledge of the effects of vehicle emissions. Purpose/Objectives: The purpose was to compare variations in environmental attitudes and knowledge amongst commuters. Design/Methods: The questionnaires were handed to train and bus commuters and mailed to motor vehicle commuters who were identified by license plate details. To measure attitude, ‘general environmental concern (GEC)’ scale was used. Environmental knowledge was measured with 31 items concerning knowledge on vehicle emissions, pollutants, and health risks. Specific environmental behaviour was measured with 5 items measuring contributions to environmental organizations. Statistical Approach: Correlation and ANCOVA (Analysis of Co-variance) were the statistical tools applied on the data. Findings/Conclusion: The level of concern between public transport commuters and private commuters was the same, and it was also same for smoky vehicle drivers and other vehicle commuters. (52) Budak et al. (2005) examined behaviour and attitude of students towards environmental issues. Design/Methods: A total of 240 undergraduate students from faculty of agriculture were randomly selected and surveyed. The questionnaire contained four parts. Part I and Part II dealt with behaviour and attitude towards environment with nine and seven items, respectively, on five-point Likert-type scale. In part III, several information sources including television, radio, newspapers, brochures, friends, family members, and journals were presented, and last part of the questionnaire asked about age, gender, education, place, and membership of environmental organizations. Statistical Approach: Descriptive statistics, ANOVA (Analysis of Variance), t-test, and correlation were the tools to examine the data. Findings/Conclusion: Majority of students indicated that their University did not provide satisfying information about environmental issues. Majority said they wanted to attend meetings, conferences, seminars, or symposiums related to environment at University and indicated that public media newspaper, television, and radio were the main sources of information. Only a small amount (5%) stated that friends and family members played important role or act about source of information about environmental issues. No significant gender differences were optioned for behaviour; but for attitude, gender differences were significant with male more reluctant towards environment issues. Differences were not significant for students’ attitude scores for their educational level; but, significant differences were obtained with behaviour having rural students score higher. (53) D’Souza (2005) studied Portugal consumer perceptions of green companies. Purpose/Objectives: The purpose was to articulate a consumer framework of environmental factors considering a range amongst these factors that affect businesses. The main objective was not to identify the greenest corporation rather to extract an understanding of environmental considerations that consumers have about a green corporation. Design/Methods: To conduct this study, students from the MBA department were selected from a University in Portugal. The data were collected using a structured questionnaire which included consumers’ perceptions on seven different dimensions. The responses were obtained on a seven-point Likert scale. Statistical Approach: Descriptive measures, partial least square regression, and structure equation modeling (SEM) were employed. Findings/Conclusion: The results suggested

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attitude towards the environment as a major contributor in making consumers’ perception of green companies, and there existed a negative relationship between the two. The next contributor was labelling of products, and the least contributor to consumers’ perceptions was attitude towards green products. (54) Gilg et al. (2005) identified the sustainable consumer and studied green consumption. Purpose/Objectives: Their aim was to examine green consumption in the context of an increasing focus on sustainable lifestyles. Design/Methods: Their research was undertaken as a part of a large ESRC-funded project examining environmental action in and around the home in Denon, UK. The instrument was a 14-page questionnaire that asked respondents about a series of pre-determined environmental actions, scoring the responses on five-point scale 1(never) to 5(always). A representative sample of 1600 households was selected both from urban and rural areas. Statistical Approach: The analysis has been carried out by factor analysis, cluster analysis, and pictorial presentation. Findings/Conclusion: Results suggested green consumers tended to hold more pro-environmental and pro-social values. The committed environmentalists usually valued wealth, personal influence, and power less than unity and other aspects of altruism. Age had a positive impact on green consumption, and males were less environmentally active. More green and liberal democrat voters were obtained as committed environmentalists. (55) Greenberg (2005) investigated concern about environmental pollution. Purpose/Objectives: The purpose was to identify the racial and ethnic differences in public concern about environment. Design/Methods: A telephonic sample survey was conducted amongst 1513 residents of New Jersey during March–May 2004. Statistical Approach: Descriptive analysis and ordinary least square regression analysis were operated upon. Findings/Conclusion: Results indicated that non-Hispanic black, non-Hispanic white, and English-speaking Hispanic Americans were significantly more concerned about environmental pollution problems than were Asian Americans and Spanish language Hispanic Americans. There were also racial/ethnic differences between these groups in their desire for government action to protect the environment, and in their personal support of the environmental movement. (56) Haron et al. (2005) examined environmental knowledge amongst Malaysians. Purpose/Objectives: The main objective of their study was to assess the level of environmental knowledge; to investigate the sources of this knowledge; determine factors that lead to different levels of knowledge; and analyse the relationship between knowledge, environmental attitude, and behaviour. Design/Methods: They carried their study in the state of Selangor in Malaysia. Sample of 734 respondents was taken by the process of random sampling. The demographic characteristics included were ethnicity, gender, residential location, housing ownership, marital status, household income, educational level, age, and household size. Statistical Approach: Descriptive statistics and multiple regression were employed. Findings/Conclusion: The results indicated that respondents’ basic or general environmental knowledge was high by scientific terms. The main sources of environmental knowledge were newspapers, television, and radio. Also, participation in environmental activities had a positive influence on knowledge, and knowledge correlated positively with environmental attitude, behaviour, and participation.

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(57) Kim and Choi (2005) examined collectivism, environmental concern, and PCE as antecedents of green purchase behaviour. Purpose/Objectives: The purpose was to develop and test a model that could explain the potential influences of consumer value orientation, general pro-environmental concerns, beliefs on green buying behaviour, and can also explicate the interrelationships amongst the constructs. Design/Methods: Data were collected through a self-administered survey distributed to 304 undergraduate students enrolled at Midwestern University. Statistical Approach: Using mean, standard deviations, correlations, Chi-square, and regression coefficients, the analysis had been proceeded. Structural equation model was also tested. Findings/Conclusion: The findings suggested statistically significant relationships between collectivism ↔ PCE, environmental concerns ↔ green purchase behaviour, and PCE ↔ behaviour. Collectivism also appeared to positively influence an individual’s tendency to buy green products. Findings also highlighted the importance of consumer attitudes towards issues closely related to the behaviour of interest in understanding the relationship between values and behaviour. They remarked that a better predictor of target behaviour could be obtained by considering the level of specificity of attitudes and behaviour or motivational factors such as personal efficacy and behavioural intensions. (58) Shanka and Gopalan (2005) explored socially responsible consumer behaviour. Design/Methods: A questionnaire was constructed with 26 statements for the purpose of assessing students’ perceptions towards socially responsible consumer behaviour. A seven-point scale ranging from 1 ‘never’ to 7 ‘always’ was utilized. Statistical Approach: Analysis was proceeded with factor analysis, mean comparison, and MANOVA (Multivariate Analysis of Variance). Findings/Conclusion: When factor analysis was performed, 15 statements formed two factors: societal and personal. Then the effects of demographic characteristics (gender, age, level of study, country, and field of study) were analysed on the combination of two factors. Only age and class levels showed statistically significant differences on dependent factors. The mean scores indicated that people aged 26 or more and post graduate students had higher level of concern than their counterparts. However, gender, country, and field of study were found insignificant determinants. (59) Schaefer and Crane (2005) addressed sustainability and consumption. Purpose/Objectives: The main objective was to examine issues of sustainability in relation to consumption. Design/Methods: The work was conceptual and literature was thoroughly studied and analysed to find out the supposed link between sustainability and consumption. Findings/Conclusion: Firstly, discussions were presented about the notions of sustainable consumption and the link between individual consumption patterns that were in need of adjustment. The issues of sustainable consumption were explored through the lens of two broadly differing conceptualizations of consumption, and four main questions were discussed: (1) How the view of consumption was related to prevalent current understandings of sustainable consumption? (2) How could sustainability be achieved following the perspectives on consumption? (3) To whom would the view of sustainable consumption appeal or not appeal? (4) What would the roles and responsibilities of different social actors be in achieving sustainability in view of consumption?

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(60) Corral-Verdugo et al. (2006) studied water conservation as a form of sustainable behaviour. Purpose/Objectives: The main question was whether or not the time perspective of people could influence their propensity to behave in an environmentally way. The prime purpose was to investigate how water conservation was related to past, present, and future orientations. Design/Methods: Three hundred individuals from the city of Hermosillo, Mexican were representative of high, middle, and low socio-economic classes. The Zimbardo’s time perspective inventory (ZIPI)9 with 56 items was utilized to identify persons of past, present, or future orientation. Water conservation behaviour was self-reported and was defined as the frequency with which the respondents engaged in actions of conserving water while washing dishes, brushing teeth, washing hands and cars. Statistical Approach: Univariate analysis and structural equation modeling were applied. In structural equation modeling, a confirmatory factor analysis and Chi-square goodness of fit was calculated. Findings/Conclusion: Mean values for the time orientation scale revealed that higher adherence to time perspective items was for future orientation and past propensity followed by present orientation. Present orientation negatively and significantly affected with positive significant affect. Women and adult were found involved in higher water conservation than their counterparts. (61) Ek and Soderholm (2006) made a study on green electricity consumption and the role of norm-motivated consumer behaviour. Purpose/Objectives: The main purpose was to provide an econometric analysis of the most important determinants of a household’s (self-reported) willingness to pay a premium for green electricity. Design/Methods: The analysis was based on postal survey responses from 655 Swedish households in four different municipalities. Statistical Approach: For the analysis, binary choice econometric framework was used. Findings/Conclusion: The results indicated that the impact of green choice on household budget of respondents largely influenced the willingness to contribute to green electricity schemes, as do the degree of perceived personal responsibility for the issue, and felt ability to affect the outcome in a positive way. Limited support was obtained for the idea that perception about others’ behaviour affects individual moral norms and ultimately behaviour. Stronger support was found for the hypothesis that the presence of a prescriptive social norm influenced the willingness to pay for green electricity. (62) Kaur (2006) examined awareness of environmental issues amongst Indian consumers. Purpose/Objectives: Her goal was to study consumer awareness for specific issues according to demographic characteristics. Design/Methods: A survey of 206 consumers located in Delhi was undertaken, using a structured non-disguised questionnaire. Quota sampling method was used. Statistical Approach: Tabulation of data was done by frequencies and percentages. Findings/Conclusion: It was found 9 The inventory identifies tendencies towards a hedonistic present (living present life in enjoyment),

a fatalistic present (perceiving own life under the control of eternal events), a positive past (an orientation towards pleasant past memories), a negative past (Living a past of unpleasant and painful events), and future orientation (the tendency to planning and anticipating events). Future orientation compels individuals to anticipate consequence of their own behaviour. Present orientation makes people prone to enjoying the immediate use of natural resources and past orientation was based on painful or funful past.

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that consumers belonging to various socio-economic categories were unevenly aware of various environmental issues and developments. As example, for some issues women were more aware and for others were men. (63) Shobeiri et al. (2006) investigated environmental attitude. Purpose/Objectives: They targeted to report the influence of gender and type of school on environmental attitude of teachers in Iran and India. Design/Methods: As sample 1004 teachers were selected through the stratified random sampling technique from 103 secondary schools of Mysore city (India) and Tehran city (Iran). The Taj Environmental Attitude Scale (TEAS) with scoring from 1 to 4 (strongly disagree to strongly agree) was employed. Statistical Approach: Mean comparison and analysis of variance were the tools of analysis. Findings/Conclusion: The findings revealed that there were significant differences between two countries, and between male and female teachers in terms of the level of their environmental attitude. Conversely, the overall comparison stressed no influence of type of school on level of attitude. (64) Devinney et al. (2006) defined the other CSR: Consumer Social Responsibility (ConSR or CnSR). Purpose/Objectives: The study aimed at answering some questions: are consumers socially responsible? If they are, how much are they willing to pay for socially responsible products? And if they are not, why is there a discrepancy between expressed beliefs and marketplace behaviour? Design/Methods/Approach: Results of different surveys and other general information were gathered. Charts and diagrams were used to make reader understand about the study results. Findings/Conclusion: The results revealed that socially responsible consumers do exist, but they differed considerably from the stereotype which was raised from popular press surveys. Demographic features such as gender, income, age, and education said little about socially responsible consumers. It was found that although some consumers were ready to pay more for products with positive social attributes, they would invariably do when the functional attributes of those products would meet their needs. Thus, they addressed that marketers had to add additional functional as well as social values to their products. (65) Bamberg and Moser (2007) 20 years after Hines et al. (1986/87) conducted a meta-analysis of psychosocial determinants of pro-environmental behaviour. Purpose/Objectives: The first goal of their paper was to assemble a body of more recent studies for an independent replication of Hines et al. meta-analytical results. The second goal was to perform a meta-analytical test of a theoretical model integrating eight psychosocial determinants of pro-environmental behaviour. Design/Methods: For the purpose of collecting studies, search focus was on papers published since 1995. First search strategy was the internet search machine. The second search strategy consisted in inspecting the content tables of 36 journals where the studies found with electronic search strategy had been published. Statistical Approach: For the purpose of combining meta-analysis and SEM model, MASEM which was called Meta-Analytical Structural Equation Modeling was used. Findings/Conclusion: The assumed mediating role of behavioural intentions was confirmed. The three predictors perceived behavioural control (PBC), attitude, and moral norms were found having average impact. Moral norm itself was seen determined by the interplay of cognitive, emotional, and social factors. Problem awareness, internal attribution,

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feelings of guilt, and social norms all contributed significantly to the prediction of moral norm. The MASEM results also provided support for social norm as more indirect determinant of intentions. Social norms were not only directly associated with moral norms but also with the perceived degree of behavioural control as well as attitude. The analysis also confirmed the association between internal attribution, social norm, and feelings of guilt. (66) Bayard and Jolly (2007) examined environmental behaviour structure and socio-economic conditions of Hillside framework. Purpose/Objectives: The aim was to capture the perceptions of farmers. Design/Methods/Approach: Using structural equation modeling and a health belief model within a conceptual economic framework, they examined the effects of perceived susceptibility, severity, benefits, and barriers on farmers’ awareness and attitudes towards environmental degradation. Findings/Conclusion: Perception of the severity of land degradation had a positive effect on an individual’s awareness and attitude, regardless of people socio-economic status. Farmers’ perceived susceptibility to land degradation on household well-being positively affected attitude at low economic levels; whereas, perceived benefits of land improvement positively influenced attitude of higher economic groups. Awareness of environmental degradation affected environmental behaviours of all economic groups, and attitude towards the problems also affected behaviour but only for high-income categories. (67) Harland et al. (2007) studied the use of Norm Activation Theory (NAT) to explain pro-environmental behaviour. Purpose/Objectives: The study was conducted with three main aims: first, determining the explanatory value of the situational activators efficacy and ability simultaneously with awareness of need and situational responsibility. The second aim was to gain insights into the effects of denial of responsibility and awareness of consequences on environmental behaviour. The third was to empirically test the mediation by personal norms. They divided their study into two parts. Study 1—Design/Methods: Respondents for study 1 were randomly sampled from the Dutch population. A postal service company selected a total of 700 addresses. The questionnaire focused on two pro-environmental behaviours. Statistical Approach: Analyses were completed through descriptive statistics, correlations, and regressions. Findings/Conclusion: The findings supported norm activation theory. The two commonly studied situational activators namely awareness of need and situational responsibility were significantly related to pro-environmental behaviour intentions. The results also suggested partial mediation of the effects of activators by personal norms. Study 2—Design/Methods: The two personality traits and manipulated awareness of need, efficacy, and ability were measured in a 2 × 2 × 2 factorial design. The participants were invited to research laboratory and were seated in separate cabins containing a computer terminal that presented information, questions, and then answers were registered. Statistical Approach: The analysis was done through mean, standard deviation, and regression. Findings/Conclusion: It was concluded that inclusion of additional activators improved the norm activation theory’s potential

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to explain pro-environmental behaviour, and personal norms significantly mediated the impact of activators on pro-environmental behaviour. (68) Dubey (2007) investigated changes in consumers’ decision-making process led by environmental information. Purpose/Objectives: To reduce the gap between environmental consciousness and environmentally friendly behaviour, a better understanding of various factors and appropriate information on environment was needed. The same was to be investigated with the objectives. Design/Methods: He utilized two-stage multiple choice questionnaire. Personal interview and telephone surveys were conducted to collect the data. A sample of 100 was selected at three localities of Bhopal city in Madhya Pradesh, India. Selection of participants was purposive and based on convenience sampling. Responses were taken on a four-point scale. Findings/Conclusion: The results revealed that people had different consciousness and knowledge depending upon their gender and age. Information on environmental assessment changed the behaviour of housewives more than the change which occurred amongst students. The findings on carbonated containers showed that the important factors in purchasing decision differed for different respondents; but, the information on global warming nevertheless affected the decision. The importance was noticed for reinforcing the link between individual information campaigns on specific environmental issues and long-term initiatives to improve environmental education, public awareness, and decision-making skills. (69) Kalantari et al. (2007) investigated factors affecting environmental behaviour. Purpose/Objectives: They aimed to find out individual and social factors affecting environmental behaviour; and to identify relationships between personal factors, environmental attitudes, and environmental behaviour. Design/Methods: The study was based on a field survey. The research population was Tehran’s residents ageing over 16 years. Using stratified sampling, the population was divided into three strata: North, Central, and South of Tehran. 1200 respondents were selected, 400 from each stratum. For the purpose of data collection, a questionnaire was used and the survey worked out through face to face interviews. Statistical Approach: For the purpose of analysis, along with descriptive statistics, some inferential techniques of correlation, t-test, and analysis of variance were utilized. Duncan tool and path analysis were also used. Findings/Conclusion: Findings concluded that there was a significant difference between men and women attitudes regarding environmental legislation and their environmental behaviour. Women strongly believed that current environmental legislation was sufficient for protection of environment whereas male emphasized that more laws were needed. Environmental behaviour was found significantly different for people with different occupations and incomes. From path analysis, it was emerged out that education and improving problem-based knowledge could change environmental attitude and increase feeling of stress. Then, these in turn prepare people to act environmentally with the help of environmental legislation. (70) Mostafa (2007) investigated gender differences in Egyptian consumers’ Green Purchase Behaviour and also showed the effects on environment knowledge, concern, and attitude. Purpose/Objectives: On the basis of literature, they asserted some hypotheses and purposed to study them in their research. Design/Methods: The questionnaire used in the study was an attractive three-page booklet with a cover-page

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of brief instructions. The first section consisted of demographic data, and the second section covered perceived environmental knowledge, environmental concern, and green purchase attitude. All the constructs used in this study were measured by various items on five-point Likert-type scale (1 = completely disagree to 5 = completely agree). Statistical Approach: Factor analysis with oblique rotation and analysis of variance were employed. Findings/Conclusion: The results revealed that there was a statistically significant difference between men and women on combined dependent variables. Men reported higher levels of perceived environmental knowledge, concern, and attitudes towards green purchase than women. Men gave correct answers to knowledge questions. Men were more concerned about environmental issues and reported more positive attitudes towards green purchase. (71) Tilikidou and Delistavrou (2007) focused on ecological consumer behaviour in Greece. Design/Methods: The authors followed a combinatorial topology approach, and the data of 10 years of persistent research studies were analysed. Statistical Approach: They employed selected non-parametric qualitative techniques in order to obtain a deeper understanding of previous data. Findings/Conclusion: Their findings remarked that three types of the ECCB’s pro-environmental purchasing behaviour (PPB), pro-environmental post purchasing behaviour, (P Post PB) and pro-environmental activities (PA) were inter-related. Thus, pro-environmental purchasers were found recyclers and activists to an extent. Greek ecologically conscious consumers were found to be well-educated people who hold relatively higher incomes. Also, the findings indicated that the general positive environmental attitude was not a very valid factor to describe pro-environmental behaviour. Further, psychographic evidence revealed that Greek ecologically conscious consumers were people with strong social values, who were not bound with material possessions, who were interested in politics, and capable of shaping social circumstances rather than being shaped by them. (72) Cornelissen et al. (2008) conducted a study on sustainable consumer behaviour. Purpose/Objectives: They questioned why people frequently fail to see themselves as environmentally conscious consumers. The reasons were proposed as hypotheses, which were tested in three studies. Findings/Conclusion: Study 1— It was concluded that the reason for this failure could be: people often prone to dismiss their more common ecological behaviours (for example: avoid littering) as non-diagnostic. The commonly performed ecological behaviours became more diagnostic for the inference of pro-environmental attitudes. Study 2—As a result, positive cueing increases the likelihood that people see themselves as consumers who were concerned with the degree to which their behaviour is environmentally responsible. Study 3—The cueing of common ecological behaviours lead participants to choose environment-friendly products with greater frequency, and even to use scrap paper more efficiently. (73) Carrus et al. (2008) combined emotions, habits, and rational choices in ecological behaviour. Purpose/Objectives: The aim was to examine the role of attitudes, subjective norms, perceived control, anticipated emotions, past behaviour, and desire

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in the prediction of pro-environmental behavioural intention. Design/Methods: The model of goal-directed behaviour (MGB) was applied to predict intentions to use public transportation instead of the private car for going to work (Investigated in study 1, N = 180), and to recycle household waste (Investigated in study 2, N = 154). Statistical Approach: Multiple regression and structural equation modeling were used to test the hypotheses. Findings/Conclusion: As expected, results of the two studies indicated that negative anticipated emotions and past behaviour were significant predictors of desire to engage in pro-environmental actions. Desire, in turn, positively predicts pro-environmental behavioural intentions. A direct link between past behaviour and intentions was also detected. (74) Finisterra do Paco and Raposo (2008) determined the characteristics of green consumers in their exploratory study. Purpose/Objectives: Their objective was to group an extensive list of variables into a set of relevant dimensions that may be useful to profile green consumers. Design/Methods: For data collection, a survey of 887 Portuguese consumers was completed aged over eighteen. The questionnaire was composed of two sections. The first part dealt with demographic characteristics of consumers, and second part was designed to examine environmental dimensions. In total, 55 statements were included, measured on a five-point scale I strongly disagree to I strongly agree. Statistical Approach: Factor analysis with varimax rotation was applied. Findings/Conclusion: Eleven factors were identified. They accepted that willingness to pay a higher price for green products depends on the consumers’ demographic and socio-economic characteristics. It also depended upon consumers’ motivations, values, and attitude towards green products. (75) Haytko and Matulich (2008) examined linkages between green advertising and environmentally responsible consumer behaviours. Purpose/Objectives: They purposed to provide a modern, reliable scale for use of academic researchers and business practitioners. Design/Methods: The study utilized Churchill’s (1979) model and a combination of several previously used measures that had not been studied simultaneously, and new items tapped the domains of attitudes towards green advertising and environmentally responsible behaviour. All ratings were done on five-point ‘strongly agree to strongly disagree’ scale. The survey was delivered to 565 undergraduate and graduate business students attending private and public Universities in Florida. Statistical Approach: During analysis, first overall reliability was assessed. Then, principal component analysis was performed to ascertain the dimensionality of each scale. Inferential statistics (t-test) was also used. Findings/Conclusion: Factor analysis reduced the attitude towards green advertising scale into four components namely cognitive and affective responses to green advertising, consumer responses to the companies and their products, consumer specific behaviours, and moral/ethical impact of green advertising. Thoughts and behaviour about the environment obtained five components: environmental activism, personal everyday thoughts and behaviours, respondent’s emotional response, environmental responsibility awareness, and understanding of environmental issues. Also, females expressed more positive attitudes towards green advertising, and exhibited more environmentally responsible behaviours than males. To examine the link between environmentally responsible behaviour and attitude towards green advertising, three

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respondent groups were created namely, environmentally responsible, environmentally apathetic, and neutral. Significant differences emerged between the groups on all green advertising questions. Those who were in environmentally responsible group had more positive attitude towards green advertising than those who were environmentally apathetic. (76) Larijani and Yeshodhara (2008) empirically studied environmental attitude amongst higher primary school teachers of India and Iran. Purpose/Objectives: The main purpose of their study was to compare Indian and Iranian teachers’ environmental attitude. Design/Methods: Their study consisted of two-stage sampling: first selection of schools and then selection of teachers. A total of 1000 teachers were selected; 500 each from both countries. The data on Indian sample was collected in Mysore city and for Iranian teachers from Hamedian city. As a research tool TEAS (Taj Environmental Attitude Scale) was applied using Likert method of summated rating procedure. Statistical Approach: Mean and multivariate analysis of variance were used as main statistical techniques. Between countries effect, between gender effect, and interaction effects were also calculated and discussed upon. Findings/Conclusion: Findings suggested that there did not exist much difference between male and female teachers of Iran. However, in India, females had higher scores towards environmental concern than male teachers. The results revealed that Iranian teachers had most favourable attitude in all the components except in wildlife. Male and female teachers also differed significantly in mostly all factors except population explosion factor. (77) Steg and Vlek (2009) found ways to encourage pro-environmental behaviour. Purpose/Objectives: They proceeded with four questions: (1) Which behaviours should be changed to improve environmental quality? (2) Which factors determine the relevant behaviour? (3) Which interventions could best be applied to encourage pro-environmental behaviour? (4) What were the interventions? Design/Methods: For getting the answers, they renewed different researches and integrated results from that. Findings/Conclusion: The findings from literature described that several theories: Theory of Planned Behaviour (TPB), Values-Beliefs-Norms (VBN), Dittmars, Theory of normative conduct, and goal framing theory contributed in literature defining factors influencing environmental behaviour. But, they emphasized that the conditions under which a particular theory is most successful in explaining behaviour needs more attention and the merits of various theories should be studied more systematically. Theory-driven approach of environmental behaviour was good. Various contextual factors like quality of public transport, the market supply of goods, and the pricing regimes could strongly affect people’ pro-environmental behaviour. They also discussed about informational and structural strategies so that environmental behaviours could be facilitated and emerge worldwide. (78) Webb et al. (2008) examined socially responsible consumption and its measurement. Purpose/Objectives: They contended that literature review suggested socially responsible consumption as a multifaceted construct involving a variety of consumer behaviours. Thus, they aimed to develop a construct for measuring socially responsible consumption. Design/Methods: Their sample consisted of 590 undergraduate and graduate students including large metropolitan University and

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two regional Universities. Statistical Approach: The data were analysed using factor analysis and t-test. External and internal reliabilities were also assessed. Findings/Conclusion: They concluded three dimensions of socially responsible consumption: one, purchasing based on firm’s CSR performance; second, use and purchase criteria; and third, reduction in use of products based on their environmental impact. (79) Alibeli and Johnson (2009) analysed environmental concern cross nationally. Purpose/Objectives: The purpose was to investigate levels of environmental concern between samples of college students in Bahrain, Jordon, Qatar, and Saudi Arabia (BJQS); and measuring the effect of country, gender, social class, and parents’ education on students’ level of environmental concern. Design/Methods: Data were obtained from the unit for community and environmental studies (UCES) in the social science research center at Mississippi State University from 1282 respondents. To measure dependent variable that was environmental concern 12 statements were selected. The scores were coded on three-point scale (1 agree, 2 neither, and 3 disagree). Statistical Approach: Exploratory factor analysis with principal component and varimax rotation, descriptive statistics, mean, range, standard deviation, and ordinary least square analysis were the tools. Findings/Conclusion: The results corroborated that respondents strongly supported the idea of coexisting with nature factor amongst the three factors (coexist with nature, master nature, and environmental efficacy) that were obtained from factor analysis. Women and middle class supported the notion of coexist with nature more than their counterparts. Bahrainis, Qataris, and Saudis indicated lower level support for coexist with nature than Jordanians. Respondents also revealed moderate support for environmental efficacy and low support for master nature component. (80) Birgelen et al. (2009) attempted to understand pro-environmental consumption behaviour that enables companies to establish reputational and competitive advantages. Purpose/Objectives: They wanted to generate new insights by analysing consumer-related factors associated with package-related behaviours regarding beverage consumption. Design/Methods: An online survey amongst 176 German respondents provided empirical support. Findings/Conclusion: The results suggested that eco-friendly purchase and disposal decisions for beverages were related to the environmental awareness of consumers and their eco-friendly attitude. Furthermore, consumers were willing to trade off almost all product attributes in favour of environmentally friendly packaging of beverages, except for taste and price. The non supported hypothesis pertains to the expectation that believing in the positive effects of own eco-friendly disposal actions will guide ecological disposal behaviour. Perceived behavioural control may thus not translate into actual disposal behaviour. (81) Cavas et al. (2009) analysed the ROSE (Relevance of Science Education) data about views of Turkish students’ on environmental challenges. Purpose/Objectives: They aimed to explore ninth-grade students’ attitudes towards the environment and their interest in learning about environmental protection with respect to gender.

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Design/Methods: The ROSE10 questionnaire was used to obtain data from students who have finished their compulsory schooling. In total, 63 high schools from 21 cities were selected. Cities were selected according to socio-economic structure of each city (low, moderate, and high GDP rate) and data were collected from 1260 respondents. Statistical Approach: Data analyses were conducted both with regard to items and total scores. Descriptive statistics, agreement index, Chi-square test, and multivariate analysis of variance were used. Findings/Conclusion: The findings revealed that Turkish students generally had favourable attitudes and interest towards environmental issues. A statistical significant gender difference was found with girls tending more favourable attitudes towards the environment. Boys were interested in effective usage and new sources of energy, and girls were interested in ensuring clean air, safe drinking water, harmful effect of loud sound and noise on hearing. (82) Chen and Kong (2009) examined Chinese consumers’ perception of socially responsible consumption (SRC). Purpose/Objectives: The study aimed to find out what the Chinese consumers were thinking and acting about corporate social responsibility. Design/Methods: The authors constructed a pool of 17 items to measure SRC according to conditions in china. These items were ordered and incorporated into a questionnaire containing 3 parts: attitude towards SRC, items on SRC, and implemented conditions of SRC. Each question was measured using a five-point rating scale anchored by ‘never true’ and ‘always true’ ranging from 1 to 5, respectively. Profession, age, marital status, and gender were taken in demographic characteristics. Statistical Approach: Data were analysed using descriptive statistics, mean, standard deviation, and count. Findings/Conclusion: The findings across demography and attitudes were reported in hierarchy. It is found that Chinese people have different perceptions on corporate social responsibility as per differences in their status, and it is only under specific conditions that consumers act in a socially responsible manner. They too suggested that married always pay more attention to CSR than unmarried. (83) Gonzalez et al. (2009) attempted to answer the question: how do socially responsible consumers consider consumption. Purpose/Objectives: Their aim was to verify the existence of different profiles of socially conscious consumers and to study their social representation of consumption. Design/Methods: The study was carried out on a convenience sample of 392 individuals including students and managers/salaried-employees of a bank. The first part of the questionnaire contained the questions on social responsiblity scale. The second part contained measures of different variables relating to consumption, and the third contained the identification sheet. Statistical Approach: Factor analysis and hierarchical ascending classification of factors to form groups were utilized for the purposes. Findings/Conclusion: Five components were obtained through factor analysis which were purchase of cause related products, helping small businesses, taking account of products’ geographical origins, reduction of volume of consumption, and firm’s behaviour. According 10 ROSE questionnaire is a Likert-type scale and included what I want to learn about, my future job,

me and the environmental challenges, my science classes, my opinion about science and technology, my out-of-school experience, and me as a scientist requires students sections and various information on them.

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to these dimensions, four groups of consumers were obtained. These groups were SRC’s, locals, good causers, and indifferent. The groups were found different from each other on several dimensions of socially responsible consumption. (84) Ha-Brookshire and Hodges (2009) explored socially responsible consumer behaviour in the form of donation of used clothing. Purpose/Objectives: The purpose was to gain an in-depth understanding of consumer disposal behaviour in a used clothing donation setting, from the perspective of consumers who have recently donated used clothing items. Design/Methods: The research design was interpretive11 in nature. For sample selection, two methods were used. First, eleven participants were selected through snowball sampling. Second, four participants were approached at a local donation site while in the process of donation of used clothing. A questionnaire was also used to get personal and demographic information. Findings/Conclusion: They interpreted the results in three stages. First, motivations for donations were explored; second was about the selection of what to donate; and third enquired about feelings of guilt. In motivations, people donated used clothing as they wanted to get rid of the stuff. It was also found as a ritual conducted as a part of spring cleaning. One reason for donating the clothes was the feeling of tiredness from old clothes and the want of something new. In this way, none of the participant mentioned social consciousness as a primary motivator for this behaviour. Further, people viewed that food and monetary donations were real donations. Instead taking donors as socially responsible, people thought those who bought merchandise from local donation centres were responsible citizens. Participants termed that they were not helping the society rather society was helping them out by accepting their used clothes. While donating feelings of guilt also appeared in two types. One was the expectation of wearing clothes again with some changes, and second source of guilt occurred when they realized how much clothing they owned that they never wore. Some other interesting findings also emerged. Close family members and friends were the first choice to give clothes, and in selecting donation sites, participants clearly expressed the convenience of the sight as most important factor. (85) Kennedy et al. (2009) attempted to understand environmental values/behaviour gap. Purpose/Objectives: They aimed to determine how many respondents acknowledge a gap between values and behaviour, and then to explore potential variables that may constraint environmentally supportive behaviours. Design/Methodology: The variables to examine the gap were grouped into individual, household, and societal components.12 The participants were asked to selfreport the gap between values and their behaviour. The questionnaire was mailed throughout ten provinces in Canada. Sampling was stratified with equal numbers of rural and urban households to attain proportional representation of the ten 11 Interpretive inquiry is described as a systematic search for deep understanding of the ways in which persons subjectively experience the social world. In-depth interview and observation were primary tools to obtain data within interpretive framework. 12 Individual variables referred to items that were controlled to a great extent by the individual. There might be outside influences also such as the role of parents and friends in shaping values which are highlighted by next two. Household variables included those influences that existed at the household level and social variables reflected social context and factors which measured it.

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provinces. The proceedings were done on 1664 completed questionnaires. Findings/Conclusion: A small group of respondents showed little concern for the environment and oriented their lifestyles around that concern. Majority indicated that they were prevented from doing what was best for the environment. A smaller percentage had strong egoistic values and nearly half reported adherence to NEP. Thus, environmental beliefs did not appear to act as a constraint. Further, time was the most important constraint amongst household variables and money was the next. (86) Oikonomou et al. (2009a) investigated the consumers’ willingness to pay for the sake of environmental protection. Design/Methods: They uniquely carried the study via e-mails using a questionnaire with SRCB scale with 30 statements. The score of statements ranged on seven-point agreement–disagreement scale. The sample of 1000 was highly inclined towards females. Statistical Approach: Factorial analysis was processed on the data. Findings/Conclusion: Six factors were obtained: willingness payment, corporate responsibility, personal sacrifices, willingness to protest, concern for the environment, and non-personal sacrifices. Their findings suggested that women were more aware and sensible to issues of environmental protection, and as the age of respondents’ increases, the awareness of environmental problems increases too. It was not possible to draw any specific conclusion for marital status and children. Highly educated were more willing to pay for environment; however, respondents with high and less income were not willing to pay a financial consideration. In comparison with working, students did not seem concerned about environment and neither was willing to pay for environment protection. (87) Oikonomou et al. (2009b) investigated via the internet the personal values in life and how they determined consumers’ environmental behaviour. Purpose/Objectives: Their purpose was to correlate the behaviour of an environmentally conscious consumer versus their values in life. A questionnaire with a range of responsible consumer behaviours was administered on 800 respondents. Statistical Approach: The analysis was done with factor analysis, percentages, and correlations. Findings/Conclusion: The findings revealed seven groups as individual factors explicitly, willingness payment, corporate responsibility, personal sacrifices, willingness to protest, concern for the environment, non-personal sacrifices, and individual awareness. Also, amongst nine forms of values only self-esteem seemed important in predicting interest in ecological issues. Women were found more environmentally aware than men. Level of education did not provide sufficient predictability of responsible environmental behaviour. (88) Ozkan (2009) examined effect of some demographic characteristics on socially responsible consumption behaviour. Purpose/Objectives: The main purpose was to investigate the effects of perceptions of consumers about their income level (income adequacy) and education level on their responsible consumption behaviours. Design/Methods: The study was conducted in Cankaya district of Ankara on women respondents. Random sampling method was used; and in total, 103 participants was included in the study. The questionnaire was divided into two parts. In the first part, demographic information was solicited. In the second part, responsible consumption behaviours were evaluated with 13 statements encoring always, usually, sometimes,

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rarely, and never. Statistical Approach: The main tools utilized were variance analysis and t-test. Findings/Conclusion: It was found that when the consumers had the opportunity to make a choice, they preferred the products less harmful to the environment. Also, when they realized the harm of any product to the environment, they did not buy that product. Consumers were too found to have a tendency of buying packaged products, and this tendency was found higher amongst the consumers who deemed their income inadequate. In addition, education level had a significant effect on behaviours and with increasing level of education, the ratio of performing responsible behaviours increased. (89) Roux and Nantel (2009) explored conscious consumption and its components. Purpose/Objectives: The purpose was to study the relationship between consumption concepts that prevailed in literature. He discussed about three aspects: socially responsible consumption, ecologically conscious consumption, and ethical consumption. Design/Methods: To study conscious consumption, a questionnaire was built with five-point scale and administered on a sample of 1338 respondents. The demographic features studied were gender and age. Statistical Approach: The correlations and proportions were examined. Findings/Conclusion: The results described that there was an overlap between the concepts of conscious consumption, and more participants identified themselves as environmentally friendly than socially responsible or ethical. (90) Sengupta et al. (2009) analysed effect of sight and gender on environmental awareness and pro-environmental behaviour. Design/Methods: Purposive sampling was applied and the sample was comprised of 97 students: 50 normally sighted and 47 visually impaired. Environmental awareness scale and pro-environmental behaviour scale were utilized. Statistical Approach: Mean, standard deviation, analysis of variance, correlation, and Fisher’s z-test were utilized. Findings/Conclusion: The results showed that gender or sight had no significant effect on environmental awareness and pro-environmental behaviour. With respect to environmental awareness, students with normal vision and those with visual impairment did not differed significantly. The relationship between awareness and action was also found similar for both the groups that were visually impaired and normally sighted. (91) Singh (2009) empirically explored socially responsible behaviour of Indian consumers. Purpose/Objectives: The main objective was to analyse socially responsible consumption behaviour across demography. Design/Methods: The sample was drawn from two cities Chandigarh and Narwana taking 100 consumers from each city. A 34-item five-point SRCB (socially responsible consumption behaviour) scale was introduced amongst the respondents. Statistical Approach: Data were analysed by comparing means and standard deviations between the groups. Regression analysis was also used. Findings/Conclusion: Study found that urban respondents scored high on all demographic categories in comparison with rural consumers. Genderwise females were found slightly more responsible than males but the difference was insignificant. Inverse relationship had been noticed between education and dependent SRCB. Younger (particularly females) demonstrated high scores on SRCB. Income levels revealed significant difference only for urban consumers. Further, age and city had been found significant predictors of SRCB.

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(92) Tan and Lau (2009) examined sustainable consumption patterns of young consumers. Purpose/Objectives: Four research questions were developed and they aimed to answer those. First question was about the significant difference between gender and consumption behaviour. Second was about the significant differences between business and non-business students regarding consumption behaviour. Third question was on the topic of the level of sustainable consumption behaviour amongst young consumers. The last undertook the cause of concern of overconsumption amongst young consumers. Design/Methods: For data collection, they selected a 15item validated scale based on four principles of sustainable consumption behaviour (selection, minimization, maximization, and segregation). The ratings were done on a four-point Likert scale from ‘1 never true’ to ‘4 true all the times’. All in all, 270 completed questionnaires were obtained from Johar, Malaysia. Statistical Approach: Mean, standard deviation, and t-test were the statistical tools applied. Findings/Conclusion: The results showed no significant difference between gender and sustainable consumption behaviour of young consumers, and no significant difference between business and non-business students. However, business students were found less environmentally oriented compared to non-business students. In addition, four factors had been identified as a barrier to buying green. These factors were (1) perceptions of inferior product quality, (2) scepticism about green marketing claims, (3) difficulty in identifying green products, and (4) price sensitivity. (93) Urban and Zverinova (2009) questioned regarding the determinants of environmentally significant behaviour in Czech Republic. Purpose/Objectives: The main objective was to test models of environmentally significant behaviour on two ISSP data sets from 1993 and 2000 surveys. Design/Methods: In both the surveys, three-stage stratified random sampling had been used to select the sample. Pooled data sets of the two surveys contained information on various behavioural intentions and particular examples of environmentally significant behaviour which were used as dependent variable in the study. These behaviours and intentions were measured either as binary or as ordinal variables. Statistical Approach: Logistic regression was the main technique. They employed binary logit model for binary dependent variables, and proportional odds model for ordinal dependent variables. Maximum likelihood method had been used for the estimation of scores. In addition, correlation matrix was also presented. Findings/Conclusion: It was shown that people were more prone to perform behaviours devoted as private-sphere environmentalism. Consumers expressed higher willingness to perform public sphere behaviour in 1993 than in 2000. Thus, it was hypothesized that higher intentions in 1993 did not actually turned into practice. Education, environmental concern, and environmental efficacy showed positive and significant effect on environmentally significant behaviour. Environmental knowledge had no significant effect on behaviours and intentions. For the types of environmentally significant behaviours, it was revealed that non-activist behaviour in public sphere and its intention formed a relatively coherent type. On the other hand, private-sphere environmentalism and environmental activism did not really form coherent types neither in terms of correlation nor in what covariates had effect on them.

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(94) Albayrak et al. (2010) clustered consumers according to their environmental concerns and skepticisms. Purpose/Objectives: They aimed to understand why some consumers behave in dissimilar ways to environmentally sensitive claims and statements from others. Design/Methods: The survey was conducted amongst the graduate students of Akdeniz University, Turkey and the instrument contained three sections. In the first section, gender, age, and monthly income were asked upon. In the second section, environmental concern was measured. In the last section, Scepticism was measured. Statistical Approach: Item-total correlations, standard deviation, item-range of responses, regression analysis, and cluster analysis were applied. Findings/Conclusion: The results showed that environmental concern had a positive and statistically significant influence on green purchase behaviour, and scepticism had a negative but statistically insignificant effect. As a result of Hierarchical clustering, three clusters were obtained; keen sceptics, fanatics, and hesitants. Keen sceptics were those who had a high level of environmental concern together with high level of scepticism which negatively influenced their green purchase behaviour. Fanatics were both environmentally concerned and optimistic about environmental claims; thus, purchased green products. Hesitants had a moderate level of environmental concern and scepticism. Accordingly, they either did not attempt to purchase green products or feel irresponsible about the environmental issues. (95) Arnocky and Stroink (2010) explained gender differences in environmentalism and elaborated upon the mediating role of emotional empathy in explaining these differences. Purpose/Objectives: They aimed to check two hypotheses. First,targeted green product purchase intentions gender differences existed in the self-reported environmental concern, cooperativeness, and behaviours. Second, emotional empathy would mediate the gender–environmentalism relationship. Design/Methods: The sample consisted of 202 undergraduate students. In the questionnaire, Emotional Empathic Tendency Scale (EETS) with 33 items quantified emotional empathy on a nine-point scale. Also, 12-item environmental concern scale, self-report commons dilemma (SRPD) scale, and 15-item scale of pro-environmental behaviour were utilized. Statistical Approach: Descriptive statistics, correlations, and t-test were applied to analyse the data. Findings/Conclusion: The results stated that age correlated with social-altruistic concern and was included as a control variable. Significant gender differences had been obtained for social-altruistic concerns and ecological cooperation. Neither biospheric nor egoistic concerns were significantly correlated with gender. Also gender did not correlate with self-reported behaviours. Emotional empathy mediated the link between gender and altruistic concern, and the relationship between gender and competitiveness in a commons dilemma. (96) Gupta (2010) examined social responsibility of Indian consumers and their attitude towards environment protection. Purpose/Objectives: The objectives were to analyse the effect of demography on socially responsible consumption behaviour, and pull out the components of consumer social responsibility. Design/Methods: The scales SRCB (socially responsible consumption behaviour), ECCB (ecologically conscious consumption behaviour), NEP (new environmental paradigm), PP (pollution problem), AL (attitude toward litter), PI (purchase intentions), and

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PCE (perceived consumer effectiveness) were borrowed from literature, assimilated in a questionnaire, and introduced amongst 200 respondents in Ambala District, Haryana. Five-point coding from ‘never true to always true’ was applied. Statistical Approach: The responses were analysed using mean comparisons, standard deviation, analysis of variance, regression analysis, correlation, factor analysis, and t-test. Findings/Conclusion: Results indicated that academic intelligence and profession showed significant differences on SRCB scale. It was also shown that environmental conscious individuals who were concerned about litter, who believed in pollution problem, and concerned about nature were more inclined towards their social responsibility, and these behaviours were found good predictors of SRCB. (97) Kim and Kim (2010) compared environmental attitude and its determinants. Purpose/Objectives: Their aim was to empirically analyse the different determinants, their state, and changes around environmental attitude in three East Asia countries: Korea, Japan, and China. Design/Methods: Data were used from fifth wave (2005) and from first wave (1980) of World Value Survey. Statistical Approach: They calculated mean of variables and implemented analysis of variance . Regression and ranking tools were also taken. Findings/Conclusion: The research showed that men had greater environmental concern and attitude. In Korea and China, male revealed greater concern, and for environmental actions, men showed higher scores than women across all three countries. The mean difference in environmental concern in Korea and Japan confirmed that younger people showed greater environmentalism. On the other hand, in Japan, people older in age expressed higher intentions to environmental action. Positive relationship between education and income had been noticed. Findings also stated that strong conservatism decreased environmental concern but increased environmental actions. Egoistic values had no significant relation to environmental concern. In Korea and China, strong egoistic values turned out to be less supportive for environmental actions. Feminism brought out both environmental concern and action in Korea; but, only environmental action in china. In Japan, no significant variations were noted even when feminism was varied. Religiosity showed a significant relationship with environmental action only in Korea. In ranking, Japan was graded first in environmental concern; whereas, China ranked first in environmental action. In conservatism and feminism, China again took the first rank. Economic value and religiosity were highest in Korea; whereas, egoistic values held highest in Japan. (98) Kiraci and Kayabasi (2010) made a preliminary study to distinguish real and spurious sustainable consumption behaviour. Purpose/Objectives: They said some behaviours might provide individuals with some economic benefits, those behaviours could be called as spurious sustainable consumption behaviour (SSCB) and the other with no economic benefits were called real sustainable consumption behaviour (RSCB). The main purpose was to analyse differences between RSCB and SSCB by comparing and contrasting the means of two behavioural styles. Design/Methods: As sample, 512 students were considered from Dumlupinar University using stratified sampling method. The questionnaire was composed by searching various studies. Statistical Approach: Descriptive analysis, t-test, and analysis of variance with post hoc comparisons were used to analyse the data. Findings/Objectives: It was noticed

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that spurious behaviours were exhibited more frequently than real behaviours. The authors found that regarding spurious behaviours, female students were higher than those of male students, and meaningful differences were noted in means of SCCB’s groups with regard to pocket money. On the other hand, RSCB had been observed frequently in the students who grew up in rural areas (villages) where the traditional life depends upon less consumption. Women also reported higher RSCB than men. (99) Lau (2010) evaluated religiosity and money ethics by moving towards socially responsible consumption. Purpose/Objectives: The primary objective was to investigate the effect of religiosity and money ethics on socially responsible consumption behaviour. Design/Methods: Three scales were used: socially responsible consumption, religiosity, and money ethics. All measurements were done on a five-point Likert-type scale from strongly disagree (1) to strongly agree (5). The questionnaires were completed by 301 undergraduate students from a private university in Malaysia. The demographic data collected were about gender, year of study, course, and home residence. Statistical Approach: Data were analysed through regression analysis and co-linearity statistics. Findings/Conclusion: Religiosity was found as a significant and the main contributor to all the dimensions of socially responsible consumption. So, a more religious consumer would likely to be supportive of company’s CSR initiatives and avoid buying from companies that discriminate amongst minorities. Religiosity also impacted consumers’ tendency to purchase and use environmentally safe products. Money ethics did not show any significant relationship with SRC dimensions, meaning that a person who placed a lot of emphasis on money might or might not be supportive of SRC concern. (100) Leonidou et al. (2010) examined antecedents and outcomes of consumer environmentally friendly attitudes and behaviour. Purpose/Objectives: The main purpose was to analyse the antecedents and outcomes of consumer environmentally friendly attitudes and behaviours. Design/Methods: Stratified random sampling was used to a nationwide sample of 500 consumers aged 15 and above. Personal interviews were conducted based on a structured questionnaire. Findings/Conclusion: Results demonstrated that pro-environmental attitude was higher for the consumers who were collectivist, long-term oriented, politically active, deontological, and law obedient. Consumers’ inward environmental attitude was found related with green purchasing behaviour, while outward environmental attitude was related with general green behaviour. It was also obtained that individuals who exhibited general ecological behaviour would enjoy more satisfaction with their lives, while consumers whose purchasing behaviour was eco-friendly would feel more satisfied with green buying options. (101) Majlath (2010) studied the role of perceived consumer effectiveness. Purpose/Objectives: It was aimed to answer the question of why people who had positive attitude towards environment not behave according to their attitude. Design/Methods: The study sample was fairly distributed amongst 102 environmentally friendly and 102 non-environmentally friendly respondents. Perceived consumer effectiveness was measured with three statements on five-grade scale expressing agreement of respondents. Statistical Approach: Mean, standard deviation, and

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t-test were used to arrive at the study’s objective. Findings/Conclusion: The statistically significant results confirmed that green consumers felt that they could contribute not only to the formation of environmental problems but to the solution of them. They also felt that their role for environment protection was little bit more significant than the non-environment friendly respondents. (102) Ramayah et al. (2010) targeted green product purchase intentions. Purpose/Objectives: The main objective was to examine (from the motivational perspective of the Theory of Reasoned Action) how individual values and attitudes influence purchase intentions of green products. Design/Methods: The study analysed data collected from 257 working respondents who were asked their views concerning their value sets, attitudes, and purchase intentions. Statistical Approach: Analysis is done through Structural Equation Modelling. Findings/Conclusion: Findings indicated that individual consequences relating to amount of effort and convenience of consumers is negatively related to intention to purchase green product. Environmental consequences are not a significant predictor of environmentally responsible purchase intention. Conservation value was found to be positively related to attitude on environmental consequences but less intensely with individual consequences, while both self-transcendence value and self-enhancement value were positively related to individual consequences. Individual consequences and self-enhancement value were negatively related to environmentally responsible purchase intention. (103) McCright (2010) tested theoretical arguments about gender differences in scientific knowledge and environmental concern. Purpose/Objectives: The main aim was to test the hypotheses which were developed on the basis of existing literature about gender differences and other determinants associated with it. Design/Methodology: The data came from the Gall up polls (2001–2008) that focused specifically on environmental issues, with nationally representative samples of adults of age 18 years or older. Gallup included three scientific knowledge items and three climate change concern items. In addition, a perceived understanding of environmental knowledge was also studied. Statistical Approach: Mean, standard deviation, percentages, OLS regression model were used for analysis of Gall up data. Findings/Conclusion: The findings on environmental knowledge displayed that white women exhibited greater climate change knowledge than men, and men reported greater perceived understanding than women. It was seen that political liberals and democrats in general public were more knowledgeable than their politically conservative and republican counterparts. Education attainment and income steered greater environmental knowledge but religiosity lead to lesser knowledge. Younger adults and whites were more knowledgeable than their older and non-white counterparts. For environmental concern women scored higher, and gender also had a direct effect on concern while controlling for knowledge. (104) Mobley et al. (2010) explored literature on environmentalism as an additional determinant of environmentally responsible behaviour (ERB) due to the fact that this literature might work as a source of environmental knowledge. Purpose/Objectives: The aim was to test the assumption that individuals who seemed knowledgeable and concerned about the environment will engage in environmentally

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responsible behaviour (ERB). It was examined whether reading three classic environmental books (Walden, A Sand County Almanac, and Silent Spring) was associated with the likelihood of engaging in ERB. Conceptualizing this activity as a formative experience and a source of environmental knowledge, it was hypothesized that reading such literature would be a stronger predictor of ERB than socio-demographic characteristics, general environmental attitudes (measured by the New Ecological Paradigm), and concern about specific environmental risks as literature dealt with till date. Design/Methods: Data were used from a large-scale Web survey hosted on National Geographic’s Web site in 2001–2002. Findings/Conclusion: The results indicated that reading environmental literature was a stronger predictor of ERB than background characteristics, but environmental concern was still a stronger predictor. (105) Rikner (2010) approached to behaviour, values, attitudes, and feelings of responsibility. Purpose/Objectives: It was targeted to look into whether or not there was a difference between a group of Waldorf teachers and a group of public teachers when it comes to behaviour, values, attitudes, and feelings of personal responsibility regarding environmental issues. Design/Methods: The sample consisted of 141 Swedish teachers of which 68 (48%) were Waldorf and 73 (52%) were public school teachers. The questionnaire contained measures of environmental behaviour, altruistic, biospheric, and egoistic values, pro-environmental attitudes, and feeling of responsibility. Statistical Approach: Standard Multiple Regression (MRA) and Multivariate Analysis of Variance (MANOVA) were performed to fetch the results. Findings/Conclusion: Analysis suggested that Waldorf teachers reported higher biospheric values, more pro-environmental behaviour, more feelings of personal responsibility, and higher altruistic values than public school teachers. Women behaved more environmentally friendly, and younger borns too reported more pro-environmental behaviour than later-borns. (106) Savita and Kumar (2010) made a comparative analysis of consumer attitude towards environment-friendly products. Design/Methods: Their sample consisted of 400 respondents from Delhi, Chandigarh, and rural areas of Haryana by equal division in these areas. The main demographic features studied were gender and residential area. As a data collection tool, a questionnaire was utilized consisting 30 items on five-point Likert-type scale including a range of dimensions of environmentfriendly products. Statistical Approach: Mean comparison and two-way analysis of variance were applied. Findings/Conclusion: Their findings revealed that genderwise there did not exist any significant difference in attitudes of people regarding environment-friendly products except after-use features. Urban people had more favourable attitude towards environment-friendly products than their rural counterparts. Also, people in urban residents had come up with favourable attitude towards raw material, packaging, and after-use features of environment-friendly products. (107) Chen and Chai (2010) studied consumers’ attitude towards environment and green products. Purpose/Objectives: Two objectives were attained: to compare gender regarding attitudes towards the environment and green products, and to investigate the relationship between attitude towards the environment and green products. To achieve the objectives five hypotheses were worked out. Design/Methods: A questionnaire was administered to 200 undergraduate students from a major private

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university in Malaysia. Statistical Approach: Factor analysis, multiple linear regression, and independent sample t-test were utilized for analysis. Findings/Conclusion: The results indicated that there was no difference between gender with regard to environmental attitudes and attitudes on green products. No significant relationship existed between attitudes for environmental protection and attitude on green products. Further, moral obligation or personal norm was seen working as an important factor for a pro-environmental behavioural disposition. (108) Xiao and Hong (2010) elaborated on gender differences in environmental behaviours. Purpose/Objectives: The purpose was to detail the Western results on gender differences to Chinese sample with taking certain important variables as control. Design/Methods: They formed two public and private environmental behaviour indices, and also combined the two to form a single behavioural index. Two mediating factors, one general environmental concern and other environmental knowledge, were also included. As a direct measure of biographical unavailability was not found; so, two personal constraints (employment and parenthood status) were asked upon. The data was used from Chinese General Social Survey (CGSS) and its environment module. Statistical Approach: Regression, confirmatory factor analysis, and path analysis with direct and indirect impacts were utilized. Findings/Conclusion: The results were indistinct that for both men and women, levels of participation in private environmental behaviours were much higher than levels of participations in public environmental behaviours. There were no significant differences in either the private or combined indices suggesting equal participation across men and women. After using controls, participations of women in private and overall index were significantly higher relative to men. However, no significant gender differences were found pertaining to public environmental behaviours. To their surprise, the interaction of gender/employment exhibited insignificant association with behaviour indices and environmental knowledge. Being a parent clearly reduced levels of participation. Parenthood had significant and negative associations with all three behaviours, while employment had no significant association. Further, respondents with higher education and more environmental knowledge tended to have significantly greater participation in all behaviours. (109) Boivin et al. (2011) studied the influence of perceived risks on buying socially responsible goods. Purpose/Objectives: The objective was to assess whether or not perceived risks actually impacted purchasing behaviour in relation to socially responsible goods. Design/Methods: Data were collected via an online survey conducted in the province of Quebec (Canada) using a random sampling method subject to age and gender quotas to guarantee population representativeness. Perceived risks associated with responsible goods were measured using a 10-point Likert scale (1 = totally disagree to 10 = totally agree). The extent of purchase of socially responsible goods was also asked, similar on a 10-point scale (1 = never to 10 = always). Statistical Approach: Exploratory factor analysis with orthogonal rotation and cluster analysis were used to reach at the objectives. Findings/Conclusion: Factor analysis revealed five distinct factors of perceived risks. (1) Psychological risk—choosing a bad product which could have a negative impact on the consumers ego, (2) Temporal risk—which was associated with the time wasted while shopping around for socially

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responsible goods, (3) Performance risk—was related to consumers’ involvement in the purchase of goods which do not work as they should, (4) Physical risk— was associated with the impact of socially responsible goods on one’s health, (5) Financial risk—measured risk of paying a high price for socially responsible goods relation to comparable goods. Their most striking result was that the performance risk and financial risks were the only risks that acted as impediments in the purchase of socially responsible goods. Further, cluster analysis grouped the products into three categories. Group 1—comprised socially responsible goods for which the financial risk acted as an impediment. Many of the goods included in this group were quite expensive. Group 2—comprised responsible goods for which no perceived risks were associated with the purchase process. Group 3—performance risk acted as an impediment and the majority of products in this group were either green products or local products. (110) Chen et al. (2011) identified effects of attitudinal and socio-demographical variables on pro-environmental behaviour. Purpose/Objectives: They aimed to study the relationship between pro-environmental behaviour and its socio-demographic predictors. They also purposed to assess the impacts of urban size and occupational status as two new predictors of this behaviour. Design/Methods: They utilized data from General social Survey (2003) administered jointly by survey research center of Hong Kong University of science and technology and department of sociology at Renmin University of China. Stratified sampling was used. Statistical Approach: Logistic regression, odds ratio, and standardized odds ratio were used to estimate effects of environmental attitude and socio-demographic attributes on pro-environment behaviour. Findings/Conclusion: The findings reported that 68% respondents were engaged in environmental talk, 71% stated engaging in recycling bags, 37% reported sort garbage, 24% reported environmental volunteering, and only 17% reported environmental litigation. It was found that all demographic variables were significant predictors of at least one pro-environmental behaviour. Being female increased the sorting garbage and recycling bags behaviour. Higher education increased environmental talk, environmental volunteering, and environmental litigation. Singles highly participated in sorting garbage, recycling bags, and environmental volunteering than their married counterparts. People who were employed instead those who were in leadership positions were found engaged in environmental litigation, talk, and also participated in environmental education programmes. Respondents in urban areas were significantly high in sorting garbage, recycling bags, and environmental volunteering dimensions. Enhanced NEP also enhanced proenvironmental behaviour specially sort garbage, environmental talk, and recycling bags. (111) Durif et al. (2011) profiled socially responsible consumers. Purpose/Objectives: They proposed a typology of socially responsible consumers and identified the leading characteristics of each profile they obtained. Design/Methods: The 49-item SRC construct was measured on Likert-type 1–10 scale ranging totally disagree to totally agree. Sample comprised 752 persons. Statistical Approach: The statistical tools were principal component analysis and cluster analysis. Findings/Conclusion: Principal component analysis extracted factors from 49 statements.

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These were citizen behaviour, behaviour focusing on protection of environment, recycling behaviour, composting behaviour, local consumption behaviour, behaviour taking into account animal protection, Deconsumption behaviour, and sustainable transport behaviour. Cluster analysis divided consumers into 6 groups: pro SRC, average –SRC, average +SRC, average SRC, pro + SRC, and anti SRC. The effect of motivation type and impediments in different forms were also noticed on identified consumer segments. (112) Robelia et al. (2011) examined environmental learning through online social networks, and how it is a contributor to environmentally responsible behaviours. Purpose/Objectives: The main aim was to examine an application within facebook.com that allowed users to post climate change news stories from other websites and comment on those stories. Design/Methods: A survey was administered and focus group interviews were conducted. Findings/Conclusion: With the survey, it came out that users of the social networking application reported above average knowledge of climate change, and that self-reported environmental behaviors increased during young people’s involvement with the facebook application. Focus groups indicated peer role modelling through interaction on the site which motivated pro-environmental behaviors. Participation in a community of like-minded users encouraged many participants to learn more about climate change and do more to limit its impact. (113) Muderrisoglu and Altanlar (2011) studied attitudes and behaviours of undergraduate students towards environmental issues. Purpose/Objectives: The aim of the study was to determine how some of the socio-demographic characteristics affect environmental attitude and behaviours of undergraduate students. Design/Methods: The study was carried out in Abant Yzzet Bayal University, Konuralp campus in Duzce and Golkoy in Bolu. The NEP scale and environmentally responsible behaviour scale both were measured on five point. Statistical Approach: Percentages, factor analysis, analysis of variance were applied on the data to carry out analysis. Findings/Conclusion: Factor analysis confirmed three factors of environmental attitude: ecocentric, technocentric, and dualcentric. Of the three factors, the most supported was the dualcentric attitude, followed by ecocentric; but, technocentric attitudes were not supported by the students. Factor analysis on environmentally responsible behaviour also confirmed three factors: activism behaviour, consumerism behaviour, and recycling behaviour. The students stated that they highly participated in consumerism behaviours followed by recycling behaviours; but, the least participation was found for the activism behaviours. Gender had a high effect both on environmental attitude and behaviour. Female students were more inclined towards ecocentric and dualcentric views, and male students were more inclined to technocentric attitudes. Male students participated more in consumerism and females participated more in recycling showing no significant difference for activism. (114) Singh and Gupta (2011) measured responsible behaviour of students by considering them as agents for social change. Design/Methods: A regional sample of 100 students from district Ambala was obtained and socially responsible consumption behaviour was assessed with the help of a questionnaire. The demographic factors included were gender, place of living, educational qualifications, field of study,

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academic intelligence, family size, family income, age, and civic sense. Statistical Approach: The findings were analysed and interpreted using regression model, mean, standard deviation, ANOVA (Analysis of Variance), and post hoc comparisons. Findings/Conclusion: The results conferred that teenagers, girls, residents of rural areas, students with excellent academic scores, having humanities as study subject, and with high civic sense were more socially responsible. The mean differences were found statistically reliable for academic intelligence and civic sense. (115) Zahedi et al. (2011) investigated awareness level regarding environmental issues in high school students of Tehran city. Purpose/Objectives: They aimed to examine whether gender, field of study, father’s and mother’s education had any effect on environmental awareness level or not. Design/Methods: Library and field methods were used to gather information. In field method, the researcher-made questionnaire was used to access required information. Classified random sampling was used in which from 19 educational zones in Tehran, 3 zones were selected by stratified random sampling. Then in each zone, one boy’s and one girl’s school was selected randomly totaling 6 schools by total sample size of 382 students. Statistical Approach: To describe data: percentages, median, frequency, Chi-square, independent t-test, and analysis of variance were used. Findings/Conclusion: The findings suggested no significant differences either for gender, field of study, father’s education, and mother’s education. They concluded that these insignificant results were in contradiction with the Western countries. Three basic reasons were obtained for the results. First, parents do not themselves pay attention to environmental issues. Second, environmental education provided at school period of parents had been inadequate or inefficient. Third, parents due to business and social engagements of machinery life are not able to transfer necessary knowledge through correct interactions with their children in this area. (116) Aman et al. (2012) studied the influence of environmental knowledge and concern on green purchase intentions. Purpose/objectives: The paper aimed at investigating the influence of environmental knowledge and concern on consumers’ green purchase intentions, and examined the effect of attitude as a mediator. Design/Methods: The study was conducted in Malaysia and data were collected from 384 Sabahan consumers. It was completed by convenience sampling approach. The questionnaire contained five sections and all the statements were measured on five-point scale from 1 (Strongly Disagree) to 5 (Strongly Agree). Statistical Approach: Theory of Reasoned Action was tested. Factor analysis, descriptive analysis, and correlation analysis were conducted. Findings/Conclusion: By applying factor analysis, a range of dimensions of environmental knowledge, concern, attitude, and green purchase intentions were derived. Then, it was revealed that dimensions of environmental concern had weak positive correlations with dimensions of environmental knowledge, attitude, and green purchase intentions. Moreover, dimensions of environmental knowledge further had weak to medium correlations with green purchase intentions, and attitude had a moderate positive correlation with green purchase intentions. (117) Darnall et al. (2012) held that consumers were becoming more knowledgeable about the environment and reflect this knowledge in their decisions of

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buying green products; but, they believed that numerous other questions too exist about consumers’ choice of various green products and services over others. Purpose/Objectives: Considering the above fact, they examined individuals’ actual green consumption. Design/Methods/Approach: The study was conducted with a sample of more than 1200 UK residents using multiple regression technique. Findings/Conclusion: They showed that consumers’ total green consumption was related to their trust on various sources which provide environmental information and knowledge to them. (118) Durif et al. (2012) questioned about whether or not perceived risks could explain the ‘green gap’ in consumption of green products. Purpose/Objectives: They purposed to understand the influence of some attributes or aspects of green products on various risks perceived by consumers. Design/Methods: To achieve the objectives, a means-end chain (MEC) technique was employed to explore the links that consumers establish between the attributes of green cleaning products, their consequences, and their perceived risks. This chain was Product attributes → Consequences → Motivations → Perceived risk dimensions. For group discussions, two groups of ten members (recruited by announcement) met in a genuine focus group room in a Canadian city for a period of 90 min. Statistical Approach: Owing to the qualitative nature of the data, the content analysis method was applied in this study. Findings/Conclusion: The findings highlighted that consumers perceive negative risks based on green products attributes, specifically with regard to the functional, financial, and temporal aspects of these products. Conversely, the perceived risks were deemed positive by consumers when it comes to physical and psychosocial aspects. (119) Carrete et al. (2012) studied confusion, credibility, and compatibility in green consumer behaviour. Purpose/Objectives: The main purpose was to contribute to a better understanding of deeper motivations and inhibitors of green consumer behaviour in the context of emerging economies. Design/Methods: In-depth interviews and observational data were used to study 15 Mexican families from four urban regions of Mexico with different incomes. Statistical Approach: Thematic analysis was used to develop and validate themes and codes. Findings/Conclusion: The findings highlighted three dominant themes related to uncertainty in the adoption of environment-friendly behaviour which were consumer confusion, trust-credibility, and compatibility. Overall, green behaviours seemed to be ingrained in the traditional heritage of savings and frugality rather than based on strong environmental values. (120) Cleveland et al. (2012) explored green creeds, green deeds, and internal environmental locus of control. Purpose/Objectives: They reported about the development of a novel construct that is internal environmental locus of control (INELOC), which captured consumers’ multifaceted attitudes towards personal responsibility and ability to affect environmental outcomes. Design/Methods: A nine-page self-report questionnaire was prepared which was divided into three sections. First section contained 176 items on a seven-point Likert scale to tap attitudes and beliefs regarding environment. Section two included 50 questions which dealt with specific environmentally friendly behaviour. Section three included demographic measures namely, sex, age, marital status, education, employment, income, and religion. Data

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were collected from sample of consumers in vicinity of a major urban University in Canada. Statistical Approach: Exploratory factor analysis and confirmatory factor analysis were performed to get the results. Findings/Conclusion: In the first analysis, INELOC scale got divided into four factors: green consumer, activist, advocate, and recycler. Depending on particular circumstances, these dimensions significantly influenced behaviours as they measured. (121) Iravani et al. (2012) studied the factors affecting young consumers for choosing green products. Purpose/Objectives: The objective was to investigate the purchasing behaviour of young Malaysian consumers towards green products based on the TPB (theory of planned behaviour) model. Design/Methods: The instrument used for the purpose was a questionnaire which was distributed amongst the students in all the faculties of the two Universities namely UTM and MMU. The analysis was carried out on 310 questionnaires. Statistical Approach: Mean, standard deviation, correlation, and regression were applied. Findings/Conclusion: The findings recorded that consumers hold positive beliefs and affirmative attitude towards environment in relation to green purchasing intentions. Most of the consumers also agreed on the importance of perceived quality of green products, and they were found moderately aligned on choosing green products despite other options available. Environmental attitude was confirmed as the strongest predictor of green purchasing intention, and consumers beliefs as the weakest. (122) Junaedi (2012) examined the role of income level in green consumer behaviour. Purpose/Objectives: It was aimed to test the influence of values orientation on environmental consciousness and to explain the effect of ecological knowledge, ecological affect, premium price, and environmental consciousness on green purchase intention. Design/Methods: Data were collected by using a questionnaire filled by 723 respondents. Purposive sampling method was used for data collection. Statistical Approach: Multi-group structural equation model was tested by using path analysis. Findings/Conclusion: The tested model had an acceptable fit. Further, findings implied that the income differences moderate the model significantly. Thus, income levels had a significant effect on green consumer behaviour. (123) Kumar (2012) targeted purchasing behaviour for environmentally sustainable products. Purpose/Objectives The aim was to ascertain the determinants of purchasing behaviour for environmentally sustainable products in Indian context. Design/Methods: Questionnaire was used to collect data, to verify the research framework, and testing the hypotheses set out. It was administered through online survey to convenience sample consisting of 235 students pursuing postgraduate and doctoral studies in the cities of Ahmedabad, India. Statistical Approach: Structural equation modeling was utilized using AMOS 4.0 with maximum likelihood estimation. Findings/Conclusion: Environmental knowledge was positively and significantly related to the attitude towards environmentally sustainable products; however, its relationship with purchase intentions was very weak and was not significant. Attitude was positively and significantly related to purchase intentions. Subjective norm was positively related to purchase intentions but statistically not significant. Perceived consumer effectiveness was also positively related to purchase intentions, and purchase intentions in turn were found significantly related to purchase behaviour.

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(124) Latif and Omar (2012) determined recycling behaviour amongst residents of Timond Island. Purpose/Objectives: The purpose was to investigate recycling behaviour in relation to attitude, materialism, and revealing the most powerful determinant of recycling behaviour. Design/Methods: The research instrument used was a questionnaire with 33 items and the sample was 62 undergraduate students of environmental marketing course from the faculty of business management, University Tekhologi MARA. Recycling behaviour and recycling attitude both were measured with self-reported items on five-point scale. The measures of individualism–collectivism and materialism were adopted from literature. Findings/Conclusion: The results depicted a positive and weak relationship between recycling attitude and behaviour. Also, recycling attitude, individualism, and materialism were not significant predictors of recycling behaviour. In this way, they confirmed that there was a gap between behaviour and attitude of respondents towards recycling. (125) Mittelman (2012) reviewed studies related to green consumer behaviour in emerging markets. Purpose/Objectives: It was aimed to find out the answer to the question whether or not traditional consumer behaviour theories developed in the west should be applied in context of emerging markets or not. Design/Methods: The method of study was literature review, and the task began by indentifying those green consumer behaviour studies which focused on consumer behaviour in emerging markets. The search for academic journals was conducted on EBSCO Business source and Google Scholar. Findings/Conclusion: While summarizing the literature, it was found that green consumer behaviour research seemed to be a burgeoning area of research in emerging markets; however, remained at the basic stage of research development. They noticed that traditional models of consumer behaviour were in application without any acknowledgement of the unique institutional contexts found in emerging markets. They challenged and rebuffed the assumption that consumers from emerging markets were poor to be green. Hence, a call was made for more sophisticated analysis in consideration with critical economic and social change in many of these countries. (126) Mohd. Noor et al. (2012) explored profiles and behaviours of green product buyers in Malaysia. Purpose/Objectives: The question posited was will consumers be willing to change their purchasing behaviour to be friendly to environment; if so, to find out characteristics of these consumer groups. Design/Methods: Survey method was used for data collection and 700 questionnaires were distributed, but a total of 616 questionnaires were used for subsequent analysis. The consumers’ green purchase behaviour was operationalized as the extent that consumers purchase environmentally relevant products. Statistical Approach: Percentages, t-test, and analysis of variance were applied on the data. Findings/Conclusion: The findings demonstrated that the level of green purchase behaviour amongst Malaysian consumers was not so much encouraging. Females significantly had higher green purchase behaviour than males. Also, the level of green purchase behaviour did not vary with age and monthly household income; but, varied across marital status in which married people showed high green purchase behaviour than singles. (127) Wan et al. (2012) performed a case study and searched recycling attitude and behaviour. Purpose/Objectives: The study aimed at investigating both

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recycling attitude and behaviour of University students and staff members, and to suggest ways to improve environmental policies and recycling facilities in University campus. Design/Methods: The study applied ‘theory of planned behaviour’ through which an instrument was developed to measure the determinants of recycling behaviour amongst the people. The survey was administered at a public University in Hong Kong, and 205 questionnaires with valid responses were exercised. Statistical Approach: A partial least square approach was used to validate the proposed model. Findings/Conclusion: The results corroborated that behavioural intentions were influenced by attitude, the subjective norms, perceived behavioural control, awareness of consequences, the moral norms, and convenience. (128) Do Paco et al. (2013) developed a green consumer behaviour model. Purpose/Objectives: The study aimed at exploring the link between environmental values, attitudes, and behaviours; as well as to develop and test a model that could be valid and applicable to a set of consumers living in different countries. Design/Methods: A sample of 1175 consumers was selected from England, Germany, Portugal, and Spain. A conceptual model was developed to test the relationships as purposed. Statistical Approach: Structural equation modeling was performed on the data. Findings/Conclusion: The results confirmed the relationship between attitudes and behaviours. They suggested certain measures which might be implemented in simultaneous testing of educational concepts across audiences in different countries. (129) Kawitkar (2013) studied the impact of eco-friendly products on consumer behaviour. Purpose/Objectives: Study was proceeded with the objectives of knowing perceptions of consumers about eco-friendly products, finding out the most popular media for promotion of eco-friendly products, and investigating about the barriers which resist free flow of the eco-friendly products in the market. Design/Methods: The primary data was collected through structured questionnaire, and the sample was fairly divided amongst consumers, retailers, shopkeepers, and distributors. A sample size of 100 was taken and the study was conducted in the Amravati region only. Findings/Conclusion: The findings concluded that people perceived eco-friendly products were not easily available in the markets, and they were not effectively promoted. Also, eco-friendly products were not found satisfying the ego and esteem needs of the consumers. Regarding purchasing decision of these products, decisions get influenced by family, children, and housewife. It was also concluded that people resist changes so the effect of eco-friendly products on consumers were found less. (130) Marques and Almeida (2013) investigated a path model of attitudinal antecedents of green purchase behaviour. Purpose/Objectives: The focus of the paper was to recommend and test a model of the effects of some specific attitudinal constructs on green purchase behaviour. Design/Methods: In the model, both direct and indirect effects were hypothesized. The data were collected from a representative sample of 419 undergraduates. Statistical Approach: Structural equation model framework was utilized. Findings/Conclusion: It was concluded that the frequency of green purchasing was dependent upon subjective knowledge regarding green issues. This knowledge was also obtained as a direct consequence of consumers’ beliefs in the effectiveness of green behaviour. The results also confirmed

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the crucial importance of trustworthiness of green marketing, and a need was felt for the strategies which can overcome consumers’ distrust. (131) Pereira Luzio and Lemke (2013) explored green consumers’ product demands and consumption processes. Purpose/Objectives: The purpose of the paper was to respond to the research gap in terms of understanding: how green consumers perceive green products in a marketplace context. The same is to be done by exploring green consumers’ product demand and consumption processes. Design/Methods/Approach: The key factors of the study were reasons to buy green products, defining green product characteristics, feeling about pricing, perceived product confidence, willingness to compromise, environmental knowledge, consideration of alternatives, products point of purchase, use, and disposal. Semi-structured in-depth interviews with Portuguese green consumers were used to discuss about the key factors as mentioned. Findings/Conclusion: It was concluded that green consumers represented an artificial segment and provides further empirical support to the definition of sustainability as a market-oriented concept. It was also suggested that mainstreaming green products were a more positive alternative than green segmentation. (132) Rezai et al. (2013) questioned about whether it was easy to go green by explaining green concept and exploring perception of consumers. Purpose/Objectives: It was purposed to determine the relationship between consumers’ socio-demographic variables and their perception towards green concept. Design/Methods: As research instrument, a structured questionnaire was designed to gather information on green consumer perception, and the study worked upon four hypotheses. Statistical Approach: Chi-square and binary logistic model were used to fulfil the objectives. Findings/Conclusion: Findings showed that some of the socio-demographic variables including education, income, age, and marital status significantly influenced consumers’ perception towards green concept. They defined consumers’ opinion about going green as the best way for saving the environment. They also highlighted that consumers should remain aware of and understand the importance of green products. (133) Roy (2013) studied the effect of green marketing on consumer behaviour. Purpose/Objectives: The aim was to realize how green marketing by corporations was affecting consumption behaviour of consumers. Design/Methods: Burdwan district in the state of West Bengal was selected and the study was conducted in Asansol sub division of it. A questionnaire was designed to find out people perception about green marketing and their awareness regarding environmental contribution of companies. Likert scale from 1 to 5 was adopted where 1 stands for strongly agree and 5 stands for strongly disagree. Statistical Approach: Descriptive statistics in the form of mean, standard deviation, percentile, and inferential statistics t-test were utilized. Findings/Conclusion: Findings indicated that as per consumers’ perception, companies needed to increase their communication with customers for moving in green direction. Also, as per responses obtained from the questionnaire, attributes like price and quality were found as more important for consumers than environmental responsibility.

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(134) Sharma and Bansal (2013) analysed environmental consciousness, its antecedents, and behavioural outcomes. Purpose/Objectives: The paper attempted to investigate the term environmental consciousness and identified its components and antecedents. Also, it was aimed to propose variables, if any, that could intervene between environmental consciousness and environmentally conscious consumer behaviour. Design/Methods/Approach: The authors reviewed extant literature to bring conceptual clarity to the term environmental consciousness and its linkages with the related variables. Findings/Conclusion: Environmental consciousness: a mental state variable was found distinct from its antecedents and associated behaviours. It was obtained as a multidimensional construct varying from low general level to high product-specific level. (135) Singh and Gupta (2013a) for the first time explored Indian consumers’ environmental attitude and ecological behaviour. Purpose/Objectives: Their aim was to explore the dimensions of environmental attitude and ecological behaviour, and to compare them with previous studies. Also, an investigation was done for the components of environmental attitude to be predictors of ecological behaviour. Design/Methods: They measured environmental attitude using 12-item NEP scale, and ecological behaviour was measured using items from SRCB and ECCB scales from literature. Both were measured on five-point scale range between ‘1’ to ‘5’ from never to always. Gender, age, city, profession, education, family size, and family income were taken as demographic variables. Statistical Approach: Factor analysis, correlation, and regression were the tools to analyse the data. Findings/Conclusion: Environmental attitude showed four components: domination over nature, concern for nature, critical level, and societal expansion. Also, ecological behaviour resulted into six factors namely, reducing damage, ecological behaviour adjustments, proactive conservation, recycling behaviour, and costly exercise. Concern for nature factor showed highest value followed by critical level, societal expansion, and domination over nature. On behavioural part, recycling behaviour had attained maximum average followed by proactive conservation, ecological behaviour adjustment, reducing damage, uncaring behaviour, and costly exercise. Environmental attitude was found moderately correlated with ecological behaviour, and components of environmental attitude significantly predicted ecological behaviour. (136) Singh and Gupta (2013b) attempted to examine gender differences with regard to environmentalism. Purpose/Objectives: The objective was to investigate how environmentalism varies across gender in India, and to show the effect of presence of moderators if any. Gender differences were analysed in three domains: attitudinal domain, behavioural domain, and overall environmentalism. Design/Methods: 52 effect sizes were calculated from different studies on a combination of attitudinal and behavioural domains. Statistical Approach: Meta-analytical research methodology, specifically, mean difference effect size popularly known as Hedges ‘g’ was used. Findings/Conclusion: Results provided evidence for notable gender differences, with female high concern for environment; but, they lack in behaviour as compared to males. Further, sample profession, age, and place of living came out as significant moderators, according to which substantial gender gap was described.

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(137) Singh and Gupta (2013c) investigated consumer willingness for environment protection. Purpose/Objectives: The prime purpose was to identify consumers who were willing to contribute for environment protection across the demography of respondents. Design/Methods: A ‘willingness to contribute’ scale was constructed by taking statements from literature and introduced on a sample of 300 consumers. Statistical Approach: Mean, Standard deviation, and analysis of variance were applied for analysing the data. Findings/Conclusion: Results inferred that males, youngs, highly educated, academically intelligent, non-business academics, married, rural residents, members of medium-sized families from high-income class were more willing to contribute for environment protection than their counterparts. (138) Patino et al. (2014) studied socially responsible marketing practices in relation with gender, race, and income. Purpose/Objectives: The purpose was to examine the importance that consumers’ place on various types of socially responsible marketing practices, and whether or not the level of this importance differed by gender, race, and income of consumers. Design/methods: A survey was designed that asked respondents about their attitude towards the various social marketing practices that were uncovered through an analysis of literature from ABI-Inform, Fordham University’s Center for Positive Marketing and focus groups. The survey was administered to 232 participants and included information regarding race, gender, and income. Statistical Approach: Analysis was done using latent class analysis (LCA) technique. Further, the results of this LCA were used to develop a correspondence analysis map. Findings/Conclusion: The outcome confirmed that key demographic factors namely income, gender, and race were very much important in understanding consumers’ perceptions of socially responsible marketing. (139) Pagiaslis and Krontalis (2014) investigated antecedents of green consumption behaviour namely Environmental Concern, Knowledge, and Beliefs. Purpose/Objectives: The study aimed at examining the interrelationships of the key constructs of environmental concern, consumer environmental knowledge, beliefs about bio-fuels, and behavioural intention (willingness to use and pay) in the context of bio-fuels. Design/Methods: Data were collected through a survey of 1695 respondents. Hypotheses were based on a literature review and a pilot study. Statistical Approach: The conceptual model was tested through structural equation modeling. Findings/Conclusion: Results showed that concern for the environment had a positive and direct impact on environmental knowledge, beliefs, and behavioural intention. Also, demography of consumers determined levels of concern for the environment and environmental knowledge. All constructs associated positively with one another delineating that the interdependence between them were important when accounting for environmental behaviour. (140) Delafrooz et al. (2014) studied effect of green marketing on consumer purchase behaviour. Design/Methods: Influence of green marketing tools had been analysed on consumer purchase behaviour. The included tools were eco-label, ecobrand, and environmental advertisement. Cluster sampling was used and Tehran city was divided into four parts which were North, South, East, and West. The Western and Northern areas of the city were selected and questionnaires were distributed to 384 people. Statistical Approach: Data were analysed using the Spearman correlation

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test and multiple regression analysis. Findings/Conclusion: The results showed that environmental advertisement had the most significant effect on consumer purchasing behaviour, and eco-brand had the least effect. (141) Agyeman (2014) analysed buying behaviour of consumers towards green products. Purpose/Objectives: The study was carried out with three prime objectives. First, to investigate the relationship between the variables that affects consumers’ buying behaviour for green products. Second was to examine those factors which affect buying behaviours of consumers for green products. Third, to identify levels of the price which consumers prefer to pay for green products. Design/Methods: Exploratory research design and convenience sampling were adopted to select the total sample size of 200 from Kancheepuram District. Primary data were collected from the respondents with the help of pre-tested structured opened and closed-ended questionnaires. The responses of the measurement were scored using five-point Likert scale. Statistical Approach: Regression and Chisquare were used to establish the relationships that existed between the variables. Findings/Conclusion: The findings revealed that there existed a significant relationship between the variables which affected consumers’ buying behaviour for green products, and these factors had major impacts on purchasing decisions of consumers. (142) Khare (2014) studied consumers’ susceptibility to interpersonal influence as a determining factor of ecologically conscious behaviour. Purpose/Objectives: The purpose was to examine the effect of consumer susceptibility to interpersonal influence (CSII) and demographics on ecologically conscious consumer behaviour (ECCB). Design/Methods: Data were collected through mall intercept technique in six cities across India. Findings/Conclusion: Factor analysis revealed two factors for ECCB scale: ecologically conscious purchase behaviour and green product attitudes. Normative, informative influence of CSII and income were predictors of ecologically conscious purchase behaviour. Normative influence emerged as predictor of green attitudes also. (143) Gupta and Singh (2015a) clustered Indian consumers as per their conservation attitude and behaviour. Purpose/Objectives: There were three specific objectives. First was to investigate causal relationship between study variables namely, conservation attitude and conservation behaviour. Second objective was to identify the segments of consumers based on these two variables; and thirdly, to attain demographic profile of segments. Design/Methods: Primary data were collected from 300 regional respondents using a structured questionnaire. Statistical Approach: Linear regression, cluster analysis, descriptive analysis, and analysis of variance with post hoc test were applied. Findings/Conclusion: Findings indicated that conservation attitude was a significant determinant of conservation behaviour, and three distinct consumer segments occurred on the dimensions of these two variables. One segment, detrimental was found completely careless and effortless; the second, hopefuls, illuminated with optimism and aspirations. Third, shining stars was the segment of legitimate conserver consumers who were strengthening the way towards sustainability with their grace. Further, the study described the conserver group as constituted with highly educated, married, and working females who live in rural places and belong to medium-sized and high-class families.

