Marketing Research 9789339219116

Table of contents :
Title
Contents
1. Introduction
Section 1: Problem Defi nition
2. Decision Problem to Research Problem
3. Research Questions and Hypotheses
Section 2: Methodology
4. Identifying the Appropriate Research Design
5. Research Designs for Gaining Insights
6. Survey Research and Questionnaire Development
7. Sampling
8. Analysis
Section 3: Using Multivariate Analysis for MR Studies
9. Interdependence Techniques for Marketing Decisions
10. Factor Analysis
11. Multi-dimensional Scaling
12. Discriminant Analysis
13. Conjoint Analysis
14. Cluster Analysis
15. Using the Marketing Research Studies
Annexures
Bibliography
Index
Author’s Profi le

Citation preview

Marketing Research A User’s Perspective

Marketing Research A User’s Perspective

Pingali Venugopal Associate Director XLRI Center for Global Management and Responsible Leadership Jamshedpur

Published by McGraw Hill Education (India) Private Limited, P-24, Green Park Extension, New Delhi 110 016. Marketing Research: A User’s Perspective Copyright © 2014, by McGraw Hill Education (India) Private Limited No part of this publication may be reproduced or distributed in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise or stored in a database or retrieval system without the prior written permission of the publishers. The program listings (if any) may be entered, stored and executed in a computer system, but they may not be reproduced for publication. This edition can be exported from India only by the publishers, McGraw Hill Education (India) Private Limited.

Print Edition: ISBN (13): 978-93-392-1911-6 ISBN (10): 93-392-1911-2 Ebook Edition: ISBN (13): 978-93-392-1913-0 ISBN (10): 93-392-1913-9 Managing Director: Kaushik Bellani Publishing Manager—Professional: Mitadru Basu Development Editor—BGR: Laxmi Singh Desk Editor: Jagriti Kundu General Manager—Sales and Business Development: S. Girish Product Manager—BGR: Priyanka Goel General Manager—Production: Rajender P Ghansela Manager—Production: Reji Kumar Information contained in this work has been obtained by McGraw Hill Education (India), from sources believed to be reliable. However, neither McGraw Hill Education (India) nor its authors guarantee the accuracy or completeness of any information published herein, and neither McGraw Hill Education (India) nor its authors shall be responsible for any errors, omissions, or damages arising out of use of this information. This work is published with the understanding that McGraw Hill Education (India) and its authors are supplying information but are not attempting to render engineering or other professional services. If such services are required, the assistance of an appropriate professional should be sought. Typeset at Ninestars Information Technologies Ltd., Chennai and printed at Rajkamal Electric Press, Plot No. 2, Phase IV, HSIIDC, Kundli, Sonepat, Haryana-131028. Cover Printer: Rajkamal Electric Press Cover Design: Code: RLCLCDLUDDYCQ

To My children Sravan and Gautam

Preface When I started teaching “marketing research” for the first time, I conducted a projective technique exercise with the students to understand their perception about the subject. The projective technique indicated that the course was felt to be quantitative and difficult to understand. Reflecting on this finding, I realized that all managers need not be experts in conducting researches; however, all managers need to be competent in knowing whether a research is good or bad and whether the research findings are good enough to be used for making a decision. That is when I decided to orient the marketing research course from the users’ perspective and make the students appreciate the process of conducting research for decision making. This orientation is the basis for the book. The objective of the book would not only be to understand the basics of marketing research but also to develop competency of the user to examine relevant aspects of the research process to ensure valid results for decision making. For this purpose the book presents different frameworks which would help the user question and minimize the error in the research process. Specifically, the focus would be on: • Differentiating the role of the user and the researcher during the research process • Understanding the basics in conducting the research • Identifying the possible sources of error • Understanding the logic of the mathematical analysis (not necessarily the formula and the mathematical calculations) • Interpreting the computer output and using it for decision making. The book is divided into three sections. Section 1 focuses on the problem definition stage, where the user and the researcher have to work together to convert the decision problem into the research problem. This process identifies the information needs for the research process. Section 2 discusses the way the information needs identified in section one are collected (methodology). This section starts by identifying the appropriate research design to enable collection of valid information. The section then discusses the process of developing a proper data collection instrument, identifying the appropriate sample and the appropriate data analysis technique to match with the research design.

viii

Section 3 describes some of the multivariate analysis used in marketing research studies. The logic and the considerations that need to be taken into account while conducting the analysis for marketing decisions are highlighted. For each analysis, focus is on its specific needs for data collection, formatting the data for the computer package, the interventions that need to taken while using the computer packages (specifically if some default options are prespecified) and the interpretation of the computer output for marketing decisions. The unique features of the book are: • The detailed description of different stages in the research process and the role of the user in the research process. • The process of converting the decision problem into research problem. • Frameworks for identifying the appropriate course of action at each stage of the research process. • Discusses the preprocessing required for the different data collection methods for multi dimensional scaling. • Check list for accepting a research study. • Case studies for different stages of the research process. • Comprehensive research proposals at the end of section 2 and comprehensive research studies at the end of the book. Pingali Venugopal

Acknowledgements At the outset I would like to thank XLRI, Jamshedpur for not only providing me an opportunity to offer Marketing Research course but also for providing an environment for research. This has helped me develop and test frameworks for different stages of the Marketing Research process. Special thanks to Fr. E Abraham, Fr. Casimir Raj and Fr. P D Thomas, Directors of XLRI. The business management students of XLRI raised relevant questions and helped me strengthen the framework while I shared my thoughts with them during my teaching. While it would not be possible to individually name each one of them, I thank all my students for their valuable inputs. The students also contributed by way of data for different research studies presented in the book. My heartfelt thanks to all of them for their efforts in collecting data. I thank Professor Prem Purwar, IIM, Lucknow, Professors R K Premarajan, Sanjeev Varshney and B K Mangaraj of XLRI who clarified some of the concepts. Sincere thanks to Professors Apalak Khatua and Basant Purohit of XLRI for their useful contributions to the book. A special thanks to the blind reviewers whose comments have significantly improved the content of the book. Thanks are due to S K Tiwary and members of XLRI computer center, and XLRI Library for the resources without which it would not have been possible to complete this manuscript. My wife, Veni, in addition to providing moral support, helped me significantly in the editing stage of this manuscript. Without her help, it would not have been possible to complete this work. My heartfelt thanks to her. Rani provided considerable help with the preparation of the manuscript. A special thanks to her. I also thank my colleagues at XLRI who provided necessary encouragement and support during the process of writing the book. I thank the editorial team of McGraw Hill for all the inputs which helped make this manuscript publishable. Also sincere thanks to McGraw for publishing this manuscript. Finally, thanks are due to all those who made small but significant contributions during the process of completion of this project. Pingali Venugopal

Contents Preface Acknowledgements Contents List of Tables List of Figures List of Boxes List of Case Studies List of Annexures

vii ix xi xii xiv xvi xvi xvii

1. Introduction

1

Section 1: Problem Definition 2. Decision Problem to Research Problem 3. Research Questions and Hypotheses

11 13 28

Section 2: Methodology 4. Identifying the Appropriate Research Design 5. Research Designs for Gaining Insights 6. Survey Research and Questionnaire Development 7. Sampling 8. Analysis

39 41 53 68 85 96

Section 3: Using Multivariate Analysis for MR Studies 9. Interdependence Techniques for Marketing Decisions 10. Factor Analysis 11. Multi-dimensional Scaling 12. Discriminant Analysis 13. Conjoint Analysis 14. Cluster Analysis 15. Using the Marketing Research Studies