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(144) Gupta and Singh (2015b) initiated to profile environmentally concerned consumers in India. Purpose/Objectives: The paper worked for determining sociodemographic and psychological characteristics of environmentally concerned consumers. Design/Methods: Data on environmental concern was collected by using NEP (New Environment Paradigm) scale from literature with other items on littering concern, pollution concern, perceived consumer effectiveness against pollution, and civic sense. Information on consumers’ demographic attributes was also obtained. Statistical Approach: A cross tab analysis along with Chi-square test of independence or association was applied by using the statistics Cramer’s V to measure the degree of association. Furthermore, profile of concerned consumers was obtained using proportional analysis. Findings/Conclusion: The findings suggested that the psychological variables according to which environmental concern significantly grows include littering concern, pollution concern, perceived consumer effectiveness against pollution, and civic sense. Besides, the significant socio-demographic variables identified comprised age, education, academic orientation, and economic status. (145) Bronfman et al. (2015) performed research on understanding attitudes and pro-environmental behaviours. Purpose/Objectives: The authors aimed to understand the attitudes and pro-environmental behaviour of Chilean Community in Santiago, Latin America, and also determined the factors that impacted the behaviours. Design/Methods: VBN (values, beliefs, norms) model was used. In the first section of the survey, environmental behaviours including power conservation, ecologically aware consumer behaviour, biodiversity protection, water conservation, rationale automobile, and ecological waste management were asked. Section two comprised five attitudinal variables namely, biospheric values, altruistic values, egotistic values, ecological vision, awareness of consequences, ascription of responsibility, and personal norms. Three-stage stratified sampling was used to select the sample. Statistical Approach: Data analysis was carried out under two phases. In the first phase, internal consistency analysis was completed followed by structural equation model in the second phase. After internal consistency analysis, scales of biodiversity protection and rationale automobile use were not studied. Findings/Conclusion: It was seen that a large share of sample was favouring responsible environmental behaviours. Overall, the results supported causal chain of relationships between variables of VBN model. Particularly, power and water conservation behaviours were explained well by VBN model, and explanatory power of the model was weak for behaviours of ecologically aware consumer behaviour and ecological waste management. Water and power conservation were highly favoured; but, ecological waste management was substantially less common pro-environmental behaviour. It was too attained that scores of younger respondents with lowest socio-economic status were lowest, and higher the socio-economic status higher the tendency to engage in high-cost behaviours such as ecological waste management and ecologically aware consumer. (146) Caluri and Luzzati (2016) analysed the antecedents of eco-friendly consumer’s choices. Purpose/Objectives: The objective was to find out the relationships between interpersonal or contextual factors, and the purchasing actions of consumers for products having low environmental impact. Design/Methods: To achieve the

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objective, data were gathered from 8001 consumers of an Italian retailers’ organization. Statistical Approach: Descriptive statistics, correlation, and OLS regression were utilized for analysis. Findings/Conclusion: A strong positive relationship was found between pro-environmental attitudes and self-declared frequency of green purchases. Further, concern for general environmental issues, attitude for the quality of the product, and knowledge about eco labels positively affected attitudes and green purchases. Next, older people and women were more prone to eco-friendly commitment, and education came out with insignificant relationship. Control variables also provided insights about the role of attitudes towards fairness of the product, brand, and towards the point of sale. Also, people with pro-social orientations were more likely to buy products which did not threaten natural resources and next generations’ rights. (147) Gupta and Singh (2017) attempted to characterize and profile global segments of responsible consumers. Purpose/Objectives: The prime purpose of the paper was to identify the determinants of responsible consumption behaviour from varied literature, and profile responsible consumers as per identified categories of determinants. Design/Methods: Content analysis was exercised on extensive literature. Findings/Conclusion: The findings notified that the responsible consumers had demographic characteristics namely females, youngsters, highly educated, academically intelligent, non-business academics, employed in high-status and leadership positions, members of small families, and married with children living at home. According to sociological features, responsible consumers were the children of highly educated parents, get full support from their family, have liberal and democratic political views, and hold time and availability to contribute for responsible acts. However, from an economic and geographic perspective, these consumers were average in income, satisfied with their income levels, not much wealthier, and majority of them live in urban areas and larger cities. The cultural features supported them as collectivists with feeling of universalism. These consumers trust others, open to change, believe in civic-cooperation, like fun, have a network as members of environmental organizations, religious with extreme religiosity, and love their country having highest national pride. As far as psychological features were concerned, they originated from a very good psyche, were initiators, and internally controlled living indulgent lifestyles, future minded, less sceptic, have high civic sense, creative, have harmony, and believe in self efficacy. They were also found environmentally concerned and settle in balance with nature. (148) Zralek (2017) discussed the trap in sustainable consumption in the form of gap in people attitude and behaviour. Purpose/Objectives: The purpose was to describe the phenomenon of attitude–behaviour gap and to explain the techniques of rationalization. Design/Methods: Individual In-depth interviews were conducted with sixteen Polish consumers who were residents of Silesian province. Statistical Approach: Qualitative analysis was done to understand the findings. Findings/Conclusion: Participants mentioned place of living, lack of information about sustainable products, low income, and misleading promotional actions as obstacles or reasons for the ‘denial of responsibility’. The participants also evoked about ‘denial

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of injury’. Moreover, ‘claim of entitlement’ was shown by two of the participants. Finally, one participant applied the strategy called ‘claim of relative acceptability’. (149) Figueroa-Garcia et al. (2018) attempted to model the social factors which determine sustainable consumption behaviour. Purpose/Objectives: The aim was to generate and test a model that defined the way in which certain social factors contributed to sustainable consumption behaviour. Design/Methods: An electronically self-administered questionnaire was utilized to collect data from 139 residents of community of Madrid. The endogenous variable for the study was ‘sustainable consumption behaviour’. The exogenous variables were ‘environmental influences, education and information, government actions, social pressure, market conditions, and demographic variables such as age, gender, and educational levels’. Statistical Approach: To test the model, partial least square structural equation modeling (PLS-SEM) was used using SmartPLS statistical software. Findings/Conclusion: Results elaborated that sustainable consumption behaviour is determined by three exogenous variables, namely, environmental influences, education and information, and market conditions. The rest of the exogenous variables were not significantly related to the endogenous variable. (150) Jaidev et al. (2018) studied about determinants of environmentally conscious consumer behaviour. Purpose/Objectives: The main objective was to examine two correlates of environmentally conscious consumer behaviour (ECCB) namely perceived consumer effectiveness (PCE) and ecological concern (EC). Design/Methods: A questionnaire containing scales of dependent variable ECCB, and independent variables PCE and EC was administered by online mode, and 207 usable responses were taken for analysis. Statistical Approach: Correlation and regression were the main techniques applied. Findings/Conclusion: Findings suggested significant relationships between independent variables (PCE ↔ EC). Also, independent variables were found significantly correlated with dependent variable (EC ↔ ECCB, PCE ↔ ECCB). It was too seen that independent variables (EC and PCE) significantly influence dependent variable (ECCB).

2.2 Literature Comprehension: Identification of Research Gaps The reviewing process brings out a range of thrust areas that becomes the basis of the present research . Abstracts of literature work reveal that extensive scholastic work in the area of consumer consumption behaviour and social responsibility has been carried out with different perspectives. On the one hand, lots of studies differ in many aspects, while several can be arranged under one domain due to intense work on similar themes. As the main focus of present work is on behaviour, it can be said that different authors have tested behavioural ideologies with different names. Literature suffers from large inconsistency and ambiguity as to what is to be included

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in this behaviour, and how it can be defined. The studies in reference to these works are cumulated and presented in Table 3.1 (Chap. 3). To measure behaviour, certain studies made efforts for the development of measurement scales. It can also be viewed from literature that behaviour of consumers underlies various components or dimensions. Some studies, instead of concentrating upon broad concept of behaviour, worked separately on any one or two components of it, for example ‘conservation behaviour’ and ‘recycling behaviour’. Also, in order to arrive at the behaviour, a class of researchers investigated the antecedents of behaviour. Environmental concern, attitude, knowledge, awareness, values, beliefs, behavioural intentions, and some others have been identified as the antecedents of behaviour. However, few studies have explored consumers’ environmental concern, attitude, and knowledge independently and separately from behaviour. Further, it can be noted that the most crucial and researched antecedent came out as environmental attitude and a wide discussion is presented in literature regarding attitude–behaviour link. Thoroughgoing from literature, it is also found that several other academics combined various antecedents of behaviour and behavioural constructs, incorporated them in the form of models, and tested the theories of behaviour formation. With the help of these antecedents, concepts of mediators and moderators are also elaborated upon. It is too attained that a group of researchers searched the attitude and behaviour with a unique name of ‘environmentalism’. Now, the most striking point is that several researchers in this study area originated from the subjects like Psychology and Sociology. Hence, the works of the above kind were organized and related to the type of researches in these subjects. But aligning the subject matters with Marketing field, many academicians also concentrated on the characteristics and profiles of consumers who can be labelled as responsibles. Authors have worked on many consumer characteristics such as demographic, economic, psychological, and personality. Studies also worked on the aspect of market segmentation to distinguish responsible consumers from common masses. Related to the same theme, consumer purchasing behaviour was separately researched in certain studies. One study also concentrated on the aspect of green advertising. Further, a noteworthy point is that it is only recently that Indian literature is contributing to the study area of consumption behaviour, social responsibility, and its allied topics. There is a dearth of literature in Indian context and proportion of studies conducted here is far less than from other parts of the world. This is a prominent research gap and makes a strong base of a study on Indian consumers. Keeping the above backdrop, classifications of literature can be noted from the Table 2.1 shown in the endnotes In this way, literature is found fragmented and a research study on the topic can be organized in several manners. Indeed, this task of literature review performed many important functions to further develop the conceptual framework of the study, and in carrying out the empirical part of it. Also, based on literature exploration and

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research gaps, the constructs for the present study are developed and objectives are set out in the next chapter. Endnote: Classification of Literature

Table 2.1 Classification of literature based on the diversity of works Source Authors’ compilation Context of studies

Studies

Development of scales

Antil (1984), Roberts (1995), Kaiser et al. (2003), Webb et al. (2008)

Studies on selected behavioural components

Conservation Behaviour: Corral-Verdugo et al. (2006), Marandu et al. (2010), Gupta and Singh (2015a) Recycling Behaviour: Latif and Omar (2012), Wan et al. (2012)

Behavioural antecedents with assessment of behaviour

Hines et al. (1986/87), Roberts and Bacon (1997), Kaiser and Shimoda (1999), Vaske and Korbin (2001), Kim and Choi (2005), Bamberg and Moser (2007), Kalantari et al. (2007), Mostafa (2007), Kennedy et al. (2009), Robelia et al. (2011), Aman et al. (2012), Marques and Almeida (2013), Pereira Luzio and Lemke (2013), Pagiaslis and Krontalis (2014), Bronfman et al. (2015), Jaidev et al. (2018)

Independent work on behavioural antecedents

Environmental concern

Pornpitakpan (2001), Walton et al. (2004), Alibeli and Johnson (2009), Gupta and Singh (2015b)

Environmental attitude

Benton and Funkhouser (1994), McMillan et al. (1995), Steel (1996), Tuna (2003), Usui et al. (2003), Shobeiri et al. (2006), Larijani and Yeshodhara (2008), Kim and Kim (2010)

Environmental knowledge/information

Banerjee and McKeage (1994), Zelezny et al. (2000), Jain and Kaur (2004), Arnocky and Stroink (2010) (continued)

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Table 2.1 (continued) Context of studies

Studies Environmental values

Karp (1996), Usui (2001)

Awareness of environmental issues/problems

Budak et al. (2005), Kaur (2006), Zahedi et al. (2011), Bickerstaff and Walker (2001), Greenberg (2005), Sharma and Bansal (2013)

Attitude–behaviour relationship

Berger and Corbin (1992), Grob (1995), Steel (1996), Wright and Klyn (1998), Kaiser et al. (1999a, b), Singh and Gupta (2013a), Zralek (2017)

Theories of behaviour formation

Hines et al. (1986/87), Grob (1995), Kaiser et al. (1999a), Siu and Cheung (1999), Follows and Jobber (2000), Stern (2000), Chan and Lau (2001), Bamberg and Moser (2007), Harland et al. (2007), Kumar (2012), Do Paco et al. (2013)

Environmentalism

Banerjee and Mckeage (1994), Zelezny et al. (2000), Jain and Kaur (2004), Arnocky and Stroink (2010), Singh and Gupta (2013b)

Characteristics and profiles of consumers

Anderson and Cunningham (1972), Kinnear et al. (1974), Webster (1975), Antil (1984), Roberts (1995), Shrum et al. (1995), Straughan and Roberts (1999), Finisterra do Paco and Raposo (2008), Gonzalez et al. (2009), Ozkan (2009), Chen et al. (2011), Durif et al. (2011), Pagiaslis and Krontalis (2014), Gupta and Singh (2017)

Segmentation of consumers

Roberts (1995), Straughan and Roberts (1999), Haytko and Matulich (2008), Gonzalez et al. (2009), Albayrak et al. (2010), Gupta and Singh (2017)

Responsible purchasing behaviour

Alwitt and Pitts (1996), Laroche et al. (2001), Bamberg (2003), Clark et al. (2003), Tanner and Kast (2003), Ek and Soderholm (2006), Boivin et al. (2011), Iravani et al. (2012), Agyeman (2014)

Green marketing-advertising issues/Attitude towards green products

Haytko and Matulich (2008), Chen and Chai (2010), Ramayah et al. (2010), Iravani et al. (2012), Roy (2013), Delafrooz et al. (2014)

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Lau, T.-C. (2010). Towards socially responsible consumption: An evaluation of religiosity and money ethics. International Journal of Trade, Economics and Finance, 1(1), 32–35. Leigh, J. H., Murphy, P. E., & Enis, B. M. (1988). A new approach to measuring socially responsible consumption tendencies. Journal of Macromarketing, 5–20. Leonidou, L. C., Leonidou, C. N., & Kvasova, O. (2010). Antecedents and outcomes of consumer environmentally friendly attitudes and behaviour. Journal of Marketing Management, 26(13–14), 1319–1344. Majlath, M. (2010). Can individuals do anything for the environment?—The role of perceived consumer effectiveness. In Symposium for Young Researchers (pp. 157–166). Marandu, E. E., Moeti, N., & Joseph, H. (2010). Predicting residential water conservation using the theory of reasoned action. Journal of Communication, 1(2), 87–100. Marques, C. P., & Almeida, D. (2013). A Path Model of Attitudinal Antecedents of Green Purchase Behaviour. Economics & Sociology, 6(2), 135–144. McCright, A. M. (2010). The effects of gender on climate change knowledge and concern in the American public. Population and Environment, 32, 66–87. McMillan, B. M., Hoban, T. J., Clifford, W. B., & Brant, M. R. (1995). Social and demographic influences on environmental attitudes. Southern Rural Sociology, 13(1), 89–107. Mittelman, R. (2012). “Green consumer behaviour in emerging markets”, exploring the new world of work. In S. Cheikhrouhou & M.-A. Vachon (Eds.), Proceedings of the Annual Marketing Division Conference of the Administrative Sciences Association of Canada, St. John’s, Newfoundland and Labrador, Canada, 33(3) (pp. 392–441). Mobley, C., Vagias, W. M., & DeWard, S. L. (2010). Exploring additional determinants of environmentally responsible behavior: The influence of environmental literature and environmental attitudes. Environment and Behaviour, 42(4), 420–447. Mohd. Noor, N. A., Mat, N., Mat, N., Mohd Jamil, C. Z., Salleh, H. S., & Muhammad, A. (2012). Emerging green product buyers in Malaysia: Their profiles and behaviours. In 3rd International Conference on Business and Economic Research (3rd ICBER 2012) (pp. 2680–2693). Mohr, L. A., Webb, D. J., & Harris, K. E. (2001). Do consumers expect companies to be socially responsible? The impact of corporate social responsibility on buying behavior. The Journal of Consumer Affairs, 35(1), 45–72. Mostafa, M. M. (2007). Gender differences in Egyptian consumers’ green purchase behaviour: The effects of environmental knowledge, concern and attitude. International Journal of Consumer Studies, 31, 220–229. Muderrisoglu, H., & Altanlar, A. (2011). Attitudes and behaviors of undergraduate students toward environmental issues. International Journal of Science and Technology, 8(1), 159–168. Oikonomou, S., Drosatos, G., Papadopoulos, T., & Oikonomou, M. (2009a). Investigate via internet the consumer’s willingness to pay for the sake of environment protection. In 2nd International Scientific Conference Titled “Energy and Climate Change” of the PROMITHEAS Network. Retrieved September 28, 2014, from http://drosatos.info/files/Promitheas1.pdf. Oikonomou, S., Drosatos, G., Papadopoulos, T., & Oikonomou, M. (2009b). Investigate via the internet personal values in life and how determine the consumer’s environmental behavior. Retrieved September 28, 2014, from http://drosatos.info/files/Promitheas2.pdf. Ozkan, Y. (2009). The effect of some demographic characteristics of Turkish consumers on their socially responsible consumption behaviours. World Applied Sciences Journal, 6(7), 946–960. Pagiaslis, A., & Krontalis, A. K. (2014). Green consumption behavior antecedents: Environmental concern, knowledge, and beliefs. Psychology and Marketing, 31(5), 335–348. Patino, A., Kaltcheva, V. D., Pitta, D., Sriram, V., & Winsor, R. D. (2014). How important are different socially responsible marketing practices? An exploratory study of gender, race and income differences. Journal of Consumer Marketing, 31(1), 2–12. Peixeira Marques, C., & Almeida, D. (2013). A path model of attitudinal antecedents of green purchase behavior. Economics and Sociology, 6(2), 135–144. Pereira Luzio, J. P., & Lemke, F. (2013). Exploring green consumers product demands and consumption processes. European Business Review, 25(3), 281–300.

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Pornpitakpan, C. (2001). The environmental concern in Thailand: Managerial implications. Journal of International Consumer Marketing, 14(2/3), 123–135. Ramayah, T., Lee, J.W.C., & Mohamad O. (2010). Green product purchase intention: Some insights from a developing country Resources. Conservation and Recycling, 54(12), 1419 - 1427. Rezai, G., Teng, P. K., Mohamed, Z., & Shamsudin, M. N. (2013). Is it easy to go green? Consumer perception and green concept. American Journal of Applied Sciences, 10(8), 793–800. Rikner, A. (2010). Waldorf teachers and environmental issues—Behavior, values, attitudes and feelings of responsibility. School of Education, Psychology and Sport Science, Master Course in Psychology, 30, 1–25. Robelia, B. A., Greenhow, C., & Burton, L. (2011). Environmental learning in online social networks: adopting environmentally responsible behaviors. Environmental Education Research, 17(4), 553 - 575. Roberts, J. A. (1995). Profiling levels of socially responsible consumer behavior: A cluster analytic approach and its implications for marketing. Journal of Marketing—Theory and Practice, Fall, 97–117. Roberts, J. A., & Bacon, D. R. (1997). Exploring the subtle relationships between environmental concern and ecologically conscious consumer behavior. Journal of Business Research, 40, 79–89. Roux, C., & Nantel, J. (2009). Conscious consumption and its components: An exploratory study. Advances in Consumer Research, 36, 903–904. Roy, H. (2013). Effect of green marketing on consumer behaviour—A study with particular reference to West Bengal (India). International Journal of Behavioural Social and Movement Sciences, 02(01), 44–55. Savita, U., & Kumar, N. (2010). Consumer attitude towards environment-friendly products: A comparative analysis. The IUP Journal of Marketing Management, IX(1 and 2), 88–96. Schaefer, A., & Crane, A. (2005). Addressing sustainability and consumption. Journal of Macromarketing, 25(1), 76–92. Schwepker, C. H., & Cornwell, B. T. (1991). An examination of ecologically concerned consumers and their intention to purchase ecologically packaged products. Journal of Public Policy and Marketing, 10(20), 71–101. Sengupta, M., Banerjee, D., & Maji, P. K. (2009). Effect of sight and gender on environmental awareness and pro-environmental behaviour amongst school students. E-Journal for Educational Research (EJAIAER), 29(1). Retrieved October 2, 2014, from http://www.aiaer.net/ ejournal/vol21109/9.Sengupta,Banerji%20and%20Maji.pdf. Shanka, T., & Gopalan, G. (2005). Socially responsible consumer behavior—Higher education students’ perceptions. In Working Paper, ANZMAC 2005 Conference: Corporate Responsibility (pp. 102–107). Sharma, K., & Bansal, M. (2013). Environmental consciousness, its antecedents and behavioural outcomes. Journal of Indian Business Research, 5(3), 198–214. Shaw, D., & Shiu, E. (2003). Ethics in consumer choice: A multivariate modeling approach. European Journal of Marketing, 37(10), 1485–1498. Shobeiri, A. M., Omidvar, B., & Prahallada, N. N. (2006). Influence of gender and type of school on environmental attitude of teachers in Iran and India. International Journal of Science and Technology, 3(4), 351–357. Shrum, L. J., McCarthy, J. A., & Lowrey, T. M. (1995). Buyer characteristics of the green consumer and their implications for advertising strategy. Journal of Advertising, XXIV, 71–82. Singh, N. (2009). Exploring socially responsible behaviour of Indian consumers—An empirical investigation. Social Responsibility Journal, 5(2), 200–211. Singh, N., & Gupta, K. (2011). Students as responsible consumers—Agents for social change. HSB Research Review, 2(2), 69–75. Singh, N., & Gupta, K. (2013a). Environmental attitude and ecological behaviour of Indian consumers. Social Responsibility Journal, 9(1), 4–18. Singh, N., & Gupta, K. (2013b). Gender differences in environmentalism in India—A meta analysis. Third Eye—A Journal of Business Review, 1(1), 1–21.

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Singh, N., & Gupta, K. (2013c). Consumer willingness for environment protection. Research paper in: Research in Business and Management—Academic and professional Perspective, 5th Annual National Conference on ‘Business and Management’ by HSB, GJU, Hisar (1st ed., pp. 286–300). Delhi: Wisdom Publications. Siu, O., & Cheung, K. (1999). A structural equation model of environmental attitude and behaviour: The Hong Kong experience. Working Paper, Centre for Public Policy Studies, Lingnan University. Retrieved October 2, 2014, from http://commons.ln.edu.hk/cgi/viewcontent.cgi?article= 1048andcontext=cppswp. Steel, B. S. (1996). Thinking globally and acting locally?: Environmental attitudes, behaviour and activism. Journal of Environmental Management, 47(1), 27–36. Steg, L., & Vlek, C. (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29, 309–317. Stern, P. C. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407–424. Straughan, R. D., & Roberts, J. A. (1999). Environmental segmentation alternatives: A look at green consumer behavior in New Millennium. Journal of Consumer Marketing, 16(6), 558–575. Tan, B.-C., & Lau, T.-C. (2009). Examining sustainable consumption patterns of young consumers: Is there a cause for concerns?. The Journal of International Social Research, 2(9), Fall, 465–472. Tanner, C. (1999). Constraints on environmental behaviour. Journal of Environmental Psychology, 19, 145–157. Tanner, C., & Kast, S. W. (2003). Promoting sustainable consumption: Determinants of green purchases by Swiss consumers. Psychology and Marketing, 20(10), 883–902. Tilikidou, I., & Delistavrou, A. (2007). The ecological consumer behaviours in Greece: Ten years of research. In Minutes 5th International Congress’ New Horizons in Industry and Business— NHIBE 2007, Rhodes 30-31/8 (pp. 476–486). Retrieved August 29, 2014, from http://eureka.lib. teithe.gr:8080/bitstream/handle/10184/797/ECCB_TD.pdf. Tindall, D. B., Davies, S., & Mauboules, C. (2003). Activism and conservation behavior in an environmental movement: The contradictory effects of gender. Society and Natural Resources, 16, 909–932. Tuna, M. (2003). Public environmental attitudes in Turkey. Retrieved September 28, 2014, from http://www.inter-disciplinary.net/ptb/ejgc/ejgc3/tuna%20paper.pdf. Urban, J., & Zverinova, I. (2009). What are the determinants of environmentally significant behavior in the Czech Republic?. CUEC Working Paper (pp. 1–17). Usui, M. A. (2001). How individual values affect green consumer behavior? Results from a Japanese survey. Global Environment Research, 5, 97–105. Usui, M. A., Vinken, H., & Kuribayashi, A. (2003). Pro-environmental attitudes and behaviors: An international comparison. Human Ecology Review, 10(1), 23–31. Vaske, J. J., & Kobrin, K. C. (2001). Place attachment and environmentally responsible behavior. The Journal of Environmental Education, 32(4), 16–21. Walton, D., Thomas, J. A., & Dravitzki, V. (2004). Commuters’ concern for the environment and knowledge of the effects of vehicle emissions. Transportation Research Part D, 9, 335–340. Wan, C., Cheung, R., & Shen, G. Q. (2012). Recycling attitude and behaviour in university campus: A case study in Hong Kong. Facilities, 30(13/14), 630–646. Webb, D. J., Lois, A. Mohr, & Katherine, E. Harris. (2008). A re-examination of socially responsible consumption and its measurement. Journal of Business Research, 61, 91–98. Webster, F. E., Jr. (1975). Determining the characteristics of the socially conscious consumer. Journal of Consumer Research, 2, 188–196. Wright, M., & Klyn, B. (1998). Environmental attitude-Behaviour correlations in 21 countries. Journal of Empirical Generalizations in Marketing Science, Three, 42–60. Xiao, C., & Hong, D. (2010). Gender differences in environmental behaviors in China. Population and Environment, 32, 88–104.

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Chapter 3

Conceptual Framework and Research Model

This chapter uses content analysis to assimilate different elements of literature to form the conceptual framework and research model. Accordingly, it outlines the theoretical base, and displays research objectives and hypotheses on which the empirical part of the book is developed. For easy grasping of its’ contents, five sections (3.1–3.5) are accessible with their allied sub-sections.

3.1 Integration of Consumption Behaviour and Social Responsibility The two concepts (consumption behaviour and social responsibility) have been separately described in Chap. 1. Here, in accordance with literature, these are defined in relation with each other as used by different authors to form distinct constructs which they either designed or borrowed from literature. Accordingly, consumption behaviour in relation to social responsibility has been visualized from many aspects and searched with distinct conceptualizations. The terminology as utilized in past studies is presented and elaborated in Table 3.1.

3.1.1 Responsible Consumption In 1973, Fisk (1973) talked about responsible consumption. As referred at that time, responsible consumption was about rational and efficient use of resources with respect to global human population. Fisk in his work mentioned that the question of consumption could not be considered from the viewpoint of any single nation, because the consumption of depletable resources in one nation necessarily affects the reservoir of resources elsewhere. So, he defined this problem at a global level, and also emphasized for its analysis with the same perspective. © Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_3

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Haron et al. (2005), Pack et al. (2005), Corral-Verdugo et al. (2006), Tan and Lau (2009), Kiraci and Kayabasi (2010), Junaedi (2012), Pagiaslis and Krontalis (2014), Zralek (2017), Figueroa-Garcia et al. (2018) Kurz (2002), Dillahunt et al. (2008), Nathaniel (2011)

Sustainable consumption/consumer behaviour

Environmentally sustainable behaviour

Peattie (1995), Elkington and Hailes (1988), Gilg et al. (2005), Finisterra do Paco and Raposo (2008), Tay (2011), Singh and Gupta (2012), Mittelman (2012), Do Paco et al. (2013), Jaidev et al. (2018)

Green consumption/consumer behaviour

Clark et al. (2003), Bamberg and Moser (2007), Harland et al. (2007), Steg and Vlek (2009), Chen et al. (2011), Bronfman et al. (2015)

Pro-environmental behaviour

Chan (2001), Kim and Choi (2005), Ramayah et al. (2010), Marques and Almeida (2013), Agyeman (2014)

Grob (1995), Kalantari et al. (2007), Xiao and Hong (2010)

Environmental behaviour

Green purchase behaviour

Alwitt and Pitts (1996), Bamberg (2003), Jaidev et al. (2018) Straughan and Roberts (1999), De Young (2000), Barr (2003), Haron et al. (2005), Muderrisoglu and Altanlar (2011), Robelia et al. (2011)

Environmentally responsible behaviour

Tindall et al. (2003), Tan and Lau (2009), Kim and Kim (2010)

Environment friendly/adjusted behaviour

Environmentally sensitive/related/conscious behaviour

Stern (2000), Gatersleben et al. (2002), Stern (2005), Urban and Zverinova (2009) Kennedy et al. (2009)

Environmentally significant behaviour

Environmentally supportive behaviour

Roberts and Bacon (1997), Tilikidou and Delistavrou (2007) Kaiser et al. (1999a, b), Kaiser et al. (2003)

Ecological behaviour

˛ It can be noted from the table that the terms of consumer behaviour and consumption behaviour are used interchangeably by researchers. However, aligning with the distinctiveness in two aspects (defined in Chap. 1); here the word consumption behaviour is preferred

Sustainable

Green

Environmental

Kinnear et al. (1974), Schwepker and Cornwell (1991)

Antil and Bennet (1979), Antil. (1984a), Antil (1984b), Roberts (1995), Mohr et al. (2001), Shanka and Gopalan (2005), Webb et al. (2008), Ha-Brookshire and Hodges (2009), Ozkan (2009)

Socially responsible consumption/consumer behaviour

Ecologically concerned consumer behaviour

Anderson and Cunningham (1972), Webster (1975), Leigh et al. (1988)

Socially conscious behaviour

Social

Ecologically conscious consumption/consumer behaviour

Fisk (1973)

Responsible consumption

Ecological

Academicians exercised the identities

Prevailing identities

Table 3.1 Conceptualizations prevailing in literature Source Authors’ compilation

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3.1 Integration of Consumption Behaviour and Social Responsibility

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3.1.2 Socially Conscious Consumption Behaviour The concept of social consciousness in consumer markets was originated by Anderson and Cunningham (1972) when growing consumer sensitivity to social and environmental problems were witnessed. At that time, marketers started evaluating and arranging market segments on the degree to which consumers accept the ‘consumercitizen concept’ and buy as individuals concerned not only with their personal satisfaction, but also with societal and environmental well being. Before this study, Berkowitz and Lutterman (1968) developed and tested a scale for measuring social responsibility and called it as Social Responsibility Scale (SRS). The scale was used to measure an individual’s traditional social responsibility. It was simply characterized as the willingness of an individual to help other persons even when there is nothing to be gained for him/her. According to these academics, the individuals who score high on SRS are more likely to make financial contributions to religious and educational institutions, active in community/church or other organizations, show intense interest in national/local/political events, and vote in elections by knowing the names of contending candidates. Thus, here the social responsibility was understood as Individual Social Responsibility (ISR), namely the responsibility of an individual as a good citizen. Afterwars, Webster (1975) defined that the SRS scale characterized social responsibility in a specific and outdated form. He affirmed that it is not obvious that a person who scores high on the general SRS, also behave consciously in his role of a consumer. He identified socially conscious consumers on the basis of some specific behaviour such as recycling; the measurement of which was totally different from what was defined by SRS. In his work, socially conscious consumption was defined as consumption in which a consumer takes into account the public consequences of his or her private consumption or attempts to use his or her purchasing power to bring about social change. Also, Leigh et al. (1988) propagated this behaviour as the perceived effect of consumer choice on social, environmental, and safety matters.

3.1.3 Socially Responsible Consumption Behaviour Some educationalists delineated the behaviour with the name of ‘socially responsible consumption behaviour’. It was described with those behaviours and purchase decisions of consumers which were related to environmental resource problems, and motivated not only with a desire to satisfy personal needs; but, also by a concern for the possible adverse consequences of their subsequent effect. To reach this definition of socially responsible consumption behaviour, Socially Responsible Consumption Behaviour (SRCB) scale was developed (Antil and Bannett 1979; Antil 1984a). This scale became a powerful measurement concept for the said behaviour. After that, Roberts (1995) marked out the socially responsible behavioural decisions as those decisions in which products and services are acquired by perceiving a positive or less

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negative influence on the environment. It may also be a behaviour which patronizes businesses that attempt to effect related positive social change. He too constructed a scale for the measurement of said behaviour using two dimensions: societal and ecological. Then, Mohr et al. (2001) identified socially responsible consumption behaviour in terms of Corporate Social Responsibility (CSR). Mohr et al. (2001) and Shanka and Gopalan (2005) exemplified consumers with SRCB traits as those basing their acquisition, usage, and disposition of products on a desire to minimize or eliminate any harmful effects and maximize the long-run beneficial impact on society. After these studies, Webb et al. (2008) developed the Socially Responsible Purchase and Disposal (SRPD) scale that reflected some developments over past measurement instruments. In line with the process of consumption, they defined three dimensions of the scale. First, purchasing based on the firm’s CSR performance; second, avoidance and use reduction of products based on their environmental impact; and third, recycling. In the words of Ozkan (2009), socially responsible consumption is a kind of consumption in which consumers’ decisions and behaviours are not only motivated by the desire to satisfy their personal needs but they also consider the results of their decisions with regard to environment and society. Till now, although research academics in this field tried to emphasize on the major aspects of consumption, but the majority of studies described behaviour related to purchasing of products and services only. Criticizing these views, Ha-Brookshire and Hodges (2009) identified that purchase-oriented SRCB research stream has resulted in a critical gap in understanding the overall consumption cycle which includes a wide range of different consumption states (have already been explained in Chap. 1: Fig. 1.4). They obtained that SRCB definition by Mohr et al. (2001) distinguished SRCB from CSR by endowing consumers’ perspective on social responsibility. However, it addressed only a part of the whole consumption experience (being concerned primarily with product or service acquisition, usage, and disposition). Mohr definition significantly failed to include other important consumption stages that might affect consumers’ future acquisition, usage, and disposition such as product information search, storage, and post-disposal evaluations of product or services. Therefore, they newly define SRCB as the behaviour of a consumer basing decisions on a desire to minimize or eliminate harmful effects and to maximize any beneficial impacts on society in one or more consumption stages of the consumption process.

3.1.4 Ecologically Concerned Behaviour Academics like Kinnear et al. (1974) and Schwepker and Cornwell (1991) mentioned about ‘ecologically concerned behaviour’. Kinnear et al. (1974) measured it by assessing the extent to which consumers intended to or actually used returnables, used fewer detergents and bought products with light packaging. This measurement comes up with two new points: first, study of intentions; and second, issue of ecological packaging. Further, Schwepker and Cornwell (1991) combined ecologically concerned consumers and their intention to purchase ecologically packaged products.

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Thus, they created purchase Intentions (PI) scale to study the intended behaviours of consumers for acquiring ecologically packaged products.

3.1.5 Ecologically Conscious Behaviour A class of researchers supplemented the literature by labelling the behaviour as ‘ecologically conscious behaviour’. In its span, Balderjahn (1988) specifically mentioned about ecological purchase and use of products. According to Roberts and Bacon (1997), ecologically conscious behaviour is about consumer decisions of purchasing (avoiding) products and services with a positive (negative) impact on the environment. These authors measured this behaviour by utilizing Ecologically Conscious Consumption Behaviour (ECCB) scale which dealt with certain dimensions including recycling of paper products, driving as little as possible, save energy/electricity, reduces reliance on foreign oil, general recycling issues, and concern about biodegradability. Tilikidou and Delistavrou (2007) conceptualized it to consist of three types. First type is the pro-environmental purchasing behaviour (PPB), second is the pro-environmental post-purchasing behaviour (P post PB), and third is the pro-environmental activities (PA).

3.1.6 Ecological Behaviour The major work on a unique behavioural identity ‘ecological behaviour’ is in the account of Kaiser and Colleagues. As apprehended by Kaiser et al. (1999a, b), ecological behaviour is a conglomerate of different environmental domains and can represent specific, as well as general behaviour for the environment; so, it is broader in sense. General behavioural indices are not specific to any one environmental aspect rather they accumulate the environment as a whole. On the other hand, specific behaviour includes recycling, composting, energy conservation, political activism, consumerism, commitment to environmental organizations, ecological farming, water conservation, and so forth. For the measurement of ecological behaviour, General Ecological Behaviour (GEB) scale was constructed (Kaiser et al. 1999a, 2003). The scale was derived from six domains: energy conservation, mobility and transportation, waste avoidance, consumerism, recycling, and social behaviour towards conservation.

3.1.7 Environmentally Significant Behaviour Stern (2000) was a novel academic who proposed the concept of ‘environmentally significant behaviour’. It was defined by its impact; the extent to which it changes the

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availability of materials or energy from the environment or alters the structure and dynamics of ecosystems or the biosphere itself. He remarks that some behaviours (such as clearing forests or disposing of household waste) directly cause environmental change and others are environmentally significant indirectly (by shaping the context in which the choices are made). He also described this behaviour from the action point of view, as this intent changes one’s behaviour in order to benefit the environment. But Stern (2000) also suggested that the intent-oriented definition is not the same as impact-oriented. It was further clarified that environmental intent is an independent cause of behaviour and emphasize the possibility that environmental intent may fail to result in environmental impact. Therefore, it was suggested that studies must adopt an impact-oriented definition to identify and target behaviours that can make a large difference to the environment. The two definitions proposed by Stern (2000) were further adopted by Gatersleben et al. (2002) and Urban and Zverinova (2009). Stern (2000) again worked on ‘Environmentally Significant Behaviour’ in 2005 (Stern 2005). According to these studies, this behaviour is explained with three different types. (1) Environmental Activism: This was described as consumers’ active involvement in environmental organizations and demonstrations. (2) Public Sphere Behaviour: • Active Participation/Environmental Citizenship—It included petitioning on environmental issues and contributing to environmental organizations. • Passive Participation—It incorporated support and acceptance of public policies, stated approval of environmental regulations, and willingness to pay higher taxes for environment protection. (3) Private-Sphere Environmentalism: It is the behaviour which is in the private sphere and involves the purchase, use, and disposal of personal and household products that have an environmental impact.

3.1.8 Environmentally Supportive Behaviour Kennedy et al. (2009) propagated a new form of behaviour which was called as Environmentally Supportive Behaviour (ESB). It was specified that individual commitment to environmental conservation may take many forms. Some people recycle, use public transport, buy local or organic products or participate in protests on environmental issues. On the other hand, some people may write letters to newspapers, help to restore damaged ecosystems, compost, or make efforts to conserve water, and energy. Stern (2000) used the term ‘environmentally significant’ but Kennedy and associates felt that replacing significant with supportive better describes the intended meaning of behaviour that reflects a positive and affective orientation towards the environment. As per them, in a broad sense, ESB refers to all those actions that are taken by consumers with the intent of benefiting natural environment or reducing negative human impacts on it.

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3.1.9 Environmentally Friendly Behaviour A new form of behavioural conceptualization was discussed with the name of Environmentally Friendly Behaviour (EFB). By EFB, Tindall et al. (2003) refer to everyday behaviours which aim to conserve the environment in various ways such as: recycling at home/work, walk/riding-bicycle/taking public transport, conserving energy, buying organic produce, reusing or mending things instead discarding, planting trees, helping to maintain parks or natural habitats, picking up litter, and compost organic waste, etc. According to them, EFB takes place in routine life and largely involves domestic and unpaid activities. Considering an open-ended approach, it can be declared that any behaviour designed to protect the environment can also come under EFB. In this way, this construct can be wider than those types of behaviour defined by Tindall and associates. Tan and Lau (2009) too viewed a bundle of environmentally friendly behavioural activities in it such as recycling, using eco-friendly products, and managing waste by separating and limiting consumption. Further, Kim and Kim (2010) included monetary donation and support for a tax or expenditure increase for environment protection in the scope of these behaviours.

3.1.10 Environmentally Related Behaviour Consistent with environmentally friendly behaviour, Bamberg (2003) talked about ‘environmentally related behaviour’. This behaviour was defined as similar to EFB but the word related (instead of friendly) contributed specificity in the meaning. As indicated by Bamberg, specific environmentally related domains include recycling, energy saving, buying eco-friendly products, or travel mode choice. In his study, he included green energy buying in environmentally related behaviour, and contained buying decisions of consumers for green electricity products and decisions to acquire information about green electricity products.

3.1.11 Environmentally Responsible Behaviour Replacing previous expression (like friendly, significant, supportive), a group of researchers exercised on a new word ‘responsible’. As per Straughan and Roberts (1999), ‘environmentally responsible behaviour’ meant for buying things that don’t damage the environment or reduce one’s personal impact on the environment. Barr (2003) studied the household waste management behaviour in environmentally responsible behaviour. But De Young (2000) mentioned that environmentally responsible behaviour is multiple determined and it seems extremely unlikely to consider it wholly a function of any single motive. Further, as given by Haron et al. (2005), environmentally responsible behaviour relates to consumption activities that benefit

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the environment or cause less harm to it. Finally, the study of Muderrisoglu and Altanlar (2011) came out as a study which had discussed about this behaviour in a wide variety of means. The behaviours in it comprise transportation, not to shop from companies that damage nature, do not use soaps and detergents that damage nature, avoid places that put food in Styrofoam containers, watching television programmes on environmental issues, avoiding detrimental gases to the environment, trying to recycle papers/newspapers, talking about environment problems, reading things written on the label of products, reading publications that focus on environmental issues, purchasing products produced from recycled materials, changing a brand which is harmful to the environment with an environment friendly brand, trying to recycle glass bottles and jars, separating the trash as to recycle or non-recycle, purchasing products packaged in reusable/recyclable containers, writing articles about environment problems, paying money for environment protection, and participating in seminars or courses about the environment.

3.1.12 Environmental Behaviour Literature unveils the term ‘environmental behaviour’ that removed the middle expressions (like significant, supportive, related, friendly, responsible) and simplified the terminology. Grob (1995) mentioned the diversity of domains in it; such as transportation to work, ownership of household electronics, separation of household waste, amount of electric energy used, and questions about ownership of car/bicycle or a pass for public transport. He also mentioned that environmental behaviour items may take two directions that are pro-environmental and anti-environmental. As per Kalantari et al. (2007), components of environmental behaviour include environmental attitudes, preparedness to act, feeling of stress, and environmental legislation. Along with Steg and Vlek (2009), environmental behaviour broadly includes all types of behaviours which change the availability of materials or energy from the environment or alters the structure and dynamics of ecosystems or the biosphere. Xiao and Hong (2010) distinguished between private environmental (household-oriented, recycling) and public environmental behaviours (community/society oriented such as protests).

3.1.13 Pro-environmental Behaviour As a supplement of views given by Grob (1995) that environmental behaviour can take two directions (pro-environmental and anti-environmental); some academics prefer to call favourable behaviour as ‘pro-environmental behaviour’. Clark et al. (2003) called it PEB which exemplifies it as an individual’s voluntary effort to

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provide an environmental public good.1 Then, Bamberg and Moser (2007) viewed pro-environmental behaviour as a mixture of two elements. First, self-interest (to pursue a strategy that minimizes one’s own health risk) and second, concern for other species and whole ecosystems (preventing air pollution that may cause risks for other’s health and/or the global climate). Harland et al. (2007) studied it by measuring behaviours like transportation means other than the car for short distances and saving of water by turning off the faucet while brushing teeth. As per Steg and Vlek (2009), pro-environmental behaviour refers to behaviour that harms the environment as little as possible or even benefits the environment. According to Rikner (2010), pro-environmental behaviour can be measured through recycling habits, purchasing of environmentally adapted products, investments in energy saving products, transportation habits, and energy consumption at home. Auxiliary to this, Chen et al. (2011) mentioned about six pro-environmental behaviours. These behaviours were sorting garbage, environmental talk, recycling bags, environmental education, environmental volunteering, and environmental litigation. Bronfman et al. (2015) included power conservation, ecologically aware consumer behaviour, biodiversity protection, water conservation, rational automobile, and ecological waste management under the broad head of pro-environmental behaviours.

3.1.14 Green Consumption Behaviour Certain studies talked about a new ideology that is ‘green consumption behaviour’. It was designated as the process of avoiding products: which endanger consumers’ health, damage the environment, consume a large amount of resources, cause unnecessary waste, and exploit other species and environment (Elkington and Hailes 1988). But Peattie (1995: p. 84) points up that purchase criteria provides only a partial picture. In addition to purchasing, consumers may respond to the green challenge in a wide variety of ways involving the means in which they themselves retain, use, and dispose of the waste. Thus, green consumption includes purchasing of green products but also other non-purchasing behaviours too. Aligning with Peattie (1995), Gilg et al. (2005) mentioned that green consumption has been defined in a variety of ways that its efficacy as a term has become somewhat meaningless. In their studies, these researchers obtained purchase decisions, habits, and recycling as three components of green consumption. It was also stated that there appear to be wider behavioural dimensions of green consumption than merely those activities which relate to green buying. Further, in the words of Finisterra do Paco and Raposo (2008), this type of behaviour has been described as one in which consumers avoid products that are

1 Public goods are goods that exhibit non-rivalry and non-excludability. Non rivalry implies that one

person’s consumption of goods does not diminish the amount available for other. Non excludability implies that once the good is provided, other people cannot be excluded from enjoying the benefits, even if they contribute nothing to its provision (Clark et al. 2003).

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likely to endanger health, cause significant damage to the environment (during production, use or disposal), cause unnecessary waste, and use materials derived from threatened species or environments. Green consumption has also evolved into two unique consumption trends: Lifestyles of Health and Sustainability (LOHAS) and Collaborative Consumption. LOHAS, a marketing term, represents a group of consumers who are environmentally, socially, and health conscious, and also considers a lifestyle that benefits the planet. In contrast, collaborative consumption refers to the trend of sharing, geared by technology and social networks that change the way businesses operate (Tay 2011). Jaidev et al. (2018) stated pro-environmental as an alternate name of green.

3.1.15 Sustainable Consumption Behaviour In today’s era when discussions relate to sustainable development and sustainability; related to these concepts, a new broad conceptualization ‘sustainable consumption behaviour’ finds a significant place in recent studies. As mentioned by Zralek (2017), the name ‘sustainable consumption’ was used for the first time in Earth Summit, 1992; however, its definition was later conceptualized in 1995 at Nordic Roundtable in Oslo. Sustainable consumption is a strategy which concentrates on new ways of managing the demand side of the economy by not only focusing on economic benefits, but by also emphasizing on environmental and social well being (Haron et al. 2005). It was defined as the use of goods and services that respond to the basic needs of consumers, brings a better quality of life, and minimizes the use of natural resources/toxic materials by considering the needs of future generation (Pack et al. 2005). In accordance with Corral-Verdugo et al. (2006), one of the most important aspects of characterizing sustainable behaviour is its extended temporal component since it includes concern for upcoming times and future generations. After that, Tan and Lau (2009) insinuated about four principles of sustainable consumption behaviour. These principles were selection,2 minimization,3 maximization,4 and segregation.5 As indicated by Kiraci and Kayabasi (2010), sustainable consumption is a consumption style that is based on the limited use of world’s resources and that looks for the best ways which do not damage or cause the least damage to natural living. These researchers mentioned that the idea of sustainable consumption desires that the individuals should decrease their levels of consumption, and change their lifestyle towards sustainability by viewing the requirements of upcoming generations. Also, this behavioural change must happen within the people by their own choice and discretion, so that there remains no need of imposing these behaviours on them.

2 It

is the behaviour of choosing environmentally friendly products and services. is the behaviour of minimizing the range of consumption. 4 It is the behaviour of maximizing functionality and extending life of the products. 5 It is the behaviour of segregating and recollecting the waste for recycling or reusing purpose. 3 It

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It can be clarified from the above discourse that the terminology as described in the literature is much cumbersome. Even after four decades of research, there is a disturbing lack of clarity in the definitions and scope of the topics. After studying and analyzing different behavioural types, it can be understood that all seem to discuss about the issues of environmental degradation, sustainability, responsibility, and the relationship between these concepts in one or the other form. Certain authors (Antil 1984b; Ozkan 2009) have accepted that there are various varying semantics, but each appears to be concerned with the same concept and define similar issues. There has also been defined an overlap between the concepts of conscious consumption (Roux and Nantel 2009). In this way, multiple identities are interrelated. In spite, in the light of dimensions of responsibility described in Chap. 1 (Fig. 1.6), a worth mentioning point is that the literature has used probably all facets of responsibility in the measurements of responsible behaviour of consumers in which either they do responsible acts or abstain from doing irresponsible ones. Now, in order to formulate the behavioural construct of the present study the ideologies (as described above) are contently analysed in next Sect. 3.2.

3.2 Formulation of Behavioural Construct: An Exploration The section is presented under two phases. Phase I—comprehending the terminology (Sect. 3.2.1) and phase II—figuring out the constituents of behaviour (Sect. 3.2.2).

3.2.1 Terminology of Responsible Behavioural Identities—A Comprehension Regarding wide terminology as used by researchers, some points came out as common. For instance, many academics have worked on the behaviour by using the word ‘environmental’ and certain have employed the term ‘ecological’. Some other studies are common in the sense that they are employing the word either ‘green’, ‘social’ or ‘sustainable’. On the other hand, studies also differ from each other for mentioning distinct other names such as in the environmental sphere (related, friendly, significant, supportive, etc.). However, in order to align the discussion with Chap. 1 where the roots of the issue of sustainable development and other concepts were arranged and defined chronologically, these terminologies too are arranged here in relation to the specific time period as revealed in Fig. 3.1. • The Initiation Period: (1960s and 1970s) First of all, Berkowitz and Lutterman (1968) constructed a social responsibility scale (SRS) and provided the literature with the concept of social responsibility. It measured the willingness of an individual to help other persons even when there was nothing to be gained for him/her. After that, Anderson and Cunningham (1972) channelized the concept of social responsibility to consumer markets, and the consumers which were identified on the basis of this social responsibility were named

Advancement in Terminology and Dimensions

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3 Conceptual Framework and Research Model {Responsible Consumption}

{Green Consumers}

{Socially Responsible Consumers}

{Socially Responsible Consumers}

{Ecologically Concerned Consumers} {Socially Conscious Consumers}

{Main focus on Socially Responsible Consumers}

{Ecological Consumers}

{Primarily focussed on the concept of Sustainable Consumers}

The Contemporary Phase

The Later Middle Eon

The Early Middle Era

The Initiation Period

1970s

1990s

1980s

2000s

Time Intervals

Fig. 3.1 Evolution and improvement in research identities. Source Authors’ compilation

as socially conscious consumers. Fisk (1973) talked about responsible consumption. Then in this period, Kinnear and Colleagues (1974) were also working on consumer environmental concern and were engaged in improving the SRS scale in the context of the ecologically concerned domain. They called these consumers ecologically concerned consumers. Afterwards, Antil and Bennett (1979) mentioned these consumers with a new name socially responsible (by replacing the word conscious with responsible) and developed a ‘socially responsible consumption behaviour’ scale. Accordingly, in this period three main terms were prevailing that were, socially conscious, environmentally concerned, and socially responsible. However, negligible attempts were made in defining the scope and distinctiveness in these terms. • The Early Middle Era (1980s) In 1980s, Antil (1984b) was engaged in testing ‘socially responsible consumption behaviour’ and now, the concept of socially responsible consumption behaviour overcame the limitation of the previous socially conscious consumer behaviour concept. Firstly, the word responsible is replaced with conscious, and the expression consumer behaviour is replaced with consumption behaviour. Both the improvements by Antil seemed significant. In radiance with the difference between consumer behaviour and consumption behaviour (refer Chap. 1), consumption seemed to be an improved elaboration. The word responsible is also better aligned with behaviour as consciousness merely suggests awareness which can instigate behaviour but not behaviour in itself. Then, Balderjahn (1988) also enlarged the concept of ecologically concerned consumers propounded by Kinnear et al. (1974), and developed a causal model to identify these consumers. In this manner, he tried to reach at the operational and practical part. Side by side, Leigh et al. (1988) reached the components of socially

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conscious consumption tendencies and major components were defined as ecological, social, health, and energy issues. In terms of scope, they also called ecologically concerned consumers as a subset of the socially conscious category (Fig. 3.2A). • The Later Middle Eon (1990s) In early 1990, Schwepker and Cornwell (1991) called a segment of consumers ecologically concerned who were concerned about the waste generated by products and their packaging. They developed purchase intentions (PI) scale to reach these consumers. Then, Roberts (1995) elaborated on socially responsible behaviour (previously described by Antil) as a broad domain, and socially conscious and ecologically conscious behaviours were studied as subsets of socially responsible behaviour. He distinguished between socially conscious and ecologically conscious terms by elaborating that socially conscious behaviour can impact particular groups within society (women, minorities or can promote causes such as religious affiliation, avoidance of sin stocks, reduction of weapon production), and ecologically conscious behaviour can impact the natural environment (the whole ecology and its diverse domains). A new SRCB scale was constructed from items of social concerns, as well as ecological considerations. Thus, socially conscious behaviour and ecologically conscious behaviour were defined as parts of socially responsible behaviour (Fig. 3.2B). Also, till now, literature was mentioning about the terms ‘ecologically concerned’ (Kinnear et al. 1974) and ‘ecologically conscious’ (Roberts 1995). Then, a study conducted by Roberts and Bacon (1997) set apart ecological concern and consciousness. 3.2(A)

3.2(B)

3.2(C) (Derived from

3.2(D)

Figure A and B)

Socially Conscious Ecologically Concerned

Socially Responsible Socially Conscious Behaviour

Ecologically Conscious Behaviour

3.2(E)

Socially Responsible

Ecological

Socially Responsible Socially Conscious Ecologically Conscious Behaviour

3.2(F) {Concentration mainly on Natural/ Biological environment}

{Natural/ Biological environment; but also human relationship with it}

{Consider the issues of ecology as well as society}



Sustainable Behaviour {Regard present status of ecology and society both ; but future generation cannot be ignored}

Socially Responsible Ecological/ Behaviour Green Environmental Behaviour Behaviour In line with the theoretical evidences found in literature, the expressions like concerned and conscious have been removed as the reader moves towards figures In literature green is termed as synonymous to pro-environmental; here, with new LOHAS and Collaborative Consumption trends, the expression ecological is also assumed tantamount to green and broader in scope than environmental

Environmental



Ecological Environmental

Fig. 3.2 Hierarchy of behavioural ideologies. Source Authors’ compilation

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They elaborated that the two are related, but the concern was defined as attitudinal part and consciousness as a behavioural constituent. This way, the word concern became outdated from behaviour; yet, consciousness still prevailed. Also, Grob (1995) in the same period investigated consumers’ ‘environmental behaviour’ and Kaiser et al. (1999a, b) defined ‘ecological behaviour’. Both the researchers removed the words concerned and conscious. Hence with these studies, consciousness too became outmoded from behavioural ideologies. Kaiser and associates also point up that ‘environmental’ and ‘ecological’ are technical terms used in the psych info database but the former is the psychological index term related to attitude, while the latter is associated with the behaviour. However, the terms are not widely distinguished by them but the word ‘ecological’ was defined as a wide elaboration of what was called as ‘environmental’. In this way, the scope of environmental activities may be less than ecological and the idea is portrayed in Fig. 3.2D. Peattie (1995), in that period was also concentrating on Green Consumption Behaviour. • The Contemporary Phase (2000s) Till now with three decades of research, some innovations occurred and academics started incorporating a full range of consumption activities in behaviour (Mohr et al. 2001; Shanka and Gopalan 2005; Webb et al. 2008). Literature became vast with ongoing researches in the field, and one more conceptualization joins ‘sustainable consumption behaviour’. As stated by Luchs and Mooradian (2012), sustainable consumption behaviour is motivated by both social and environmental considerations. Researchers working on sustainable behaviour also added a temporal component and future-oriented role of consumption in their discussions. Therefore, the term is a wide elaboration and a development over previous prevailing identities. Certain other academics, who although utilized past prevailing identities, but started considering the concept of sustainable development and sustainability in their work. For instances, Gilg et al. (2005), in their paper on green consumption mentioned that the term green is typically used interchangeably with pro-environmental and can be taken as a move towards sustainable lifestyles. Tanner and Kast (2003) described that an important component of sustainable consumption is the purchase of green products. Kim and Choi (2005) compared ecological consumption choices with sustainable, and concluded that these should be future and group-oriented. In the past, Antil (1984b) also emphasized upon the future-oriented aspect of socially responsible behaviour by saying that the future role of socially responsible behaviour should not be disregarded. Consequently, it can be concluded that the scope of sustainable consumption is widest than other ideologies as it emphasizes the future-oriented consumption philosophies. The fact is that all the concepts are interrelated except for some differences in terms of scope. These are blended together in a hierarchy in Fig. 3.2F to draw synergy. Behavioural Construct—Name Identification Due to the ambiguity in terminology, Gilg et al. (2005) suggested to redefine the language of consumption for realigning it towards a sustainable future. Therefore, it is crucial to evolve a significant impression for the behavioural measurement.

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In simpler terms, when social responsibility (whether for environment and society) gets intermingled in the process of consumption (pre-acquisition to post-disposal), the behaviour that consumers display can be called responsible. Here, at best, not to indulge in the controversy of environment, green, ecological, social, and sustainable, the behavioural construct will be called as ‘Responsible Consumption Behaviour (RCB)’; explicitly, Consumption Behaviour + Social Responsibility = Responsible Consumption Behaviour. Fisk (1973) had also discussed about Responsible Consumption; but here, the construct ‘Responsible Consumption Behaviour (RCB)’ is unique in itself and broad in its purview. Towards the present purpose, it is preferred to call ‘responsible consumption behaviour’ a construct rather than a concept, and it is wide enough to admit all the conceptualizations that prevail all over literature; it also relates consumer consumption patterns with their responsibility towards environment protection and sustainability with both present and future role.

3.2.2 Constituents of Responsible Consumption Behaviour (RCB)—An Elaboration With the explanation of differing ideologies, one more fact can be highlighted that the span of studies conducted in each period was limited and circumvents around those problems noticed in any particular era as defined in Chap. 1. But as environmental problems became wide and the problem of environment protection turned into the issue of sustainability, studies started concentrating upon broader aspects and the scale of their measurements became wider. Whatsoever are the names of the measurements, the components of behavioural identities as studied by researchers’ intersect with each other and the subject matters remain almost similar. In line with the process of consumption, some researchers (Kinnear et al. 1974; Stern 2000; Gilg et al. 2005; Ek and Soderholm 2006; Webb et al. 2008; Tan and Lau 2009; Mondejar-Jimenez et al. 2011) talked about purchasing, usage, and disposal behaviours and measured their constituents. A group of other elegant academics (Karp 1996; Chan 2001; Kim and Choi 2005; CorralVerdugo et al. 2006; Koda 2012) concentrated only upon one domain like activities related to only purchasing; while others (Roberts and Bacon 1997; Kurz 2002; Bamberg 2003; Kaiser et al. 2003; Tindall et al. 2003; Tan and Lau 2009; Chen et al. 2011) independently searched for behaviours but did not categorize them under any of the consumption domain (like purchasing, usage, disposal). A class of researchers has also talked about many of those acts which are out of the purview of consumption process but are termed as some socially responsible activities (Haytko and Matulich 2008; Isildar and Yildirim 2008; Yuksel 2009; Chen et al. 2011).

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Along these lines, here the construct ‘responsible consumption behaviour (RCB)’ is assumed to underlie five broad domains.6 These domains are inconsistent with domains of consumption behaviour as explained in Chap. 1 excluding ‘behaviour of evaluating’. But, the antecedents of behaviour which are further elaborated in this chapter work for consumers to evaluate their behaviour in different domains regarding responsible activities. So, evaluation is not directly included in behaviour, but the antecedents can work in this direction which will be noted from the further text. A domain of allied socially responsible behaviours is also appraised. Specifically, the domains of study are: (1) (2) (3) (4) (5)

Responsible Purchasing Domain Responsible Usage Domain Responsible Maintenance Domain Responsible Disposal Domain Domain of Allied Socially Responsible Behaviours

Here, instead of integrating the activities only in one broad construct (RCB), these are separately considered in each domain, because one consumer may not behave conscientiously in all types of behaviours. In some instances, environmentally responsible products may be costly and involve more time in searching; but, the opposite alternatives may be cheaper and cost less: driving own vehicle is more comfortable than sharing with others or using public transport; irresponsible use of energy, water, and other natural resources brings no immediate pain; use and throw behaviour is much easy than retaining or sustaining the things; and unsustainable means of disposal are totally effortless instead enduring for recycling or reusing. Liegeois and Cornelissen (2006) quote similar views and remark that each choice confronts consumers with self conflict that is the choice between an easy solution that harm the environment and a sustainable substitute for which they themselves sacrifice. Consumers can set aside their own benefits amongst these choices, and their level of engagement in responsible behaviour may vary as to their power and premises to perform in each domain. Consequently, it is preferred to explore responsible activities in diverse domains. First four domains directly relate to the consumption process (Chap. 1: Fig. 1.4). Last domain of ‘allied socially responsible activities’ is separate from the consumption process and the activities underlie it are completely distinguished from those in the initial four domains. (1) Responsible Purchasing Domain: This domain underlies purchasing of those products which do not harm the environment, and also behaviours which lead to buying of such offerings.

6 It is significant to note that a number of environmental and social issues exist and one study cannot

entertain all of them. Thereby, each domain reflects some selected socially responsible acts and the process of measurement of each behavioural domain is defined further in Chaps. 4 and 5.

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(2) Responsible Usage Domain: This domain strikes on the sustainable and responsible manners of consuming, as usage behaviour puts an environmental impact in terms of ways and manners in which the acquisitions/resources are consumed. (3) Responsible Maintenance Domain: Responsible maintenance calls for keeping the offerings into good conditions by regular check and repair for expanding the life of products so that they last longer. (4) Responsible Disposal Domain: This domain covers the disposal, as well as post-disposal activities of consumers. It has much significance as it connotes tasks of proper handling of household waste, and waste outside the home. (5) Domain of Allied Socially Responsible Behaviours: The activities with significant measurements in past studies are considered here. For example: behaviours of petitioning, joining environmental organizations, support for public policy, environmental talk, donation for environment purpose, etc. Suitably, in the present study ‘responsible consumption behaviour (RCB)’ refers to a behaviour in which consumers reposition themselves towards sustainable substitutes in full range of consumption process that is from pre-acquisition and acquisition to disposal and post-disposal; and also reveal those accompanying responsible activities which can benefit the environment and crucial for a healthy and sustainable living in society. In this way, ‘responsible consumption behaviour’ not only safeguards the present but also keep an eye on the stake of the future generation. To this point, this chapter has discussed about the behavioural ideologies and their constituents. However, while studying behavioural identities, it has been observed that the formation of responsible behaviour is not something that happens accidently and immediately. Rather, it undergoes a definite process and a range of factors are involved that determine the behaviour of an individual or group of individuals. Accordingly, literature discusses about certain antecedents which help in forming favourable behaviour. Section 3.3 discusses about these antecedents.

3.3 Behavioural Antecedents—An Exploration The origin of the concepts of behavioural antecedents is as old as the beginning of the domain of studying behaviour itself. The views of certain research academics as described in the following paragraph substantiate the idea of behavioural antecedents. At first, Kinnear et al. (1974) obtain that a consumer may behave in an ecological concerned manner without being aware that he/she is doing so. Hence, a buyer in the consumer marketplace must express concern for ecology, and according to that concern, he/she must indicate ecological purchasing behaviour. After that, Webster (1975) exclaims that consumers may behave responsibly only when they will be aware of any environmental problem, and about the opportunities to buy products and services which are responsive to that problem. He too said that with awareness, the process of responsible behaviour formation will start and consumers then form a positive attitude/superior perception that they have a power to favourably influence

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the problem situation. Antil (1984b) too stresses on the point that first of all, a consumer should perceive a problem which leads to the belief that his/her individual efforts will help contribute to the solution of the problem, only then the behaviour is possible. Clark et al. (2003) also claim that favourable behaviour originates from values, beliefs, and attitudes that orient individuals toward particular actions. These views are emphasized by Ozkan (2009) by stating that the consumer first needs to believe in the existence of problems related to sustainability and the need for finding solutions to these problems. Then, he/she must believe that his or her personal efforts can contribute to the quest of the solution to these problems. Stern (2000) point up that it is the environmental intent which is an independent cause of the behaviour. Related to it, Mondejar-Jimenez et al. (2011) elaborate on a process in which environmental concern goes into positive attitude and knowledge, and with possibilities to solve a problem, finally consumers behave in ecological manners. These views imply some unique points based on which some antecedents of behaviour are recognized here, are boldfaced in the text below, and explained further. (1) Consumers must be well informed, aware and knowledgeable  Environmental Knowledge/Environmental Awareness (2) Consumers must be concerned about environment protection/Consumers must believe in the existence of problems  Environmental Concern (3) Consumers must have a positive attitude for protecting the environment  Environmental Attitude (4) Consumers must have the belief that they can do something for the welfare of the environment and their efforts can make a difference  Perceived Consumer Effectiveness for Environment (5) Consumers must be intended to contribute to environment purpose  Willingness/Intentions/Commitment to Behave Responsibly

3.3.1 Environmental Knowledge (EK) Environmental knowledge is defined as a general knowledge of facts, concepts, and relationships concerning the natural environment and its major ecosystems. It involves people knowledge about the environment, key relationships leading to environmental impacts, an appreciation of the whole system, and knowledge about the collective responsibilities necessary for sustainable development (Mostafa 2007), In the words of Finisterra do Paco and Raposo (2008), it refers to the extent to which an individual knows and is aware of environmental issues. Environmental knowledge is categorized differently by different authors. The types include: • Normative knowledge: Values are termed as normative knowledge (Kaiser et al. 1999a, b).

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• Factual knowledge: Factual knowledge measures cognitive aspects (Maloney and Ward 1973), and it is also referred to as behavioural knowledge (Kaiser et al. 1999a, b). • Objective and Subjective knowledge: How much a person actually knows is called objective knowledge, and subjective knowledge is the self-assessed knowledge about how much a person thinks he/she knows (Barber et al. 2009). So, one’s belief can be called as one’s stable subjective knowledge. • Abstract knowledge: It relates to knowledge concerning environmental issues, problems, causes, solutions, and so on (Mostafa 2007). • Concrete knowledge: Behavioural knowledge that can be utilized and acted upon (Mostafa 2007). • Ecological knowledge: Knowledge of the extent and causes of various types of pollution (Weidenboerner 2008). • Trade off knowledge: Knowledge of the trade off costs associated with implementing pollution abatement programmes (Weidenboerner 2008).

3.3.2 Environmental Concern (EC) Environmental concern has been defined in a wide variety of ways and in its definitions, it is a very complicated and unstable concept (Albayrak et al. 2010). Simply stated, concern for the environment can be called environmental concern. But, the environment itself has different elements which make its’ structure very much complex. Therefore, as defined in the literature, EC is not a specific term but is interchangeably used to refer to the whole range of environmentally related perceptions, emotions, values, knowledge, attitude, and behaviour (Bamberg 2003). According to Siu and Cheung (1999), a positive attitude towards the environment refers to environmental concern, and Finisterra do Paco and Raposo (2008) relate this attitude with environmental consequences. Milfont and Duckitt (2004) also mention that EC is the term typically used in empirical literature to refer to Environmental Attitude which itself is a multifaceted term and has been defined differently by academics. EC may also represent values of respondents about the relationship between: the environment and society, individual and environment, and perceptions of respondents about specific environment problems (Kalantari et al. 2007). Kim and Choi (2005) and Kim and Kim (2010) too treat EC as an individual’s general orientation towards the environment, his/her concern, evaluation, and attitude towards facts related to environmental issues, and accept that it is the degree of worry about the state and nature of environmental problems. Looking beyond, along with peoples’ worry and awareness for the environment, EC is also stated as the supported attempts of individuals for solving these problems and the level of their willingness for contributing to such attempts (Alibeli and Johnson 2009; Albayrak et al. 2010).

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3.3.3 Environmental Attitude (EA) It has been established that attitude develops towards something, towards an object, and it is a learned predisposition to respond in a favourable or unfavourable manner with respect to that object. Favourable indicates the positive attitude and unfavourable means negative evaluating reactions concerning a person, a group, or a phenomenon (Budak et al. 2005; Rikner 2010). Taken from this perspective, environmental attitude is termed as a human attitude towards environment as an object and a learned predisposition to respond consistently in a favourable and unfavourable manner with respect to the environment (Heberlein 1981; Budak et al. 2005). Supplementing these designations, Milfont and Duckitt (2004) outlined it as the collection of beliefs, affect and behavioural intentions a person holds regarding environmentally related activities or issues. Then, Milfont (2009) asserted it as a psychological tendency that is expressed by evaluating perceptions and beliefs regarding the natural environment, including factors affecting its quality, with some degree of favour or disfavour. Tuna (2003) accepted it as respondents’ responses for given environmental issues: might be environmental degradation, pollution, the relationship between society and environment, global warming, and any other matter. Kim and Kim (2010) found EA as a kind of cognitive evaluation of environmental issues. As indicated by them, the positive or favourable attitude towards the environment is taken as pro-environmental while the reverse is called anti-environmental attitudes. The positive or pro-environmental attitude too can be called as environmentalism (Tuna 2003).

3.3.4 Perceived Consumer Effectiveness (PCE) Perception is said to be a method by which physical sensations are selected, organized, and interpreted to provide some meaning (D’Souza 2005). Aligning with the problem of environmental pollution, Kinnear et al. (1974) defined PCE as a measure of the extent to which respondents believe that an individual consumer can be effective in pollution abatement. With the passage of time as the environmental problems became wider, Roberts (1995) defined PCE as a measure of the subject’s judgment in the ability of the individual consumer to affect environmental resource problems. Further, Straughan and Roberts (1999) stated that consumers’ attitude and responses to environmental appeals are a function of their belief that they can positively influence the outcome of environmental problems, this belief is referred to as PCE. It is also seen as an element of self-concept, and as the output of a self-perception, self evaluation process (Berger and Corbin 1992).

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3.3.5 Willingness/Intentions/Commitment to Behave Responsibly The keenness of an individual for contributing to environment protection and maintaining its sustainability is exercised differently in literature by using dissimilar terminology. Some authors preferred the term willingness to pay (Laroche et al. 2001; Ek and Soderholm 2006; Oikonomou et al. 2009). Some specified it as willingness to protect (Oikonomou et al. 2009), and Dietz et al. (1998) and Barken (2004) employed the term willingness to sacrifice. Tuna (2003) operationalized a term environmental commitment, and Caluri and Luzzati (2016) invoked on eco-friendly commitment. A component entreated with the tag verbal commitment was also raised (Hines et al. 1986/87; Siu and Cheung 1999) Numerous studies had utilized this willingness/commitment as a component of environmentally responsible/socially responsible consumption behaviour (Benton and Funkhouser 1994; Dietz et al. 1998; Tuna 2003; Siu and Cheung 1999; Oikonomou et al. 2009; Caluri and Luzzati 2016), and various others focused on it as an independent construct separate from other concepts (Laroche et al. 2001; Ek and Soderholm 2006). A unique class of researchers had also replaced the word willingness with behavioural intent or intentions, and instead of showing this intention as a component or subscale of any behavioural identity, it was implemented as a direct and immediate predictor of actual environmental behaviour (Kaiser et al. 1999b; Stern 2000; Mondejar-Jimenez et al. 2011). Irrespective of the names, it has been defined that behavioural intentions are the immediate antecedent of actual behaviour, and if an individual is willing to contribute for environmental causes, it is most likely that he/she will behave in identical ways. Now, it can be noted that a range of antecedents has been studied in literature and defined in a variety of aspects. Many times researchers combined various terminologies in any one antecedent type (like in environmental concern: concern, attitude, belief, values, behaviour are combined). Likewise, the intended meaning of any particular antecedent becomes somewhat ambiguous. Indeed, attempts were also made in the literature to simplify the overall situation. In order to discriminate them, one such attempt has been found in the work of Alwitt and Pitts (1996). Attempting for a difference between EC and EA, they elaborate that general concern has an indirect rather than direct influence on behaviour. General concern for the environment (which is ultimately the environmental concern) can make individual’s attitude towards certain environmental issue or problem (which is the specific environmental concern and can be termed as environmental attitude) may be in positive or negative direction, then this specific attitude may cause behavioural intent to perform or not to perform environmentally (for that particular issue or problem), by which actual responsible behaviour is manifested that may range from most environment friendly to most environment unfriendly (Kaiser et al. 1999b; Mondejar-Jimenez et al. 2011). With this, it can be clarified that EC is general in nature, and in specific contexts instead of environmental concern, the term environmental attitude (EA) must be preferred. A person may be concerned for the environment as a whole (General → EC), but attitude may be different for different environmental issues or problems

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(specificity → EA). As an instance, the view that the environment must be protected is a general concern for the environment; but, conservation or recycling can protect the environment is a specific attitude towards these two issues. These specific attitudes can generate intentions to conserve/recycle. Consequently, a consumer with the given resources (given resources means consumers must have opportunities to behave environmentally), as induced by this intent can become actually engage in conservation behaviour/recycling behaviour. Also, PCE is defined as different from attitude; thus, it is dissimilar from environmental concern also. An attitude represents an evaluation of individual’s beliefs or feelings about an entity (such as environment); PCE on the other hand, represents an evaluation of the self in the context of that entity (Berger and Corbin 1992; Majlath 2010). Sometimes, people show a positive attitude towards the environment, but one can feel totally helpless in his/her ability to have an impact on the problem through his/her efforts. As in the previous example, suppose consumers sense that there is a need for environment protection (EC) and view that recycling or conservation (EA) can protect it; but still, they may perceive that recycling is a very effortful and difficult task, and it will not make any difference what they do as an individual alone to conserve the resources. This type of thought implies lack of PCE. Although having a positive attitude, the destination of favourable behaviour will never be achieved because of inferior perceptions. Further, Siu and Cheung (1999) found intentions as a significant mediator of the attitude–behaviour relationship. Although certain theories of attitude–behaviour relationship exists in which the Theory of Reasoned Action (TRA) is foremost; later, extended by the Theory of Planned Behaviour (TPB). However, on the basis of the above elaborations, for the research work in this book a ‘Theory of Responsible Behaviour Formation (TRBF)’ is developed. This theory exclaims that firstly a consumer must be concerned for preservation of environmental sustainability {general environmental concern}; this concern helps in making that consumer’s favourable attitude for specific means and mechanisms by which sustainability can be realized {specific environmental attitude}; which in turn creates intentions/commitment for putting hands into those means {intentions/commitment to behave responsibly}; and finally, induced by these intentions, the consumer becomes actually engaged in that particular type of responsible behaviour {actual responsible behaviour}. But the point to be noted is that, attitude turns into actions only if the consumer is of the perception that his/her initiatives and individual efforts will be valuable and worthy in this direction {perceived consumer effectiveness}. The conceptual model based on this theory is revealed in Fig. 3.3. Figure 3.3 suggests that actual responsible behaviour is a function of people behavioural intentions, intentions generate with a specific environmental attitude, which ultimately operate with general environmental concern. Also, it can be noted that there may be a direct link between general environmental concern and actual behaviour, but specific environmental attitude and behavioural intentions can mediate it. Similarly, the direct association of attitude and behaviour can be mediated by behavioural intentions. There is another variable ‘perceived consumer effectiveness’ which can significantly intervene between attitude and intentions. In this way, attitude and intentions are working as mediators, and as suggested by Berger and Corbin (1992), ‘perceived Consumer Effectiveness’ has been shown as a moderator variable.

3.4 Antecedents of Responsible Consumption Behaviour …

Moderator Variable Independent Variable

Perceived Consumer Effectiveness

Mediator Variable 1

General Environmental Concern

Specific Environmental Attitude

{Highly Unconcerned to Highly Concerned}

{Extreme Negative to Extreme Positive}

137

{Intense Inferior to Intense Superior} Mediator Variable 2

Willingness/Intentions/ Intentions to

Commitment Behave to Behave Responsibly Environmentally

{Most Unfavourable to Most Favourable}

Dependent Variable

Actual Actual Responsible Consumption Behaviour Behaviour

{Anti-Environmental to Pro-Environmental}

Parentheses {} indicate range of particular paradigm Environmental knowledge is not included in the model due to its enlarged dimensions Direct and Indirect/Mediating effects [ ], Moderated effect [ ]

Fig. 3.3 A conceptual model for ‘theory of responsible behaviour formation’. Source Developed by authors

3.4 Antecedents of Responsible Consumption Behaviour: Decision for the Present Context Although varieties of antecedents have been researched in past studies, however, ‘attitude’ as an antecedent has been in the most controversy (Singh and Gupta 2013). Majority of the studies on this theme concentrated on attitude–behaviour link; affirmed that there always remains a gap between people attitude and behaviour, and tried to find out the reasons of this gap with their own gracious ways (Roberts and Bacon 1997; Wright and Klyn 1998; Kaiser et al. 1999b; World Business Council for Sustainable Development 2008). Factually, the literature supports that attitude and behaviour are causally related and Wright and Klyn (1998) propounded that the direction of attitude–behaviour link can have four possibilities: (i) Attitude causes behaviour (ii) Behaviour causes attitude (iii) Attitude and behaviour are reciprocally causative (iv) Attitude and behaviour are unrelated. Literature in the present field has supported the first notion that attitude is a predictor of behaviour (Kaiser et al. 1999b; Tilikidou and Delistavrou 2007). The relationship of attitude–behaviour and the power of attitude in determining behaviour have also been examined in literature, but have remained in disagreements. For instances, after analyzing environmental attitude–behaviour link, Hines et al. (1986/87); Roberts and Bacon (1997); and Budak et al. (2005) found moderate correlations. Hines et al. (1986/87) reported a moderate relationship (r = 0.347) in a meta-analysis of 51 outcomes; while Meta analyzing 17 studies, Eckes and Six (1994) found still less (r = 0.26). However, Kaiser et al. (1999b) established environmental attitude as a powerful predictor of ecological behaviour. Following these

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notions, encompassed by all other determinants, this study concentrates upon attitude as a superimposed predictor of behaviour. Likewise, attitude is the behavioural antecedent which is studied in this research. Further those researchers (Alwitt and Pitts 1996; Heberlein 2012) are followed here, who in their researches, had distinguished attitudes based on generality and specificity. As instances: Heberlein (2012: p. 64) mentioned that the general attitude correlates better only with general behaviour and can seldom predict specific behaviours. Kim and Choi (2005) also said that advanced predictors of behaviour could be achieved by specificity both in behavioural and attitudinal measurements. Hence, the principle of specificity is considered, and attitude is defined in two domains. (1) General Attitudinal Domain (2) Specific Attitudinal Domain. (1) General Attitudinal Domain: General attitudinal domain mainly implies a broad-spectrum of consumers concern for the protection of environment and maintenance of its sustainability which can encompass various sorts of activities that affect the environment. In this way, the general attitudinal domain takes the question of sustainability and means of its achievement as a whole. Here, the domain represents two constructs ‘Concern for Sustainable Future (CSF)’ and ‘Commitment to Initiate (CI)’. • Concern for Sustainable Future (CSF): ‘Concern for sustainable future’ as a general attitude implies an attitude of consumers which is customary for the formation of their specific attitude towards the means by which sustainability can be achieved. In literature, the general attitude of consumers is popular with the name of ‘environmental concern (EC)’. But beyond the protection of the environment, in the light of today’s problem of healthy endurance and maintenance of sustainability, a similar construct ‘Concern for Sustainable Future (CSF)’ is considered. • Commitment to Initiate (CI): ’Commitment to initiate’ measures the extent to which consumers consign that they are enthusiastic and prepared for environmental actions. Literature encounters that although people accept that something must be done for maintenance of sustainability; but, when it comes to their own performance, their saying contradicts with their doing. Accordingly, there has been obtained a gap between attitude and actions. The reason may be the lack of commitment to contribute to the said purpose. So, another construct ‘Commitment to Initiate (CI)’ in general attitudinal domain attempts to measure the level of this commitment of consumers, induced by which they may act responsibly. (2) Specific Attitudinal Domain: Specific attitudinal domain, as its name implies deals with consumers’ attitude only for some specific environment issues/problems, and also about solutions by which these problems can be trounced. The broad construct in the specific attitudinal domain is named ‘Attitude towards Sustainable Living (ASL)’.

3.4 Antecedents of Responsible Consumption Behaviour … Hypotheses regarding direct Influences (Above Arrows)

139 Hypotheses regarding Mediation

Causal Variable → Mediator(s) → Dependent Variable

H4a: CSF → ASL → RCB H4b: CSF → ASL → CI H5a: CSF → CI → RCB H5b: ASL → CI → RCB H6: CSF → ASL → CI → RCB

 

CSF is assumed as an independent variable, RCB a dependent variable, and ASL and CI both as mediator variables For taking a practical stance, pictorial presentation of C-A-C-B model is different from conceptual model previously shown in fig. 3.3

Fig. 3.4 A Schematic C-A-C-B Model of Integrated Conceptual Framework. Source Developed by authors

Now, at a practical level, the conceptual model (Fig. 3.3) is somehow changed and an analytical working model is formulated. This model is designated as ‘C-AC-B (Concern → Attitude → Commitment → Behaviour) model’ (Fig. 3.4) to be empirically investigated further. The model is motivated by the theoretical model revealed in Fig. 3.3. However, going for the need of maintaining sustainability, names of constructs, and their measurements are transformed for a new appearance. The construct ‘concern for sustainable future’ is working on the place of ‘environmental concern’. The previous construct ‘environmental attitude’ is replaced with ‘attitude towards sustainable living’. ‘Commitment to Initiate’ takes the position of ‘willingness/intentions/commitment to behave responsibly’ and the ‘actual responsible behaviour’ is obvious with the construct ‘responsible consumption behaviour’. The development and measurement process of these constructs are defined in Chaps. 4 and 5. This C-A-C-B model has been worked further empirically in Chap. 6. It is also important to note that C-A-C-B model incorporates only mediator variables and does not consider any moderators (such as perceived consumer effectiveness in the theoretical model). It is so because MacKinnon (2011) advocated that mediating variables describe the process, and moderating variables access the intervention across groups either in the form of change in relationship amongst variables or the change in effect of one variable on other. Since the prime focus of this model is on the process of behaviour formation, only mediators are considered. It is also done to avoid the complexity which may be generated by testing both mediator and moderator effects in a single model. Further, the model which is to be tested is a Serial Mediation Model7 as it is assumed that one of the mediators is a cause of 7 There can be three forms of mediation, i.e. simple mediation, parallel mediation, and serial media-

tion. Simple mediation model assumes only one mediator. Conversely, parallel and serial mediation both the models have two mediators, but they are different. Parallel mediation model assumes that

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another mediator. Now, as the study is based upon consumers, next discussion offers credentials about ‘responsible consumers’. A Demarcation to Responsible Consumers Consistent with different responsible consumption concepts, literature endows distinctive appellations to consumers who in any research work designated as responsible. Some authors preferred the terms: green consumers, environmentally responsible consumers, environmentally-ecologically conscious consumers, environmentallyecologically concerned consumers, or ecological consumers. On the other hand, in some places, these consumers are also opined as socially responsible consumers, socially conscious consumers, and sustainable consumers. Throughout literature, these expressions are interchangeably used with almost similar meanings. Here, with the definition of behavioural construct ‘responsible consumption behaviour’; it is preferred to call them as ‘responsible consumers’. Also, in relation to the study of behavioural antecedents, the point has been regarded that sometimes consumers may seem to behave responsibly; but, instead of concern for sustainability and positive attitude for the cause, the behaviour may get motivated by certain other kinds of sentiments, like: saving of money, health consciousness, family care, etc. Such behaviours are derived from self-seeking intentions and not because of noble intentions of environment protection and sustainability; so, being egoistic, they cannot be entitled as responsible. Accordingly, ‘responsible consumers’ in this book has been defined in an exclusive manner in the following way. To the purpose, ‘responsible consumers’ are those who primarily show ‘concern for sustainable future’; being concerned, positive ‘attitude towards sustainable living’ develops in them and transpires their ‘commitment to initiate’ for sustainability; going through this process, they behave in responsible manners for environment protection and preservation of sustainability. It is so because concern through attitude and commitment channelizes itself towards ‘responsible consumption behaviour’. In this way, it will not be wrong if we say that scores or grading of these consumers must remain high from other consumers on all the measurements whether concern, attitude, commitment, and behaviour. Accordingly, it can also be said that for these consumers, the ‘theory of responsible behaviour formation (TRBF)’ must be true. To this point, it has been articulated that how much was already known on the topic, and how the researchers have organized their studies. Accordingly, the conceptual framework and the analytical working model for the research work in this book have been decided. Next, it is determined, what types of questions need to be answered. Thereby, research objectives are set out and worked further in Part IV. These objectives are motivated by the distinct ways and manners of research conducted in literature as was accomplished in Chap. 2.

the two mediators are not causally related. However, serial mediation model assumes a causal chain that one of the mediators is a cause of other mediators (Hayes 2013).

3.5 Research Objectives and Hypotheses

141

3.5 Research Objectives and Hypotheses 3.5.1 Objectives and the Reasoning (1) To explore the dimensions underlying behavioural and attitudinal constructs. (2) To examine the extent to which consumers adopt each behavioural kind8 and to affirm the extent of their attitudinal viewpoints. (3) To investigate the theory of responsible behaviour formation by empirically testing C-A-C-B model. (4) To identify consumer segments as per behavioural and attitudinal dimensions. (5) To anticipate the proportion of responsible consumers in Indian Market. (6) To analyse the characteristics of identified segments, their profiles, and distinctiveness. It has already been clarified that the study is based upon behavioural and attitudinal constructs. At the outset, the structure underlying supposed constructs will be explored by operating on the first objective. Secondly, behavioural and attitudinal dimensions as will be obtained in the first objective will be contrasted (objective 2). Then, C-A-C-B model remains to be tested in differing behavioural kinds (objective 3). Rationale behind objectives 4 and 5 is to have a divide between responsible/not responsible consumers due to the fact that there exist attitudinal and behavioural dissimilarities amongst consumers. Lastly, it is assumed that different consumer segments may be a result of the dissimilar composition of consumers and differing circumstances through which they pass out. In view of that, consumers in one segment must have different features and attributes which separates them from consumers in other segments; so, objective 6 worked on this point. Accordingly, the characteristics of ‘responsible consumers’ will also be identified.