121 123 131 149 165 182 199 208

Annexures Bibliography Index Author’s Profile

235 243 255 262

xii

LIST OF TABLES 3.1: 4.1: 4.2: 4.3: 4.4: 4.5: 5.1: 6.1: 7.1: 7.2: 7.3: 7.4: 8.1: 8.2: 8.3: 8.4: 8.5: 8.6: 8.7: 8.8: 8.9: 9.1: 9.2: 10.1: 10.2: 10.3: 10.4: 10.5: 10.6: 10.7: 11.1: 11.2: 11.3:

Strubler’s quality improvement questioning Top 5 indoor activities survey response summary Questions and responses for a class exercise on reading habit of the faculty Motivators as perceived by the channel members Sequence of motivational schemes for channel members Framework for identifying the research design Sale of cars in India Comparison of survey methods Quota sampling grid for study on awareness of anti smoking campaign Suggested sampling plan mapped to the research design framework States divided based on voter turnout (2004 general elections) Quota sampling grid for studying the voters’ behaviour Gender-wise classification of students opting for different courses Comparison of batches 2009 and 2010 Ranking of cars before and after the campaign Focus group characteristics Knowledge of ITES vs gender Importance of information sources for a career choice (in percent) Importance of factors influencing interest for a career choice Importance of factors during evaluation of choice of company Quota sampling format for the conclusive study Service improvement grid Perception scores of oil companies Data format for R-factor analysis Initial eigen values Initial communality Initial factor matrix component matrix Communalities Rotated component matrix Partial rotated matrix of 4BT study Similarity/dissimilarity matrix for television channels of 1998 Calculating the stress value Stress improvement table

30 42 45 49 49 50 64 71 91 93 94 95 100 102 104 106 109 109 110 110 114 128 128 136 137 137 140 140 143 144 150 152 152

xiii

11.4: 11.5: 11.6: 11.7: 11.8: 11.9: 11.10: 11.11: 11.12: 12.1: 12.2: 12.3: 12.4: 12.5: 12.6: 12.7: 12.8: 12.9: 12.10: 12.11: 12.12: 12.13: 12.14: 12.15: 12.16: 13.1: 13.2: 13.3: 13.4: 13.5: 13.6: 13.7: 13.8: 13.9: 13.10: 13.11: 13.12: 13.13: 13.14:

Coordinates for TV channels on two dimensions Paired ranking for TV channels Conditional ranking for branded shirts Pair X pair matrix for conditional ranking Row totals of conditional ranking pairs Rank for the brand pairs Dissimilarity matrix for branded shirts Traid method of data collection Partial data set of attributes for soaps Data format showing the inclusion of a grouping variable Tolerance test for “enter” method Goodness of fit for discriminant analysis for Hyundai Santro and Fiat Uno Standardised canonical discriminant function coefficients Unstandardised canonical discriminant function coefficients Structure matrix for Hyundai Santro and Fiat Uno Study Group centroids for Hyundai Santro and Fiat Uno Partial case-wise results Classification results Mean scores for different attributes for Santro and Uno Eigen value for three-group discriminant analysis Test of significance for a three-group discriminant analysis Standardised canonical discriminant function coefficients for a three-group analysis Structure matrix for a three-group discriminant analysis Group centroids for a three-group discriminant analysis Classification matrix for a three-group discriminant analysis Ranking of concept cards for soaps for two segments Segment 1—Lather X fragrance Segment 1—Fragrance X effect on skin Segment 1—Lather X effect on skin Segment 2—Lather X fragrance Segment 2—Fragrance X effect on skin Segment 2—Lather X effect on skin Attributes and levels for the car study Concept cards based on Latin square design A subset of attributes and levels for cars Latin square design for three attributes and three levels Orthogonal plan for car conjoint analysis Orthogonal plan with hold out cards Ranking of car on three attributes, each at two levels

153 155 156 157 158 159 160 162 162 167 168 169 169 170 171 171 172 172 173 174 174 174 175 175 175 184 184 185 185 185 185 186 186 187 188 188 188 190 191

xiv

13.15: Calculation of utilities for cars (three attributes at two levels each) 13.16: Distribution of preferred levels 14.1: Non-hierarchical clustering for soaps 14.2: Comparison of hierarchical and non-hierarchical clustering output 14.3: Initial cluster centres 14.4: Iteration history 14.5: Distances between final cluster centres 14.6: Number of cases in each cluster 14.7: Partial list of cluster membership 14.8: Final cluster centres 15.1: Content analysis of TAT 15.2: Parent’s perceptions of impact of TV on children (in percent) 15.3: HBM dimensions 15.4: Sampling design to study effectiveness of anti-smoking campaign

191 196 202 202 205 205 205 206 206 206 214 216 219 222

LIST OF FIGURES 1.1: 1.2: 1.3: 2.1: 2.2: 2.3: 2.4: 2.5: 3.1: 3.2: 3.3: 3.4: 4.1: 5.1: 5.2: 5.3: 5.4: 5.5:

Marketing planning process Need for marketing research Marketing research process Decision problem Process of developing research problem Dummy test: the process Decision tree approach to the environmental consciousness issue Conceptual model: quality of students Modified 5Ws and 1H for identifying research questions Different levels of information that has to be collected in research studies Rationale for developing hypotheses Classification of reasons for overspeeding using the behavioural framework Diagrammatic representation of the state-of-mind information Methods for focus group discussions Airline stimuli Detergent stimuli Cereal stimuli Car breakdown

2 4 7 15 16 17 19 26 31 32 33 37 46 54 59 59 60 65

xv

6.1: 7.1: 7.2: 8.1: 8.2: 8.3: 8.4: 8.5: 8.6: 8.7: 8.8: 8.9: 8.10: 9.1: 9.2: 9.3: 9.4: 9.5: 9.6: 10.1: 10.2: 10.3: 10.4: 10.5: 11.1: 11.2: 12.1: 13.1: 13.2: 13.3: 13.4: 13.5: 13.6: 14.1: 15.1: 15.2: 15.3: 15.4: 15.5: 15.6:

Process of converting research questions into a questionnaire Methods of sampling Appropriateness of a sampling frame Types of analysis Time spent for different secondary activities (hours/months) Tractor purchase: satisfaction levels in percent Preference for different brands of tractors A typical normal distribution curve Types of errors Importance of information sources for a career choice Importance of factors influencing interest for a career choice Importance of factors during evaluation of a choice of company Decision tree for usage of broadband Relative position of selected car brands Consumer information processing and marketing decisions Service improvement grid for oil companies Service improvement grid for oil company 1 Service improvement grid for oil company 2 Service improvement grid for oil company 3 The initial data set reduced to a smaller number of derived factors Scree plot method Logical depiction of extraction of factors Graphical depiction of rotation of factors Influencing the brand perceptions Euclidian distance of brands on the perceptual map Perceptual map for TV channels Graphical representation of two-group discriminant analysis Importance of attributes and levels Relative importance of features Utility of brand Utility of price Utility of health Utility of free flow Hierarchical clustering for soaps Hours of TV viewing by children Type of TV programmes watched TV characters children imitate TV acts children imitate Smoking behaviour Frequency of smoking

69 86 89 97 98 98 99 99 101 109 110 111 112 125 126 129 129 130 130 132 138 141 141 145 151 153 166 192 194 194 195 195 196 200 215 216 217 217 225 225

xvi

15.7: 15.8: 15.9: 15.10: 15.11: 15.12: 15.13: 15.14: 15.15: 15.16: 15.17: 15.18: 15.19: 15.20: 15.21: 15.22: 15.23: 15.24: 15.25:

Reasons for smoking Awareness of anti-smoking campaign Communication of the anti-smoking campaign Content of the anti-smoking campaign Perception on the effectiveness of the anti-smoking campaign Perception on the impact of anti-smoking campaign Anti-smoking campaign’s impact on social consequences Effectiveness of the personalities used in the anti-smoking campaigns Perception on the frequency of the anti-smoking campaign Perception of the target group (age) of the anti-smoking campaign Perception that the anti-smoking campaign has a rural focus Perception on the extent of addiction to smoking Perception on the timing when people get addicted to smoking Smoking: Role of peer group Perception on the difficulty to quit smoking Media and recall of anti-smoking campaigns Recall of message in anti-smoking campaign Reasons for quitting smoking Most dangerous disease due to smoking

226 226 227 227 227 228 228 229 229 229 230 230 231 231 231 232 232 233 233

LIST OF BOXES 1.1: 3.1: 5.1: 5.2: 6.1: 7.1: 11.1: 11.2:

Intuition and decision making Five Ws and one H Responses for detergent and plane stimuli Sample responses for the projective test of car breakdown Scales of measurement National Sample Survey Round 61, 2004–2005 MDS for branded shirts MDS for soaps

2 29 61 66 73 92 160 163

LIST OF CASE STUDIES 2.1: 2.2: 3.1: 3.2: 4.1:

Starting an advertising agency in Jamshedpur Conceptualizing the process of business schools ranking Testing effectiveness of “vote for the right candidate” campaign Overspeeding Designing an anti-smoking campaign

22 25 34 36 51

xvii

5.1: 6.1: 7.1: 8.1: 8.2: 8.3: 9.1: 10.1: 12.1: 13.1: 14.1: 15.1: 15.2:

Mindset of Indian consumer: implications for car marketers Brand decision process for televisions Choosing appropriate representative to the Parliament (2009) Research study on choice of career in medical transcription Comprehensive research proposal: broadband Comprehensive research proposal impact of advertising on the use of helmets Perception of oil companies Perceptions for soft drinks Deodorant soap for men Conjoint analysis for salt brands Awareness of RTI act Attitude of parents towards TV viewing by children Comprehensive research study evaluating anti-smoking advertising campaign

63 79 94 105 111 116 127 145 176 193 203 211 218

LIST OF ANNEXURES 1: 2: 3:

Research briefs Analysis tools using excel Pre-processing of triad data

235 237 239

1

Introduction

Learning Objectives At the end of the chapter, the reader would be familiar with: The role of marketing research in the marketing planning process The boundaries for marketing research The sources of error while conducting research studies An overview of the research process Special topics covered in the chapter: Estimate versus guesstimates User’s (decision maker’s) considerations for conducting research

The role of marketing research (MR) in the marketing planning process should be clearly understood by the decision makers to derive maximum benefit from MR studies. There are several marketing decisions that would be taken during the entire marketing process and many of them may not require input from MR. However, all these decisions require information which may be obtained from sources other than MR. Several companies have excellent marketing information systems (MISs) to provide information to make decisions. This chapter identifies the role of MR in the marketing planning process and when information from MR should be used to derive maximum benefit.

MARKETING PLANNING PROCESS The marketing planning process aims at (a) satisfying the needs of the customer, (b) satisfying the customers’ requirements better than the competition and (c) communicating and making the product available at the right price taking into account the marketing environment (adopted from Pingali 2010).

2

Marketing Research: A User’s Perspective

Marketing planning is essential for the success of any product/brand, and the success of the marketing planning process depends on information available for making the different marketing decisions. Information about consumers, competitors, marketing environment is essential to formulate the marketing plan. Again, information on the impact the different marketing strategies had on the consumers and competitors needs to be collected to review (evaluate) the marketing programme. The marketing planning process is shown in Figure 1.1. The shaded cells (in Figure 1.1) show the information needs. Consumer need analysis

Product market fit

Consumer behaviour

Marketing environment

Competitive analysis

SWOT analysis

Marketing plan

Review Figure 1.1: Marketing planning process Source: Adopted from Pingali (2010)

Sources of Information As stated, information represents the key resource in any decision-making process. While, marketing research is a tool known for getting information for marketing decisions, information can be obtained from other sources also.

Box 1.1: Intuition and decision making Despite the apparent lack of any scientific evidence of its accuracy, most successful entrepreneurs place a high value on intuition for making decisions; particularly, where there is a large amount of information, and where the data is conflicting. Turner (2009) states that the unconscious mind processes over 2 million pieces of information at a time, it filters out superfluous information and only brings to the conscious awareness a tiny fraction of all the information. Due to its much greater processing power, the unconscious mind (intuition) is much better suited to making decisions with large amounts of data. Turner, Lisa (2009), “Intuition: It’s science not magic”, http://ezinearticles.com/ ?Intuition—Its-Science-Not-Magic&id=3366303 downloaded 21 September 2010.

Introduction

3

A lot of information is obtained from the field force and this is integrated into the MISs. Intuition is also a good source of information. Intuition comes from experience and puts the past into the context (Time-Management guide.com 2005). Also refer to Box 1.1. When several decisions can be made using information from MIS then what is the role of MR and how would it be different from MIS? Marketing Information System versus Marketing Research The process of collecting and analysing information on an ongoing basis for making marketing decisions forms part of marketing information systems (Green et al. 1993). Companies which have been in business for several years generally have built systems to integrate their existing information sources to the decision-making process using MISs. Thomas and Ahmed (1999) found large companies in Asia are using market intelligence as an input for their strategic management system and decision making. While MIS provides information on a continuous basis, marketing research is an ad hoc activity. Piercy (1980) differentiates the two by stating that marketing information system uses existing sales data and published information and marketing research generates new information. That is, when a decision maker feels that he/she cannot make a decision with the existing information and there is a need for obtaining some additional and relevant information then MR should be used.

NEED FOR MARKETING RESEARCH Mitchell and Davies (1995) claim that products could be introduced without market research, but they would be associated with a higher risk of failure. Green et al. (1993) state that research “will reduce the level of uncertainty in making a decision”. Piercy (1980) also states marketing research helps “in reducing, though never removing, the risk and uncertainty”. Marketing research, therefore, would be required when the decision makers face uncertainty and risk with the decision. Rational decision models state that MR is required when the benefit in terms of uncertainty/risk reduction is greater than the marginal cost of conducting the research (Ritchie and Brindley 2001). That is, while several decisions are being made, the need for MR is only when the decision is perceived to be risky and the available information is not found to be adequate or seen to be biased to make a decision. Risk perception is however a characteristic of the decision maker/decision making unit (Figure 1.2). Thus, while for some the decision may be perceived as risky, it may not be perceived as risky by some others. Bettis and Hall (1982) and Bowman (1982) also state that the same set of events influencing risk may be viewed differently by different decision makers, resulting in different levels of risk perceptions. Therefore, it is possible that a decision problem may be

Marketing Research: A User’s Perspective

4

perceived as risky by one decision maker necessitating marketing research, whereas, not so risky for another decision maker and not requiring any input from research.

Decision problem

Risk perception of decision maker

Need for marketing research

Figure 1.2: Need for marketing research

The need for MR is therefore specific to the decision maker and not the problem. If the risk associated with the decision is perceived to be high, then the manager would be interested in unbiased information to help him/her take an informed choice. Since MR is all about obtaining unbiased information, it is important to note that the process of obtaining the information is very important. The process of obtaining the information is detailed in the official definition of MR: Definition of Marketing Research Marketing research is the function that links the consumer, customer, and public to the marketer through information–information used to identify and define marketing opportunities and problems; generate, refine, and evaluate marketing actions; monitor marketing performance; and improve understanding of marketing as a process. Marketing research specifies the information required to address these issues, designs the methods for collecting information, manages and implements the data collection process, analyzes, and communicates the findings and their implications. Official Definition of Marketing Research approved (October 2004) by the American Marketing Association (AMA 2004). Since MR is a tool of risk reduction, it is quite possible that a decision maker may opt for MR to justify most decisions. It is therefore important for the decision maker to also know the boundaries of MR.

BOUNDARIES FOR MARKETING RESEARCH “Marketing research does not make decisions and it does not guarantee success” (FAO 1997). Marketing managers may seek information from MR studies, however, it is marketing managers who should make the final marketing decision and not go purely by the researcher’s recommendation. For example, a research had indicated that noodles would not be accepted by Indian consumers as they have a variety of snacks to choose from, and secondly noodles would not suit the tastes and preferences of Indians. However, this information did not

Introduction

5

deter the launch of the product in India. The manager used this knowledge to design a marketing strategy which has made noodles a great success. The second observation that MR does not guarantee success suggests that MR can indicate the current market condition and not the future situation. Therefore, using current information for developing a marketing plan for the future requires the managerial skills of the decision maker not just the information from MR. The decision maker should also consider the following aspects. Marketing Research and Time Conducting MR is a time-consuming process. In case there is an urgency of making a decision, then the manager has to do without research and should use guesstimates (discussed in detail below). Marketing Research and Secrecy While, MR is seen as a tool for the successful introduction of a product (Mitchell and Davies 1995), Armstrong (2001) states that the drawback of (market research) field studies is that there is a loss of secrecy. A competitor sensing the idea being studied and having a greater risk-taking ability may introduce the concept without going through MR. In such a situation, the decision maker who has gone in for MR may lose his/her time advantage. Marketing Research and Cost Elliott and Jobber (1995) state that the high cost of marketing research keeps many firms from conducting it. Burns and Bush (1998) found marketers doing a trade-off between collecting information through MR and time and cost. Marketing Research and Error The marketing research process consists of different steps (discussed in detail later) and each step has a potential of bringing in an error in the information. To specify the research problem incorrectly would mean that the information obtained would not help answer the decision problem. This is known as a “surrogate information” error (Mitchell and Davies 1995). Sampling error may occur if respondents refuse to co-operate, or refuse being included in the survey. Uninformed response is a source of response error, or error that results from inaccurate responses to survey questions (Schneider and Johnson 1994). Schneider (1985) found that exerting response-inducing techniques (including personification, monetary incentive, offering a summary of the study and follow-up/callback) considerably increased uninformed response (Schneider 1985). Similarly, the results from experimental studies are accurate only if the variables being tested are free from other influences.

6

Marketing Research: A User’s Perspective

Again, biases might be introduced from the interaction between the interviewer and interviewee (Mitchell and Davies 1995). Mitchell and Davies also suggest that MR can produce erroneous results because of the use of wrong analytical techniques or misinterpretation of data. All these suggest that there is a high chance of error creeping into the information being collected by MR and the success of MR depends on the effort taken to minimize the error. It is therefore not surprising to note that “research studies contribute far less to corporate marketing effectiveness than they should do” [Clancy and Krieg (2000)]. Gibson (2000) also state that “marketing research has fallen short of its potential”.

MR: DECISION MAKER’S CONSIDERATIONS The decision maker should consider the following to improve the usefulness of MR. Need of the Research has to be Established While, Grieg (2003) points out that marketing research studies are done for different marketing decisions, MR should be taken up only when the risk associated with the decision is high. As stated earlier, it should be noted that MR should not be used to justify a decision made. Estimate (Near Accurate Information) Versus Guesstimate A manager faced with a decision situation may also face a time constraint. He/she may have to decide between information based on MR or a guesstimate. Since, the need for MR is based on the risk perception of the decision maker, if the company supports the manager to take quick decisions to be competitive, then the manager may be encouraged to use guesstimates based on experience and intuition. For example, there are many possible forecasting methods to forecast demand for tourism industry, however, in practice, relatively few of these have been used for forecasting, and planning is done on guesstimates (Witt and Witt 1993). Piercy (1980) also found that managers preferred information that is 75% right and available on time as against information that is 95% right and available after some time and at a high cost. Decision Maker should not Do the Research The role of the decision maker and the researcher should be clearly demarcated once the problem definition is done (problem definition is discussed in the next section). The researcher should take over after identifying the problem and then conduct the research. If the decision maker is involved in

Introduction

7

conducting the research then there is a possibility of getting biased results. Ritchie and Brindley (2001) found that the decision maker may selectively choose information that supported their initial perceptions and overlook any data that might challenge their perceptions and choices. The Decision Maker should However be Critical of the Process The MR process includes a series of steps to identify, collect and analyse information to address the decision process (Figure 1.3). Since there is a possibility of error creeping in at different stages of the decision process, the decision maker should critically question the different steps of the research process and ensure that the research is conducted properly and the validity of the study is high. This becomes more important because the decision maker should not do the research. Piercy (1978) points out that there is a possibility of the researcher’s subjectivity getting reflected in the study and the role of the decision maker is to question that subjectivity.

Research brief

Research problem

Research questions/ hypothesis Purpose of study

Type of information Research design

Type of analysis

Instrument design

Sampling design

Data analysis

Interpretation Figure 1.3: Marketing research process

8

Marketing Research: A User’s Perspective

User and Doer The above two points indicate the need for the “doer” and “user” of MR to be different. Henceforth in the book, any reference to the doer would mean the person/agency conducting the research and any reference to the user would mean the decision maker who uses the MR studies for decision making. The subsequent chapters would focus on each stage of the research process with a view of minimizing the error. The role of the doer and user during the different stages would also be discussed to ensure that valid information is obtained.

OVERVIEW OF THE BOOK Chapter 1 described when marketing research is required along with the boundaries for conducting research studies. The chapter also provided an overview of the research process and the possible sources of error. The chapter concluded by stating that the doer and user of MR should be different. The subsequent chapters of the book are divided into three sections. Section 1 focuses on what information should be collected (termed as problem definition). This section contains two chapters. Chapter 2 describes the process of converting the research brief into the research problem. It describes three methods: the interaction method, the process of conceptualization and the decision tree approach. The conceptualization process could apply an existing concept or develop a new concept. Chapter 3 suggests frameworks for converting the research objectives into research questions and hypotheses. Section 2 describes the methodology to be adopted for the research. This section has five chapters. Chapter 4 develops a framework for identifying the appropriate research design based on the type of information to be collected and the purpose for which the information is being collected. While Chapter 5 describes the research designs for gaining insights, Chapter 6 describes the survey research method and how a questionnaire has to be developed. Chapter 7 describes the different methods of sampling and suggests sampling methods suited for the different research designs. Chapter 8 describes the different types of analysis and suggests guidelines for choosing the method of analysis to be used. The chapter also discusses types of errors while testing hypothesis. Section 3 describes some of the multivariate analysis used for MR studies. Chapter 9 discusses how the interdependence analysis can be used for marketing decisions taking segmentation and positioning as examples. The subsequent chapters describe the model or the logic for the respective analysis and focus primarily on how the data should be collected, formatted for computer packages and how the output needs to be interpreted (the mathematical calculations which are not important for the user have not been discussed, except where required). Factor analysis is described in Chapter 10,

Introduction

9

multidimensional scaling in Chapter 11, discriminant analysis in Chapter 12, conjoint analysis in Chapter 13 and cluster analysis in Chapter 14. The book concludes with Chapter 15, which provides a check list for users for “using MR studies”.