3.5.2 Hypotheses The research hypotheses as described below originated from C-A-C-B model and are linked to the assessment of objective 3. These are also apparent in Fig. 3.4 in symbolic form. 8 Behavioural

kind and attitudinal viewpoints imply the dimensions of behaviour and attitude respectively as will be attained by operating on the first objective.

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• Hypotheses Pertaining to Direct Influences Hypotheses Concerning Effect of Concern for Sustainable Future (CSF) H1a: Concern for Sustainable Future (CSF) has a significant positive effect on Responsible Consumption Behaviour (RCB). H1b: Concern for Sustainable Future (CSF) has a significant positive effect on Attitude towards Sustainable Living (ASL). H1c: Concern for Sustainable Future (CSF) has a significant positive effect on Commitment to Initiate (CI). Hypotheses Concerning Effect of Attitude towards Sustainable Living (ASL) H2a: Attitude towards Sustainable Living (ASL) has a significant positive effect on Responsible Consumption Behaviour (RCB). H2b: Attitude towards Sustainable Living (ASL) has a significant positive effect on Commitment to Initiate (CI). Hypothesis Concerning Effect of Commitment to Initiate (CI) H3: Commitment to Initiate (CI) has a significant positive effect on Responsible Consumption Behaviour (RCB). • Hypotheses Pertaining to Mediation Effects Hypotheses Concerning Mediation by Attitude towards Sustainable Living (ASL) H4a: Attitude towards Sustainable Living (ASL) mediates the effect of Concern for sustainable future (CSF) on Responsible Consumption Behaviour (RCB). H4b: Attitude towards Sustainable Living (ASL) mediates the effect of concern for sustainable future (CSF) on Commitment to Initiate (CI). Hypotheses Concerning Mediation by Commitment to Initiate (CI) H5a: Commitment to Initiate (CI) mediates the effect of Concern for Sustainable Future (CSF) on Responsible Consumption Behaviour (RCB). H5b: Commitment to Initiate (CI) mediates the effect of Attitude towards Sustainable Living (ASL) on Responsible Consumption Behaviour (RCB).

3.5 Research Objectives and Hypotheses

143

Hypotheses Concerning Serial Mediation H6: Attitude towards Sustainable Living (ASL) and Commitment to Initiate (CI) serially mediates the effect of Concern for Sustainable Future (CSF) on Responsible Consumption Behaviour (RCB). Overall, the chapter presented a maiden attempt in describing the behavioural and attitudinal constructs of the study and rationale of their existence. It involved a description of the development of the theory of formation of responsible behaviour; research model; objectives and hypotheses. Now, next Part III (Chap. 4), offers a discussion on the methodology as adopted for achieving the objectives and testing of the hypotheses.

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Part III

Research Methodology

This section entails Chap. 4: Methodological Procedures and Techniques. Certainly, this is the chapter prepared about which kind of methodology is adopted for achieving objectives.

Chapter 4

Methodological Procedures and Techniques

The theoretical framework, conceptual model, objectives, and hypotheses have already been outlined in the previous chapter. Now, this chapter elaborates on the methodology adopted for carrying out the empirical part of this book. Mainly, it is divided into two sections. Section 4.1 deals with the research design, and has eight subdivisions under which the process and phases of all the methodological procedures and techniques are detailed. Further, Sect. 4.2 presents the sample characteristics.

4.1 Research Design Research design is a framework or blueprint for conducting the research to arrive at the objectives, and test the hypotheses set out. This design as adopted is defined under various steps highlighted in Fig. 4.1.

4.1.1 Designing Phases of Research By and large, exploratory, descriptive, and causal designs are the major classifications of research designs. Malhotra and Dash (2012: p. 81) emphasize that the distinctions between these classifications are not absolute and a given research may involve more than one type of design to serve several purposes. The same is the case with the present study as it comes across and is completed under exploratory and conclusive phases. Firstly, exploration of literature is done, and then insights gained from this exploratory research are quantified by two broad dimensions of conclusive research namely, descriptive research and causal research.

© Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_4

151

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4 Methodological Procedures and Techniques

Fig. 4.1 Research design: procedure and phases

Step 1: Designing Exploratory, Descriptive and Causal phases of research

Step 2: Construction of Data Collection Form

Step 3: Decision on Sample Sampling Process

Size

and

Step 4: Gathering Data: Distribution and Collection Step 5: Preliminary Analysis: Editing and Coding

Step 6: Transcription Preparation

and

Final

Data

Step 7: Assessment of Reliability

Step 8: Development of Plan of Analysis

4.1.1.1

Exploratory Phase

The research presented in this book can be said as exploratory in nature at two levels. The first level is the discovery of significant variables. The second is the discovery of relationships between identified variables. At the first level of exploration, a range of variables have been explored (Chap. 3), and as to the suggestions of Krishnaswami and Ranganatham (2005: p. 35), that a single study cannot be able to account for all the variance in any complex social phenomenon, the area for inclusion of variables was delimited specifically to attitude and behaviour. For this selection literature survey method of exploration was adopted. By exploring literature and after identification of research gaps, research objectives and hypotheses were defined in Chap. 3. Exploration and validation of dimensions of attitude and behaviour are done in the exploratory phase in Chap. 5.

4.1 Research Design

4.1.1.2

153

Descriptive Phase

Descriptive research is conducted to segment consumers (in order to identify responsible consumers), describe the characteristics of identified segments, and for developing their profile (Chaps. 7 and 8). At this point, the most common method of descriptive research design, i.e. single cross sectional design (sample survey research design) is utilized. It means that information from the sample respondents is acquired only once.

4.1.1.3

Causal Phase

The study, however, cannot be defined as completely causal but the element of causality remains here as it operates on the objective of obtaining evidence regarding cause and effect relationship between attitude and behaviour (Consider C-A-C-B model). Krishnaswami and Ranganatham (2005: p. 43) define that in any causal research the nature of relationship between independent variables and dependent variables is perceived and stated in the form of causal hypothesis, and according to Malhotra and Dash (2012: p. 80), the main method of causal research is experimentation. Being into line, the causal hypotheses were tested and sorts of experiments were completed by controlling mediator variables in the working analytical models in Chap. 6.

4.1.2 Construction of Data Collection Instrument As this research involves a study on consumers, primary source remains the main method of data collection, and utilizes a questionnaire as a means of data collection instrument. Initially, the main task in questionnaire preparation was the identification of appropriate scales for the measurement of behavioural and attitudinal constructs. So to begin with, two scales for the measurement of behaviour and attitude, respectively, were developed from the literature. Subsequently, the final research instrument is prepared by integrating these scales in the form of a set of questions in the questionnaire.

4.1.2.1

Development of Scales of Measurement

A large number of research articles and papers provided insights for the measurements of supposed constructs of attitude and behaviour. The statements which properly represented the constructs are either selected from literature or are constructed. Preparation of scales of measurement passed through the following steps as shown in Fig. 4.2.

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4 Methodological Procedures and Techniques

Fig. 4.2 Process of development of scales

Development of Theory, C-A-C-B Model and Constructs

Generation of Initial Pool of Items

Development of Variables Measuring Constructs

Selection of Pre-test Sample and Data Collection from it

Selection of Reduced Items

Coding Responses and Reliability

Validity Check and Preparation of Final Scale

(1) Development of Theory, C-A-C-B Model, and Constructs: The task of preparing scales of measurement begins by developing the constructs of interest in the study, and by defining their theoretical definitions which states the meaning and the central idea behind them (already defined in Chap. 3). (2) Development of variables measuring constructs: This step undergoes two sub steps. • Generation of Initial pool of items: Preliminary collection of scale items is based on theory, literature, and upon qualitative research including discussions with experts. While generating an initial pool of items, it is tried to obtain items in ways so that these can become consistent with theoretical definitions of their constructs. • Selection of Reduced Items: Firstly, the procedure of read-write-deleterewrite was used. Then, the items were shown to knowledgeable persons including academics of psychology, management, and commerce in Kurukshetra University for their views and directions. Consequently, ambiguous and irrelevant statements were removed, and certain statements were revised. This reduced version of scales consisted of forty statements in behavioural construct, thirty-eight in attitudinal construct, and thirty-two others for measurement of personality features. Next, the pilot survey is detailed out. (3) Selection of Pre-Test Sample and Data Collection: As regards pilot survey, 50 respondents were selected in the pre-test sample; composed of 20 teachers of Kurukshetra University and 30 students pursuing postgraduate courses from the same University. This selection was based on accidental sampling. The preferred items were introduced before this sample on a five-point agree-disagree Likert

4.1 Research Design Table 4.1 Pilot-test reliability

155 Constructs

N

Cronbach alpha

Behavioural (B)

40

0.917

Attitudinal (A)

38

0.844

Personality (P)

32

0.764

scale for attitudinal and personality statements, and a scale of always true to never true for behavioural statements. (4) Coding and Reliability Measurement: The data for the pilot study are coded (rating 1–5, reverse coding is also done wherever necessary) into the statistical programme SPSS (Version 20). The reliability of the scales and overall statements is judged using the Cronbach alpha coefficient (Table 4.1). The alpha value above 0.9 indicates high consistency amongst behavioural items. Alpha value for attitudinal measurement is also good (α = 0.844). However, the statements to measure personality characteristics had a low value of coefficient alpha than statements of other constructs, though well above 0.7, a suggested cut-off (Leary, 2004: p. 66). (5) Validity Check and Preparation of Final Scales: Validity has different forms but here content validity of the final scale is examined. Content validity also referred to as face validity is a subjective method; however, validity check was done with this method in the form of experts’ opinion. For finalizing the scales, University academics and researchers who were initially contacted for inclusion of statements were contacted again to critically examine these items regarding the domain of the constructs being measured (defined in Chap. 3). Based on their interpretation and feedback, minor changes were done in the wording of some of the items and final scales were obtained. A more formal and sophisticated evaluation of validity was further tested by examining construct validity (as per domains of behavioural and attitudinal constructs) in three forms: convergent validity, discriminant validity, and nomological validity (Chap. 5).

4.1.2.2

Development of Final Research Instrument: The Questionnaire

Initially, the pre-test questionnaire was prepared in English language. After getting the final draft, the same was translated into Hindi language in order to reduce response errors. For the purpose of verification, back translation into English was also completed and again with the help of experts’ opinions, the corrections if any were done. While preparing a questionnaire for any research, Malhotra and Dash (2012: p. 294) delineate a series of steps. Aligning with these steps, the final questionnaire is drafted under the following stages.

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(1) Interviewing Method: The questionnaire survey is self-administered. According to suggestions provided by Parasuraman et al. (2005: p. 277) self-report measurements are chosen as the most straight forward method in which respondents had to mark their responses on the questionnaire. (2) Structure of Questions: Parasuraman et al. (2005: p. 165) refer to the format of the questionnaire, and state it as a function of level of structure and disguise desired during data collection. As per these guidelines, the two aspects are fully considered. The questionnaires both in English and Hindi language consisted of four sections. First three sections of the questionnaire were completely structured. Here, the questions were presented verbatim to every respondent with five fixed response categories on which respondents had to check off appropriate positions that best describe them. For Section A: Behavioural Response these categories were Always True, Often True, Sometimes True, Rarely True, Never True. For the next two sections Section B: Attitudinal Response and Section C: Personality Features the categories were labelled as Strongly Agree, Agree, Somewhat Agree-Somewhat Disagree, Disagree, Strongly Disagree. In this way, five-point Likert scale has been used. Also, these sections were non-disguised in nature and the purpose was obvious to respondents. Consequently, regarding these three sections, the questionnaire can be termed as a structured non-disguised questionnaire. In Section D of personal information, some structured and non-structured questions were assorted. Questions for age, religion, educational qualifications, field of study, profession, and years of marriage were non-structured and socalled open-ended questions. This was done with a view to generate refined data (for example the exact age of respondent). In structured questions: gender, place of living, marital status, parenthood, type of family, and type of housing were of dichotomous type with two answer choices. However, for variables classes where the respondent’s score is above 60%, family income, and mode of commuting respondents enjoy more than two answer choices. So, amongst structured type, these questions were multiple-category questions. In addition, two variables that had indicated a relationship with responsible behaviour in literature were also added in the questionnaire. These were family support and religious strength or religiosity. Each of these variables was measured with two statements and is described in Endnotes at the end of this chapter. (3) Content of Questions: The wordings of the statements were clear and easily understandable which was confirmed with content validity during the pilot testing of the questionnaire. A logical order was followed for the arrangement of statements in the constructs. In the final questionnaire, the statements were sufficiently varied and unequivocal to minimize erratic fluctuations in the responses. Descriptions of the contents about the final statements of the questionnaire are presented next.

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157

Behavioural Measurement In the final version of the questionnaire, 40 statements were employed to measure ‘responsible consumption behaviour’ with its specified domains in Chap. 3. Here, the measurements are described domain wise. • Responsible Purchasing Domain: The subscale of responsible purchasing domain accentuated eight items from literature. The scale included choices for environmentally safe products and products less harmful to the environment (Tindall et al. 2003; Kim and Choi 2005), people switch over from brands causing environmental damage (Roberts and Bacon 1997), refusal of products with unnecessary packaging (Tindall et al. 2003), buying of energy-efficient products (Roberts and Bacon 1997; Ozkan 2009), and buying of products in refillable and reusable containers (Schwepkeer and Cornwell 1991; Siu and Cheung 1999; Kiraci and Kayabasi 2010). • Responsible Usage Domain: The second behavioural domain of usage contained fourteen behaviours. The items tapped behaviour of saving energy by using renewable energy methods like solar energy (Siu and Cheung 1999), and defreezing food before heating up (Mondejar-Jimenez et al. 2011). Some more items captured behaviours like switching off the engine of the vehicle while waiting on crossings (Kaiser et al. 2003), turnoff unused electrical energy (Finisterra do Paco and Raposo 2008), conservation of water by turning off taps, using filled water (instead flowing) to wash utensils (Gilg et al. 2005; Kiraci and Kayabasi 2010), utilizing used water (like after washing clothes) for cleaning of the floor (Isildar and Yildirin 2008), and wait for a full load of clothing for washing. Other behaviours include walking, riding a bicycle, carpooling or using of public transport as maximum as possible (Roberts and Bacon 1997; Tindall et al. 2003; Webb et al. 2008); preferring handkerchief instead tissue paper (Siu and Cheng 1999), use of both sides of paper (Isildar and Yildirin 2008), and taking of own bag while shopping so that plastic carrier can be avoided (Gilg et al. 2005; Kiraci and Kayabasi 2010; Xiao and Hong 2010). • Responsible Maintenance Domain: Maintenance can be in the form of reusing, sustaining, upholding or donating already used products so that products’ usage life span can be expanded, which may lead towards producing less amount of litter. This domain of maintenance behaviour underlined three statements. Accordingly, preference of reusable mugs/glasses (Tindall et al. 2003), repairing and reusing of things instead of discarding (De Young 2000; Tindall et al. 2003), and savingreusing of plastic shopping bags had been taken into consideration (Xiao and Hong 2010). • Responsible Disposal Domain: Fourth subscale of behavioural domain was added with seven items for the measurement of responsible disposition activities of consumers. The behaviours of maintaining the surroundings neat and clean (such as after some activities like picnic) were adopted from Tindall et al. (2003) and Kaiser et al. (2003). Two more items were constructed keeping in view the habits of consumers of putting the waste and wrappers into the dustbin. Another item regarding recycling and taking the garbage to the nearest recycling bins were

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derived from Finisterra do Paco and Raposo (2008). Willingness to contribute to municipalities for garbage collection that may aid in the safe disposal of waste was extracted from Berger and Corbin (1992). Recycling is an activity for which sorting of garbage is necessary, and consumer intention for the same was measured by the statement taken from the work of Uitto et al. (2004). • Domain of Allied Socially Responsible Behaviours: Besides, the four domains of consumption as discussed above, certain other activities had been measured and prescribed in literature in the domain of social responsibility. Some of these acts were also included in the present set of measurements. These measurements include: donation to groups who work for environment protection (Finisterra do Paco and Raposo 2008), environmental talk (Budak et al. 2005; Xiao and Hong 2010) and obeying environmental laws. Some other statements were constructed to define parking related behaviour, and social activities like playing music systems at less volume so that the neighbourhood does not get disturbed. Attitudinal Measurement Combining general and specific domains, 38 items were used in the measurement of attitudes. • General Attitudinal Domain: Eleven items were utilized from the literature by slightly changing the wordings to suit Indian situations. Seven of these statements measured concern of consumers for having a sustainable future. Attitude for the problem of increasing carbon-dioxide in the atmosphere leading to global warming was taken from Xiao and Hong (2010). People feeling that human consumption activities are a major cause of global warming (McCright 2010), and their sensible opinions that solutions of environment and sustainability problems can be done by changing the lifestyle (Tuncer et al. 2005) were also asked. Two items regarding people worry for a worse future were taken from Environment Value Survey (EVS) 2006. In this domain, four different statements were also included to define people’s commitment to do something for the environment and their dedication to take initiatives for the purpose. Three of the statements were used from Cavas et al. (2009), and one other is adopted from Finisterra do Paco and Raposo (2008). • Specific Attitudinal Domain: In specific attitudinal domain, twenty-seven items were selected related to various environmental issues regarding which attitude of consumers was sought. Three of the items were altered from the work of Boivin et al. (2011), and measured people’s attitudes towards sustainable products. Another three statements to measure people’s attitudes for the increasing waste problem were used from Alwitt and Pitts (1996). Awareness and attitude for recycling and for sorting of garbage to accomplish recycling activities were exercised from Berger and Corbin (1992), Laroche et al. (2001), and Finisterra do Paco and Raposo (2008). To tap pollution and conservation issues such as conservation of water, four items were constructed as to the visible scenario in India. These included: people thinking that pollution problem can be minimized by shifting from private to public transportation or walking/cycling, people thought that

4.1 Research Design

159

the huge amount of water gets wasted when the water unnecessarily keep flowing from roof tanks, and people forethought that much electricity and other resources get wasted in parties/decorations at many occasions in India. The parking related attitudes were captured by Kaiser et al. (1999a, b). Two other statements were constructed to measure the extent of the feelings of people that music systems and loudspeakers cause unnecessary noise, and their support for the ruling of not using music systems and DJ’s late night. Besides these, four of the items were also taken from the study of Tantawi et al. (2009) which were related to religion and rationality as religion can be a powerful weapon to change the world in optimistic directions and can give people a sensible divergence. Personality Measurement Sixteen personality traits of consumers namely emotionality, collectivism, persistence, determination, curiosity, orientation, demonstration, hedonism, rationality, sociability, patience, dominance, confidence, conservatism, motivation, and courage were considered to be measured in this study. Two statements were used for measuring each trait; one statement with favourable wordings and second was going in an unfavourable direction measuring the reverse. It was done in order to create a bipolar form of responses (because the purpose was to classify respondents into less-more categories according to these traits). The rationale for mixing up positivenegative statements was similar to the rationale for having a mixture of favourable and unfavourable statements on a Likert scale. In this way, thirty-two statements were used which were self-constructed. However, while constructing the items, the work of Cattell et al. (1993) on the Sixteen Personality Factor Questionnaire (16PF) was reviewed for gaining directions. 16PF as a multiple choice personality questionnaire measures certain fundamental traits of human personality. It was originally developed by Raymond B. Cattell in 1949. Since then, five versions of it have been published. The most recent fifth version was published in 1993. For the same purpose, online reports of the Institute for Personality and Ability Testing on the same were also cited (www.ipat.com). Originally, these items were created in Hindi language. In the English version of the questionnaire, Hindi to English translation was completed. Verification of the suitability was done by back translating the statements in Hindi language, and by making discussions with knowledgeable experts. Obtaining Personal Information Here, questions related to respondents’ demographic, sociological, geographic, cultural, and economic attributes were asked upon. A combination of close-ended, multiple-choice, and open-ended questions were combined as described (while defining the structure of questions). All these attributes are articulated in Table 4.7 with their categories. Questionnaire for all the measurements can be studied from the annexure. However, only English version of the Questionnaire is revealed in the annexure; if any reader wants Hindi version, the same can be availed from the authors.

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4.1.3 Sample Size and Sampling Process 4.1.3.1

Sample Size

Regarding the estimation of sample size, Hair et al. (2006: p. 318) and Zikmund and Babin (2006: pp. 422–423) provide a statistical approach (numerical formula) based on three aspects. The first aspect is the level of confidence desired in the estimate, second is the variability, and third is the degree of precision considered necessary in estimating population characteristics. Mathematically, the formula is: N = [(Z × S) ÷ E]2 where Z Standardized value that corresponds to the desired confidence level S Estimate of the standard deviation: the variability E Acceptable magnitude of error: required degree of precision. • Z: Decision of standardized value: A generally accepted confidence level 95% was regarded as recommended by Zikmund and Babin (2006: p. 420). At this confidence level, the specified value of z is 1.96. • S: Decision of sample standard deviation: The letter S, has been used in the formula as the measure of standard deviation of range. This is the measure of the heterogeneity of the population in statistical terms. According to Zikmund and Babin (2006: pp. 422–423), a rule of thumb says that the value of standard deviation should be expected to be 1/6th of the range of measurement. Aligning with this argument, as five-point measurement has been used in the study; on this five-point scale with lowest value 1 and highest value 5, the range came out to be equal to 4 (5 – 1). Accordingly, the estimated standard deviation of the range was considered equal to 1/6th of this value, calculated as 0.667 {4 × (1/6)}. • E: Acceptable Magnitude of Error: In line with Hair et al. (2006: p. 318); E, is the amount of statistical precision specified by the researcher and normally stated as a percentage. For this study, data precision to be ±4% was preferred. This value was set slightly below from the level of significance (α = 5%) exercised upon in the study. As follows, after substituting all the values in the formula, the required sample size was 1068 approximately calculated as N = [(1.96 × 0.667) ÷ 0.04] = 1068.178. Inadvertently, the decision of sample size was taken to be specifically 1000.

4.1.3.2

Sampling Procedure

This study had been designed for and restricted to North Indian people. To help address the sampling needs of the study in most efficient and effective manner,

4.1 Research Design

161

Table 4.2 Divisions of 21 districts of Haryana Ambala division

Gurgaon division

Rohtak division

Hisar division

Ambala, Panchkula, Kaithal, Kurukshetra, Yamunanagar

Palwal, Faridabad, Mahendragarh, Gurgaon, Mewat, Rewari

Karnal, Jhajjar, Rohtak, Panipat, Sonipat

Fathehbad, Bhiwani, Jind, Hisar, Sirsa

Source Statistical abstract of Haryana, 2011–2012: pp. 13-17

sampling was completed in three phases by combining few methods of sampling in suitable ways. • Phase I: In the first stage, three geographical areas were considered from North India including the State of Haryana, National Capital Territory: Delhi, and Northern Union Territory: Chandigarh. This selection was based upon non-randomness (on convenience) as these areas can be opportunely reached by the researcher. The size of the sample that is 1,000 was distributed in the selected areas in the ratio of 3:1:1 that is 600 from Haryana, and 200 each from Delhi and Chandigarh. • Phase II: Here, the selection of representative districts of Haryana was completed. For the same, the four administrative divisions of Haryana were regarded. These divisions are shown in Table 4.2, and are considered from statistical abstract of Haryana (2011–12) issued by Department of Economic and Statistical Analysis, Haryana (official website: esaharyana.gov.in). Ambala, Rohtak, and Hisar divisions comprise of five districts each, and the Gurgaon division contains six districts. It was decided to include all the districts of Ambala and Rohtak divisions from where the respondents were to be selected. However, from Gurgaon and Hisar divisions one district from each was selected. Gurgaon district was preferred from the Gurgaon division, and Hisar district from the Hisar division. Like so, the selection of districts was based on disproportionate stratified random sampling in terms of overrepresentation of Ambala and Rohtak divisions and with the underrepresentation of Gurgaon and Hisar divisions. Consequently, twelve districts were selected and the size of the sample from Haryana (N = 600) was apportioned in equal ratio in these districts that is 50 (600 ÷ 12) respondents from each selected area. • Phase III: The selection of respondents was completed from Delhi, Chandigarh, and from the main cities of each selected district of Haryana. It was well thought out that the preparation of the sampling frame was very much problematic (as no specific definition of consumer subsists in the study). Hence, it was decided that probability sampling methods were someway cumbersome to apply. Consequently, going with the directions of Trochim (2005: p. 56), purposive sampling was utilized with two of its methods in combination: The first method was proportional quota sampling, and second was snowball sampling. Trochim (2005: pp. 57–58); Krishnaswani and Ranganatham (2005: p. 140) and Malhotra and Dash (2012: p. 338) mention the advantages of sampling of this type concerning homogeneity of sample respondents in terms of important demographic and psychographic characteristics.

162 Table 4.3 Expected gender composition of the sample

4 Methodological Procedures and Techniques Gender

Population count*

Population proportion

Estimated number in sample

Male

623724248

51.5

515

Female

586469174

48.5

485

Total

1210193422

100

1000

Source *Census of India (2011)

Method I: Quota Sampling: Moving towards quota sampling, gender has been selected as a control characteristic. The decision was taken on the basis of researchers’ judgment to the fact that gender differences had been found as the most important attribute studied in literature, and had also been remained the most crucial and critical in its findings (Singh and Gupta 2013). With this decision, the composition of the Indian population was discerned as indicated by gender. The data for this composition was based upon the results of the Census of India (2011) issued by Ministry of Home Affairs, Government of India (official website: censusindia.gov.in/). Table 4.3 highlights the quota division and estimated number of male-female respondents in the study. As the total estimated sample size of the study was 1000, the table describes that according to gender, approximately 515 males and 485 females were to be included in the sample to make it proportionally equal to gender composition in the Indian population. Method II: Snowball Sampling: Snowball sampling method proved to be a fruitful idea because approaching a large number of respondents such as 1000 was much troublesome. Accordingly, a beginning was done by identifying some respondents who met the criteria of quota as decided. Then, they were asked to recommend others who could be better consulted for the study. In this way, a network was set up between the respondents, and a questionnaire survey was administered.

4.1.4 Gathering Data—Distribution and Collection of Questionnaires Keeping in mind, the limitation of non-response and improper response that usually happens in consumer studies, the survey was administered to a large number of participants than estimated. Indeed, while distributing the questionnaires two main factors were kept in mind. The first factor was related to the requirements of the size of the sample from each geographical area (Sect. 4.1.3.2: Phase I and II), and the second was in accordance with quota needs as decided according to the gender composition of the Indian population (Sect. 4.1.3.2: Phase III). On the basis of these two factors, the administration and collection of questionnaires are illustrated in the discourse below.

50

Gurgaon

Gurgaon division

200

Delhi

a depicts

less than estimated response rate in the districts

1000

200

Grand total

600

Chandigarh

50

50

Sonipat

50

Panipat

Hisara

50

Rohtak

50

Kurukshetra 50

50

Yamunanagar

Jhajjara

50

Kaithal

50

50

Panchkula

Karnal

50

Ambala

Hisar division

Rohtak division

Ambala division

Estimated sample size

Total data from Haryana

HARYANA

Geographical areas

Table 4.4 Questionnaire survey over selected geographical areas

1100

220

220

660

55

55

55

55

55

55

55

55

55

55

55

55

Questionnaires delivered

1020

202

207

611

52

44a

52

53

50

47a

51

55

53

50

52

52

Questionnaires received

92.73

91.82

94.09

92.58

94.55

80.00

94.55

96.36

90.91

85.45

92.73

100

96.36

90.91

94.55

94.55

Response rate (%)

1000

200

200

600

50

50

50

50

50

50

50

50

50

50

50

50

Final used sample

4.1 Research Design 163

164

4.1.4.1

4 Methodological Procedures and Techniques

Administration and Collection of Questionnaire (Area Wise)

Table 4.4 pinpoints both the estimated and observed questionnaire data collected from all the selected geographical areas. Leaving two districts of Haryana, the response rate in all the areas was above 90%, and in the district Kurukshetra 100% response rate came out. However, in the two districts (Jhajjar and Hisar) less than estimated response appeared. Consequently, to make the observed responses equal to estimated, three more questionnaires were distributed in the Jhajjar district, and six more were filled out from the district Hisar. As any specific category of respondents has not been prescribed in the study for inclusion and exclusion of any participant from the survey, the task of substituting respondents was not considered to be problematic. Decisively, from the received questionnaires, after sorting 50 questionnaires from each of the twelve districts of Haryana, and 200 questionnaires each from Delhi and Chandigarh; a total sample of 1000 was achieved as estimated. The procedure of sorting was based upon the process of editing (later described in Sect. 4.1.5). Questionnaires with large missing and inconsistent responses were completely discarded. But if only two or three responses were missing, the missing values were replaced with the mean values while performing the analysis.

4.1.4.2

Administration and Collection of Questionnaire (Gender Wise)

Table 4.5 administers the composition of sample respondents in terms of gender, both according to quota as was initially decided (in the form of expected count) and also the original composition of respondents in the sample (the observed frequencies). It can be seen that observed male-female composition came out as slightly different from prior anticipations. So, the Chi-square goodness of fit test was performed to see the appropriateness of the observed count in lieu of the expected one. The calculated Chisquare value of the test came out as insignificant (χ 2 = 0.577). Hence, the observed composition of sample respondents in terms of gender fits the data well and not significantly different from the estimated composition. Accordingly, the sampling needs of the study got accomplished as was described in phase III of the sampling procedure. Table 4.5 Questionnaire survey compatible with gender quota Gender

Male Female Total

Composition of gender (count)

Composition of gender (proportions)

Expected

Expected

515

Observed 527

485

473

1000

1000

51.5 48.5 100

Percentage deviation (%)

Standardized residuals

Observed 52.7

+2.33

+0.53

47.3

−2.47

−0.54

100

Test of goodness of fit χ2 = 0.577 df = 1 p = 0.467

4.1 Research Design

165

4.1.5 Preliminary Analysis 4.1.5.1

Editing

The process of editing was completed under two stages. • Field Edit: While taking back the filled questionnaires from the respondents, a quick examination was made in order to ensure that all fields were correctly filled up or not to remedy fieldwork deficiencies. • Final Edit: Here, before constructing a final data sheet, the questionnaires were checked again. It was decided to discard a complete questionnaire, if large part of responses in it seem missing. But, if only some of the responses were missing they were to be replaced with mean values. However, no serious violations were noted and only a few questionnaires were rejected in the editing process and substituted with new ones. 4.1.5.2

Coding

In this part, the edited responses were transformed in the form ready for subsequent analysis. The coding process is completed into two steps. • Transforming responses into meaningful categories: Structured questions in the questionnaire did not require categorization. However, for the purpose of analysis, open-ended and multiple-choice questions are classified into meaningful and manageable categories. This categorization is presented and described in Table 4.7 along with their numerical coding. • Assigning numerical codes: To facilitate manipulation and subsequent analysis of responses, numerical codes were assigned to the responses and quantification was done so that adequate mathematical techniques can be performed. The coding process is shown in two parts. Part I: Coding for Behavioural, Attitudinal, and Personality Measures: The first three sections (A, B and C) of the questionnaire measured behaviour, attitude, and personality features of respondents with fixed response categories. The response of each statement was coded ranging from 1 to 5. Several items were reverse scored to avoid response bias (Table 4.6). Part II: Coding for Personal Responses: Part D (personal information) of the questionnaire was a combination of open-ended and close-ended (dichotomous and multiple-choice categories) questions in which a range of demographic, sociological, cultural, geographic, and economic characteristics of respondents were enquired. Before going into the analysis, firstly open-ended and multiple-choice questions were transformed into a meaningful and manageable set of categories. These categories and their numerical coding are shown in Table 4.7.

166

4 Methodological Procedures and Techniques

Table 4.6 Rating of scale items of constructs Section A (behavioural response)

Section B (attitudinal response) and Section C (personality characteristics)

Anchor labels

Coding

Reverse coding

Anchor labels

Coding

Reverse coding

Always true

5

1

Strongly agree

5

1

Often true

4

2

Agree

4

2

sometimes true

3

3

Somewhat agree-somewhat disagree

3

3

Rarely true

2

4

Disagree

2

4

Never true

1

5

Strongly disagree

1

5

Level of measurement

Ordinal scale (single statement, one respondent)

Level of measurement

Ordinal scale (single statement, one respondent)

Interval scale (summated scale, 1000 respondents)

Interval scale (summated scale, 1000 respondents)

4.1.6 Transcription and Survey Database Preparation At this juncture, a data file was arranged to record data for all the fields of the questionnaire. The processing was completed in Statistical Package for Social Sciences (SPSS–Version 20). 4.1.6.1

Preparation of Variable View

Before entering the numeric data into SPSS, most of the options in the variable view menu of SPSS work sheet were used. The variable view menu as prepared for analysis is highlighted in Fig. 4.3. 4.1.6.2

Preparation of Data View

The data input into SPSS followed a matrix format in data view, where the variables appeared on the column heading and data for respondents were entered along with rows. In total, there were 130 variables (40 Behavioural, 38 Attitudinal, 32 Personality Variables, and 20 Variables of personal Information) and 1000 respondents. Consequently, the data set in the form of a matrix of size 1000 × 130 was acquired (Fig. 4.4). This prepared ‘survey database’ is analyzed from diverse perspectives in the further discourse on this chapter, and in Part IV of the book to arrive at the objectives. Hence, the source for all figures and tables presented in this chapter and in all other chapters is: Authors’ own analysis of survey database. However, with the further reading of the book, it will become clearer to the readers that this database is also extended for more variables which either are converted from original variables or are derived after statistical analysis in Part IV: Analyses and and Interpretations.

4.1 Research Design

167

Table 4.7 Numerical coding of questions related to personal information Variables

Categories

Coding

Measurement level

Male

1

Nominal

Female

2

15–24 (young)

1

Demographic attributes Gender1 Age2

25–40 (adult)

2

41–65 (middle and old aged)

3

School level

1

Graduation level

2

Post-graduation and higher education

3

Arts and natural sciences

1

Law and business

2

Pure sciences and technical

3

Academically poor

1

Academically fair

2

Academically good

3

Academically excellent

4

Academically superior

5

Marital status6

Married

1

Unmarried

2

Parenthood

With children

1

Without children

2

Years of marriage

1–5

1

6–15

2

16–25

3

26–44

4

Earners

1

Non-earners

2

Joint

1

Educational level3

Academic orientation4 (field of study)

Academic

intelligence5

Profession7

Ordinal

Ordinal

Nominal

Ordinal

Nominal Nominal Ordinal

Nominal

Sociological characteristics Family type8 Family size9

Family structure10 (gender wise)

Nuclear

2

2–5 (small)

1

6–10 (medium)

2

11 and above (large)

3

Females > Males

1

Females = Males

2

Nominal Ordinal

Nominal

(continued)

168

4 Methodological Procedures and Techniques

Table 4.7 (continued) Variables Family structure (age wise)

Household

support11

Categories

Coding

Females < Males

3

Mature > Young

1

Mature = Young

2

Mature < Young

3

Low support

1

High support

2

Hindus

1

Sikhs

2

Islamics

3

Others

4

Measurement level Nominal

Ordinal

Cultural characteristics Religion12

Religiosity13

Low

1

High

2

Rural

1

Urban

2

Not commute

1

Commute

2

Walking/cycling

1

Private vehicles

2

Public vehicles

3

Less than 15,000

1

15001 to 80,000

2

Greater than 80,000

3

Own houses

1

Rental houses

2

Nominal

Ordinal

Geographic characteristics Place of living14 Commuting15 Mode of commuting

Nominal Nominal Nominal

Economic characteristics Family income16

Home

ownership17

Ordinal

Nominal

˛ Superscripts numbers indicate the endnotes in which the descriptions of variables have been highlighted

4.1.7 Assessment of Reliability Reliability of any construct or scale signifies the consistency and stability in the results it produces. After the preparation of the final worksheet, the internal consistency form of reliability was measured by applying two of the methods specifically Alpha and Split-Half methods. A more reliable assessment of test items was also carried out by using statistics produced by SPSS in the form of Item-Total Statistics.

4.1 Research Design

169

Fig. 4.3 Survey database: SPSS worksheet (variable view)

Fig. 4.4 Survey database: SPSS worksheet (data view)

4.1.7.1

Alpha Method

Firstly, internal consistency was checked with coefficient alpha as shown in Table 4.8. The table is mainly divided into two halves, displaying reliability and descriptive statistics. As a rule of thumb researchers’ consider a measure to have adequate reliability if the coefficient alpha exceeds 0.70 (Leary 2004: p. 66). However, going with some other guidelines, a value of 0.60 was also deemed acceptable (Ganguly et al. 2009: p. 32). The statistical values for Cronbach alpha stated high internal consistency amongst the scale items thus implied high reliability. Table 4.8 unveils that initially there were 40 statements in behavioural construct and the value of alpha came out as 0.882 but the item-total-statistics as highlighted in Table 4.9 affirmed that two of the items (B_3 and B_11) were insignificantly correlated and thus these were removed from the scale. In this way, the final behavioural scale included 38 statements with an

170

4 Methodological Procedures and Techniques

Table 4.8 Internal consistency: alpha method Constructs

Number of items

Alpha

Scale’s descriptive statistics Mean

Variance

S. D.

Scaled

Weighted

Scaled

Weighted

Scaled

Weighted

Behavioural (B)

40

0.882

151.03

3.776

404.718

0.253

20.118

0.503

38

0.895

144.66

3.807

410.806

0.284

20.268

0.533

Attitudinal (A)

38

0.846

143.19

3.768

199.975

0.138

14.141

0.372

Personality (P)

32

0.845

110.45

3.452

187.336

0.183

13.687

0.428

improved coefficient alpha of 0.895. Meant for attitudinal and personality scales alpha values were again good (α Attitude = 0.846; α Personality = 0.845).

4.1.7.2

Item-Total-Statistics

Allied with the value of coefficient alpha, Item-Total-Statistics were also calculated. This implies the computation of Cronbach alpha in case of a need to remove any particular item from the test items to increase internal consistency. Corrected-ItemTotal-Correlation, as shown in the Table 4.9, is the Pearson correlation coefficient between the scores on the individual item and the sum of the scores on the remaining items. High correlations imply that high or low scores on one question are associated with high or low scores on others. The column Alpha if Item Deleted informs the effect on the reliability of the scale with the removal of a particular item from it. These statistics showed no need to obliterate any of the items for attitudinal and personality measures (no correlation was inconsequential and alpha values only improved marginally if any of the items get removed from the list). Hence, with no further modification, attitudinal and personality scales were used as they were formerly. However, behavioural construct has been transformed to 38 items from 40 items based on Corrected-Item-Total-Correlation.

4.1.7.3

Split-Half Method

Table 4.10 contains the outcome of reliability analysis based on the Split-Half method. The correlation between the two halves of any construct is labelled on the output as Correlation between two Forms. These are the estimates of the reliability of the scales if it has half the items. The estimates of these correlations were 0.683, 0.605, and 0.649 for the specified constructs. Statistically, these values were acceptable for the claim of adequate reliability. The equal length Spearman-Brown coefficient reports the reliability of the entire scale if it is to be constructed with two equal parts. If the number of items on each of the two parts is not equal, the unequal length of the Spearman-Brown coefficient can be used to estimate the reliability of

−0.121

0.193

0.420

-0.045

0.450

0.389

0.477

0.553

0.553

0.489

0.450

0.557

B10_ENGINE

B11_STATUS

B12_FOOD

SH_1

SH_2

SH_3

SH_4

SH_5

MW_1

ERA_1

0.427

GB_3

B9_SOLAR

0.447

0.388

EFC_4

0.380

GB_1

GB_2

0.523

EFC_3

0.877

0.878

0.878

0.876

0.877

0.878

0.879

0.878

0.888

0.879

0.883

0.879

0.879

0.879

0.880

0.877

0.889

0.322

EFC_2

B3_THROW

0.881

0.430

EFC_1

A19_PARTIES

A15_BANNED

AMW_3

AMW_2

AMW_1

CE_4

CE_2

CE_3

CE_1

OGM_3

OGM_2

OGM_1

A1_CURBING

CSF_6

CSF_5

CSF_4

CSF_3

CSF_2

CSF_1

Item identities

0.879

Attitudinal measures

Cronbach’s alpha if item deleted

Item identities

Corrected item-total correlation

Behavioural measures

0.454

0.268

0.428

0.474

0.440

0.442

0.415

0.453

0.399

0.002

0.205

0.105

0.392

0.456

0.381

0.386

0.485

0.488

0.479

Corrected item-total correlation

Table 4.9 Item-total-statistics: behavioural, attitudinal, and personality measures

0.839

0.844

0.840

0.839

0.840

0.840

0.840

0.839

0.840

0.851

0.845

0.849

0.841

0.840

0.841

0.841

0.838

0.839

0.839

Cronbach’s alpha if item deleted

P19_DOWNCAST

P18_INDIVID

P17_MOODS

P16_CHALLENGE

P15_RAELY

P14_EVERYDAY

P13_FULL

P12_CHANGE

P11_FACE

P10_RELATIVES

P9_LUCK

P8_SPARE

P7_EXPRESS

P6_WORRY

P5_WORK

P4_CIRCUM

P3_GIVES

P2_GROUP

P1_TV

Item identities

Personality measures

0.394

0.360

0.277

0.348

0.273

0.245

0.425

0.412

0.264

0.346

0.308

0.325

0.369

0.308

0.365

0.429

0.426

0.386

0.257

Corrected item-total correlation

(continued)

0.839

0.840

0.843

0.841

0.843

0.844

0.839

0.840

0.843

0.841

0.842

0.841

0.840

0.842

0.840

0.839

0.839

0.840

0.843

Cronbach’s alpha if item deleted

4.1 Research Design 171

0.215

0.462

0.506

0.478

0.309

0.202

B29_PCKAGE

MW_3

B31_NEAT

ERA_2

ERA_3

B34_PARK

0.538

0.527

AD_2

SSC_3

0.476

AD_1

0.551

0.428

MW_2

SSC_2

0.488

WC_3

0.487

0.338

WC_2

0.224

0.173

B22_TAP

B36_DISTURB

0.363

WC_1

SSC_1

0.591

B20_LEAKS

0.877

0.877

0.883

0.878

0.883

0.881

0.878

0.878

0.878

0.883

0.877

0.878

0.879

0.878

0.881

0.884

0.880

A33_RELPROC

A32_RELIINS

A31_HINDU

A30_TRAFFIC

CI_3

CI_2

CI_1

NR_3

NR_2

NR_1

ET_2

ET_1

SM_2

SM_1

CN_2

CN_1

RP_2

RP_1

Item identities

0.877

Attitudinal measures

Corrected item-total correlation

Item identities

Cronbach’s alpha if item deleted

Behavioural measures

Table 4.9 (continued)

0.271

0.145

0.141

0.349

0.412

0.425

0.413

0.375

0.447

0.527

0.412

0.138

0.214

0.176

0.437

0.455

0.342

0.152

Corrected item-total correlation

0.844

0.847

0.848

0.842

0.840

0.840

0.840

0.841

0.840

0.838

0.840

0.848

0.845

0.846

0.839

0.839

0.842

0.847

Cronbach’s alpha if item deleted

P32_PEACE

P31_COMFORT

P30_DISLIKE

P29_LOSE

P28_ORDERS

P27_SMALL

P26_TURST

P25_BADLUCK

P24_BUSY

P23_HARDLY

P22_LIVING

P21_NEWFACTS

P20_SHIRK

Item identities

Personality measures

0.164

0.439

0.501

0.496

0.414

0.440

0.291

0.360

0.310

0.437

0.218

0.409

0.356

Corrected item-total correlation

(continued)

0.846

0.838

0.836

0.836

0.839

0.838

0.843

0.840

0.842

0.838

0.845

0.839

0.840

Cronbach’s alpha if item deleted

172 4 Methodological Procedures and Techniques

0.277

0.203

RI_2

RI_3

0.882

0.881

0.073

Corrected item-total correlation 0.850

Cronbach’s alpha if item deleted

Item identities

Personality measures Corrected item-total correlation

˛ Item identities reveal the short abbreviations as used in SPSS Variable View; the full statements can be read out from Annexure

0.252

RI_1

A35_DIFFICULT

Item identities

0.882

Attitudinal measures

Cronbach’s alpha if item deleted

Item identities

Corrected item-total correlation

Behavioural measures

Table 4.9 (continued) Cronbach’s alpha if item deleted

4.1 Research Design 173

174

4 Methodological Procedures and Techniques

Table 4.10 Split-half reliability of all measures Particulars Cronbach alpha

Behavioural

Attitudinal

Personality

Part 1

Part 2

Part 1

Part 2

Part 1

Part 2

α-value

0.848

0.802

0.797

0.713

0.743

0.752

N of items

19

19

19

19

16

16

Correlation between two forms

0.683

Spearman-brown coefficient

Equal length

0.812

0.754

0.787

Unequal length

0.812

0.754

0.787

0.807

0.754

0.787

Guttman split-half coefficient

0.605

0.649

the overall scale. Since the two parts of all the scales were of equal length, the two coefficients were identical. The Guttman split-half coefficient is another estimate of the reliability of the overall scale. It does not assume that the two parts are equally reliable or have the same variance; hence, the reliability coefficient produced remains somewhat smaller. In the present case, this coefficient was the same as the Spearman-Brown coefficient for attitudinal and personality measures. Finally, separate values of Cronbach alpha were also discovered for each of the two divisions of the scales in the upper part of the table. All the values were statistically up to the standard. After these analyses, the major requirements of checking of data were complete, and thus the task was put forward for analyzing it.

4.1.8 Plan for Analyses Techniques of analysis are exploited keeping in view the objectives. These objectives are worked upon in Chaps. 5, 6, 7, and 8. The utilization of techniques as per objectives can be studied as under shown in Table 4.11. It is important to note that allied statistics that is customary to be used in any technique are explained in the chapters of analysis not here. From the above, it can be noted that combinations of Univariate (one variable involved), Bivariate (involvement of two variables), and Multivariate (more than two variables involved) techniques were applied depending upon the objectives. Most of the statistical tests were utilized by clicking the Analyse option in the Menu Bar on the top of the variable view window of SPSS work sheet. Two commands transform and graphs were also utilized where necessary. Results of structural equation modeling were obtained with the help of statistical software Analysis Movement of Structures (AMOS) Version 20. Microsoft Excel (2007) was also utilized for some calculations and for making charts and graphs. For certain calculations, online calculators are also utilized (described in further chapters where utilized). Figure 4.5 is apparent with all the tools and techniques as utilized for data analysis based on the nature of data. The description of these techniques is as follows:

4.1 Research Design

175

Table 4.11 Research objectives and allied techniques of analysis Research objectives

Chapter no.

Analysis techniques

Objective 1

Chap. 5

Mean, Standard Deviation, Pearson Product Moment Correlation, t-test for significance of Correlations, Principal Component Analysis (PCA), Confirmatory Factor Analysis (CFA)

Objectives 2 and 3

Chap. 6

Mean, Standard Deviation, Coefficient of Variation, z-test for differences of Means, Pearson Product Moment Correlation, t-test for significance of Correlations, Path Analysis with Direct and Indirect Effects

Objectives 4 and 5

Chap. 7

Cluster Analysis, ANOVA with Scheffe Post Hoc Test, Multiple Discriminant Analysis, Percentage/Proportion

Objective 6

Chap. 8

Two-Way Cross Tabulation, Chi-Square, Cramer’s V, Frequency/Count, Percentage/Proportion, z-test for difference between two proportions

• Simple Mean: It is applied for summarizing the relevant measure of a construct into a single scale and for comparing any two constructs. This mean has also been used for comparing the scores of respondents in one cluster from another. • Standard Deviation and Coefficient of Variation: By using standard deviation and coefficient of variation, statistical dispersion is calculated along with simple mean. • Frequency/Count: While cross tabulating the segmentation division of sample consumers according to their preferred attributes, frequencies are calculated. • Percentages/Proportions: In cross-tabulation, in addition to frequency, percentages have been utilized to investigate the proportionate share of respondents in terms of a range of their characteristics. Also, the calculation of percentages aided in estimating segment membership of respondents. • Two-Way Cross-Tabulation: Contingency tables are prepared by combining two categorical variables. • Chi-square: Where the cross-tabulations are involved featuring two variables, this test is applied to see whether they significantly associate with each other or not. • Cramer’s V: Chi-square test looks at the statistical significance but does not tell the strength of association. So, for this purpose, Cramer’s V is utilized. • Z-Test: Z-test has been applied in its three forms. Typical null hypothesis in this test maintains the assumption of no significant difference in the specified mean values. Z-Test for Testing of Population Mean: To compare the computed mean values of each construct with its test value.

One Way ANOVA

Z-Test of difference between two Means and Proportions

Two or More Samples

Chi-Square

Percentages/ Proportions

Frequencies/ Count

One Sample

Non Metric Data

Fig. 4.5 Summary of statistical techniques applied

Z-Test for Testing of population Mean

Coefficient of Variation

Standard Deviation

Simple Mean

One Sample

Metric Data

Univariate Techniques

One-Way Analysis of Variance (ANOVA)

Metric as well as non-Metric Data

Cramer’s V (Measure of Association)

Chi-Square as a test of Independence or No Association

Cross Tabulation (Two Way)

Product Moment Correlation t-test for testing of significance of correlation coefficient

Non Metric Data

Metric Data

Bivariate Techniques

Data Analysis Tools Used

Principal Component Analysis

Variable Interdependence

Cluster Analysis

Interobject Similarity

Interdependence Techniques

⇒Path Analysis

⇒Confirmatory Factor Analysis

Structural Equation Modeling

Multiple Discriminant Analysis

More Than One Dependent Variable

Dependence Techniques

Multivariate Techniques

176 4 Methodological Procedures and Techniques

4.1 Research Design

177

Z-Test for difference between Two Means: With the help of this test, the differences between the mean values of behavioural and attitudinal components have been examined. Z-Test for Difference between Two Proportions: While combining various features of segments of consumers, inferential statistics in the form of z-test for difference between two sample proportions was utilized. • One Way Analysis of Variance (ANOVA): For finding out differences in mean values of more than two groups, the ANOVA technique is preferred. To compare the group means amongst the identified segments, it is employed. The technique is combined with the Scheffe Post hoc test for examining the differences in paired mean values. The null hypothesis here assumes that all the group means are equal. • Pearson Product Moment Correlation: Product moment correlation is exercised upon to summarize the strength of the relationship between two metric variables namely attitudinal components and behavioural dimensions. • t-test: The significance of correlations is tested with a t-test for testing the significance of the bivariate correlation coefficient. • Principal Component Analysis (PCA): By using PCA, dimensions underlined attitudinal, behavioural, and personality constructs are derived. For reducing the variables, numerical scores are calculated for each dimension in the form of summated scales and substituted for original variables. • Cluster analysis: The technique of cluster analysis is employed to classify respondents into groups. • Multiple Discriminant Analysis: Discriminant analysis is utilized to examine whether any significant difference exists between consumer clusters obtained in terms of attitudinal and behavioural variables, and for evaluating the accuracy of the classification of respondents into identified clusters. • Structural Equation Modeling (SEM): To test the C-A-C-B model as developed in the study, this technique for the estimation of a series of dependence relationships amongst a set of constructs has been applied in two forms: Confirmatory factor analysis (measurement model) and path analysis (structural model). Confirmatory Factor Analysis (CFA): It involves the allocation of relevant measured items under each construct to each of its latent constructs identified by principal component analysis in the study. Path Analysis: Path analysis is attained as a special case of SEM only with the structural model but no measurement model. This analysis prescribes the relationship between the constructs of C-A-C-B model, and thus investigates the process of responsible behaviour formation.

178

4 Methodological Procedures and Techniques

4.2 Sample Characteristics 4.2.1 Classification Based on Demographic Characteristics Table 4.12 describes the demographic characteristics of sample respondents. The majority of respondents were male (N = 527; % = 52.7) and aged between 25 to 40 years (N = 455; % = 45.5). Educational qualifications suggested that highly Table 4.12 Demographic classification of sample respondents Attributes

Categories

Frequency

Percentage

Gender

Male

527

52.7

Female

473

47.3

Young (15–24)

417

41.7

Age

Education

Academic orientation

Adults (25–40)

455

45.5

Middle and old age (41–65)

128

12.8

Schooling (10th or 12th)

261

26.1

Graduates

344

34.4

Postgraduates

298

29.8

Higher education

97

9.7

Law and business

270

27.0

Arts and natural sciences

343

34.3

Science and technical

320

32.0

Missing Academic intelligence

67

6.7

Academically poor

123

12.3

Academically fair

228

22.8

Academically good

268

26.8

Academically excellent

192

19.2

Academically superior

189

18.9

Unmarried

541

54.1

Married

459

45.9

Parenthood

Parents

379

82.6

80

17.4

Years of marriage

1–5

204

44.4

6–15

130

28.3

16–25

82

17.9

26–44

43

9.4

Earning

619

61.9

Non-earning

381

38.1

Marital status

Not parents

Profession

4.2 Sample Characteristics

179

educated individuals were least in proportion to be exact 9.7% as compared to respondents who were middling in education (%Graduates = 34.4). The majority also belonged to Arts and Natural Sciences subject field (N = 343). Also, a major proportion of the sample was unmarried (% = 54.1). Amongst married, mainstream were found newly married and parents. Maximum people were obtained as earners (N = 619) and the rest of the sample was composed of students and female house-makers which were taken into one group of non-earning respondents.

4.2.2 Classification Based on Sociological Characteristics The sociological characteristics of respondents are depicted in Table 4.13. A major part of respondents resided in nuclear (% = 51.4) and small-sized families (% = 60.0). As given by family composition, 27.8% of families have more number of females than males. On the other hand, 35.3% of families were with the same number of males and females while 36.9% of families have more males in comparison with females. Also, respondents who belong to the families in which mature members were more in number than young, were in a high proportion (% = 73.2). As per variable family support, a high proportion of respondents feel that they get high support from their families in purchasing and other decisions (N = 551; % = 55.1). Table 4.13 Classification based on Sociological Attributes Attributes

Categories

Frequencies

Percentage

Family type

Joint

486

48.6

Nuclear

514

51.4

Small sized (2–5)

600

60.0

Medium sized (6–10)

335

33.5

65

6.5

Females > Males

278

27.8

Females = Males

353

35.3

Females < Males

369

36.9

Family size

Large sized (11 and Above) Composition of family (gender wise)

Composition of family (age wise)

Household support

Mature > Young

99

9.9

Mature = Young

169

16.9

Mature < Young

732

73.2

Low

449

44.9

High

551

55.1

180

4 Methodological Procedures and Techniques

Table 4.14 Classification based on cultural features

Attributes

Categories

Frequency

Percentage

Religion

Hindus

889

88.9

Sikhs

72

7.2

Muslims

17

1.7

Jainis

8

0.8

Buddhists

7

0.7

Christians Religiosity

7

0.7

Low

591

59.1

High

409

40.9

Table 4.15 Geographic classification of sample respondents Attributes

Categories

Frequency

Percentage

Place of living

Rural

321

32.1

Urban

679

67.9

Commuting

Yes

946

94.6

54

5.4

Type of commuting

Walking/Footers

135

14.3

Cycling/Cyclers

83

8.8

384

40.6

No

Own (N = 444; % = 46.9)

Two Wheeler Cars

60

6.3

Pubic (N = 284; % = 30.0)

Bus

189

20.0

Train

74

7.8

Others

21

2.2

4.2.3 Classification Based on Cultural Characteristics The composition of sample respondents according to two cultural attributes is evident from Table 4.14. An extremely high proportion of respondents was Hindus (N = 889; % = 88.9). This sample characteristic aligns with the composition of the Indian population because affiliation to Hinduism is the key religious characteristic of Indian people. Also, most of the people were showing low religious strength. This implies that the greater part of the people is of the nature that the religion to which they profess has little effect on their decisions and they are not much influenced by it.

4.2.4 Classification Based on Geographic Characteristics In Table 4.15, as stated by a variable place of living, 67.9% of respondents lived in urban areas and 32.1% belonged to rural places. The majority, 94.6% stated that they

4.2 Sample Characteristics

181

Table 4.16 Classification based on economic attributes Attributes

Categories

Frequency

Percentage

Family income

Less than 15,000 (low class)

220

22.0

15,001–50,000 (middle class)

548

54.8

50,001–80,000

136

13.6

96

9.6

Living in own houses

764

76.4

Living in rental dwellings

236

23.6

(High class)

Greater than 80,000 Home ownership

commute from one place to another for their education or job purposes; while only 5.4% defined that they do not commute on a regular basis. For the commuters, a high percentage share namely 46.9% revealed that they employ their own vehicle, specifically two-wheelers (N = 384; % = 40.59). Amongst those who were commuters of public transportation (N = 284; % = 30.0), a major proportion of sample commute by buses (N = 189; % = 20.0). The rest of the commuters can be categorized as footers (N = 135; % = 14.3), and cyclers (N = 83; % = 8.8).

4.2.5 Classification According to Economic Variables Classification of respondents according to economic variables is presented in Table 4.16. Family income has four categories. The highest percentage of respondents appeared in the category with earnings 15,001 to 50,000; thus, can be said average in economic status. Another variable home ownership specified that the percentage of respondents in the group who live in rental dwellings (% = 23.6) is less than those people who live in their own houses (% = 76.4). Thus, the basic requirement of shelter was found to be fulfilled for the major part of the sample. Reading up to this point, it could be understood that the next part Analyses and Interpretations is based on a range of methodological procedures and statistical techniques. Correspondingly, this chapter becomes the basis for futher organization of this book. EndNotes 1 2

Gender is a natural occurring phenomenon and in the study implies a biological state: male and female. Age of respondents’ ranges between 15 to 65. Three categories of this variable were prepared. Respondents ageing between 15 to 24 fall in the category of young group. Adult group was considered as a group of people aged between 25 to 40 years. People ageing above 40 came under old aged group.

182

3

4 Methodological Procedures and Techniques

Three categories of education were considered. In the first category, the respondents having secondary education were kept. Third category was an accumulation of highly educated persons. Also, the middle category could be defined as the category of moderately educated respondents. 4 Academic orientation denoted the field of study. Respondents enjoyed an open choice in the questionnaire, and for the analysis, the similar study disciplines were merged into one category. In this way, three classifications were accessible (Table 4.7). 5 Academic intelligence represented an individual’s academic achievements and was assessed with the students’ first division (above 60%) in the levels of his/her education. Respondents’ having first division in any one level was taken as academically fair, in two levels as academically good, in three levels as academically excellent, and in four or upper levels he or she was termed as academically superior. However, no response of first division in any of the educational level gave the impression of poor academic records. 6 In combination, marital status, parenthood and years of marriage were the replacement for variable family life cycle stage, analyzed in literature. If the respondent stated that they were married, another two questions were asked: years of marriage, and about parenthood. 7 While coding the variable profession, school teachers, college lecturers, government employees, private sector employees, business owners were captured in the earning class, and remaining were the students and female house-makers: the non-earners. 8 Regarding type of family, respondents were simply asked whether they live in joint or nuclear families. 9 Number of persons in the household makes up the family size. The cataloguing was finished by taking up to 5 member families as small, 6–10 members in medium sized families, and above 10 members in large families. 10 Family structure referred to the composition of family. It had been viewed from two perspectives: first, as per gender composition, families were observed where male-female numbers were equal, less than or greater than males. Second, similar classification was considered for age wise composition. Members ageing 40 and below was taken in the young category, and members aged 41 and above was grouped into the set of mature category. 11 The data for household support was acquired interval from the questionnaire by utilizing two statements for its measurement (Shown in Annexure). Family support implied the extent of feelings of respondents about the support and cooperation of their families they experience in their decisions. To analyse the same, the scores on this variable was divided into two halves from median score (M = 4.5). Respondents scoring less than median were taken into group of people who feel less supported from the side of their families and those scoring equal and high were taken in the group who get enhanced family support. 12 Religion was asked from respondents by offering an open choice. Subsequently, while coding data, it was classified in four categories. Denominations Hindus,

4.2 Sample Characteristics

13

14 15

16

17

183

Sikhs and Muslims were taken as separate categories. However denominations: Buddhists, Jainis, Christian were described under one roof named other religions. Religiosity is the extent to which an individual is committed to the religion he or she possess, and the faith in the teachings of such religion. The variable in its original form was interval scaled, measured from two statements (shown in Annexure), but classified into two categories form median split (M = 3). Similar to variable family support, respondents who scored below this value were merged into first category (low religiosity), while the respondents who scored equal to or above median score were combined into the group of people having high religiosity. Place of living simply pointed towards respondents’ rural or urban inhabitants. Basically, under variable commuting there were two broad classifications: people might be commuters or non-commuters. The commuters were further asked for their mode of commuting. Then three classifications were accomplished: footers-cyclers (Walk-Cycle), private vehicle commuters, and public vehicle commuters. To determine economic status, family income was enquired upon. Low income category included individuals with monthly family income below Rs. 15,000. Middle class responded to income category between Rs. 15,000 to Rs. 80,000, and those having monthly family income above Rs. 80,000 were regarded in high income class. For asking about home ownership, respondents were requested to mention whether they had their own houses or live in rental dwellings; hence, two classifications were depicted in Table 4.7.

Websites http://www.ipat.com/Pages/homepage.aspx

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Part IV

Analyses and Interpretations

Part IV is about attainment of research objectives. Chapter 5: Exploration and Validation of Behavioural-Attitudinal Dimensions in it explores and validates behavioural and attitudinal components which form the basis of testing of research model in Chap. 6: Model Specification and Theory Testing. Further, the dimensions are utilized to segment consumers in Chap. 7: Segmentation of Consumers and Identification of Responsibles. Afterwards, these segments are profiled in Chap. 8: Characterizing and Profiling Responsible Consumer Segments.

Chapter 5

Exploration and Validation of Behavioural–Attitudinal Dimensions

In response to objective 1, this chapter explores the dimensions of diverse domains of behavioural and attitudinal constructs which have already been specified in Chap. 3. The task of exploration is completed with identification of components using principal component analysis (PCA). Afterwards, measurement model of structural equation modeling (SEM) i.e. confirmatory factor analysis (CFA) is exercised to validate the explored dimensions. The preparation of ‘survey database’ was highlighted in Chap. 4 (Sect. 4.1.6), which here becomes the basis of empirical analysis. All the tables and figures in this chapter are the resultant of statistical analysis on this database. A broad discussion about the techniques of principal component analysis and confirmatory factor analysis is beyond the scope of this book. However, certain works are referred for guidance. PCA is proceeded upon with the guidelines of Hair et al. 2006, Churchill et al. 2010, and Malhotra and Dash 2012. Confirmatory factor analysis is exercised by following those authors who had worked on CFA or SEM including Bentler and Bonett 1980, Tanaka and Huba 1985, Mulaik et al. 1989, MacCallum 1990, Hu and Bentler 1999, Schumacker and Lomax 2004, Hair et al. 2006, Hancock and Mueller 2006, Hooper et al. 2008, Byrne 2010, Gatignton 2010, Malhotra and Dash 2012, and Singh and Gupta 2014/15. While organising this chapter, two main sections (5.1 and 5.2) are presented. Sect. 5.1 is for behavioural domains, and sect. 5.2 envisages attitudinal domains.

5.1 Domains of Behavioural Construct: Responsible Consumption Behaviour (RCB) Chapter 3 has already been articulated that behavioural construct instigates five domains, analyses upon which are presented as follows.

© Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_5

189

190

5 Exploration and Validation of Behavioural–Attitudinal Dimensions

5.1.1 Responsible Purchasing Domain To analyse the responsible purchasing behaviour; firstly, descriptive statistics are calculated. The corresponding mean and standard deviation values for each variable are amenable from Table 5.1 The two items which reflect consumers’ buying ¯ = 4.29; X ¯ = 4.02). All of energy-efficient products attained high mean values (X other mean values are well above the average of 3: as the measurement is done on a five-point scale. Variables V2 and V4 each attained alike and least mean val¯ = 3.46). This suggests people moderate level of ues amongst all other values (X behaviour of refusal for products which harm the environment. These mean values are somewhat low than other mean values may be because people are less aware and uncertain about the products which may harm the environment or may have the least knowledge about the other safest alternatives. The inter-item correlations are also depicted in the right side of the table under column labelled correlation matrix. The statistical values of correlations illustrate that all the statements are significantly correlated with each other (p < 0.001). Item V1 is strongly associated with statement V2 and V3. In comparison, the degree of its correlation with V4 is bit low. The relationships of this variable (V1) with other statements are also significant. Variables V2 ↔ V3 and variables V3 ↔ V4 are also strongly associated with each other (r = 0.425; r = 0.414). V5 and V6 are too significantly correlated. In this way, co-relational analysis suggests that these seven variables might be reduced to two components. It can be noted that variables 1–4 seem to go together as with variables 5–7. The lower part of the table shows the statistical values of two necessary conditions for applying principal component analysis that are Bartlett’s Test of Sphericity (BTS statistics) and KMO measure of sampling adequacy. The BTS statistics is significant (χ2 = 1266.944; p < 0.000) depicting that the variables of purchasing domain are significantly correlated with each other which has already been analysed by correlational analysis. The KMO measure of sampling adequacy (KMO = 0.790) is also well above the suggested value of 0.5 (Hair et al. 2006: p. 122; Churchill et al. 2010: p. 518) which gives additional confidence to go ahead with interpreting the results. Figure 5.1 shows a scree plot which produces a graphical representation of the number of components on the x-axis and corresponding Eigenvalues (EV) on the yaxis. As seven variables are furnished into the principal component analysis as input, the x-axis describes seven components which explain a total of 100% variance. But as a general rule for retaining the components, the scree test provides the first two components with Eigenvalues greater than one. The scree is visible with a small black square on the figure. It can be further noted that the first Eigenvalue is between 2.5 and 3 (EV = 2.722) and the second is between 1.0 and 1.5 (EV = 1.073). After that, all Eigenvalues are less than one and the scree line is tending to become horizontal to the x-axis. In this sense, the variation explained by the first component is 2.722/7 = 38.88 %, and the second component explains 1.073/7 = 15.33 % of variations, respectively. Taken together these components account for 54.21% of variance. This matches with

3.60

3.46

3.84

3.46

3.64

4.02

4.29

V1

V2

V3

V4

V5

V6

V7

0.697 0.425b 0.315b 0.162b 0.199b 0.149b

0.475b 0.332b 0.262b 0.209b 0.166b

KMO = 0.790

0.129b

0.229b

0.244b

0.777

V4

Approx. Chi-Square = 1266.944; df = 21; p = 0.000

0.278b

0.257b

0.370b

0.414b

0.605

V3

0.182b

0.225b

0.828

V5

0.323b

0.831

V6

b Correlations

left triangular matrix denotes Pearson Bivariate Correlations and the diagonal elements are the Measures of Sampling Adequacy (MSA) significant at 0.001 significance level Statements representing variables V1–V7 are presented in Table 5.2

a Lower

Bartlett’s Test of Sphericity

V2

0.492b

0.654

V1

Correlation matrixa

Kaiser-Meyer-Olkin measure of sampling adequacy

1.152

1.117

1.198

1.181

1.210

1.212

1.170

Descriptive statistics ¯ X S.D.

Variables

Table 5.1 Responsible purchasing domain: descriptive statistics and bivariate correlations

0.852

V7

5.1 Domains of Behavioural Construct … 191

192

5 Exploration and Validation of Behavioural–Attitudinal Dimensions

Fig. 5.1 Responsible purchasing domain: scree plot for number of components

♦ ♦

Values in rectangular shapes connote Initial Eigen values: Before Varimax Rotation. Bold Faced Values are percentage of explained variance: Before Varimax Rotation.

the previous prediction in the co-relational analysis as the seven variables are now reduced to two components. In Table 5.2, all essential output of Principal Component Analysis is revealed. The Eigenvalues as shown in the first column of the table are obtained after rotation, thus these are different from previous Eigenvalues evident from Fig. 5.1. The total variation explained by the two components is the same, however, the variance explained by individual components has changed as rotation occurs. It can be seen that these components together explain approximate 54.206% of variance. After rotation, the first component accounts for 32.298% (2.261/7) of variance and the second component 21.908% (1.1534/7) of variance. The total variance explained is well above the suggested value of 50% for a robust solution as given by Churchill et al. (2010: p. 524) in case of exploratory studies. All the loadings are very high with the exception of only one variable GB1 (λ = 0.441) but this is also well above 0.3, a strand suggested for a larger sample by Hair et al. (2006: p. 112). The column V.E. represents the variance explained by each statement which is square of loading. As the loading of GB3 is extremely high (λ = 0.800), the variance explained is also high (V.E. = 64%). The same is the case with other variables. To obtain reliability and validity of these components, internal consistency (Cronbach alpha), and Average Variance Explained (AVE) measures are employed. The alpha coefficients for the components suggest internal consistency amongst the items within a component. The average variance explained for the first component is 0.526 and for the second component, it is 0.460. AVE with a value of 0.5 or above is acceptable (Hair et al., 2006: p. 636; Cleveland et al., 2012: p. 299). For the first component it is above the limit but in the second case, slightly misses 0.50 guideline. These values of AVE and the high component loadings of the variables imply the convergent validity of the obtained components. As the components are varimax rotated, the high loadings of variables in one component and low in other tend to rely on the discriminant validity. According to the loadings of variables on the components, these two components are named Eco-Friendly Choice (EFC) and Green Buying (GB). Statements in the first component reflect the selection or choice of the product before actual buying takes place, and the second component is directly

5.1 Domains of Behavioural Construct …

193

Table 5.2 Responsible purchasing domain: results of principal component analysis Components

Variables

Statements

λ

V.E.

α

AVE

Component 1 Eco-Friendly Choice Eigenvalue = 2.261; % of variance = 32.298 Cumulative variance = 32.298%

V1/EFC1

I usually make a special effort not to buy products unsafe to environment

0.783

0.613

0.735

0.526

V2/EFC2

I generally switch from brands causing environmental damage

0.763

0.580

V3/EFC3

When I have a choice between two equal products, I purchase the one I believe is not/less harmful to environment

0.712

0.507

V4/EFC4

To reduce packaging waste, I usually refuse products with unnecessary packaging or plastic covering

0.634

0.402

V5/GB1

I prefer buying products in refillable containers to minimize packaging waste

0.441

0.194

0.490

0.460

V6/GB2

I buy energy efficient household appliance/products despite high price

0.743

0.552

V7/GB3

Even if they are more expensive, I have installed CFL or other energy efficient bulbs in my house to save energy

0.800

0.640

Component 2 Green Guying Eigenvalue = 1.534; % of variance = 21.908 Cumulative variance = 54.206%

λ stands for component loadings and V.E. means variance explained α is Cronbach Alpha: measure of internal consistency AVE represents Average Variance Explained: measure of convergent validity

related to buying. Thus, the names given to these components also reflect nomological validity for them. These three types of validity measures provide a very good indication of the construct validity for the components. Thus, the two components (EFC and GB) are kept here. Before interpreting the components, it is important to identify the component model fit. Confirmatory factor analysis is a technique used for the same. It is applied here to access the reliability of the whole model provided

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♦

Standardized Component Loadings are significant at p < 0.001.

Fig. 5.2 Responsible purchasing domain: measurement model for two dimensional component structure

by principal component analysis, devising that responsible purchasing behaviour of consumers’ works under two dimensions EFC and GB. A two-stage CFA model is shown in Fig. 5.2. As customary, observed variables are shown in rectangles and unobserved (latent) components are represented in ovals. The model is a two-stage confirmatory model in which EFC and GB are the first layer latent components, and RPB is the second layer unobserved construct. Under the label variable count, it is shown in the Fig. 5.2 that there are a total of 19 variables in the model in which seven are observed and twelve are unobserved. From dependence point of view, there are nine endogenous variables of which seven are directly observed and the other two are latent components (EFC; GB) which when joined to RPB (second layer latent construct) becomes endogenous. All endogenous variables (EFC1 to EFC4; GB1 to GB3; EFC and GB) are associated only with its assumed latent construct (there are no cross loadings). It is obvious to have some of the measurement error which also associates with each measurement. The same is described by indicating the small circles from e1 to e9. Beneath, the title of model notes, the number of distinct sample moments are calculated using the formula [p(p + 1)/2] in which p is the number of observed variables which are seven. Hence, sample moments comes out to be 28 [7(7 + 1)/2]. A total of 15 parameters are to be estimated. Thus, the degrees of freedom attained for the model is 13 (28 – 15). The statistical values above the arrows represent the standardized component loadings and those above rectangles and ovals are the squared multiple correlations (square of loadings). The model simply shows that the first level latent construct EcoFriendly Choice is associated with four variables and the second construct Green Buying is related with three variables. These latent constructs further become endogenous as both depend upon a further latent construct Responsible Purchasing Behaviour

5.1 Domains of Behavioural Construct …

195

(RPB). All the standardized component loadings are showing that each latent construct is significantly related to its’ observed variables and able to explain a sufficient amount of variance in them. The confirmatory loadings once again affirm the convergent validity as with the exception of only one component loading, all the loadings are greater than 0.50: a suggested cut off (Ganguly et al. 2009: p. 15). However, this low loading (0.46) is also acceptable for convergent validity because a criterion of component loadings 0.30 or above is also given by Zakuan et al. (2010: p. 164). They regarded 0.30 as the acceptable level of loading for a claim of convergent validity, and mentioned that loadings below this value show absence of convergent validity. Now, the decision rests for the fit to this estimated model. The discourse below works in this direction. There is a huge discussion for deciding the fit of any estimated measurement model comparing it with saturated model and independence model. Various statistics for measures of absolute fit, incremental fit, and parsimonious fit have been developed overtime; but, no single statistics under these categories can best describe the strength of model’s fit. So, as a basis of deciding model fit in this book Singh and Gupta (2014/15) is preferred, owing to their work on synthesizing the statistics on fit estimation in structural equation modeling. The fit of the model is evaluated based on Absolute fit measures and Incremental fit measures. Parsimonious fit measures are not described as they are specifically invented only for inter-model comparisons and not appropriate for evaluating the fit of any single model (Hair et al., 2006: p. 636; Malhotra and Dash, 2012: p. 701). In this way, Parsimonious fit measures are not required as the inter-model comparison is not the purpose here. The Chi-square value as shown in Fig. 5.2 (χ2 = 95.440; df = 13; p < 0.000) rejects the null hypothesis of a good fit model because the significant Chi-square value shows that estimated covariance matrix differs from observed covariance matrix. But the Chi-square value is not a good criterion to obtain model fit as it is too sensitive to sample size. Thus, the other indices of model fit are presented in Table 5.3 which proves that this model actually fits the data quite well. Model fit statistics are displayed under three columns for three models separately. The saturated model is the one which is the ideal model with Chi-square zero (0.000), Goodness of fit measures having score 1.00 and badness of fit with values 0.000. Independence model, as the name implies, suggests independence and no correlation. So, it may be the worst possible happening for the estimated model. It can be observed that goodness of fit measures are very low under the column representing the Independence model and the badness of fit values are very high. The statistical values of incremental fit are also worst (0.000) in this model. Model fit measures for the working model is shown under the column ‘estimated model’. Absolute fit indices are further divided into two parts: goodness of fit and badness of fit. The rule says that goodness of fit measures must be very close to 1 and badness of fit indices must be near to zero for having a respectable model fit. Below the category of goodness of fit measures, both the values of GFI (0.973) and AGFI (0.942) are perfect. For the category of badness of fit, the ratio of Chisquare to its degrees of freedom (χ2 /df = 7.342) is above 5 (a cut off suggested by researchers), somewhat problematic. The value of root mean residual (RMR = 0.059)

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Table 5.3 Responsible purchasing domain: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability

Goodness of Fit Index (GFI)

0.973

1

0.654

Perfect

Adjusted Goodness of Fit Index (AGFI)

0.942

–

0.538

Perfect

CMIN/DF(χ2 /df)

7.342

–

60.523

Less acceptable

Root Mean Square Residual (RMR)

0.059

0.00

–

Marginally acceptable

Root Mean Square Error of Approximation (RMSEA)

0.080

–

0.244

Marginally acceptable

Absolute fit indices Goodness of fit

Badness of fit

Incremental fit indices Normed Fit Index (NFI)

0.925

1.000

0.000

Good

Relative Fit Index (RFI)

0.879

–

0.000

Marginal

Incremental Fit Index (IFI)

0.934

1.000

0.000

Good

Tucker-Lewis Index (TLI)

0.893

–

0.000

Marginal

Comparative Fit Index (CFI)

0.934

1.000

0.000

Good

0.864

–

Model reliability Composite Reliability (CR)

Great

is quite acceptable and index RMSEA is on the edge of rejection (0.08) but below the upper threshold of 0.1, thus marginally accepted. For Incremental fit indices or baseline comparisons, the indicators NFI (0.925), IFI (0.934), and CFI (0.934) all exceed from the suggested value of 0.9 except RFI (0.879) and TLI (0.893) but these values too can be said quite marginally accepted. The composite reliability (CR = 0.864) of the whole model is also very high from the suggested 0.70,1 a commonly used threshold value (Hair et al., 2006: p. 612). Accordingly, a combination of all 1 However

0.70 is given, but Hair et al. (2006: p. 612) have also mentioned that 0.70 is not an absolute standard and values below it is also acceptable if the research is exploratory in nature. As going with Ganguly et al. (2009: p. 32), 0.60 is also acceptable.

5.1 Domains of Behavioural Construct …

197

these indices provides evidence of a good model fit and a good reliability of the model. This highlights that the components are worth noticing and interpretable. This is accomplished next. Interpretation of Components • Eco-Friendly Choice (EFC) We, as consumers have unlimited choices and can decide what to buy and what to ask for. The component is significant and denotes consumer selection of ecofriendly products. It takes into account the question: when we are given choices, why not behave in an eco-friendly manner and ask for products not/less harmful to the environment. The items in the component show consumers’ efforts to buy environmentally safe products and their switching from products unsafe to the environment. It also captures transformation in consumers’ choices and their refusal of products with heavy packaging or plastic covering because a rational consumer can better understand that the piles of packaging waste not only sore the eyes but also harm the eco-systems. In short, the component describes a type of action of ‘responsible consumers’ in which they explore brands and shortlist amongst them for tuning up their shopping radar with the environment. • Green Buying (GB) The next component is named Green Buying. The component put forth the economize electricity efforts of consumers with the buying of energy-conserving products. Today, energy efficiency is the main agenda of policy discussions and to meet out this, the energy-efficient bulbs like CFL is one option which may save energy. The two items in this component attain consumers’ attraction and buying of some energy-saving appliances and bulbs which may become a mechanism to save energy. Also, a statement denotes buying of products in refillable/reusable containers. This kind of purchasing behaviour eliminates the need for raw material, as well as contributes to tackle with the problem of waste. Overall, the component seems to tap energy saving and waste-reducing behaviour by buying green products, so well is named ‘green buying’.

5.1.2 Responsible Usage Domain Table 5.4 describes mean and standard deviation for each variable of usage domain. ¯ = 4.41; s = 0.961) shows peoples’ high engageThe high mean for variable V3 (X ¯ = 4.18) reveals ment in electricity conservation behaviour. Next mean value (X peoples’ good habit of using both sides of the paper for their work like printing, etc. All other average values are well above 3 which denote that people do not practice these behaviours frequently or on a regular basis, but at times try to do these responsible activities in their day-today life. The lower left triangular matrix denotes the correlation matrix in the table. With exception of the correlation between V1 ↔

3.76

4.18

4.41

3.65

3.56

3.48

3.39

3.81

V1

V2

V3

V4

V5

V6

V7

V8

0.822 0.403b 0.253b 0.260b 0.173b 0.204b 0.303b

0.359b 0.279b 0.290b 0.202b 0.055 ns 0.244b

Bartlett’s Test of Sphericity

0.344b

0.236b

0.184b

0.827

V5

KMO = 0.811

0.329b

0.239b

0.344b

0.426b

0.811

V4

Approx Chi-Square = 1416.973; df = 28; Sig. 0.000

0.275b

0.132b

0.166b

0.350b

0.367b

0.802

V3

0.287b

0.252b

0.799

V6

0.370b

0.754

V7

b Correlations

left triangular matrix denotes Pearson Bivariate Correlations and the diagonal elements are the Measures of Sampling Adequacy (MSA) significant at 0.001 significance level ns Correlations not significant (p > 0.05) Statements representing variables V1–V8 are presented in Table 5.5

a Lower

V2

0.299b

0.823

V1

Correlation matrixa

Kaiser-Meyer-Olkin measure of sampling adequacy

1.169

1.387

1.356

1.198

1.252

0.961

1.081

1.276

Descriptive statistics ¯ X S.D.

Variables

Table 5.4 Responsible usage domain: descriptive statistics and bivariate correlations

0.827

V8

198 5 Exploration and Validation of Behavioural–Attitudinal Dimensions

5.1 Domains of Behavioural Construct …

199

V7 (r = 0.055), all the correlations are highly significant (p < 0.001). Two of the correlations are above 0.4 (V2 ↔ V3 = 0.403; V4 ↔ V5 = 0.426) and eight others are above 0.3 showing a moderate level of correlation. Other correlations are low but significant. BTS statistics which is also highly significant (χ2 = 1416.973; df = 28; p < 0.000) provides indication for performing principal component analysis on these variables. The statistical value of KMO test of sampling adequacy falls in a good range (KMO = 0.811), and again confirms the eligibility of performing principal component analysis on the data. The scree plot considers Eigenvalues on the y-axis and the number of components on the x-axis. As a criterion for retaining the number of components, the latent roots (Eigenvalues) and scree test both suggest a two-component solution. First Eigenvalue is 2.936 and second is 1.108 (before rotation is carried out). But the next all Eigenvalues are less than 1, thus making no practical reason to retain other numbers of components. These two components explain 50.545% of variance, however, slightly but again well above the 50% threshold. The breakup of this variance is 36.70% for the first component and 13.85% for the second. As shown in the Table 5.5, the first component explains 27.153% of variance which is calculated by dividing the Eigenvalue (2.172) by a number of variables (N = 8). Next component explains 23.392% of the variance, thus these components taken together account for 50.545% of variance. Initially (before rotation) variance explained by the first component was 36.70%; rotation transferred a part of this variation (9.55%) to the second component. So, the second component is now able to explain more variance than in the previous situation of no rotation, because it has taken the proportionate variance left by the first component (13.85% + 9.55% = 23.40%) (Fig. 5.3 and Table 5.5). Next columns present the items, item loadings, explained variance by each statement, and the reliability and validity coefficients for the components. All item loadings are high, therefore, explains a sufficient amount of variance. Component one has noteworthy loadings for five variables. All loadings are positive with the high loading of variable SH3 (λ = 0.770), thus it explains 59.3% of variance in the component. Variables SH1, SH2, and SH5 explains 52.9%, 38.9%, and 29.6% of variance, and the least variance is explained by SH4 (25%). Component 2 is highly related with three variables and is amply explained by the variables WC2 (its loading on the component is high). In the light of these loadings, the components are named Sustainable Habits (SH) and Water Conservation (WC). The measure of reliability Cronbach Alpha is quite good for the components. However, the values of AVE are not so much encouraging. In this sense, we lack in claiming a good level of construct validity. However, component names in this domain are noteworthy. This gives a direction that ‘responsible usage domain’ of consumers’ consumption behaviour is characterized by two components. To validate these results, a two-stage confirmatory component analysis is performed and the model is shown in Fig. 5.4. There are 8 observed and 13 unobserved variables totalling 21 variables. These can also be classified as exogenous (N = 11) and endogenous (N = 10) variables. The figure has two latent variables in ovals that are manifested by eight observed variables in rectangles. Next, e1, e2……e10 are the error terms associated with each observed

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Table 5.5 Responsible usage domain: results of principal component analysis Components

Variables

Statements

λ

V.E.

α

AVE

Component 1 Sustainable Habits Eigenvalue = 2.172; % of variance = 27.153% Cumulative variance = 27.153%

V1/SH1

I prefer handkerchief instead of tissue paper to reduce trash

0.727

0.529

0.703

0.411

V2/SH2

I use both sides of paper for writing or printing

0.624

0.389

V3/SH3

I conserve energy by turning off lights/fans when not in use in home work/institution

0.770

0.593

V4/SH4

Whenever possible, I walk, ride bicycle, car pool or use public transport to help in reducing air pollution

0.496

0.246

V5/SH5

I prefer my own bag while shopping than a plastic carrier provided by a shop

0.544

0.296

V6/WC1

I use minimum water while bathing, soaping, or washing

0.625

0.391

0.560

0.484

V7/WC2

After washing clothes, I use the remaining water for clearing the floor

0.797

0.635

V8/WC3

To save water, I wait until there is a full load of clothing for washing

0.652

0.425

Component 2 Water Conservation Eigenvalue = 1.871; % of variance = 23.392% Cumulative variance = 50.545%

λ Stands for Component Loadings and V.E. means Variance Explained α is Cronbach Alpha: Measure of Internal Consistency AVE represents Average Variance Explained: Measure of Convergent Validity

variable as it is impossible in practice to have error-free measurement and the essence of CFA is that it accounts for the same. Error terms are also associated with firstorder latent constructs because it is a two-stage model and these latent constructs work as observed variables for the second layer. The single-headed arrows represent the causal relationships. Values over arrows are the standardized loadings and the other values are the square of these loadings. The figure also provides the value of Chi-square statistic which is large enough to reject the hypothesis of a good fit. But as it has already been defined that being sensitive to sample size, can reject even good models, so, this sole value is not sufficient to decide about model fit. Therefore,

5.1 Domains of Behavioural Construct …

201

Fig. 5.3 Responsible usage domain: scree plot for number of components

♦ ♦

Values in rectangular shapes connote Initial Eigen values: Before Varimax Rotation. Bold Faced Values are percentage of explained variance: Before Varimax Rotation.

other important model fit indices are taken in the next table to determine the actual fit of the data to the model. As presented before in Sect. 5.1, the saturated model is the best possibility and independence model is the worst possible situation. The goodness of the estimated model depends upon both of them. The indices are divided into two sections: absolute and incremental fit. The portion of goodness of fit measures in absolute indices reveals that the value of GFI (0.967) and AGFI (0.938) both are perfect. According to the fit of badness, RMR (0.070) and RMSEA (0.078) lie in the range of marginal fit. χ2 /df is 7.108; provided indeed a very large Chi-square value on given degrees of freedom, combining with other indices it can be accepted. Incremental fit values NFI, IFI, and CFI all are above 0.9. Other two RFI and TLI are also marginal. Accordingly, the data has an adequate fit to the model. Composite reliability also states that the reliability of this model is quite high from the standard (CR = 0.791). Accordingly, after all this analysis, the interpretations are outlined next. Interpretation of Components • Sustainable Habits (SH) The way people consume/move/use is a result of both individual and collective habits which they develop over time. Unlike purchasing behaviour, everyday life actions are governed by habits and routines than by deliberate and rational choices. Human activities that are integrated into this component seem contributing little but these acts can make big differences in reality. The statement with high loading (SH3) shows the energy-saving behaviour but different from the previous component green buying. The essence is that buying only CFL or energy-efficient products is not a solution. Can a consumer be called responsible if he/she has bought CFL, installed it at a place, but left it out lighting whole day unnecessarily? Hence, this behaviour is different from purchasing and comes under ‘responsible usage domain’. Trash as a consequence of consumption is the main problem of waste generation. Previous components (EFC; GB) also indirectly tap waste reduction behaviour in purchasing domain but trash reducing behaviour with preferring handkerchief and using both sides of the paper are important in usage. The simple looking behaviours of minimizing paper use can also contribute

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Table 5.6 Responsible usage domain: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability

Goodness of Fit Index (GFI)

0.967

1.000

0.638

Perfect

Adjusted Goodness of Fit Index (AGFI)

0.938

–

0.534

Perfect

CMIN/DF (χ2 /df)

7.108

–

50.784

Root Mean Square Residual (RMR)

0.070

0.00

0.360

Marginally acceptable

Root Mean Square Error of Approximation (RMSEA)

0.078

–

0.223

Marginally acceptable

Absolute fit indices Goodness of fit

Badness of fit Less acceptable

Incremental fit indices Normed Fit Index (NFI)

0.905

1.000

0.000

Good

Relative Fit Index (RFI)

0.860

–

0.000

Marginal

Incremental Fit Index (IFI)

0.917

1.000

0.000

Good

Tucker-Lewis Index (TLI)

0.877

–

0.000

Marginal

Comparative Fit Index (CFI)

0.917

1.000

0.000

Good

0.791

–

Reliability Composite Reliability

Good

in saving the amount of trees which are cut down for the purpose. Also, one of the best things people can do is to minimize the use of their own vehicles which can help in reducing emissions and the global warming. In this sense, their move to public transportation provides their ideal action alternative for saving the planet. Reduction in air pollution is again the measurement of this component with item SH4. The item SH5 positions toward carrying own bags while shopping and show the minimum use of poly bags. If all these and similar habits develop in people in everyday routines, the planet can be made no less than heaven to live. Thus, the component is better called Sustainable Habits.

5.1 Domains of Behavioural Construct …

♦

203

Standardized Component Loadings are Significant at p < 0.001

Fig. 5.4 Responsible usage domain: measurement model for two dimensional component structure

• Water Conservation (WC) Conservation does not imply remaining distant from resources rather calls for a wise and careful use of them. There is no doubt that water is a life-sustaining resource; conservation of it means using this life-sustaining resource wisely, caring for it, and utilizing it really to the necessary levels. Today, India is suffering from the problem of safe drinking water. Wastage of water can be reduced if it is used wisely. Use of minimum water for everyday activities such as waiting for a full load of clothing for washing so that less quantity of water may be used is the measurement of the component. The statement WC2 highlights the maximum use of water as it can provide salvage value by employing the used water (such as after washing clothes) for cleaning of dirty floors, and depicts consumers’ preference for the same act. In short, it can be said that for conserving water, this component targets those behaviours for which usually an irresponsible person will never bother off; but a rational responsible being can surely sense that with small efforts also, they can make a reduction in water usage and can contribute saving life on the planet. With these behaviours, it can be said that if consumers may be engaged in these activities, most probably they can do even little things for leaving the unsustainable activities by which water is wasted.

5.1.3 Responsible Maintenance Domain The descriptive statistics in Table 5.7 reveal that the mean values for these three statements are above the average of 3 on a five-point scale showing a moderate level of engagement of people in these activities. The significant correlations display that

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Table 5.7 Responsible maintenance domain: descriptive statistics and bivariate correlations Descriptive statistics ¯ X S.D

Correlation matrixa

V1

3.79

1.185

0.632

V2

3.96

1.144

0.242b

0.626

V3

3.75

1.141

0.303b

0.310b

Variables

V1

V2

0.598

KMO = 0.617

Kaiser-Meyer-Olkin measure of sampling adequacy Bartlett’s Test of Sphericity

V3

Approx. Chi-Square = 224.156; df = 3; p = 0.000

a Lower left triangular matrix denotes Pearson Bivariate Correlations and the diagonal elements are

the Measures of Sampling Adequacy (MSA) significant at 0.001 significance level Statements representing variables V1–V8 are presented in Table 5.8

b Correlations

these are likely to form a component in component analysis. The KMO (0.617) here suggests adequacy of sample. The result of Bartlett’s test (χ2 = 224.156; df = 3; p = 0.000) rejects the hypothesis that variables are independent of each other. Hence, the assumptions are fulfilled for conducting the principal component analysis. The scree plot portrays a single Eigenvalue greater than one, thus these three variables are said to converge into a component. This one component is able to explain 52.385% of variance (1.572/3) and is again greater than the level of 50%. The Eigenvalue (EV = 1.572) given in Table 5.8 shows that the variation explained in this component is 52.385%. As only one component is produced, no rotation Table 5.8 Responsible maintenance domain: results of principal component analysis Components

Variables

Statements

λ

V.E.

α

AVE

Component 1 Minimizing Mastage Eigenvalue = 1.572; % of variance = 52.385 Cumulative variance = 52.385%

V1/MW1

I prefer reusable mugs/glasses instead of disposable cups/glasses for beverage to avoid unnecessary waste

0.703

0.494

0.544

0.524

V2/MW2

I save and reuse plastic shopping bags/poly bags so that they can be used again instead wasting them

0.709

0.503

V3/MW3

To maximize the usage, I often repair and reuse things instead of discarding and buying new ones

0.758

0.575

λ Stands for Component Loadings and V.E. means Variance Explained α is Cronbach Alpha: Measure of Internal Consistency AVE represents Average Variance Explained: measure of convergent validity

5.1 Domains of Behavioural Construct …

205

Fig. 5.5 Responsible maintenance domain: scree plot for number of components

♦ ♦

Values in rectangular shapes connote Initial Eigen values: Before Varimax Rotation. Bold Faced Values are percentage of explained variance: Before Varimax Rotation.

occurred. Hence, the two Eigenvalues are the same as displayed in Fig. 5.5 and Table 5.8. The loadings of variables on this single component are quite high. The component explains approximate 50% of the variance in two of the items (MW2; MW3). The third variable is an exception whereby variance explained is high due to its high loading (VE = 57.50%). The reliability (α = 0.544) and convergent validity (AVE = 0.524) values are also acceptable. There is no question of discriminant validity as the variables load only on a single component. Minimizing Wastage (MW ) is the best suited name to the component which stands for the aspect of Nomological Validity. The validity of this model is also confirmed by zero-order CFA in which all variables load only on a single latent construct as specified in Fig. 5.6. The loadings in the model are found significant. There are three observed variables (measured directly) and four are the unobserved (three error terms; one latent construct). These observed variables can also be classified as endogeneous because they depend upon the latent construct. The number of sample moments here is found same as number of parameters. Accordingly, the model is a just identified model

♦ Standardized Component Loadings are Significant at p < 0.000

Fig. 5.6 Responsible maintenance domain: zero order confirmatory measurement model

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5 Exploration and Validation of Behavioural–Attitudinal Dimensions

(Equivalent to Saturated Model: the ideal one). The calculated degrees of freedom are zero resulting in a zero Chi-square value. As can also be seen in Table 5.6, all the model fit indices of estimated model are identical to saturated model, therefore, ideal. The goodness of fit indices are perfectly one (1.00) and badness of fit are zero (0.00). Composite reliability of the model is also quite well (CR = 0.651). Incidentally, this one component (MW) representing responsible maintenance behaviour of consumers is worth interpretable. Interpretation of Component • Minimizing Wastage (MW ) Three statements employed in maintenance domain transformed into one component; and cumulatively named minimizing wastage. Minimizing wastage can be defined as a process of reducing the amount of waste produced by a person or a society. This component becomes significant with the notion that wastage can be minimized by maximizing the functionality of goods and extending their life to maximum utilization. In India, many goods lost their usage appeal and get a place in the garbage even before their useful life expires. Hence, in between the continuum of usage behaviour and disposal behaviour, pre-disposal responsibility establishes its part that proper maintenance and maximum utilization is required so that things may get less and less accumulated as waste. This is implied in the present component. Use of disposables has become a part of the lifestyle in India for making activities smaller and life easier. The first responsible behaviour (MW1) is significant in this domain as an action alternative for preferring re-useable mugs/glasses over disposables that minimize the waste generation. Although, today we all know that plastic bags do great harm to the environment since their inception, these are the part of human life. It is a fact that their use may not be avoided fully but can be minimized. Reusing plastic bags and use of reusable shopping bags (made of other materials) may save our landfills from converting into dirty dumpsites as a large amount of plastic papers and poly bags are rebooted there from trash holes. The repair can even also be much better, which is described by the third statement (MW3). The statement is discernible of maximizing and extending the life of a product by newly enlivening it with the same functions or maybe with entirely different functions. The component thus differentiates amongst consumers who give a nice treat to the environment and try to reduce the potential harmful effect on it (Table 5.9).

5.1.4 Responsible Disposal Domain A perusal of descriptive statistics (Table 5.10) for the items used to measure the responsible disposal behaviour of respondents shows that the mean scores fall within the range of 3.62–4.12. Hence, respondents’ obtain compatible mean scores on all

5.1 Domains of Behavioural Construct …

207

Table 5.9 Responsible maintenance domain: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability

Goodness of Fit Index (GFI)

1

1

0.859

Ideal

Adjusted Goodness of Fit Index (AGFI)

–

–

0.717

–

CMIN/DF(χ2 /df)

0.000

–

74.856

Ideal

Root Mean Square Residual (RMR)

0.000

0.000

0.271

Ideal

Root Mean Square Error of Approximation (RMSEA)

–

–

0.272

–

Absolute fit indices Goodness of fit

Badness of fit

Incremental fit indices Normed Fit Index (NFI)

1.000

1.000

0.000

Ideal

Relative Fit Index (RFI)

–

–

0.000

–

Incremental Fit Index (IFI)

1.000

1.000

0.000

Ideal

Tucker-Lewis Index (TLI)

–

–

0.000

–

Comparative Fit Index (CFI)

1.000

1.000

0.000

Ideal

0.651

–

Reliability Composite Reliability

Adequate

¯ = 4.12) reveals consumers’ prethe disposal measures. The high mean of V3 (X paredness for contributing to recycling if recycling opportunities will be provided to them. However, they are somewhat hesitant and less willing to pay for the same purpose (lowest mean = 3.62). It can be concluded by the correlation matrix that all bivariate correlations are significant (p < 0.001). Correlation between V1 and V2 is highest amongst all (r = 0.48). Other degrees of Pearson correlation are low, thus two components may be obtained which will be confirmed by Eigenvalues in the component analysis. KMO as a test of sampling adequacy and Bartlett’s Test as a test of hypothesis of no correlation between the variables specify that both the test values are exceedingly significant.

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Table 5.10 Responsible disposal domain: descriptive statistics and bivariate correlations Variables

Descriptive statistics ¯ X S.D.

Correlation matrixa V1

V2

V3

V4

V1

3.67

1.216

0.558

V2

3.86

1.202

0.480b

0.559

0.775

0.129b

0.163b

0.670

0.128b

0.283b

0.611

0.111b

0.151b

0.293b

V3

4.12

V4

3.86

0.871

0.124b

V5

3.62

1.061

0.124b

Kaiser-Meyer-Olkin measure of sampling adequacy Bartlett’s Test of Sphericity

V5

0.649

KMO = 0.593

Approx. Chi-Square = 488.980; df = 10; p = 0.000

a Lower left triangular matrix denotes Pearson Bivariate Correlations and the diagonal elements are

the Measures of Sampling Adequacy (MSA) significant at 0.001 significance level Statements representing variables V1–V7 are presented in Table 5.11

b Correlations

KMO is well above 0.5 and a highly significant Chi-square value (χ2 = 488.980; df = 10; p < 0.000) rejects the hypothesis of no correlation. Therefore, confirming that principal component analysis is a good idea for clubbing these variables into some components. In Fig. 5.7, scree on the plot is not very clear. Therefore, a criterion of Latent Roots is best employed and the square is marked on the second component. Two Eigenvalues can be seen greater than one, and after the third component all Eigenvalues become below one; accordingly, two components are preserved. These two components account for almost 60% of data variations described as 36.05 (1.803/5) + 23.42 (1.171/5). Fig. 5.7 Responsible disposal domain: scree plot for number of components

♦ ♦

Values in rectangular shapes connote Initial Eigen values: Before Varimax Rotation. Bold Faced Values are percentage of explained variance: Before Varimax Rotation.

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Table 5.11 Responsible disposal domain: results of principal component analysis Components

Variables

Statements

λ

V.E.

α

AVE

Component 1 Appropriate Disposal Eigenvalue = 1.491; % of variance = 29.811 Cumulative variance = 29.811%

V1/AD1

After a picnic, I leave the place as clean as it was originally

0.854

0.729

0.649

0.728

V2/AD2

I keep the waste with me and search for the dustbin to put them away

0.853

0.728

Component 2 Recycling Intentions Eigenvalue = 1.483; % of Variance = 29.658 Cumulative Variance = 59.469%

V3/RI1

I am prepared to take my household garbage to the nearest recycling bins if provided

0.626

0.392

0.481

0.491

V4/RI2

I am willing to sort garbage for appropriate disposal

0.787

0.619

V5/RI3

I am ready to pay more to municipalities/government for garbage collection for safe long term disposal

0.679

0.461

λ Stands for Component Loadings and V.E. means Variance Explained α is Cronbach Alpha: Measure of Internal Consistency AVE represents Average Variance Explained: Measure of Convergent Validity

The first column of Table 5.11 presents the Eigenvalues and percentage of variance explained. The total variance explained by two components are same, but the variances explained by individual components vary after rotation. In comparison with the previous 36.05%, now the first component deals with 29.81% of variance. The variance which has been left by this component (6.24%) is gained by the second and it’s explained variance now increases to 29.658%. The loadings are obtained after rotations. Component one is highly explained by both the statements that underlie it. Their component loadings are quite the same. Next three statements are significantly correlated with the second component with the high correlation of RI2. It can also be seen from the table that internal consistency for the first component is quite well (α = 0.649), however, below the standard for the second component (α = 0.481). The AVE values (AVE = 0.728; AVE = 0.491) state convergent validity. As the variables highly load on single components, it also confirms discriminant validity. According to the items in each component, their names Appropriate Disposal (AD) and Recycling Intentions (RI) are also nomologically valid. In radiance, these two components are both validated and reliable. This principal component analysis offers two dimensions of responsible disposal behaviour. The CFA diagram (Fig. 5.8) with standardized solution illustrates the two-stage model, where latent variables are in ovals and observed are in rectangles. The unique components or errors in the other terms are also associated with each endogenous variable. As it is a CFA model, there are no cross-loadings and each

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♦

Standardized Component Loadings are significant p < 0.001.

Fig. 5.8 Responsible disposal domain: measurement model for two dimensional component structure

variable is caused by only one latent construct. In total there are 15 variables (5 observed + 10 unobserved or 8 exogenous + 7 endogenous). The degrees of freedom calculated for the model is 4. The standardized loadings over the one-sided arrows represent the correlation between each observed variable and the corresponding latent component. First considering the indicators of appropriate disposal, they are 0.67 and 0.71, and considering recycling intentions, component loadings are 0.43, 0.64, and 0.43, respectively. A good deal of the variance in each observed variable is accounted for with the exception of RI1 and RI3. The correlation of latent construct (responsible disposal behaviour) is exceedingly high with recycling intentions but only moderate with appropriate disposal. This shows that the component recycling intentions is more robust and significant in the disposal domain as compared to the component appropriate disposal. The Chi-square value for this model (χ2 = 9.555) is significant on the given degrees of freedom but not highly significant (p < 0.05). The model fit will further be confirmed by other fit indices. The two measures of ‘goodness of fit’ under ‘absolute fit measures’ are very near to 1; the values are classified as perfect (GFI = 0.996; AGFI = 0.986). The badness of fit indices are very low and statistically these are close to zero. Going for baseline comparisons (Incremental Fit) all indices are again above 0.95 and perfect. Composite reliability of the model is again very good. Thereby all these statistical values inclined towards concluding a perfect fit of data to the model. On the whole, this model gives us two dimensions of responsible disposal behaviour namely Appropriate Disposal (AD) and Recycling Intentions (RI) (Table 5.12).

5.1 Domains of Behavioural Construct …

211

Table 5.12 Responsible disposal domain: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability estimated model

Goodness of Fit Index (GFI)

0.996

1.000

0.827

Perfect

Adjusted Goodness of Fit Index (AGFI)

0.986

–

0.741

Perfect

CMIN/DF (χ2 /df)

2.389

–

49.021

Root Mean Square Residual (RMR)

0.022

0.000

0.221

Very Low

Root Mean Square Error of Approximation (RMSEA)

0.037

–

0.219

Very Low

Absolute fit indices Goodness of fit

Badness of fit Low

Incremental fit indices Normed Fit Index (NFI)

0.981

1.000

0.000

Perfect

Relative Fit Index (RFI)

0.951

–

0.000

Perfect

Incremental Fit Index (IFI)

0.989

1.000

0.000

Perfect

Tucker-Lewis Index (TLI)

0.971

–

0.000

Perfect

Comparative Fit Index (CFI)

0.988

1.000

0.000

Perfect

Reliability and validity Composite Reliability (CR)

0.802

–

Great

Interpretation of Components • Appropriate Disposal (AD) It is true that as consumers, everybody is throwing away far too much waste. Even it is more awful that many consumers amongst us don’t dispose it adequately in the dustbins. The heaps of rubbish can be seen in many places because the waste does not get a place in the dustbins. A consumer plays many roles in the consumption process. As an end-user, we as consumers can exercise more power to positively contribute to a green planet. This component is something denoting to this particular aspect. It is true that businesses even the big one is cursed because their

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packaging finishes up as roadside waste. But factually, they are the uncaring consumers who elect to dispose them in inappropriate ways. Most often, the packages contain the required information of disposal, but often consumers consider that these are addressed to someone else. In this way, businesses are very well playing their social responsibility of informing citizens about where to and how to dispose product packages. Now, it is the obligation of consumers to comply with the same. Some consumers may be careless contributing nothing to society other than the debris (visual pollution) to the places. On the other side, some may understand their duty to make the public places clean and free from visual pollution. They responsibly dispose the waste into the bins provided for. Hence, in ‘responsible disposal domain’, this component aptly is called appropriate disposal. • Recycling Intentions (RI) In-between three R’s (Reduce, Reuse, and Recycle), the Reduce (conservation) and Reuse (minimizing wastage) are the healthier options to minimize the probable waste. But then also, waste production cannot become zero. Recycling remains the best option to reduce the landfills and minimize the need to extract more materials from nature and to circumvent large amount of waste. If said on the part of consumers, they may become active for household recycling. But, in India, there is least possible knowledge for the same, and everybody is not in a position to take the burden of recycling on their own. The government can initiate the same in all areas and consumers can support by playing parts necessary on their levels. Therefore, the statements were designed to capture behavioural intentions of consumers for recycling which is established as an immediate predictor of actual behaviour (Mondejar-Jimmenz et al., 2011). Sorting of household garbage and taking it to the recycling bins may be the two different behaviours consumers are expected to perform. These same intentions are defined by two statements (RI1; RI2). The garbage collection and its recycling may also involve a huge cost for the authorities. In this direction, the next statement in the component detains whether the consumers are willing to give a small part of their income to this cause or are indifferent. Recycling Intentions thus is the best suited name to this component.

5.1.5 Domain of Allied Socially Responsible Behaviours The average values and standard deviation of the six statements are visible from Table 5.13. Variables V5, V4 and V6 attained high mean values in descending order followed by variables V1, V2, and V3. These values suggest that people sense their responsibility in a civil society and remain distinct from the activities by which the society gets disturbed. They also obey laws but their environmental activism (environmental talk: ERA2 and donation for environment purpose: ERA3) is low compared to other behaviours fall under this range. There are total 6 C2 = 15 bivariate correlations represented in the lower left triangular matrix. These correlations lie in

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213

Table 5.13 Domain of allied socially responsible behaviours: descriptive statistics and bivariate correlations Correlation matrixa

Variables

Descriptive statistics ¯ X S.D.

V1

V1

4.04

1.036

0.831

V2

3.35

1.162

0.379b

0.0.757

V3

3.19

1.248

0.276b

0.428b

0.0.716

1.081

0.294b

0.256b

0.132b

0.800

1.049

0.380b

0.316b

0.207b

0.424b

0.786

1.171

0.304b

0.334b

0.169b

0.470b

0.511b

V4 V5 V6

4.25 4.30 4.16

V2

Kaiser-Meyer-Olkin measure of sampling adequacy Bartlett’s Test of Sphericity

V3

V4

V5

V6

0.760

KMO = 0.776

Approx. Chi-Square = 1266.944; df = 21; p = 0.000

a Lower left triangular matrix denotes Pearson Bivariate Correlations and the diagonal elements are

the Measures of Sampling Adequacy (MSA) significant at 0.001 significance level Statements representing variables V1–V7 are presented in Table 5.14

b Correlations

a low to a moderate level and all are significant. The measures of sampling adequacy on the diagonal elements are also quite good. The KMO value (KMO = 0.776) too clarifies the adequacy of the sample for component analysis. A significant result of Bartlett’s test is obtained suggesting that there exists at least a set of variables which is linearly related to form a component. There are six components shown on the horizontal axis as there are six variables to be analysed in this domain. The vertical axis is prepared with associated Eigenvalues for each component. The scree is visible at the second and third component but two components are appropriate as the Initial two Eigenvalues are greater than one, after that less than it. All these components account for 100% variations in data; and since the variations explained by the first two components are approxiate 62%, retaining two components here will make lose approximate 38% of data variations (Fig. 5.9). The variation that is accounted by the components in the data is 62.25%, very high from the suggested cut off. Taken separately, the first component explains 34.38% and the second component 27.86% of the variance. Component one is highly represented by ERA3 as it accounts for the high amount of variance (λ = 0.852; VE = 0.726). On the other hand, loadings for the second component are all quite similar and above 0.70, therefore, all three variables very well explain this component. There is an acceptable level of consistency amidst the statements as given by Cronbach alpha. Construct validity of the components is also maintained. Both values of AVE are above 0.5 confirming convergent validity, and low cross-loadings (after varimax rotation) described their discriminant validity. Given the loadings of variables on the components; these are named environmentally relevant activities (ERA) and sustainable societal conduct (SSC). Both these names seem logical and significant. Now, they can be said to have nomological validity too (however this form of validity is

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Table 5.14 Domain of allied socially responsible behaviours: results of principal component analysis Components

Variables

Statements

λ

V.E.

α

AVE

Component 1 Environmentally Relevant Activities Eigenvalue = 2.063; % of variance = 34.38% Cumulative variance = 34.38%

V1/ERA1

I obey environment laws and rules to keep environment safe

0.535

0.286

0.628

0.529

V2/ERA2

I talk to people when they harm the environment to persuade that person to stop that activity

0.759

0.576

V3/ERA3

I donate to groups working for safeguarding of environment

0.852

0.726

V4/SSC1

I always ensure that my way of living and activities do not disturb others

0.784

0.615

0.725

0.606

V5/SSC2

I always park my vehicle suitably not to block others’ way

0.754

0.568

V6/SSC3

Though I like listening music at high volume but caring for the neighbourhood, I keep it low

0.796

0.634

Component 2 Sustainable Societal Conduct Eigenvalue = 1.672; % of variance = 27.86 Cumulative variance = 62.25%

λ Stands for Component Loadings and V.E. means Variance Explained α is Cronbach Alpha: Measure of Internal Consistency AVE represents Average Variance Explained: Measure of Convergent Validity

not empirical rather subjective). All the three are the indicators of construct validity for the components. Once again, the CFA model is a two-stage model (Fig. 5.10) linking construct allied socially responsible behaviours to ERA and SSC which are defined by ERA1 to ERA3 and SSC1 to SSC3, respectively. Concerning component SSC, SSC2 and SSC3 have the highest standardized loadings (λ = 0.71; λ = 0.73) and loading of SSC1 (λ = 0.62) is least. Concerning another component; ERA explains 51% of variation in ERA2. The values of R2 are low for the other two variables (ERA1: R2 = 34%; ERA3: R2 = 27%) given their low standardized loadings in comparison with ERA2. Coming to the next stage, it can be interpreted that exceedingly high percentage (85%) of variation in environmentally relevant activities is explained by the allied socially responsible behaviours. Almost 50% is also explained in sustainable societal conduct but far below from the former. Thus, the component ERA is a candidate of

5.1 Domains of Behavioural Construct …

215

Fig. 5.9 Domain of allied socially responsible behaviours: scree plot for number of components

♦ ♦

♦

Values in rectangular shapes connote Initial Eigen values: Before Varimax Rotation. Bold Faced Values are percentage of explained variance: Before Varimax Rotation.

Standardized Component Loadings are significant p < 0.001.

Fig. 5.10 Domain of allied socially responsible behaviours: measurement model for two dimensional component structure

higher representation of the second stage construct. The significant Chi-square value (χ2 = 55.669) maintains that the observed covariance matrix of the data is different from estimated, thus the model does not seem to fit the estimated data. But, it has already been defined that this single approach of model fit identification is criticized by many researchers and for the reason, other techniques of model fit are devised. These techniques are worked out on the basis of Chi-square of the estimated and null model. In Table 5.15, these model fit values are explained.

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Table 5.15 Domain of allied socially responsible behaviours: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability estimated model

Goodness of Fit Index (GFI)

0.981

1.000

0.631

Perfect

Adjusted Goodness of Fit Index (AGFI)

0.951

–

0.484

Perfect

CMIN/DF(χ2 /df)

6.959

–

83.766

Root Mean Square Residual (RMR)

0.053

0.000

0.361

Marginally acceptable

Root Mean Square Error of Approximation (RMSEA)

0.077

–

0.288

Marginally acceptable

Absolute fit indices Goodness of fit

Badness of fit Least acceptable

Incremental fit indices Normed Fit Index (NFI)

0.956

1.000

0.000

Perfect

Relative Fit Index (RFI)

0.917

–

0.000

Good

Incremental Fit Index (IFI)

0.962

1.000

0.000

Perfect

Tucker-Lewis Index (TLI)

0.928

–

0.000

Good

Comparative Fit Index (CFI)

0.962

1.000

0.000

Perfect

0.880

–

Reliability Composite Reliability

Great

χ2 /df yields a value of 6.959; RMR is 0.053 and RMSEA is 0.077. GFI is near to 1.00, AGFI is also its neighbour (AGFI = 0.951) indicating a flawless absolute fit. The fitness from incremental fit indices is also achieved given that all indices of baseline comparisons are greater than 0.9 thus their acceptability is perfect. Composite reliability of model is also exceptionally good (CR = 0.880). In this way, the model can be said having a perfect fit to the data and valid to be operated upon further.

5.1 Domains of Behavioural Construct …

217

Interpretation of Components • Environmentally Relevant Activities (ERA) Some of the consumers’ activities which are highlighted by the component are consumers’ obedience for environmental legislation, communication with people regarding environmental matters, and promotion of environmental organizations by helping them out with donations or material benefits. These activities seem significant and relevant for spreading/diffusing environmental concern amongst the general public and so is named environmentally relevant activities. These activities are, however, different from the consumption domain (purchasing to disposal) but necessary for enhancing environmental concern and awareness which is the root cause of pro-environmental behaviour of consumers. • Sustainable Societal Conduct (SSC) Next component describes people carefulness to remain responsible in their everyday living so that their neighbourhood or the people in their surroundings do not feel disturbed or annoyed by their activities. Playing music mildly is pleasant, but beyond a level it becomes a disturbing noise. Inappropriate use of music systems, many a time, creates nuisance and disturbance in the society. So, the third statement taps people who perform this activity sensibly. Improper parking is also a common problem today. From small to large cities, population increase and increasing number of vehicles on roads are creating this problem, and people can be seen competing for parking space. Sometimes there is parking on ‘No Parking’ areas. If everyone park suitably, this may reduce the time of others looking for available parking spaces, noise, and congestion problems. The next statement defines this responsible act of people. The name sustainable societal conduct in this direction, seems appropriate to the component. It may be expected that by resorting to these sustainable conducts cities can be made more people-centric and a better place to live.

5.2 Domains of Attitudinal Construct It has been demarcated in Chap. 3 that attitudinal field reflects two domains: general and specific. These domains are analysed in further sections of the chapter.

5.2.1 General Attitudinal Domain The constructs of the general attitudinal domain are analysed using confirmatory factor analysis (CFA) to substantiate the reliability of the constructs, and to which extent the items measure the two intended constructs: the validity aspect. In this section, principal component analysis is not applied because exploration is not a purpose here. The statements under each of the construct in this domain are based

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on the preconceived thoughts underlying theoretical support from the literature. Hair et al. (2006: p. 91) has also described this issue by stating that based on any theoretical support or researchers’ prior opinion, the exploratory component analysis takes only a confirmatory approach and then there is only a need to assess the degree to which the data meet the expectations of researchers. This can best be done by CFA.

5.2.1.1

Concern for Sustainable Future (CSF)

Six statements under this construct attempts to measure concern of consumers for the cause of sustainability. Confirmatory factor analysis is used to test the validity and reliability of the items forming the construct. At the outset, the mean and standard deviation of each item is considered and the degrees of correlations are verified between any two items as reflected from Table 5.16. The column labelled ‘descriptive statistics’ tells us about the average and dispersion values for the items under investigation. The sub column under it labelled as ‘mean’ gives values of simple average. The column next to it shows ‘standard deviation’. It can be seen that mean values vary between 3.88 to 4.44. Variations in data from mean are quite little as depicted by the standard deviation values. High mean values (above 4) for five variables (CSF1, CSF2, CSF3, CSF4, and CSF6) clearly show that people accept human consumption activities as the matter of serious concern as the present lifestyle is detrimental to sustainability. The mean value of the variable CSF5 is low from the mean values of the above five variables but greater than 3 (the average on a five-point scale), revealing that although people know that they themselves are responsible for the deterioration of environment; but, still not much worried regarding the future that may be in dark with their deleterious consumption behaviours. There are a total of 15 correlations in the lower left triangular part of the table under the heading ‘correlation matrix’. All correlations are statistically significant. Table 5.16 Concern for sustainable future: descriptive statistics and bivariate correlations Descriptive Statistics

Correlation matrixa

Mean

S.D.

CSF1

CSF1

4.31

0.872

1.00

CSF2

4.44

0.745

0.347b

1.00

CSF3

4.05

0.926

0.345b

0.341b

1.00

0.801

0.300b

0.267b

0.427b

1.00

0.287b

0.328b

0.277b

1.00

0.298b

0.302b

0.264b

0.252b

Variables

CSF4

4.05

CSF5

3.88

0.991

0.263b

CSF6

4.35

0.767

0.247b

a Lower

CSF2

CSF3

CSF4

left triangular matrix denotes Pearson Bivariate Correlations significant at 0.001 significance level The statements representing the variables are displayed in Table 5.18

b Correlations

CSF5

CSF6

1.00

5.2 Domains of Attitudinal Construct

♦

219

Standardized Component Loadings Significant at p < 0.001.

Fig. 5.11 Concern for sustainable future: zero order confirmatory measurement model

Six of these correlations are moderate (above 0.30), only one correlation is above 0.40 (r = 0.427), and eight correlations are above 0.25. As the variables are significantly correlated, they can be expected to form a set. Figure 5.11 is a zero-order confirmatory component model for the construct CSF. Six observed variables CSF1 to CSF6 are connected to the latent construct ‘Concern for Sustainable Future (CSF)’ in that these six observed variables are endogenous. One latent construct (CSF) and the six error variance (e1 to e6) are unobserved and exogenous. The sample moments are based on the number of observed variables and are 21 for this model [6(6 + 1)/2]. Twelve parameters are to be estimated. In this way, calculated degrees of freedom is 9 (21 – 12). On the specified degrees of freedom, Chi-square of model is significant (CMIN = 21.307) at 5% significance level. The results of model fit are presented in Table 5.17. The fit is perfectly acceptable for the present set of data. The goodness of fit indices are perfectly near to 1 (GFI = 0.993; AGFI = 0.983) and measures of lack of fit are also low and near to zero (RMR = 0.014; RMSEA = 0.037). The ratio of CMIN/DF is 2.367 much below from the upper threshold of 5. All the incremental fit statistics are also above 0.95 and perfect. Composite reliability of the model is also greater than 0.6, a strand suggested as a cut off for having composite reliability (Ganguly et al., 2009: p. 32). Next, the item loadings and explained variance in Table 5.18 reveal that due to the high loading (λ = 0.66), the construct is highly represented by the variable CSF3. It means that the explained variance is also high and equals 44% approximately. Variable CSF6, however, contributes less and explains 23% of variations but is significant (λ = 0.48). With the exception of item CSF6, all other items display convergent validity. Internal consistency based on item-total statistics of the items is also good as measured by coefficient alpha (α = 0.720). In this way, analysis observes that the statements are valid and reliable indicators of the construct and it is worth noticeable and interpretable. • Concern for Sustainable Future (CSF) Sustainable future implies a future with healthy endurance and sustainable living which every individual dream off. The construct ‘concern for Sustainable Future’

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Table 5.17 Concern for sustainable future: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability

Goodness of Fit Index (GFI)

0.993

1

0.680

Perfect

Adjusted Goodness of Fit Index (AGFI)

0.983

–

0.552

Perfect

CMIN/DF (χ2 /df)

2.367

–

61.956

Root Mean Square Residual (RMR)

0.014

0.00

0.189

Very Low

Root Mean Square Error of Approximation (RMSEA)

0.037

–

0.247

Very Low

Absolute fit indices Goodness of fit

Badness of fit Low

Incremental fit indices Normed Fit Index (NFI)

0.977

1.000

0.000

Perfect

Relative Fit Index (RFI)

0.962

–

0.000

Perfect

Incremental Fit Index (IFI)

0.987

1.000

0.000

Perfect

Tucker-Lewis Index (TLI)

0.978

–

0.000

Perfect

Comparative Fit Index (CFI)

0.987

1.000

0.000

Perfect

0.721

–

Reliability Composite Reliability

Good

as its name implies attempts to measure the same: general concern of human being for maintenance of this sustainable future. Today, it is a well known fact that human consumptive and unsustainable lifestyle is the primary cause of the problem of unsustainability the world is facing. Since the industrial revolution, human activities have been the primary cause of global warming and a major contributor to climate change. The process of behaviour transformation begins when everybody themselves feel and accept this fact that there is a need to change this destructive lifestyle aligning it with the demand for sustainability. From this aspect, two items (CSF1 and CSF3) consider consumers’ conformity that deleterious consumption behaviour is the main cause of environmental problems and global warming. With

5.2 Domains of Attitudinal Construct

221

Table 5.18 Concern for sustainable future: results of confirmatory factor analysis Variables

Statements

λ

V.E.

α

CSF1

I agree that the increasing carbon dioxide in the atmosphere is one of the factors causing global warming

0.54

0.29

0.720

CSF2

I think if individual stop damaging the environment, it will help in improving life for everyone

0.55

0.30

CSF3

I view human consumption activities as the primary cause of global warming

0.66

0.44

CSF4

In my opinion the ultimate solution for environmental problems depends on drastic changes in our lifestyle

0.57

0.33

CSF5

Often I feel that the lifestyle we live is impossible to maintain in long run

0.50

0.25

CSF6

I consider future generation deserves equally as those living now

0.48

0.23

this, two other statements (CSF5; CSF6) confirm their thinking about future generations. Remaining two statements (CSF2; CSF4) go together that people feel a need to change their consumptive behaviour for maintaining a sustainable living on Earth.

5.2.1.2

Commitment to Initiate (CI)

Three statements are utilized as a measure of consumer ‘commitment to initiate’ for environment protection. The descriptive measures and the degrees of correlation in-between these are evident from Table 5.19. It is revealed in the table that the mean values for the three statements are quite similar and above 3.0. These values reflect a moderate level of commitment of consumers regarding the cause of sustainability. The correlation matrix is manifesting that correlation between CI1 and CI2 is moderate (r = 0.341), and correlations between CI1 ↔ CI3 (r = 0.172), and CI2 ↔ CI3 (r = 0.219) are low. All three correlations are statistically noteworthy at Table 5.19 Commitment to Initiate: descriptive statistics and bivariate correlations Descriptive statistics

Correlation matrixa

Mean

S.D.

CI1

CI1

3.73

0.940

1.00

CI2

3.75

0.832

0.341b

1.00

1.100

0.172b

0.219b

Variables

CI3 a Lower

3.70

CI2

left triangular matrix denotes Pearson Bivariate Correlations b Correlations significant at 0.001 significance level The statements representing the variables are displayed in Table 5.21

CI3

1.00

222

5 Exploration and Validation of Behavioural–Attitudinal Dimensions

♦

Standardized Component Loadings Significant at p < 0.001.

Fig. 5.12 Commitment to Initiate: zero order confirmatory measurement model

0.001 significance level. The results of zero-order confirmatory model are shown in Fig. 5.12. This model is a zero-stage confirmatory model in which the oval represents unobserved construct CI, and observed variables from CI1 to CI3 (N = 3) are highlighted with rectangles. All the standardized loadings appear significant. Loading for the first two variables are above 0.50 and the third variable has a loading of 0.33. The significance of loadings shows that the variables are reliable indicators of their latent construct. The squared multiple correlations (above rectangles) provide information on how much variance the latent construct accounts for in the observed variables. The figure also depicts that degrees of freedom for the model is zero (df = 0) which is calculated by subtracting the number of parameters that are to be estimated (N = 6) from the number of distinct sample moments (N = 6). As in this case, the number of parameters to be estimated is equal to the number of sample moments; therefore, degrees of freedom is zero and the model is a just identified model having an ideal fit. The ideal model fit values are displayed in Table 5.20. The absolute goodness of fit values (GFI = 1.000; RMR = 0.000) and Incremental fit indices NFI, IFI, and CFI touch their upper level of 1; hence, ideally accepted for the model. The composite reliability (0.735) is again above the limit of 0.6 as suggested. The loadings are interpreted in the next Table 5.21 and after it, the construct is interpreted. Table 5.21 explains that item CI2 (λ = 0.66) robustly load on the construct followed by item CI1 (λ = 0.52). In this way, 44% and 27% of the variance in CI2 and CI1, respectively, are explained by the construct CI. Indeed, the loading for the third statement is significant; but, item analysis revealed that after removing statement CI3 from the analysis, the reliability increased to 0.506 from the previous level of 0.475. Hence, two items (CI1 and CI2) is retained for further analysis, and this construct is thus composed of two statements.

5.2 Domains of Attitudinal Construct

223

Table 5.20 Commitment to Initiate: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability

Goodness of Fit Index (GFI)

1.000

1

0.885

Ideal

Adjusted Goodness of Fit Index (AGFI)

1.000

–

0.771

Ideal

61.453

Absolute fit indices Goodness of fit

Badness of fit CMIN/DF(χ2 /df)

–

–

Root Mean Square Residual (RMR)

0.000

0.00

0.154

Root Mean Square Error of Approximation (RMSEA)

–

–

0.246

Ideal

Incremental fit indices Normed Fit Index (NFI)

1.000

1.000

0.000

Relative Fit Index (RFI)

–

–

0.000

Incremental Fit Index (IFI)

1.000

1.000

0.000

Tucker-Lewis Index (TLI)

–

–

0.000

Comparative Fit Index (CFI)

1.000

1.000

0.000

0.735

–

Ideal

Ideal

Ideal

Reliability Composite Reliability

Good

Table 5.21 Commitment to Initiate: results of confirmatory factor analysis Variables

Statements

λ

V.E.

α

αa

CI1

I am willing to have environmental problems solved even if this means sacrificing many personal goods

0.52

0.27

0.475

0.506

CI2

I am willing to take any initiative to protect the environment

0.66

0.44

CI3

I believe that the solution of environment problems should be left to experts/government

0.33

0.11

a Indicates

coefficient alpha (α) after deleting CI3 from the analysis

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5 Exploration and Validation of Behavioural–Attitudinal Dimensions

• Commitment to Initiate (CI) The construct which is a conglomerate of two statements is named ‘commitment to initiate’. Commitment means dedication or promise for any cause or an engagement or duty that restricts free-riding. It measures consumers’ instigation for the protection of the environment and maintaining its sustainability. These attempts for taking initiatives in environmental directions is much needed and welcomed in the present scenario. The statements under it measure consumer commitment to generate a clean and healthy environment with the least environmental impact possible. It also holds a very good psyche of individuals that little bit contribution from their side adds up to a whole lot and also acts as an encouragement to others so that they too can join the street. It is very much important to have an assenting concern but the superimposed power is consumers’ own supported ingenuity to act for the purpose. The first statement shows their willingness to solve environmental problems even on the expense of their own personal benefits, and second reveals their commitment and undertaking to do anything for the environmental cause. It is the measurement of the component that whether consumers are optimistic about their role as initiators to act upon all possibilities in their reach or are fragile by nature.

5.2.2 Specific Attitudinal Domain—Attitude towards Sustainable Living (ASL) The items in this domain point towards some of the specific aspects, the constituents of which are derived through principal component analysis first, and then validated using confirmatory factor analysis. To begin with the principal component analysis in the specific attitudinal domain, the mean and standard deviation of nineteen statements are observable from Table 5.22. The lowest mean value is 2.68 (V13) and the highest mean is obtained as 4.28 (V6). Mean values of ten variables amongst these nineteen variables are above 4.0 implying people intense apprehension regarding the issues measured by these statements. For ¯ V7 ) = 4.27 states that people strongly feel for the eradication of example, Mean (X ¯ V2 = 4.20 directs to believe in plastic bags seeking a need for a law. Average of V2 X people view that air pollution can be decreased by resorting to public transportation and other means such as walking and cycling for small distances. Further, mean values between a range of 3 and 4 show a level of the modest approach of people regarding what type of attitude is captured by these variables. The least mean value ¯ = 2.68) implies people feeling that environment friendly products are hard to (X locate in Indian markets. So, people may perceive that their time can get wasted for searching about these products. Slightly high mean values from this value, the mean ¯ V11 = 2.88 also suggest that people associate price risk to ¯ V12 = 3.19; X values X these products and feel for their low quality.

4.20

3.95

4.08

4.17

4.28

4.27

4.21

4.18

3.97

2.88

3.19

2.68

3.13

3.70

4.16

4.03

3.39

3.39

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

V15

V16

V17

V18

V19

1.034

1.098

1.051

0.815

1.100

1.198

1.076

0.983

1.157

0.803

0.782

0.805

0.892

0.894

0.784

0.835

0.911

0.862

0.868 0.341b 0.336b 0.263b 0.365b 0.340b 0.429b 0.289b 0.338b −0.012 ns

0.055c −0.090 ns −0.087 ns

0.037 ns 0.266b 0.236b 0.102c 0.043 ns

0.323b

0.119b

0.291b

0.224b

0.238b

0.254b

0.250b

0.208b

0.008 ns

0.086c

0.027 ns

0.035 ns

0.191b

0.213b

0.141b

0.041 ns

0.076c

V2

0.226b

0.848

V1

0.910

S.D.

¯ X

4.14

Correlation matrixa

Descriptive statistics

V1

Variables

0.131b

0.073d

0.264b

0.299b

0.064d

0.045 ns

−0.067d

−0.003 ns

−0.025 ns

0.243b

0.311b

0.327b

0.232b

0.325b

0.296b

0.326b

0.895

V3

0.134b

0.102c

0.251b

0.292b

0.167b

0.048 ns

−0.005 ns

0.030 ns

0.042 ns

0.264b

0.262b

0.311b

0.227b

0.280b

0.268b

0.888

V4

Table 5.22 Attitude towards sustainable living: descriptive statistics and bivariate correlations

0.136b

0.098c

0.266b

0.251b

0.166b

0.067d

−0.041 ns

0.086c

0.023 ns

0.252b

0.253b

0.260b

0.154b

0.262b

0.894

V5

0.089c

0.077c

0.171b

0.278b

0.109b

0.062d

−0.020 ns

0.023 ns

0.007 ns

0.239b

0.243b

0.434b

0.312b

0.853

V6

0.125b

0.073d

0.252b

0.255b

0.132b

0.004 ns

−0.070d

0.079c

0.019 ns

0.210b

0.310b

0.385b

0.894

V7

(continued)

0.098c

0.056d

0.282b

0.279b

0.105b

−0.001 ns

−0.063d

0.103c

−0.179b

0.419b

0.539b

0.822

V8

5.2 Domains of Attitudinal Construct 225

−0.156b

0.042 ns

−0.104b

−0.007 ns

0.060d

0.213b

0.121b

0.129b

0.047 ns

−0.121b

0.063d

−0.078d

0.020 ns

0.101c

0.304b

0.194b

0.073d

0.083c

V11

V12

V13

V14

V15

V16

V17

V18

V19

0.131b

0.103b

0.092c

−0.109b

0.180b

0.124b

0.268b

0.141b

0.551

V11

−0.030 ns

−0.016 ns

0.062d

0.043 ns

0.257b

0.153b

0.267b

0.640

V12

−0.026 ns

0.030 ns

0.058d

0.042 ns

0.112b

0.034 ns

0.559

V13

−0.044 ns

−0.050 ns

0.101c

−0.015 ns

0.247b

0.571

V14

−0.024 ns

−0.038 ns

0.142a

0.107b

0.717

V15

0.112b

0.102c

0.447a

0.798

V16

Statements representing variables V1–V19 are presented in Table 5.23

d Correlations significant at 0.05 significance level. ns Correlations not significant (p > 0.05)

a Lower left triangular matrix denotes Pearson Bivariate Correlations and the diagonal elements are the Measures of Sampling Adequacy (MSA) b Correlations significant at 0.001significance level. c Correlations significant at 0.01 significance level

Bartlett’s Test of Sphericity; Approx. Chi-Square = 3588.646; df = 171; p = 0.000

Kaiser-Meyer-Olkin measure of sampling adequacy; KMO = 0.814

0.883

0.409b

V10

V10

0.848

V9

Correlation matrixa

V9

Variables

Table 5.22 (continued)

0.258b

0.113b

0.760

V17

0.310b

0.665

V18

0.647

V19

226 5 Exploration and Validation of Behavioural–Attitudinal Dimensions

5.2 Domains of Attitudinal Construct

227

Below label correlation matrix; the off-diagonal elements are the bivariate correlations and diagonal elements are the measures of sampling adequacy. There can be seen a pattern and range of correlations in the correlation matrix (lower left triangular part). Some correlations are moderate (range 0.25 to 0.50), some are rather very weak (below 0.25), and the correlation between V8 and V9 is moderately high (r = 0.539). Certain statistical values of correlations are insignificant too (p > 0.05) asserting that these statistical values are not different from zero and correlations are just because of chance. This pattern suggests a stream: variables may flow and go together in groups; hence, implies that these variables can be reduced to some small number of constituents to be further operated upon. The values of BTS and KMO are also significant which affirm that principal component analysis is preferable for the data. There were nineteen attitudinal statements to be component analysed; so horizontal axis (Fig. 5.13) represents nineteen variables as nineteen components. Eigenvalues of the first five components are greater than 1 explaining 50.538% of variance. Therefore, these five components are retained replacing the original nineteen variables into a smaller set of five constituents. Component one explains 4.262/19 = 22.43% variance and the least of variance (5.33%) is explicated by the fifth component (1.012/19). These variations are calculated from initial Eigenvalues that are obtained before rotation. For the interpretability of these components, varimax rotation is applied. Total variance remaining same, the percentage of variance explained for individual components changes after rotation which can be noted from Table 5.23. Component one

♦ ♦

Values in rectangular shapes connote Initial Eigen values: Before Varimax Rotation. Bold Faced Values are percentage of explained variance: Before Varimax Rotation.

Fig. 5.13 Attitude towards sustainable living: scree plot for number of components

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5 Exploration and Validation of Behavioural–Attitudinal Dimensions

Table 5.23 Attitude towards sustainable living: results of principal component analysis Components

Variables

Statements

λ

V.E.

α

AVE

Component 1 Sustainable Attitude Eigenvalue = 3.548; % of Variance = 18.674 Cumulative Variance = 18.674%

V1/CE1

Sometimes, I worry about present generations’ unsustainable habits.

0.457

0.209

0.804

0.329

V2/CE2

I think pollution problem can be minimized by shifting from private to public transportation or walking/cycling

0.639

0.408

V3/CE3

It upsets me when I see people use too much water and damage environment

0.468

0.219

V4/CE4

I sense a requirement for an intelligent system to stop over flowing water from roof tanks

0.415

0.172

V5/AMW1

I sense a need to do something immediately to reduce the amount of waste thrown away.

0.407

0.166

V6/AMW2

I agree that there is a lot of waste produced in India in parties/occasions or religious moments

0.547

0.299

V7/AMW3

In my view, government should totally ban plastic bags and disposable containers

0.520

0.270

V8/NR1

Recycling reduces pollution and is important to save natural resources

0.769

0.591

(continued)

5.2 Domains of Attitudinal Construct

229

Table 5.23 (continued) Variables

Statements

λ

V.E.

V9/NR2

I feel that the government should pass law for recycling

0.688

0.473

V10/NR3

I think of a law for all household’s garbage to be separated into different classes for recycling

0.695

0.483

Component 2 Civic Norms Eigenvalue = 1.717; % of Variance = 9.038 Cumulative Variance = 27.711%

V11/CN1

I feel that many a times music systems and loudspeakers cause unnecessary noise and nuisance in society

0.746

0.557

V12/CN2

I support the ruling of not using music systems/DJ’s late night.

0.780

0.608

Component 3 Overcoming Green Myopia Eigenvalue = 1.459; % of Variance = 7.681 Cumulative Variance = 35.393%

V13/OGM1

I feel that environment friendly products are high priced products.

0.444

0.197

V14/OGM2

I believe that products made of recycled material are of lower quality

0.689

0.475

V15/OGM3

In Indian markets, environment friendly products are not readily available/easily recognizable.

0.788

0.621

V16/ET1

I would oppose any environmental regulation that would restrict my lifestyle

0.765

0.585

V17/ET2

I think there is a need to spread environmental education among Indians

0.636

0.404

Components

Component 4 Environmental Thinking Eigenvalue = 1.446; % of Variance = 7.611 Cumulative Variance = 43.004%

α

AVE

0.605

0.583

0.465

0.431

0.401

0.495

(continued)

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5 Exploration and Validation of Behavioural–Attitudinal Dimensions

Table 5.23 (continued) Components

Variables

Statements

λ

V.E.

α

AVE

Component 5 Sustainable Mobility Eigenvalue = 1.432; % of Variance = 7.534 Cumulative Variance = 50.538%

V18/SM1

Aligning with demand for parking space, I support raising parking fees in cities

0.755

0.570

0.472

0.551

V19/SM2

I am willing to pay extra for intelligent parking systems

0.729

0.531

λ Stands for Component Loadings and V.E. means Variance Explained α is Cronbach Alpha: Measure of Internal Consistency AVE represents Average Variance Explained: Measure of Convergent Validity

has lost 3.76% of its variance and proportionately it is gained by the other four components. For example, now the fifth component is able to explain 7.534% of variation compared to a relative amount of 5.33% before rotation. These statistics for the five components are visible in Table 5.23. The Eigenvalues shown in the first column of Table 5.23 are obtaine after varimax rotation. The cumulative variance explained by these components remains the same as observed before rotation (50.538%). It can be seen that the Eigenvalue for the first component is high (λ = 3.548). In this regard, it explains 18.674% of variance. Four other Eigenvalues are less than this value but well above the criteria of 1. These four components account for 9.038%, 7.681%, 7.611%, and 7.534% of variations, respectively. The loadings for each statement are displayed in the fourth column of Table 5.23 with their explained variation in its adjacent column. The loadings for the first component vary in the range of 0.407–0.769 meaning that variable NR1 is the robust variable and variable AMW1 has the least impact. The loadings for the second, fourth, and fifth components are all above 0.70; and these are composed of two statements each. Component three is a conglomerate of three items explaining the highest variation of OGM3 (VE = 62.1%) and the least variation of OGM1 (VE = 19.7%). For the variance extracted measures (AVE), component 2 and component 5 have a value of 0.583 and 0.551, respectively, which exceeds the recommended level substantially. Similarly, components 3 and 4 with AVE scores of 0.495 and 0.431, respectively, are marginally lower than 0.5, quite satisfactory on this criterion. But component one has a value of 0.33, notably failing short of recommended 0.50. Cronbach alpha for the first component (SA) is exceptionally good (α = 0.804), and for the third component also reasonable (α = 0.605). Altogether, it is obtained by this analysis that component one is highly reliable but not so much convincing because of the unacceptable value of average variance explained. This is a worth mentioning point that sustainable attitude is a very wide term in literature, and a set of various types of attitudes of people make it. The statements which in principal component analysis load only on one component (named

5.2 Domains of Attitudinal Construct

231

sustainable attitude: SA), factually are a combination of three different types of attitude of people. First four statements CE1 to CE4 reflect an attitude towards conserving ecology; statements AMW1 to AMW3 pinpoints the attitude towards the problem of mounting waste, and items NR1–NR3 is visible regarding a need for recycling. Highest sharing of variance between these statements make them members of one component in principal component analysis; but very low convergent validity push us to rely on the face validity of statements in which three different sub-components seem to underlie this one module. Therefore, to validate these seven components, one stage confirmatory model is apparent in Fig. 5.14.

♦

Standardized Component Loadings significant p < 0.000.

Fig. 5.14 Attitude towards sustainable living: One stage confirmatory measurement model

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5 Exploration and Validation of Behavioural–Attitudinal Dimensions

Confirmatory attitudinal model (Fig. 5.14) is a one stage model2 in which the observed variables are defined by one layer3 of latent constructs (as shown in large ovals). The one-sided arrows are the causation between the observed (in rectangles) and unobserved (in ovals) variables. Left part of the figure denotes that the total number of variables in this model is 45 of which 19 are observed and 26 are unobserved. These can also be categorized as endogenous (N = 19) and exogenous variables (N = 26). Observed variables are endogenous which depends upon the unobserved or latent variables. As marked in Table 5.23, four statements (CE1–CE4) are assumed to underlie component named Attitude towards Conserving Ecology (ACE). Two components Anticipating Mounting Waste (AMW) and Need for Recycling (NR) each is assumed to combine three statements. The remaining variables which load on their supposed constructs are identical to what is observed in the principal component analysis. The error terms are also associated with each measurement (e1–e19). In total, 59 parameters are to be estimated by the model: 19 loadings, 19 error variances and 14 correlations. Given the number of distinct sample moments (N = 190) and the number of parameters to be estimated (N = 59), the degrees of freedom for the model is 131. The Chi-square is significant at these degrees of freedom (p < 0.001). All the item loadings are greater than 0.30 as maintained by Zakuan et al. (2010), for a marginal claim of convergent validity. Correlations between latent variables are significantly below unity, and it supports the discriminant validity of the model (Ganguly et al. 2009: p. 32). The results of model fit are given by Table 5.24. This confirmatory measurement model with seven components is found to fit the data robustly as the goodness of fit values are well within the acceptable ranges (CFI = 0.945; AGFI = 0.920; χ2 /df = 4.393; RMR = 0.043; RMSEA = 0.058). Except for RFI, all incremental fit indices are above 0.8 and marginally accepted. Consequently, these seven components are worth noticeable and interpretable in the specific attitudinal domain. The interpretation section takes into account what type of consumer attitude these components symbolize for. Interpretation of Components • Attitude towards Conserving Ecology (ACE) The Earth cannot sustain us if we go on using up everything as if there is no tomorrow. If we don’t take care of what we have left, soon there will be nothing to plunder. Accordingly, conservation is crucial for the attainment of sustainability. There may be many ways in everyday routines by which ecology can be conserved. This component talks about some of them. The first statement (CE1) is 2A

first-order confirmatory factor model is selected for the attitudinal domain because a secondorder factor model (as used for all behavioural domains) assumes that first-order dimensions will affect the second-order latent construct in the same way (Malhotra and Dash, 2012: p. 706). This assumption holds well in behavioural domains but not in attitudinal because there has not been defined any specific sphere of influence for the construct, as in behavioural part. 3 One layer means instead of specifying a higher order construct, the covariance between one stage latent constructs are freely estimated (double-headed arrows between latent constructs).

5.2 Domains of Attitudinal Construct

233

Table 5.24 Attitude towards sustainable living: model fit measures Model fit indices

Estimated model

Saturated model

Independence model

Acceptability

Goodness of Fit Index (GFI)

0.945

1.000

0.585

Perfect

Adjusted Goodness of Fit Index (AGFI)

0.920

–

0.539

Perfect

CMIN/DF(χ2 /df)

4.393

–

21.138

Acceptable

Root Mean Square Residual (RMR)

0.043

0.000

0.151

Very Low

Root Mean Square Error of Approximation (RMSEA)

0.058

–

0.142

Very Low

Absolute fit indices Goodness of fit

Badness of fit

Incremental fit indices Normed Fit Index (NFI)

0.841

1.000

0.000

Marginally Acceptable

Relative Fit Index (RFI)

0.792

–

0.000

Less Acceptable

Incremental Fit Index (IFI)

0.872

1.000

0.000

Marginally Acceptable

Tucker-Lewis Index (TLI)

0.831

–

0.000

Marginally Acceptable

Comparative Fit Index (CFI)

0.871

1.000

0.000

Marginally Acceptable

a general attitude which targets people forethought regarding the need of small behavioural changes by which efforts can be resorted towards preserving ecology. In attempting for the conservation of ecology, the concept of eco-mobility4 is popular worldwide. Nowadays, in the wake of adverse environmental consequences, eco-mobility is encouraged for clean air, noise avoidance, energy efficiency, low greenhouse gas emission, and such kind of delinquencies. Regarding attitude towards this behavioural change, it is a predicament that majority of consumers amongst us prefer comfort, use car for commuting and most often individually

4 Eco-mobility implies environment friendly modes of transport and reduce/remove people reliance

on private motor vehicles. Thus, it promotes passaging (the use of public transport such as buses, trams, trains, ferries and collective taxis) and non-motorized transport (walking, cycling, nonmotorized scooters, push scooters and walking aids).

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5 Exploration and Validation of Behavioural–Attitudinal Dimensions

(carrying one person only). Sensing towards this, the item CE2 states about walking or riding a bicycle for short distances which can significantly reduce the amount of pollution generated each day. Consumers may express this attitude by becoming thoughtful for cutting down personal carbon footprint by choosing public transport instead of driving privately as public transport uses far less resources than cars. The next two statements verbalize about consumer attitude for water conservation. Since each one of us depends upon water for life, it is our responsibility to adopt even the little means by which water may be saved for generations to come. By this way, statement CE3 shows people worry for excessive use of water and the subsequent item (CE4) reveals their sensibility for tackling with the problem of flowing water from roof tanks which with no use get totally wasted in drains. All this shows their attitude to always endure for saving every drop of water even with little means possible so at best this attitude is called attitude towards conserving ecology. • Anticipating Mounting Waste (AMW ) Next component anticipating mounting waste measures the same as its name implies: consumers’ views that there is a lot of waste produced in India and this problem is going to hype in the years to come. Two statements (AMW1; AMW2) remain in the agreement of consumers that plenty of waste in India is produced by many activities, and the lifestyle of ours (parties/movements) furnishes the fuel to the fire. The thrown poly bags become totally useless and accumulate as waste into our landfills. Vast tracts of land are fast becoming barren because of indiscriminate and unnecessary use of poly bags. Connecting to this aspect, next item (AMW3) detains consumers’ support for the idea of abolishing plastic bags which end up as litter and fouls the landscapes. No government or organization can fight alone against plastic and win it. What is needed is the army of the common man to knock down this enemy of the environment. All these attitudes point toward averting the rising levels of waste by closing down the use of poly bags and other unsustainable activities. This keenness for the problem can work for waste minimization behaviour in the form of reduced consumption, re-usage or restraining it. • Need for Recycling (NR) Recycling is a process of reusing waste resources (natural or man-made) which slows down consumption and has multi-fold effect. It saves energy, conserves natural resources, and converts the old discarded products into new useful ones. Our landfills are dumped with waste and recycling could be a better option. This component contains three statements and seems taping an attitude of consumers about the need to develop and expand recycling services in India on a continuing basis. The prime item (NR1) enquires about the same approach of respondents, whether they think of and feel for having such kind of recycling amenities. Giving consideration to recycling and perceiving it as the best option for dealing with the waste, people may think of a law for it. Sorting of garbage is also important for advancing recycling, so there should also be a law requiring people to do the same. Hence, statements NR2 and NR3 obtain their feeling that a law must be passed for sorting garbage and making recycling mandatory.

5.2 Domains of Attitudinal Construct

235

• Civic Norms (CN) The component civic norms is a summation of two statements. The word civic implies having to do with a city and the word norms connote for a standard of achievement or behaviour that is required and designated as emblematic. Civic Norms, according to this component refers to some norms and standards that should be a part of society, and one of them relating to reducing/eliminating both the noise pollution and noise nuisance. Amongst various sources of noise, music players and loudspeakers cause unnecessary and unwarranted sound. Beyond a limit, this sound disturbs community life and results in restricting people to work and relax. The first statement in this direction measures the extent of agreement of respondents for the unnecessary noise that music systems and loudspeakers produce. The second one is a measure for well-timed and appropriate use of DJs (a type of music system). Therefore, the item obtains consumers’ opinion and their support for the ruling of confiscation of late night DJs. Actually, various components determine the extent of noise and nuisance such as location, frequency, duration, intensity, and time of the day. These components are implied in the measurement of the current statements. In any area/society, frequent and continuous use of music systems and DJs with such an intrusive volume cause stress to neighbouring residents, for example, students can’t concentrate and study. Time of the day is also important. The effects of late night noise when the environment is even calm give greater weight than the same noise occurring during the day. In no doubt, lesser noise can make the environment more friendly and we can make ours and others life pleasant. Hence, the norms for regulated and timely use of music players refer to the civic norms in the component, and ask for people opinions on the alleged aspect. • Overcoming Green Myopia (OGM) Myopia means lack of imagination or shortsightedness. The term is employed in the component as in the explanations of the items the level of myopia of consumers is captured regarding sustainable products. Sustainable products include many types; eco-friendly, recyclable, and green names finish up under this category. The main problem that marketers of these products are facing is the same consumer shortsightedness because of lack of facts and information about these products which hinder consumers’ green purchase. Majority of Indian consumers are price conscious; so, first statement (OGM1) talks about consumers’ perception about the price the supposed sustainable products hold. Consumers can compromise over price only if they are offered with the best quality. Likewise, the second statement asks for the same. The last item relates to time risk associated with green buying because in lack of knowledge/awareness people view that they are not able to recognize and mark the availability of these products in the market. By perceiving the kinds of myopias, they may remain hesitant to transform their choices into buying green products. On the other hand, some other consumers becoming rational may have noble perceptions regarding these products. It may be anticipated that consumers who come under the later segment may comprehend and become able to make green their overwhelming preference. So, the component

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is rightly called overcoming green myopia, and differentiates between myopic and other sensible consumers. • Environmental Thinking (ET ) Two statements which form another component in the principal component analysis are cumulatively named Environmental Thinking. First item of the component (ET1) states for complying with and respect of consumers for environmental regulations. Having respect, ‘responsible consumers’ will prefer to be abiding by the environmental rules and regulations of their country even if they have to sacrifice their present lifestyle. The next thought for a need of environmental education (EI2) for Indians highlights a lack of it in Indian public (if consumers agree on the item). The measurement of people agreement with this item also shows their feeling that perhaps by diffusing environmental education and concern, environmentally relevant acts can be persuaded. Statement 1 was worded anti-environmental but reverse coding matched it with the second statement. Hence, the two jointly tap thinking of consumers from an environmental perspective, so the given name to the component is justified. • Sustainable Mobility (SM) The term mobility here includes transportation, travel, and accessibility. Sustainable mobility in this sense pertains to sustainable development which applies to mobility. With population explosion, urban crawl, and an increasing number of owners of private vehicles, there is a need to think for comprehensive solutions intended for eco-mobility. Connecting with eco-mobility, eco-responsible solutions of parking with eco-park scheme is an ambivalent instrument that serves both transportation and traffic regulations. Related to it, if people admit on some standards and apply them in routine life the concept of sustainable mobility can be made practicable. Accordingly, as the name of the component entails, the two statements here deal with the attitude of consumers regarding appropriate parking and assessment of parking facilities. In recent times, new eco-parking technologies for private vehicles are offered to consumers which give improved access to parking space, as well as better management of parking areas. These parking areas may involve some construction and running costs; so, facilities can be provided to consumers on payment basis. Therefore, the first statement (SM1) directs on consumers’ support for raising parking fees in cities. It is also essential to ensure people standpoint about whether they themselves intend to pay for the same purpose or not and this view of consumers’ is captured by the second item (SM2). The encouraging outlook of people in both kinds of measurements may boost sustainable mobility positively, hence the name of the component. With this chapter, the first objective is abided by the exploration of different components of behavioural and attitudinal constructs. The chapter concludes that

5.2 Domains of Attitudinal Construct

237

domains of ‘responsible consumption behaviour (RCB)’ namely: responsible purchasing, responsible usage, responsible disposal, and allied socially responsible behaviours originate with two components each. However, the domain of responsible maintenance reflects only one component. General attitudinal domain highlights the general concern of consumers for a sustainable future and their commitment to act responsibly. Further, in specific attitudinal domain, the analysis too seems to yield an interpretable pattern of seven types of people specific attitudes by which efforts can be geared up to maintain a sustainable and healthy living (Table 5.25). With these set of components, in the next chapter, the ‘theory of responsible behaviour formation (TRBF)’ is investigated by empirically testing ‘C-A-C-B (Concern→Attitude→Commitment→Behaviour)’ model in each of the behavioural domain.

Table 5.25 Summarization of behavioural and attitudinal dimensions Constructs

Domains

Dimensions/Components

Behavioural Construct: Responsible Consumption Behaviour (RCB)

Responsible Purchasing Behaviour (RPB)

Eco-Friendly Choice (EFC)

Responsible Usage Behaviour (RUB)

Sustainable Habits (SH)

Responsible Maintenance Behaviour (RMB)

Minimizing Wastage (MW)

Responsible Disposal Behaviour (RDB)

Appropriate Disposal (AD)

Allied Socially Responsible Activities

Environmentally Relevant Activities (ERA)

Green Buying (GB) Water Conservation (WC)

Recycling Intentions (RI)

Sustainable Societal Conduct (SSC) Attitudinal Constructs: General and Specific

General Attitudinal Domain

Concern for Sustainable Future (CSF) Commitment to Initiate (CI)

Specific Attitudinal Domain: Attitude towards Sustainable Living (ASL)

Attitude towards Conserving Ecology (ACE/CE) Anticipating Mounting Waste (AMW) Need for Recycling (NR) Civic Norms (CN) Overcoming Green Myopia (OGM) Environmental Thinking (ET) Sustainable Mobility (SM)

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References Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in analysis of covariance structures. Psychological Bulletin, 88, 588–606. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications and programming (2nd ed., pp. 1–391). New York, London: Taylor and Francis Group. Churchill, G. A., Iacobucci, D., & Israel, D. (2010). Marketing research (4th ed., pp. 1–673). Cengage Learning Editions. Cleveland, M., Kalamas, M., & Laroche, M. (2012). “It’s not easy being green”: exploring green creeds, green deeds, and internal environmental locus of control. Psychology and Marketing, 29(5), 293–305. Ganguly, B., Dash, S. B., & Cyr, D. (2009). Website characteristics, trust and purchase intention in online stores: An empirical study in Indian context. Journal of Information Science and Technology, 6(2), 22–44. Gatignton, H. (2010). Confirmatory factor analysis in statistical analysis of management data (2nd ed., pp. 1–387). New York, Dordrecht, Heidelberg, London: Springer. Gupta, K. & Singh, N. (2014/15). Fit estimation in structural equation modeling: A synthesis of related statistics. HSB Research Review, 8(2); 9(1), 20–27. Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (2006). Multivariate data analysis (5th ed., pp. 1–700). Dorling Kindersley (India) Pvt. Ltd, Pearson Education, Inc., New Delhi. Hancock, G. R., & Mueller, R. O. (2006). Structural equation modeling (pp. 1–427). United States of America: Information Age Publishing. Hooper, K., Coughlan, J., & Mullen, M. R. (2008). Structural equation modeling; guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53–60. Hu, L., & Bentler, P. M. (1999). Cut-off criteria for fit indices in co-variance structure analysis: Conventional criteria vs. new alternatives. Structural Equation Modeling, 6(1), 1–55. Malhotra, N. K., & Dash, S. (2012). Marketing research: An applied orientation (6th ed., pp. 1–929). Pearson Education Inc, New Delhi: Dorling Kindersley (India) Pvt. Ltd. MacCallum, R. C. (1990). The Need for Alternative Measures of Fit in Covariance Structure Modeling. Multivariate Behavioral Research 25 (2):157–162. Mondejar-Jimenez, J. A., Cordente-Rodriguez, M., Meseguer-Santamaria, M. L., & Gazquez-Abad, J. C. (2011). Environmental behavior and water saving in Spanish housing. International Journal of Environmental Research, 5(1), 1–10. Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness of fit indices for structural equation models. Psychological Bulletin, 105, 430–445. Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (pp. 1–487). New Jersey, London: Lawrence Erlbaum Associates, Publishers Mahwah. Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38, 197–201. Zakuan, N., Yusof, S. M., Mat Saman, M. Z, & Mohd Shaharoun, A. (2010). Confirmatory factor analysis of TQM practices in Malaysia and Thailand automotive industries. International Journal of Business and Management, 5(1), 160–175.

Chapter 6

Model Specification and Theory Testing

This chapter presents the results of the analysis for objectives 2 and 3, and also works for the testing of hypotheses developed in Chap. 3. Sections 6.1 and 6.2 corresponds to objective 2, and Sects. 6.3–6.9 are devoted to objective 3. In this line, it envisages the C-A-C-B model for its empirical investigation about variables assumed as mediators in different behavioural domains. Mainly, the technique of path analysis is used to test the ‘theory of responsible behaviour formation (TRBF)’. Since an extensive elaboration on mediation analysis and path analysis does not come under the purview of this book, to make the text reader friendly, we only briefly explain mediation by path analysis further in the chapter. For attaining these objectives, firstly, various attitudinal and behavioural components (as are derived in the previous chapter) are converted into observed variables. Hair et al. (2006: pp. 116–119) have provided a range of methodological procedures for converting the unobserved constructs into observed variables. They talked about many choices. Amongst these, the first is about the use of a surrogate variable. Secondly, there are component scores generated by SPSS and similar software. Third, loadings of statements under a component may be used as weights, and then weighted average may be applied to generate statistical values. Lastly, summated scales are also an option to be exercised upon. The first method (selection of surrogate variable) may have its importance but the use of a single variable ignores the other considerable items underlying a construct. Spector (1992: p. 10) also points up that a single item has a large error component, thus surrogate variable is notoriously an unreliable measure of any supposed construct. In the second method of component scores, a latent component can be obtained as a linear combination of all variables underlying it; because, component scores are computed on the basis of loadings of all the items whether with low or high loading. However, in practice and interpretation, almost all researchers take into account only those statements in a component for which the loadings are too high or significant. Thus, from the viewpoint of interpretability, this technique also has little value. Connected with the third selection, the use of only significant and high factor loadings as weights for obtaining any construct’s score may seem advantageous; but © Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_6

239

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again, there exist no specifications and guidelines about which kind of loadings can be utilized as weights. The loadings tend to alter in principal component analysis, zero-order confirmatory models, and in one-stage or two-stage confirmatory models. Therefore, this approach too seems a subjective approach for getting pleasing results. Keeping in mind the above backdrop, the method of summated scales is selected. In this, the variables that go unidirectional under one component are summed up and averaged into its unobserved or latent construct. The same method is propounded by Malhotra and Dash (2012: p. 854). Psychological researchers are of the view that the summated scale reduces measurement error by using responses of respondents to a set of related items instead of relying on a single item. Spector (1992: p. 10) has reported that when summated scales are constructed, the errors of measurement are assumed to average approximately zero, resulting in an estimate of the true and reliable score. These scales are also able to represent the multiple aspects of a construct in a single measure representing what is held common. In essence, the ‘survey database’ highlighted in Sect. 4.1.6 of Chap. 4 is extended for the dimensions of behavioural and attitudinal constructs (by adding and averaging variables underlying a particular dimension) from Chap. 5, and analysis in this chapter is performed on these dimensions. In this way, all the tables and figures here came out as a resultant of empirical analysis on the ‘extended survey database’. Before proceeding further for the model testing, Sects. 6.1 and 6.2 examine the dimensions of behavioural and attitudinal constructs using descriptive statistics and inferential z statistics.

6.1 Behavioural Dimensions: A Descriptive Analysis The summated scales of behavioural dimensions are highlighted in Fig. 6.1. Table 6.1 provides the descriptive statistics and the test of significance of mean results of behavioural dimensions. As can be seen from the table, in all the dimensions, there are substantive mean differences from the test value of 3.0 (p < 0.001). Statistical values of standard deviations are quite low, and coefficients of variation explain the percentage of variations in the responses given by respondents. Therefore, regarding consistency, the component RI is favoured the most (CV = 16.49%), and high variations are displayed in the component AD (CV = 27.63%). The ranks in the last column are given to the constructs according to their average values. Amongst components of behavioural domains, SSC has obtained 1st rank   X = 4.237 . It implies that people are heavily engaged in activities that promote a   sustainable society. This is followed by GB and SH X = 3.979; X = 3.909 which reveal that consumers are also engaged in purchasing of energy-saving products, and develop such habits by which we all can make  RI and MW  sustainability a reality. attain approximately similar average values X = 3.864; X = 3.836 which reflect a moderate engagement of respondents in the acts of minimizing the wastage and their intentions to contribute towards recycling acts. Mean values for the remaining components point up that these behaviours are not the overwhelming preferences

6.1 Behavioural Dimensions: A Descriptive Analysis

241

Fig. 6.1 Extended survey database for summated scores of behavioural dimensions as new variables

  of consumers. For example, mean value for WC X WC = 3.560 shows that people make efforts for conserving water but not encouragingly. Similarly, they are somehow related to environmental talk and some other activities considerable in environmental   domain as shown by the average value of ERA X = 3.525 .

6.2 Attitudinal Dimensions: A Descriptive Analysis The scales for attitudinal dimensions are shown in Fig. 6.2 in the form of a SPSS data sheet. There were two attitudinal domains, and Table 6.2 describes the mean results and the results of significance testing for each dimension under them. Like the behavioural domain, all mean values are statistically significant and differ from the test value of 3.0 (p < 0.001). Standard deviation and values of coefficients of variation lie in the range of 0.552–0.908 and 13.21%–26.58%, respectively. In general attitudinal domain, the high mean of CSF is a symbol of respondents’ high concern for having a sustainable future over their unsustainable lifestyle   X = 4.177 . Overall, this shows that people accept their consumptive lifestyle as the primary cause of global warming. Thus, they are in high concert  it to  of changing tune up with the demands of sustainability. Next mean value of CI X = 3.741 clarifies that although majority of the people accept consumption as a demolisher of our natural environment, and can very well predicts its disastrous consequences, yet are not able to accept freely their readiness and initiatives in this direction. Under specific

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6 Model Specification and Theory Testing

Table 6.1 Behavioural dimensions: descriptive statistics and significance testing Summated constructs

Mean

S.D.

C.V. (%)

S.E.

Mean difference

Z statistics

Rank

Responsible purchasing domain Eco-friendly choice (EFC)

3.588

0.890

24.80

0.028

0.588

20.892*

VII

Green buying (GB)

3.979

0.813

20.43

0.026

0.979

38.069*

II

Responsible usage domain Sustainable habits (SH)

3.909

0.784

20.06

0.025

0.909

36.666*

III

Water conservation (WC)

3.560

0.954

26.80

0.030

0.560

18.578*

VIII

0.836

21.80

0.026

0.836

31.587*

V

Responsible maintenance domain Minimizing wastage (MW)

3.836

Responsible disposal domain Appropriate disposal (AD)

3.764

1.04

27.63

0.033

0.764

23.212*

VI

Recycling intentions (RI)

3.864

0.637

16.49

0.020

0.864

42.855*

IV

Domain of allied socially responsible behaviour Environmentally relevant activities (ERA)

3.525

0.872

24.74

0.027

0.525

19.024*

IX

Sustainable societal conduct (SSC)

4.237

0.884

20.86

0.028

1.237

44.222*

I

*p < 0.001 (Two-Tailed)

  attitudinal domain, the component AMW attains a very high average X = 4.239 . This presents a unique scenario that people strongly believe in the problem of waste which is hoisting day by day. The main cause is the use of plastic bags so they agree for  or minimize their use. The average of NR is also exceedingly high  abandoning X = 4.117 and explains that as consumers predict mounting waste problems, they agree that we require all means and mechanisms by which these problems can be tackled. Recycling can be a solution, so people feel its need and think about laws  for its promotion. The mean value for the component ACE X = 4.093 enlightens people’ feeling about saving ecology which can be conserved by using public transport or remaining conscious about water conservation. The average value of  ET is not very much encouraging X = 3.416 ; so, it can be said that people are somewhat sceptical about environmental education. It may be due to the ambiguity of constituents of environmental education or why it is required. Similarly, they are

6.2 Attitudinal Dimensions: A Descriptive Analysis

243

Fig. 6.2 Extended survey database for summated scores of attitudinal dimensions as new variables

also in obscurity regarding environmental legislation or whether these regulations can  work for restricting the unsustainable lifestyle of individuals. Average of CN X = 4.094 reveals people’ respect for the norms in a civil society. They do agree that music systems and loudspeakers create nuisance and noise  in society; so, support the ruling for their limited and regulated use. Mean of SM X = 3.388 is again above average but not very much high. This becomes an evidence of conflict for conferring answers about raising parking fees and their own contribution for the same.  OGM has come out with the least mean value X = 2.917 exhibiting that majority of sample disapprove sustainable products by perceiving them lacking in quality and because of their less/infrequent availability.

6.3 Investigation of C-A-C-B Model: An Empirical Approach This section represents and again analytically explains the C-A-C-B model by specifying attitudinal antecedents in each behavioural domain. Figure 6.3 reproduces the previous model from Chap. 3 into a more refined form so that it can be worked upon empirically.

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6 Model Specification and Theory Testing

Table 6.2 Attitudinal dimensions: descriptive statistics and significance testing Summated constructs

Mean

S.D.

C.V. (%)

S.E.

Mean difference

Z statistics

Rank

General attitudinal domain Concern for sustainable future (CSF)

4.177

0.552

13.21

0.017

1.177

67.460*

II

Commitment to initiate (CI)

3.741

0.726

19.41

0.023

0.741

32.243*

VI

Specific attitudinal domain Overcoming green myopia (OGM)

2.917

0.747

25.61

0.024

−0.083

−3.502*

IX

Attitude towards conserving ecology (ACE)

4.093

0.596

14.56

0.019

1.093

57.994*

V

Anticipating mounting waste (AMW)

4.239

0.605

14.27

0.019

1.239

64.779*

I

Need for recycling (NR)

4.117

0.636

15.45

0.020

1.117

55.547*

III

Environmental thinking (ET)

3.416

0.908

26.58

0.029

0.416

14.471*

VII

Civic norms (CN)

4.094

0.796

19.44

0.025

1.094

43.455*

IV

Sustainable mobility (SM)

3.388

0.863

25.47

0.027

0.388

14.218*

VIII

*p < 0.001 (Two-Tailed)

It has already been defined in Chap. 3 that C-A-C-B model integrates concern, attitude, commitment, and responsible behaviour by setting cause and effect relationship between these variables. The view that sustainability must be preserved is a concern (measured by CSF); consumers’ belief that various means such as conservation or recycling can do this, is their specific attitude (measured by dimensions of ASL) towards these and similar issues. These specific attitudes can generate a commitment to instigate for conserving/recycling or other issues (measured by CI). Consequently consumers, as induced by their commitment, can become actually engaged in responsible consumption behaviour (measured by dimensions of RCB). Two variables ‘concern for sustainable future (CSF)’ and ‘Commitment to Initiate (CI)’ are common to all behavioural domains. Specific attitude and responsible behaviour are further divided into different components. Bringing into light this

6.3 Investigation of C-A-C-B Model: An Empirical Approach

AMW

NR

ET

Assumptions

ACE

CN

OGM

SM

Attitude towards Sustainable Living (ASL)

H1b

Concern for Sustainable Future (CSF) H1c

♦

245

H1a

H2b

Commitment to Initiate (CI)

‘A’* → ‘B’*

CA

OGM → RPB

RPB H2a

ACE → RUB

RUB MW

Responsible Consumption Behaviour (RCB)

AMW → MW NR → RI

RI

ET → ERA ERA

H3

CA → SSC

SSC

*‘A’ stands for attitudinal constituents and ‘B’ implies the behavioural components

Fig. 6.3 C-A-C-B model concerning attitudinal specificity in behavioural domains

notion, this multifaceted model is simplified and each particular attitudinal component is coupled with allied behavioural type. The right part of the figure corresponds to these causal relationships under heading assumptions. For example, the attitudinal component OGM which shows consumers’ attitude towards sustainable products are employed to predict the behaviour of responsible purchasing (RPB). Similarly, their anticipation regarding increasing waste problem (AMW) is used to reflect their activities of minimizing the waste (MW) and so forth. RPB here is an average of its two components (EFC and GB) in ‘responsible purchasing domain’. RUB is created by averaging two components namely SH and WC for ‘responsible usage domain’. MW is the only component under ‘responsible maintenance domain’. RI is the second component under ‘responsible disposal domain’. The first component AD is not utilized in the model. It is so because the only attitudinal component which matches to this domain is NR. Theoretically, it is anticipated to associate with RI; thus, AD has no significant role to play in the examination of the model in ‘responsible disposal domain’ and has been left out. Unlike RPB and RUB, two components of the ‘domain of allied socially responsible behaviours’ (ERA and SSC) are analysed separately because of their isolated attitudinal companions; specifically ET and CA (Component CA is a combination of scores on two components namely, CN and SM). In this way, here, ‘domain of allied socially responsible behaviours’ will be analyzed with its two sub-domains: environmentally relevant activities and sustainable societal conduct. Further, from statistical point of view, this model is a Serial Mediation Model (as previously discussed in Chap. 3) and is investigated in the above specified domains by using the technique of path analysis. Various authors had described path analysis namely, Duncan 1966, Austin and Wolfle 1991, Austin and Calderson 1996, Wolfle 1999–2003, and Rucker et al. 2011. Path analysis can be viewed as a special case of structural equation modeling (SEM) with only a structural model but no measurement model (Malhotra and Dash, 2012: p. 716). Factually, path analysis extends the use of

246

6 Model Specification and Theory Testing

regression, and the bivariate correlation amongst two variables is decayed into four components. These are the direct effect (DE), indirect effect (IE), spurious effect (SE), and the unanalyzed component. Corresponding to it, mediation is typically the standard for testing theories regarding process (Rucker et al. 2011: p. 35) by analyzing both causal (DE and IE) and non-causal effects (SE). Any variable in a path model is considered as a mediator if it significantly carries the influence of a given independent variable (IV) on a given dependent variable (DV). However, there are four conditions to claim mediation as described by (Baron and Kenny, 1986: p. 1176). Based on the conditions, either total/full/complete/perfect and parital mediation can be claimed. Full mediation suggests that the process of influence of independent variable on dependent variable is fully explained, and there is no need to further test the effect of any other variable. In case of partial mediation there is a clear implication that the indirect effect of other variables can also be claimed. However, both the conditions of full and partial mediation obtain that the original direct effect (when any mediator is not included) must reduce/drop substantially after inclusion of mediator for one to argue either full or partial mediation (Rucker et al. 2011). Practical experience notices that full mediation has rarely been experienced by researchers; whereas, partial mediation can be the most pervasive case. Starting from the next section, empirical testing of the models is described in all the responsible behavioural domains set out in this study. In line with the previous chapter, path model fit is tested using guidelines from Gupta and Singh (2014/15). Indirect/Mediating effects are tested by using Sobel Test, Aroian Test, Goodman Test, and Bootstrapping.1 Calculations of Sobel test, Aroian test, and Goodman test are done by using online calculator developed by Preacher and Leonardelli (2001). Bootstrapping is done with Process Macro for SPSS v3.4 by Hayes (2019). Results of testing of direct and indirect effects are revealed in Endnotes.