SUMMARY The chapter starts with the need for information in the marketing planning process and differentiates the MISs (which are in-built systems) from the MR studies (which are project-based studies). The chapter highlights the fact that the need for the MR depends on the risk taking ability of the decision maker. An overview of the research process and the possible sources of error are highlighted. While mentioning that the user should not be directly involved in the research process, the chapter not only indicates that the doer and user are different but also stresses the fact that the user should be critical of each stage of the research process to ensure the validity of the information for decision making.

Section 1 Problem Definition Section 1 focuses on what information should be collected (termed as problem definition). This section contains two chapters. Chapter 2 describes the process of converting the decision problem into the research problem. It describes three methods: the interactive method, the process of conceptualization and the decision tree approach. The conceptualization process could apply an existing concept or develop a new concept. Chapter 3 suggests frameworks for converting the research objectives into research questions and hypotheses. The chapters in the section are highlighted in the overview diagram.

Research brief Research problem Research questions/ hypothesis Purpose of study

Type of information Research design

Type of analysis

Instrument design Sampling design Data analysis Interpretation

2

Decision Problem to Research Problem

Learning Objectives At the end of the chapter, the reader would be familiar with: Differentiating between the decision problem and the research problem Understanding the research brief Identifying the appropriate method for converting the decision problem (research brief) into a research problem Special topics covered in the chapter: Interactive method, applying existing concept, developing a new concept or decision tree approaches for defining the research problem.

The first step of the research process would be to shift from the decision process to the research process by determining the information required for the decision maker. This is called the problem definition stage. This is the most important stage as all the subsequent stages are dependent on this stage. Again  this stage determines “what information” needs to be collected, and any error at this stage would get compounded throughout the research process. This chapter describes the process of converting the decision problem to a research problem.

PROBLEM DEFINITION The process of research starts with understanding the decision problem and converting it into a research problem by identification of the research objectives. Research objective defines the overall information required for the marketing research studies (e.g. understand the perceptions of the brands). FAO (1997) states that the researcher has to translate the decision-maker’s problem

Marketing Research: A User’s Perspective

14

into research objectives that are “clear, concise, attainable, measurable and quantifiable”. These research objectives are then broken down into a comprehensive set of research questions that would provide detailed information to address the decision area (discussed in the next chapter). These two steps, identifying the research objectives and breaking it down into research questions, forms part of the problem definition phase. Notwithstanding its relevance, problem definition is not properly defined in many marketing research studies (Chapman 1989). In general, decision makers feel that marketing problems are known, easy to define and clear. Chapman (1989) points out that often the decision maker does not know what the precise problem is as problems are usually “buried under a heap of symptoms” and problem definition requires probing into the symptoms to identify the problem. For example, drop in sales is not a problem, it is only a symptom. The real problem may be with the acceptance of the brand, pricing, distribution, etc. It therefore becomes important to identify the real problem to find the appropriate solution. This stage of converting the decision problem to research problem is therefore very crucial. This process starts with the user stating the decision problem as a research brief to the doer.

RESEARCH BRIEF A research brief is a problem statement given by the decision maker to the researcher. The problem may arise out of a situation where the manager is unable to make a decision with the available information. This need for decisions could arise out of opportunities identified during the marketing environment analysis (e.g. a new segment which could be tapped) or symptoms identified after implementing the marketing programme (e.g. low sales). If the risk associated with the decision is high then there is a need for research and this problem is translated into a research brief (Figure 2.1). This research brief is communicated to the researcher. For example, in 1995, a company1 wanted a research study and gave the following research brief: Our sales are increasing every year, but we do not know why. We are wondering whether the increase in sales would be sustainable. Examples of other research briefs are given in Annexure 1.

1

Name withheld

Decision Problem to Research Problem

Marketing environment analysis

Marketing strategy

15

Marketing implementation

Symptoms (e.g. low sales, etc.)

Opportunity (e.g. new segment to be tapped)

Need to make decision

Risk perception high

Need for research Prepare research brief Figure 2.1: Decision problem

CONVERTING THE RESEARCH BRIEF TO THE RESEARCH PROBLEM The research brief given above and those given in Annexure 1 show the variation in the way the research briefs are prepared. Some are very precise stating what study is required and some are not clear as to what is the problem. Thus in the latter case, the decision maker and the researcher should work jointly to think through the research brief to identify the research problem. Even when the research brief is clearly specified, the researcher should interact with the decision maker to confirm that the problem has been clearly defined and also convert the research problem into specific information needs. The different approaches to identify the research problem (information needs) from the decision problem, based on the clarity of the research brief are given in Figure 2.2. For example, the research brief which states that the “sales is dropping” is only suggesting the outcome without identifying the problem. There is a need to identify the problem as the decision maker is not aware of what corrective action to take to boost the sale (i.e. the decision area is not clear). On the other hand, if the decision maker knows that the sales is dropping because the consumers have developed a wrong perception and the company needs to change the perception to improve sales then the decision area is said to be clear. In general, when the research brief specifies only an outcome which depends on several factors (as drop in sales) or specifies only symptoms, then the research brief can be classified as “decision area

Marketing Research: A User’s Perspective

16

not clear”. If the decision area is not clear then an interactive method should be used to convert the research brief into research objectives. On the other hand, if the decision area is clear, then the process of converting the decision problem to the research problem is either by conceptualizing the problem or using a decision tree approach. Conceptualization process could be by using an existing concept or by developing a new concept. Research brief

Decision area not clear

Decision area clear

Use interactive process

Conceptualization

Use existing concept

Use decision tree approach

Develop a new concept

Figure 2.2: Process of developing research problem

Interactive Method If a decision maker is unable to provide a clear problem statement, then the interaction method is appropriate. Interactive process as the name suggests requires both the decision maker and the researcher to interact to identify the relevant information needed to address the decision problem and define them as research objectives. While converting a decision problem to a research problem, care should be taken that the decision maker should not dominate the process; the decision maker should work with an open mind (jointly with the researcher) to identify the information required. In other words, the researcher and the decision maker should work as “equals”. This interaction process would also act as a “dummy test” (Figure 2.3) as this would help clarify the decision area as well as the information required for the decision. The researcher should start by asking the decision maker general questions like, “Why do you feel there is a need for research?” “What do you (don’t)

Decision Problem to Research Problem

17

know about the situation?” The answers to these questions should lead to a tentative problem statement (Chapman 1989). Starting with the tentative problem statement, the research should then probe the decision maker sequentially with the following questions: 1. Why not make a decision using existing knowledge instead of conducting marketing research? 2. If the decision cannot be made with the existing information, what additional information is required for making the decision? 3. Assume the additional information is available. 4. If the information would help in making the decision, then it becomes a research objective. If the information is not relevant, then the questioning goes back to Point 2. 5. Is the information identified in Point 3 sufficient to make the decision? If yes, go to Point 6. If no, go to Point 2. 6. If more than one research objective is present, then the research objectives have to be prioritized based on the criticality of the information required. Case 2.1 describes an interactive process for starting an advertising agency in Jamshedpur. The dummy test will help the decision maker to: • Question and concretize the problem • Identify the research objectives • Prioritize them (in case there is a budget constraint, then it could help in deciding how the budget should be spent across the different research objectives).

Identify possible problem

Assume information is available

Will it answer the decision area?

Yes Then it becomes research objective

No Can decision be No No made with existing What other information is required? information?