6.4 Responsible Purchasing Domain: Path Analysis In Fig. 6.4, each rectangle represents an observed variable as already been explained created by summing and averaging the items in a construct. The figure shows that CSF is considered to be the only observed exogenous variable as its variance is assumed to be caused entirely by variables not in this model. The error terms are the unobserved exogenous variables. OGM, CI, and RPB are endogenous variables meaning that their variances are considered to be explained in part by other variables. So, paths are directional and are drawn towards these endogenous variables from exogenous variables. In this sense, variance in RPB is theorized to result from variance in CSF, 1 Bootstrapping

is a non-parametric method of testing indirect effects based on resampling with a replacement which is done many times. From each of these samples, the indirect effect is computed and a sampling distribution can be empirically generated. Very typically a confidence interval is computed in it, and it is checked to determine if zero is in the interval. If zero is not in the interval, then the researcher can be confident that the indirect effect is different from zero and significant (Kenny 2018).

6.4 Responsible Purchasing Domain: Path Analysis

247

Correlation Matrix of Input Variables CSF

OGM

CI

CSF

1.000

OGM

0.074**

1.000

CI

0.372*

0.009ns

1.000

RPB

0.340*

-0.008ns

0.267*

♦

*p < 0.001; **p < 0.05;

ns

RPB

1.000

p > 0.05

Fig. 6.4 Free model: a path model for responsible purchasing domain

OGM, and CI. Variance in CI is caused by CSF and OGM. Similarly, variance in OGM is assumed to be caused only by CSF. It can be observed that no variable is hypothesized to affect its dependent variable only directly; both the direct, as well as indirect (mediating) effects are hypothesized. Statistically, the following hypotheses are tested using this model. H1: Concern for Sustainable Future (CSF) has a significant positive effect on: (a) Responsible Purchasing Behaviour (RPB) (b) Overcoming Green Myopia (OGM) (c) Commitment to Initiate (CI). H2: Overcoming Green Myopia (OGM) has a significant positive effect on: (a) Responsible Purchasing Behaviour (RPB) (b) Commitment to Initiate (CI). H3: Commitment to Initiate (CI) has a significant positive effect on: Responsible Purchasing Behaviour (RPB) H4: Overcoming Green Myopia (OGM) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Responsible Purchasing Behaviour (RPB). (b) Concern for Sustainable Future (CSF) on Commitment to Initiate (CI). H5: Commitment to Initiate (CI) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Responsible Purchasing Behaviour (RPB) (b) Overcoming Green Myopia (OGM) on Responsible Purchasing Behaviour (RPB). H6: Overcoming Green Myopia (OGM) and Commitment to Initiate (CI) serially mediates the effect of: Concern for Sustainable Future (CSF) on Responsible Purchasing Behaviour (RPB)

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6 Model Specification and Theory Testing

Amongst these hypotheses, the first three hypotheses examine the direct effect of one exogenous variable on its allied endogenous variable. Hypotheses 4, 5, and 6, however, investigate the mediating effects. Maximum Likelihood Estimation (MLE) method in path analysis provides estimates for effects. The results of these hypotheses testing are shown in two tables. Table 6.3 corresponds to the first three hypotheses (H1–H3) and Table 6.4 displays the results for the assertions of mediation effects (H4, H5, and H6).

6.4.1 Hypotheses Testing for Direct Causal Effects By reading Table 6.3, the hypotheses, the causal paths assumed, the effects (direct, indirect, and spurious) and model fit statistics can be noted. Both the absolute and incremental model fit statistics are ideal: indicators of goodness and badness of fit are the values 1 and 0, respectively. The column labelled total effect provides the values for product moment coefficient of correlation for the two variables assumed in a causal path. In path analysis, this total effect is decomposed into a number of components which are shown in the table as direct, indirect, and spurious effects. The total causal effect comes out by summing the direct and indirect effects. Indirect effects are the sum of all the possible indirect causal paths going from one cause to one effect. Direct effect estimates are also provided in Fig. 6.4 above the arrows. The indirect paths and their magnitude are visible from the Table 6.3. The spurious effect is due to both exogenous and endogenous variables caused by some other set of variables. For instance, consider path CI → RPB; in Fig. 6.4, both CI and RPB are explained by CSF and OGM. Therefore, some of the effect of CI on RPB may be because they share common causes (what is not common is causal). In this sense, the hypotheses of the assumed effect of one causal variable on another are talked about for the direct effects. The hypotheses tested for these direct effects are described below. H1a: This hypothesis states about the significant positive effect of CSF on RPB. The total effect is 0.340; and in path analysis, this effect is decomposed into two parts namely direct and indirect effect. The direct effect is straight forward (β = 0.282) and the indirect effect is (IE = 0.058), because of OGM and CI as two intervening variables. The statistical value of the direct effect is the standardized beta which is positive and highly significant. Other variables (OGM and CI) remaining constant, this value indicates 0.28 standard deviation increase in behaviour of responsible purchasing with every one standard deviation increase in consumer concern for having a sustainable future. Therefore, the hypothesis is strongly supported. H1b: Regarding this hypothesis, the direct effect is the same as the total effect. This is due to no intervening and common variables for this path assumed (see Fig. 6.4). This effect is in hypothesized direction, is significant, however, the magnitude is somewhat low (β = 0.074; p < 0.05). Considering the significance and direction of the effect, the hypothesis is accepted.

0.074**

0.373*

−0.03ns

−0.02ns

0.162*

CSF → OGM

CSF → CI

OGM → RPB

OGM → CI

CI → RPB

H1b

H1c

H2a

H2b

H3

–

–

−0.003ns

−0.001ns

–

0.267*

0.009ns

−0.008ns

0.372*

0.074**

0.340*

*p < 0.001; **p < 0.05; ns p > 0.05; NS : Not Significant at 95% Confidence Interval Significance testing of direct effects and indirect effects is shown in the Endnotes

Incremental fit

0.162*

−0.018ns

−0.033ns

0.372*

0.074**

0.340*

Total effect (1 + 2 + 3)

NFI = 1.000 IFI = 1.000 CFI = 1.000

0.105

0.027

0.025

–

–

–

Total causal effect (1 + 2)

Goodness of fit ≈GFI = 1.000

Badness of fit ≈RMR = 0.000

–

–

OGM → CI → RPB = −0.0032ns

CSF → OGM → CI = −0.001ns

–

CSF → OGM → CI → RPB = − 0.00022NS

CSF → CI → RPB = 0.06*

0.058*

Spurious effect (SE) (3)

Absolute fit indices

Model fit statistics

0.282*

CSF → RPB

H1a

CSF → OGM → RPB = −0.0021ns

Causal paths

Direct effect (β) (1)

Causal paths

Indirect effects (IE) (2)

Causal paths and indirect effects

Causal paths and direct effects

Hypotheses

Table 6.3 Responsible purchasing domain: results of path analysis

Fully Supported

Not Supported

Not Supported

Fully Supported

Fully supported

Fully supported

Support for hypotheses

6.4 Responsible Purchasing Domain: Path Analysis 249

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6 Model Specification and Theory Testing

Table 6.4 Responsible purchasing domain: summary results for mediation effects Hypotheses

Causal paths

H4a

Direct effect

OGM CSF

Model fit statistics

Constrained model

Free model

0.28*

0.28*

RPB

Constrained model Absolute fit Goodness of fit GFI = 0.999 AGFI = 0.996

Badness of fit χ 2 /df = 0.709 RMR = 0.006 RMSEA = 0.000

Support for hypotheses Not supported

Incremental fit NFI = 0.995; RFI = 0.986; IFI = 1.002; TLI = 1.006; CFI = 1.000 H4b

0.37*

OGM CSF

0.37*

CI

Absolute fit Goodness of fit GFI = 0.999 AGFI = 0.996

Badness of fit χ 2 /df = 0.709 RMR = 0.006 RMSEA = 0.000

Not supported

Incremental fit NFI = 0.995; RFI = 0.986; IFI = 1.002; TLI = 1.006; CFI = 1.000 H5a

0.34*

CI CSF

RPB

0.28*

Absolute fit Goodness of fit GFI = 0.987 AGFI = 0.873

Badness of fit χ 2 /df = 25.932 RMR = 0.023 RMSEA = 0.158

Partially supported

Incremental fit NFI = 0.915; RFI = 0.488; IFI = 0.918; TLI = 0.498; CFI = 0.916 (continued)

6.4 Responsible Purchasing Domain: Path Analysis

251

Table 6.4 (continued) Hypotheses

Causal paths

H5b

CI OGM

Direct effect

Model fit statistics

Constrained model

Free model

−0.03ns

−0.03ns

RPB

Constrained model Absolute fit Goodness of fit GFI = 0.987 AGFI = 0.873

Badness of fit χ 2 /df = 25.932 RMR = 0.023 RMSEA = 0.158

Support for hypotheses Not supported

Incremental fit NFI = 0.915; RFI = 0.488; IFI = 0.918; TLI = 0.498; CFI = 0.916 H6

OGM RPB

CSF

Mediating effect = − 0.00022

Insignificance shown by Bootstrapping

Not supported

CI

*p < 0.001; ns p > 0.05

H1c: Testing this hypothesis, CSF significantly and positively influences CI. The indirect effect via OGM is insignificant and the direct effect for the path is 0.373, again highly significant given an observed significance level of 0.000. Hence, support for the hypothesis is again provided by the data. It can be interpreted that if concern for having a sustainable future will go up by one standard deviation, the commitment of consumers to contribute for the environment purpose will go up by 0.373 standard deviation. H2a: This hypothesis for a positive effect of OGM on RPB is neither supported in magnitude nor in direction. Both the total and direct effects are insignificant. Spurious effect for an amount equal to 0.025 exists because OGM and RPB both are caused by CSF in the model. Overall, OGM is not able to determine people’ behaviour of responsible purchasing . Statistically, the effect is not more than zero (β = −0.03; p > 0.05). H2b: Here, the standardized beta (direct effect: β = −0.02; p > 0.05) is not significant and is negligible. In line with the previous hypothesis, the insignificant path coefficient also rejects this hypothesis of the causal effect of OGM on CI. H3: As per this hypothesis, an antecedent to RPB is CI. The total effect of CI on RPB (0.267) is segregated as direct effect and spurious effect. CSF and OGM are the two variables and common influencers both for CI and RPB; thus, a part of the effect is spurious (SF = 0.105). Leaving this spurious component, the standardized path coefficient (β = 0.162; p < 0.001) indicates support of data for acceptance of the hypothesis.

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6 Model Specification and Theory Testing

In summary, this analysis confirms that concern for sustainable future is a very important factor which works behind people engagement in the behaviour of searching and buying of green/environment friendly products. This concern is also contributing for consumer commitment to protect the environment and work for environmental sustainability. This commitment further determines responsible buying. Overcoming green myopia, however, does not determine responsible purchasing and consumer commitment. This finding here is unique in itself. Spreading light, it was expected that if people will be less/not myopic about green products, this will confirm their sensible attitude regarding these products; then being committed for the cause, they will engage in actual buying. Contrary to these expectations, sample results came out with the finding that the majority of Indian people are actually highly sceptic and insensitive about green products. This is confirmed by a significantly low mean   value X = 2.917; p = 0.000 of OGM (Table 6.2). This low mean value implies negative thoughts of people; thus, demote responsible purchasing behaviour.

6.4.2 Hypotheses Testing for Mediation Effects Other three hypotheses deal with the power of mediating variables in mediating the direct effects. These mediating effects are actually the indirect effects depicted in Table 6.3. For testing the true mediation, the procedure adopted by Ganguly et al. (2009: p. 36-37) and Malhotra and Dash (2012: p. 856) is accomplished. Decades ago, the procedure was originated from the works of Baron and Kenny (1986). In the words of Rucker et al. (2011) and Malhotra and Dash (2012), the same process is in standard practice to test mediation effects with minimal modifications. For testing H4 and H5, the earlier model (which is called the free model: indirect paths opened) are compared with the constrained models (indirect paths controlled: constrained to 0). These constrained models are shown in Figs. 6.5 and 6.6. The summary results are shown in Table 6.4. H4a: Stated by this hypothesis, OGM is expected to mediate a part of the causal effect of CSF on RPB. As revealed by the table, in the constrained model (Fig. 6.5) the direct effect of CSF on RPB is significant (β = 0.28). This direct effect is compared with the direct effect in the free model (Fig. 6.4). It is also 0.28 and significant at 0.1% significance level. There is no change in the value of direct effect when the path from OGM to RPB was opened in the free model. This states that OGM has no intervening power that works between CSF and RPB. The model fit statistics which is exceptionally good for the constrained model also clarify that the path OGM → RPB has no notable value, and it is contributing nothing to the model. So, the hypothesis of mediation by OGM is not confirmed here. H4b: It can be observed from the model that OGM is also hypothesized as a mediator in between CSF and CI. The mediation can be notified if, with the introduction of OGM, the direct effect of CSF on CI significantly gets reduced. But in both models (constrained as well as free model), this direct effect is same (β = 0.37) and no reduction is noticed while studying it from constrained (Fig. 6.5) to free

6.4 Responsible Purchasing Domain: Path Analysis

253

Fig. 6.5 Constrained model of responsible purchasing domain: mediating effect of OGM

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 8 Degrees of Freedom (10 – 8) = 2 Chi-Square Statistic CMIN = 1.418 Probability Level = 0.492

Fig. 6.6 Constrained model of responsible purchasing domain: mediating effect of CI

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 9 Degrees of Freedom (10 – 9) = 1 Chi-Square Statistic CMIN = 25.932 Probability Level = 0.000

model (Fig. 6.4). The indirect effect is negligible (IE = −0.001). Accordingly, the hypothesis is not supported by the data and gets rejected. The model fit indices which are also exceptionally good, state that the variable OGM has no statistical power in the model, neither direct nor indirect. H5a: In this hypothesis, an assertion was made about the mediating role of variable CI between CSF and RPB. For the purpose, path CI to RPB is constrained (Fig. 6.6).

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6 Model Specification and Theory Testing

According to path estimate in this model, CSF has a statistically significant direct effect equal to 0.34 standard deviations on RPB (β = 0.34; p = 0.000). The effect from the level of 0.34 in the constrained model significantly decreased to 0.28 in the free model (Fig. 6.4). The difference (0.34 − 0.28 = 0.06) is the indirect effect of CSF on RPB from the path CI as presented in Table 6.3. This implies that CI has a statistical power to mediate the relationship between CSF and RPB by 17.65% [(0.34 − 0.28)/0.34]. The model fit statistics for the constrained model is not well which suggests that path CI → RPB has its own importance in the model, so when the path is controlled, the Chi-square (χ 2 = 25.932) and badness of fit (RMSEA = 0.158) are very much high. Incremental fit indices (RFI and TLI) are also poor. The path considerably gets reduced but still, the direct effect of 0.28 is significant. Therefore, the claim here is only for partial mediation by CI, and the hypothesis in this way is partially supported. H5b: The hypothesis predicts that CI may also intervene between variables OGM and RPB. The beta coefficient in case of the route OGM → RPB before and after controlling the path CI → RPB is insignificant, and same in constrained as well as in free model (β = −0.03; p > 0.05). Thus, the analysis provided no empirical support for relying on this hypothesis. Accordingly, is rejected and it is concluded that CI has no statistical power to influence the effect of OGM on RPB. H6: This hypothesis assumes a serial mediation that CSF influences OGM, OGM influences CI which then goes into RPB. But, the mediating path coefficient (−0.00022) is found having no statistical value. The significance of this coefficient is tested with Bootstrapping (Process Macro developed by Andrew F. Hayes as already been mentioned). The boot lower-limit confidence interval (LLCI) and boot upperlimit confidence interval (ULCI) are shown in endnotes in Table 6.18. These lower and upper limits are opposite in sign signifying that zero comes in this interval. This is an indication that the indirect path coefficient is insignificant. Hence, this hypothesis is rejected. In conclusion, CI has an important role in mediating the effect of CSF on RPB. On the other hand, OGM has no such power. The reason is obvious; as the condition of mediation (Baron and Kenny 1986) says that the mediating variable must also have a significant direct effect on outcome variable assumed. Here, when there is no significant total effect of OGM on RPB (β = −0.03; p > 0.05), there could not be any mediation possible by this variable. Perhaps because of the reason serial mediation too could not happen. Also, the effect of OGM on RPB is not mediated by CI. The reason for this is again extended from the previous part. The condition of mediation of a significant effect of the causal variable (OGM) on the outcome variable (RPB) is not fulfilled here. The effect is not mediated since there was no overall effect of OGM on RPB to mediate. The direct effect (beta value) is negligible (β = −0.03; p > 0.05).

6.5 Responsible Usage Domain: Path Analysis

255

6.5 Responsible Usage Domain: Path Analysis Like ‘responsible purchasing domain’, there are four observed variables in which three ACE, CI, and RUB are endogenous, conversely, CSF is exogenous. Error variances (e1, e2, e3) are exogenous and are unobserved. These errors are the stray causes or causes outside the model. The paths are going towards endogenous variables from exogenous variables. It is hypothesized in the model that high concern for sustainable future develops an affirmative attitude of consumers for the modes by which ecology can be conserved. This concern and attitude both can work in enhancing consumer commitment to initiate for environment protection. Given all these antecedents, people then behave responsibly for resource conservation and exercise responsible usage behaviour. This hypothetical model is examined by using path analysis and ported in Fig. 6.7. It is noted that χ 2 statistic of the proposed model is zero. Thus, the model is just identified model and has a saturated fit. The path coefficients (above arrows in the Fig. 6.7) indicate the direct effect of a variable assumed to be a cause, on another variable taken as an effect. These path coefficients are standardized partial regression coefficients. Interpreting these coefficients, the following hypotheses are further tested. H1: Concern for Sustainable Future (CSF) has a significant positive effect on: (a) Responsible Usage Behaviour (RUB). (b) Attitude towards Conserving Ecology (ACE) (c) Commitment to Initiate (CI). H2: Attitude towards Conserving Ecology (ACE) has a significant positive effect on: (a) Responsible Usage Behaviour (RUB) (b) Commitment to Initiate (CI). H3: Commitment to Initiate (CI) has a significant positive effect on: Responsible Usage Behaviour (RUB) H4: Attitude towards Conserving Ecology (ACE) mediates the effect of:

Correlation Matrix of Input Variables CSF CSF

ACE

CI

ACE

0.587*

1.000

CI

0.372*

0.378*

1.000

RUB

0.396*

0.391*

0.218*

♦

Fig. 6.7 Free model: a path model for responsible usage domain

RUB

1.000

*p < 0.001

1.000

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6 Model Specification and Theory Testing

(a) Concern for Sustainable Future (CSF) on Responsible Usage Behaviour (RUB). (b) Concern for Sustainable Future (CSF) on Commitment to Initiate (CI). H5: Commitment to Initiate (CI) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Responsible Usage Behaviour (RUB) (b) Attitude towards Conserving Ecology (ACE) on Responsible Usage Behaviour (RUB). H6: Attitude towards Conserving Ecology (ACE) and Commitment to Initiate (CI) serially mediates the effect of: Concern for Sustainable Future (CSF) on Responsible Usage Behaviour (RUB)

6.5.1 Hypotheses Testing for Direct Causal Effects The summary of required statistics to test the proposed hypotheses is revealed by the Table 6.5 in which decomposition of correlations into direct, indirect, and spurious effects are shown. Except for one direct effect in the case CI → RUB; all other estimated path coefficients are significant (p < 0.001) with the sign of influence as hypothesized (positive). H1a: In radiance with the first hypothesis, CSF and RUB are found significantly correlated (r = 0.396). This relationship gets segregated into two components (direct effect and indirect effect) in this path model. An examination of causal path CSF → RUB provides a significant beta coefficient (β = 0.245; p = 0.000). From this aspect, the hypothesis is fully supported. H2b: This hypothesis of a positive direct effect of CSF on ACE is highly accepted. The path indicates a direct effect value of 0.587 which is significant at 0.1% significance level. This shows the profound effect of CSF on ACE. H1c: The variables of this hypothesis (CSF and CI) are found moderately correlated. This correlation is shown in Table 6.5 under the column labelled total effect. Accumulated in total effect, an effect equal to 0.143 is indirect, due to ACE being an intervening variable. Rest of the effect is direct effect. As the path coefficient (β = 0.229) is highly significant (p = 0.000), this hypothesis again is strongly accepted. H2a: According to this hypothesis, ACE is assumed to influence RUB directly. The total effect of ACE on RUB is 0.391; wherein, in mathematical terms, an effect equal to 0.149 is spurious because of CSF being a common source of influence both for ACE and RUB. A nominal effect (0.010) is indirect which ACE has on RUB via CI. Leaving these aspects, standardized beta which is highly significant (β = 0.232; p = 0.000) gives the power to rely on the hypothesis; accordingly, it is strongly accepted. H2b: In this case, a direct positive effect of ACE on CI is hypothesized. From a total effect of 0.378, 0.134 is spurious. It can be seen from the Fig. 6.7 that both the variables get influenced by CSF; therefore, this much of effect is due to CSF being a shared cause. If this spurious part is ignored, the direct effect of ACE on CI is 0.244,

0.587*

0.229*

0.232*

0.244*

0.039ns

CSF → ACE

CSF → CI

ACE → RUB

ACE → CI

CI → RUB

H1b

H1c

H2a

H2b

H3

0.039ns

0.244*

0.242*

0.372*

0.587*

0.218*

0.378*

0.391*

0.372*

0.587*

0.396*

*p < 0.001; ns p > 0.05; NS : Not Significant at 95% Confidence Interval Significance testing of direct effects and indirect effects is shown in the Endnotes

Incremental fit

0.178

0.134

0.149

–

–

0.396*

Total effect (1 + 2 + 3)

NFI = 1.000 IFI = 1.000 CFI = 1.000

–

–

0.010ns

0.143*

–

__

Total causal effect (1 + 2)

Goodness of fit ≈GFI = 1.000

Badness of fit ≈RMR = 0.000

–

–

ACE → CI → RUB = 0.01ns

CSF → ACE → CI = 0.143*

–

CSF → ACE → CI → RUB = 0.0057NS

CSF → CI → RUB = 0.0092ns

0.151*

Spurious effect (SE) (3)

Absolute fit indices

Model fit statistics

0.245*

CSF → RUB

H1a

CSF → ACE → RUB = 0.136*

Causal paths

Direct effect (β) (1)

Causal paths

Indirect effects (IE) (2)

Causal paths and indirect effects

Causal paths and direct effects

Hypotheses

Table 6.5 Responsible usage domain: results of path analysis

Fully supported

Fully supported

Fully supported

Fully supported

Fully supported

Fully supported

Support for hypotheses

6.5 Responsible Usage Domain: Path Analysis 257

258

6 Model Specification and Theory Testing

both significant and in the hypothesized direction. Hence, a one standard deviation elevation in ACE causes 0.244 standard deviation increase in CI and the hypothesis is accepted. H3: An assumption was made here that CI positively affect RUB, thus has a significant influence on it. CI and RUB are observed to be significantly correlated as the total effect is 0.218 which is significant. But other than direct effect the more statistical power is due to CI and RUB sharing common causes CSF and ACE, which allocate part of their statistical strength to both the variables. Spurious effect (SE = 0.178) is greater in size than the direct effect (β = 0.039). This direct effect is insignificant at 5% level of significance. Thus, we find no empirical support for the claim of the hypothesis and it is rejected. From all these empirical discussions, it is clarified that concern for sustainable future is a significant determinant of attitude towards conserving ecology, commitment to initiate, and responsible usage behaviour. Attitude towards conserving ecology also significantly determines responsible usage behaviour. Contrary to the expectations, commitment to initiate is not a significant determinant of this behaviour. The reason may be that the variable ACE has more statistical power in analysis than CI. The mean value of CI is statistically significant but far less than ACE. Thereby, if people are engaged in the activities of environment conservation it is due to their formed attitude for preserving the ecology and seeing it a compulsion to live a healthy and sustainable life.

6.5.2 Hypotheses Testing for Mediation Effects Next Table 6.6 is prepared to confirm the mediation effects as hypothesized in H4, H5, and H6. For the same, a comparison is presented between free model (Fig. 6.7) and after constraining the intervening paths in the constrained models (Figs. 6.8 and 6.9). H4a: As assumed in this hypothesis, ACE mediates the effect of CSF on RUB. To confirm the same, constrained model (Fig. 6.8) is contrasted with free model (Fig. 6.7). CSF has a significant direct effect of an amount equal to 0.37 in the constrained model. The model in which path ACE → RUB is opened, the value noticeably gets reduced to 0.25. Although the beta value in the free model is reduced but still significant, so it is interpreted that ACE partially mediates the assumed effect. Consequently, the hypothesis also is partially supported. The model fit which becomes less encouraging after controlling for the said path also supports the mediation. H4b: The mediation by ACE between the path CSF and CI is inherent in this hypothesis. Regarding the same, the path going from ACE to CI is constrained to zero. Figure 6.8 describes that when the effect of ACE is constrained, CSF’s direct effect on CI is equal to 0.37 and is significant (p = 0.000), however, in the previous model which is termed as a free model (Fig. 6.7) when this path was open, this effect was 0.23, significant at 0.1% significance level. The reduction (0.143) is equal to indirect effect which is going from CSF to CI by the way of ACE which is significant.

6.5 Responsible Usage Domain: Path Analysis

259

Table 6.6 Responsible usage domain: summary results for mediation effects Hypotheses

Causal paths

H4a

ACE CSF

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.37*

0.25*

Absolute fit

RUB

Goodness of fit GFI = 0.960 AGFI = 0.799

Badness of fit χ 2 /df = 43.624 RMR = 0.031 RMSEA = 0.207

Support for hypotheses Partially supported

Incremental fit NFI = 0.896; RFI = 0.687; IFI = 0.898; TLI = 0.692; CFI = 0.897 H4b

0.37*

ACE

0.23*

RUB

CSF

Absolute fit Goodness of fit GFI = 0.960 AGFI = 0.799

Badness of fit χ 2 /df = 43.624 RMR = 0.031 RMSEA = 0.207

Partially supported

Incremental fit NFI = 0.896; RFI = 0.687; IFI = 0.898; TLI = 0.692; CFI = 0.897 H5a

0.25*

CI CSF

RUB

0.25*

Absolute fit Goodness of fit GFI = 0.999 AGFI = 0.992

Badness of fit χ 2 /df = 1.577 RMR = 0.005 RMSEA = 0.024

Not supported

Incremental fit NFI = 0.998; RFI = 0.989; IFI = 0.999; TLI = 0.996; CFI = 0.999 (continued)

260

6 Model Specification and Theory Testing

Table 6.6 (continued) Hypotheses

Causal paths

H5b

CI

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.24*

0.23*

Absolute fit

RUB

ACE

Goodness of fit GFI = 0.999 AGFI = 0.992

Badness of fit χ 2 /df = 1.577 RMR = 0.005 RMSEA = 0.024

Support for hypotheses Not supported

Incremental fit NFI = 0.998; RFI = 0.989; IFI = 0.999; TLI = 0.996; CFI = 0.999 H6

ACE RUB

CSF

Mediating effect = 0.0057

Insignificance shown by Bootstrapping

Not supported

CI

*p < 0.001

Fig. 6.8 Constrained model of responsible usage domain: mediating Effect of ACE

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 8 Degrees of Freedom (10 – 8) = 2 Chi-Square Statistic CMIN = 87.247 Probability Level = 0.000

Although this indirect effect is significant but the direct effect after reduction (β = 0.23) too remains significant. Hence, ACE partially mediates the effect of CSF on CI. Consequently, the hypothesis is partially supported. H5a: Another variable CI between CSF and RUB is assumed to mediate the causal relationship from CSF to RUB. Figure 6.9 corresponds toward the model in which path CI → RUB is constrained to zero. A comparison of this constrained model with the free model suggests that the statistical value of the direct effect of CSF on RUB

6.5 Responsible Usage Domain: Path Analysis

261

Fig. 6.9 Constrained model of responsible usage domain: mediating effect of CI

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 9 Degrees of Freedom (10 – 9) = 1 Chi-Square Statistic CMIN = 1.577 Probability Level = 0.209

remains static in both the models (β = 0.25; p = 0.000). As no change is noticed, it is claimed that CI has no intervening power. The statistics of model fit (both Absolute and Incremental) also confirms that if the path is controlled, the fit still remains exceptionally good. Therefore, the hypothesis is rejected and commitment has no power of mediation in the model. H5b: It is assumed in the hypothesis that in line with CSF → RUB, CI also is assumed to intervene in the route ACE → RUB. For testing this assumption, the constrained model (Fig. 6.9) is match up with free model (Fig. 6.7). The direct effect of ACE on RUB is 0.24 in the constrained model and 0.23 in the free model. The reduction is nothing but just because of rounding off the figures, so mediation by CI cannot be claimed by the analysis. The model fit also says that if CI is eliminated from the model, even then, the model fit remains quite perfect. Hence, this postulation is rejected by the data. H6: This hypothesis of serial mediation is rejected on the ground of insignificance of indirect effect coefficient (IE = 0.0057). As per bootstrapping results zero comes in the confidence interval −0.0044 (LLCI) to 0.0204 (ULCI). This highlights that the indirect effect is no different from zero and has no relevance. In this way, the hypothesis is rejected. Unlike the previous path model (purchasing domain), the supremacy of the attitudinal component (ACE) in this model is more than a general commitment for mediating the proposed relationships. The reason itself is clear from the analysis. CI has no direct effect on RUB (β = 0.04; p > 0.05). So, conditions for mediation as propounded in mediation analysis are not fulfilled. In this case, CI becomes powerless for arbitrating any significant link in the model whether between CSF and RUB or ACE and RUB. Probably because of it the serial mediation too cannot happen.

262

6 Model Specification and Theory Testing

6.6 Responsible Maintenance Domain: Path Analysis In Fig. 6.10, the variable MW is associated with CSF, AMW, and CI. Thus, theoretically, the behaviour of minimizing wastage is expected to explain by consumer concern for maintaining a sustainable future, anticipation of consumers regarding troubling waste problem, and their commitment to do something for the cause. There is only one arrow pointing towards AMW; so it can be explained only by a single variable (CSF). CI can be predicted by general concern (CSF) and specific attitude for minimizing the waste problem (AMW). From this aspect, CSF is the only observed variable which is exogenous too. The error variances (e1, e2, and e3) are the unobserved exogenous variables. As AMW, CI, and MW are all determined by other variables(s), these will be referred to as observed endogenous variables. There are 10 distinct sample moments calculated by the formula [p(p + 1)/2] where p is the number of observed variables [4(4 + 1)/2]. The number of parameters to be estimated is also 10. So, this model is a just identified model with calculated Chi-square value as 0.000. To investigate different direct effects and indirect effect (the effect via assumed mediators) tables 6.7 and 6.8 are prepared; following statistical hypotheses are remained to be tested here. H1: Concern for Sustainable Future (CSF) has a significant positive effect on: (a) Minimizing Wastage (MW) (b) Anticipating Mounting Waste (AMW) (c) Commitment to Initiate (CI). H2: Anticipating Mounting Waste (AMW) has a significant positive effect on: (a) Minimizing Wastage (MW) (b) Commitment to Initiate (CI). H3: Commitment to Initiate (CI) has a significant positive effect on: Minimizing Wastage (MW) H4: Anticipating Mounting Waste (AMW) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Minimizing Wastage (MW). (b) Concern for Sustainable Future (CSF) on Commitment to Initiate (CI). H5: Commitment to Initiate (CI) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Minimizing Wastage (MW) (b) Anticipating Mounting Waste (AMW) on Minimizing Wastage (MW). H6: Anticipating Mounting Waste (AMW) and Commitment to Initiate (CI) serially mediates the effect of: Concern for Sustainable Future (CSF) on Minimizing Wastage (MW).

0.588*

0.278*

0.196*

0.159*

−0.039ns

CSF → AMW

CSF → CI

AMW → MW

AMW → CI

CI → MW

H1b

H1c

H2a

H2b

H3

−0.039ns

0.159*

0.190*

0.372*

0.588*

0.132*

0.323*

0.354*

0.372*

0.588*

0.391*

NS :

*p < 0.001, > 0.05; Not Significant at 95% Confidence Interval Significance testing of direct effects and indirect effects is shown in the Endnotes

ns p

NFI = 1.000 IFI = 1.000 CFI = 1.000

0.171

0.164

0.164

–

–

0.391*

Total effect (1 + 2 + 3)

Goodness of fit ≈GFI = 1.000

–

–

−0.006ns

0.094*

–

–

Total causal effect (1 + 2)

Incremental fit

Badness of fit ≈RMR = 0.000

–

–

AMW → CI → MW = −0.0064ns

CSF → AMW → CI = 0.094*

–

CSF → AMW → CI → MW = −0.004NS

CSF → CI → MW = −0.011ns

0.101*

Spurious effect (SE) (3)

Absolute fit indices

Model fit statistics

0.291*

CSF → MW

H1a

CSF → AMW → MW = 0.118*

Causal paths

Direct effect (β) (1)

Causal Paths

Indirect effects (IE) (2)

Causal paths and indirect effects

Causal paths and direct effects

Hypotheses

Table 6.7 Responsible maintenance domain: results of path analysis

Not supported

Fully supported

Fully supported

Fully supported

Fully supported

Fully supported

Support for hypotheses

6.6 Responsible Maintenance Domain: Path Analysis 263

264

6 Model Specification and Theory Testing

Table 6.8 Responsible maintenance domain: summary results for mediation effects Hypotheses

Causal paths

H4a

AMW CSF

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.40*

0.29*

Absolute fit

MW

Goodness of fit GFI = 0.977 AGFI = 0.884

Badness of fit χ 2 /df = 24.406 RMR = 0.025 RMSEA = 0.153

Support for hypotheses Partially supported

Incremental fit NFI = 0.938; RFI = 0.814; IFI = 0.940; TLI = 0.821; CFI = 0.940 H4b

0.37*

AMW CSF

0.28*

CI

Absolute fit Goodness of fit GFI = 0.977 AGFI = 0.884

Badness of fit χ 2 /df = 24.406 RMR = 0.025 RMSEA = 0.153

Partially supported

Incremental fit NFI = 0.938; RFI = 0.814; IFI = 0.940; TLI = 0.821; CFI = 0.940 H5a

0.28*

CI CSF

MW

0.29*

Absolute fit Goodness of fit GFI = 0.999 AGFI = 0.992

Badness of fit χ 2 /df = 1.544 RMR = 0.006 RMSEA = 0.023

Not supported

Incremental fit NFI = 0.998; RFI = 0.988; IFI = 0.999; TLI = 0.996; CFI = 0.999 (continued)

6.6 Responsible Maintenance Domain: Path Analysis

265

Table 6.8 (continued) Hypotheses

Causal paths

H5b

CI AMW

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.19*

0.20*

Absolute fit

MW

Goodness of fit GFI = 0.999 AGFI = 0.992

Badness of fit χ 2 /df = 1.544 RMR = 0.006 RMSEA = 0.023

Support for hypotheses Not supported

Incremental fit NFI = 0.998; RFI = 0.988; IFI = 0.999; TLI = 0.996; CFI = 0.999 H6

AMW MW

CSF

Mediating effect = − 0.004

Insignificance shown by Bootstrapping

Not supported

CI

*p < 0.001

6.6.1 Hypotheses Testing for Direct Causal Effects H1a: In this hypothesis, MW is assumed to be directly and significantly caused by CSF. Total causal effect (addition of direct and indirect effect) of CSF on MW is 0.391 which is significant. From this total effect, the direct effect (β = 0.291) is also significant and is in the hypothesized direction. Thus, CSF significantly and directly instigates the behaviour of minimizing wastage. Averting statistically, the hypothesis is fully supported. H1b: This hypothesis contends that CSF directly and significantly affects AMW. The beta coefficient for the assumed path is very much high and statistically significant (β = 0.588; p = 0.000). This path coefficient is equal to the correlation coefficient (total effect) as the dependent variable (AMW) here is a function of a single independent variable (CSF). After seeing the magnitude and direction of this effect, the hypothesis is accepted, and it is claimed that people anticipation of mounting waste is a function of their concern and vision for a sustainable future. H1c: This hypothesis talks about the effect of CSF on CI. It can be said that CSF can affect CI directly, as well as indirectly via AMW. Therefore, the total causal effect here is 0.372 decomposed into two components namely direct effect (β = 0.278) and indirect effect (IE = 0.094). This shows that the assumption in this hypothesis is supported by the data with 0.278 standard deviation increase in commitment if concern increases by one standard deviation. H2a: As per this hypothesis, the conceptions for waste problem determine people’ engagement in the activities significant for minimizing wastage, specifically AMW

266

6 Model Specification and Theory Testing

leads to MW. A positive and significant beta value (β = 0.196; p = 0.000) is conclusive for the acceptance of this hypothesis. It maintains that as people anticipate the problem of rising waste, they try to behave in manners by which wastage can be minimized. H2b: Consumer anticipations of increasing waste problem is hypothesized as a cause which determines their commitment to contribute for environmental purpose. There has been observed a moderate correlation between these variables (r = 0.323; p = 0.000). From this relationship, an amount equal to 0.164 is sorted out by path analysis as a spurious effect because both AMW and CI are commonly explained by CSF. After deducting this value from the overall relationship, the direct effect (β = 0.159) is found significant and noteworthy. This implies 0.16 standard deviation activist change in behaviour of minimizing wastage if expectation of increasing waste problem elevates by one standard deviation. H3: As assumed, CI is not able to explain the behaviour of minimizing waste of people. The assumed path is neither in hypothesized direction nor significant in magnitude (β = −0.039; p > 0.05). Therefore, it can be said that CI has no statistical power to predict a kind of people’ behaviour in which they try to maintain the things responsibly so that the problem of waste can be reduced. Hence, this hypothesis is rejected. From these hypotheses testing results, the variables CSF and AMW are supported for having significant roles in the model. However, CI has no such position. It is not able to determine the responsible behaviour of waste minimization. Probably, this is happening because the attitude of consumers for waste problem is more robust than their commitment: average of AMW (4.24) variable is greater than the average of CI (3.74).

6.6.2 Hypotheses Testing for Mediation Effects H4a: This hypothesis is to investigate the mediating power of AMW between the causal path CSF → MW. The review of conditions of mediation suggests that the mediating effect of AMW is indeed present. When the mediating path through CSF to MW is controlled, the direct effect of CSF on MW is found significant and positive (β = 0.40; p = 0.000) which is dropped by 27.5% (β = 0.29; p = 0.000) when the same mediating path is opened in the free model (Fig. 6.10). This notable reduction in the beta coefficient states a partial mediation by the variable AMW. Therefore, this hypothesis is partially supported. H4b: The hypothesis states for the mediating power of AMW regarding the relationship between CSF and CI. The model in which the effect of AMW is constrained to zero is 6.11 which is compared with the free model 6.10. The direct effect of CSF on CI is 0.37 in Fig. 6.11. When the mediating path from AMW to CI is opened, a reduction equal to 0.09 can be noticed which is the indirect effect, going towards CI through AMW. As the indirect effect is significant, there is evidence of mediation. Since, direct effect in the free model is too significant, full mediation cannot be

6.6 Responsible Maintenance Domain: Path Analysis

267

Correlation Matrix of Input Variables CSF

AMW

CI

CSF

1.000

AMW

0.588*

1.000

CI

0.372*

0.323*

1.000

MW

0.391*

0.354*

0.132*

♦

MW

1.000

*p < 0.001

Fig. 6.10 Free model: a path model for responsible maintenance domain

Fig. 6.11 Constrained model of responsible maintenance domain: mediating effect of AMW

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 8 Degrees of Freedom (10 – 8) = 2 Chi-Square Statistic CMIN = 48.813 Probability Level = 0.000

claimed. Consequently, the hypothesis is partially supported. The fit indices of the constrained model which are not good (χ 2 /df and RMSEA are even not acceptable) also emphasize on the mediating role of variable AMW in the model. H5a: The constrained model, in which the direct effect of CI on MW is controlled, is displayed in Fig. 6.12. The direct effect of CSF on MW in this model is 0.28. In the free model, this path coefficient is 0.29. Both the effects are statistically the same. So, it is concluded that CI has no intervening power between and the hypothesis is not supported here. H5b: The hypothesis verbalizes for the mediating power of CI between the path AMW → MW. The constrained model (Fig. 6.12) here is again compared with the free model (Fig. 6.10). Statistically, there is no significant difference between the two path coefficients: β = 0.19 (constrained model); β = 0.20 (free model). In this way, again CI has no mediating power between AMW and MW. Similar to the above result, this hypothesis too is not sustained. H6: Here, the hypothesis of serial mediation is again rejected due to insignificance of indirect effect (IE = −0.004; CI = −0.0170 to 0.0037). Hence, it can be concluded that AMW and CI taken together are not able to mediate the effect of CSF on MW.

268

6 Model Specification and Theory Testing

Fig. 6.12 Constrained model of responsible maintenance domain: mediating effect of CI

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 9 Degrees of Freedom (10 – 9) = 1 Chi-Square Statistic CMIN = 1.544 Probability Level = 0.214

In summary, as expected AMW mediate the link between CSF and MW. But, opposite to the expectations, CI has not strengthened its position in mediating either of the path CSF → MW and AMW → MW. The probable reason for both these insignificant outcomes is the same as described before. When CI is not able to significantly determine MW, it cannot mediate any other direct effect going towards it. The proposition of serial mediation is also not accepted.

6.7 Responsible Disposal Domain: Path Analysis This proposed model in Fig. 6.13 specifies direct causal relationships between three exogenous variables (CSF, NR, and CI) and RI as one endogenous variable. CSF can influence directly as well as indirectly through NR and CI. CI can also get affected by NR in the model; thus NR can lead to RI directly or by the way of CI also. In this sense, exogenous variables NR and CI become endogenous for their causes, and CSF is thus the sole exogenous variable. For the statistical testing of these causal effects of one variable on another, following hypotheses are conceptualized. H1: Concern for Sustainable Future (CSF) has a significant positive effect on: (a) Recycling Intentions (RI) (b) Need for Recycling (NR) (c) Commitment to Initiate (CI). H2: Need for Recycling (NR) has a significant positive effect on: (a) Recycling Intentions (RI) (b) Commitment to Initiate (CI). H3: Commitment to Initiate (CI) has a significant positive effect on: Recycling Intentions (RI)

6.7 Responsible Disposal Domain: Path Analysis

269

H4: Need for Recycling (NR) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Recycling Intentions (RI) (b) Concern for Sustainable Future (CSF) on Commitment to Initiate (CI). H5: Commitment to Initiate (CI) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Recycling Intentions (RI) (b) Need for Recycling (NR) on Recycling Intentions (RI). H6: Need for Recycling (NR) and Commitment to Initiate (CI) serially mediates the effect of: Concern for Sustainable Future (CSF) on Recycling Intentions (RI).

6.7.1 Hypotheses Testing for Direct Causal Effects Table 6.9 presents the summary of statistics of path analysis. The total effect is the effect of one cause on its assumed effect if only that specific cause would be the sole independent variable (other independent variables not being included in the model). This gives all the power of prediction only to that variable and standardized partial regression coefficient (direct effect in path analysis) becomes equal to total effect (bivariate correlation). As there is more than one variable assumed as predictors for some particular relationships such as CSF → RI; this total effect decomposes into direct, indirect, and spurious effects. The power of direct effect shed light on the hypotheses H1, H2, and H3 which are scrutinized next. H1a: Two variables of this hypothesis (CSF and RI) are found strongly and significantly related to each other (r = 0.501; p = 0.000). In terms of cause and effect relationship, CSF can escort to RI directly, as well as passing through NR and CI (indirect effect). As a confirmation of the hypothesis for a direct effect of CSF and RI, the beta coefficient for direct effect is observed as 0.311 suggesting a 0.31 standard deviation upturn in recycling intentions if the concern of consumers elevates by one standard deviation. Consequently, this hypothesis is accepted both for significance and direction of effect assumed. H1b: It is obvious from Fig. 6.13 that there is only one arrow which is pointing at NR, all the way through CSF. Thus, the entire correlation is the direct effect. Looking at the significance and magnitude of this effect (β = 0.528; p = 0.000), the hypothesis is fully supported by the data. H1c: Here, CSF is in a position of influencing CI. It can be noted that CSF can affect CI directly (CSF → CI), as well as indirectly through NR (CSF → NR → CI). Therefore, factually the total effect is the summation of direct (β = 0.263) and indirect effect (IE = 0.110). As direct effect is significant, the hypothesis is fully supported. H2a: In the statistical language of this hypothesis, NR significantly and positively brings the consumer intentions for recycling (RI). A significant moderate correlation (r = 0.437; p = 0.000) is noticed between these variables (NR → RI). The statistical

0.528*

0.263*

0.190*

0.206*

0.239*

CSF → NR

CSF → CI

NR → RI

NR → CI

CI → RI

H1b

H1c

H2a

H2b

H3

0.239*

0.206*

0.239*

0.372*

0.528*

0.421*

0.345*

0.437*

0.372*

0.528*

0.501*

*p < 0.001; Sig. : Significant at 95% Confidence Interval Significance testing of direct and indirect effects is shown in the Endnotes

Incremental fit

0.182

0.139

0.198

–

–

0.501*

Total effect (1 + 2 + 3)

NFI = 1.000 IFI = 1.000 CFI = 1.000

–

–

0.050*

0.110*

–

–

Total causal effect (1 + 2)

Goodness of fit ≈GFI = 1.000

Badness of fit ≈ RMR = 0.000

–

–

NR → CI → RI = 0.050*

CSF → NR → CI = 0.110*

–

CSF → NR → CI → RI = 0.027Sig.

CSF → CI → RI = 0.062*

0.189*

Spurious effect (SE) (3)

Absolute fit indices

Model fit statistics

0.311*

CSF → RI

H1a

CSF → NR → RI = 0.101*

Causal paths

Direct effect (β) (1)

Causal paths

Indirect effects (IE) (2)

Causal paths and indirect effects

Causal paths and direct effects

Hypotheses

Table 6.9 Responsible disposal domain: results of path analysis

Fully supported

Fully supported

Fully supported

Fully supported

Fully supported

Fully supported

Support for hypotheses

270 6 Model Specification and Theory Testing

6.7 Responsible Disposal Domain: Path Analysis

271

Correlation Matrix of Input Variables CSF CSF

NR

CI

NR

0.528*

1.000

CI

0.372*

0.345*

1.000

RI

0.501*

0.437*

0.421*

♦

RI

1.000

1.000

*p < 0.001

Fig. 6.13 Free model: a path model for responsible disposal domain

value is highly noticeable but magnitude equal to 0.198 is spurious, as CSF has impinged on both NR and RI. This spurious effect is not causal, thus the total causal effect (0.437 − 0.198 = 0.239) is integrated with both direct and indirect effects. A direct part in this causal effect (β = 0.190; p = 0.000) is positive and significant, elaborating that if consumers feel for a need of recycling, they will be more intended to do the same. Factually, this hypothesis is accepted. H2b: Inherent in this hypothesis is the extent to which NR is able to influence CI. There is no intervening variable between path NR → CI, but the model has CSF as a common influencer of both these variables. Therefore, a statistical value (SE = 0.139) in correlation can be taken as sham. The significant beta value (β = 0.206; p = 0.000) shows approximately 0.21 standard deviation increase in commitment with 1 standard deviation increase in consumers’ sensations that we require recycling. Hence, the hypothesis for the assumed effect is accepted. H3: In this hypothesis, the commitment of consumers to initiate is assumed to generate their intentions for recycling. The spurious component (SE = 0.182) again holds its position because CSF and NR are the two common determinants both for CI and RI. If this spurious part is left out, the direct effect (β = 0.239) is in a positive direction and highly significant (p = 0.000). This positive effect clarifies that if commitment increases by one standard deviation, intentions for recycling will definitely increase, and the increase will be approximately 0.24 standard deviations. In this way, we accept the hypothesis and conclude that recycling intentions of consumers are a function of their commitment to initiate for the same.

6.7.2 Hypotheses Testing for Mediation Effects To test the hypotheses related to mediation effects, Table 6.10 is prepared for analyzing the statistical figures. Similar to previous evaluated parts for mediation effects, the analysis is carried on with two additional models. First, paths from NR are constrained to zero and then the path CI → RI is evaluated similarly. A comparison is made between constrained and free model and the results for these hypotheses testing are described as follows:

272

6 Model Specification and Theory Testing

Table 6.10 Responsible disposal domain: summary results for mediation effects Hypotheses

Causal paths

H4a

Direct effect

NR CSF

Model fit statistics

Constrained model

Free model

0.40*

0.31*

RI

Constrained model Absolute fit Goodness of fit GFI = 0.966 AGFI = 0.829

Badness of fit χ 2 /df = 36.722 RMR = 0.031 RMSEA = 0.189

Support for hypotheses Partially supported

Incremental fit NFI = 0.921; RFI = 0.762; IFI = 0.923; TLI = 0.767; CFI = 0.922 H4b

0.37*

NR CSF

0.26*

CI

Absolute fit Goodness of fit GFI = 0.966 AGFI = 0.829

Partially supported

Partially supported

Incremental fit NFI = 0.921; RFI = 0.762; IFI = 0.923; TLI = 0.767; CFI = 0.922 H5a

0.37*

CI CSF

0.31*

RI

Absolute fit Goodness of fit GFI = 0.967 AGFI = 0.675

Partially supported

Partially supported

Incremental fit NFI = 0.925; RFI = 0.550; IFI = 0.926; TLI = 0.553; CFI = 0.926 H5b

0.24*

CI NR

RI

0.19*

Absolute fit Goodness of fit GFI = 0.967 AGFI = 0.675

Partially supported

Partially supported

(continued)

6.7 Responsible Disposal Domain: Path Analysis

273

Table 6.10 (continued) Hypotheses

Causal paths

Direct effect Constrained model

Model fit statistics Free model

Constrained model

Support for hypotheses

Incremental fit NFI = 0.925; RFI = 0.550; IFI = 0.926; TLI = 0.553; CFI = 0.926 H6

NR RI

CSF

Mediating effect = 0.027

Significance shown by Bootstrapping

Partially supported

CI

*p < 0.001

H4a: As maintained by this hypothesis, NR mediates the direct effect of CSF on RI. Examining for the same, in the constrained model (Fig. 6.14) the direct effect coefficient is seen as 0.40 which is significant at 0.001 level of significance. This statistical value in the free model becomes 0.31 (p < 0.001). The fluctuation from the previous level is the indirect effect of CSF on RI via NR (Table 6.9). When this path is controlled, the model fit indices for the constrained model are also not so much encouraging. Accordingly, the mediating power of NR is confirmed and on the basis of the significance of the beta coefficient in the free model; the hypothesis is partially supported by the data. H4b: This hypothesis tests whether NR is able to mediate the relationship between CSF and CI. In the free model, the correlation coefficient (r = 0.37) is decomposed into two components; direct component is equal to 0.263 and indirect component is equal to 0.110. This indirect effect is statistically significant (p = 0.000). Due to significant value, this proposition of mediation is partially accepted. Fig. 6.14 Constrained model of responsible disposal domain: mediating effect of NR

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 8 Degrees of Freedom (10 – 8) = 2 Chi-Square Statistic CMIN = 73.444 Probability Level = 0.000

274

6 Model Specification and Theory Testing

Fig. 6.15 Constrained model of responsible disposal domain: mediating effect of CI

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 9 Degrees of Freedom (10 – 9) = 1 Chi-Square Statistic CMIN = 69.486 Probability Level = 0.000

H5a: This hypothesis says that CI mediates the direct link between CSF and RI. According to the conditions of mediation, the path CI → RI is controlled in Fig. 6.15 (constrained model) and then it was open in Fig. 6.13 (free model). A comparison is made between the constrained model and free model. The direct effect coefficient fluctuates by 16.2% [(0.37 − 0.31)/0.37] for the path CSF → RI by going from constrained model to free model. Its value in the constrained model is 0.37 and is decreased by 0.06 becoming 0.31 in the free model. The comparison reveals that CI is able to partly mediate the relationship between CSF and RI; accordingly, able to influence some degree of direct effect. In this way, the hypothesis is partially supported. H5b: It can be seen from the Fig. 6.13 that variable CI is working between NR and RI, thus is an intervening variable in the path NR → RI. For investigating the statistical power of intervention, the hypothesis is developed. Controlled model (Fig. 6.15) exclaims that there is a 0.24 standard deviation increase in RI with one standard deviation increase in NR. Contrary to it, only a 0.19 standard deviation increase is visualized in the free model (Fig. 6.13). The reduction of 0.05 confirms for a partial mediation of CI for the direct effect of NR on RI. In this way, partial support is extended for this hypothesis. H6: Contrary to earlier domains, the mediating effect in this domain is significant. The significance is asserted on the 95% confidence interval in which both the signs are identical (positive). This shows the zero does not come in this interval; so, the indirect effect is different from zero. Accordingly, the hypothesis is accepted. In the previous models, the direct path of CI to behaviour was very weak (insignificant); so CI played no part in mediation analysis in the models tested in earlier domains. However, here CI significantly determines RI and this determination also provides it with the power of mediation. Also, with significant serial mediation, it can surely be said that concern for sustainable future converts into people attitude

6.7 Responsible Disposal Domain: Path Analysis

275

that there is a need for recycling. This attitude converts into a commitment for the cause and finally, people intend to get involve in recycling activities.

6.8 Environmentally Relevant Activities: Path Analysis Figure 6.16 is constructed for the path analytical model showing the way for environmentally relevant activities. As customary for path analytical models in this study, all the four variables are observed (leaving the stray causes: error variances). ET, CI, and ERA are endogenous, and the only exogenous variable is CSF. It can be observed from the figure that CSF is assumed to cause ERA directly, as well as indirectly via ET and CI. Similarly, ET may cause ERA directly and this too can have an indirect effect via CI. Following hypotheses are considered in this section to be tested by path analysis. H1: Concern for Sustainable Future (CSF) has a significant positive effect on: (a) Environmentally Relevant Activities (ERA) (b) Environmental Thinking (ET) (c) Commitment to Initiate (CI). H2: Environmental Thinking (ET) has a significant positive effect on: (a) Environmentally Relevant Activities (ERA) (b) Commitment to Initiate (CI). H3: Commitment to Initiate (CI) has a significant positive effect on: Environmentally Relevant Activities (ERA) H4: Environmental Thinking (ET) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Environmentally Relevant Activities (ERA) (b) Concern for Sustainable Future (CSF) on Commitment to Initiate (CI). H5: Commitment to Initiate (CI) mediates the effect of:

Correlation Matrix of Input Variables CSF CSF

ET

CI

ERA

1.000

ET

0.163*

1.000

CI

0.372*

0.186*

1.000

ERA

0.293*

0.086**

0.314*

Note: * p < 0.001 and ** p < 0.05

Fig. 6.16 Free model: a path model for environmentally relevant activities

1.000

276

6 Model Specification and Theory Testing

(a) Concern for Sustainable Future (CSF) on Environmentally Relevant Activities (ERA) (b) Environmental Thinking (ET) on Environmentally Relevant Activities (ERA). H6: Environmental Thinking (ET) and Commitment to Initiate (CI) serially mediates the effect of: Concern for Sustainable Future (CSF) on Environmentally Relevant Activities (ERA).

6.8.1 Hypotheses Testing for Direct Causal Effects H1a: According to this hypothesis, CSF is able to determine ERA. A significant correlation (r = 0.293) is noticed between these variables (Table 6.11); so, it can be said that CSF has a total of this much effect on ERA. But out of this statistical value, the indirect effect is equal to 0.090. Therefore, the direct effect is 0.204 and it is highly significant. Accordingly, because of this direct effect of CSF on ERA, the hypothesis is strongly accepted. H1b: As it is popularized in this study that concern can determine the attitude of people towards something (towards an object), people concern for a sustainable future is assumed to affect their thinking for the causes of enrichment of environment. A significant beta coefficient (β = 0.163; p = 0.000) says that concern notably influence people’ thinking for environment purpose. In accordance with this result, the hypothesis is fully supported. H1c: The next variable hypothesized to be determined by CSF is CI. It has already been analysed in previous models that CI significantly and positively gets influenced by CSF in all the models of behavioural domain. In this model also, the direct effect is positive and significant (β = 0.351; p = 0.000). Consequently, this hypothesis also gets full support from the data. H2a: As per this hypothesis, ET is assumed to be a significant determinant of consumer activities relevant in the environmental domain (ERA). A very low correlation is observed between the attitudinal component (ET) and the behavioural component (ERA). This correlation, however, is significant (p < 0.05) but not as much as needed so that one variable may have a significant influence on its assumed effect. Same is the outcome of the path analysis model which provides a statistical value of 0.009 for the assumed direct effect of ET on ERA. This value is highly insignificant (p > 0.05) and even leads to conclude for a zero effect. Hence, the assumption is not fulfilled by the data. H2b: Peoples’environmental thinking is assumed as a cause of enhancing their commitment to initiate in environmental direction, statistically ET → CI . The empirical analysis gives power to this hypothesis and confirms its applicability. Environmental thinking is seen elevating commitment to initiate by approximately 0.13 standard deviations as visualized by significant direct effect coefficient (β = 0.129; p = 0.000). This statistical value replies for the full support of the hypothesis.

6.8 Environmentally Relevant Activities: Path Analysis

277

H3: Next premise states for a positive effect of CI on ERA. Firstly, these two variables (CI and ERA) are seen for their co-relational value which is found to be 0.314 with an observed significance of 0.000. As a thumb rule, this value is taken as the total effect of CI on ERA. Added in this value, a value equal to 0.077 is held as the spurious effect of CI on ERA which means this effect is just because these variables share CSF and ET as two common causes (the cause CSF being more robust). In this sense, the refine analytical value is the value of direct effect which for this path (CI → ERA) is 0.237 and significant (p = 0.000). Relying on the significance of the magnitude of this path coefficient, the hypothesis is firmly accepted.

6.8.2 Hypotheses Testing for Mediation Effects H4a: For analyzing the mediating effect of ET, this hypothesis is developed. Environmental thinking has no significant direct effect on environmentally relevant activities as analysed in H2a. Thus, one condition of mediation that the mediator variable must have a significant influence on the dependent variable is not fulfilled. In consequence, possibly ET could not mediate the link between CSF and ERA. An identical result is provided by mediation analysis (Table 6.12). After constraining the path ET → ERA, the direct effect value (CSF → ERA = 0.20) is the same as in the free model (Fig. 6.16). As follows, by this finding the hypothesis is not supported here. H4b: The previous hypothesis for the mediation of ET between path CSF to ERA was not supported; but here by viewing the results of the constrained and free model, opposite of the previous case is noticed. The direct effect (however, only slightly but significantly) gets reduced in the free model when ET is not constrained. The direct effect of 0.37 reduced to 0.35 in the free model. The significance of this reduction Fig. 6.17 Constrained model of environmentally relevant activities: mediating effect of ET

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 8 Degrees of Freedom (10 – 8) = 2 Chi-Square Statistic CMIN = 18.898 Probability Level = 0.000

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6 Model Specification and Theory Testing

Fig. 6.18 Constrained model of environmentally relevant activities: mediating effect of CI

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 9 Degrees of Freedom (10 – 9) = 1 Chi-Square Statistic CMIN = 53.233 Probability Level = 0.000

(IE = 0.02) is confirmed, and support is obtained for the assertion of mediation by ET. Consequently, the hypothesis is partially supported. H5a: As per this hypothesis, variable CI also has the power to mediate the link between CSF and ERA. The second constrained model (Fig. 6.18) in which path CI → ERA is held to be zero (no direct effect is assumed by CI) indicates that CSF has a significant direct effect on ERA with a power of 0.29. This effect size reasonably decreases in the free model and becomes 0.20. This striking decrease in statistical value is the indirect effect of CSF on ERA via CI (Table 6.11). Hence, it can be said that CI is able to mediate a part of the effect of CSF on CI. The path which is constrained has its own importance in the model; this can be said on the basis of model fit statistics. Indices (including AGFI, χ 2 /df, RMSEA, RFI, and TLI) become even worst if the path is controlled. Stating all these facts, the hypothesis is partially supported by the data (Table 6.12). H5b: This hypothesis of mediation of CI in the path ET to ERA becomes redundant and not acceptable in the light of previous analysis. ET has no significant effect on ERA (β = 0.01; p > 0.0.05). When there is no effect, there could not be any mediation possible. Thus, the decrease of 0.031 in the value of direct effect (ET → ERA) from the constrained model (Fig. 6.18) to free model (Fig. 6.16) even is significant but has no notable value. Hence, the hypothesis is rejected. This decision of rejection is based on the recommendations given by Kenny (2018). The author states that sometimes a researcher gets a statistically significant indirect effect but no statistical evidence that the independent variable causes the dependent variable. It is suggested that in such cases one should never make any claim of complete or partial mediation. So, going with this description the hypothesis is not acceptable. H6: Here, it is found that ET is not able to mediate the effect of CSF on ERA. But, CI does the same. If these mediators are taken together in a causal order for testing serial mediation, we find that mediation happens. The indirect effect is, however,

0.163*

0.351*

0.009ns

0.129*

0.237*

CSF → ET

CSF → CI

ET → ERA

ET → CI

CI → ERA

H1b

H1c

H2a

H2b

H3

–

–

0.030*

0.021*

–

0.314*

0.186*

0.086**

0.372*

0.163*

0.293*

*p < 0.001; **p < 0.05; ns p > 0.05; Sig. : Significant at 95% Confidence Interval Significance testing of direct effects and indirect effects is shown in the Endnotes

Incremental fit

0.237*

0.129*

0.039ns

0.372*

0.163*

0.293*

Total effect (1 + 2 + 3)

NFI = 1.000 IFI = 1.000 CFI = 1.000

0.077

0.057

0.047

–

–

–

Total causal effect (1 + 2)

Goodness of fit ≈GFI = 1.000

Badness of fit ≈RMR = 0.000

–

–

ET → CI → ERA = 0.0312*

CSF → ET → CI = 0.021*

–

CSF → ET → CI → ERA = 0.005Sig.

CSF → CI → ERA = 0.084*

0.090*

Spurious effect (SE) (3)

Absolute fit indices

Model fit statistics

0.204*

CSF → ERA

H1a

CSF → ET → ERA = 0.0016ns

Causal paths

Direct effect (β) (1)

Causal paths

Indirect effects (IE) (2)

Causal paths and indirect effects

Causal paths and direct effects

Hypotheses

Table 6.11 Environmentally relevant activities: results of path analysis

Fully Supported

Fully Supported

Not Supported

Fully Supported

Fully Supported

Fully Supported

Support for hypotheses

6.8 Environmentally Relevant Activities: Path Analysis 279

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6 Model Specification and Theory Testing

Table 6.12 Environmentally relevant activities: summary results for mediation effects Hypotheses

Causal paths

H4a

ET CSF

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.20*

0.20*

Absolute fit

ERA

Goodness of fit GFI = 0.991 AGFI = 0.954

Badness of fit χ 2 /df = 9.449 RMR = 0.028 RMSEA = 0.092

Support for hypotheses Not supported

Incremental fit NFI = 0.944; RFI = 0.833; IFI = 0.950; TLI = 0.848; CFI = 0.949 H4b

0.37*

ET CSF

0.35*

CI

Absolute fit Goodness of fit GFI = 0.991 AGFI = 0.954

Badness of fit χ 2 /df = 9.449 RMR = 0.028 RMSEA = 0.092

Partially supported

Incremental fit NFI = 0.944; RFI = 0.833; IFI = 0.950; TLI = 0.848; CFI = 0.949 H5a

0.29*

CI CSF

ERA

0.20*

Absolute fit Goodness of fit GFI = 0.975 AGFI = 0.747

Badness of fit χ 2 /df = 53.233 RMR = 0.040 RMSEA = 0.229

Partially supported

Incremental fit NFI = 0.843; RFI = 0.057; IFI = 0.845; TLI = 0.058; CFI = 0.843 (continued)

6.8 Environmentally Relevant Activities: Path Analysis

281

Table 6.12 (continued) Hypotheses

Causal paths

H5b

CI ET

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.04ns

0.01ns

Absolute fit

ERA

Goodness of fit GFI = 0.975 AGFI = 0.747

Badness of fit χ 2 /df = 53.233 RMR = 0.040 RMSEA = 0.229

Support for hypotheses Not supported

Incremental fit NFI = 0.843; RFI = 0.057; IFI = 0.845; TLI = 0.058; CFI = 0.843 H6

ET ERA

CSF

Mediating effect = 0.005

Significance shown by Bootstrapping

Partially supported

CI

*p < 0.001; ns p > 0.05

very low (IE = 0.005) but significant (LLCI 0.0032 to ULCI 0.0143). Hence, the hypothesis of serial mediation is accepted for this domain. As indicated by the conditions of mediation, one of the mediators (ET) has no significant direct effect on the dependent variable (ERA). Although this condition of mediation is not fulfilled yet the serial mediation occurred in this domain. It may be due to more power of CI in determining ERA and significant effect of ET of CI too. ET alone is weak for mediation; however, with CI it works in the process of behaviour formation though with least impact.

6.9 Sustainable Societal Conduct: Path Analysis The model (Fig. 6.19) is analogous to other models. Two observed variables (CSF, CI) are identical as utilized in previous path analytical models. Attitudinal variables namely CN and SM are found significant in this domain and both are termed as indicators of civil attitude of people which can relate to their civil (societal) acts. The behavioural variable which is observed as well as endogenous is SSC. By the means of this model, it is expected that the concern for a sustainable future is also meaningful from the aspect of social sustainability and insightful attitude of people towards communal sustainability is a compulsion for it. This attitude may set a path to reach the destination of those activities of people which can be regarded as sustainable. Probably, this civil attitude may also relate with a commitment to

282

6 Model Specification and Theory Testing

Correlation Matrix of Input Variables CSF

CA

CI

CSF

1.000

CA

0.379*

1.000

CI

0.372*

0.281*

1.000

SSC

0.465*

0.315*

0.310*

♦

SSC

1.000

* p < 0.001

Fig. 6.19 Free model: a path model for sustainable societal conduct

initiate and can predict it firmly. Then, it can also be an anticipation that if people will be heartily committed for maintaining communal sustainability, this will give a boost to their respectable societal activities. These theoretical anticipations are next illustrated in the form of some propositions to be tested by this model. H1: Concern for Sustainable Future (CSF) has a significant positive effect on: (a) Sustainable Societal Conduct (SSC). (b) Civil Attitude (CA) (c) Commitment to Initiate (CI). H2: Civil Attitude (CA) has a significant positive effect on: (a) Sustainable Societal Conduct (SSC) (b) Commitment to Initiate (CI). H3: Commitment to Initiate (CI) has a significant positive effect on: Sustainable Societal Conduct (SSC) H4: Civil Attitude (CA) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Sustainable Societal Conduct (SSC) (b) Concern for Sustainable Future (CSF) on Commitment to Initiate (CI). H5: Commitment to Initiate (CI) mediates the effect of: (a) Concern for Sustainable Future (CSF) on Sustainable Societal Conduct (SSC) (b) Civil Attitude (CA) on Sustainable Societal Conduct (SSC). H6: Civil Attitude (CA) and Commitment to Initiate (CI) serially mediates the effect of: Concern for Sustainable Future (CSF) on Sustainable Societal Conduct (SSC).

6.9.1 Hypotheses Testing for Direct Causal Effects H1a: It can be noted that the total causal effect of CSF on SSC is 0.465. The segregated part of this causal effect is a direct effect and an indirect effect. The indirect effect

0.379*

0.310*

0.139*

0.164*

0.137*

CSF → CA

CSF → CI

CA → SSC

CA → CI

CI → SSC

H1b

H1c

H2a

H2b

H3

__

__

0.022*

0.062*

__

Badness of fit ≈ RMR = 0.000

__

__

CA → CI → SSC = 0.0224*

CSF → CA → CI = 0.062*

__

CSF → CA → CI → SSC = 0.0085Sig.

CSF → CI → SSC = 0.0434*

0.104*

*p < 0.001; Sig. : Significant at 95% Confidence Interval Significance testing of direct effects and indirect effects is shown in the Endnotes

Goodness of fit ≈ GFI = 1.000

Absolute fit indices

Model fit statistics

0.362*

CSF → SSC

H1a

CSF → CA → SSC = 0.053*

Causal paths

Direct effect (β) (1)

Causal paths

Indirect effects (IE) (2)

Causal paths and indirect effects

Causal paths and direct effects

Hypotheses

Table 6.13 Sustainable societal conduct: results of path analysis

0.137*

0.164*

0.162*

0.372*

0.379*

0.465*

Total causal effect (1 + 2)

0.310*

0.281*

0.315*

0.372*

0.379*

0.465*

Total effect (1 + 2 + 3)

NFI = 1.000 IFI = 1.000 CFI = 1.000

Incremental fit

0.173

0.117

0.153

__

__

__

Spurious effect (SE) (3)

Fully supported

Fully supported

Fully supported

Fully supported

Fully supported

Fully supported

Support for hypotheses

6.9 Sustainable Societal Conduct: Path Analysis 283

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6 Model Specification and Theory Testing

(IE = 0.104) is the summation of three indirect paths (Table 6.13). As hypothesized, the magnitude and significance of path coefficient (β = 0.362; p = 0.000) reveals that the first hypothesis is firmly accepted because of a noteworthy direct effect of CSF on SSC. H1b: Concern for a sustainable future is the only predictor of civil attitude. So, there is no indirect effect and the total causal effect and direct effect are same in size, as well as in direction (β = 0.379; p = 0.000). Hence, the hypothesis is fully accepted, and it is concluded that CSF has a positive and significant effect on CA. H1c: It is hypothesized here that CSF is able to explain variations in CI. A direct effect value is equal to 0.310 which is significant (p < 0.000). This effect size states that concern can substantially determine the commitment of people. In view of this, the hypothesis is supported by the data. H2a: This hypothesis describes for a significant effect of civil attitude on sustainable societal conduct. There is a moderate correlation (r = 0.315) between the variables assumed in this causal path (r = 0.315). CSF being a common influencer, the spurious effect is equal to 0.153. The value of the indirect effect is 0.022. Against this hypothesis, beta value is obtained as equal to 0.139 and significant at 0.1% significance level. The value is good enough to claim a substantive assumed effect. Stating both the magnitude and significance of this value, the hypothesis is accepted. H2b: The hypothesis says for a significant and positive effect of civil attitude on the commitment to initiate. The correlation between these two variables is equal to the total effect (r = 0.281; p = 0.000) which is significant. But value equal to 0.117 is spurious as CA and CI both are jointly influenced by CSF. In this sense, the total causal effect is 0.164. As there is no intervening variable between them, the total causal effect and direct effect both are same. Hence, this direct effect (β = 0.164; p = 0.000) point up 0.16 standard deviation increase in commitment if the civil attitude changes positively with one standard deviation. As this beta value is significant and positive, the hypothesis is accepted. H3: Assertion in this hypothesis is about the direct effect of CI on SSC. Amongst a total effect of 0.310, effect equal to 0.173 is spurious (CSF and CA being two common causes) and remaining (0.310 − 0.173 = 0.137) is causal which is identical to direct effect and states a rise of near about 0.14 standard deviation in sustainable civil conduct if commitment increases by one standard deviation. As the value is statistically reliable, the hypothesis is fully supported by the data. Rest of the three hypotheses are about mediation effects. So are tested by controlling the mediating variables CA and CI. The required outcome is shown in Table 6.14. Figure 6.20 corresponds to H4 for which paths from CA are constrained to zero. The adjoining Fig. 6.21 is to test H5 and the controlled path here is from CI.

6.9 Sustainable Societal Conduct: Path Analysis

285

Table 6.14 Sustainable societal conduct: summary results for mediation effects Hypotheses

Causal paths

H4a

CA CSF

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.41*

0.36*

Absolute fit

SSC

Goodness of fit GFI = 0.977 AGFI = 0.884

Badness of fit χ 2 /df = 24.235 RMR = 0.033 RMSEA = 0.153

Support for hypotheses Partially supported

Incremental fit NFI = 0.922; RFI = 0.767; IFI = 0.925; TLI = 0.774; CFI = 0.925 H4b

0.37*

CA CSF

0.31*

CI

Absolute fit Goodness of fit GFI = 0.977 AGFI = 0.884

Badness of fit χ 2 /df = 24.235 RMR = 0.033 RMSEA = 0.153

Partially supported

Incremental fit NFI = 0.922; RFI = 0.767; IFI = 0.925; TLI = 0.774; CFI = 0.925 H5a

0.40*

CI CSF

SSC

0.36*

Absolute fit Goodness of fit GFI = 0.990 AGFI = 0.898

Badness of fit χ 2 /df = 20.812 RMR = 0.023 RMSEA = 0.141

Partially supported

Incremental fit NFI = 0.967; RFI = 0.800; IFI = 0.968; TLI = 0.808; CFI = 0.968 (continued)

286

6 Model Specification and Theory Testing

Table 6.14 (continued) Hypotheses

Causal paths

H5b

CI CA

Direct effect

Model fit statistics

Constrained model

Free model

Constrained model

0.16*

0.14*

Absolute fit

SSC

Goodness of fit GFI = 0.990 AGFI = 0.898

Badness of fit χ 2 /df = 20.812 RMR = 0.023 RMSEA = 0.141

Support for hypotheses Partially supported

Incremental fit NFI = 0.967; RFI = 0.800; IFI = 0.968; TLI = 0.808; CFI = 0.968 H6

CA SSC

CSF

Mediating effect = 0.0085

Significance shown by Bootstrapping

Partially supported

CI

*p < 0.001

Fig. 6.20 Constrained model of sustainable societal conduct: mediating effect of CA

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 8 Degrees of Freedom (10 – 8) = 2 Chi-Square Statistic CMIN = 48.469 Probability Level = 0.000

6.9 Sustainable Societal Conduct: Path Analysis

287

Fig. 6.21 Constrained model of sustainable societal conduct: mediating effect of CI

Notes for Model Number of Distinct Sample Moments = 10 Number of Parameters to Be Estimated = 9 Degrees of Freedom (10 – 9) = 1 Chi-Square Statistic CMIN = 20.812 Probability Level = 0.000

6.9.2 Hypotheses Testing for Mediation Effects H4a: As per this hypothesis, CA can intervene between CSF and SSC or in other words, mediate a part of the total causal effect of CSF on SSC. For testing the same, direct effect (CSF → SSC) in the constrained model (Fig. 6.20) is contrasted with the direct effect in the free model (Fig. 6.19). In the constrained model, the direct effect is equal to 0.41 and fall substantially to 0.36 in the free model. The decreased amount (0.41 − 0.36 = 0.05) is equal to the indirect effect of this path as shown in Table 6.13. The value has been decreased significantly, but still, the direct effect is highly significant in the free model. Therefore, we can claim only partial mediation, and the hypothesis is partially supported. H4b: For testing the mediation by CA, its path CA → CI is constrained and set as zero (Fig. 6.20). In the free model (Fig. 6.19) in which intervening variable CA is doing the job, the direct effect reduced by a value of 0.062. This value is statistically significant and manifesting the indirect effect of CSF on CI by the medium of CA. Hence, the data is supporting the assumption of mediation by CA and the hypothesis is partially accepted. H5a: There is another variable CI which works in between CSF and SSC in the path model. Statistically, the hypothesis talks about the mediation effect of this variable. A beta value for the path (CSF → SSC) is 0.40 in the constrained model. Comparing this value with the free model (Fig. 6.19), a decrease of 0.04 is noticed. Table 6.13 verifies this value as the indirect effect coefficient which CSF has on SSC via CI and is significant; so, the hypothesis gets a partial support in the study.

288

6 Model Specification and Theory Testing

H5b: This hypothesis considers the mediation of the direct effect of CA on SSC by the mediating variable CI. The constrained model (Fig. 6.21) is compared with the free model (Fig. 6.19). Constraining CI → SSC gives a statistical power of 0.16 to the path CA → SSC (this is the direct effect in the constrained model). This power gets reduced slightly in the free model when CI is also working (β = 0.14; p = 0.000). Although, the decrease of 0.02 (0.16 − 0.14) in the value seems nominal but is significant. Therefore, it is concluded that CI mediates the effect, and thus the hypothesis gets partial support. H6: Like the previous two domains, indirect effect (IE = 0.0085) is again significant. Accordingly, the hypothesis is accepted. Therefore, the process of behaviour formation of SSC beginning from CSF going through CA and CI is justified here, leading towards the acceptance of the hypothesis. The chapter dealt with a descriptive analysis of attitudinal and behavioural dimensions, and tested the ‘theory of responsible behaviour formation (TRBF)’ with CA-C-B model in differing behavioural domains. In this way, objectives 2 and 3 are responded in it. Concluding objective 2, from the behavioural aspect, people are found highly engaged in the activities of sustainable societal conduct followed by green buying and sustainable habits. Then, the engagement is noticed in recycling intentions and minimizing wastage. People are moderately engaged in appropriate disposal, eco-friendly choices, water conservation, and environmentally relevant activities. Further, the attitudinal viewpoints are found utmost favourable in order for AMW, CSF, NR, CN, and ACE. Least level of attitude is found only for OGM. Conclusive for objective 3, summarized results can be studied from Tables 6.15 and 6.16. In the domains of ‘responsible disposal’, ‘environmentally relevant activities’, and ‘sustainable societal conduct’, the process of behaviour formation is accepted. Thus, the ‘theory of responsible behaviour formation’ is accepted for these domains. Besides this, in other domains, this theory is supported partially as only one mediator at-a-time becomes able to mediate, and serial mediation could not happen. These results can be attributed to the different levels of concern, attitude, commitment, and behaviour of consumers which may result in distinctive segments of consumers. Next Chap. 7 carries out the analysis in this regard.

H1c: CSF → CI

H1b: CSF → ASL

Accepted

CSF → SSC

Accepted Accepted Accepted Accepted Accepted

CSF → CI

CSF → CI

CSF → CI

CSF → CI

CSF → CA

CSF → CI

Accepted

CSF → ET Accepted

Accepted

CSF → NR

CSF → CI

Accepted Accepted

CSF → AMW

Accepted

Accepted

CSF → ERA

CSF → ACE

Accepted

CSF → RDB/RI

Accepted

Accepted

CSF → RMB/MW

CSF → OGM

Accepted Accepted

CSF → RPB

H1a: CSF → RCB

Acceptance/Rejection of domain wise hypotheses

CSF → RUB

Domain wise hypotheses

Main hypotheses

Table 6.15 Integration of hypotheses testing of direct effects: domain wise results

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

Fully accepted Concern for Sustainable Future (CSF) has a significant positive effect on Commitment to Initiate (CI) in all domains

Fully accepted Concern for Sustainable Future (CSF) has a significant positive effect on Attitude towards Sustainable Living (ASL) in all domains

Acceptance/Rejection of main hypotheses ⎫ Fully accepted ⎪ ⎪ ⎪ Concern for Sustainable Future ⎪ ⎪ ⎪ ⎪ (CSF) has a significant positive ⎪ ⎪ ⎬ effect on Responsible Consumption Behaviour (RCB) ⎪ ⎪ ⎪ ⎪ in all domains ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

(continued)

CI is an indicator for general attitude, and is same in all domains. CSF has a significant positive effect on CI in all domains; so, the main hypothesis is fully accepted

OGM, ACE, AMW, NR, ET, CA; are the dimensions of ASL. Since, the hypothesis is accepted in all domains; main hypothesis is also fully accepted

RPB, RUB, RMB, RDB, ERA, SSC; all are the indicators or domains of construct RCB. Since, the hypothesis is accepted in all domains; the main hypothesis is also fully accepted

Explanation

6.9 Sustainable Societal Conduct: Path Analysis 289

H3: CI → RCB

H2b: ASL → CI

Accepted

CA → SSC

Rejected Rejected Accepted Accepted Accepted

CI → RMB/MW

CI → RDB/RI

CI → ERA

CI → SSC

CA → CI

CI → RUB

Accepted

ET → CI Accepted

Accepted

NR → CI

CI → RPB

Accepted Accepted

AMW → CI

Accepted

Rejected

ET → ERA

ACE → CI

Accepted

NR → RDB/RI

Rejected

Accepted

AMW → RMB/MW

OGM → CI

Rejected Accepted

OGM → RPB

H2a: ASL → RCB

Acceptance/Rejection of domain wise hypotheses

ACE → RUB

Domain wise hypotheses

Main hypotheses

Table 6.15 (continued)

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

Partially accepted Commitment to Initiate (CI) has a significant positive effect on Responsible Consumption Behaviour (RCB) in four domains

Partially accepted Attitude towards Sustainable Living (ASL) has a significant positive effect on Commitment to Initiate (CI) in five domains

Acceptance/Rejection of main hypotheses ⎫ Partially accepted ⎪ ⎪ ⎪ Attitude towards Sustainable ⎪ ⎪ ⎪ ⎪ Living (ASL) has a significant ⎪ ⎪ ⎬ positive effect on Responsible Consumption Behaviour (RCB) ⎪ ⎪ ⎪ ⎪ in four domains ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

CI is tested in all behavioural domains; since, the hypothesis is accepted in four out of six domains; main hypothesis is partially accepted

Dimensions of ASL are tested for their effect on CI. Since, the hypothesis is accepted in five out of six domains; main hypothesis is partially accepted

Dimensions of ASL are tested for their effect on domains of RCB. Since, the hypothesis is accepted in four out of six domains; main hypothesis is partially accepted

Explanation

290 6 Model Specification and Theory Testing

H5b: ASL → CI → RCB

H5a: CSF → CI → RCB

Rejected

AMW → CI → RMB/MW

Accepted

CSF → CI → SSC Rejected

Accepted

CSF → CI → ERA

ACE → CI → RUB

Accepted

CSF → CI → RDB/RI

Rejected

Rejected

CSF → CI → RMB/MW

OGM → CI → RPB

Rejected

Accepted

CSF → CA → CI

CSF → CI → RUB

Accepted

CSF → ET → CI Accepted

Accepted

CSF → NR → CI

CSF → CI → RPB

Accepted

CSF → AMW → CI

Accepted

CSF → CA → SSC Accepted

Rejected

CSF → ET → ERA

CSF → ACE → CI

Accepted

CSF → NR → RDB/RI

Rejected

Accepted

CSF → AMW → RMB/MW

CSF → OGM → CI

Accepted

CSF → ACE → RUB

H4b: CSF → ASL → CI

Rejected

CSF → OGM → RPB

H4a: CSF → ASL → RCB

Acceptance/Rejection of domain wise hypotheses

Domain wise hypotheses

Main hypotheses

Table 6.16 Integration of hypotheses testing of indirect/mediation effects: domain wise results

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

Partially accepted CI mediates the effect of ASL on RCB in two out of six domains

Partially accepted CI mediates the effect of CSF on RCB in four out of six domains

Partially accepted ASL mediates the effect of CSF on CI in five out of six domains

Acceptance/Rejection of main hypotheses ⎫ Partially ⎪ ⎪ ⎪ accepted ⎪ ⎪ ⎪ ⎪ ASL mediates ⎪ ⎬ the effect of ⎪ CSF on RCB in ⎪ ⎪ ⎪ ⎪ four out of six ⎪ ⎪ ⎪ domains ⎭

(continued)

Since, CI is able to mediate the effect in two domains, the hypothesis is accepted partially

Since, CI is able to mediate the effect in four domains, the hypothesis is accepted partially

Since, specific attitudinal components are mediating the effect in five domains; the hypothesis is accepted partially

Since, specific attitudinal components are mediating the effect in four domains; the hypothesis is accepted partially

Explanation

6.9 Sustainable Societal Conduct: Path Analysis 291

H6: CSF → ASL → CI → RCB

Main hypotheses

Table 6.16 (continued)

Rejected Rejected Accepted Accepted Accepted

CSF → AMW → CI → RMB/MW

CSF → NR → CI → RDB/RI

CSF → ET → CI → ERA

CSF → CA → CI → SSC

Accepted

CA → CI → SSC

CSF → ACE → CI → RUB

Rejected

ET → CI → ERA Rejected

Accepted

NR → CI → RDB/RI

CSF → OGM → CI → RPB

Acceptance/Rejection of domain wise hypotheses

Domain wise hypotheses

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ Partially accepted ASL and CI serially mediate the effect of CSF on RCB in three out of six domains

Acceptance/Rejection of main hypotheses

Since, ASL and CI is able to mediate the effect in three domains, the hypothesis is accepted partially

Explanation

292 6 Model Specification and Theory Testing

6.9 Sustainable Societal Conduct: Path Analysis

293

Table 6.17 Responsible purchasing domain: significance testing of direct effects Causal paths

Coefficient estimate (β)

CSF → RPB

Std. error of β

0.282*

0.032

CSF → OGM

0.074**

CSF → CI

0.373*

OGM → RPB OGM → CI

Critical ratio (CR)

P-Value Sig.

8.880

0.000

0.032

2.340

0.019

0.029

12.671

0.000

−0.03ns

0.029

−1.018

0.309

−0.02ns

0.029

−0.619

0.536

0.162*

0.032

5.126

0.000

CI → RPB

*p < 0.001; **p < 0.05; ns p > 0.05

Table 6.18 Responsible purchasing domain: significance testing of indirect effects Indirect paths

Path coefficients

Type of tests

t-Statistic

Standard error

P-Value

CSF → OGM → RPB (0.074 × 0.030)

−0.0021ns

Sobel test

−0.944

0.002

0.345

Aroian test

−0.878

0.003

0.380

Goodman test

−1.028

0.002

0.304

CSF → CI → RPB (0.373 × 0.162)

0.06*

Sobel test

4.711

0.013

0.000

Aroian test

4.698

0.013

0.000

Goodman test

4.723

0.013

0.000

CSF → OGM → CI (0.074 × −0.018)

−0.001ns

Sobel test

−0.599

0.002

0.549

Aroian test

−0.553

0.002

0.580

Goodman test

−0.660

0.002

0.509

OGM → CI → RPB (−0.018 × 0.162)

−0.0032ns

Sobel test

−0.616

0.005

0.538

Aroian test

−0.605

0.005

0.545

CSF → OGM → CI → RPB (0.074 × −0.02 × 0.162)

−0.0002NS

Goodman test

−0.628

0.005

0.530

Bootstrapping (95% Confidence Interval)

0.0006

LLCI

ULCI

−0.0017

0.0009

*p < 0.001; **p < 0.05; ns p > 0.05; NS : Not Significant

Table 6.19 Responsible usage domain: significance testing of direct effects Causal paths

Coefficient estimate (β)

Std. error of β

CSF → RUB

0.245*

0.036

6.862

0.000

CSF → ACE

0.587*

0.026

00.925

0.000

CSF → CI

0.229*

0.035

6.452

0.000

ACE → RUB

0.232*

0.036

6.481

0.000

ACE → CI

0.244*

0.035

6.869

0.000

CI → RUB

0.039ns

0.031

1.256

0.209

*p < 0.001; **p < 0.05; ns p > 0.05

Critical ratio (CR)

P-Value Sig.

294

6 Model Specification and Theory Testing

Table 6.20 Responsible usage domain: significance testing of indirect effects Indirect paths

Path coefficients

CSF → ACE → RUB (0.587 × 0.232)

0.136*

CSF → CI → RUB (0.229 × 0.039)

0.009ns

CSF → ACE → CI (0.587 × 0.244)

0.143*

ACE → CI → RUB (0.244 × 0.039)

0.010ns

CSF → ACE → CI → RUB (0.587 × −0.244 × 0.039)

0.0074NS

Type of tests

t-Statistic

Standard error

P-Value

Sobel Test

6.197

0.022

0.000

Aroian Test

6.191

0.022

0.000

Goodman Test

6.203

0.022

0.000

Sobel Test

1.235

0.007

0.217

Aroian Test

1.222

0.007

0.222

Goodman Test

1.250

0.007

0.211

Sobel Test

6.661

0.022

0.000

Aroian Test

6.655

0.022

0.000

Goodman Test

6.667

0.021

0.000

Sobel Test

1.238

0.008

0.216

Aroian Test

1.226

0.008

0.220

Goodman Test

1.251

0.008

0.211

Bootstrapping (95% Confidence Interval)

0.0062

LLCI

ULCI

−0.0044

0.0204

*p < 0.001; **p < 0.05; ns p > 0.05; NS : Not Significant

Table 6.21 Responsible maintenance domain: significance testing of direct effects Causal paths

Coefficient estimate (β)

Std. error of β

Critical ratio (CR)

P-Value Sig.

CSF → MW

0.291*

0.037

7.958

0.000

CSF → AMW

0.588*

0.026

23.004

0.000

CSF → CI

0.278*

0.036

7.731

0.000

AMW → MW

0.196*

0.036

5.466

0.000

AMW → CI

0.159*

0.036

4.423

0.000

−0.039ns

0.031

−1.243

0.214

CI → MW

*p < 0.001; **p < 0.05;

ns p

> 0.05

6.9 Sustainable Societal Conduct: Path Analysis

295

Table 6.22 Responsible maintenance domain: significance testing of indirect effects Indirect paths

Path coefficients

CSF → AMW → MW (0.588 × 0.196)

0.118*

CSF → CI → MW (0.278 × −0.039)

CSF → AMW → CI (0.588 × 0.159)

−0.011ns

0.094*

AMW → CI → MW (0.159 × −0.039)

−0.0064ns

CSF → AMW → CI → MW (0.588 × 0.159 × −0.039)

−0.004NS

Type of tests

t-Statistic

Standard error

P-Value

Sobel Test

5.293

0.022

0.000

Aroian Test

5.288

0.022

0.000

Goodman Test

5.298

0.022

Sobel Test

−1.242

0.009

Aroian Test

−1.232

0.009

0.218

Goodman Test

−1.252

0.009

0.211

0.214

Sobel Test

4.335

0.022

0.000

Aroian Test

4.331

0.022

0.000

Goodman Test

4.339

0.022

0.000

Sobel Test

−1.210

0.005

0.226

Aroian Test

−1.182

0.005

0.237

Goodman Test

−1.240

0.005

0.215

Bootstrapping (95% Confidence Interval)

0.0051

LLCI

ULCI

−0.0170

0.0037

*p < 0.001; **p < 0.05; ns p > 0.05; NS : Not Significant

Table 6.23 Responsible disposal domain: significance testing of direct effects Causal paths

Coefficient estimate (β)

Std. error of β

CSF → RI

0.311*

0.031

9.994

0.000

CSF → NR

0.528*

0.027

19.645

0.000

CSF → CI

0.263*

0.034

7.745

0.000

NR → RI

0.190*

0.031

6.169

0.000

NR → CI

0.206*

0.034

6.063

0.000

CI → RI

0.239*

0.028

8.483

0.000

*p < 0.001; **p < 0.05;

ns p

> 0.05

Critical ratio (CR)

P-Value Sig.

296

6 Model Specification and Theory Testing

Table 6.24 Responsible disposal domain: significance testing of indirect effects Indirect paths

Path coefficients

Type of tests

t-Statistic

Standard error

P-Value

CSF → NR → RI (0.528 × 0.190)

0.101*

Sobel test

5.849

0.017

0.000

Aroian test

5.842

0.017

0.000

Goodman test

5.855

0.017

0.000

Sobel test

5.732

0.011

0.000

Aroian test

5.710

0.011

0.000

Goodman test

5.754

0.011

0.000

Sobel test

5.787

0.019

0.000

Aroian test

5.781

0.019

0.000

Goodman test

5.794

0.019

0.000

Sobel test

4.941

0.010

0.000

Aroian test

4.918

0.010

0.000

Goodman test

4.963

0.010

0.000

Bootstrapping (95% Confidence Interval)

0.0076

LLCI

ULCI

0.0158

0.0454

CSF → CI → RI (0.263 × 0.239) CSF → NR → CI (0.528 × 0.206)

0.062*

0.110*

NR → CI → RI (0.206 × 0.239)

0.050*

CSF → NR → CI → RI (0.528 × 0.206 × − 0.239)

0.027Sig.

*p < 0.001; **p < 0.05; ns p > 0.05; Sig. : Significant Table 6.25 Environmentally relevant activities: significance testing of direct effects Causal paths

Coefficient estimate (β)

Std. error of β

CSF → ERA

0.204*

0.032

6.390

CSF → ET

0.163*

0.031

5.220

0.000

CSF → CI

0.351*

0.029

11.896

0.000

ET → ERA

0.009ns

0.030

0.286

0.775

ET → CI

0.129*

0.029

4.358

0.000

CI → ERA

0.237*

0.032

7.394

0.000

*p < 0.001; **p < 0.05;

ns p

> 0.05

Critical ratio (CR)

P-Value Sig. 0.000

6.9 Sustainable Societal Conduct: Path Analysis

297

Table 6.26 Environmentally relevant activities: significance testing of indirect effects Indirect paths

Path coefficients

Type of tests

t-statistic

Standard error

P-Value

CSF → ET → ERA (0.163 × 0.009)

0.0016ns

Sobel test

0.300

0.005

0.765

Aroian test

0.294

0.005

0.769

Goodman test

0.305

0.005

0.760

Sobel test

6.317

0.013

0.000

Aroian test

6.302

0.013

0.000

Goodman test

6.333

0.013

0.000

Sobel test

3.396

0.006

0.001

Aroian test

3.361

0.006

0.001

Goodman test

3.432

0.006

0.001

Sobel test

3.813

0.008

0.000

Aroian test

3.788

0.008

0.000

Goodman test

3.839

0.008

0.000

Bootstrapping (95% confidence interval)

0.0028

LLCI

ULCI

0.0032

0.0143

CSF → CI → ERA (0.351 × 0.237) CSF → ET → CI (0.163 × 0.129) ET → CI → ERA (0.129 × 0.237) CSF → ET → CI → ERA (0.163 × 0.129 × −0.237)

0.084*

0.021*

0.031*

0.005Sig.

*p < 0.001; **p < 0.05; ns p > 0.05; Sig. : Significant Table 6.27 Sustainable societal conduct: significance testing of direct effects Causal path

Coefficient estimate (β)

Std. error of β

Critical ratio (CR)

P-Value Sig.

CSF → SSC

0.362*

0.031

11.694

0.000

CSF → CA

0.379*

0.029

12.932

0.000

CSF → CI

0.310*

0.031

9.893

0.000

CA → SSC

0.139*

0.030

4.658

0.000

CA → CI

0.164*

0.031

5.232

0.000

CI → SSC

0.137*

0.030

4.586

0.000

*p < 0.001; **p < 0.05;

ns p

> 0.05

298

6 Model Specification and Theory Testing

Table 6.28 Sustainable societal conduct: significance testing of indirect effects Indirect paths

Path coefficients

Type of tests

t-Statistic

Standard error

P-Value

CSF → CA → SSC (0.379 × 0.139)

0.053

Sobel test

4.367

0.012

0.000

Aroian test

4.356

0.012

0.000

Goodman test

4.378

0.012

0.000

Sobel test

4.154

0.010

0.000

Aroian test

4.137

0.010

0.000

Goodman test

4.171

0.010

0.000

Sobel test

4.904

0.013

0.000

Aroian test

4.891

0.013

0.000

Goodman test

4.916

0.013

0.000

Sobel test

3.457

0.006

0.001

Aroian test

3.422

0.007

0.001

Goodman test

3.493

0.006

0.000

Bootstrapping (95% confidence interval)

0.0048

LLCI

ULCI

0.0057

0.0242

CSF → CI → SSC (0.310 × 0.137) CSF → CA → CI (0.379 × 0.164) CA → CI → SSC (0.164 × 0.137) CSF → CA → CI → SSC (0.379 × 0.164 × − 0.137)

0.043*

0.062*

0.022*

0.0085Sig.

*p < 0.001; **p < 0.05; ns p > 0.05; Sig. : Significant

Endnotes: Domain Wise Significance Testing of Direct and Indirect Effects Responsible Purchasing Domain See Tables 6.17 and 6.18. Responsible Usage Domain See Tables 6.19 and 6.20. Responsible Maintenance Domain See Tables 6.21 and 6.22. Responsible Disposal Domain See Tables 6.23 and 6.24. Environmentally Relevant Activities See Tables 6.25 and 6.26. Sustainable Societal Conduct See Table 6.27 and 6.28.

References Austin, J. T., & Calderon, R. (1996). Theoretical and Technical contributions to structural equation modeling: An updated annotated bibliography. Structural Modeling, 3, 105–175. Austin, J. T., & Wolfle, L. M. (1991). Annotated bibliography of structural equation modeling: Technical work. British Journal of Mathematical and Statistical Psychology, 44, 93–152.

References

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Baron, R. M., & Kenny, D. A. (1986). The Moderator–Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. Duncan, O. D. (1966). Path Analysis: Sociological Examples. American Journal of Sociology, 72(1), 1–16. Ganguly, B., Dash, S. B., & Cyr, D. (2009). Website characteristics, trust and purchase intention in online stores: An empirical study in Indian context. Journal of Information Science and Technology, 6(2), 22–44. Gupta, K., & Singh, N. (2014/15). Fit estimation in structural equation modeling: A synthesis of related statistics. HSB Research Review, 8(2); 9(1), 20–27. Hair, Jr J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2006). Multivariate data analysis (5th ed., pp. 1–700). New Delhi: Dorling Kindersley (India) Pvt. Ltd., Pearson Education, Inc. Hayes, A. F. (2019). The Process Macro for SPSS, SAS, and R, Process v3.4. downloaded January 10, 2020 http://processmacro.org/download.html. Kenny, D. A. (2018). Mediation. Retrieved January 10, 2020 from http://davidakenny.net/cm/ mediate.htm#SE. Malhotra, N. K., & Dash, S. (2012). Marketing research: An applied orientation (6th ed., pp. 1–929). New Delhi: Dorling Kindersley (India) Pvt. Ltd, Pearson Education, Inc. Preacher, K. J., & Leonardelli, G. J. (2001). Calculation for the Sobel test: An interactive calculation tool for mediation tests . Retrieved September 6, 2013, from http://quantpsy.org/sobel/sobel.htm. Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E. (2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5(6), 359–371. Spector, P. E. (1992). Summated rating scale construction: An introduction (pp. 1–73). Newbury Park London New Delhi: Sage Publications. Wolfle, L. M. (1999) Sewall wright on the method of path coefficients: An annotated bibliography. Structural Equation Modeling: A Multidisciplinary Journal, 6(3), 280–291. Wolfle, L. M. (2003). The Introduction of Path Analysis to the Social Sciences, and Some Emergent Themes: An Annotated Bibliography. Structural Equation Modeling: A Multidisciplinary Journal, 10(1), 1–34.

Chapter 7

Segmentation of Consumers and Identification of Responsibles

This chapter deals with objectives 4 and 5 for segmenting consumers based on a range of their attitudinal and behavioural dimensions extended in survey database (see Chap. 5), analyses upon which upturned into all the figures and tables here. Four sections from 7.1 to 7.4 in association with certain sub-sections under them reveal the empirical analysis. From this perspective, to have a divide between consumers, cluster analysis is applied. Both the approaches of hierarchical and non-hierarchical methods of cluster analysis are combined and applied in two stages. The first stage employs the hierarchical procedure in which Euclidean distance is chosen as the similarity measure and Ward’s method1 is exercised to develop a number of clusters amenable. Afterward, the second phase uses k-means clustering as a non-hierarchical procedure to fine-tune the results of Ward’s method for further empirical testing, labelling of clusters, and exploring cluster membership of sample respondents. Statistical guidelines are followed from Krishnaswami and Ranganatham 2005, Parasuraman et al. 2005, Trochim 2005, Hair et al. 2006, Zikmund and Babin 2006, Churchill et al. 2010, and Malhotra and Dash 2012.

7.1 Hierarchical Clustering 7.1.1 Determination of Variables All the attitudinal and behavioural variables are selected to be included in the cluster variants (as per definition of responsible consumers in Chap. 3). The essence is to find out the groups of consumers who can be called responsibles and others who can be simply singled out from the responsible category. The number of clusters is 1 Ward’s

Method is employed as it is best suited with squared Euclidian Distance Measure (Hair et al. 2006: pp. 486–496).

© Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_7

301

302

7 Segmentation of Consumers and Identification of Responsibles

Table 7.1 Agglomeration schedule Stage

No. of clusters

Cluster combined Cluster 1

Cluster 2

Coefficients

Per-cent change (%)

Stage cluster first appears

Next stage

Cluster 1

Cluster 2

–

982

963

993

990

10

2

145

4079.121

991

9

4

49

4177.404

2.37

983

985

994

992

8

9

23

4306.118

2.41

980

984

997

993

7

2

19

4443.066

3.08

990

978

998

994

6

4

12

4601.823

3.18

991

988

996

995

5

5

13

4765.876

3.57

989

979

997

996

4

1

4

4972.847

3.56

987

994

998

997

3

5

9

5215.864

4.89

995

992

999

998

2

1

2

5794.545

11.09

996

993

999

999

1

1

5

7439.097

28.38

998

997

0

determined by using the procedure of agglomeration within hierarchical clustering as displayed in Sect. 7.1.2.

7.1.2 Selection of Number of Clusters Agglomeration schedule is customary to determine the number of suitable clusters in the data structure by measuring the increase in heterogeneity occurring from the combination of two clusters. It is done by evaluating the changes in the coefficients at each stage of the hierarchical process.2 Table 7.1 corresponds to this agglomeration schedule used as a criterion of stopping rule on a particular stage of clustering to settle on the number of clusters. Table 7.1 pinpoints that there were a total of 999 stages in the agglomeration schedule as there was a sample of 1000 respondents. The results of the last ten stages that are devised to retain the number of clusters are depicted in the table. Stage 990 explains that respondents whose entry was marked on the 2nd and 145th row, respectively, in the SPSS data sheet are combined at this stage according to the measure of Euclidean distance. The column labelled ‘coefficients’ displays this squared Euclidean distance between these two respondents (2 and 145) and other ones.The column which is marked as ‘stage cluster first appears’ point towards each stage at which the respondents are combined before a particular stage. As given, in the first row of stage 990, respondent 2 was combined in stage 982 and respondent 145 2 Hair

et al. (2006: p. 506) suggests a reasonable approach to determine the number of clusters is to measure the percentage change in heterogeneity that is how different observations in one cluster are from another cluster.

7.1 Hierarchical Clustering

303

Fig. 7.1 Elbow plot for the number of clusters

was combined in stage 963 before this stage. The last column ‘Next Stage’ implies the stage at which the respondents are combined with any other respondent after a particular stage. The first entry 993, here, says that this is the next stage in which respondent 2 again joins. In stage 993, companion of respondent 2 is respondent 19 (row for stage 993 can be studied). Similarly, on the basis of coefficients, all the respondents are combined by making a pecking order in successive stages. Two columns of the table namely ‘coefficients’ and ‘per cent change’ are significant to recognize the number of clusters based on the large increases in coefficients. It can be seen that ‘coefficients’ up to stage 996 indicate fairly homogenous clusters as the per cent change in coefficient at one stage to their previous stage divulges minimal changes. At stage 997, there can be noted a 4.89% change in coefficient from its previous level. The change becomes more spectacular (11.09 and 28.38%) in its subsequent stages (stage 998 and 999). Accordingly, basing on the analysis from agglomeration schedule, three useful clusters can be selected to work upon in k-means clustering. The same result of three cluster solution is obtained by the ‘Elbow Plot’ provided in Fig. 7.1. The Elbow Plot shown in Fig. 7.1 is similar to the scree plot as utilized for obtaining the number of components in Principal Component Analysis (Chap. 5). It is prepared by plotting the percentage change in squared Euclidean distance that range between 2.37% and 28.38% on the y-axis, and the number of clusters on the corresponding xaxis. The results are presented only up to ten cluster solution as showing 999 clusters in one figure is very much cumbersome. In the figure, an elbow is indicated by a small square. This point explains that after three clusters the line decreases sharply and flattens out with no further substantial drops (up and downs). Thus, the marginal gain by adding an additional cluster drops after this position giving an angle (the elbow) on this point in the graph. In radiance, similar to the previous result given by the agglomeration schedule, a three cluster solution is preferred for the present data.

304

7 Segmentation of Consumers and Identification of Responsibles

7.2 Non-hierarchical Clustering Coming to the next stage, a three cluster solution is pre-specified in k-means clustering basing on the result of hierarchical clustering. The most useful output provided by SPSS is about the final cluster centres and distances between the final cluster centres. Final cluster centres represent the average values of the final three clusters on all the attitudinal and behavioural variables used as the basis of clustering. On the other hand, distances between final cluster centres verbalize about the separation of one cluster from another. As revealed in matrix 7.2, the obtained clusters are well separated from each other. As can be expected, distance between Cluster I and Cluster III is highest (distance = 4.370). Cluster I and Cluster II are separated by an amount of 2.500 and Cluster II and Cluster III are estranged to an extent of distance 2.072 (Table 7.2).

7.2.1 Representation of Clusters According to Means In line with these final cluster centres (mean scores) on all the fourteen variables (dimensions of attitude and behaviour), Fig. 7.2 represents the trend that these clusters follow on the input variables. Attitudinal and behavioural variables with average scores on the three clusters connect to the bottom of the figure. The average values (final cluster centres) as marked with each dot range between 2.74 and 4.75. Rhombus-shaped marks in red colour represents the mean values for Cluster I. Square Table 7.2 Distances between final cluster centres

Fig. 7.2 Representation of final cluster centres

Clusters

Cluster I

Cluster II

Cluster I

–

–

Cluster II

2.500

–

Cluster III

4.370

2.072

7.2 Non-hierarchical Clustering

305

dots with yellow colour show mean values of Cluster II, and triangular spots with green colour depicts the mean values for Cluster III. The mean values for Cluster I lie between a range of 2.74 (variable RUB)–3.43 (variable CSF). This range for Cluster II is from 2.85 (variable OGM) to 4.15 (variable SSC), and for Cluster III is 2.99–4.75 (for variables OGM and SSC). An inspection of the graph visualizes a clear-cut trend for the three clusters according to mean on the variables. There is a hierarchy in this trend. The level of the first cluster is below the second and the second is ultimately below the third cluster. It means that respondents in the third cluster attain utmost average scores on all the attitudinal and behavioural variables as compared to the 2nd and 1st cluster. Likewise, the 2nd cluster has obtained mean values over and above the 1st cluster; however, variable OGM is an exception. Breaking the ascending order (trend of hierarchy), the mean value of the second cluster on variable OGM is marginally less than the 1st cluster. But this mean difference (M.D. = 2.88–2.85 = 0.03) is just by chance as statistically insignificant (p > 0.05). This is proved in Sect. 7.3 (Table 7.5) where mean differences are tested using ANOVA F-statistics. Consequently, Cluster I and Cluster II have identical mean values on this particular variable. As can be based on these average values, the clusters are labelled next.

7.2.2 Labelling the Clusters Labelling and naming of clusters involve examining the mean values of the objects contained in the cluster on each of the variables. The mean scores are already mentioned in Sect. 7.2.1. Cluster I has relatively low mean values both on attitudinal and behavioural dimensions. Cluster II is the mediocre and Cluster III hits the highest points. On the basis of these statistical values, the clusters are labelled as Red segment, Yellow segment, and Green segment (that is why the spots in the previous graph 7.2 are displayed in these particular colours). The naming of these segments and the number of cases in each cluster is diagrammed into the next pie chart for easy observation and understanding (Fig. 7.3). As per specification provided by the labels of these segments, the red segment is displayed with red colour, the yellow segment is presented with yellow colour, and obviously the green segment is projected to reflect green colour. The pie graph reveals that the red segment of consumers comprises 139 respondents out of the sample of 1000, thus estimated as nearly 14%. This segment is named ‘Apathetics and Imprudents’. Next, almost 40% of the sample respondents are a conglomerate of people who after clustering are members of the yellow segment. They are 395 in number and named ‘Aesthetics and Hopefuls’. The third segment which is the green segment constitutes approximate 47% of sample respondents (N = 466) and this assemblage is named ‘Aspirants and Illuminators’. Before interpreting and stating

306

7 Segmentation of Consumers and Identification of Responsibles

Red Segment

Green Segment Yellow Segment

Fig. 7.3 Symbolizing consumer segments with Pie-chart

the reasoning behind naming these segments, the significant/insignificant mean differences amongst these segments and the validity of cluster solution on the basis of defined attitudinal and behavioural variables are analysed.3

7.3 Test of Significance and Validity of Cluster Solution For examining the significance of the difference between three clusters for the attitudinal and behavioural variables, we need cluster membership as an independent variable. So, the membership of respondents in each segment as specified by cluster analysis is saved as a new variable in SPSS data file and is visible in Fig. 7.4 under heading clusters (last column). The entry ‘1’ in this column represents the red segment, the label ‘2’ is specified for the yellow segment, and the green segment is marked with entry ‘3’.

7.3.1 Tests of Significance Between Means of Segments In Table 7.3, the first two columns tell us about the attitudinal–behavioural dimensions and variables. Column labelled ‘means’ depicts the statistical values of the arithmetic average for the three clusters. Next column ‘standard deviation’ presents 3 The

significance of mean differences is tested using one-way ANOVA, and validity of cluster solution is determined using discriminant analysis. Facca and Allen (2011) suggested testing validity of cluster solution with discriminant analysis to have the truly classified cases and the percentage of correct classification.

7.3 Test of Significance and Validity of Cluster Solution

307

Fig. 7.4 Extended survey database for cluster membership (clusters) as a new variable

the measures of dispersion of data values from the mean value of a particular series. These values reveal that dispersion from the mean within a segment for the given variables is not much extensive. The standard error in the next column represents the measurement of sampling error in the present sample distribution; the statistical values are acceptable for the present sample survey. It has already been shown that for the measures of central tendency (means), the red segment is below the yellow segment, and the yellow segment is below the green segment. This was a mean comparison completed between segments on the basis of which labels and names have been supplied to the identified three clusters. Contrary to this analysis, here a comparison is presented for the average values within a particular segment for all the input variables. It can be sighted from the mean values within the red segment that these have a range between 2.745 and 3.477. The mean on behavioural dimensions pinpoint that the consumers in this segment are not enthusiastic on any of behavioural measure. Similar to it, the mean on attitudinal dimensions are also not encouraging. These consumers just show some slight level of concern for having a sustainable future and are somewhat agreed on the side of increasing problems of waste, seeking a need for recycling for the removal of these tribulations. In between the yellow segment, the scale of mean values varies from 2.849 to 4.154. People in this segment are concerned and conscious of sustainable living. Analogous to the red segment, consumers in the yellow segment too anticipate the problems of increasing waste and demand for its solutions such as recycling but in quantitative terms they are above the red segment on these measures. On the behavioural side, they are highly engaged in activities necessary for having a sustainable society. Moving towards the green segment, the extremes for the mean values are 2.986 on the lower side and

2.844

3.030

2.753

2.755

ERA

SSC

3.248

CI

RDB

3.320

CA

RMB

3.129

ET

2.745

3.365

NR

RUB

3.477

AMW

2.898

3.333

ACE

RPB

2.881

OGM

Behavioural dimensions

3.427

CSF

Attitudinal dimensions

4.154

3.430

3.670

3.589

3.657

3.543

3.501

3.528

3.184

4.09

4.142

3.976

2.849

4.096

4.750

4.089

4.191

4.341

4.189

4.192

4.090

4.047

3.697

4.364

4.548

4.419

2.986

4.470

0.660

0.713

0.490

0.719

0.656

0.620

0.745

0.732

0.850

0.781

0.730

0.694

0.601

0.649

Red N = 139

0.735

0.736

0.510

0.743

0.553

0.626

0.729

0.570

0.906

0.549

0.490

0.488

0.737

0.418

Yellow N = 395

Standard deviation Green N = 466

Red N = 139 Yellow N = 395

Means

Variables

Dimensions

Table 7.3 Descriptive statistics for consumer segments

0.410

0.627

0.442

0.531

0.495

0.493

0.523

0.530

0.843

0.449

0.383

0.357

0.788

0.348

Green N = 466

0.056

0.060

0.042

0.061

0.056

0.526

0.063

0.062

0.072

0.066

0.062

0.059

0.051

0.055

Red N = 139

0.037

0.037

0.026

0.037

0.028

0.032

0.037

0.029

0.046

0.028

0.025

0.025

0.371

0.021

Yellow N = 395

Standard error

0.019

0.029

0.021

0.025

0.023

0.023

0.024

0.025

0.039

0.021

0.018

0.017

0.365

0.016

Green N = 466

308 7 Segmentation of Consumers and Identification of Responsibles

7.3 Test of Significance and Validity of Cluster Solution

309

4.750 on the upper point. Leaving variable OGM and ET, the average on all variables are above 4. Consequently, the consumers who come under this segment have a positive attitude and do behave in optimistic ways thinking for the power of ‘responsible consumption behaviour’. Although consumers in this segment are observed as exceptionally good on all measures, they too suffer from myopia regarding sustainable products as measured by variable OGM. On this variable, their attitude seems no different from red and yellow segments. So, it is important to analyse the differences between group means for all variables between these three segments and to validate the cluster solution. In this regard, the next part corresponds to the ANOVA test of significant/insignificant mean differences and discriminant analysis for validating the cluster solution. Table 7.4 reveals the between group and within group variances, the mean square, degrees of freedom, Wilks’ Lambda, ANOVA F-statistics, observed significance, and the value of the strength of association4 of independent variable (segments) with specific dependent variables (attitudinal and behavioural variables). Wilks’ Lambda is the ratio of within group sum of squares to the total sum of squares and implies the total variation in discriminant scores not explained amongst groups. So, a small Lambda denotes that group means appear to differ and is important to analyse the output of discriminant analysis (Sect. 7.3.2). The value of Wilks’ Lambda for variable SSC is least; accordingly, its associated F-statistic is highest. Both these statistics clarify noteworthy mean differences between three clusters on this variable. As the Wilks’ Lambda is highest for variable OGM (λ = 0.992), its associated F value is least (F = 3.847) showing that the within group variability is large compared to total variability for this variable. However, all the mean differences are found statistically significant at 0.1% significance (except OGM) but the mean differences for variable OGM are significant at a 5% significance level. This investigation states that the three segments significantly differ from each other regarding their ‘attitude towards sustainable living’ and also on the dimensions of responsible behaviour. Since significant F-statistics are obtained, strength of association (ω2 ) between dependent variables and independent variable is also considered. The calculated ω2 for each variable indicates the amount of variance which approximately these three segments account for in each of attitudinal and behavioural variable. Since the value of ω2 is high for variable SSC, the segments account for approximate 55% and the highest variance in this variable. Nearly 44% and 41% of variances are explained for variable RUB and RDB. The least variance (0.6%, almost negligible) is explained in variable OGM. All other associations are quite well and lie in the range of low to moderate namely 8.3%–39.8%. Due to the significance of F-statistics, the next important question to be answered is whether all the three or any particular pair of segments differ from each other on the specified variables. It is because the significance of F implies that at least one of the group means or two of the group means are significantly different from each 4 The strength of association (ω2 ) between dependent clusters and independent variables is calculated

manually using the formula [SSB − (K − 1)MSW] ÷ [SST + MSW]. The abbreviations in the formula are visible in Table 7.4 and SST stands for ‘total sum of squares’ (SSB + SSW).

279.780

159.580

292.935

430.682

RMB

RDB

ERA

SSC

113.259

CI

232.955

86.001

CA

RUB

69.661

ET

208.819

107.423

NR

RPB

128.870

AMW

*p < 0.001, **p < 0.05

Behavioural dimensions

4.264

135.276

120.914

ACE

CSF

Attitudinal dimensions

2

2

2

2

2

2

2

2

2

2

2

2

2

2

215.341

146.468

79.790

139.890

116.477

104.409

56.629

43.000

34.830

53.712

64.435

67.638

2.132

60.457

351.435

466.901

226.583

419.991

294.060

320.721

413.651

332.793

753.949

296.810

236.391

219.575

552.458

183.361

Sum of squares within groups (SSW)

Mean square between groups (MSB)

Sum of squares between groups (SSB)

df (K)

Error variance

Cluster variance

OGM

Variables

Dimensions

Table 7.4 Tests of significance and association

997

997

997

997

997

997

997

997

997

997

997

997

997

997

df

0.352

0.468

0.227

0.421

0.295

0.322

0.415

0.334

0.756

0.298

0.237

0.220

0.554

0.184

Mean square within groups (MSW)

0.449

0.614

0.587

0.600

0.558

0.606

0.785

0.795

0.915

0.734

0.647

0.619

0.992

0.603

Wilks’ Lambda

610.909*

312.761*

351.088*

332.079*

394.913*

324.569*

136.491*

128.823*

46.059*

180.420*

271.761*

307.118*

3.847**

328.727*

F

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.022

0.000

Sig.

0.550

0.384

0.412

0.398

0.441

0.393

0.213

0.204

0.083

0.264

0.351

0.380

0.006

0.396

ω2

310 7 Segmentation of Consumers and Identification of Responsibles

7.3 Test of Significance and Validity of Cluster Solution

311

other. So, beyond the conclusion of the significance of F-statistics, post hoc test is followed up and shown in the subsequent part of this section. Amongst the numerous post hoc tests, Scheffe post hoc procedure is utilized. A paired comparison of consumer segments is presented in Table 7.5. Scheffe post hoc test is used for multiple comparisons. Column one and two correspond to each attitudinal and behavioural variable and in its adjacent column segments of consumers are shown in pairs. The mean differences of each pair are calculated by deducting means from one segment from the mean of its correspondent. For example, the mean difference (−0.669) is calculated after deducting mean of the yellow segment from the mean of the red segment (3.43–4.10 = −0.669). This depicts that the red segment differs from the yellow segment regarding its mean on variable CSF by this much amount. Similarly, all other mean differences are checked. The observed significance levels for each pair confirm that comparing in parts the segments noticeably differ from each other on all variables. However, the mean differences are insignificant for red-yellow and red-green segments for variable OGM. Red-yellow pair is also not significant on variable ET. Therefore, it can be said that on these specific variables the differences in mean values just occur by chance; however, consumers are not considerably distinctive on these variables. The effect size5 of each pair on each variable is also calculated. Except for OGM, in all the pairs, the effect size is highest for the red–green pair. This effect size depends upon differences in means. As the difference in means is highest for the red-green segment on variable SSC (M.D. = −1.995; p = 0.000), its effect is also highest amongst all other classifications (ES = 3.363). The mean difference is highly insignificant and nominal for red-yellow segments on variable OGM (M.D. = −0.031; p = 0.914); appropriately its size of effect is also minor and negligible (ES = 0.042).

7.3.2 Discriminant Analysis and Validation of Segments To validate the final cluster solution for the three identified segments of consumers, cluster membership is used as a non-metric grouping variable (dependent) and attitudinal and behavioural dimensions are the metric independent variables in discriminant analysis. George and Mallery (2006: p. 279) have defined the preparation of pooled within-group correlation matrix6 as the prime step of the discriminant procedure to examine the extent of multicollinearity7 between variables, and it has been prepared first to analyse the output of the discriminant analysis. 5 The

effect sizes in ANOVA have been interpreted and defined by Laroche et al. (2002: p. 274). They mentioned that a coefficient of 0.01 can be interpreted as a low effect size, 0.06 as a medium effect size, and if any coefficient exceeds 0.15 it is termed as large effect size. 6 The correlation matrix in discriminant analysis is called as pooled within-group correlation matrix as it involves the average correlation for the two or more correlation matrices for each variable pair (George and Mallery 2006: p. 279). 7 In the words of Churchill et al. (2010: p. 498), correlation coefficient between any pair of predictors that exceeds 0.80 is considered to be the existence of multicollinearity in that pair of variables. Here

312

7 Segmentation of Consumers and Identification of Responsibles

Table 7.5 Pair-wise post hoc multiple comparisons Dimensions

Variables

Pairs

Mean difference

Standard error

Sig.

Effect size (ES)

Attitudinal dimensions

CSF

Red «» Yellow

−0.669*

0.042

0.000

−1.560

Yellow «» Green

−0.375*

0.029

0.000

−0.873

Red «» Green

−1.043*

0.041

0.000

−2.433

0.031ns

0.073

0.914

0.042

Yellow «» Green

−0.137***

0.051

0.026

−0.185

Red «» Green

−0.106ns

0.072

0.336

−0.143

Red «» Yellow

−0.643*

0.046

0.000

−1.372

Yellow «» Green

−0.443*

0.032

0.000

−0.945

Red «» Green

−1.086*

0.045

0.000

−2.316

Red «» Yellow

−0.665*

0.048

0.000

−1.365

Yellow «» Green

−0.406*

0.033

0.000

−0.834

Red «» Green

−1.071*

0.047

0.000

−2.199

Red «» Yellow

−0.727*

0.054

0.000

−1.331

Yellow «» Green

−0.273*

0.037

0.000

−0.500

Red « » Green

−0.999*

0.053

0.000

−1.831

Red « » Yellow

−0.054ns

0.086

0.820

−0.062

Yellow « » Green

−0.514*

0.059

0.000

−0.591

Red « » Green

−0.568*

0.086

0.000

−0.653

Red « » Yellow

−0.208**

0.057

0.001

−0.361

Yellow « » Green

−0.518*

0.039

0.000

−0.897

Red « » Green

−0.726*

0.056

0.000

−1.257

Red « » Yellow

−0.253*

0.064

0.000

−0.393

Yellow « » Green

−0.589*

0.044

0.000

−0.914

Red « » Green

−0.842*

0.062

0.000

−1.307

Red « » Yellow

−0.644*

0.056

0.000

−1.135

Yellow « » Green

−0.649*

0.039

0.000

−1.145

Red « » Green

−1.294*

0.055

0.000

−2.280

Red « » Yellow

−0.913*

0.054

0.000

−1.681

Yellow « » Green

−0.532*

0.037

0.000

−0.979

Red « » Green

−1.445*

0.052

0.000

−2.660

Red « » Yellow

−0.745*

0.064

0.000

−1.148

Yellow « » Green

−0.752*

0.044

0.000

−1.159

Red « » Green

−1.500*

0.063

0.000

−2.307

Red « » Yellow

−0.640*

0.047

0.000

−1.343

Yellow « » Green

−0.521*

0.033

0.000

OGM

ACE

AMW

NR

ET

CA

CI

Behavioural dimensions

RPB

RUB

RMW

RDB

Red «» Yellow

−1.093 (continued)

7.3 Test of Significance and Validity of Cluster Solution

313

Table 7.5 (continued) Dimensions

Variables

Pairs

Red « » Green

−1.160*

0.046

0.000

−2.436

ERA

Red « » Yellow

−0.377*

0.067

0.000

−0.384

Yellow « » Green

−0.959*

0.047

0.000

−0.977

Red « » Green

−1.336*

0.066

0.000

−1.360

Red « » Yellow

−1.398*

0.059

0.000

−2.357

Yellow « » Green

−0.597*

0.041

0.000

−1.006

Red « » Green

−1.995*

0.057

0.000

−3.363

SSC

*p < 0.001; **p < 0.01; ***p < 0.05;

Mean difference

ns p

Standard error

Sig.

Effect size (ES)

> 0.05

The pooled within-group correlation matrix (Table 7.6) indicates low correlations between the independent variables. In this sense, multicollinearity is unlikely to be a problem as the pooled correlation coefficients between all pairs of independent variables are less than the suggested threshold of 0.80 and proceedings can be done further with the output. The next step of significance/insignificance of discriminant functions is displayed in Table 7.7. In the present case, as the three groups of the dependent variable are analysed, two discriminant functions emerged. Discriminant analysis is meaningful if the estimated discriminant functions are statistically significant; otherwise the estimates differ only due to sampling error. This is measured by the statistic Wilks’ λ as appears in Table 7.7. By and large, a Lambda near to zero is taken as exceptionally good. It can be noted from the table that Lambda associated with first function is 0.127 (near to zero) which suggests greater discriminatory power of this function and that the model separates respondents into three segments effectively. This statistical value explains that only 12.7% of variance in the discriminant scores is not explained. Transformed Chi-square of this Wilks’ λ (2046.133) with 28 degrees of freedom is significant beyond 0.01% significance level. Wilks’ λ for the second function is 0.786 with a transformed Chi-square of 238.606 at 13 degrees of freedom which is again significant beyond 0.01% level of significance. This indicates significant discrimination between the three groups on the specified attitudinal and behavioural variables by the second function also. Thus, the two functions together significantly discriminate amongst the three segments. Also, the Eigenvalue which is the ratio of betweengroup differences to with-in group differences for the first function is 5.202; it shows that the variance between segments is more than the variance within a segment. This function accounts for 95% of the explained variance. Canonical correlation for this function is 0.916 which is the simple correlation between the discriminant scores of respondents and their corresponding category membership. The second function the degree of correlations is not exceedingly high from this value of 0.80 for multicollinearity to be a problem.

0.043

0.065

−0.101

−0.001

ERA

SSC

−0.035

−0.009

0.055 −0.040

−0.046

0.068

−0.026

−0.087

0.065

0.163

−0.145

0.001

RDB

0.034

RMB

0.096 0.046

−0.032

0.043

−0.039

0.157

0.007

0.145

0.047

0.161

0.014

RUB

0.173

CA

0.305

0.332

−0.055

0.241

−0.159

−0.032

0.016

ET

−0.067

0.309

NR

1.000

0.342

1.000

AMW

−0.020

0.153

0.343

AMW

1.000

ACE

−0.044

−0.081

0.326

ACE

RPB

0.047

OGM

OGM

CI

1.000

CSF

CSF

Variables

Table 7.6 Pooled within-groups correlation matrix

−0.020

−0.098

0.113

−0.025

−0.031

−0.018

0.185

0.120

−0.051

1.000

NR

0.071

−0.123

−0.131

−0.112

−0.120

−0.165

0.062

−0.108

1.000

ET

0.039

0.032

0.203

−0.091

−0.143

−0.024

0.090

1.000

CA

0.017

0.038

0.090

−0.223

−0.098

−0.025

1.000

CI

−0.016

0.244

0.115

0.017

0.275

1.000

RPB

0.016

0.097

0.121

0.333

1.000

RUB

−0.015

−0.019

0.039

1.000

RMB

0.025

0.064

1.000

RDB

0.055

1.000

ERA

1.000

SSC

314 7 Segmentation of Consumers and Identification of Responsibles

5.0

0.272a

2

100.0

0.463

2

1 through 2

95.0

5.202a

1

0.916

Test of function (s)

95.0

Wilks’ Lambda Canonical correlation

Function

Cumulative (%)

% of variance

Eigenvalues

Eigenvalues

Table 7.7 Variance and significance of discriminant functions

0.786

0.127

Wilks’ Lambda

238.606

2046.133

Chi-square

13

28

df

0.000

0.000

Sig.

7.3 Test of Significance and Validity of Cluster Solution 315

316

7 Segmentation of Consumers and Identification of Responsibles

has a small Eigenvalue 0.272 and accounts for the remaining 5% variance. In this way, both the functions significantly discriminate between the segments; however, the first function is likely to be superior because of large Eigenvalue and highest variance explanation. For obtaining classification statistics in discriminant output, prior probabilities are based on the proportions of consumers in each segment (Table 7.8). In the total sample of 1000 respondents, 139 were the members of the red segment, consequently its prior probability is 0.139 (139/1000). Similarly based on the size of groups, the yellow segment has a prior probability of 0.395 and probability associated with the green segment is 0.466. The results for the classification procedure are also presented in the same Table 7.8. The diagonal entries (under each column labelled N) represent the number of individuals correctly classified and the off-diagonal represents the incorrect classifications. The entries under the column actual segment sizes show the number of individuals actually in each of the three segments. The entries in the row labelled segment sizes by discriminant analysis show the number of individuals assigned to each segment by discriminant functions. It is evident by the classification that the number of individuals correctly assigned to the red segment is 133 whereas 6 of its original members are incorrectly assigned to the yellow segment by the analysis. Similarly, the correct number of classification of the yellow segment is equal to 390, 5 of its members are defined as members in the green segment in this case. Classification analysis also states that 6 of the members of the green segment are assigned to the yellow segment. In this way, the classification accuracy percentage of the discriminant functions for the three segments is 95.7% for the red segment; and 98.7% for yellow and green segments each. In total there are 17 respondents who are misclassified in a group (6 + 5 + 6), and 983 of consumers (133 + 390 + 460) are correctly identified in their segments. In this way, the hit ratio (percentage of correctly classified groups) of overall classification accuracy is 98.3% and reveals that segments are very well discriminated on the selected variables of attitude and behaviour. Also, the part below the table gives the leave one out classification which is used for cross-validating these results in which 97.7% cases are correctly classified and this ratio is akin to the original hit ratio 98.3%. Now, as both the discriminant functions are significant and the classification ratio is too worthy, respondents are analysed for their discriminant scores and predicted group membership based on these scores. Predicted group membership in discriminant analysis and respondents’ discriminant scores on the two functions are saved as a new variable in the data file (Fig. 7.5). The last two columns of Fig. 7.5 show the scores of each respondent. On the basis of these scores, respondents are classified in the segments in discriminant analysis. The entries under discriminant membership depict ‘1’ for red segment, ‘2’ for yellow segment, and ‘3’ for green segment. The classification statistics have already revealed that 17 respondents are not classified in their defined segments, so are in disagreement to be arranged under either label red, yellow, or green. For instance, the last row (entry 23) can be studied, cluster analysis defined this respondent in green segment (entry 3 under heading cluster) but discriminant analysis positioned this respondent in yellow segment (entry 2 under heading discriminant membership). Discriminant scores of

Leave one out classification

Cross-validation with original cases

12.7

408

6

390

12

402

40.8

1.3

98.7

8.6

40.2

465

460

5

0

465

460

0.0

46.5

98.7

1.3

0.0

46.5

98.7

1.3

1000

466

395

139

1000

466

395

139

N

100

100

100

100

100

100

100

100

%

Actual segment sizes

97.7% of cross-validated grouped cases correctly classified

127

% of cross-validation

0.0

0

0.0

0

91.4

Green

127

Yellow

Red

13.3

1.3

0 5

98.3% of original grouped cases correctly classified

6

4.3 98.7

%

133

0.0

6 390

N

Hit ratio

0

Green

0.0

95.7

%

Green

1.000

Total

Segment sizes by discriminant analysis

0

133

N

%

N

0.466 Yellow

0.395

0.139

Green

Red

Yellow

Red

Yellow

Red

Segments

Classification and predicted group membership

Prior probability for segments

Prior probability for determining classification

Table 7.8 Prior probability and classification results

7.3 Test of Significance and Validity of Cluster Solution 317

318

7 Segmentation of Consumers and Identification of Responsibles

Fig. 7.5 Discriminant scores and predicted group membership

these respondents are analysed by the way of Fig. 7.6, so that misclassifications can be sorted out for further investigation, size of each segment, and interpretation of the segments. The group centroid of red, yellow, and green segments are shown in Fig. 7.6 in the adjoining table. These group centroids are the average of discriminant scores for each respondent. As shown in the table, the average discriminant scores of green segment are positive on both the functions and high on the first function (2.101),

Group Centroids Segments

Fig. 7.6 Sorting of misclassified cases and segment sizes

Function

Function

1

2

Red

-4.497

0.790

Yellow

-0.897

-0.611

Green

2.101

0.283

7.3 Test of Significance and Validity of Cluster Solution

319

whereas this mean score is least for red segment on the first function (−4.497) and it is high on the second (0.790). If a respondent’s discriminant score (as shown in Fig. 7.5) is near to group centroid of green segment, he/she is the predicted member of it. Otherwise based on the obtained discriminant scores he/she become the member either in yellow and red counterparts. To enquire about these misclassifications, Fig. 7.6 utilizes values of discriminant scores for the first function on the horizontal axis and values of function two on the vertical axes. With their defined colours, members of three segments can be identified on the graph. The cases that may be misclassified in the discriminant analysis are those which come under the defined area on graph marked with two rings. The respondents those come under this particular area overlap in neighbourhood segment. They are the people who in cluster analysis were members in a particular defined segment but predicted group membership in discriminant analysis marked them as the member in their environs segment. In this way, these respondents are not truly mutually exclusive and contentious for their segment membership. Consequently, the 17 respondents who are misclassified in the discriminant analysis are removed from the sample for further analysis of characterizing and profiling Indian segments of ‘responsible consumers’. Hence, further results utilize data of 983 (1000–17) respondents. Of these respondents, 133 being in the red segment, 360 lie in the yellow segment, and another 460 are existent in the green segment. These segment sizes with their changed proportionate share are also evident from blurs in Fig. 7.6.

7.4 Interpretation of Segments 7.4.1 Red Segment: Apathetics and Imprudents It has already been mentioned that the first cluster is identified with red colour and named ‘Apathetics and Imprudents’. Red is one of the primary colours, and mostly everywhere, a symbol of risk and danger. The consumers in this segment are those who have attained least mean values on all the dimensions of attitude and behaviour. They are called Apathetics because apathy implies an attitude of indifference. These types of people are not concerned, interested, or enthusiastic regarding maintenance of sustainability (as their mean are low on attitudinal dimensions). They are Imprudents as well because imprudence entails no care for the results of an action. People in this segment neither show good sense, lack good judgment about the ways by which sustainable living can be apprehended in the real world, nor disclose any of the sustainable activities. Their low mean values on behavioural dimensions leave a mark of irresponsibility for them to be described as Imprudents. They are in the red zone as these types of people are a threat and a hindrance in maintaining sustainability. They contribute nothing rather than annoying the environment and society by their unsustainable activities. So, the segment is shown with the colour of danger and

320

7 Segmentation of Consumers and Identification of Responsibles

named so. It is satisfactory from society’s viewpoint that this segment is constituted with only a small part of sample respondents (N = 133; % = 13.5).

7.4.2 Yellow Segment: Aesthetics and Hopefuls The segments of consumers who cannot be articulated as robust but as per their mean values are quite compatible on both attitudinal and behavioural domains come under yellow segment and are termed as ‘Aesthetics and Hopefuls’. Aesthetics, because they are above Apathetics, are concerned for sustainable living and to some extent show interest in the causes by which sustainability can be maintained (measurement of specific attitudinal domain). On average, they are also farther than their previous counterpart (red segment) in ranking on behavioural dimensions. In this sense, they are hopefuls as society can expect their sustainable actions that may contribute to sustainable and healthy living. This group is given the tag yellow because they are almost prepared to go to environmental directions. Here, this colour symbolizes their optimism, idealism, vitality, and endurance. This segment constitutes 39.7% (N = 390) of the sample.

7.4.3 Green Segment: Aspirants and Illuminators Green is known as a symbol of life, good health, luck, vigour, and a colour reflecting trust. From an environmental point of view, all over the world, green is associated with nature and environment protection. Consumers in this segment are more vigorous and prominent both on attitudinal and behavioural measures. Hence, they are named ‘Aspirants and Illuminators’. They are aspirants because they are the rational people, aware and have ambitions to do something for environment preservation. Likewise, they are illuminators for society having an unusual degree of intellectual enlightenment in themselves because of which they behave in environmentally and socially desirable manners. By endowing with fame and splendour, they are definitely illuminating the way towards sustainability with their grace. It is a welcome sign that there is a silver lining in the form of these consumers who comprise the majority of the sample respondents (N = 460; % = 46.8). The analyses obtained that although there may be many types of consumers in the real world, here a stylized market of three consumer segments is observed. Accordingly, objectives 4 and 5 are attained. Green segment emerged out as the segment of ‘responsible consumers’; and the estimated proportion of these consumers is approximate 47% in the Indian market. Now, profiles of ‘responsible consumers’ (green segment) and their counterparts are explored in the next chapter with a further variable ’clusters’ extended in survey database.

References

321

References Churchill, G. A., Iacobucci, D., & Israel, D. (2010). Marketing research (4th ed, pp. 1–673.). Cengage Learning Editions. Facca, T. M., & Allen, S. J. (2011). Using cluster analysis to segment students based on self-reported emotionally intelligent leadership behaviors. Journal of Leadership Education, 10(2), 72–96. George, D., & Mallery, P. (2006). SPSS for windows step by step: A simple guide and reference, 13.0 update (6th ed., pp. 1–386). Pearson Education. Hair, Jr J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2006). Multivariate Data Analysis. 5th Edition, Dorling Kindersley (India) Pvt. Ltd, Pearson Education, Inc., New Delhi, pp. 1–700 Krishnaswami, O. R., & Ranganatham, M. (2005). Methodology of research in social sciences (2nd ed., pp. 1–445). Mumbai: Himalaya Publishing House. Laroche, M., Bergeron, J., Tomiuk, M.-A., & Barbaro-Forleo, G. (2002). Cultural differences in environmental knowledge, attitudes, and behaviour of Canadian consumers. Canadian Journal of Administrative Sciences, 19(3), 267–283. Malhotra, N. K., & Dash, S. (2012). Marketing research: An applied orientation (6th ed., pp. 1–929). New Delhi: Dorling Kindersley (India) Pvt. Ltd, Pearson Education, Inc. Parasuraman, A., Grewal, D., & Krishan, R. (2005), Marketing research-first indian adaptation (3rd Reprint, pp. 1–683). USA: Houghton Mifflin Co.; India, New Delhi: Biztantra-An Imprint of Dreamtech Press. Trochim, W. M. K. (2005). Research methods (2nd ed., pp. 1–355). Atomic Dog Publishing; India, New Delhi: Biztantra-An Imprint of Dreamtech Press. Zikmund, W. G., & Babin, B. J. (2006). Exploring marketing research (9th ed., pp. 1–848). Cengage Learning.

Chapter 8

Characterizing and Profiling Responsible Consumer Segments

This chapter as a rejoinder of objective 6 confronts for profiling segments of consumers (as identified in the previous chapter) according to a range of influencing variables. These influencing variables are accessible from sections C: personality features and D: personal information of the questionnaire (see Chap. 4, Sects. 4.1.2.2 and 4.1.5.2). The extended survey database for cluster membership as a new variable from Chap. 7 provides the data support here. Indeed, endnotes clarify that the used survey database is again a sequel of the previously extended survey database. Statistical results are amenable from various tables and figures prepared by analyzing this database. In the organization of this chapter, the classification of influencing variables is provided in Sect. 8.1. Sections 8.2 and 8.3, respectively, obtain cross-tabulation of respondents, and consumer membership of the segments. Finally, Sect. 8.4 integrates various features of ‘responsible consumers’ that differentiate them from their correspondents, thus presents their unique profile. Like the previous chapter, in this chapter too, Krishnaswami & Ranganatham 2005, Parasuraman et al. 2005, Trochim 2005, Hair et al. 2006, Zikmund & Babin 2006, Churchill et al. 2010, and Malhotra & Dash 2012 are the main statistical guides.

8.1 Characterization and Categorization of Influencing Dimensions For identifying ‘responsible consumers’, initial studies focused mainly upon demographic determinants; but at times, other kinds of determinants have also been added to the list. These determinants are visible in the literature with divergent terminology. For some instances, Hines et al. (1986/87) examined factors affecting environmental behaviour under three categories: cognitive, psychosocial, and demographic. Clark et al. (2003) abridged internal and external as the two broad classes, and Kennedy et al. (2009) congregated individual, household, and societal categories. Further, Kim and Kim (2010) divided the variables according to the social structural approach and © Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_8

323

324

8 Characterizing and Profiling Responsible Consumer Segments

social value approach. A comparison of these classifications with each other confirms that the difference rests only in categorization; however, the notions of researchers regarding the variables under different categories intersect with each other. Incidentally, to reach the profile of ‘responsible consumers’, firstly, the need is to standardize these dimensions to reach a common cognizance. Since consumer behaviour is an interdisciplinary concept, here, the standardization is completed on the basis of those study disciplines from where the influencing determinants of behaviour originate. For cases in point, psychological determinants originate from the study field Psychology; Economics provides economic determinants; sociological determinants are in use from Sociology and Social Psychology; Geography gave a foundation to geographic determinants; and Archaeology/Cultural Anthropology is the base of having cultural factors. In view of that, Table 8.1 responds to the arrangement of causal variables under each determinant category and their categorization as utilized in the study. Viewing horizontally, Table 8.1 presents segmentation dimensions under six divisions. Vertically, it is divided into two columns: the first obtains the variables under each category of determinant; and second is the representative of the categories underlying each variable as operated upon further. The first potion ‘demographic determinants’ corresponds to the demography of respondents. ‘Sociological factors’ indicate behaviour of people when they are operating in a group, and hold that consumers are shaped by their environment and surroundings as they live and operate within it. The impact of family is the most dominating influence in this category. Next, ‘cultural factors’ are the robust influencer of consumer behaviour as it is the culture which determines what is acceptable or unacceptable, important or unimportant, right or wrong, and workable or unworkable in a society. Geography effects consumer behaviour as people who live in identical geographic conditions have similar needs and wants instead of those living in other areas, thus may have identical behaviour. The second last basis in the table is the ‘economic causes’ which are the indicators of purchasing power and affluence of individuals. The last dimension speaks about the ‘personality traits’ of consumers. In the words of Hoyer et al. (2009: p. 418), personality consists of the distinctive patterns of behaviours, tendencies, qualities, or personal dispositions that make one individual different from another. These patterns are internal characteristics that individuals are born with or that result from the way they have been raised. Sixteen personality traits were targeted and measured (Chap. 4). A correlation matrix between these traits suggested that many were significantly correlated with each other, and thus can be reduced to some components. Accordingly, at the outset, these are analysed using principal component analysis and four components are obtained which are used as a replacement of these sixteen variables. Afterward, these four components are categorized into two (less-more) categories from median splits as with the requirement of the Chi-square test (analysed in Sects. 8.2 and 8.3). Full details and procedure of principal component analysis and classification of variables in the category of personality traits are presented in Endnotes.

8.1 Characterization and Categorization of Influencing Dimensions

325

Table 8.1 Segmentation bases and their categories Segment basis

Classifications utilized in the study

Demographic determinants Gender

Male, female

Age

15–24 (young), 25–40 (adults), 41–65 (middle- and upper-aged) Range of age: 15–65

Education

School level, graduation level, postgraduation, and higher education

Academic orientation

Arts and natural sciences, law and business, pure sciences and technical

Academic intelligence

Academically poor, academically fair, academically good, academically excellent, academically superior

Marital status

Married, unmarried

Parenthood

with children, without children

Years of marriage

1–5 (newly married couples or married couples having kids), 6–15 (married couples having teenage child ), 16–25 (married couples having grown-up children), 26–44 (married couples and children live separately)

Profession/earning

Earners, non-earners

Sociological determinants Family type

Joint family, nuclear family

Family size

2–5 persons (small-sized), 6–10 (medium-sized), 11 and above (large-sized)

Composition of family

• Females > Males, Females = Males, Females < Males • Mature > Young; Mature = Young, Mature < Young

Household support

Low household support, high household support

Cultural determinants Religion

Hindus, Sikhs, Islamic, Others (Jains, Buddhists, Aesthetes, Christians)

Religiosity

Less religiosity, high religiosity

Geographic determinants Place of living

Rural, urban

Commuting

Not commute, commute (walking/cycling, use of private, public vehicles)

Economic determinants Family income

Less than 15,000 (low-income group), 15,001–80,000 (middle-income group), greater than 80,000 (high-income group)

Home ownership

Living in own houses, living in rental houses

Personality determinants Objective directed

Less-directed individuals, more-directed individuals

Social directed

Less-directed individuals, more-directed individuals

Self-directed

Less-directed individuals, more-directed individuals

Emotions directed

Less-directed individuals, more-directed individuals

326

8 Characterizing and Profiling Responsible Consumer Segments

8.2 Cross-Tabulation and Consumer Membership: Chi-Square and Proportional Analysis For the purpose of obtaining consumer membership in the identified segments as per different categories of influencers (Table 8.1), two-way cross-tabulations are completed. Segmentation division (three segments: Red, Yellow, and Green) is taken as the dependent variable and each of the ‘determinants’ as independent variables. Along with cross-tabulation, the Chi-square test for significant/insignificant association is performed. Chi-square test has been run by using online calculators developed by Lowry (2001–2014), Preacher (2001), and Social Science Statistics (2013). For this test, all independent variables are converted to a categorical measure as per its requirement (the required description has already been detailed in Chap. 4). The significance of Chi-square implies that the membership of consumers into segments varies according to a specific attribute for which that particular result relates. Cramer’s V as a measure of association further confirms the degree and significance of dependence of consumer membership. According to the custom of cross-tabulation, the dependent variable (segments) is displayed column-wise, and the independent variables (consumer characteristics) are depicted row-wise in all the tables described afterward. Besides this cross-tabulation, proportional analysis sheds additional light on any prescribed correspondence between the variables. In any cross-tabulation, this proportional analysis can be presented either row-wise or column-wise. It means that percentages can be computed in different directions, such as horizontally (row percentages) or vertically (column percentages). In this regard, Churchill et al. (2010: p. 339) mentioned a fundamental rule of calculating percentages that these should always be calculated in the direction of causal or effect factor for making the right comparisons. Accordingly, to make the analysis comparable, and to further profile consumers, these percentages are calculated row-wise (in the direction of independent variables) and subsequently analysed in Sect. 8.3.

8.2.1 Demographic Determinants and Segmentation Table 8.2 describes cross-tabulated frequencies as per each demographic determinant. The first column reveals the variables, and the second highlights the categories underlying each. The analysis is extended in a four-phased compass. In the first part, the column labelled ‘frequencies’1 presents the cross-tabulation of segments of consumers with each demographic feature. Each cell value depicts the observed

1 The

total of frequencies in each category is 983 since the data for 17 respondents who were misclassified into identified segments in the discriminant analysis are removed in this part. Contrary to it, the total for the categories of variables ‘parenthood’ and ‘years of marriage’ is 455 as it is obvious that only the married ones are the respondents here.

Academic orientation

Educational level

36 (32.9)

49 (42.3)

Arts and natural sciences

16 (52.6)

Higher

Law and business

59 (45.6)

Graduates

21 (17.3)

Aged

58 (34.8)

49 (61.0)

Adult

Schooling

63 (54.7)

Young

49 (62.2)

Female

Age cohorts

84 (70.8)

Male

Gender

112 (136.5)

123 (106.1)

143 (154.3)

143 (133.7)

104 (102.0)

26 (50.8)

174 (178.9)

190 (160.3)

205 (182.5)

185 (207.5)

176 (158.2)

103 (123.0)

230 (182.0)

135 (157.7)

95 (120.3)

81 (59.9)

228 (211.0)

151 (189.1)

206 (215.3)

254 (244.7)

337

262

389

337

257

128

451

404

460

523

Total +3.8% (+0.59)

−10.8% (−1.56) +12.3% (+1.67)

+18.7% (+1.57) −21.3% (−1.68)

+15.8% (+1.03)

+9.4% (+0.54)

14.5

+11.3% (+1.42)

−17.9% (−2.1)

V of Cramer = 0.095 p = 0.002

13.7

χ2 = 16.581 df =4 p = 0.002

−16.3% (−1.8)

+15.9% (+1.64)

17.5 4.1

−7.3% (−0.91)

−69.6% (−5.05)

−14.4% (−1.81) +26.3% (+3.56)

+7% (+0.8)

+29.4% (+1.99)

V of Cramer = 0.186 p = 0.000

22.6

χ2 = 67.686 df =4 p = 0.000

−21% (−2.3)

+66.8% (+3.94)

+2% (+0.2)

16.4

+35.2% (+2.73)

−48.8% (−3.48)

+21.3% (+0.88)

10.9

+8% (+1.17)

−2.8% (−0.37)

15.6

10.7

16.1

Red segment

−19.7% (−1.54)

V of Cramer = 0.140 p = 0.000

V of Cramer = 0.107 p = 0.004

Test of association

Proportions

+18.5% (+2.35)

χ2 = 38.618 df =4 p = 0.000

χ2 = 11.253 df = 2 p = 0.004

Test of significance

Inferential statistics

+15.3% (+1.13)

−20.1% (−2.77)

−4.3% (−0.63)

Green segment

Yellow segment

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Variable categories

Demographic variables

Table 8.2 Demographic variables and segmentation: cross-tabulation and test of association/dependence

33.2

46.9

36.8

42.4

40.5

20.3

38.6

47.0

44.6

35.4

Yellow segment

52.2

39.3

59.1

40.1

37.0

63.3

50.6

37.4

44.8

48.6

100

100

100

100

100

100

100

100

100

100

Total

(continued)

Green segment

8.2 Cross-Tabulation and Consumer Membership … 327

Profession

Years of marriagea

Parenthooda

Marital status

Demographic variables

24 (10.3)

4 (5.4)

16-25

26-44

65 (50.6)

11 (16.0)

6-15

Non-earning

18 (25.3)

6 (10.4)

Without children

1-5

51 (46.6)

57 (61.6)

Married

With children

76 (71.4)

164 (148.4)

9 (12.6)

17 (24.0)

31 (37.4)

76 (59.0)

35 (22.4)

88 (100.6)

133 (180.5)

257 (209.5)

145 (175.0)

30 (25.0)

41 (47.8)

86 (74.5)

108 (117.6)

42 (50.2)

233 (224.8)

265 (212.9)

195 (247.1)

151 (148.8)

374

43

82

128

202

83

372

455

528

−14.2% (−0.98)

+56% (+2.65) +28.7% (+2.21) −17.1% (−1.05) −29.1% (−1.42) −28.4% −1.01

−42.3% (−1.36) −28.9% (−1.45) −31.4% (−1.26) +133.6% (+4.28) −25.7% −0.6 +10.5% (+1.28)

+15.4% (+1.33)

−12.5% (−1.25)

+9.4% (+0.64)

+28.5% (+2.02)

+3.6% (+0.54)

−26.3% (−3.54)

−7.4% (−0.58)

17.4

29.3

8.6

8.9

7.2

13.7

−17.2% (−2.27)

V of Cramer = 0.134 p = 0.000

V of Cramer = 0.199 p = 0.000

V of Cramer = 0.166 p = 0.002

9.3 χ2 = 17.575 df =2 p = 0.000

χ2 = 35.887 df =5 p = 0.000

χ2 = 12.5 df = 2 p = 0.002

12.5

+19.8% + 0.99

−8.2% (−0.89)

−16.3% (−1.15)

+24.5% (+3.57)

+22.7% (+3.28)

14.4

Red segment

−21.1% (−3.31) V of Cramer = 0.220 p = 0.000

Test of association

Proportions

9.5 χ2 = 47.634 df =2 p = 0.000

Test of significance

Inferential statistics

+1.5% (+0.18)

Green segment

+6.4% (+0.54)

+5.9% (+0.67)

136 (128.4)

317

−24.6% (−1.55)

30 (39.8)

Yellow segment

Total

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Unmarried

Pure sciences and technical

Variable categories

Table 8.2 (continued)

43.9

20.9

20.7

24.2

37.6

42.2

21.0

29.2

48.7

42.9

Yellow segment

38.8

69.8

50.0

67.2

53.5

50.6

66.9

58.2

36.9

47.6

100

100

100

100

100

100

100

100

100

100

Total

(continued)

Green segment

328 8 Characterizing and Profiling Responsible Consumer Segments

Academic intelligence (level—higher education)

Academic intelligence (level—graduation)

Academic intelligence (level—schooling)

Demographic variables

0 (1.09)

7 (20.30)

Academically excellent

Academically fair

25 (20.72)

Academically good

4 (0.003)

23 (22.43)

Academically fair

Academically poor

17 (8.55)

18 (36.91)

Academically good

Academically poor

43 (38.10)

Academically fair

15 (9.64)

6 (5.01)

40 (35.80)

32 (36.55)

36 (39.57)

19 (15.07)

33 (30.40)

32 (31.38)

19 (22.23)

10 (14.26)

3 (7.41)

48 (38.90)

40 (39.72)

46 (43.00)

4 (16.38)

42 (25.69)

21 (26.52)

8 (18.79)

25

13

95

97

105

40

93

96

68

+19.7% (+0.44) +55.6% (+1.73)

+604.1% (+4.55) −100% (−1.05)

0.0

31.0

−59.6% (−1.62) −29.9% (−1.13)

7.4

25.8

+23.4% (+1.46) V of Cramer = 0.221 p = 0.000

+11.7% (+0.7)

−65.5% (−2.95)

χ2 = 37.971 df =8 p = 0.000

+0.7% (+0.04)

−12.5% (−0.75)

+20.6% (+0.94)

+2.5% (+0.12)

21.9

+7% (+0.46)

−9% (−0.57)

V of Cramer = 0.218 p = 0.000

42.5

+98.9% (+2.89)

−75.6% (−3.06)

44.8

60.3

11.2

Red segment

+26% (+1.01)

χ2 = 32.071 df =6 p = 0.000

V of Cramer = 0.265 p = 0.000

Test of association

Proportions

19.4

+8.6% (+0.47)

−51.2% (−3.11)

χ2 = 35.989 df =4 p = 0.000

Test of significance

Inferential statistics

+63.5% (+3.22)

+2% (+0.11)

+12.9% (+0.79)

−20.8% (−1.07)

−57.4% (−2.49)

−14.5% (−0.68)

41 (26.99)

315 (285.0) +51.9% (+2.7)

+10.5% (+1.78)

226 (241.6)

609

−6.5% (−1)

68 (82.4)

−17.5% (−1.59)

Green segment

Yellow segment

Total

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Academically poor

Earning

Variable categories

Table 8.2 (continued)

60

46.0

42.1

33.0

34.3

47.5

35.5

33.3

27.9

37.1

Yellow segment

40

23.0

50.5

41.2

43.8

10.0

45.2

21.9

11.8

51.7

100

100

100

100

100

100

100

100

100

100

Total

(continued)

Green segment

8.2 Cross-Tabulation and Consumer Membership … 329

Total

6 (3.23)

2 (3.93)

5 (8.17)

Academically excellent

Academically brilliant

61 (72.11)

39 (34.70)

29 (28.53)

121 (106.72)

49 (51.36)

39 (42.23)

187

90

74

36.5% (−1.31)

+12.4% (+0.73)

−49.2% (−0.97) 13.1% (−1.11)

+1.6% (+0.09)

+85.5% (+1.54)

Yellow segment

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Academically good

Variable categories

a Total for the categories is 455: data subsist only for married respondents Total of academic intelligence in all levels is in accordance with people in each educational level

Demographic variables

Table 8.2 (continued)

+65.3% (+1.38)

−4.6% (−0.33)

−7.7% (−0.5)

Green segment

Test of significance

Test of association

Inferential statistics

2.7

2.2

8.1

Red segment

Proportions

32.6

43.3

39.2

Yellow segment

64.7

54.4

52.7

Green segment

100

100

100

Total

330 8 Characterizing and Profiling Responsible Consumer Segments

8.2 Cross-Tabulation and Consumer Membership …

331

count with expected count2 in parentheses. By the way of this table, a 2 × 3 crosstabulation is exhibited for variables gender, marital status, parenthood, and profession. However, age cohorts, educational level, and academic orientation depict a 3 × 3 classification. The variable years of marriage is presented with four categories, thus its cross-tabulation can be defined as a table of 4 × 3 size, similar is applicable to academic intelligence. The second part shows Percentage Deviations3 with Standardized Residu4 als in parentheses. These deviations and residuals are the basis for significance/insignificance of Chi-square statistics. The third part draws inferences about the association of consumer segments with each demographic feature, therefore it is named inferential statistics. Looking at the observed significance values (p-values), it can be concluded that observed frequencies noticeably differ from expected frequencies. The same difference is reinforced with the percentage deviation and considerable residuals. In this sense, it is remarked that the assumption of no association of Chi-square is rejected here, and all the demographic variables are found significantly associated with the identified segments. V of Cramer further measures the degree of association. The variables’ age (χ2 = 38.618; V of Cramer = 0.140), educational qualifications (χ2 = 67.686; V of Cramer = 0.186), academic intelligence, marital status (χ2 = 47.634; V of Cramer = 0.220), and years of marriage (χ2 = 35.887; V of Cramer = 0.199) associate with the dependent variable at 0.1% significance level (p = 0.000). Indeed, the significance for variables gender (χ2 = 11.253; V of Cramer = 0.107), academic orientation (χ2 = 16.581; V of Cramer = 0.095), parenthood (χ2 = 12.5; V of Cramer = 0.166), and profession (χ2 = 17.575; V of Cramer = 0.134) can be obtained at 1% probability level. The association of parenthood (χ2 = 9.106; V of Cramer = 0.141) is noteworthy at 5% level of significance. The last portion of the table is marked with the heading proportions. The mathematical values in each cell obtain the proportionate share of respondents in the columns of red, yellow and green segments. These values are calculated by considering the total of respondents in a particular causal category. Statistically, the observed count into each cell is divided by the corresponding row total. For example, the value 84 in the very first cell implies that from a total of 523 male respondents 84 fall into the category of the red segment. Similarly, the cell value 49 remarks the same for females. But the two numbers 84 and 49 are not comparable as the base total is unequal (523 for males and 460 for females). Hence, categorical data is made 2 Expected count in each cell assumes that there is no association between the two variables of interest

and mathematically Chi-square value than becomes zero stating no association or dependence. Therefore, if these frequencies are remarkably different from observed frequencies, a dependence relationship may be achieved with Chi-square significance. 3 Percentage deviation is the measure of the degree to which an observed Chi-square cell frequency (observed frequency) differs from the value that would be expected on the basis of the null hypothesis (expected frequency). The resulting value is then given a positive sign if the observed count is greater than expected and a negative sign if the reverse is true. Statistically, [{(O − E)/E} × 100]. 4 The standardized residual for a cell in a Chi-square table is a version of the standard normal deviate. The Chi-square value that results from a Chi-square analysis is equal to the sum of the squares of the standardized residuals. Statistically, [{O − E}/{SQRT(E)}].

332

8 Characterizing and Profiling Responsible Consumer Segments

comparable by equalizing the total of all categories to 100 (Cent%). As an instance, for taking the percentage of males who lies in the red segment, the male frequency into the segment (N = 84) is divided by the total count of male respondents (N = 523) and the result is multiplied by 100 [(84 ÷ 523) × 100]. A similar calculation is applied to the rest of the cell values. These proportions are further utilized for profiling purpose in Sect. 8.3.1.

8.2.2 Sociological Determinants and Segmentation As with the same line, Table 8.3 presents the result of variables in the sociological category and contains identical messages as Table 8.2. The first variable is ‘type of family’ which can be accepted for its association with the segments at a 10% probability level. The second variable ‘family size’ does not associate at all; however, the composition of family (gender-wise) associates with a 5% significance level. Age-wise analysis shows the association at a 1% level of significance. The next variable ‘household support’ associates highly with a given significance of 0.000 (χ2 = 21.204; df = 2). This provides confidence that the membership of consumers in the segments varies according to espousal people get from their family. Further, a perusal of a 3 × 3 matrix for the variable ‘family size’ brings into light the fact of its insignificance (χ2 = 2.986; p > 0.05). The degree of association for that reason is also not substantial (V of Cramer = 0.039; p > 0.05). This variable indeed does not associate significantly with the segments; still, the calculated percentages present a distinctive picture of membership of consumers into each segment (analysed in Sect. 8.3.2).

8.2.3 Geographic Determinants and Segmentation Two of the geographic influences ‘place of living’ and ‘commuting’ are captured in the study. Highly considerable Chi-square values [(place of living: χ2 = 13.997; p = 0.001), (commuting: χ2 = 23.991; p = 0.001)] determine that there is a significant difference in observed and expected frequencies. As follows, Cramer’s V determines that the association of place of living (V of Cramer = 0.119; p = 0.001) is slightly greater than the association of commuting (V of Cramer = 0.111; p = 0.001). Overall, it can be concluded that the membership of respondents into segments differs from their living place and change in the place (commuting). Regarding the membership, the procedure as defined above is processed. The percentages are considered in the direction of causal variables and are evident from the right side of the table under column ‘proportions’, interpreted in Sect. 8.3.3.

Composition of family (age-wise)

Composition of family (gender wise)

16 (12.3)

25 (22.1)

92 (98.6)

Mature = Young

Young > Mature

59 (49.2)

Males < Females

Mature > Young

42 (46.9)

Females = Males

10 (8.8)

Large

32 (36.8)

37 (44.4)

Medium

Female > Males

86 (79.8)

Small

68 (68.33)

Nuclear

Family size

65 (64.67)

Joint/extended

Type of family

279 (289.2)

83 (64.7)

28 (36.1)

120 (144.4)

148 (137.7)

122 (107.9)

31 (25.8)

120 (130.1)

239 (234.1)

218 (200.36)

172 (189.64)

358 (341.1)

55 (64.7)

47 (42.6)

185 (170.3)

157 (162.4)

118 (127.3)

24 (30.4)

171 (153.5)

265 (276.1)

219 (236.32)

241 (223.68)

729

163

91

364

347

272

65

328

590

505

478

Total

−3.5% (−0.6)

−6.7% (−0.67)

+4.9% (+0.91)

−27.9% (−2.44)

+10.4% (+0.68)

−22.4% (−1.35) +28.3% (+2.28)

+8.6% (+1.12)

−16.9% (−2.03)

+13.4% (+0.63)

+30% (+1.05)

+19.8% (+1.39)

−3.3% (−0.42)

−7.3% (−0.82)

−21.1% (−1.16)

+11.4% (+1.41)

−4% (−0.67)

+7.5% (+0.88)

+13.1% (+1.36)

−13% (−0.79) −10.5% (−0.72)

+20.2% (+1.03)

−7.8% (−0.89)

−16.6% (−1.11) +13.7% (+0.41)

+2.1% (+0.32)

+8.8% (+1.25)

−0.5% (−0.04) +7.7% (+0.69)

+7.7% (+1.16)

−9.3% (−1.28)

+0.5% (+0.04) −7.3% (−1.13)

Green segment

Yellow segment

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Variable categories

Sociological variables

χ2 = 16.55 df = 4 p = 0.002

χ2 = 11.938 df =4 p = 0.018

χ2 = 7.612 df = 4 p = 0.107

χ2 = 5.808 df = 2 p = 0.055

Test of significance

V of Cramer = 0.092 p = 0.002

V of Cramer = 0.078 p = 0.018

V of Cramer = 0.062 p = 0.107

V of Cramer = 0.077 p = 0.055

Test of association

Inferential statistics

Table 8.3 Sociological variables and segmentation: cross-tabulation and test of association/dependence

12.6

15.3

17.6

16.2

12.1

11.8

15.4

11.3

14.6

13.5

13.6

Red segment

Proportions

38.3

50.9

30.8

33.0

42.7

44.9

47.7

36.6

40.5

43.2

36.0

Yellow segment

49.1

33.7

51.6

50.8

45.2

43.4

36.9

52.1

44.9

43.4

50.4

100

100

100

100

100

100

100

100

100

100

100

Total

(continued)

Green segment

8.2 Cross-Tabulation and Consumer Membership … 333

84 (59.8)

49 (73.2)

Low

High

Household support

Total

220 (214.6)

170 (175.4) 272 (253.2)

188 (206.8) 541

442

−9.1% (−1.31)

−3.1% (−0.4) +2.5% (+0.37)

+40.5% (+3.13) −33.1% (−2.83) +7.4% (+1.18)

Green segment

Yellow segment

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Variable categories

Sociological variables

Table 8.3 (continued)

χ2 = 21.204 df =2 p = 0.000

Test of significance V of Cramer = 0.147 p = 0.000

Test of association

Inferential statistics

9.1

19.0

Red segment

Proportions

40.7

38.5

Yellow segment

50.3

42.5

Green segment

100

100

Total

334 8 Characterizing and Profiling Responsible Consumer Segments

10 (7.3)

40 (28.8)

48 (59.0)

35 (37.9)

Not commute

Walk/cycling

Private

Public

78 (90.5)

Urban

Commuting and commuting type

55 (42.5)

Rural

Place of living

115 (111.1)

160 (173.0)

100 (84.5)

15 (21.4)

290 (265.4)

100 (124.6)

130 (311.0)

228 (204.0)

73 (99.7)

29 (25.3)

301 (313.1)

159 (146.9)

280

436

213

54

669

314

Total

−3.9% (−0.68) +14.8% (+0.74)

+9.3% (+1.51) −30% (−1.39)

−13.8% (−1.32)

+18.3% (+1.69) −7.5% (−0.99) +3.5% (+0.37)

+38.8% (+2.08) −18.6% (−1.43) −7.6% (−0.47)

−0.8% (−0.09)

+11.7% (+1.68)

−26.8% (−2.67)

+8.2% (+1)

−19.7% (−2.2)

+29.5% (+1.92)

+36.9% (+1)

Green segment

Yellow segment

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Variable categories

Geographic variables

χ2 = 23.991 df = 6 p = 0.001

χ2 = 13.997 df =2 p = 0.001

Test of significance

V of Cramer = 0.111 p = 0.001

V of Cramer = 0.119 p = 0.001

Test of association

Inferential statistics

Table 8.4 Geographic variables and segmentation: cross-tabulation and test of association/dependence

12.5

11.0

18.8

18.5

11.7

17.5

Red segment

Proportions

41.1

36.7

46.9

27.8

43.3

31.8

Yellow segment

46.4

52.3

34.3

53.7

45.0

50.6

Green segment

100

100

100

100

100

100

Total

8.2 Cross-Tabulation and Consumer Membership … 335

336

8 Characterizing and Profiling Responsible Consumer Segments

8.2.4 Economic Determinants and Segmentation Table 8.5 is visible with an analysis of two economic influencers namely ‘family income’ and ‘home ownership’. In total, these two factors describe the economic standing of consumers which may have an effect on their attitude and behaviour. With this given effect, consumers may be found having placed either in the red, yellow, or green segment. Observed and expected frequencies are revealed under the heading ‘frequencies’ as a custom adopted in this part of the analysis. Just looking at the observed and expected count, one can get hunches regarding reliable differences between the two. The same is obtained from the percentage deviations and the values of the Chi-square test which are statistically noticeable. Next is the statistics of the test of association (V of Cramer). Stated by the significance levels, this measure reckons on weak but considerable associations. For obtaining the membership of consumers, the proportional analysis can be seen by looking at the right side of the table. The percentages as revealed by the table are calculated based on the total of the rows in any category. Further, these percentages are analysed in the form of proportionate share for profiling the segments of consumers in Sect. 8.3.4.

8.2.5 Cultural Determinants and Segmentation Table 8.6 gives the frequencies, measures of the test of significant association, and proportions for each of the two variables under the cultural category of influencers. It is clear from the observed significance levels for both the variables that the calculated Chi-square can be termed substantial by considering a 10% level of significance (preligion = 0.054; preligiosity = 0.065). With this marginal significance, Cramer’s V is a reflection of a feeble association between the dependent (consumer segments) and independent variables (religion and religiosity). Supplementing the result, computed percentages are shown in the last portion of the table. These percentages are further analysed with the help of bar graphs to obtain membership of respondents into each segment in Sect. 8.3.5.

8.2.6 Personality Determinants and Segmentation In the initial column of Table 8.7, four components named: objective directed traits, social directed traits, self-directed traits, and emotions directed traits are highlighted. Extremely high significance (p = 0.000) affirms that consumer membership robustly depends upon the personality features as measured by these four components. Cramer’s V further measures the degree of dependence in which the association of objective directed traits is strongest amongst all (V of Cramer = 0.346). The right side of the table again is visible with the proportions computed for attaining the

91 (101.6)

42 (31.4)

Rental

34 (31.1)

High

Own

59 (72.5)

Middle

Home Owner−Ship

40 (29.4)

Low

Family Income

94 (92.0)

286 (298.0)

97 (91.3)

201 (212.7)

92 (86.1)

96 (108.6)

374 (351.4)

99 (107.6)

276 (250.8)

85 (101.5)

232

751

230

536

217

Total

+4.8% (+0.46)

−1.5% (−0.25)

−10.4% (−1.05) +33.8% (+1.89)

+6.3% (+0.6)

−5.5% (−0.8)

−18.6% (−1.59) +9.3% (+0.52)

+6.9% (+0.64)

+36.2% (+1.96)

Yellow segment

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Variable categories

Economic variables

−13.5% (−1.42)

+4.2% (+0.79)

−8% (−0.83) χ2 = 7.595 df = 2 p = 0.022

χ2 = 13.965 df =4 p = 0.007

−16.3% (−1.64) +10% (+1.59)

Test of significance

Green segment

V of Cramer = 0.088 p = 0.022

V of Cramer = 0.084 p = 0.007

Test of association

Inferential statistics

Table 8.5 Economic variables and segmentation: cross-tabulation and test of association/dependence

18.1

12.1

14.8

11.0

18.4

Red segment

Proportions

40.5

38.1

42.2

37.5

42.4

Yellow segment

41.4

49.8

43.0

51.5

39.2

Green segment

100

100

100

100

100

Total

8.2 Cross-Tabulation and Consumer Membership … 337

71 (70.1)

62 (62.9)

Low

High

2 (2.7)

Others

Religiosity

6 (2.3)

Islamic

168 (184.5)

222 (205.5)

11 (7.9)

8 (6.7)

26 (28.6)

235 (217.6)

225 (242.4)

7 (9.4)

3 (8.0)

38 (33.7)

412 (409.0)

465

518

20

17

72

+8% (+1.15) −8.9% (−1.21)

−1.5% (−0.12)

+38.6% (+1.09)

−26.1% (−0.43) +1.3% (+0.11)

+18.6% (+0.48)

+160.9% (+2.44)

−9% (−0.48)

−17.9% (−0.56)

8 (9.7)

Sikh

−0.5% (−0.09)

345 (346.8)

874

−1.1% (−0.12)

117 (118.3)

Hindus

Religion

Yellow segment

Total

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Variable categories

Cultural variables

+8% (+1.18)

13.3

13.7

35.3

11.1

13.4

Red segment

−7.2% (−1.12)

V of Cramer = 0.075 p = 0.065

V of Cramer = 0.079 p = 0.054

Test of association

10.0 χ2 = 5.462 df = 2 p = 0.065

χ2 = 12.371 df =6 p = 0.054

Test of significance

Proportions

−25.2% (−0.77)

−62.3% (−1.76)

+12.8% (+0.74)

+0.7% (+0.15)

Green segment

Inferential statistics

Table 8.6 Cultural variables and segmentation: cross-tabulation and test of association/dependence

36.1

42.9

55.0

47.1

36.1

39.5

Yellow segment

50.5

43.4

35.0

17.6

52.8

47.1

Green segment

100

100

100

100

100

100

Total

338 8 Characterizing and Profiling Responsible Consumer Segments

Emotions directed traits

Self-directed traits

114 (68.7)

19 (64.3)

More

47 (65.5)

More

Less

86 (67.5)

35 (71.6)

More

Less

98 (61.4)

Less

28 (64.1)

More

Social directed traits

105 (68.9)

Less

Objective directed traits

200 (188.5)

190 (201.5)

159 (192.0)

231 (198.0)

194 (209.9)

196 (180.1)

143 (188.1)

247 (201.9)

256 (222.3)

204 (237.7)

278 (226.5)

182 (233.5)

300 (247.5)

160 (212.5)

303 (221.8)

157 (238.2)

475

508

484

499

529

454

474

509

Total

χ2 = 44.545 df = 0 p = 0.000

−22.1% (−3.37)

−14.2% (−2.19)

−5.7% (−0.81) +6.1% (+0.84)

+65.9% (+5.46) −70.4% (−5.65)

+15.2% (+2.26)

+22.7% (+3.42)

−17.2% (−2.38)

−28.2% (−2.28)

+21.2% (+3.33)

χ2 = 72.966 df = 4 p = 0.000

χ2 = 67.130 df = 2 p = 0.000

−24.7% (−3.6)

+16.7% (+2.35)

−7.6% (−1.1)

−51.1% (−4.32)

χ2 = 117.554 df = 2 p = 0.000

−34.1% (−5.26) +36.6% (+5.45)

Test of significance

V of Cramer = 0.273 p = 0.000

V of Cramer = 0.213 p = 0.000

V of Cramer = 0.261 p = 0.000

V of Cramer = 0.346 p = 0.000

Test of association

Inferential statistics Green segment

+27.4% (+2.25)

+8.8% (+1.18)

−24% (−3.29)

−56.3% (−4.51) +59.5% (+4.67)

+22.3% (+3.17)

+52.5% (+4.35)

Yellow segment

Red segment

Green segment

Red segment

Yellow segment

Percentage deviation (standardized residuals)

Frequencies [observed (expected)]

Variable categories

Personality variables

Table 8.7 Personality variables and segmentation: cross-tabulation and test of association/dependence

4.0

22.4

9.7

17.2

6.6

21.6

5.9

20.6

Red segment

Proportions

42.1

37.4

32.9

46.3

36.7

43.2

30.2

48.5

Yellow segment

53.9

40.2

57.4

36.5

56.7

35.2

63.9

30.8

Green segment

100

100

100

100

100

100

100

100

Total

8.2 Cross-Tabulation and Consumer Membership … 339

340

8 Characterizing and Profiling Responsible Consumer Segments

apportionment of respondents into each segment. It can be seen that the percentages are highest in the cell under the green segment for the group of people where these traits are robustly intermingled in people personality (more-directed group).