Is the information sufficient to make the decision? Yes

Yes

Prioritize research objectives based on criticality of information

No need for MR

Figure 2.3: Dummy test: the process

18

Marketing Research: A User’s Perspective

For example, if the research brief was “sales is dropping” then the interactive process should help narrow down the possible causes by questioning and allowing the decision maker to look at the possible reasons and think through them and eliminate those which are not likely to be a problem and finally arrive at the most possible cause(s) that require correction to arrest the sales drop. Here it is important for the researcher to be familiar with the market/product to ask relevant questions and help the decision maker to identify the problem. Decision Tree Approach A decision tree is one of the most systematic tools of decision-making theory and practice. The decision tree is helpful when the decision can be modelled as a multistage problem. In a decision tree approach, the information needs are sorted hierarchically like the branches of a tree (Sánchez-Úbeda 1999). That is, a decision tree is essentially a diagram that represents outcomes at different levels of the problem (Time-Management Guide 2010). The decision tree was found to be an effective tool for examining the impact of both behavioural and demographic variables on private label purchasing (Moore and Carpenter 2010). The decision tree should start with basic questions and then broken down to specifics. This is explained using the case of environmental consciousness. The research brief for environment consciousness is given below and the decision tree to identify the information needs is given in Figure 2.4. Research Brief Given the generally deteriorating state of the environment, both locally and globally, the extent of concern about environmental problems amongst the people of India has been of considerable importance. Since no empirical information about environmental consciousness is available, the government needs to know what level of environmental consciousness is present in India. The research brief can be classified (as per Figure 2.2) under the head of decision area clear as the research brief clearly specifies that it is interested in understanding the level of environmental consciousness. Since environment consciousness is a multistage concept, a decision tree approach is used to identify the information needs for the study. The decision tree approach starts with the basic question about what does environment means to the general public. Different people could have different views about environment, so there is a need to profile the people based on their understanding of the word environment. Based on the level of understanding, the government would need to educate people about what constitutes environment. The next level questioning would be to understand whether people are aware of the need to protect environment. Here again there is a need to segment the population on their awareness about

Decision Problem to Research Problem

19

environmental protection to develop suitable programmes for environmental protection. In the same way, the questioning could continue to “understand the desire, knowledge and willingness to act”. At each stage, the profiling is done to identify the different groups. Information about the reading habits are also collected to decide on “how the group can be reached” (Figure 2.4). Environment protection

What does environment mean to the respondent

Aware of the importance of protecting environment

No desire to protect environment

Readership behaviour

Profile citizens on the basis of how they define environment

Profile citizens based on their awareness about the importance of protecting environment

Not aware of importance of protecting environment

Desire to protect environment

Knowledge of what degrades environment

Identify factors which facilitate/hinder actions to protect environment Willing to act

Readership behaviour

Readership behaviour

Profile customers based on desire to act

Identify what needs to be communicated and to whom Profile customers based on factors which facilitate/hinder actions to protect environment based on factors Identify media to be used to communicate to different groups

Figure 2.4: Decision tree approach to the environmental consciousness issue

Marketing Research: A User’s Perspective

20

In general, the information obtained by adopting different methods for converting the decision area which is clear to the research problem should yield similar information needs. This is shown by using the same case of environmental consciousness by using another method, “conceptualization using an existing concept”. Conceptualization Conceptualization is another method used to identify the information needs when the decision area is clear. Conceptualization is a process where an issue is broken down into variables such that the relationship between them is identified. Unlike the decision tree approach, here the variables need not be hierarchical. Chapman (1989) emphasizes the importance of conceptual thinking at the initial stage of marketing research to obtain relevant information. Conceptualization could be either by: • Applying an existing concept or • Developing a new concept Applying the Existing Concept Applying an existing concept or developing a new concept depends on the availability of acceptable models explaining the concept. If models are already present, they could be used to identify the information needs for the research. For example, if a research is focused in studying the motivation level of the sales force, then there are models (e.g. expectancy theory) which would help a researcher convert the decision problem into a research problem. This process is explained using the above research brief of environmental consciousness. Here environmental consciousness is broken down into information needs using an existing concept of “hierarchy-of-effect” (Ambler 2000). In hierarchy-of-effect models, AIDA is a well-accepted marketing model which aims to obtain the “attention (awareness), interest, desire and action” stages an individual goes through before changing his/her behaviour. Each stage is broken down into specific data points. The information needs identified for the environment conscious problem using the AIDA model is given below: 1. Awareness a. What is the meaning of environment? i. Immediate surroundings ii. National level iii. Global level b. What causes damage to the environment? i. Plastic ii. Pollution

Decision Problem to Research Problem

21

iii. Aerosols iv. Cutting of trees, etc. c. What is the extent of damage? i. Water pollution ii. Air pollution iii. Tsunami iv. Floods v. Global warming, etc. d. Who damages? i. Industries ii. Individuals iii. Developed nations, etc. 2. Interest a. Why protect? i. For present generation ii. For future generation b. Possible actions i. Avoid plastics ii. Car pooling iii. Reduce wastage of water, electricity, etc. 3. Desire a. Attitudes and motives towards protecting environment b. Corrective actions–who can do? i. Industry ii. Individual iii. Government iv. Non-governmental organisations, etc. 4. Action a. What are you doing? i. Avoid plastics ii. Car pooling iii. Wastage of water, electricity, etc. Developing a New Concept When no existing concept can be applied to understand the problem, a new conceptualization needs to be done to understand the phenomenon. For example, Dunning (1981) has conceptualized foreign direct investment and international expansion in retail using three dimensions (ownership, location and internationalisation). The relevant information identified is given below:

•

Ownership Asset ownership Transactions

Marketing Research: A User’s Perspective

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•

•

Location Overall attractiveness of a country Push factors Market size Cost of land Cost of labour Pull factors Cultural differences Regulations Internalisation How are company’s secrets handled?

Halepete et al. (2008) later used the above dimensions to evaluate Wal-Mart’s expansion in Germany and South Korea. They also used the model to “understand the possible advantages and disadvantages for Wal-Mart entering the Indian retail market”. This research would however be treated as applying an existing model of Dunning (1981) for converting the decision problem to a research problem. The process of developing a new concept needs to work backwards from the outcome to the input variables. The detailed process is explained in the case of “Conceptualizing the process of business schools ranking”2 (Case 2.2).

SUMMARY The chapter describes the process of converting a decision process into a research problem. Three methods, depending on the clarity of the decision problem, are suggested. Interaction method should be used when the decision problem is not clear. Here the user and researcher should adopt the dummy test to finalize the research objectives. Conceptualization and decision tree approach are applicable when there is clarity in the decision problem. Case studies are given for different approaches.

CASE 2.1 Starting an Advertising Agency in Jamshedpur Jamshedpur, located in Jharkhand, is a major industrial centre. Companies like Tata Steel, Tata Motors, Tata Power, Lafarge Cement, Telcon, BOC Gases, Praxair, Timken India, Tinplate, etc. are located in Jamshedpur. The automobile cluster with around 300

While the conceptualization could be generalised, the background is specific to India.