8.3 Identification of Consumer Membership This section sheds light on a range of characteristics of respondents based on which they become members in a particular segment. For distinguishing between extremely responsibles (members in the green segment), less responsibles (members in the yellow segment), and not responsibles (members of the red segment), each specific feature is separately analysed by displaying proportionate share from the previous Sect. 8.2 in the form of bar charts. The membership is based on the highest proportion of any category in any segment which is similar to the traditional vote-counting method as defined by Pal and Davar (2001: p. 624). As this method conspires, the category with the highest proportion is noted and in this way; the class with utmost proportion is declared the winner. But an important point is worth considerable that one group cannot be singled out over the other with negligible differences in proportionate shares. The place of any category is fixed under any segment only if its proportion significantly differs from the proportion of its neighbouring counterpart. Statistically, if differences in proportions are insignificant, one category cannot be favoured over the other. So, to avoid subjectivity, here the inferential statistics in the form of z-test for the difference between two population proportions is also utilized to play its part and provide accurate and embellished results. The calculations are completed by using online z-test calculator for two population proportions from Social Science Statistics (2013).

8.3.1 Demographic Variables and Segmentation • Gender and Segment Membership It has already been marked that gender and segment membership are reliably associated. To obtain consumer membership in segments according to gender, Fig. 8.1 is visible with the number of segments on the x-axis and proportionate share on the y-axis. The data table below this figure confers that both males and females are hierarchically distributed in the three segments meaning that consumers’ membership in the red segment is less than the yellow segment, and ultimately the percentage share in the red segment is below the green. In this way, both males and females in the majority are the members of the green segment. But as per the purpose of obtaining membership, the comparison rests between the categories of the male and female groups within each segment. Table 8.8 further contrasts these groups and statistically demonstrates the results of the z-test.

8.3 Identification of Consumer Membership

341

Fig. 8.1 Gender-wise segmentation

Table 8.8 Gender-wise intra-segment comparison: paired differences and test of relevance Segments

Gender

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Male

523

0.161

0.054

0.022

2.467

0.014

Female

460

0.107 0.092

0.031

2.942

0.003

0.038

0.032

1.191

0.234

Yellow segment

Male

523

0.354

Female

460

0.446

Green segment

Male

523

0.486

Female

460

0.448

(a) Red Segment: At this juncture, it can be seen that 16.1% of the male sample falls under the red segment whereas 10.7% are female respondents. Accordingly, the male proportion is high compared to their female counterparts, and the proportionate difference is also statistically noteworthy at a 5% significance level (Z = 2.467; p = 0.014). Consequently, males with high proportion get a place in the segment of the improvidents. (b) Yellow Segment: Contrary to the above result, more of the female sample (%Female = 44.6) gets nearer to the yellow segment, and the percentage of males (%Male = 35.4) arrive after them. The difference in these proportions is significant too at 1% level of significance (z = 2.942; p = 0.003). So, going with results females are the strong contenders to be stated as ‘aesthetics and hopefuls’ in comparison with males. (c) Green Segment: Here, males can be slightly privileged over females as their share is a bit high (%Male = 48.6 > %Female = 44.8); but, as affirmed by the highly insignificant z-statistics (Z = 1.191; p = 0.234), the difference in proportions (48.6 − 44.8 = 3.8) just occurs because of chance and with this small difference males cannot be given the advantage over females. In conclusion, gender has not much influence in the segment and shining stars may be both males and females.

342

8 Characterizing and Profiling Responsible Consumer Segments

Fig. 8.2 Age-wise segmentation

• Age and Segment Membership Segment membership according to consumer age is analysed with Fig. 8.2 and Table 8.9 in which three combinations are presented for examining the difference in proportions (3 C2 = 3). (a) Red Segment: Within the red segment, a U kind relationship can be noted. The results state that apathetic young consumers are high in proportion (% = 15.6). The percentage of adult consumers is least (% = 10.9) and the highest proportion is observed for middle and upper-aged respondents (% = 16.4). An insignificant proportionate difference can be seen for young and upper-aged groups (z = 0.216; p = 0.826). A similar z-test procedure for the other two combinations clarifies that young and middle-aged respondents significantly differ from adult consumers. As a result, with statistically identical and different proportions from the adult group, both young and upper-aged respondents are found as the members of this segment. (b) Yellow Segment: The bars in the figure for the yellow segment obtain a negative association of age. While comparing with upper-aged respondents, the proportion of adult respondents is high and ultimately the percentage share for young consumers is higher than adults (%Young = 47.0 > %Adult = 38.6 > %Upper Aged = 20.3). Considering the three combinations for significant–insignificant differences, Table 8.9 reveals that the proportionate differences for all the three possible pairs are statistically reliable. Consequently, with a high proportion, youngsters get a place amongst ‘aesthetics and hopefuls’. (c) Green Segment: Opposite to what is presented in the yellow segment, age positively relates here. The young consumers who have attained a high percentage in the yellow segment are attaining the least part. Adults are holding their midposition and the middle and upper-aged people obtain extremely high percentage share (%Upper Aged = 63.3 > %Adult = 50.6 > %Young = 37.4). These percentages

8.3 Identification of Consumer Membership

343

Table 8.9 Age-wise intra-segment comparison: paired differences and test of relevance Segments

Age

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Young

404

0.156

0.047

0.023

2.032

0.042

Adult

451

0.109

Adult

451

0.109

0.055

0.036

1.683

0.093

Middle and aged

128

0.164

Young

404

0.156

0.008

0.037

0.216

0.826

Middle and aged

128

0.164

Young

404

0.470

0.084

0.034

2.480

0.013

Adult

451

0.386

Adult

451

0.386

0.183

0.042

3.843

0.000

Middle and aged

128

0.203

Young

404

0.470

0.267

0.043

5.361

0.000

Middle and aged

128

0.203

Young

404

0.374

0.132

0.034

3.879

0.000

Adult

451

0.506

Adult

451

0.506

0.127

0.049

2.542

0.011

Middle and aged

128

0.633

Young

404

0.374

0.259

0.049

5.149

0.000

Middle and aged

128

0.633

Yellow segment

Green segment

themselves provide an indication for major variations, and a confirmation is provided by the z values which are notably greater than the critical values at the specified significance levels. In this way, young and adults are simply out of the responsible category, and upper-aged people regain their place here. • Educational Level and Segment Membership Education is the most powerful weapon which can be used to change the world in optimistic ways. The educational level of consumers is studied here (Fig. 8.3 and Table 8.10) so that as per education the membership of consumers can be finalized. (a) Red Segment: It is well known in the study that red is the segment of ‘apathetic and imprudent’ people who are in no concert regarding sustainability. As per expectations, this is the effect of less education by which people cobble together in the majority in this segment. It is apparent from the figure that the proportion of least educated people is high if compared with the proportions of their other two counterparts, and thus this segment is dominated by less-educated individuals.

344

8 Characterizing and Profiling Responsible Consumer Segments

Fig. 8.3 Education-wise segmentation

The proportion of highly educated people is only marginal and the difference in proportions for school and graduate-level people is not significant (z = 1.548; p = 0.121). Therefore, leaving the highly educated group, the other two are affiliated with this segment. (b) Yellow Segment: The bars in the figure do not articulate a clear picture regarding the approval of any one category. The proportionate shares are very close to each other. Further, the z-test too declares that the differences in all of the three pairs are insignificant (p > 0.05). Accordingly, it may be accepted that the marginal differences occur just because of chance. Hence, education seems to have no prominent influence amid the yellow segment; and according to education, people here may be less, middling, or highly educated. (c) Green Segment: Here, the proportional analysis sharpens upon a positive relationship of education. As expected, highly educated group attains high share (% = 59.1); the less educated group has least share; (% = 37.0) and the modest educated (graduates) is at the middle position (% = 40.1). The people of higher education who find a place in this segment significantly differ from their other counterparts as is accessible from the extremely significant z-values (z = 5.106 and z = 5.499). However, no significant difference emerges out in between groups of school and graduate-level people (z = 0.768; p = 0.441). Accordingly, as the share of highly educated respondents differs fundamentally from the other two, they conquer and get a place amongst the responsibles. • Academic Orientation and Segment Membership Figure 8.4 visualizes the breakdown of the respondent groups into each segment in radiance with their academic orientation. The percentage share is visible below the figure as per which membership of consumers is confirmed. Further, the analysis with the z-test is highlighted in the associated Table 8.11. (a) Red Segment: It can be seen within this segment that uppermost percentages (% = 14.5; % = 13.7) are gained by consumers who have studied ‘natural sciences’

8.3 Identification of Consumer Membership

345

Table 8.10 Education and intra-segment comparison: paired differences and test of relevance Segments

Educational qualifications

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

School level

257

0.226

0.051

0.033

1.548

0.121

Graduate level

337

0.175

Graduate level

337

0.175

0.134

0.023

5.919

0.000

Higher education

389

0.041

School level

257

0.226

0.185

0.028

7.225

0.000

Higher education

389

0.041

School level

257

0.405

0.019

0.410

0.466

0.638

Graduate level

337

0.424

Graduate level

337

0.424

0.056

0.036

1.540

0.124

Higher education

389

0.368

School level

257

0.405

0.037

0.039

0.947

0.342

Higher education

389

0.368

School level

257

0.370

0.031

0.040

0.768

0.441

Graduate level

337

0.401

Graduate level

337

0.401

0.190

0.037

5.106

0.000

Higher education

389

0.591

School level

257

0.370

0.221

0.039

5.499

0.000

Higher education

389

0.591

Yellow segment

Green segment

and ‘business subjects’, respectively. The proportionate share of people of science background is comparatively less (% = 9.5). As there are three categories of variables, three combinations are obtained to test significant/insignificant proportionate differences. In connection with these combinations, only the difference between arts–science combination is statistically reliable at a 10% level of significance (z = 1.735; p = 0.084). On the other hand, the remaining two are insignificant. In this way, statistically, respondents from business and arts study fields obtain a place here.

346

8 Characterizing and Profiling Responsible Consumer Segments

Fig. 8.4 Segmentation as per academic orientation

(b) Yellow Segment: Here, proportions are in favour of business academics (% = 46.9) followed by the people originated from pure sciences (% = 42.9). The proportionate share of arts academics (% = 33.2) is very far away from these two values and significantly differs from its other two correspondents (z = 3.407; p = 0.001 and z = 2.555; p = 0.011). As no significant difference comes out for the groups of business and science academics (z = 0.964; p = 0.337), it is well thought-out that both are the contenders to be considered amongst ‘aesthetics and hopefuls’. (c) Green Segment: The values below the bars in the green segment shortlist respondents from arts background in the category of ‘aspirants and illuminators’ due to their high percentage (% = 52.2). Behind them, there are the academics of science (% = 47.6) and business education (% = 39.3), respectively. Further, with the z-test, it comes out that the difference in the proportion of arts–science academics is only because of chance; statistically, the two percentages can be regarded similar (z = 1.176; p = 0.238). In this way, with arts academics science people too are associated with the group of green consumers. • Academic Intelligence and Segment Membership It has already been mentioned that the variable ‘academic intelligence’ is analysed in three parts. Figure 8.5 and Table 8.12(i), (ii), (iii) become evident with the results. (a) Red Segment: Academically poor respondents have significantly high proportion in this segment than their correspondents (% = 60.3; % = 42.5; % = 31.0). Their proportionate shares significantly differ from the other categories when compared using the z-test for proportionate differences. Hence, the apathy of academically poor people is clearly visible with their high membership; and it can be said that low level of education, moreover, less-effective education put downward pressure on the attainment of superior attitude and performance of exclusive behaviour.

8.3 Identification of Consumer Membership

347

Table 8.11 Academic orientation and intra-segment comparison: paired differences and test of relevance Segments

Academic orientation

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Law and business

262

0.137

0.008

0.029

0.279

0.779

Arts and natural sciences

337

0.145

Arts and natural sciences

337

0.145

0.050

0.025

1.735

0.084

Science and technical

317

0.095

Law and business

262

0.137

0.042

0.027

1.583

0.114

Science and technical

317

0.095

Law and business

262

0.469

0.137

0.040

3.407

0.001

Arts and natural sciences

337

0.332

Arts and natural sciences

337

0.332

0.097

0.038

2.555

0.011

Science and technical

317

0.429

Law and business

262

0.469

0.040

0.042

0.964

0.337

Science and technical

317

0.429

Law and business

262

0.393

0.129

0.041

3.140

0.002

Arts and natural sciences

337

0.522

Arts and natural sciences

337

0.522

0.046

0.039

1.176

0.238

Science and technical

317

0.476

Law and business

262

0.393

0.083

0.041

2.003

0.045

Science and technical

317

0.476

Yellow segment

Green segment

348

8 Characterizing and Profiling Responsible Consumer Segments

(i)

(ii)

(iii)

Fig. 8.5 Academic intelligence and segmentation

8.3 Identification of Consumer Membership

349

Table 8.12 Academic intelligence and intra-segment comparison: paired differences and test of relevance Segments

Academic intelligence

N

Proportions

Difference

S.E

z-statistics

Sig

0.155

0.078

1.956

0.050

0.409

0.072

5.319

0.000

0.254

0.065

3.733

0.000

0.054

0.073

0.736

0.459

0.076

0.074

1.019

0.308

0.022

0.069

0.318

0.749

0.101

0.058

1.669

0.095

0.334

0.065

4.522

0.000

0.233

0.067

3.412

0.001

0.206

0.088

2.481

0.013

(i) Red segment

Yellow segment

Green segment

Academically poor

68

0.603

Academically fair

96

0.448

Academically poor

68

0.603

Academically good

93

0.194

Academically fair

96

0.448

Academically good

93

0.194

Academically poor

68

0.279

Academically fair

96

0.333

Academically poor

68

0.279

Academically good

93

0.355

Academically fair

96

0.333

Academically good

93

0.355

Academically poor

68

0.118

Academically fair

96

0.219

Academically poor

68

0.118

Academically good

93

0.452

Academically fair

96

0.219

Academically good

93

0.452

Academically poor

40

0.425

(ii) Red segment

(continued)

350

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.12 (continued) Segments

Yellow segment

Academic intelligence

N

Proportions

Academically fair

105

0.219

Academically poor

40

0.425

Academically good

97

0.258

Academically poor

40

0.425

Academically excellent

95

0.074

Academically fair

105

0.219

Academically good

97

0.258

Academically fair

105

0.219

Academically excellent

95

0.074

Academically good

97

0.258

Academically excellent

95

0.074

Academically poor

40

0.475

Academically fair

105

0.343

Academically poor

40

0.475

Academically good

97

0.330

Academically poor

40

0.475

Academically excellent

95

0.421

Academically fair

105

0.343

Academically good

97

0.330

Academically fair

105

0.343

Difference

S.E

z-statistics

Sig

0.167

0.090

1.927

0.054

0.351

0.083

4.868

0.000

0.039

0.060

0.651

0.516

0.145

0.048

2.867

0.004

0.184

0.052

3.418

0.001

0.132

0.092

1.464

0.144

0.145

0.092

1.596

0.110

0.054

0.094

0.578

0.562

0.013

0.067

0.195

0.842

0.078

0.069

1.135

0.259 (continued)

8.3 Identification of Consumer Membership

351

Table 8.12 (continued) Segments

Green segment

Academic intelligence

N

Proportions

Academically excellent

95

0.421

Academically good

97

0.330

Academically excellent

95

0.421

Academically poor

40

0.100

Academically fair

105

0.438

Academically poor

40

0.100

Academically good

97

0.412

Academically poor

40

0.100

Academically excellent

95

0.505

Academically fair

105

0.438

Academically good

97

0.412

Academically fair

105

0.438

Academically excellent

95

0.505

Academically good

97

0.412

Academically excellent

95

0.505

Academically poor

13

0.310

Academically fair

25

0.000

Academically poor

13

0.310

Academically good

74

0.081

Difference

S.E

z-statistics

Sig

0.091

0.070

1.302

0.194

0.338

0.068

3.828

0.000

0.312

0.069

3.557

0.000

0.405

0.070

4.416

0.000

0.026

0.070

0.374

0.711

0.067

0.071

0.948

0.342

0.093

0.072

1.293

0.197

0.310

0.128

2.944

0.003

0.229

0.132

2.385

0.017

(iii) Red segment

(continued)

352

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.12 (continued) Segments

Yellow segment

Academic intelligence

N

Proportions

Difference

S.E

z-statistics

Sig

0.288

0.129

4.141

0.000

0.283

0.129

4.740

0.000

0.081

0.032

1.55

0.121

0.022

0.015

0.748

0.453

0.027

0.012

0.832

0.407

0.059

0.035

1.748

0.080

0.054

0.034

1.953

0.051

0.005

0.019

0.248

0.803

0.140

0.169

0.823

0.412

0.068

0.149

0.461

0.646

Academically poor

13

0.310

Academically excellent

90

0.022

Academically poor

13

0.310

Academically brilliant

187

0.027

Academically fair

25

0.000

Academically good

74

0.081

Academically fair

25

0.000

Academically excellent

90

0.022

Academically fair

25

0.000

Academically brilliant

187

0.027

Academically good

74

0.081

Academically excellent

90

0.022

Academically good

74

0.081

Academically brilliant

187

0.027

Academically excellent

90

0.022

Academically brilliant

187

0.027

Academically poor

13

0.460

Academically fair

25

0.600

Academically poor

13

0.460

Academically good

74

0.392 (continued)

8.3 Identification of Consumer Membership

353

Table 8.12 (continued) Segments

Green segment

Academic intelligence

N

Proportions

Difference

S.E

z-statistics

Sig

0.027

0.148

0.184

0.857

0.134

0.142

0.990

0.322

0.208

0.113

1.810

0.070

0.167

0.111

1.480

0.139

0.274

0.104

2.683

0.007

0.041

0.077

0.530

0.596

0.066

0.066

1.011

0.313

0.107

0.062

1.737

0.082

0.170

0.152

1.048

0.294

0.297

0.130

1.976

0.048

Academically poor

13

0.460

Academically excellent

90

0.433

Academically poor

13

0.460

Academically brilliant

187

0.326

Academically fair

25

0.600

Academically good

74

0.392

Academically fair

25

0.600

Academically excellent

90

0.433

Academically fair

25

0.600

Academically brilliant

187

0.326

Academically good

74

0.392

Academically excellent

90

0.433

Academically good

74

0.392

Academically brilliant

187

0.326

Academically excellent

90

0.433

Academically brilliant

187

0.326

Academically poor

13

0.230

Academically fair

25

0.400

Academically poor

13

0.230

Academically good

74

0.527 (continued)

354

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.12 (continued) Segments

Academic intelligence

N

Proportions

Difference

S.E

z-statistics

Sig

0.314

0.128

2.117

0.034

0.417

0.122

2.995

0.003

0.127

0.114

1.098

0.271

0.144

0.111

1.274

0.204

0.247

0.104

2.387

0.017

0.017

0.078

0.217

0.826

0.120

0.068

1.794

0.073

0.103

0.063

1.649

0.099

Academically poor

13

0.230

Academically excellent

90

0.544

Academically poor

13

0.230

Academically brilliant

187

0.647

Academically fair

25

0.400

Academically good

74

0.527

Academically fair

25

0.400

Academically excellent

90

0.544

Academically fair

25

0.400

Academically brilliant

187

0.647

Academically good

74

0.527

Academically excellent

90

0.544

Academically good

74

0.527

Academically brilliant

187

0.647

Academically excellent

90

0.544

Academically brilliant

187

0.647

(b) Yellow Segment: Though the bars in the specified figures to some extent move in favour of some particular categories, they do not articulate a clearer view. Reading the z-test it can be noted from the tables, that none of the pairs shows a significant result of proportionate difference. However, with Table 8.12(iii) z-test comes out in favour of fair academic record holders with the difference of this category from its good and brilliant performers. But more appropriately, it can be said that academic intelligence does not say much about the members

8.3 Identification of Consumer Membership

355

of the yellow segment but it is sure that academically they are above the people who belong to the red segment. (c) Green Segment: In this segment, educational intelligence has a positive effect. The proportions of academically poor respondents are significantly low from the other categories. In this way, the green segment is a segment of people who are not only educated but ‘educated effectively’. • Marital Status and Segment Membership Figure 8.6 and Table 8.13 stand out to obtain results of membership for marital status. (a) Red Segment: The difference of just 1.9% in the proportions in two categories itself elaborate that this difference is not considerable. Insignificant z-test further confirms the same (z = 0.869; p = 0.384). Therefore, the people in this segment can be both unmarried and married. (b) Yellow Segment: A long bar for the category of unmarried respondents puts them under the yellow segment (%Unmarried = 48.7 > %Married = 29.2). The finding is also extremely valid as confirmed by the significant z-statistics (z = 6.231; p = 0.000). Accordingly, this segment comprises the unmarried respondents. Fig. 8.6 Marital status and segmentation

Table 8.13 Marital status and intra-segment comparison: paired differences and test of relevance Segments

Marital status

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Unmarried

528

0.144

0.019

0.022

0.869

0.384

Married

455

0.125

Yellow segment

Unmarried

528

0.487

0.195

0.030

6.231

0.000

Married

455

0.292

Green segment

Unmarried

528

0.369

0.213

0.031

6.674

0.000

Married

455

0.582

356

8 Characterizing and Profiling Responsible Consumer Segments

(c) Green Segment: Contrary to the result in the yellow segment, here married ones secure their prime position. The gap between the two percentages (58.2 − 36.9 = 21.3) is noticeable with substantially significant z-statistics (z = 6.674; p = 0.000). In this way, the green segment approvingly accommodates married people, and this feature distinguishes them from the people in the other two segments. • Parenthood and Cluster Membership The effect of this variable in each segment appears from the next lines aiding with Fig. 8.7 and Table 8.14.

Fig. 8.7 Parenthood and segmentation

Table 8.14 Parenthood and intra-segment comparison: paired differences and test of relevance Segments

Parenthood

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

With children

372

0.137

0.065

0.034

1.618

0.105

Without children

83

0.072

With children

372

0.237

0.185

0.058

3.190

0.000

Without children

83

0.422

With children

372

0.626

0.120

0.060

2.021

0.043

Without children

83

0.506

Yellow segment

Green segment

8.3 Identification of Consumer Membership

357

(a) Red Segment: The share of married people having children is high contrasted with people who are not parents (%Parents = 13.7 > %Not Parents = 7.2). However, there exists a difference between these two values (Difference = 6.5%) but weighing with sample size in each category, the same comes out as insignificant. Accordingly, the result cannot be settled in support of any one category and the people here may be parents or not. (b) Yellow Segment: This segment obtains a high percentage of people who are married but are not parents (% = 42.2). The z-test also inclines towards high proportion of this group (z = 0.319; p = 0.000). Consequently, married respondents without children constitute the segment. (c) Green Segment: Married people with children secure their position here with their major percentage (% = 62.6). Given the relevance of differences of proportions, this category is publicized as linked with the green segment. • Years of Marriage and Segment Membership There are four classifications in this variable and the results are visible in Fig. 8.8 and Table 8.15. (a) Red Segment: It is crystal clear from the part of the red segment in the figure that the respondents who have been classified in the category ‘16–25 years of marriage’ attain a major percentage. Their high proportion is amply noticeable as reliably differ from the proportions of the other three corresponding classifications. This result enlarges the previous finding of two variables ‘marital status’ and ‘parenthood’ that all the married respondents are not apathetic; but, environmental apathy is the spotlight feature of people who have grown-up children.

Fig. 8.8 Years of marriage and segmentation

358

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.15 Years of marriage and intra-segment comparison: paired differences and test of relevance Segments

Years of marriage

N

Red segment

1–5 6–15 1–5

202

0.089

82

0.293

16–25 1–5

Yellow segment

Difference

S.E

z-statistics

Sig

202

0.089

0.003

0.032

0.094

0.928

128

0.086 0.204

0.054

4.389

0.000

0.004

0.049

0.083

0.936

0.207

0.056

3.925

0.000

0.007

0.051

0.140

0.889

0.200

0.067

2.547

0.011

0.134

0.051

2.534

0.011

0.169

0.056

2.751

0.006

0.167

0.071

2.089

0.037

0.035

0.059

0.049

0.960

0.033

0.073

0.442

0.660

0.002

0.076

0.026

0.976

0.137

0.054

2.464

0.014

0.035

0.065

0.535

0.589

0.163

0.078

1.957

0.050

0.172

0.069

2.487

0.013

0.026

0.081

0.316

0.749

0.198

0.089

2.123

0.034

202

0.089

26–44

43

0.093

6–15

128

0.086

16–25

82

0.293

6–15

128

0.086

26–44

43

0.093

16–25

82

0.293

26–44

43

0.093

1–5

202

0.376

6–15

128

0.242

1–5

202

0.376

82

0.207

16–25 1–5

Green segment

Proportions

202

0.376

26–44

43

0.209

6–15

128

0.242

16–25

82

0.207

6–15

128

0.242

26–44

43

0.209

16–25

82

0.207

26–44

43

0.209

1–5

202

0.535

6–15

128

0.672

1–5

202

0.535

82

0.500

16–25 1–5

202

0.535

26–44

43

0.698

6–15

128

0.672

16–25

82

0.500

6–15

128

0.672

26–44

43

0.698

16–25

82

0.500

26–44

43

0.698

8.3 Identification of Consumer Membership

359

(b) Yellow Segment: As noticeable, this segment holds a major part of respondents who may be newly married or married with kids (% = 37.6). On the basis of this percentage, it can be said that the group meaningfully differs from its other three correspondents due to the significance of z-statistics (z = 2.534; z = 2.751; z = 2.089); and thus they assure their part amongst hopefuls. (c) Green Segment: The percentage for the two categories ‘children living separately’ (%26–44 = 69.8) and ‘married having teenage child’ (%6–15 = 67.2) exceeds here from the other two. The marginal difference in percentages just occurs because of chance as can be noted with the insignificant z-test value (z = 0.316; p = 0.749). However, the proportionate share of these two categories is significantly different from the other two groups (z = 2.464; z = 1.957; z = 2.123). Consequently, with the highest grades the groups as described above hold their position in the responsible category. • Profession and Segment Membership As defined in Sect. 8.2.1, profession contains two classifications. One classification is for the non-earning people while the other is the category of earning group. Figure 8.9 and Table 8.16 reveal consumer membership in each segment as per profession. (a) Red Segment: The proportion of non-earners (% = 17.4) is high compared to earners (% = 11.2). There is a significant difference in the two groups as determined by the calculated z-value (z = 2.757) which is greater than the critical value at a 1% significance level (p = 0.006). In this way, the red segment encompasses the non-earning group. (b) Yellow Segment: In line with the previous result, again the non-earning group reveals a high share (%Non-Earning = 43.9 > %Earning = 37.1). The z-value (z = 2.116; p = 0.034) also provides 95% confidence for the respondents who are non-earning to be included in this segment. (c) Green Segment: The result in this segment superimposes earning people over non-earning since their share is high (%Earning = 51.7 > %Non-Earning = 38.8) by Fig. 8.9 Segmentation as per profession

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8 Characterizing and Profiling Responsible Consumer Segments

Table 8.16 Profession and intra-segment comparison: paired differences and test of relevance Segments

Family support

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Non-earning

374

0.174

0.062

0.023

2.757

0.006

Earning

609

0.112

Yellow segment

Non-earning

374

0.439

0.068

0.032

2.116

0.034

Earning

609

0.371

Green segment

Non-earning

374

0.388

0.129

0.032

3.935

0.000

Earning

609

0.517

12.9% which is also highly vigilant with significant z-value (z = 3.935; p = 0.000). Along these lines, earning people are the members in this segment of ‘responsible consumers’ and leave behind their non-earning counterparts.

8.3.2 Sociological Variables and Segmentation • Type of Family and Segment Membership According to the total count of respondents into the two categories of ‘type of family’, the percentage share is evident from the data table below Fig. 8.10. The result of the z-test for proportionate difference can be studied from Table 8.17. (a) Red Segment: Within this segment, regarding proportionate share no considerate difference emerges out. The magnitude of two proportions are identical Fig. 8.10 Type of family and segmentation

8.3 Identification of Consumer Membership

361

Table 8.17 Type of family and intra-segment comparison: paired differences and test of relevance Segments

Type of family

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Joint Nuclear

478

0.136

0.001

0.022

0.046

0.960

505

0.135

Yellow segment

Joint

478

0.360

0.072

0.031

2.306

0.021

Nuclear

505

0.432

Green segment

Joint

478

0.504

Nuclear

505

0.434

0.070

0.032

2.198

0.028

both mathematically (%Joint = 13.6; %Nuclear = 13.5) and statistically with high insignificance of z-value (z = 0.046; p = 0.960). Thus, it makes no difference whether the people who fall under the red segment belong to joint or nuclear families. (b) Yellow Segment: Members of nuclear families are in high proportion with a considerable difference of 7.2% from the members of joint families. Further, z-statistics makes clear that the difference is noticeable (z = 2.306; p = 0.029). Accordingly, the majority of the people here are found associated with nuclear families. (c) Green Segment: This segment embraces with the members of joint families as specified by their high percentage share (%Joint = 50.4 > %Nuclear = 43.4). The significant z-value also favours their high share (z = 2.198; p = 0.028). With this finding, affiliation to joint families comes out as an important feature of ‘responsible consumers’. • Family Size and Segment Membership With the insignificance of Chi-square statistics (Table 8.3), family size is not statistically associated with the number of segments but beyond this insignificance, the proportional analysis further presents a distinct scenario of consumer membership. (a) Red Segment: An investigation of Fig. 8.11 reveals that in the red segment large and small-sized families attain a little high share from medium-sized families; but, statistically the proportions are similar as z-test confirms no significant difference between any of the pairs considered (z = 1.401; z = 0.930; z = 0.173). Hence along with the previous variable type of family, family size also does not influence segment membership within this segment. (b) Yellow Segment: Here, again the percentage share of large and small-sized families is high compared to their medium-sized counterparts. The difference in the proportion of medium and large-sized families become visible at a 10% probability level (z = 1.681; p = 0.093); but, the other two pairs obtain insignificant results. In this manner, the people in this group are supposed to arise from either small or large-sized families.

362

8 Characterizing and Profiling Responsible Consumer Segments

Fig. 8.11 Family size and segmentation

(c) Green Segment: Members of medium-sized families are at the top with their high share (% = 52.1). This share is also significantly greater than their small (z = 2.093; p = 0.037) and medium-sized counterparts (z = 2.239; p = 0.025). Accordingly, membership in medium-sized families is an important attribute of ‘responsible consumers’ (Table 8.18). • Composition of Family and Segment Membership – Composition of Family in Terms of Gender The membership is highlighted with a bar graph in Fig. 8.12 and z-test is visible with allied Table 8.19(i). Notation M is used for male members and F symbolizes female household members. (a) Red Segment: In Fig. 8.12, amongst the three small rectangles of the red segment, the size of the rectangle for the third category (F < M) is higher than the other two categories. However, the z-test discloses that the proportionate difference in any of the pairs is not considered. Accordingly, the ratio of male–female in any family structure does not matter a lot for people who get associated with the red segment. (b) Yellow Segment: It is clear from the figure that the percentage share of families in which females are in a larger number is up from the other two groupings (% = 44.9). But, the z-test in Table 8.19(i) further confers that their proportion is not statistically different from the families in which the male–female composition is equal. Consequently, both groups fall under the segment. (c) Green Segment: Here, the families with greater male composition overlay the other two classifications by gaining a 50.8% share. Further, the z-test confirms that this percentage is significantly greater than the category of families where females are less in number than males. But, in families where male–female ratio

8.3 Identification of Consumer Membership

363

Table 8.18 Family size and intra-segment comparison: paired differences and test of relevance Segments

Family size

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Small sized

590

0.146

0.033

0.023

1.401

0.159

Medium sized

328

0.113

Medium sized

328

0.113

0.041

0.048

0.930

0.352

Large sized

65

0.154

Small sized

590

0.146

0.008

0.047

0.173

0.865

Large sized

65

0.154

Small sized

590

0.405

0.039

0.033

1.160

0.246

Medium sized

328

0.366

Medium sized

328

0.366

0.111

0.067

1.681

0.093

Large sized

65

0.477

Small sized

590

0.405

0.072

0.065

1.119

0.263

Large sized

65

0.477

Small sized

590

0.449

0.072

0.034

2.093

0.037

Medium sized

328

0.521

Medium sized

328

0.521

0.152

0.066

2.239

0.025

Large sized

65

0.369

Small sized

590

0.449

0.080

0.063

1.233

0.219

Large sized

65

0.369

Yellow segment

Green segment

Fig. 8.12 Composition of family (gender-wise) and segmentation

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8 Characterizing and Profiling Responsible Consumer Segments

Table 8.19 Family composition and intra-segment comparison: paired differences and test of relevance Segments

Family Size

N

Proportions

Difference

S.E

z-statistics

Sig

F>M

272

0.118

0.003

0.026

0.114

0.912

F=M

347

0.121

F=M

347

0.121

0.041

0.026

1.566

0.116

FM

272

0.118

0.044

0.028

1.567

0.116

FM

272

0.449

0.022

0.040

0.548

0.582

F=M

347

0.427

F=M

347

0.427

0.097

0.036

2.667

0.008

FM

272

0.449

0.119

0.039

3.058

0.002

FM

272

0.434

0.018

0.040

0.447

0.653

F=M

347

0.452

F=M

347

0.452

0.056

0.037

1.494

0.136

FM

272

0.434

0.074

0.040

1.849

0.064

FY

91

0.176

M=Y

163

0.153

M=Y

163

0.153

MY

91

0.176

MY

91

0.308

M=Y

163

0.509

M=Y

163

0.509

MY

91

0.308

MY

91

0.516

M=Y

163

0.337

M=Y

163

0.337

(continued)

8.3 Identification of Consumer Membership

365

Table 8.19 (continued) Segments

Family Size

N

Proportions

MY

91

0.516

M %Less Support = 42.5). In this way, the finding moves in favour of the group of people who are highly supported by their families.

8.3.3 Geographical Variables and Segmentation • Place of Living and Segment Membership The proportional analysis here presents a unique picture of consumer membership in specific segments as with place of living, characterized in Fig. 8.15 and Table 8.21. Fig. 8.15 Segmentation as per place of living

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8 Characterizing and Profiling Responsible Consumer Segments

Table 8.21 Place of living and intra-segment comparison: paired differences and test of relevance Segments

Place of living

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Rural

314

0.175

0.058

0.025

2.477

0.013

Urban

669

0.117

Yellow segment

Rural

314

0.318

0.115

0.033

3.437

0.001

Urban

669

0.433

Green segment

Rural

314

0.506

0.056

0.034

1.647

0.101

Urban

669

0.450

(a) Red Segment: The findings confirm that respondents from rural background attain high proportionate share as compared to their urban counterparts (%Rural = 17.5 > %Urban = 11.7). Statistically, the difference of proportions can be confirmed with 95% confidence (z = 2.477; p = 0.013). Hence, in-between apathetic people of the red segment the place is higher for rural respondents. (b) Yellow Segment: Respondents who reside at urban places obtain high share when compared to their rural correspondents. The difference in this proportion is also noteworthy at a 1% significance level (z = 3.437; p = 0.006) which gives additional confidence to rely on the outcome. (c) Green Segment: Although the calculated proportions extend the support for people belonging to rural areas, due to inconsequential z-statistics (z = 1.647; p = 0.101) they cannot be stated as winners to be included amidst responsibles. Thus, more appropriately both rural and urban people are the candidates to be considered here. • Commuting and Segment Membership The segment membership according to commuting is described with the help of Fig. 8.16 and Table 8.22. (a) Red Segment: Apparently, bars in the red segment mark two types of respondents ‘non-commuters and commuters who either walk or ride a bicycle’, different from ‘private and public vehicle commuters’ due to their high and statistically similar percentages (z = 0.051; p = 0.960). Accordingly, both of these groups are regarded as members of this segment. (b) Yellow Segment: In the yellow segment, the high percentage (% = 46.9) is visible for the respondents who prefer walking or cycling to reach their destinations followed by the group of public vehicle commuters (% = 41.1). The difference of proportions for these two categories is highly insignificant (z = 1.286; p = 0.197). Indeed, it can also be seen that the proportion of private vehicle commuters (% = 36.7) can also be termed identical with public vehicle commuters (% = 41.1) because of insignificance of z-statistics (z = 1.181; p = 0.238). But, with the two highest proportions footers–cyclers (walk-ride) and public vehicle commuters are elaborated as members here.

8.3 Identification of Consumer Membership

369

Fig. 8.16 Commuting and segmentation

(c) Green Segment: It can be noticed from the figure that this segment is assimilated with non-commuters since their percentage share is the highest (% = 53.7). But public and private vehicle commuters are also allied with them (%Public = 52.3; %Private = 46.4) as the paired comparison with z-test does not recommend one group over any other category due to the insignificance of z-statistics. For that reason, with the exception of only one commuting category (walk–ride), responsibles may be non-commuters or commuters. It also does not make a difference whether they are the public vehicle commuters or private vehicle commuters.

8.3.4 Economic Variables and Segmentation • Family Income and Segment Membership The proportions and z-test for the three categories of variable ‘family income’ are revealed in Fig. 8.17 and Table 8.23. The membership of consumers can be confirmed from the undergoing discourse. (a) Red Segment: Low-status families can be seen as having significant high share from their middle-income counterparts (z = 2.723; p = 0.007). However, their proportion is no different from high-status group (z = 1.024; p = 0.308). With these findings, low-class and high-class families are retained in the makeup of this segment. (b) Yellow Segment: In line with the above result, a negligible proportionate difference arises between the percentage share of low and high-class families (Difference = 0.2%); statistically, the obvious result of inconsideration of z-test (z =

370

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.22 Commuting and intra-segment comparison: paired differences and test of relevance Segments

Commuting

Red segment

Not commute

Yellow segment

Green segment

Walking/cycling

N

Proportions

Difference

S.E

z-statistics

Sig

54

0.185

0.003

0.059

0.051

0.960

0.075

0.055

1.610

0.107

0.060

0.056

1.183

0.238

0.078

0.031

2.725

0.006

0.063

0.033

1.929

0.054

0.015

0.025

0.612

0.542

0.191

0.070

2.532

0.011

0.089

0.065

1.226

0.219

0.133

0.068

1.835

0.066

0.102

0.041

2.490

0.013

0.058

0.045

1.286

0.197

0.044

0.037

1.181

0.238

0.194

0.075

2.620

0.009

0.014

0.072

0.194

0.849

0.073

0.074

0.984

0.327

0.180

0.040

4.318

0.000

0.121

0.044

2.704

0.007

0.059

0.038

1.541

0.124

213

0.188

Not commute

54

0.185

Private vehicle

436

0.110

Not commute

54

0.185

Public vehicle

280

0.125

Walking/cycling

213

0.188

Private vehicle

436

0.110

Walking/cycling

213

0.188

Public vehicle

280

0.125

Private vehicle

436

0.110

Public vehicle

280

0.125

Not commute

54

0.278

Walking/cycling

213

0.469

Not commute

54

0.278

Private vehicle

436

0.367

Not commute

54

0.278

Public vehicle

280

0.411

Walking/cycling

213

0.469

Private vehicle

436

0.367

Walking/cycling

213

0.469

Public vehicle

280

0.411

Private vehicle

436

0.367

Public vehicle

280

0.411

Not commute

54

0.537

Walking/cycling

213

0.343

Not commute

54

0.537

Private vehicle

436

0.523

Not commute

54

0.537

Public vehicle

280

0.464

Walking/cycling

213

0.343

Private vehicle

436

0.523

Walking/cycling

213

0.343

Public vehicle

280

0.464

Private vehicle

436

0.523

Public vehicle

280

0.464

8.3 Identification of Consumer Membership

371

Fig. 8.17 Segmentation as per family income

Table 8.23 Family income and intra-segment comparison: paired differences and test of relevance Segments

Economic status

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Low class

217

0.184

0.074

0.030

2.723

0.007

Middle class

536

0.110

Middle class

536

0.110

0.038

0.027

1.476

0.139

High class

230

0.148

Low class

217

0.184

0.036

0.035

1.024

0.308

High class

230

0.148

Low class

217

0.424

0.049

0.040

1.249

0.211

Middle class

536

0.375

Middle class

536

0.375

0.049

0.039

1.223

0.223

High class

230

0.422

Low class

217

0.424

0.002

0.047

0.043

0.968

High class

230

0.422

Low class

217

0.392

0.123

0.040

3.060

0.002

Middle class

536

0.515

Middle class

536

0.515

0.085

0.039

2.157

0.031

High class

230

0.430

Low class

217

0.392

0.038

0.047

0.816

0.412

High class

230

0.430

Yellow segment

Green segment

372

8 Characterizing and Profiling Responsible Consumer Segments

0.043; p = 0.968). There can also be seen a difference in the percentage share of these two categories, but the differences are again statistically not reliable (p > 0.05). In this way, regarding income there is less transparency and it can be said that this feature has no notable effect for the consumers of the yellow segment. (c) Green Segment: The percentage of middle class category (% = 51.5) significantly exceeds from the other two categories of low class (% = 39.2: z = 3.060; p = 0.002) and high class (% = 43.0; z = 2.157; p = 0.031). Therefore, members of middle-class families are originating as ‘responsible consumers’. • Home Ownership and Segment of Consumers In accordance with the residence of respondents in their own or rental houses, it is seen whether this variable makes any difference within segment membership or not. The analysis is visible from Fig. 8.18 and Table 8.24. (a) Red Segment: Residence in own or rental houses clearly distinguish respondents here and mark them as people who do not possess their own house (%Rental = 18.1 > %Own Houses = 12.1). The finding can also be relied with a significant z-value (z = 2.336; p = 0.019). (b) Yellow Segment: On the basis of insignificance of calculated z-statistics (z = 0.656; p = 0.509), it is contended that the feature does not say much about the Fig. 8.18 Home ownership and segmentation

Table 8.24 Home ownership and intra-segment comparison: paired differences and test of relevance Segments

Home ownership

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Own house

751

0.121

0.060

0.028

2.336

0.019

Rental

232

0.181

Yellow segment

Own house

751

0.381

0.024

0.037

0.656

0.509

Rental

232

0.405

Green segment

Own house

751

0.498

0.084

0.037

2.239

0.025

Rental

232

0.414

8.3 Identification of Consumer Membership

373

question of who are the members here. The people amongst the yellow segment may live in their own houses or may not. (c) Green Segment: Opposite from the above two categories, here the share of respondents having their own houses is greater. The difference in proportions is also remarkable with a 5% probability level (2.239; p = 0.025). Accordingly, owners get a place here.

8.3.5 Cultural Variables and Segmentation • Religious Denomination and Segment Membership Not robustly, but religion has been observed significantly related to the segments of consumers as specified by the 10% significance level. Religious denominations are displayed in four categories, and the share of each category into each segment is noticeable from Fig. 8.19. The z-test for proportionate difference can be studied from Table 8.25. (a) Red Segment: Concerning the height of bars, it can be noticed that the respondents of Islamic denomination attain high share (% = 35.3). z-test as displayed in Table 8.25 confirms the significance of their high share from the share of the other three denominations. Hence, with a significantly high percentage they are the members in the red segment. (b) Yellow Segment: As obtained in the yellow segment, the respondents who fall under the ‘others’ category attain the highest share (% = 55.0), followed by believers of Islamic (% = 47.1), Hindu (% = 39.5), and Sikh (% = 36.1) religions. Further, the z-test substantiates no significant difference between any

Fig. 8.19 Religion and segmentation

374

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.25 Religion and intra-segment comparison: paired differences and test of relevance Segments

Religion

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Hindus

874

0.134

0.023

0.039

0.554

0.582

0.219

0.116

2.592

0.010

0.034

0.068

0.442

0.660

0.242

0.122

2.465

0.014

0.011

0.077

0.140

0.889

0.220

0.139

1.575

0.114

0.034

0.059

0.568

0.569

0.076

0.122

0.635

0.529

0.155

0.112

1.400

0.162

0.110

0.134

0.840

0.401

0.189

0.125

1.525

0.126

0.079

0.164

0.479

0.631

0.057

0.061

0.931

0.352

0.295

0.094

2.415

0.016

0.121

0.108

1.072

0.285

0.352

0.110

2.619

0.009

0.178

0.122

1.409

0.159

0.174

0.141

1.188

0.234

Sikhs

Yellow segment

Green segment

72

0.111

Hindus

874

0.134

Islamics

17

0.353

Hindus

874

0.134

Others

20

0.100

Sikhs

72

0.111

Islamics

17

0.353

Sikhs

72

0.111

Others

20

0.100

Islamics

17

0.353

Others

20

0.133

Hindus

874

0.395

Sikhs

72

0.361

Hindus

874

0.395

Islamics

17

0.471

Hindus

874

0.395

Others

20

0.550

Sikhs

72

0.361

Islamics

17

0.471

Sikhs

72

0.361

Others

20

0.550

Islamics

17

0.471

Others

20

0.550

Hindus

874

0.471

Sikhs

72

0.528

Hindus

874

0.471

Islamics

17

0.176

Hindus

874

0.471

Others

20

0.350

Sikhs

72

0.528

Islamics

17

0.176

Sikhs

72

0.528

Others

20

0.350

Islamics

17

0.176

Others

20

0.350

8.3 Identification of Consumer Membership

375

of the combinations considered on the basis of these categories. Therefore, it can be stated that people may be devoted to any religion; all have similar teachings and gives hope to society for people sustainable attitudes and actions. (c) Green Segment: The followers of Sikhism dominate the green segment with their major percentage (% = 52.8) followed by the respondents of Hinduism (% = 47.1). These proportions are alike as can be concluded with the inconsideration of z-statistics (z = 0.931; p = 0.352). Consequently, the followers of Sikhism and Hinduism are the candidates of inclusion in the segment. • Religiosity and Segment Membership According to religiosity, an intra-segment comparison is presented in Fig. 8.20 and Table 8.26. (a) Red Segment: Although there is a difference of only 0.4% between low and high groups, weighing with the size of sample, z-test demonstrates a significant difference in the two categories at a10% level of significance. Accordingly, the group having low religious strength dominates here. (b) Yellow Segment: Alike with the above category, the percentage share of high religious group is significantly less than the group with low religiosity [(% = 36.1 < % = 42.9); (z = 2.176; p = 0.029)]. Accordingly, the respondent group which falls here has weak religious strength. Fig. 8.20 Religiosity and segmentation

Table 8.26 Religiosity and intra-segment comparison: paired differences and test of relevance Segments

Religiosity

Red segment

Low level High level

Yellow segment

Low level

518

0.429

High level

465

0.361

Low level

518

0.434

High level

465

0.505

Green segment

N

Proportions

Difference

S.E

z-statistics

Sig

518

0.137

0.004

0.022

0.183

0.086

465

0.133 0.068

0.031

2.176

0.029

0.071

0.032

2.228

0.026

376

8 Characterizing and Profiling Responsible Consumer Segments

(c) Green Segment: People with high religiosity attain a significant high percentage share of 50.5% as against with a 43.4% share of people who fall in low religiosity group. Hence, opposite to the result of the above two segments, the respondents with high religious strength situate somewhere amongst responsibles in this segment.

8.3.6 Personality Variables and Segmentation • Objective Directed Traits and Segment Membership Figure 8.21 and Table 8.27 obtain the membership of less and more objective-driven individuals in between the segments. (a) Red Segment: According to objective directed traits, the group of respondents who are less oriented towards their goals attain significantly high share (% Fig. 8.21 Objective directed traits and segment membership

Table 8.27 Objective directed traits and intra-segment comparison: paired differences and test of relevance Segments

Categories

N

Proportions

Difference

S.E

Red segment

Less

509

0.206

0.147

0.029

6.737

0.000

More

474

0.059 0.183

0.031

5.860

0.000

0.331

0.030

10.393

0.000

Yellow segment Green segment

Less

509

0.485

More

474

0.302

Less

509

0.308

More

474

0.639

z-statistics

Sig

8.3 Identification of Consumer Membership

377

= 20.6; z = 6.737; p = 0.000), consequently, getting a place amongst an environmentally detrimental section of society. (b) Yellow Segment: Here also, the group having the least scores on objective directed traits constitute the segment with their major share (%Less = 48.5 > %More = 30.2). The significance is also visible from Table 8.27 at a 0.1% significance level (z = 5.860; p = 0.000). (c) Green Segment: In the green segment, the respondents who assemble under group more objectively directed individuals attains 63.9% share as against the percentage of only 30.8% from the former group of less-directed individuals. The statistically significant difference in these proportions (Difference = 33.1%) empirically confirms that the second group is the candidate of inclusion amongst aspirants and shining stars. • Social Directed Traits and Segment Membership The analysis with bar charts (Fig. 8.22) and z-test (Table 8.28) for social directed traits is confirmed from the next lines. Fig. 8.22 Social directed traits and segment membership

Table 8.28 Social directed traits and intra-segment comparison: paired differences and test of relevance Segments

Categories

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Less

454

0.216

0.150

0.022

6.855

0.000

More

529

0.066 0.065

0.031

2.077

0.038

0.215

0.031

6.736

0.000

Yellow segment Green segment

Less

454

0.432

More

529

0.367

Less

454

0.352

More

529

0.567

378

8 Characterizing and Profiling Responsible Consumer Segments

(a) Red Segment: Fig. 8.22 states that the group of less socially directed individuals are proportionately high (%Less = 21.6) compared to their correspondents (%More = 6.6). The groups are statistically departed with a difference of 15% in the proportions as stated by z-test (z = 6.855; p = 0.000). So, less-directed group divulges into the red segment. (b) Yellow Segment: Here, a difference of 6.5% is obtained between two groups and the z-test confirms the reliability of this high share of less-oriented individuals by providing significant results at a 5% probability level (z = 2.077; p = 0.038). (c) Green Segment: Here, the second group conquers over the first by gaining 21.5% more share. Table 8.28 also reflects extremely high significance for the difference in their favour (z = 6.736; p = 0.000). • Self-Directed Traits and Segment Membership Again there are two less and more categories of self-directed traits and Fig. 8.23 entails segment membership on the basis of these traits. Table 8.29 further sheds light on the significance of the findings. Fig. 8.23 Self-directed traits and segment membership

Table 8.29 Self-directed traits and intra-segment comparison: paired differences and test of relevance Segments

Categories

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Less

499

0.172

0.075

0.022

3.439

0.001

More

484

0.097

Yellow segment

Less

499

0.463

0.134

0.031

4.293

0.000

0.209

0.031

6.566

0.000

Green segment

More

484

0.329

Less

499

0.365

More

484

0.574

8.3 Identification of Consumer Membership

379

(a) Red Segment: In line with the previous results of objective directed and social directed traits, the red segment here is again composed of individuals who score less on self-directed traits (% = 17.2). Moving towards the percentage difference, the share of their counterpart (% = 9.7) is also significantly less from them (z = 3.439; p = 0.001). (b) Yellow Segment: Less-directed group attains 13.4% (46.3% − 32.9%) more share in the yellow segment if compared to their more-directed counterparts. Taken statistically, the gap is noteworthy with extreme significance (z = 4.293; p = 0.000). In view of this fact, membership of the less-directed group is confirmed here. (c) Green Segment: As anticipated, the group of people who are highly directed by their own self acquires prominent share amongst aspirants and shining stars (%More = 57.4). The inferential statistics in the form of z-test is in also their favour (z = 6.566; p = 0.000). Therefore, high self-concepts make people members of the green segment. • Emotions Directed Traits and Segment Membership The results for two categories of emotions directed traits can be studied from Fig. 8.24 and Table 8.30. (a) Red Segment: 22.4% of the less-directed individuals fall under this segment whereas this percentage is only marginal (% = 4) for more-directed group. Just looking at these percentages, it can be said that least directed group is at the extreme and the same is confirmed by highly significant z-statistics (z = 8.434; p = 0.000). (b) Yellow Segment: From the least directed group, here 37.4% respondents confirm their place while amongst more-directed individuals 42.1% come under this segment. Thus, there is a difference of 4.7% but noted from z-test, the same is not statistically considerable (z = 1.505; p = 0.131). Hence, it is concluded that Fig. 8.24 Emotions directed traits and segment membership

380

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.30 Emotions directed traits and intra-segment comparison: paired differences and test of relevance Segments

Categories

N

Proportions

Difference

S.E

z-statistics

Sig

Red segment

Less

508

0.224

0.184

0.021

8.434

0.000

More

475

0.040

Yellow segment

Less

508

0.374

0.047

0.031

1.505

0.131

More

475

0.421

Green segment

Less

508

0.402

0.137

0.031

4.302

0.000

More

475

0.539

here the results are not favouring any one group and both less or more-directed individuals are supported. (c) Green Segment: With 13.7% high share, the group of more-directed individuals is at the prime position than their least directed equivalents. Statistically also, this high share is preferential as demonstrated by notable z-statistics (z = 4.302; p = 0.000). In radiance with this result, aspirants and shining stars are those who are directed by the component of emotionality and collectivism in them.

8.4 Profiling of Responsible Consumers In view of a range of features as defined in Sect. 8.3, the purpose of profiling the ‘responsible consumers’ (members of the green segment) and their counterparts is completed here.

8.4.1 Integration of Consumers’ Attributes—Attempt of Profiling Table 8.31 snapshots a range of characteristics of respondents into each segment; and in this part, a variety of attributes of consumers are assimilated for obtaining the profiles.

8.4.1.1

Profile of Red Segment

The third column of Table 8.31 embraces the features of consumers in the red segment. As stated by a combination of demographic features, red is a segment of young and middle-aged males having up to college-level education from business and arts subjects, academically their performance is poor. Specifically, they are the nonearners and as designated by their marital status, may be married or unmarried.

• Unmarried • Married • With children • Without children • Married couples having grown-up children

• Non-earning

Marital status

Parenthood/parental status

Years of marriage

Earning/profession

• Small sized • Medium sized • Large sized

• Academically poor

Academic intelligence

Family size

• Law/business • Arts and natural sciences

Academic orientation

• Nuclear families • Joint families

• Schooling • Graduation

Educational qualifications

Type of family

• Young • Middle–upper aged

Age cohorts/birth order

Sociological traits

• Males

Gender

Personal or demographic traits

Red segment

Segmenting variables

Segmentation dimensions

Table 8.31 Profiling of segments: incorporating distinctive features

• Small sized • Large sized

• Members of nuclear families

• Non-earning

• Newly married couples or married couples having kids

• Without children

• Unmarried

• Academically mediocre

• Law/business • Science and technical

• Schooling • Graduation • Higher

• Young

• Females

Yellow segment

• Medium sized

(continued)

• Members of joint families

• Earning

• Married couples having teenage child • Married couple and children live seperately

• With children

• Married

• Academically superior

• Arts and natural sciences • Science and technical

• Highly educated

• Middle and upper-aged

• Males • Females

Green segment

8.4 Profiling of Responsible Consumers 381

Economic traits

Cultural traits

Geographical traits

Segmentation dimensions

• Low class • High class • Non-owners

Home ownership

• Low religiosity • High religiosity

Religiosity

Family income

• Islamics

Religious denominations

• Non-commuters • Walk-ride bicycle

• Low support

Household support

Commuting

• Mature > Young • Mature = Young • Young > Mature

Family composition (age-wise)

• Rural

• Female > Male • Female = Male • Female < Male

Family composition (gender-wise)

Place of living

Red segment

Segmenting variables

Table 8.31 (continued)

Others Islamics Hindus Sikhs

• Owners • Non-owners

• Low class • Middle class • High class

• Low religiosity

• • • •

• Walk-ride bicycle • Public vehicle commuters

• Urban

• Owners

• Middle class

• High religiosity

• Sikhs • Hindus

• Non-commuters • Private vehicle • Public vehicle

• Rural • Urban

• High support

• Mature > Young • Young > Mature

• Mature = Young

• Low support • High support

• Female < Male • Female = Male

Green segment

• Female > Male • Female = Male

Yellow segment

(continued)

382 8 Characterizing and Profiling Responsible Consumer Segments

• Less-directed • Less-directed • Less-directed • Less-directed

Objective directed

Social directed

Self-directed

Emotions directed

Personality traits

Red segment

Segmenting variables

Segmentation dimensions

Table 8.31 (continued)

• Less-directed • More-directed

• Less-directed

• Less-directed

• Less-directed

Yellow segment

• More-directed

• More-directed

• More-directed

• More-directed

Green segment

8.4 Profiling of Responsible Consumers 383

384

8 Characterizing and Profiling Responsible Consumer Segments

Combining with age, two subgroups may be anticipated in which young may be unmarried or married without children. Alternatively, here it can be said that middle and upper-aged respondents may be married having grown-up children. Coming to sociological dimensions, it can be said that the family type, size, and structure have no influence, but respondents underlying the red segment get very low support from their family. Geography supports them as non-commuter rural people and if they commute, only walk or ride a bicycle to their destination. Therefore, it can be firmly understood that their mobility is restricted within their own village or town and they are not much geographically spread from their living areas. Further, cultural traits bring them out as followers of the Islamic religion with less-religious strength. According to economic standing, the people here either belong to the low or highclass group and specifically they do not own their houses and live in rental dwellings. As far as personality features are concerned, people are not encouraged by affirmative personality features. They neither focus on their objectives in life nor are socially oriented. They have no self-direction and are also much far from their emotional affinity.

8.4.1.2

Profile of Yellow Segment

According to demography, the yellow segment comprises young females who according to education may be less or highly educated. They attain education from business and science subjects and their academic performance is much better than people in the red segment. These females are non-earning and may be unmarried, newly married, or may have kids. From a sociological viewpoint, the majority of them live in small-sized nuclear families in which males are comparatively less or equal in ratio with females. Similarly, in the family composition of these females, the aged household members are approximately equal to the members who may be young. These females generally get extended family support, but in some cases families are also not so much supportive. Geographical dimensions state that they belong to urban living areas and also commute within (as they walk or ride a bicycle) or across cities (as they use both public and private transport). Economic status does not matter a lot as the people here may belong to low, middle, or high-class families. Their living in rental or own houses also does not make a difference. Considering personality features, they are less objective-driven, not much socially concerned, encompass little self-enhancement, and somewhat also lack emotions with attaining least scores on emotionally oriented traits. In this way, the yellow segment can be said as a composition of well educated young females who may be unmarried and newly married. As they come out to be non-earners, they may be those who are engaged in the process of their high education and even after marriage continuing with their studies.

8.4 Profiling of Responsible Consumers

8.4.1.3

385

Profile of Green Segment

Irrespective of their gender, the basic image of ‘responsible consumers’ emerging from demographic determinants is that of the aged, highly educated, academically insightful, non-business academics, who are earning people, and married having children. Established by sociological determinants, they are the members of joint but medium-sized families. In their family structure, the number of male members is either greater than or equal to female members. In terms of age, the majority of family members may be mature or young. The type of consumers also gets extended support of their family members for showing responsible acts in their consumption behaviour. In relation to economic determinants, ‘responsible consumers’ are also not wealthier and come under the middle-class category having average socio-economic status. They live in their own houses, thus may be satisfied as their basic need for shelter is fulfilled. Geographical dimensions hold that they typically come from larger cities whether from urban or rural living places. Expressly, these people are those who do not commute on a daily basis; however, commuters may use their own vehicle or can also travel by public transportation. Next in the cultural category, religion is the most important variable that shapes the differing kinds of values in their followers. The altruistic and biospheric values of every religion teach the people about concern for others and the environment. Particularly, the followers of Sikhism and Hinduism fall into this segment, who by construing that nature is the reflection of God on Earth, tend to be religious with extreme religiosity. Personality traits further confer that people here are mature in their thinking and social behaviour. As they are high on personality traits, it can be said that they think objectively, give importance to society, are self-guided, and believe in collective working.

8.4.2 Inter-comparison of Features of Segments As there are three segments, mathematically three combinations are obtained for elaborating on a paired comparison. The undergoing discourse confirms some attention-grabbing points of similarities and dissimilarities that stuck between them.

8.4.2.1

Red-Yellow

An assessment of a range of features of the red segment with the yellow segment points out that more firmly, the people in both segments are distinct on their gender. Red is a segment of young males while yellow is especially associated with young females. On the one hand, males of the red segment are usually from the disciplines of arts and natural sciences while preferential courses of females of the yellow segment are pure sciences and technical education. A major part of both the segments are unmarried but married respondents too fall here. In the red segment, males are the fathers of grown-up children; but, the yellow segment holds married females having kids. The majority of males of red segment live in rural areas and females of the yellow segment

386

8 Characterizing and Profiling Responsible Consumer Segments

reside in urban living places. Family structure has no notable influence in the red segment; whereas in the yellow segment, females are larger in family composition. On the rest of the traits, the segments are almost similar with no remarkable differences. Further, when these two segments are compared with the segment of ‘responsible consumers’ (green segment), several striking points emerge and explained under the next two paragraphs.

8.4.2.2

Red-Green

Red and green segments stand out against each other on some inimitable features which discriminate the segment of irresponsibles from responsibles. Middle and upper-aged male respondents come under both the segments but education and academic intelligence separates them from each other. In the red segment, respondents have middle-level education; but, in the green segment they are highly educated with superior academic records. Comparing to the red segment, male composition remains high in the green segment. While the consumers in the red segment are non-earners and get less family support, in the green segment, they belong to earning class and also get full support from their family which can boost their morale. They are also distinct in cultural and economic traits. Further, as given by personality attributes, respondents in the red segment are impoverished in their personality traits; but, respondents in the green segment have affirmative and accustomed personality features beyond conventional.

8.4.2.3

Yellow-Green

Attributes like age, marital status, parental status, years of marriage, and earning capacity define the difference between these two segments. Whereas in the yellow segment, the majority of people are the members of small or large-sized nuclear families, in the green segment they are from medium-sized joint families. Ratio of female members is high over male members in the yellow segment while the reverse is true in the green segment. The proportion of mature and young household members is also almost equal in the yellow segment; but, in green the structure of families is skewed towards aged and young household members. Religiosity and homeownership again have a notable influence. Further, the activities of the green segment are guided by the objectives, emotions, and social motives; people underlying the yellow segment however live in their own convenience.

8.4 Profiling of Responsible Consumers

387

Fig. 8.25 Extended survey database for personality variables (variable view)

Overall, in an attempt to answering objective 6, this chapter presented a distinct and unique profile of ‘responsible consumers’ who because of their superior attitude and actions originate as a distinct and fruitful consumer segment to green marketers. Leaving only ‘family size’, the associations of all other variables are found significant. Consequently, it is concluded that the sense of responsibility does not originate in vacuum; but, as consumers differ from each other on many of the important characteristics, some affirmative features fill in them this essence, and they become distinctive from other persons. At the end of this chapter, part IV is complete and all the objectives of this study have been accomplished. Now, findings and implications are discussed in the next part Conclusions and Practicality. Endnotes: Results of Principal Component Analysis on Personality Attributes For analyzing personality attributes; at the outset, scores of consumers on the two statements measuring a particular trait were added and averaged by using compute variable option in transform command in SPSS. In this way, data of thirty two statements were first reduced to sixteen traits. In Table 8.32, the mean values of these traits are shown which range between 2.815 to 3.789. The values of standard deviation are not so much high, so it can be concluded that scores of respondents on these variables are not widely spread. The internal consistency of the statements was also good with an alpha coefficient of 0.835. In fact, these traits were to be seperately analysed; but, their inter-item correlation matrix suggested significant correlations between them except two correlations that are V1 ↔ V9 and V3 ↔ V6 (Table 8.33). Kaiser-Mayer-

388

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.32 Descriptive statistics for personality variables Variables

Attributes

¯ X

S.D.

Variables

Attributes

¯ X

S.D.

V1 [P1 + P17]

Emotionality

3.403

0.913

V9 [P9 + P25]

Rationality

3.353

0.882

V2 [P2 + P18]

Collectivist

3.368

0.770

V10 [P10 + P26]

Sociability

3.536

0.741

V3 [P3 + P19]

Persistence

3.672

0.776

V11 [P11 + P27]

Patience

3.114

0.898

V4 [P4 + P20]

Determination

3.671

0.745

V12 [P12 + P28]

Dominance

3.789

0.702

V5 [P5 + P21]

Curiosity

3.770

0.852

V13 [P13 + P29]

Confidence

3.777

0.767

V6 [P6 + P22]

Orientation

3.405

0.793

V14 [P14 + P30]

Conservatism

3.467

0.825

V7 [P7 + P23]

Demonstration

3.531

0.788

V15 [P15 + P31]

Motivation

3.166

0.788

V8 [P8 + P24]

Hedonism

3.389

0.741

V16 [P16 + P32]

Courage

2.815

0.755

Cronbach Alpha: 0.835; Split-Half Reliability: Part I (N = 8) = 0.719; Part II (N = 8) = 0.746 ˛ Full statements of Personality measurements can be read out from Annexure

Olkin test and Bartlett’s test of sphericity were also significant (Table 8.33) devising that these variables might be reduced to a small number of factors. Accordingly, the principal component analysis was employed for reducing them to the manageable level. Analyzing through principal component analysis with varimax rotation, these 16 variables resulted in four components having Eigenvalues greater than one and explaining 52.206% variance (Table 8.34). According to the loadings of the attributes, factors were named objective directed traits, social directed traits, self-directed traits, and emotions directed traits. It is revealed that the component ‘objective directed traits’ is a conglomerate of five attributes. ‘Social directed traits’ and ‘self-directed traits’ accumulates four attributes each, and the component ‘emotions directed traits’ is a combination of three traits. Then, summated scales were constructed into SPSS file for each of the measurement (Figs. 8.25 and 8.26) and by exercising upon ‘categorize variable’ option; each of these four components were categorized into two parts from median split (Table 8.35). Respondents’ scoring less than median were categorized in less-directed group for that particular trait and those gaining scores above or equal to median were grouped into high directed category. Then, this transformed data were used for analysis of profiling consumers based on their personality characteristics.

0.395** 1

0.292** 0.470**

0.235**

0.287**

0.236** 0.301**

0.246**

0.110**

0.260** 0.174**

0.199**

0.290**

0.235** 0.496**

0.211**

0.278**

0.205** 0.208**

0.171**

0.124**

0.211**

0.270**

0.138**

0.106***

−.012ns

0.277**

0.112**

0.222**

0.155**

0.100***

0.163**

0.071*

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

V15

V16

V5

V6

V8

V9

V10

V11

V12

V13

V14

V15

Approx.Chi-square = 3679.859; df = 120; p = 0.000

KMO = 0.870

0.211** 0.123** 0.187** 0.180** 0.141** 0.251** 0.190** 0.241** 0.132** 0.160** 0.286** 0.298**

0.321** 0.221** 0.120** 0.245** 0.256** 0.268** 0.250** 0.262** 0.397** 0.340** 0.330** 1

0.282** 0.323** 0.190** 0.301** 0.284** 0.219** 0.262** 0.244** 0.259** 0.422** 1

0.428** 0.371** 0.140** 0.330** 0.233** 0.283** 0.264** 0.313** 0.404** 1

0.334** 0.326** 0.229** 0.326** 0.298** 0.272** 0.289** 0.247** 1

0.276** 0.226** 0.134** 0.274** 0.134** 0.353** 0.132** 1

0.229** 0.211** 0.252** 0.219** 0.311** 0.192** 1

0.256** 0.153** 0.086*** 0.296** 0.225** 1

0.124** 0.175** 0.216** 0.393** 1

*Correlations are significant at the 0.05 level (2-tailed) **Correlations are significant at the 0.001 level (2-tailed) ***Correlations are significant at 0.01 level (2-tailed) ns Correlations are not significant (p > 0.05)

Bartlett’s test of sphericity

V7

0.272** 0.308** 0.213** 1

0.114** 0.229** 1

0.345** 1

1

V4

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy

0.299**

0.269**

0.334**

0.212**

0.287**

0.207**

0.051ns

0.310**

1

0.421**

V3

V2

V2

1

V1

V1

Table 8.33 Inter-item correlation matrix

8.4 Profiling of Responsible Consumers 389

390

8 Characterizing and Profiling Responsible Consumer Segments

Table 8.34 Personality variables: results of principal component analysis Components

Attributes

Measurement

Loadings (λ)

Consistency

Component 1 Objective directed traits Eigenvalue = 2.692 % of variance = 16.827 Cumulative variance = 16.827

Persistence

Never gives up easily/gives up easily

0.748

Cronbach α = 0.753

Determination

Ambitious/apathetic

0.700

Curiosity

Curious/mundane

0.553

Dominance

Dominant/submissive

0.481

Confidence

Confident/unsure

0.701

Component 2 Social directed traits Eigenvalue = 1.979 % of variance = 12.367 Cumulative variance = 29.193

Demonstration

Introvert/extrovert

0.620

Hedonism

Fun loving/dejected

0.789

Sociability

Friendly/antagonistic

0.454

Conservatism

Like changes/do not like changes

0.404

Component 3 Self-directed traits Eigenvalue = 1.878 % of variance = 11.739 Cumulative variance = 40.933

Rationality

Never gives value to luck/superstitious

0.570

Patience

Compassionate/short tempered

0.574

Motivation

Self-motivated/uninspired

0.465

Courage

Courageous/imprudence

0.801

Component 4 Emotions directed traits Eigenvalue = 1.804 % of variance = 11.273 Cumulative variance = 52.206

Emotionality

Emotional/unemotional

0.784

Collectivism

Collectivist/individualist

0.669

Orientation

Future oriented/lives in present

0.583

Cronbach α = 0.625

Cronbach α = 0.607

Cronbach α = 0.590

8.4 Profiling of Responsible Consumers

391

Fig. 8.26 Extended survey database for personality variables (data view)

Table 8.35 Partition points: medians

Components

Median

Objective directed traits

3.700

Social directed traits

3.500

Self-directed traits

3.000

Emotions directed traits

3.333

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Krishnaswami, O. R., & Ranganatham, M. (2005). Methodology of research in social sciences (2nd ed., pp. 1–445). Mumbai: Himalaya Publishing House. Lowry, R. (2001–2014). Chi-Square, Cramer’s V and Lambda-For a rows by columns contingency table. On line Calculator. Retrieved September 6, 2013, from http://vassarstats.net/newcs.html. Malhotra, N. K., & Dash, S. (2012). Marketing research: An applied orientation (6th ed., pp. 1–929). New Delhi: Dorling Kindersley (India) Pvt. Ltd, Pearson Education, Inc. Pal, Y., & Davar, S. C. (2001). Meta-analysis in research: An introduction. In P. P. Arya, & Y. Pal (Eds.), Research methodology in management theory and case studies (pp. 621–631). New Delhi: Deep and Deep Publications Pvt. Ltd. Parasuraman, A., Grewal, D., & Krishan, R. (2005), Marketing research-first indian adaptation (3rd Reprint, pp. 1–683). USA: Houghton Mifflin Co.; India, New Delhi: Biztantra-An Imprint of Dreamtech Press. Preacher, K. J. (2001). Calculation for the Chi-Square test: An interactive calculation tool for chisquare tests of goodness of fit and independence [computer software]. Retrieved September 6, 2013, from http://www.quantpsy.org/chisq/chisq.htm. Social Science Statistics. (2013). Chi-Square calculator. Retrieved September 6, 2013, from http:// www.socscistatistics.com/tests/chisquare/. Social Science Statistics. (2013). Z-test calculator for two population proportions. Retrieved September 6, 2013, from http://www.socscistatistics.com/tests/ztest/. Trochim, W. M. K. (2005). Research methods (2nd ed., pp. 1–355). Atomic Dog Publishing; India, New Delhi: Biztantra-An Imprint of Dreamtech Press. Zikmund, W. G., & Babin, B. J. (2006). Exploring marketing research (9th ed., pp. 1–848). Cengage Learning.

Part V

Conclusions and Practicality

In Chap. 9, the target is to summarize and integrate the contents of everything presented up to the point. However, Chap. 10: Implications and Research Directions focuses and elaborates upon the implications, suggestions, and further research directions emerged from the findings.

Chapter 9

Findings and Discussions

This chapter summarizes the analysis, and revives the significant findings emerging from it. Three sections are presented. The first section highlights the research problem, questions, and the reasoning for working on particular objectives. The second section reflects the process of development of the questionnaire, sampling utilized, and characteristics of the sample. In the third section, the main findings are abridged allied with each objective. Here, while presenting the results two main aspects are considered: support/contradiction from the literature, and development of a theory. Via support or contradiction, it is noticed as to where the findings fit in the literature, and if it is not consistent with the literature, theory entails the likely assumptions and explanations for a particular finding.

9.1 An Approach to Study’s Theme 9.1.1 Evolution of Thought The world is passing through a critical phase when economic development in its present form seems unsustainable, and this is the most debatable issue of the time. Searching for the causes of environmental delinquency, a potent fact came out that the main reason for environmental malady is increasing consumption and production levels that are rapidly depleting the natural resources and creating an ecological imbalance. As the issue relates to both production and consumption, it became the implicit and shared responsibility of both corporations and consumers. From this viewpoint, Corporate Social Responsibility (CSR) and Consumer Social Responsibility (CnSR) have evolved as the two concepts in business and marketing literature. In fact, at first, discussions began for the responsibility of corporations (CSR); but, with the superimposed power of consumers who direct corporations’ actions, the paradigm shifted towards inserting responsibility of consumers (CnSR). The © Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_9

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present study concentrated on the second aspect and searched for the responsibility of consumers according to various domains of consumption behaviour. From the last four decades, researchers and academicians are searching for the aspect of consumption behaviour in reference to the social responsibility of consumers from different perspectives and diverse angles. The studies are conducted in search of segments of ‘responsible consumers’ and the factors that impel them towards responsible acts. But, the literature with different findings has been jumbled with a number of results, and awfully still has not been reached to a common cognizance. The structure of influencing factors as well as the definition of ‘responsible consumers’ has also not been yet specified. Moreover, Indian consumers have been less attended and searched only on few dimensions. Accordingly, thoroughgoing from the literature, the study was designed on Indian consumers and emphasized on people ‘Concern for Sustainable Future (CSF)’, ‘Attitude towards Sustainable Living (ASL)’, ‘Commitment to Initiate (CI)’, and ‘Responsible Consumption Behaviour (RCB)’. Objectively, the research presented in this book answered six broad questions (research objectives) which aroused due to research gaps evident from the literature.

9.1.2 Reasoning Behind Objectives It has already been defined in Chap. 3 that different responsible attitudinal and behavioural identities prevailed in the literature, albeit in measurement all tell the same story. So, at the outset, the structure underlying attitudinal and behavioural constructs were explored by operating on objective 1. Secondly, the findings in the literature exclaimed on a number of factors which contribute to the formation of responsible behaviour. In different studies, environmental concern was noticed influencing behaviour but the effect was stated weak as attitude mediated this link. Supplementing this result, attitude has also been defined influencing behaviour even though the attitudinal kind which is able to influence any specific behaviour was least specified. Therefore, ‘Theory of Responsible Behaviour Formation (TRBF)’ was developed for this research work by considering the outcomes and suggestions of previous studies, and a C-A-C-B model was tested in differing behavioural kinds (objectives 2 and 3). Rationale behind some more objectives was to have a divide between responsible/not-responsible consumers due to the fact that there exist attitudinal and behavioural dissimilarities amongst consumers (objectives 4 and 5). Finally, it was assumed that if different consumer segments exist, probably it is because of the dissimilar composition of people and differing circumstances through which they pass out. Accordingly, people in different segments must have different features and attributes which separate them from others, and objective 6 worked on this point. While working on objectives, the definitions of ‘responsible consumption behaviour’ and ‘responsible consumers’ (which were specified in Chap. 3) were given full consideration, and analysis was completed accordingly.

9.2 Methodological Viewpoints

397

9.2 Methodological Viewpoints 9.2.1 Development of Research Instrument The instrument for this research was developed based on the literature review and subsequently revised according to suggestions of expert evaluators to ensure its content and face validity. It was properly ensured that the statements represent domains of the construct and reflect concepts they were intended to measure. To assess attitude and behaviour, mixed statements were used from the initial pool of items from previous studies which were deemed suitable in Indian circumstances. Also, the wordings of many statements were changed, and then the questionnaire was pre-tested on a sample of 50 respondents.

9.2.2 Sampling and Sample Profile Data collected from 1000 residents from North India were processed in analysis. Sixty per cent i.e. 600 respondents were selected from Haryana (A Northern State), and 200 each from Delhi and Chandigarh. Delhi is the Capital of India and Chandigarh is the Capital of the State of Haryana. Besides, Delhi and Chandigarh both are the Northern Union territoires. The sample is constructed keeping in view the demographic structure in India and was fairly divided between male– female, young–old, income-wise, and family-wise. An overview of the sample profile showed that gender-wise sample was leaning towards males with a 52.7% share. India is a country of youthful population; accordingly, they are high in number (N = 872). The same is applicable to the middle class who with 55% constituted the largest part of the sample. Stated by education, approximately two-fifth were highly educated with non-business subjects. Over half was unmarried and the majority amongst married were found to be parents. Earners were also high in ratio contrasted with non-earners (ratio = 1.6:1). The sample of urban respondents (N = 679) was approximately twice as with rurals (N = 321). Respondents with Hindu denomination highly represented the sample. Also, majority of the sample belonged to small-sized nuclear families, as is the living trend visible in many areas of the country. Transcribing data of 1000 respondents with the above sociodemographic profile, each of the six objectives were arrived at with suitable statistical tools and techniques. From the next part, the major findings as emerged from the analysis are shown allied with each objective.

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9.3 Main Findings: Summarization Findings are presented in alignment with each of the six objectives investigated in the study.

9.3.1 Objective 1: To Explore the Dimensions Underlying Attitudinal and Behavioural Constructs According to the purpose, for the identification of the structure of dimensions of attitudinal and behavioural constructs, principal component analysis with varimax rotation was applied. Later, the explored components and their measurement models were validated by using confirmatory factor analysis with the maximum likelihood estimation method. A number of fit indices for the confirmation of model fit were also presented (Chap. 5). • The analysis on the first behavioural domain ‘responsible purchasing’ pointed out two components namely Eco-friendly choice (EFC) and Green Buying (GB). The component eco-friendly choice came out as a summation of four statements and described the cautious behaviour of people while selecting the products. Green buying, however, showed the behaviour of actual purchasing with an integration of three statements. Further, the model of purchasing behaviour with two sub-dimensions revealed an acceptable fit and composite reliability. Also, when compared with the literature, the collection of studies stands out in support of these two components and the matching studies are highlighted in Table 9.1. • The ‘responsible usage domain’ also originated with two components named Sustainable Habits (SH) and Water Conservation (WC). The behaviours underlined first component sustainable habits revealed the actions like use of public transport, use of own shopping bags, and conservation of energy and natural resources. So, it can be said that this component symbolizes a kind of behaviour if develops into everyday routines, the living will become harmonious and healthy. The second component water conservation prompted a very careful and wise use of water really for necessary levels. The measurement model of ‘responsible usage domain’ with these two latent dimensions was found acceptable with a good fit and adequate composite reliability. The behaviours as defined by a number of authors become prominent in favour of these components and are presented in Table 9.2. • The sphere of ‘responsible maintenance’ attained only one component Minimizing Wastage (MW ) in which three behavioural types merged into one. The factor model was validated with zero-order confirmatory analysis, which statistically appeared with an ideal fit. This component popularized the behaviours of reducing waste in society by maximizing the functionality of goods and using them to their maximum. The studies in support of this component are presented in Table 9.3.