2

Decision Problem to Research Problem

23

automobile ancillaries is located in Adityapur, adjoining Jamshedpur. The region also boasts of excellent mineral resources. The Problem Impressed by the number of industrial houses in and around Jamshedpur and by the fact that there was no good advertising agency in Jamshedpur, Bhuvan, a management graduate from a premier management school, decided to set up a high quality advertising agency in Jamshedpur. While the agency was set up with high expectations, the agency was not able to garner any business from any of the big industrial houses. It was catering to some local traders and hotels who wanted advertising services on an ad hoc basis. Research Brief Bhuvan decided to take the help of a marketing research agency to find out why his organisation was not able to get any business and what his future course of action should be. He approached a research agency and described the mission of his organisation was to set up an advertising agency that would provide “high quality advertising solutions”. He thought Jamshedpur was a natural choice, given its huge potential and his familiarity and contacts with executives in the industrial houses in Jamshedpur. Bhuvan wanted the research agency to identify why he was not getting any “big accounts”. Converting to a Research Problem The researcher adopted an interactive process to identify the research problem. The relevant parts of the interactive process are given below: Researcher: Bhuvan: Researcher: Bhuvan:

Researcher: Bhuvan: Researcher: Bhuvan:

Researcher: Bhuvan: Researcher: Bhuvan:

Quality advertising means… Providing complete advertising support to an organisation. This includes creating, designing and producing advertisements as well as ensuring that the advertisements are released as planned. What does it take to provide good quality advertising services? Creativity, contacts and finances. I have a team of management graduates from premier management schools. We have the knowledge of the market; we have good financial support and know some executives in the companies. Anything else? You need good photographers, models, printing houses, etc. Does Jamshedpur have all these? No, Jamshedpur does not have professional photographers. Some great models have started their career here, but they moved to other cities. As far as printers are concerned, there are some OK printers; but not high quality ones. Then how do you develop high quality advertising. Could this be the problem? Not sure… given the type of advertising we did, we did not require such high professional help. But if we get a “big account” maybe it would be a constraint. So, do you feel that this could be a possible problem? It is a chicken and egg situation. Which comes first?

24

Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher:

Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan: Researcher: Bhuvan:

Marketing Research: A User’s Perspective

Ok, let us look at it differently, if there were good support facilities, could you have got the “big accounts”. Maybe, maybe not… Could there be another problem? What could be the other reasons for not getting any business? Does it have anything to do with the way we approach the companies? How do you approach? Interaction is mainly through email and telephone. Are you not getting an appointment to meet them directly. The concerned persons are not in Jamshedpur. ??? Most of the big companies have their sales offices in the metros. Now, could that be a problem. Do they prefer some agency where they can interact personally? … And would it mean that the potential for your agency is limited only to companies having sales offices in Jamshedpur. Thus, of the two problems (support facilities and location of sales office) which do you think is more critical? Maybe, location of the sales office. Have you got business from any company which has a sales office in Jamshedpur? Most of the big companies we are looking at have their sales office in Kolkata or other metros. Ok, why not shift your office to Kolkata or some other metro? There would be too much competition. But you have the capability… I know, but we may not get any business in a big city. Why? You also have contacts. Here in Jamshedpur, not outside. Your team could impress upon them to give business. Are they going to take a risk by giving the account to us? What? They do not know who we are. Does it mean… We do not have any past accounts to boast about. Therefore, could that be the problem. You need “reputation”. You have to build it or acquire it. Maybe you should start with smaller accounts and build reputation or hire a person with reputation. … makes sense.

Decision Problem to Research Problem

25

CASE 2.2 Conceptualizing the Process of Business Schools Ranking3 Many companies choosing between management schools for recruiting management graduates face a dilemma, as different surveys adopting different methodologies and using different parameters come out with different ranking. Students also face a similar dilemma. The students would have no doubts if they get admission into the top business schools, but the problem would be if they get admission calls from other institutes which are ranked differently by different surveys. Thus, there is a need to develop an understanding of ranking of institutes that could help companies/students make an informed choice. Since surveys use different variables and different methodologies, there is a need to conceptualize the process of B-school ranking to help students and recruiters to identify the appropriate survey. Alternatively, help the student/recruiter to select relevant information from different surveys. The process of conceptualization should start with the desired outcome. From the recruiters point of view, the ranking should help them identify institutes, which could give them students who can perform in their job and provide them highest “recruiters’ satisfaction”. Again, prospective MBA students join business schools to build a good career, which also means they have to provide “recruiters satisfaction”. Thus, for both the recruiters and the prospective MBA students a common conceptualization can be done (conceptualization for attracting faculty or doctoral students would be different as the outcome they are looking for would be different). Recruiters’ satisfaction would influence the company to visit campuses for placement. Placement in an institute would, however, be affected by external factors as detailed below, therefore placement may not be a good measure for ranking. • Need in the company could determine the number of vacancies as well as the profile of students required. If a company is looking for a specific profile, it may opt for an institute known to possess such students (e.g. if a company requires students for industrial management, they would prefer going to a school offering such a specialisation). • Timing of placement in an institute could also influence the companies visiting the campuses. The dates of the placement in an institute may not suit the company, or the dates may clash with other institutes. In such situations, the companies may skip an institute though they would have been interested in selecting students from that institute. • Institute factors. A company would not have visited an institute as it was not given an appropriate “day slot”4 during the campus recruitment process. • Again placement would put new institutes at a disadvantage as they do not have alumni and therefore lack on the recruiters’ perception.

3 This insight written by the author first appeared in the October–November 2004 issue of the Smart Manager magazine that accompanied the TCS Smart Manager Case Contest. Reproduced with permission. 4 Some business schools in India provide companies specified slots during which period the companies could visit the campuses for recruitment. The slots allotted to the companies are based on the image the companies have amongst the students. Thus, getting a “good day slot” is perceived to be important by the companies.

Marketing Research: A User’s Perspective

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The ranking should therefore focus on the antecedents to “recruiters’ satisfaction”. Recruiters’ satisfaction would be influenced by the quality of students passing out from the institute. Here, quality of students for recruiters would mean that the students should have (a) cognitive skills/logical reasoning, (b) interpersonal skills (team orientation, leadership, etc.) and (c) technical skills.

Cognitive abilities/logical reasoning

Recruiters’ satisfaction

Quality of students

Interpersonal skills (team orientation/ leadership)

Academic environment

Faculty, academic infrastructure industry interface, etc. admission process

Curriculum related

Courses on individual behaviour, ethics

Extracurricular activities

Management value

Student bodies, sports, etc. Ownership, faculty satisfaction faculty–student interaction

Placement Technical knowledge/ skills

Influenced by external factors

Need in the company, e.g. vacancy, profile of student

Timing of placement

Output variables

Background/ experience

Institute factors: e.g. company ranking, availability of students Throughput

Antecedents (Input data)

Figure 2.5: Conceptual model: quality of students

Decision Problem to Research Problem

27

Working further backwards, the input variables that would influence each of these dimensions of quality should be identified. For example, cognitive skills would be influenced by the academic environment, which comprises of the faculty, academic infrastructure, industry interface, admission process, etc. (These are only indicative; the complete list of antecedents needs to be identified.) Similarly, interpersonal skills would depend on curricular (courses on organisational behaviour, ethics, etc.), extracurricular (student bodies, sports, etc.) and management values (ownership of institute by the management, student–faculty interaction, etc.). This way a complete list of variables which would cumulatively define the quality of students can be identified. Therefore, ranking process should measure these antecedents and combine them by giving appropriate weights to arrive at an overall score for each institute (this could then help in the ranking of the different schools). This process of working from the antecedents would not put the new institutes at a disadvantage, as information on the input variables would be available for all institutes. The process of conceptualizing would also help eliminate double counting (like adding input and output parameters) and eliminating variables not relevant e.g. “gross expenditure” used by one study. (This put a new institute at an advantage because it spent a lot of money on infrastructure development during the year of the study as compared to other institutes.) The conceptualization of B-Schools ranking is given in Figure 2.5.