9.3 Main Findings: Summarization

399

Table 9.1 Responsible purchasing behaviour and matching studies Author(s)

Matching behaviours harmonizing with the components

Component eco-friendly choice and previous studies Berger and Corbin (1992)

• Avoid products with Styrofoam packaging

Karp (1996)

• Avoid products with plastic covering • Avoid company products which harm the environment

Elkington and Hailes (1988)

• Avoidance of products which damage environment, consume a large amount of resources, cause unnecessary waste, exploit other species

Roberts (1995)

• Avoid aerosol containers

Gilg et al. (2005)

• Avoid aerosols and toxic detergents

Shanka and Gopalan (2005)

• Avoid products which cause environmental damage, pollute air-water, harm endangered plants and animals

Isildar and Yildirim (2008)

• Avoid products with CFC (Chlorofluorocarbon)

Finisterra do Paco and Raposo (2008)

• Switching from products for ecological reasons • Avoid unnecessary packaging and aerosol containers

Ozkan (2009)

• Avoidance of heavily packaged products, chemical products, aerosol products, products harmful to the environment

Muderrisoglu and Altanlar (2011)

• Switch from brands harmful to the environment • Stop buying from companies that disregard the environment • Avoid aerosol-Styrofoam containers

Component green buying and previous studies Berger and Corbin (1992)

• Buying biodegradable products

Karp (1996)

• Buying products made with recycled material

Roberts (1995)

• Buy products: low in pollutants/available in recyclable containers

Siu and Cheung (1999)

• Buying rechargeable batteries • Buying products in recyclable containers

Bamberg (2003)

• Purchasing of eco-friendly products

Gilg et al. (2005)

• Buying energy-efficient appliances/bulbs, recyclable products

Webb et al. (2008)

• Purchasing based on a firm’s CSR performance

Finisterra do Paco and Raposo (2008)

• Buy energy-efficient appliances and biodegradable products • Buying products in refillable containers

Ozkan (2009)

• Buying energy-efficient and recyclable products

Rikner (2010)

• Purchasing environmentally adapted and energy-saving products

Muderrisoglu and Altanlar (2011)

• Purchasing of recycled products

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Table 9.2 Responsible usage behaviour and matching studies Study and author(s)

Matching behaviours harmonizing with the components

Component sustainable habits and previous studies Webster (1975)

• Disconnect car’s pollution control device

Berger and Corbin (1992)

• Carpool-walk; use public transport

Roberts and Bacon (1997)

• Driving as little as possible • Save energy

Siu and Cheung (1999)

• Use of handkerchief instead of tissue paper • Seldom use of a car and lift • Turn off unused electrical appliances

Bamberg (2003)

• Travel mode choice

Kaiser et al. (2003)

• Mobility and transportation

Harland et al. (2007)

• Transportation means other than cars for short distances

Isildar and Yildirim (2008)

• Public transportation • Use paper with both sides

Ozkan (2009)

• Travel mode behaviour (use bus/bicycle)

Yuksel (2009)

• Leave the comfort of a car • Conservation of limited resources

Kiraci and Kayabasi (2010)

• No demand for ATM receipt to conserve paper

Rikner (2010)

• Changing transportation habits • Less energy consumption

Durif et al. (2011)

• Sustainable transport behaviour

Component water conservation and previous studies Kaiser et al. (1999), Corral-Verdugo et al. (2003)

• Water conservation

Kurz (2002)

• Conservation of water

Harland et al. (2007)

• Turning off the faucet

Isildar and Yildirim (2008)

• Close taps

Mondejar-Jimenez et al. (2011)

• Turning off the taps • Showering instead of bathing

Gilg et al. (2005)

• Turn off the tap • Reduce shower • Wash when full load for clothing

Corral-Verdugo et al. (2006)

• Conserve water while washing dishes, brushing teeth, washing hands, washing vehicles/cars

Kiraci and Kayabasi (2010)

• Water saving

9.3 Main Findings: Summarization

401

Table 9.3 Responsible maintenance behaviour and matching studies Study and author(s)

Matching behaviours harmonizing with the components

Tindall et al. (2003)

• Reuse or mend things instead of discarding

Isildar and Yildirim (2008)

• Lessen the amount of waste

Tan and Lau (2009)

• Managing waste by limiting consumption • Maximizing functionality and life of products • Reuse of waste

Kaiser et al. (2003)

• Waste avoidance

Durif et al. (2011)

• De-consumption behaviour

Ha-Brookshire and Hodges (2009)

• Used clothing donation behaviour

• The last stage of consumption decisions is responsible disposal which stated the responsibility of consumers as end users. Two components Appropriate Disposal (AD) and Recycling Intentions (RI) with two and three statements respectively were identified. Appropriate disposal defined consumers’ sense of freeing public places from visual pollution by using dustbins. Recycling intentions captured the intentions of people to be a part of recycling activities as the same is a remarkable solution for tackling the problem of waste. The fit of the model was perfect with exceedingly high composite reliability. These components also matched with a number of studies which worked on identical behavioural patterns. A perusal of these studies is shown in Table 9.4. • Different from purchasing to disposal behaviours, two types of allied responsible behaviours were also discovered: Environmentally Relevant Activities (ERA) and Sustainable Societal Conduct (SSC). The former highlighted the activities significant for diffusing environmental concern and knowledge amongst the general public, and the latter component put forth those activities resorting to which cities can be made more people-centric and a better place to live. The behaviours from the literature which are consistent with the behaviours that underlie these components are defined in Table 9.5. • Attitudinal field was divided into two parts: General and Specific. The findings in the general attitudinal domain were confined to two constructs namely ‘Concern for Sustainable Future (CSF)’ and ‘Commitment to Initiate (CI)’. The construct ‘concern for sustainable future’ beyond the measurement of general environmental concern (as mentioned in the literature) obtained a broad perspective, and measured consumers’ preferences and thinking for a sustainable future over their consumptive lifestyle. The second construct ‘commitment to initiate’ targeted dedication of consumers to be engaged in responsible acts, and captured their optimism regarding their role as initiators to act upon all possibilities of maintaining sustainability in their reach. The model fit for the construct ‘concern for sustainable future’ was perfect and it was ideal for ’commitment to initiate. Further, the construct ‘concern for sustainable future’ can be termed equivalent to the construct environmental concern (EC) as defined in a number of studies. Some of these matching studies are depicted in Table 9.6. ‘Commitment to ini-

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9 Findings and Discussions

Table 9.4 Responsible disposal behaviour and matching studies Author(s)

Matching behaviours harmonizing with the components

Component appropriate disposal and previous studies Berger and Corbin (1992)

• Separate pile of garbage for recycling

Grob (1995)

• Separation of household waste into different bins

Tindall et al. (2003)

• Pick up litter • Compost organic waste

Tan and Lau (2009)

• Managing waste by separating it

Durif et al. (2011)

• Composting behaviour

Component recycling intentions and previous studies Karp (1996), Bamberg (2003), Tindall et al. (2003), Lau (2010), Kiraci and Kayabasi (2010), Durif et al. (2011)

• Recycling behaviour

Roberts and Bacon (1997)

• Recycle paper

Roberts (1995)

• Recycle paper and household trash

Hunter et al. (2004)

• Sort recycle

Gilg et al. (2005)

• Recycle (glass, newspaper, plastic bottles)

Shanka and Gopalan (2005)

• Recycle (plastic, cardboard, aluminium, magazines)

Tan and Lau (2009)

• Recollecting the waste for recycling

Muderrisoglu and Altanlar (2011)

• Recycling (jars, bottles, aluminium cans, sort trash for recycling)

tiate’ can be understood compatible with intentions/commitment/willingness to sacrifice/pay/contribute/protect for environment discussed by certain studies. • A structure of seven sub-dimensions was further recognized by analysing 19 statements of the specific attitudinal domain ‘Attitude towards Sustainable Living (ASL)’. The first dimension attitude towards conserving ecology (ACE) was significant in measuring the attitude of consumers for the conservation of natural resources. The second component anticipating mounting waste (AMW ) captured consumers’ sensations regarding increasing waste problems. The Third, Need for Recycling (NR), considered views of consumers for recycling resorting to which problem of waste can be tackled. The component overcoming green myopia (OGM) acted as a symbol for measuring the attitude of consumers regarding sustainable products. Environmental thinking (ET ), as another component, tapped consumers’ respect and honour for environmental legislation and highlighted the need for environmental education in India. Next two components civic norms (CN) and sustainable mobility (SM) collectively captured the aspect of consumers’ approach that as humans are a part in civil society, it is their obligation to avoid the tasks by which society gets disturbed or annoyed, therefore with the name of Civil Attitude (CA) these two components were analysed as

9.3 Main Findings: Summarization

403

Table 9.5 Allied socially responsible activities and matching studies Author(s)

Matching behaviours harmonizing with the components

Component environmentally relevant activities and previous studies Berger and Corbin (1992)

• Write to the government on environment issues

Karp (1996)

• Work for environmental organizations and the contribution of money

Siu and Cheung (1999)

• Joining of activities organized by green bodies

Stern (2000), Urban and Zverinova (2009)

• Petitioning, joining environmental organizations • Support for public policies and their acceptance

Tindall et al. (2003), Hunter et al. (2004), Ozkan (2009)

• Donate money, sigh petitions/write letters • Representation of an environmental body

Finisterra do Paco and Raposo (2008)

• Help environmental groups by donating money • Protest/demonstration to preserve the environment

Isilidar and Yildirim (2008)

• Membership of environmental organizations/NGO’s • Participation in films, meetings on environmental issues

Yuksel (2009)

• Joining environmental demonstrations and signature campaigns • Donate to institutions that act to protect the environment • Join education and courses about the environment

Chen et al. (2011)

• Environmental talk, environmental education, environmental volunteer, environmental litigation

Muderrisoglu and Altanlar (2011)

• Talk to others on environmental matters • Donate and vote to protect the environment • Read and watch events for environmental issues

Component sustainable societal conduct and previous studies Kaiser et al. (2003)

• Social behaviour

Yan and She (2011)

• Promotion of social and ethical progress • Defend one’s national interest

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Table 9.6 General attitudinal domain and matching studies Concern for sustainable future (CSF)

Kinnear et al. (1974), Dunlap et al. (1978), Shetzer et al. (1991), Roberts and Bacon (1997), Bamberg (2003), Kim and Choi (2005), Mostafa (2007), Finisterra do Paco and Raposo (2008), Albayrak et al. (2010), Kim and Kim (2010)

Commitment to initiate (CI)

Hines et al. (1986/87) Dietz et al. (1998), Kaiser et al. (1999), Siu and Cheung (1999), Stern (2000), Laroche et al. (2001), Tuna (2003), Barken (2004), Oikonomou et al. (2009), Mondejar-Jimenez et al. (2011), Caluri and Luzzati (2016)

Table 9.7 Specific attitudinal components and matching studies Overcoming green myopia (OGM)

Matches with ‘green purchase attitude scale’ by Mostafa (2007) and Chan (2001) measurement of ‘attitude towards green purchases’

Attitude towards conserving ecology (ACE)

Compatible with a number of studies which measured people’s attitude towards conservation of resources in one form or the other. Major studies include Randolph and Troy (2008), Dolnicar and Hurlimann (2010), Marandu et al. (2010), Gilbertson et al. (2011), Malodia (2013)

Anticipating mounting waste (AMW)

Corresponds to ‘attitude toward Litter’ factor of Schwepker and Cornwell (1991) and ‘waste avoidance attitude’ of Kaiser et al. (2003)

Need for recycling (NR)

Consistent with ‘attitude to recycling’ measurement of Tonglet et al. (2004)

Civic norms (CN)

Can be termed similar measurement like Owen and Videras (2006) measurement of ‘Civic-minded individuals’

one. Statistically, the confirmatory factor model of these seven components was acceptable, and certain components are also obtained in harmony with a number of previous studies reflected in Table 9.7.

9.3.2 Objective 2: To Examine the Extent to Which Consumers Adopt Each Behavioural Kind and to Affirm the Extent of Their Attitudinal Viewpoints To arrive at this objective, descriptive statistics in the form of mean and standard deviation was utilized; z-test, as an inferential statistics, was applied for testing the mean differences.

9.3 Main Findings: Summarization

405

• Aligned with the descriptive analysis and mean comparison of attitudinal and behavioural components, the findings indicated significant differences in the level ¯ = 4.177), of people’s attitude and behaviour. With a high average value of CSF (X ¯ = 4.239), NR (X ¯ = 4.117), and ACE (X ¯ = 4.093), the attitude of AMW (X people were observed as very much positive on these aspects, and significantly ¯ = 2.917) was consistent for their myopia regarding low mean value of OGM (X sustainable products. The mean values of all behavioural components were above 3.0; however, component SSC attained a high average of 4.237. So, it can be said that people actively engage in activities necessary for having a sustainable society (measurement of SSC) while on other environmental acts (responsible purchasing, responsible usage, minimizing wastage, and recycling intentions), they are not proactively engaged. As a reason for this dissimilar attitude and behaviour, it may be said that a high attitude on the part of consumers may be the people’s inner feeling as environmental problems are mounting day by day; but, behaviour many a time depends upon outside factors and when the things are beyond people’s control, they cannot behave in a manner aligning with their concern and desire. In India, situational factors also work, people are least environmentally educated, and they have less information about the ways for going environmentally responsible. The activities of SSC factor such as not using music systems or proper parking may be in their own control and these behaviours directly influence them; so, perhaps society is a more concrete aspect for them and the environment is conceptualized as abstract. Further, people’ high engagement in behaviour SSC and comparatively less engagement in other behaviours point out the thoughts of Steg and Vlek (2009), according to which behaving in one type of behavioural sphere may be consumers’ own choice while other kinds of behaviours may be thought of as challenging and a burden.

9.3.3 Objective 3: To Investigate Theory of Responsible Behaviour Formation by Empirically Testing C-A-C-B Model As per this objective, the ‘C-A-C-B (Concern → Attitude → Commitment → Behaviour)’ model as defined in the study was tested in Chap. 6 by using path analysis with Sobel test, Aroian test, Goodman test, and Bootstrapping. To verify the overall model, broadly, six hypotheses were tested for direct and indirect/mediating effects and the findings are as follows. However, there are eleven hypotheses in total if these six hypotheses are considered with their sub-hypotheses.

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9 Findings and Discussions

H1a: Concern for Sustainable Future (CSF) Has a Significant Positive Effect on Responsible Consumption Behaviour (RCB)

• As far as the question of impact of concern on behaviour was estimated, concern for sustainable future significantly determined all types of behaviours and had been observed as a robust predictor of sustainable societal activities (β = 0.362), followed by recycling intentions (β = 0.311), behaviour of minimizing wastage (β = 0.291), responsible purchasing behaviour (β = 0.282), and behaviour of responsible usage (β = 0.245). A significant but least impact of concern had been observed on the behavioural component namely, environmentally relevant activities (β = 0.204). These findings in which behaviour of consumers was found a function of their superior and noble concern matches Balderjahn (1988) for stating that attitude towards ecologically conscious living resulted into ecologically responsible buying and using of products. The reason for the dissimilarities in the predicting power of concern as is evident with different beta values in each behavioural type can be defined in statistical terms. Taking the two extremes, one with the highest effect (SSC) and the second with the least effect (ERA) of concern, the mean difference of CSF and SSC (Mean Difference = 0.060) is least as compared to the mean difference amongst CSF and ERA (Mean Difference = 0.652). So, it can be said that on a five-point measurement, the level of consumer concern and their behaviour of societal activities coincide and then the concern is highly influencing this behavioural kind. On the other hand, there is a wide difference in the level of concern and people engagement in environmentally relevant ¯ = 4.177), it is mediocre for activities. While the average value of CSF is high (X ¯ ERA (X = 3.525). Accordingly, CSF is able to highly determine SSC as compared to ERA.

9.3.3.2

H1b: Concern for Sustainable Future (CSF) Has a Significant Positive Effect on Attitude Towards Sustainable Living (ASL)

• The second hypothesis of a positive impact of concern on attitude also stood accepted for all attitudinal types. CSF was able to explain the highest (approximately 58%) variations both in ACE (β = 0.587) and AMW (β = 0.588). Above 50% variations were also explained in NR (β = 0.528), but CSF was slightly determining environmental thinking (βET = 0.163) and people’s attitude towards green products (βOGM = 0.074). The reasons behind this dissimilar power may again be attributed to the levels at which people are showing their concern and attitude. The level of concern making (CSF) is very much high as compared to attitude towards sustainable products (OGM) (Mean Difference = 1.260). So, concern is only slightly estimating this attitude. Conversely, the difference in the level of consumer concern with their attitude towards conserving ecology and for minimizing wastage (Mean Difference = 0.026) is less. Therefore,

9.3 Main Findings: Summarization

407

it can be said that remaining at the same juncture, both of the attitudes are firmly predicted by concern with identical determining power as defined.

9.3.3.3

H1c: Concern for Sustainable Future (CSF) Has a Significant Positive Effect on Commitment to Initiate (CI)

• The finding in response to this hypothesis obtained that concern for a sustainable future was significantly deciding the commitment of consumers for contributing towards responsible acts in all the behavioural domains. The beta coefficients were obtained high for responsible purchasing (β = 0.373), environmentally relevant activities (β = 0.351), and sustainable societal conduct (β = 0.310). However, for responsible maintenance and disposal domains, the coefficients were 0.278 and 0.263, respectively, which were slightly below from the coefficients of the abovesaid domains. Nevertheless, concern too defined commitment in ‘responsible usage domain’ (β = 0.229), however slightly below from other domains. Therefore, the inner commitment of consumers could be explained as circumscribed by their concern. If people would be concerned about maintaining sustainable living, then, they would be committed to responsible behaviour.

9.3.3.4

H2a: Attitude Towards Sustainable Living (ASL) Has a Significant Positive Effect on Responsible Consumption Behaviour (RCB)

• The hypothesis of prediction of behaviour from attitude was supported for the behaviour of responsible usage as determined by attitude towards conserving ecology (β = 0.232), behaviour of minimizing wastage as established by attitude towards anticipating mounting waste (β = 0.196), recycling intentions as effected by attitudinal component need for recycling (β = 0.190), and sustainable societal conduct when ascertained by civil attitude (β = 0.139). However, overcoming green myopia was not determining the behaviour of responsible purchasing, and environmental thinking was also found unable in determining environmentally relevant activities (βOGM = −0.03, βET = 0.009; p > 0.05). The contradictory and main finding that OGM does not influence people’s RPB is unique in itself. Spreading light, it was expected that if people will be less/not myopic about green products, this will confirm their sensible attitude and they will engage in buying such kinds of products. Contrary to expectations, sample results came out with the finding that the majority of Indian people are actually highly sceptic and insensitive about green products. The low mean value (significantly low mean = 2.92) implied negative thoughts which can demote responsible purchasing behaviour. When people are not able to overcome their myopic thoughts, how this

408

9 Findings and Discussions

so-called negative attitude can translate into the positive outcome of responsible purchasing. Attitudinal component environmental thinking, however, can be said as a non-responsive attitude. Although people at a moderate level think about the environment, the attitude is not predicting similar behaviour defined by the component environmentally relevant activities.

9.3.3.5

H2b: Attitude Towards Sustainable Living (ASL) Has a Significant Positive Effect on Commitment to Initiate (CI)

• Integrating results for testing of this hypothesis, all attitudinal dimensions were found significant determinants of consumers’ commitment to initiate except that OGM is weak in determining it (β = −0.02; p > 0.05). Component ACE was found robust amongst all with a standardized beta of 0.244, followed by NR whose power is determined by a beta value of 0.206. ET also has a significant but least impact on commitment (β = 0.129). This way the hypothesis of a significant impact of attitude on commitment is accepted but purchasing domain is an exception. The finding that OGM has no effect on commitment is not surprising rather clear with the finding in the above hypothesis. When the attitude of consumers is very much negative on this dimension, it can never be converted into a commitment for the same.

9.3.3.6

H3: Commitment to Initiate (CI) Has a Significant Positive Effect on Responsible Consumption Behaviour (RCB)

• It was observed that the hypothesis of the direct effect of commitment on behaviour was not supported in two domains. Commitment to initiate neither explained responsible usage (β = 0.039) nor behaviour of minimizing wastage (β = −0.039). Considering other domains, the predicting power of CI was found utmost in predicting RI (β = 0.239), ERA (β = 0.237), and RPB (β = 0.162). Quite least but the significant prediction of commitment was noted for sustainable societal conduct with a beta value of 0.137. The explanation of no direct effect of commitment on responsible behaviour can be explained on the ground of the content of the spurious effect (SE) in the total correlation. Although the total effect of CI on RUB (responsible usage) was significant (r = 0.218), large content of this effect was spurious (SE = 0.178), meaning that interaction between independent variables was stronger. On a similar ground, CI has no effect on the behaviour of MW.

9.3 Main Findings: Summarization

9.3.3.7

409

H4a: Attitude Towards Sustainable Living (ASL) Mediates the Effect of Concern for Sustainable Future (CSF) on Responsible Consumption Behaviour (RCB)

• It was emerged out from the analysis that the hypothesis of mediation by attitude between concern and behaviour was not supported for components OGM and ET (insignificant indirect effects), least supported for CA (Indirect Effect = 0.05), and highly supported for NR, ACE, and AMW with indirect effects of 0.09, 0.13, 0.11, respectively. Therefore, findings obtained that concern may or may not affect behaviour through attitude formation for any particular cause. If however, the result is not supported for OGM and ET, it is because both the components have also been observed powerless for their direct effect on behaviour. In this way, the conditions of mediation were not fulfilled.

9.3.3.8

H4b: Attitude Towards Sustainable Living (ASL) Mediates the Effect of Concern for Sustainable Future (CSF) on Commitment to Initiate (CI)

• It came out from the analysis that the indirect effects of concern on commitment through attitude were found significant in all the behavioural domains except responsible purchasing where OGM was not acting as a mediator in between concern and commitment (indirect effect was not present). The robust mediation was visible for attitude towards conserving ecology and the need for recycling, where the indirect effects were highly significant (Indirect effect = 0.14; Indirect effect = 0.11). With statistically less but significant mediating power anticipating mounting waste (Indirect effect = 0.09), civil attitude (Indirect effect = 0.06), and environmental thinking (Indirect effect = 0.02) also carried the influence of concern on commitment. Accordingly, it can be said that concern affects commitment through attitude formation. The outcome that OGM was not carrying the concern to behaviour is obvious, because, as per one necessary condition of mediation, the mediating variable should have direct influence on the dependent variable in which OGM lacks. Here, the condition of mediation was not satisfied, hence no mediation could become possible.

9.3.3.9

H5a: Commitment to Initiate (CI) Mediates the Effect of Concern for Sustainable Future (CSF) on Responsible Consumption Behaviour (RCB)

• It was extracted from the analysis that CI worked as a mediator between CSF-RPB and CSF-RI with the same indirect effect (Indirect effect = 0.06). The mediating

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power of commitment was highest in between concern and environmentally relevant activities (Indirect effect = 0.09). To some extent, it also mediated the direct effect of concern on SSC (Indirect effect = 0.04). Therefore, it can be said that concern goes into behaviour when the sense of commitment develops in people. However, the same is not true in the study of responsible usage and maintenance behaviour. For the two domains of responsible usage and maintenance behaviour, the reason for no mediation is the same as defined with the previous attitudinal variable OGM. There has not been observed any direct effect of commitment on behaviour; as the effect of the mediator on the dependent variable is a necessary condition of mediation and it is lacked in the model, the result of no mediation is apparent.

9.3.3.10

H5b: Commitment to Initiate (CI) Mediates the Effect of Attitude Towards Sustainable Living (ASL) on Responsible Consumption Behaviour (RCB)

• It was obtained that commitment was able to mediate the effect of need for recycling on recycling intentions (Indirect effect = 0.05), and to some extent effect of civil attitude on sustainable societal conduct (Indirect Effect = 0.02). In other domains, commitment was not mediating any of the attitude–behaviour links. This finding can again be attributed to a reason that only in domains of ‘responsible disposal and sustainable societal conduct’, both attitudinal components and commitment showed a direct effect. In other models, only one of the constructs either attitude (domains of responsible usage and maintenance) or commitment (domains of responsible purchasing and environmentally relevant activities) showed the effect. Hence, conditions of mediation were not fulfilled, thus no mediation could become possible.

9.3.3.11

H6: Attitude Towards Sustainable Living (ASL) and Commitment to Initiate (CI) Serially Mediates the Effect of Concern for Sustainable Future (CSF) on Responsible Consumption Behaviour (RCB)

• Testing the serial mediation, it was attained that attitude of consumers by converting into commitment was able to mediate the effect of concern on behaviour in three domains explicitly, ‘responsible disposal, environmentally relevant activities, and sustainable societal conduct’. In ‘responsible disposal’ and ‘sustainable societal conduct’ domains, the conditions of mediation were fulfilled; thereby, serial mediation was assuredly interpreted in these domains. In contrast, the attitudinal component environmental thinking as a mediator was weak in the domain of ‘environmentally relevant activities’. It exhibited no effect (highly insignificant)

9.3 Main Findings: Summarization

411

on behaviour; yet converting into commitment {on which its direct effect (0.129) was significant (p = 0.000)}, the power of mediation was shown. Indeed, serial mediation is claimed in this domain too but we lack confidence in contending mediation. Undeniably, the ‘Theory of Responsible Behaviour Formation (TRBF)’ was developed on conceptual grounds in Chap. 3 and tested with the hypothesis of serial mediation in different domains of behaviour allied with the suitable attitudinal dimension. The theory is acceptable in some domains but not in others. The reason may be the low to high levels of attitude, power of attitudinal components as mediators in the models, and makeup of low/high levels of different kinds of behaviour. Possibly because of this, in some domains both mediators were good in mediating; but, in some other domains, only one component was playing its role of mediation. • To fathom out, with testing of these hypotheses in objective 3, it can be concluded for the overall C-A-C-B model that the ‘theory of responsible behaviour formation’ is fully accepted for domains of ‘responsible disposal’, ‘environmentally relevant activities’, and ‘sustainable societal conduct’. In other domains, either attitudinal components mediate the concern–behaviour link or commitment does the same. Both attitude and commitment did not work simultaneously for mediation. Thus, the process remains partially true. This partial process came out as Concern → Attitude → Behaviour, for domains of responsible usage and responsible maintenance, and Concern → Commitment → Behaviour for the domain of responsible purchasing. With these findings, it is affirmed that responsible behaviour formation is not something immediate; it evidently depends upon concern which is the initiator and when transforming into attitude, commitment or any of the two, induce consumers for responsible acts with possibilities in their reach.

9.3.4 Objective 4: To Identify Consumer Segments as Per Behavioural and Attitudinal Dimensions As per the definition of ‘responsible consumers’ in this book, the analysis which corresponds to this objective was presented in Chap. 6, where cluster analysis was performed for obtaining the segments of consumers on the basis of their attitudinal and behavioural attributes. ANOVA with Scheffe post hoc test was exercised for testing the significance/insignificance of mean differences. • Three segments of consumers namely red, yellow, and green with least, mediocre, and high mean values on attitudinal and behavioural components respectively, emerged out from hierarchical clustering. Consequently, the findings presented the division of Indian consumers in these three segments, and the tags red, yellow, and green reflected risk, hope, and optimism. The identification of three segments

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is in line with Parker et al. (2004) and Albayrak et al. (2010). In line with Parker et al. (2004), the red segment matches with their segment ‘consumers’; the yellow segment takes the position of ‘steady middle’ group, and the ‘conserver’ group is visible with the green segment. However, Roberts (1995) and Gonzalez et al. (2009) attained four segments; accordingly, regarding the number of segments, these researchers are in contradiction, but are supported for the claim that there is a differentiated market of consumers and there exist diverse divisions of ‘responsible consumers’. Along the lines of mean analysis, consumers in the red segment were outlined ‘apathetics and imprudents’. The people here were characterized apathetic because of indifferent attitude and no concern; likewise, they were imprudents because of no interest and enthusiasm for environmental actions. People in the yellow segment were delineated ‘aesthetics and hopefuls’; aesthetics because somehow they were found concerned and having a positive attitude towards the means of sustainable living. Their environmental actions were slight encouraging and they might be expected to continuously perform their responsible behaviour, so named hopefuls. Finally, the green segment was associated with people who had been made distinct from the other two segments by defining them ’aspirants and illuminators’. To be called aspirants, the word symbolized their rationality, awareness, and affirmative attitude of maintaining sustainability. With their responsible actions, they light up and strengthen the way towards sustainability so at best called illuminators.

9.3.5 Objective 5: To Anticipate the Proportion of Responsible Consumers in Indian Market Here, discriminant analysis was worked out for the validation of cluster solution. While validating the cluster solution, a hit ratio of 98.3% was attained meaning that 983 respondents were correctly classified in their segments and rest of the 17 respondents were misclassified. Accordingly, data for 983 respondents were analysed to obtain the proportion of ‘responsible consumers’. • Finally, 133 respondents (13.50%) were placed in the red segment, 390 (39.70%) got a place in the yellow segment, while 460 (46.80%) respondents were kept in the green segment. So, it is a welcome sign that the detrimental red segment is an accumulation of just 13.5% of the sample, and a majority 46.80% care for the environment and perform with the sense of responsibility. In this way, the Indian consumer market is found partially constituted with ‘responsible consumers’ as their proportion is in majority amongst sample consumers (members of the green segment).

9.3 Main Findings: Summarization

413

9.3.6 Objective 6: To Analyse the Characteristics of Identified Segments, Their Profiles, and Distinctiveness Accomplishment of the purpose was completed according to consumers’ demographic, sociological, cultural, geographic, economic, and personality dimensions. Chi-square with Cramer’s V was applied as a test of checking association/dependence. Proportional analysis with z-test for the difference between two proportions was utilized for obtaining segment membership and profile characteristics. • Based on the results of the Chi-square test and Cramer’s V, it was realized that demographic, sociological, cultural, geographic, economic, and personality variables significantly associate with segment membership. Accordingly, it was obtained that membership of consumers into red, yellow, and green segments differed according to these attributes. Only the variable family size was found to be insignificant and the significance of variables, type of family, religion, and religiosity, was claimed on a stringent level of 10% significance (0.05 < p < 0.10). • The analysis for profiling concluded that the red segment incorporated young and middle-aged males having up to college-level education. They were from business and arts subjects but academically their performance was poor. They were the nonearners and as designated by their marital status, may be married or unmarried. Family type, size, and structure had no influence but respondents underlying the red segment attained very low support from their family. Geography supported them as non-commuters rural people and if they commute, they only walk or ride a bicycle to their destination. Cultural traits defined them as followers of the Islamic religion with less religious strength. According to economic standing, the people here either belonged to low- or high-class group, did not own their houses, and live in rental dwellings. As per personality features, they were also not encouraged by affirmative personality features. • The yellow segment included young females who according to education might be less or highly educated, and belong to business and science subjects. Their academic performance was also found much better than people in the red segment. These females were non-earning and get extended support from their families. They might be unmarried, newly married, or married having kids. Geographical dimensions stated that they belong to urban living areas. Considering personality features, they were found less objective-driven, not much socially concerned, encompass little self-enhancement, and somewhat also lack emotions with attaining least scores on emotionally oriented traits. • As can be inferred, the basic image of ‘responsible consumers’ emerged from demographic determinants was that of the aged, highly educated, academically insightful, non-business academics, the earning people, married and having children. Gender differences were least relevant in the green segment. Established by sociological determinants, the people were the members of joint but mediumsized families. In their family structure, the number of male members was either greater than or equal to female members. In terms of age, the majority of family

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members were originated as mature or young. The type of consumers also comprehended with high support of their family members. In relation to economic determinants, ‘responsible consumers’ came out as not wealthier. Geographical dimensions hold no notable influence in this segment. In the cultural category, people were found to be followers of Sikh and Hindu religions. Personality traits further conferred that people here think objectively, give importance to society, are self-guided, and believe in collective working. To visualize major characteristics that differentiate consumers in one segment from another, the findings are presented concerning each determinant category.

9.3.6.1

Demographic Characteristics and Differentiation in Segments

• In accordance with the first finding, ‘responsible consumers’ may be both males and females. The literature encountered with hefty discussions regarding these gender differences, and a large number of studies favour females and put them under the responsible category. Indeed, studies also exist which support men. On the part of females, several studies (Straughan and Roberts 1999; Zelezny et al. 2000; Laroche et al. 2001; Tindall et al. 2003; Budak et al. 2005; Oikonomou et al. 2009; Alibeli and Johnson 2009; Lee 2009; Singh 2009; Singh and Gupta 2011) are in support. Regarding males, the studies conducted by Hunter et al. (2004) and Xiao and Hong (2010) provide assistance. A huge discussion is presented in the literature regarding gender differences in environmentalism (Singh and Gupta 2013). Some studies favour females and other support males. This outcome can be attributed to a range of reasons as happened in the literature. The fibre of responsibility of females may be defined because of her motherly care, compassion, co-operation, and selflessness. The gender socialization process as mentioned by Zelezny et al. (2000) holds well in India. Girls from their childhood are taught to look after their families and care about the members. They are brought up and internalize their motherhood mentality (McCright 2010). This works for the enhancement of their environmental attitude which in turn leads to the performance of environmental behaviours. On a second aspect, as per McCright (2010), as men have more representation in hard carriers like science and engineering, they have greater scientific knowledge about environment and climate change; thus, being knowledgeable they perform responsibly. In spite of all discussions of gender differences, in the present research, both male and female attain a position in the responsible category of the green segment. Accordingly, the finding gets espousal of the academics like Engel et al. (1993) who mention that there has been a dramatic decline in gender differences in the recent era. Biehl (1991) is also relevant to be quoted who criticize the eco-feminist theory for excess focus on mystical relation between women and nature and not on the existent conditions of women which becomes necessary to examine.

9.3 Main Findings: Summarization

415

• The next finding fostered aged respondents being more responsible for their young and adult counterparts. This is in agreement with Shanka and Gopalan (2005) and Oikonomou et al. (2009) as they suggest that people turn conscious about societal aspects as age increases. Rikner (2010) is also supported for pointing up younger borns for their pro-environmental behaviour than later borns. However, results by Straughan and Roberts (1999) and Singh (2009) are contradicted for publicizing younger groups for their socially responsible behaviour. This result can be defined based on two main arguments: the life cycle effect and cohort effect given by Torgler and Garcia-Valinas (2007). A life cycle effect which is tantamount with ageing effect is there due to consumers’ being at a certain stage of age, and the cohort effect is the result of belonging to a specific generation which covers the attitudes formed in different ages. This implies that people in a generational cohort experience similar historic and economic conditions and face-related restrictions and possibilities. On a similar ground, the group of middle and aged people is apparently in the green segment because the respondents in this age cohort have experienced the same economic and environmental conditions in India. There has been a rapid change in the environment in recent times and the people in this age group can easily sense the level of environmental deterioration in the present times over past times, which is crossing all the limits. Opposite to this, the group of young adults remains behind due to their birth order. They are born when there are sufficient technological developments in India, and have rarely experienced the previous conditions of scarcity and draughts. Technology has provided them with an abundance of things of their choices. Perhaps because of this reason they term environment as a tool to fulfil their needs and are not in a part of the green segment. From another perspective, concern of the young section of society may be less because given the high number of two-carrier families and single parents, teenagers often shop for themselves and they are alienated from society. They are only characterized by good grades and high carrier aspirations. They only tend to find success and achievement in being at the cutting edge of technology and try to balance only their own work and personal lives. • An obvious result of the literature once again became evident with the finding in favour of highly educated who were originated members of the green segment with maximum share (% = 59.1). Various studies (Laroche et al. 2001; D’Souza 2005; Tilikidou and Delistavrou 2007; Alibeli and Johnson 2009; Urban and Zverinova 2009; Xiao and Hong 2010; Chen et al. 2011) hold up that education positively influences green behaviour and educated people are more likely to show it over less educated; and consequently, are consistent with the finding. However, some authors (Shanka and Gopalan 2005; Oikonomou et al. 2009; Singh and Gupta 2011) are contradicted for obtaining the negative effect of education in their results. As a probable reason, it can be said that education is the most powerful weapon that transmits values to consumers. Obviously, the well educated individuals who know

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about environmental problems must have strong support of environmental protection as they are better aware of the possible damage. • A specific feature which emerged out from the results stated that ‘responsible consumers’ have studied arts and science subjects in their education. Finding of Singh and Gupta (2011) assists this outcome by favouring people who have studied humanities, and the finding of Tan and Lau (2009) is contradicted, being in favour of business students. Arts and science people were obtained higher on environmental parameters than business students. The reason may be that since long, only profitability was the subject matter of the businesses and the same was the educational curricula. Now replacing the old theories, the new concepts of business and marketing signify that the survival in the market will not be possible unless and until the social responsibilities of businesses will be satisfied, and a dimension of this social responsibility deals with environmental protection and sustainability (Rahman 2011). Accordingly, the business study area is also enlarging and the B-schools in India are aligning the stream with subjects like social responsibility and environmental management in anticipation of an affirmative change. These optimistic transformations are making themselves visual in the present finding as business students are a part of the yellow segment with considerable share that is 46.9%. • For their responsibility, an outcome on the variable academic intelligence came out in favour of academically insightful people. The study by Singh and Gupta (2011) is preferred for having a direct relationship between academic intelligence and attainment of responsibility. The results in favour of academically insightful people state that intelligence level affects intellectual level. This result simply points out that educational qualifications are important but the psychic efficiency that is attained during the educational process is more vital. There is a difference between those who are just ‘educated’ and those who can be called ‘educated effectively’ and the same difference is noticeable here. • Pronouncing with marital status and parenthood, the result obtained married people who were parents in the segment of responsibles. There are mixed findings in the literature for this variable. Where Chen et al. (2011) is contradicted for bolstering unmarried respondents, the study by Mondejar-Jimenez et al. (2011) provides support for aligning towards married people. The particular cause of the sense of environmental responsibility of the married respondents was defined in the literature as the ‘parenthood effect’. To be specific with females, the parenthood effect and the motherhood mentality both work when this mentality actually changes to motherly care. The same is applicable to fathers, as their responsibility is to provide safety to their family in all the ways; it makes them prepare to look out for the welfare of the environment; ultimately, for the sake of their children.

9.3 Main Findings: Summarization

417

• Located from the years of marriage, parents of teenagers and families where children live separately fall amongst responsibles. Anderson and Cunningham (1972) is supported for studying a similar variable ‘family life cycle stage’ as a determinant but this factor fails to significantly discriminate respondents as to the degree of social responsibility in their study. Hence, for the same they are disagreed with. The probable reason for parents of teenagers might be their care and concern for the future generation as their own children will be a part of that. However, the next counterpart having adult kids might be free from their children’s responsibility and now may have extensive time by which they can contribute to responsible acts, and can think as to how the contribution towards sustainability can be enhanced. • It can be concluded that being placed in the green segment, working class showed more responsible attitude and behaviour. This result promotes the matching findings by Gupta (2010), McCright (2010) and Chen et al. (2011). There may be two probable reasons for the result. First is the same, the lifecycle and cohort effect as explained earlier in age, because the working class is matured as compared to their younger nonworking student counterparts. The second may be financial support with earnings which is the power of earners than non-earners.

9.3.6.2

Sociological Characteristics and Differentiation in Segments

• Based on the next finding, membership in joint families appeared out as a special feature of ‘responsible consumers’. The explanation of findings can be extended with the influence and experience of persons when they are members of nuclear or joint families. Generally, in nuclear families both the parents remain working and have less time for their children. As a reason, the child gets mentally separated from their family. However, opposite to it, family members devote more time to each other in joint families and beyond the influence of only parents, the influence of other family members becomes available for the children. When we turn the eye on the foregoing days in India, most of the families were joint, and children spent almost the first 6 formative years of their life within home and family values then get transferred in them. Now, as most of the families are geographically separated from grandparents and other relatives, the values of children who will eventually become our future leaders depend heavily on sources outside the family. The result of age can again be reinforced with this result as middleand upper-aged people have been a part of joint families and our young generation mostly belongs to nuclear families. The youngsters are behind the older because they are being debarred from this process of value transmission, and then they lack the relation with heritage and yearning for roots. • Association of respondents with medium-sized families further turned out a special attribute of ‘responsible consumers’ which is compatible with Singh and

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Gupta (2011). According to them, small families show high socially responsible consumption behaviour than medium or large. The rationale in favour of medium-sized families can be given on the ground of the health concerns and well being of the family from the adverse consequences of a deteriorating environment. However, it is true too that humans can think for the society only after the family. A very big family and then rising personal problems hardly annoy them for rational thinking to do something for others or society beyond conventional. The same justification may apply for the finding of large-sized families which are not covered amongst responsibles. The foundation for the conclusion of small families has already been discussed as it contrasts with the above result of nuclear families. • Given by family structure in terms of gender composition, members of those families where male composition is either high or equal to females came under ‘responsible consumers’. By stating in terms of age, the families of ‘responsible consumers’ were of the type where the number of mature and young members both may be more. Parker et al. (2004) is supported for studying a similar variable. Their result stated that non-conserver consumer group had children and teenagers in their family composition than the group of conservers. • Household support notably obtained people who get high support from their families in the profile of ‘responsible consumers’. Kennedy et al. (2009) is agreed with for stating that family support is very important and without it green behaviour gets restricted. The finding in a very deep manner implies that support of relations and personal dynasty have always been considered as a social security blanket, and this support can too work as a safety coverlet for the maintenance of environmental sustainability.

9.3.6.3

Geographic Characteristics and Differentiation in Segments

• The insignificant results of z-test highlighted that ‘responsible consumers’ may reside both in rural places or urban ones. Budak et al. (2005), Kiraci and Kayabasi (2010), and Singh and Gupta (2011) all collaborate in favour of rural people, and Chen et al. (2011) is supported for stating that residents of urban areas put hands in environmental activities. This finding may be regarded based on some logical grounds. Rural people are more close to nature in their everyday activities and religiosity amongst them may also be the basis for their responsible acts. The environment is called ‘Mother Nature’ in Indian villages and is worshipped under different rituals and festivals. Their high connectivity and religiosity become the cause of their high concern. However, urban living, what is called the ‘lifestyle’ is much more different from rural areas and our civilized society is engaged in polishing and decorating the cages in which humanities and carefulness will be kept imprisoned. It may only be the effect of education and

9.3 Main Findings: Summarization

419

other opportunities due to which they get a substantial share in the green segment and also give hope by taking the highest part in the yellow segment. The results can too be contrasted with the finding of the ‘type of family’. Though conventional family structures are breaking down and joint families are few in urban areas, values, traditions, and norms laid down by family may be inherently visible in the mature family members even while staying in nuclear families. • In radiance with the analysis, commuting has no noteworthy effect. The proportionate shares were found statistically symmetrical for non-commuters and private–public vehicle commuters. Accordingly, Walton et al. (2004) provide assistance to the finding.

9.3.6.4

Economic Characteristics and Differentiation in Segments

• Stated by the finding, ‘responsible consumers’ belonged to middle-class families who were found middling in earnings. Studies of Laroche et al. (2001), Alibeli and Johnson (2009), and Singh and Gupta (2011) confirm that the middle class express strong support for the preservation of the environment, so these studies are consistent with. However, Tilikidou and Delistavrou (2007) and Torgler and Garcia-Valinas (2007) are opposed for supporting high class for their environmental behaviour. In line with the reason given by Singh and Gupta (2011), the matter of less social responsibility with the rich may be uselessness; most often this results in carelessness. The matter with the low-class families is obviously the lack of financial resources; because the hard-living circumstances and tough situations through which they pass set a limit for environmental activities to them. Indeed, as high-class families obtain membership in the yellow segment, the argument of Torgler and Garcia-Valinas (2007) is worthy to quote that high-income groups get least pressurized by economic problems. So, they can support all the steps for the protection of the environment and solve environmental problems, maybe it is a starting in India too. • Home ownership significantly separated responsibles from their counterfeits and owners of houses turned up as the members of the green segment. Laroche et al. (2001) is going against for stating the variable as insignificant. The result of home ownership may be closely related to the family type. With the separation of families, there has been a rapid trend of living in rental houses; so the households of rental houses may be nuclear families. Contrary to it, a majority amongst those who live in their own houses may belong to joint families. This gives an important characteristic to the ‘responsible consumers’ that the basic need of shelter is fulfilled for them. So, they can think beyond their physiological need and contribute to society. The reason stated by Mostafa (2007) is also substantial who quoted that concern and responsibility towards the environment is a post-material

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value that develops amongst people once the more basic needs of food and safety have been met.

9.3.6.5

Cultural Characteristics and Differentiation in Segments

• The finding for variable religion popularizes followers of Sikhism and Hinduism in the group of responsibles. The variable was found significant and the finding is in tune with Dietz et al. (1998). Anuar et al. (2012) too can be supported for maintaining that religion significantly influences consumers’ intention to purchase ‘cause-related products (a dimension of socially responsible products)’. Mondejar-Jimenez et al. (2011) again assist in mentioning that religious participations are related to positive behaviour in social matters. It has been accepted that Christians’ religious values negatively influence the formation of environmentalism in the Western society and this religion has a correlation with lower environmental concern (Kim and Kim 2010). Further, the result is based upon the opinions of Guth et al. (1995) that Confucianism religions stress not only on anthropocentrism but on an organism worldview also. • Given the result, high religiosity makes people highly responsible and members of the green segment. Guth et al. (1995) provide assistance to the result with the notions that environmentalism differs according to the strength of religious commitment. According to McCright (2010), religious strength or religiosity can affect environmental behaviour indirectly via environmental concern. Religiosity is also seen impacting consumers’ tendency to purchase and use environmentally safe products and a more religious consumer too comes out to be supportive of CSR initiatives of companies that help the needy, victims of disasters, and avoid buying from those who discriminate against minorities (Lau 2010). Accordingly, all these studies have been collaborated with the finding. Dietz et al. (1998), however, go against the result for stating that religious strength is not significantly related to pro-environmental behaviour. The explanation is quite obvious; the religions of Hinduism and Sikhism stress that nature is the reflection of God on Earth, and with many religious stories devotees believe and consider nature as their appetizer. So, high religiosity denotes that people have high commitment and faith in religious values; when their religion does not permit them free riding and disturbance with nature, people cannot dare to do the tasks outside their religious premises.

9.3.6.6

Personality Characteristics and Differentiation in Segments

• Findings for personality domain indicated that the people who were more objective-driven, social-driven, self-directed, and emotions directed in their life behave responsibly. A number of studies stand up and sustain these findings.

References

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• As ‘responsible consumers’ were found objectives driven, the result by Antil (1984) can be sustained for defining socially responsible consumers as more confident in personal ability and as those persons who take direct actions. • The finding for social directed individuals match with the finding of Triandis (1993), McCarthy and Shrum (1994), and Kim and Choi (2005) as they all established collectivists to be friendlier to the environment while individualists were found unfriendly. Anderson and Cunningham (1972) provide support for obtaining socially conscious consumers as those individuals who were less alienated from society. According to Kinnear et al. (1974), Webster (1975), and Karp (1996), ecologically concerned consumers are open to new ideas and in line with Gilg et al. (2005), conservatives are less engaged in green activities, accordingly providing support here. As per Owen and Videras (2006), civic-minded individuals are more likely to participate in the goals and efforts of social movements than free riders; thus they again are assisted. • In line with the next finding of emotions directed traits, McCarthy and Shrum (1994) address that people having positive emotions of fun and enjoyment positively performs recycling behaviour. Given by Mondejar-Jimenez et al. (2011), the persons with more solidarity or harmony in their personality recycle more and also engage themselves in environmental events. Both the findings favour emotions directed individuals to be responsibles; thus these studies provide a base to present results. • The finding of self-directed individuals matches with Tilikidou and Delistavrou (2007) for mentioning that those who think they are capable of shaping circumstances rather than being shaped by them are environmentalists. Schwepker and Cornwell (1991) can well be afforded for espousing people with internal locus control (persons who can control their behaviours). Berger and Corbin (1992) and Laroche et al. (2001) also hold up people who have faith in their self-efficacy. As stated by Finisterra do Paco and Raposo (2008) and Kennedy et al. (2009), consumers with high perceived behavioural control have more intense environmental behaviours than others. Accordingly, all these studies are consistent with the present finding. In essence, this chapter summarized the major findings which became apparent from analysing the data. At many places, the results matched with past studies; however, when compared with the literature, some outcomes were contradictory. The probable reasons for the specific outcomes were discussed and many assumptions were also kept by keeping in mind the Indian context. Whatsoever, the findings as appeared have several implications for different sections of society. Indeed, inherent in these findings, useful directions can be given for carrying out further research. With these views, the next chapter corresponds to the implications and directions emerged from this research.

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Chapter 10

Implications and Research Directions

Based on the findings, the chapter provides considerations and implications to marketers and policymakers (Sect. 10.1). It points towards some indispensable concerns needed from society. Besides, certain future research directions are also offered (Sect. 10.2).

10.1 Recommendations and Implications 10.1.1 Recommendations to Marketers There are several inferences of the findings according to which strategies are recommended for successful management of marketing activities. Here, implications are segregated in line with significant activities that a marketer has to perform. Strategies for Market Segmentation • The results prove that marketers can easily divide their target consumers into groups and can effectively apply STP (segmenting, targeting, positioning) model in accordance with consumers’ sense of ‘concern for sustainable future’, ‘attitude towards sustainable living’, and ‘responsible consumption behaviour’. • Green marketers and businesses can frame and flourish different marketing strategies for the distinct consumer segments as identified in the study. They can communicate with these groups differently as per their requirements. Instances are provided as below: Green Segment: Since people in the Green Segment are self-enthusiastic, they only need to be reminded that their attitude and behaviour for maintaining sustainability makes a lot of difference. Green marketers or the companies which rely on green © Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0_10

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repositioning efforts can derive financial and other benefits by positioning this segment, and offering practical solutions of sustainability to them. Yellow Segment: Members of the yellow segment need a bit of promotional efforts/motivation so that they take responsible actions, and share the burden of environmental responsibility. They may be further encouraged by providing them with more opportunities for environment protection. Red Segment: Consumers who are placed in the red segment requires substantial empowerment through which they can be motivated. They need more push-up as people in this group have a very low level of attitude. Some type of promotional campaigns and rewarding proposals may be organized to elicit their attitude and put them on the line of a sustainable track. Marketers who rely on mass marketing in case of green products need to understand the rationale for concentrated marketing (concentration on the green segment) and differentiated marketing (differentiated offerings for the three segments). The findings of this research offer a unique strategy to green marketers entitled as SLG (●Stop, ●Look and ●Go) strategy. ●Stop: The stop strategy is for the red segment. Marketers may stop promoting green products to this segment as they first need sensitization in terms of greenness. Time, financial and other resources can get wasted by including them in the common promotional programs. ●Look: This strategy is for consumers of the yellow segment which may prove potential green buyers. Hence, marketers can look at them in the hope of their responsible actions and can include these people in promotional programs. ●Go: Obviously, the word ‘go’ stands for the green segment. Green marketers can easily go for the marketing of green products to consumers of this segment with minimal efforts. The results prove that all categories of determinants significantly affect consumers’ membership into segments. Now, it is on the part of the marketer to choose either demographic segmentation or geographic segmentation. The decision must be taken based upon the nature and usage of marketers’ offerings. Going with survey results, it can be said that ‘responsible consumers’ hold major share amongst Indian consumers. Therefore, marketers’ need a comprehension on the profile of this segment as identified in the study in terms of aged, highly educated, academically insightful, non-business academics, earning people, married and having children, members of joint but medium-sized families, followers of Sikhism and Hinduism, and who belong to the middle class and live in their own houses.

Strategies for Implementing Marketing Mix • One implied finding in this study highlights that people require some product or technique by which flowing water from roof tanks can be harvested. Indeed, certain types of products for the same are available in the Indian market but need

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modifications to be used beneficially. It provides marketers with a cue to develop such green products that suit this requirement. The Research and Development departments of companies can work in this direction. Before framing plans to develop a new product, marketers must know about consumers’ views and knowledge regarding green products. This task will create curiosity amongst consumers in the offerings, and marketers can then easily capitalize that interest. Findings prove that green segment constitutes a large part of the sample. Thus, it can be anticipated that the acceptability of sustainable products may increase in the marketplace. Therefore, ecological compatibility can be developed and capitalized as a strategic advantage by marketers. A significant finding points up that consumers think of green products as high in price than the value they offer. So, marketers have to maintain compatibility between price and quality. In the Indian economy, where low-income segment constitutes a significant part of the population, it is obligatory for marketers to provide lower priced alternatives of products which can be afforded by consumers. Some people said that they face difficulty in locating green products in Indian markets. Thus, a complete and updated knowledge must be provided to them. It points towards appropriate strategies for the distribution mix. Many products can get failed in the market because of less or no interest of consumers. So, test marketing should be in use by every marketer as a strategic weapon before full-scale production and distribution.

Strategies for Studying Consumer Behaviour and Consumer Education • The findings on determinants highlight that ‘responsible consumption behaviour’ of consumers can be determined by multiple factors. In this manner, for the understanding of consumer behaviour there is a need to first understand the determinants which drive such behaviour. • Certain behaviours may be difficult to perform and some may be easy. So, one consumer may not be responsible for all aspects. Some may initiate to conserve but may not be so good for recycling. Thus, marketers can address rightly by understanding the difficulty which consumers face in performing responsible behaviour. • Marketers may obtain different kinds of responses from consumers regarding the purchase of green products. At a point of time, some consumers may perhaps buy energy-efficient products and at the same time the products made of recycled material are denied. It may be because the latter case brings no immediate benefits such as a reduction in electricity bills in the former case. Thus, marketers require a comprehension of the buying motives of individual consumers or group of consumers. • Due to the gap between consumer attitude (saying) and actions (doing), marketers are advised to follow the actual behaviour of consumers such as their past actions and not to rely just on dictums.

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• Consumer behaviour is so dynamic that for a new choice, lots of products lose their usage appeal and accumulate as waste even before their natural life expires. However, the optimum use of a product contributes to waste reduction. Real-time data and marketing research can identify the frequent changes in consumer tastes and can guide product development. Besides, re-usage and recycling should also be promoted. • Communication with consumers (specifically commuters) must be with appropriate media planning and scheduling. Online and social networking marketing may be today’s good options for the same. • The benefits of a sustainable lifestyle get collectively accrued in future; but, often consumers do not want to compromise on their present ease and comfort. So, advertising messages should stress on future-oriented and rational appeals that their present unsustainable actions will harm their own survival on the planet. • As a motivation, a rewarding system may be elicited. Consumers may be encouraged by the rewards that they can get in turn with their green choices, for which they may be rewarded as the best consumer of the month from time to time. As a result, they may become a part of the sustainability arrangements. • The foremost important task for marketers is to educate consumers about green products, green claims, and eco-labels. The way in which the products are used significantly contributes to the overall environmental impact of these products. • Significant low mean value of component Overcoming Green Mypoia (OGM) leads to green consuming barriers for consumers and ultimately creates hurdles in green activities of corporations. There is a requirement that these kinds of myopias of people can be removed.

10.1.2 Implications for Public Policy In radiance with findings, it is crucial to know, what government and policymakers can do regarding sustainability. Here, the following points offer some guidelines. • Education and academic intelligence are found to be the main factors which distinguish between red and green segment. Accordingly, both government and non-government organizations should formulate and promulgate educational and user-friendly strategies to sensitize public environmental concern and knowledge. When they come to know about the delinquencies of their unsustainable lifestyle, the acceptance of green alternatives may become easy for them. • It is crucial to inform consumers about choices of products, transportation, and leisure patterns that support environmental sustainability and establish a better quality of life. Such facilities can be made available to practicize responsible behaviour. • The awareness of any problem can become a strong support and a weapon for its removal. For the same, policymakers’ may benefit more by making the general public informed and updated on current environmental issues, problems, and the

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possible ways of their solution. The government may also discourage unsustainable behaviour by imposing laws and regulations where required. For the same, concepts like ‘environmental tax’ and ‘polluter pays’ can be strengthened in India. The policymakers can play a vital role by introducing environmental subjects as part of the educational curriculum. However, environmental education has been enlarged in the school and college education curricula but there is a strong case for its improvement by enforcing the same in practice. There must also be efforts to increase environmental knowledge amongst the masses. Provision for recycling amenities and proper parking facilities should get due attention from policy planners. It cannot be denied that poor people always think about their survival and their thought only sticks around satisfaction of their physiological needs. However, for sustainable development collective efforts are required. The government is doing well with its plans for removing poverty and unemployment. But there is still much to do to promote the living standard of people with developing concern for environmental conservation. The finding on variable religion suggests that religious values can also promote environmental sustainability, thus must be endorsed amongst masses. The religious leaders and institutions can become instrumental in this sphere by coordinating and standardizing such values. There is a need to create awareness for a clean, green, and sustainable living. It can be done through seminars, workshops, exhibitions, or other similar options. Street plays can be organized for less-educated people. This may help in forming their favourable attitude which can lead to favourable behaviour. Also, consumers often lack knowledge or may have an abundance of contradictory information which misguides them. Therefore, information with its updates is also a vital and obligatory part to be performed well. The finding can become a base for the mission of ‘Clean India or Swach Bharat’ and ‘Smart City’ concepts which have been introduced in India recently. In radiance with the findings, policy planners for the above causes can become more operative.

10.1.3 Suggestions to Different Societal Sections • The main cause of unsustainability is chiefly anthropogenic, so it is the human consumption behaviour and their lifestyles that must be altered and controlled. Consumers as part of society can play a big role in enhancing green practices; thus, they need to take initiatives and revolutionary actions regarding protection of the environment. If consumers are more willing to help solve environmental problems, there will be less need for government interventions.

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• As learners and future caretakers of the society, youngsters need to develop a sense of responsibility in them. In this regard, the media can play a significant role in providing social advertising and information. • People need to understand that though they have an abundance of choices, and are living in a technological era, any success cannot sustain for long if it climbs upward on the expense of society and environment. In the long run, the future generations will be the sufferers. • Individual level of concern is necessary for having a healthy and sustainable future along with the acceptance of personal responsibility. But, it became clear from the results that less support from family hinders people’ responsible acts. So, all the members in any household in our civil society must think about it. • Open negotiations may be instigated amongst government, industrial sector, and households to recognize their duties towards the environment so that they can adopt appropriate behaviour. • In India, often women face household barriers and social restrictions in going environmental. So, there is a strong case for different sections of society to provide women with prospects and opportunities as they can prove the most powerful helping hand for environment protection and sustainability. • There is a need to enhance environmental values; because these values help in making people concerned and responsible for the environment and society.

10.2 Limitations and Further Research Directions 10.2.1 Limitations To a greater extent, most relevant points were considered and all possible efforts were made to maintain objectivity, validity, and reliability of the findings. In spite, there may be certain caveats as described below which must be kept in mind while generalizing the results. • Although the data collection instrument (the questionnaire) was prepared with great care and it was properly ensured that the respondents must understand the statements exactly as were directed to them; however, measurement error and errors (if any) due to misunderstanding of the survey items cannot be eliminated. • Given the nature of the topic and the measurement of attitude and behaviour through self-reporting, it is not possible to rule out the chance of socially desirable response patterns of some of the sample respondents. • The sampling area is restricted to North India specifically to the State of Haryana, National Territory: Delhi, and Union Territory: Chandigarh. To make more generality of results other states could also have been included.

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• The sample, however, is sufficient but was restricted to 1000 respondents. As the results are based upon a sample survey and not on the census, sampling errors cannot be denied. • Cross-sectional survey data were used which might put a limit on the true cause– effect relationship between attitude and behaviour that could be obtained with longitudinal designs. • The behaviour of consumers is flexible and not static; the findings do not articulate and determine if the responses of people may differ over a period of time. • Data have been analysed with a wide range of statistical techniques. The limits within which these techniques operate, themselves put limitations on the generalizability of the results. For example, The technique of SEM is sensitive to sample size. Hence, as sample size varies results could also differ. • Techniques such as principal component analysis, cluster analysis, and discriminant analysis have multiple methods. The choice of any one method depends upon a range of factors. The results may be different if any other method dissimilar to as utilized in the present research is employed in further studies. • Even though the findings of this research match with the literature and confirm to external validity, certain results may relate only with some particular consumer markets having similar sample profile, hence, cannot be generalized to a larger extent.

10.2.2 Directions for Future Research • The area of research in this field is burgeoning in India and the opportunities, not only in India but all over the world, are plentiful. The present research is worthy of becoming a springboard for future researches with its theoretical and practical importance. Consequently, this invites a number of avenues for further research. • The past studies on ‘responsible consumers’ and ‘responsible consumption behaviour’ were fragmented due to copious terminology and backgrounds. This study integrates and synergizes the literature to form a new construct. Thus, it provides a platform to further develop this subject. Also, in order to avoid any inconsistency and confusion the researchers are directed to remain precarious in properly using the appropriate terms for their measurements. • Since it has been proved that specific responsible behaviour types are a result of the formation of that kind of attitude, it is being possible to develop which kind of ecological behaviour we desire from people. By this way, we can transform our society as we desire. This research is a step in strengthening the literature for the formation of such theory. • Even if the constructs as used in this study reached acceptable levels of validity and reliability, a few constructs also revealed them really to the necessary level. The findings of these constructs can again be tested. Establishment of ‘Scale Norms’

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•

10 Implications and Research Directions

will be of immense significance. A significant point of fact is relevant which is about verifying the predictive validity of the scales. Identification of different components of attitude and behaviour imply that both are multidimensional. People can perform well in one dimension while others may not behave well. Similarly, their attitude may be favourable for one aspect but not for others. There must be endeavours to find out reasons for dissimilarity in behaviours. The C-A-C-B model that is used and tested in the present research is based on four components in which mediating variables in the models only partially mediate the link between independent and dependent variables. This clearly indicates that there may be other variables which may work as mediators in the process of behaviour formation. Further studies can search for the same variables. Despite the insignificant direct effect of ET on ERA, indirect effect in serial mediation was found significant for ‘environmentally relevant activities’. This finding needs more assessments and can be cross-examined in research studies about whether or not the claim of serial mediation in this domain is customary. What should be done when a researcher encounters such situations? The research model incorporates and studies the effects of mediator variables. Indeed, there may be a range of variables which may act and affect as moderators and suppresser variables. Accordingly, the concern–behaviour gap may also be found out in terms of the moderator effect. There must be endeavours to find out reasons for the gap in consumer attitude and behaviour, and how this gap can be eliminated. Regarding differences between attitude and behaviour much could be learned from longitudinal studies. The findings may prove useful for the creation and maintenance of projects like waste management, green energy, and green electricity in India. The study entails certain selected behaviours in each domain, however, there may exist other types of behaviours which have not been analysed in the study, and other researchers can explore them. The process of behaviour formation starts with concern. Factually, concern itself is said to be a function of many other variables like values, beliefs, norms, etc. These variables are not included in the study and there is no antecedent assumed for concern. Further studies can be initiated in this direction. The finding regarding variable gender puts a question on former gender socialization and math-science education principles. Today, as educated women are venturing out from their homes for similar jobs with men, their role in homes and society is changing. So, more research is needed in this area with young samples particularly in developing countries like India where the situations are just changing pace with the modern era. New studies may endeavour to discover ways of getting favourable environmental actions of the red segment of consumers. Lack of environmental knowledge and awareness may be a reason; so, environmental knowledge and opportunities to behave environmentally must also be studied separately in India.

?

Green Segment

Moderate

?

Yellow Segment

?

Red Segment

?

?

High

?

Low

Fig. 10.1 Unidentified segments: for future consideration

435

Continuum as Per Behavioural Characteristics

10.2 Limitations and Further Research Directions

Low

Moderate

High

Continuum as per Attitudinal Characteristics

• Of great significance for subsequent research would be the extension of knowledge about constraints of ‘responsible consumption behaviour’ like contextual factors. Subsequent researches may entail more interdisciplinary cooperation for the same. • One important area of allied research is the investigation of how values of Indian consumers are changing and then the consequences of changing lifestyle can be tested for environmental and sustainability context. • The segments as identified in the study are shown at the diagonals in the adjoining Fig. 10.1 but future studies can investigate the answers if any for the question mark areas. Any other sample consumers can be tested for the same. • The study profiled segments of consumers on the basis of a range of specific consumer characteristics; but, product attributes also play an important role. Practically, segmentation may be a combination of these different consumer accreditations in consideration with the nature of products. Further research, in turn, can respond to the preferred answers to marketers by assimilating both kinds of information. By conceptualizing ‘responsible consumption behaviour’ from domain-specific perspective, and by incorporating their corresponding attitudinal antecedents, it is hoped that this deliberation can now move towards a greater level of specificity in studies related to the topic and similar ones. It is also regarded that the division of consumers and separation of irresponsible segments from responsible ones will be fruitful for marketers, government, and consumers themselves. The findings will definitely leave a remark on the mind of the readers and will divert their thinking on the track of their own responsibility for environment protection and maintenance of its sustainability. As a point of fact, the successful gaining of sustainable development depends upon the participation and contribution from our millions of hands. So, we leave a comprehension: why not begin with ourselves?

Annexure: Questionnaire

Subject—Consumption Behaviour and Social Responsibility This questionnaire was prepared for collecting primary data on the above topic. For the easy understanding of the reader, it has been presented here in a different way from which was used for data collection.

Section A: Behavioural Response The following questions ask √ about consumption behaviour that is carried out in routine life. Please select ( ) the response that best describes your behaviour within your everyday life decisions. There are no right or wrong answers.

Item identities

Statements

Never true

Rarely true

Sometimes true

Often true

Always true

Coding

(1)

(2)

(3)

(4)

(5)

Reverse Coding

(5)

(4)

(3)

(2)

(1)

Total response

Responsible purchasing domain EFC1

I usually make a special effort not to buy products unsafe to environment

59 {5.9}

108 {10.8}

290 {29.0}

265 {26.5}

278 {27.8}

1000 {100}

EFC2

I generally switch from brands causing environmental damage

77 {7.7}

122 {12.2}

320 {32.0}

225 {22.5}

256 {25.6}

1000 {100}

B3

I prefer to buy one-time use product because of throw away convenience (R)

174 {17.4)

179 (17.9)

288 (28.8)

221 (22.1)

138 (13.8)

1000 {100}

(continued)

© Springer Nature Singapore Pte Ltd. 2020 K. Gupta and N. Singh, Consumption Behaviour and Social Responsibility, Approaches to Global Sustainability, Markets, and Governance, https://doi.org/10.1007/978-981-15-3005-0

437

438

Annexure: Questionnaire

(continued) Item identities

Statements

Never true

Rarely true

Sometimes true

Often true

Always true

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse Coding

(5)

(4)

(3)

(2)

(1)

EFC3

When I have a choice between two equal products, I purchase the one I believe is not/less harmful to the environment

47 {4.7}

118 {11.8}

187 {18.7}

243 {24.3}

405 {40.5}

1000 {100}

GB1

I prefer buying products in refillable containers to minimize packaging waste

56 {5.6}

140 {14.0}

209 {20.9}

302 {30.2}

293 {29.3}

1000 {100}

GB2

I buy energy efficient household appliances/products despite high price

27 {2.7}

107 {10.7}

133 {13.3}

290 {29.0}

443 {44.3}

1000 {100}

EFC4

To reduce packaging waste, I usually refuse products with unnecessary packaging or plastic covering

75 {7.5}

131 {13.1}

274 {27.4}

303 {30.3}

217 {21.7}

1000 {100}

GB3

Even if they are more expensive, I have installed CFL or other energy efficient bulbs in my house to save energy

42 {4.2}

78 {7.8}

75 {7.5}

163 {16.3}

642 {64.2}

1000 {100}

Responsible usage domain B9

I use renewable energy methods like solar energy

267 {26.7}

228 {22.8}

203 {20.3}

190 {19.0}

112 {11.2}

1000 {100}

B10

I switch off the engine while waiting on railway crossing/traffic lights

49 {4.9}

117 {11.7}

184 {18.4}

187 {18.7}

463 {46.3}

1000 {100}

B11

I use my car instead public transport to maintain my status (R)

319 {31.9}

140 {14.0}

252 {25.2}

141 {14.1}

148 {14.8}

1000 {100}

B12

To save energy, I defreeze food before heating up

81 {8.1}

120 {12.0}

172 {17.2}

219 {21.9}

408 {40.8}

1000 {100}

SH1

I prefer handkerchief instead of tissue paper to reduce trash

52 {5.2}

157 {15.7}

182 {18.2}

201 {20.1}

408 {40.8}

1000 {100}

SH2

I use both sides of paper for writing or printing

23 {2.3}

70 {7.0}

163 {16.3}

197 {19.7}

547 {54.7}

1000 {100}

SH3

I conserve energy by turning off lights/fans when not in use in home or work/institution

9 {0.9}

82 {8.2}

41 {4.1}

230 {23.0}

648 {64.8}

1000 {100}

SH4

Whenever possible, I walk, ride bicycle, carpool, or use public transport to help in reducing air pollution

60 {6.0}

145 {14.5}

222 {22.2}

228 {22.8}

345 {34.5}

1000 {100}

(continued)

Annexure: Questionnaire

439

(continued) Item identities

Statements

Never true

Rarely true

Sometimes true

Often true

Always true

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse Coding

(5)

(4)

(3)

(2)

(1)

SH5

I prefer my own bag while shopping than a plastic carrier provided by a shop

52 {5.2}

145 {14.5}

291 {29.1}

220 {22.0}

292 {29.2}

1000 {100}

B20

I turn off taps and repair leaks to conserve water

16 {1.6}

73 {7.3}

80 {8.0}

211 {21.1}

620 {62.0}

1000 {100}

B22

I use filled instead of running water to wash utensils (R)

118 {11.8}

136 {13.6}

194 {19.4}

249 {24.9}

303 {30.3}

1000 {100}

WC1

I use minimum water while bathing, soaping, or washing

103 {10.3}

101 {10.1}

124 {12.4}

178 {17.8}

494 {49.4}

1000 {100}

WC2

After washing clothes, I use the remaining water for cleaning the floor

123 {12.3}

167 {16.7}

219 {21.9}

184 {18.4}

307 {30.7}

1000 {100}

WC3

To save water, I wait until there is full load of clothing for washing

44 {4.4}

116 {11.6}

182 {18.2}

299 {29.9}

359 {35.9}

1000 {100}

Responsible maintenance domain MW1

I prefer reusable mugs/glasses instead of disposable cups/glasses for beverages to avoid unnecessary waste

36 {3.6}

127 {12.7}

231 {23.1}

222 {22.2}

384 {38.4}

1000 {100}

MW2

I save and reuse plastic shopping bags/poly bags so that they can be used again instead of wasting them

38 {3.8}

81 {8.1}

202 {20.2}

238 {23.8]

441 {44.1}

1000 {100}

MW3

To maximize the usage, I often repair and reuse things instead of discarding and buying new ones

45 {4.5}

99 {9.9}

237 {23.7}

295 {29.5}

324 {32.4}

1000 {100}

Responsible disposal domain AD1

After a picnic, I leave the place as clean as it was originally

69 {6.9}

109 {10.9}

215 {21.5}

295 {29.5}

312 {31.2}

1000 {100}

AD2

I keep the waste with me and search for the dustbin to put them away

47 {4.7}

113 {11.3}

184 {18.4}

250 {25.0}

406 {40.6}

1000 {100}

B29

After opening the product, I just throw away the package anywhere (R)

47 {4.7}

117 {11.7}

156 {15.6}

173 {17.3}

507 {50.7}

1000 {100}

B31

I keep my surroundings neat and clean

12 {1.2}

70 {7.0}

139 {13.9}

293 {29.3}

486 {48.6]

1000 {100}

RI1

I am prepared to take my household garbage to the nearest recycling bins if provided

9 {0.9}

34 {3.4}

93 {9.3}

560 {56.0}

304 {30.4}

1000 {100}

(continued)

440

Annexure: Questionnaire

(continued) Item identities

Statements

Never true

Rarely true

Sometimes true

Often true

Always true

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse Coding

(5)

(4)

(3)

(2)

(1)

RI2

I am willing to sort garbage for appropriate disposal

25 {2.5}

57 {5.7}

140 {14.0}

593 {59.3}

185 {18.5}

1000 {100}

RI3

I am ready to pay more to municipalities/government for garbage collection for safe long-term disposal

41 {4.1}

116 {11.6}

231 {23.1}

407 {40.7}

205 {20.5}

1000 {100}

Domain of allied socially responsible behaviours ERA1

I obey environment laws and rules to keep the environment safe

13 {1.3}

82 {8.2}

193 {19.3}

277 {27.7}

435 {43.5}

1000 {100}

ERA2

I talk to people when they harm the environment to persuade that person to stop that activity

73 {7.3}

161 {16.1}

290 {29.0}

295 {29.5}

181 {18.1}

1000 {100}

ERA3

I donate to groups working for safeguarding the environment

108 {10.8}

179 {17.9}

332 {33.2}

182 {18.2}

199 {19.9}

1000 {100}

B34

At convenience, I park anywhere on roads or streets (R)

430 {43.0}

194 {19.4}

172 {17.2}

124 {12.4}

80 {8.0}

1000 {100}

SSC1

I always ensure that my way of living and activities do not disturb others

23 {2.3}

88 {8.8}

87 {8.7}

218 {21.8}

584 {58.4}

1000 {100}

B36

When washing vehicle/car, I do not bother that the water may disturb public paths (R)

470 {47.0}

158 {15.8}

181 {18.1}

89 {8.9}

102 {10.2}

1000 {100}

SSC2

I always park suitably not to block others’ way

14 {1.4}

84 {8.4}

108 {10.8}

174 {17.4}

620 {62.0}

1000 {100}

SSC3

Though I like listening to music at high volume, but caring for the neighbourhood, I keep it low

39 {3.9}

88 {8.8}

121 {12.1}

180 {18.0}

572 {57.2}

1000 {100}

Note (1) Item Identities imply the identity of a particular item as used in SPSS Variable View and are italicized for those statements which are not analysed in the study owing to statistical reasons. The letter R in the brackets is a symbol for the reverse coded statements. (2) The numbers in the cells denote the frequency of responses and the figures in parentheses imply percentages.

Section B: Attitudinal Response In this part, the purpose is to obtain an attitude regarding some aspects of how to protect the environment √ and maintain sustainability. If you fully agree or disagree with a statement, tick ( ) either extreme or if you find your view in the middle, tick

Annexure: Questionnaire

441

that response best describes your opinion between the two ends. There are no right or wrong answers.

Item identities

Statements

Strongly disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

Total response

General attitudinal domain A1

I think protecting the environment should be a top priority even at the risk of curbing economic growth

11 {1.1}

56 {5.6}

139 {13.9}

503 {50.3}

291 {29.1}

1000 {100}

CSF1

I agree that the increasing carbon dioxide in the atmosphere is one of the factors causing global warming

21 {2.1}

25 {2.5}

73 {7.3}

389 {38.9}

492 {49.2}

1000 {100}

CSF2

I think if individuals stop damaging the environment, it will help in improving life for everyone

8 {0.8}

21 {2.1}

43 {4.3}

382 {38.2}

546 {54.6}

1000 {100}

CSF3

I view human consumption activities as the primary cause of global warming

19 {1.9}

49 {4.9}

144 {14.4}

439 {43.9}

349 {34.9}

1000 {100}

CSF4

In my opinion the ultimate solution for environment problems depends on drastic changes in our lifestyle

8 {0.8}

45 {4.5}

116 {11.6}

556 {55.6}

275 {27.5}

1000 {100}

CSF5

Often I feel that the lifestyle we live is impossible to maintain in the long run

27 {2.7}

86 {8.6}

140 {14.0}

478 {47.8}

269 {26.9}

1000 {100}

CSF6

I consider that future generation deserves equally as those living now

7 {0.7}

30 {3.0}

48 {4.8}

436 {43.6}

479 {47.9}

1000 {100}

(continued)

442

Annexure: Questionnaire

(continued) Item identities

Statements

Strongly disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

CI1

I am willing to have environmental problems solved even if this means sacrificing many personal goods

22 {2.2}

77 {7.7}

247 {24.7}

453 {45.3}

201 {20.1}

1000 {100}

A35

I feel that it is very difficult for an individual to do anything to safeguard the environment (R)

61 {6.1}

223 {22.3}

124 {12.4}

387 {38.7}

205 {20.5}

1000 {100}

CI2

I am willing to take any initiative to protect the environment

22 {2.2}

57 {5.7}

201 {20.1}

592 {59.2}

128 {12.8}

1000 {100}

CI3

I believe that the solution of environment problems should be left to experts/government (R)

238 {23.8}

440 {44.0}

156 {15.6}

118 {11.8}

48 {4.8}

1000 {100}

Specific attitudinal domain CE1

Sometimes I worry about present generation’s unsustainable habits

24 {2.4}

38 {3.8}

94 {9.4}

458 {45.8}

386 {38.6}

1000 {100}

AMW1

I sense a need to do something immediately to reduce the amount of waste thrown away

6 {0.6}

35 {3.5}

95 {9.5}

510 {51.0}

354 {35.4}

1000 {100}

AMW2

I agree that there is a lot of waste produced in India in parties/occasions or religious moments

21 {2.1}

24 {2.4}

101 {10.1}

363 {36.3}

491 {49.1}

1000 {100}

CE3

It upsets me when I see people use too much water and damage environment

21 {2.1}

48 {4.8}

170 {17.0}

481 {48.1}

280 {28.0}

1000 {100}

(continued)

Annexure: Questionnaire

443

(continued) Item identities

Statements

Strongly disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

CE2

I think pollution problem can be minimized by shifting from private to public transportation or walking/cycling

16 {1.6}

40 {4.0}

75 {7.5}

465 {46.5}

404 {40.4}

1000 {100}

CE4

I sense a requirement for an intelligent system to stop overflowing water from roof tanks

10 {1.0}

43 {4.3}

124 {12.4}

507 {50.7}

316 {31.6}

1000 {100}

CN1

I feel many a times music systems and loudspeakers cause unnecessary noise and nuisance in society

14 {1.4}

21 {2.1}

117 {11.7}

485 {48.5}

363 {36.3}

1000 {100}

SM1

Aligning with the demand for parking space, I support raising parking fees in cities

58 {5.8}

170 {17.0}

241 {24.1}

391 {39.1}

140 {14.0}

1000 {100}

A15

I think vehicles should be banned in the markets for sustainable mobility

33 {3.3}

81 {8.1}

188 {18.8}

447 {44.7}

251 {25.1}

1000 {100}

CN2

I support the rule of not using music systems/DJs late night

34 {3.4}

65 {6.5}

140 {14.0}

363 {36.3}

398 {39.8}

1000 {100}

ET1

I would oppose any environmental regulation that would restrict my lifestyle (R)

129 {12.9}

309 {30.9}

218 {21.8}

250 {25.0}

94 {9.4}

1000 {100}

SM2

I am willing to pay extra for intelligent parking systems

61 {6.1}

136 {13.6}

250 {25.0}

457 {45.7}

96 {9.6}

1000 {100}

A19

I see too much electricity is wasted in parties/decorations

14 {1.4}

65 {6.5}

162 {16.2}

423 {42.3}

336 {33.6}

1000 {100}

(continued)

444

Annexure: Questionnaire

(continued) Item identities

Statements

Strongly disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

AMW3

In my view, government should totally ban plastic bags and disposable containers

22 {2.2}

21 {2.1}

105 {10.5}

373 {37.3}

479 {47.9}

1000 {100}

NR1

Recycling reduces pollution and is important to save natural resources

15 {1.5}

28 {2.8}

67 {6.7}

512 {51.2}

378 {37.8}

1000 {100}

NR2

I feel that the government should pass law for recycling

10 {1.0}

40 {4.0}

53 {5.3}

558 {55.8}

339 {33.9}

1000 {100}

NR3

I think of a law for all household’s garbage to be separated into different classes for recycling

13 {1.3}

41 {4.1}

139 {13.9}

581 {58.1}

226 {22.6}

1000 {100}

RP1

Just thinking of keeping separate piles of garbage for recycling is too much trouble (R)

111 {11.1}

359 {35.9}

258 {25.8}

229 {22.9}

43 {4.3}

1000 {100}

RP2

I would be willing to recycle only if there will be a monetary reward for the same (R)

163 {16.3}

422 {42.2}

212 {21.2}

144 {14.4}

59 {5.9}

1000 {100}

A30

In my point of view religious processions cause unnecessary traffic jam on roads and produce too much noise

17 {1.7}

69 {6.9}

188 {18.8}

452 {45.2}

274 {27.4}

1000 {100}

A31

I support Hindu rituals to flow away the material into water after a Pooja, although it may pollute the water (R)

113 {11.3}

193 {19.3}

249 {24.9}

348 {34.8}

97 {9.7}

1000 {100}

(continued)

Annexure: Questionnaire

445

(continued) Item identities

Statements

Strongly disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

A32

In my observation, religious institutions are not propagating environment friendly actions

30 {3.0}

103 {10.3}

217 {21.7}

511 {51.1}

139 {13.9}

1000 {100}

A33

From my viewpoint all types of processions including religious should be banned/curtailed

58 {5.8}

136 {13.6}

256 {25.6}

407 {40.7}

143 {14.3}

1000 {100}

OGM1

I feel that environment friendly products are high priced products (R)

101 {10.1}

214 {21.4}

253 {25.3}

332 {33.2}

100 {10.0}

1000 {100}

OGM2

I believe that products made of recycled material are of lower quality (R)

71 {7.1}

335 {33.5}

355 {35.5}

191 {19.1}

48 {4.8}

1000 {100}

OGM3

In Indian markets, environment friendly products are not readily available/easily recognizable (R)

50 {5.0}

215 {21.5}

202 {20.2}

429 {42.9}

104 {10.4}

1000 {100}

ET2

I think there is a need to spread environmental education amongst Indians

48 {4.8}

118 {11.8}

156 {15.6}

440 {44.0}

238 {23.8}

1000 {100}

Note (1) Item Identities imply the identity of a particular item as used in SPSS Variable View and are italicized for those statements which are not analysed in the study owing to statistical reasons. The letter R in the brackets is a symbol for the reverse coded statements. (2) The numbers in the cells denote the frequency of responses and the figures in parentheses imply percentages.

Section C: Personal Characteristics The following statements describe a person as he/she generally sees himself/herself in personal life. If you are fully satisfied or highly unsatisfied with a statement check

446

Annexure: Questionnaire

√ ( ) accordingly below “strongly disagree” or “strongly agree”. You can also see √ yourself ( ) somewhere between these ends. There are no right or wrong answers.

Item identities

Statements

Strongly Disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

P1

I often get sentimental seeing emotions in films/TV serials

44 {4.4}

159 {15.9}

127 {12.7}

483 {48.3}

187 {18.7}

1000 {100}

P2

While working in a group, I admire group goals over personal

7 {0.7}

74 {7.4}

143 {14.3}

518 {51.8}

258 {25.8}

1000 {100}

P3

I am not a person who gives up very easily

14 {1.4}

50 {5.0}

96 {9.6}

455 {45.5}

385 {38.5}

1000 {100}

P4

I am always prepared for work, whatever may be the circumstances

5 {0.5}

50 {5.0}

125 {12.5}

512 {51.2}

308 {30.8}

1000 {100}

P5

On hearing a new word, immediately I try to find its meaning

17 {1.7}

81 {8.1}

137 {13.7}

469 {46.9}

296 {29.6}

1000 {100}

P6

I often worry about me and my family’s future

10 {1.0}

63 {6.3}

91 {9.1}

469 {46.9}

367 {36.7}

1000 {100}

P7

During a conversation, I can easily express my opinions and views

20 {2.0}

66 {6.6}

156 {15.6}

548 {54.8}

210 {21.0}

1000 {100}

P8

I can easily spare myself for work as well as entertainment/fun

8 {0.8}

61 {6.1}

168 {16.8}

571 {57.1}

192 {19.2}

1000 {100}

P9

I give little value to luck while performing my work/job

36 {3.6}

127 {12.7}

219 {21.9}

445 {44.5}

173 {17.3}

1000 {100}

P10

I maintain good relationships with my relatives and friends

7 {0.7}

44 {4.4}

98 {9.8}

509 {50.9}

342 {34.2}

1000 {100}

P11

The hard circumstances which I face can rarely make me angry

46 {4.6}

174 {17.4}

255 {25.5}

427 {42.7}

98 {9.8}

1000 {100}

(continued)

Annexure: Questionnaire

447

(continued) Item identities

Statements

Strongly Disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

P12

I become ready to change myself if somebody corrects me

5 {0.5}

23 {2.3}

88 {8.8}

574 {57.4}

310 {31.0}

1000 {100}

P13

I mostly complete my work with full confidence and efficiency

8 {0.8}

30 {3.0}

106 {10.6}

534 {53.4}

322 {32.2}

1000 {100}

P14

I usually change my ways of doing everyday tasks

31 {3.1}

182 {18.2}

234 {23.4}

395 {39.5}

158 {15.8}

1000 {100}

P15

It is only rarely that I need any reward as a motivation for work

25 {2.5}

179 {17.9}

238 {23.8}

436 {43.6}

122 {12.2}

1000 {100}

P16

Preferably I read challenging material and accept challenging tasks

36 {3.6}

154 {15.4}

263 {26.3}

429 {42.9}

118 {11.8}

1000 {100}

P17

People’s moods and feelings do not affect me (R)

100 {10.0}

396 {39.6}

180 {18.0}

247 {24.7}

77 {7.7}

1000 {100}

P18

Normally, I feel convenient working individually (R)

57 {5.7}

271 {27.1}

193 {19.3}

362 {36.2}

117 {11.7}

1000 {100}

P19

I feel downcast by the failures and problems at work (R)

113 {11.3}

328 {32.8}

246 {24.6}

269 {26.9}

44 {4.4}

1000 {100}

P20

Sometimes I shirk/neglect work (R)

140 {14.0}

329 {32.9}

244 {24.4}

239 {23.6}

48 {4.8}

1000 {100}

P21

I am not very much customary to know/learn new facts/things (R)

218 {21.8}

422 {42.2}

147 {14.7}

162 {16.2}

51 {5.1}

1000 {100}

P22

I admire living in the present than in future which is uncertain (R)

94 {9.4}

183 {18.3}

184 {18.4}

397 {39.7}

142 {14.2}

1000 {100}

P23

I can hardly express my feelings (R)

106 {10.6}

343 {34.3}

238 {23.8}

271 {27.1}

42 {4.2}

1000 {100}

P24

I find myself a kind of person who is always busy in his/her work (R)

59 {5.9}

301 {30.1}

217 {21.7}

326 {32.6}

97 {9.7}

1000 {100}

(continued)

448

Annexure: Questionnaire

(continued) Item identities

Statements

Strongly Disagree

Disagree

Somewhat agree Somewhat disagree

Agree

Strongly agree

Total response

Coding

(1)

(2)

(3)

(4)

(5)

Reverse coding

(5)

(4)

(3)

(2)

(1)

P25

I believe that unhappy things in life are due to bad luck (R)

103 {10.3}

330 {33.0}

223 {22.3}

265 {26.5}

79 {7.9}

1000 {100}

P26

I am suspicious in trusting people like relatives/neighbours (R)

97 {9.7}

271 {27.1}

224 {22.4}

289 {28.9}

119 {11.9}

1000 {100}

P27

Sometimes I become aggressive even on small matters (R)

87 {8.7}

263 {26.3}

179 {17.9}

375 {37.5}

96 {9.6}

1000 {100}

P28

I enjoy giving orders as I consider my views superior than others (R)

153 {15.3}

396 {39.6}

219 {21.9}

177 {17.7}

55 {5.5}

1000 {100}

P29

I tend to lose confidence when criticized (R)

175 {17.5}

357 {35.7}

223 {22.3}

204 {20.4}

41 {4.1}

1000 {100}

P30

I dislike changes as I feel difficulty in adjusting to a new atmosphere (R)

187 {18.7}

353 {35.3}

240 {24.0}

180 {18.0}

40 {4.0}

1000 {100}

P31

For me comfort/convenience is very important to work smoothly (R)

74 {7.4}

273 {27.3}

217 {21.7}

332 {33.2}

104 {10.4}

1000 {100}

P32

I live a very simple and peaceful life (R)

48 {4.8}

96 {9.6}

126 {12.6}

459 {45.9}

271 {27.1}

1000 {100}

Note (1) Item Identities imply the identity of a particular item as used in SPSS Variable View. The letter R in the brackets is a symbol for the reverse coded statements. (2) The numbers in the cells denote the frequency of responses and the figures in parentheses imply percentages.

Section D: Personal Information Here, respondents are directed towards giving some personal information regarding them. The information provided herein will be used for academic purposes only. Thus, respondents are encouraged to provide true information with honesty.

Annexure: Questionnaire

449

Age:

Gender:

Male / Female

Place of Living: Rural / Urban

Educational Qualiications:

Classes where you score above 60%: Field of Study:

10th /

12th / Graduation / PG / Higher (specify)

Profession/Occupation:

Religion:

Type of Housing:

Marital Status:

City:

Unmarried / Married

Years of Marriage:

Own / Rental / Any Other (specify)

If married, are you a Parent too:

Yes / No

Type of Family: Joint / Nuclear

Total Number of Persons in Your Family:

Composition of Family: Age/Gender

Male

Female

Total

Up to14 15–40 41–65 Above 65 Total

Family Income (Monthly): Up to Rs. 15000/15000 to 50000/50000 to 80000/Above 80000 How do you move to work/education: Walking/Cycling/Own (Two-Wheeler/Car)/Public (Bus/Train/Others)

vehicle

Household Support: Sr. no.

Statements

Never

Rarely

Sometimes

Often

Always

Total response

1.

My family is very supportive of me

10 {1.0}

16 {1.6}

101 {10.1}

196 {19.6}

677 {67.7}

1000 {100}

2.

I am free to decide on my own

20 {2.0}

33 {3.3}

249 {24.9}

275 {27.5}

423 {42.3}

1000 {100}

Note The numbers in the cells denote the frequency of responses and the figures in parentheses imply percentages.

450

Annexure: Questionnaire

Religious Strength/Religiosity: Sr. no.

Statements

Never

Rarely

Sometimes

Often

Always

Total response

1.

I am highly active in religious activities

56 {5.6}

96 {9.6}

370 {37.0}

232 {23.2}

243 {24.3}

1000 {100}

2.

My decisions get affected by my religion

243 {24.3}

150 {15.0}

327 {32.7}

156 {15.6}

124 {12.4}

1000 {100}

Note The numbers in the cells denote the frequency of responses and the figures in parentheses imply percentages.