3

Research Questions and Hypotheses

Learning Objectives At the end of the chapter, the reader would be familiar with: Differences between research objectives, research questions and hypotheses The process of developing a complete list of research questions Special topics covered in the chapter: Modified 5Ws and 1H questioning and behavioural framework for identifying the research questions

The interactive method in the problem definition stage helps in formulating the research objectives that would guide the marketing research process. The research objective needs to be broken down into a set of research questions. The research questions delineate the information that needs to be collected from the research. Answers to these research questions would provide the necessary information for the decision maker to address the problem. (Research questions are different from the “final questionnaire” questions. The research questions will be subsequently translated into a format suitable for inclusion in a questionnaire. This is discussed in Chapter 6.) Research questions may be converted into hypotheses. Hypotheses are possible answers to the research questions or they explain the relationship between two research questions. Once the research questions/hypotheses are formulated, the research methodology is designed. This chapter describes how to formulate research questions and hypotheses.

FORMULATING RESEARCH QUESTIONS The effectiveness of the research would be based on how well the research questions are defined. Additional research questions identified after data collection may not be of any value. It is therefore important that a complete set

Research Questions and Hypotheses

29

of research questions are identified before commencing the data collection process. Failure to identify a complete set of research questions would mean a gap in the information and a possible error. While there is no standard format for formulating research questions, some frameworks would help identify the complete set of information. It may also be advisable to have the involvement of the decision maker during the formulation of the research questions. Moreover, as Chapman (1989) states that “the research objectives/research questions serves as an implicit contract (between the decision maker and the researcher) by describing what the marketing research study must accomplish”. The decision tree and conceptualization process, as described in Chapter 2, would identify the necessary research questions directly. Thus, if these approaches are used during the initial phase then there may not be a need to identify the research questions again. Two approaches for breaking down research objectives into research questions are suggested: • Modified “5Ws and 1H” questioning • Using the behavioural framework Modified “5Ws and 1H” A standard questioning pattern of “5Ws and 1H” is generally an accepted tool to gather information (also refer to Box 3.1). The “5Ws and 1H” are: • Who? • What? • Where? • When? • Why? • How?

Box 3.1: Five Ws and one H The “Five Ws” (and 1 H) were memorialized by Rudyard Kipling in his “Just So Stories” (1902), in which a poem accompanying the tale of “The Elephant’s Child” opens with: I keep six honest serving-men (They taught me all I knew); Their names are What and Why and When And How and Where and Who. http://en.wikipedia.org/wiki/Five_Ws downloaded 20 October 2010

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From a marketing perspective, the 5Ws and 1H matches with the 7Os framework. • Who participates in the buy? Organisations • What constitutes the market? Occupants • What does the market buy? Objects • Where does the market buy? Outlets • When does the market buy? Occasions • Why does the market buy? Objectives • How does the market buy? Operations While some claim that this approach is basic and does not provide the depth, the robustness of this approach would depend on the extent of probing on these questions that one goes through. They need to question each W (and the 1H) many times. For example, Strubler (2010) expands the “5Ws” and “1H” for the quality improvement programme (Table 3.1). Table 3.1: Strubler’s quality improvement questioning WHO 1. Does it? 2. Is doing it? 3. Should be doing it? 4. Else can do it? 5. Else should do it?

WHAT 1. To do? 2. Is being done? 3. Should be done? 4. Else can be done? 5. Else should be done?

WHERE 1. To do it? 2. Is it done? 3. Should it be done? 4. Else can it be done? 5. Else should it be done?

WHEN 1. To do it? 2. Is it done? 3. Should it be done? 4. Else to do it?

WHY 1. Is it done? 2. Do it? 3. Do it there? 4. Do it then? 5. Do it that way?

HOW 1. To do it? 2. Is it done? 3. Should it be done? 4. Can it be done elsewhere? 5. Is another way to do it?

Source: Strubler (2010)

The architect of the Toyota Production System (Scribd.com, 2010), Taiichi Ohno, described the repeated questioning as “the basis of Toyota’s scientific approach”. He focused on repeating “why” five times. Taiichi Ohno states that “by this, the nature of the problem as well as its solution becomes clear”. The tool has seen widespread use beyond Toyota and is now also used within Six Sigma methodology. A suggested framework for the marketing research combines the ideas of Strubler and Toyota (Figure 3.1). Here “what, who, when and where” of the behaviour are questioned “why” several times to identify perceptual and attitudinal factors (how behaviour is influenced).

Research Questions and Hypotheses Who behaves When do they act Where do they act What action

Question why several times

31

How behaviour is influenced

Figure 3.1: Modified 5Ws and 1H for identifying research questions

For a usage and attitude studies, the what, who, when, where and how would give an understanding on: What: determines the product decision, the brand decision and the important attributes Who: makes the decision (individual or joint), who are the influencers, their credibility When: determines the gap between desire and purchase Where: determines the place of purchase and the role the place would have on the brand and product decision Repeating why few times will identify “how behaviour is influenced” (perception and attitudinal). Refer to Case 3.1 for detailed research questions developed using this approach. Using the Behavioural Framework The behavioural framework approaches the information needs by starting with the outcome and working backwards to identify the variables that influence the behaviour and the variations in behaviour, if any. For instance, a marketing decision maker is interested in understanding the behaviour of the consumers to help him/her know about the sales. But the knowledge of behaviour alone is not enough if the decision maker has to influence the buyers, and hence the decision maker should know the state of mind of the buyers (what influences led to the decision). So, the research question should probe into the state of mind which influences the behaviour. State-of-mind information is at multiple levels. The levels include: • Preferences • Opinions • Knowledge • Perceptions and attitudes • Motives The discussion of the different levels of state of mind is taken up in detail in Chapter 4. While a consumer may be interested in purchasing the brand of his/her preference, sometimes preferences may not convert into purchase as behaviour may be influenced by situational factors (e.g. availability). Thus, research questions should also capture these factors.

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In addition to this information, the decision maker should also know the consumer groups (state of being variables) with specific state-of-mind orientation so that marketing programmes can be directed to different groups. A formal research should therefore develop research questions that can capture information on behaviour, situational factors, state of mind and state of being variables (Figure 3.2). Behaviour

State of mind

State of being

Situational factors Figure 3.2: Different levels of information that has to be collected in research studies

The need for behavioural framework can be understood by looking at the responses to a study on overspeeding (Case 3.2). As mentioned, there is no standard basis for arriving at the research questions. The different frameworks only provide a basis for questioning the problem to arrive at a possible complete set of research questions and minimize the “surrogate information error”. Inferring from Chapman (1989), it can be assumed that sufficient probing is not done by researchers to identify a complete set of research questions. The user should therefore take adequate care to check if the research questions provide a complete set of information to answer the research objective.

HYPOTHESIS Research questions are generally open ended and provide a broad understanding of the issue. Thus, research questions are sometimes converted into hypotheses to narrow the scope of the data collection to some specific dimensions of the research questions. That is, the broader concept of research question is expressed in terms of specific variables as a possible answer to the research question (see later part of this section for examples). But the hypotheses need to be tested to be proven correct. Hypotheses can also take the form of a statement indicating the relation between two or more variables. The variables could be derived from one or more research questions, and this relation also has to be tested. Hypotheses are therefore expressed as variables that are measurable. They provide the following benefits (Reference for Business.com, 2010): • Establish the focus and direction of data collection • Determine the dimensions of the research question that would be considered in a study • Define operational definition of the variables

Research Questions and Hypotheses

33

Since hypotheses narrow the focus of the data collection, they should be developed with a great deal of thought. Hypotheses should therefore be developed based on literature support or a sound market knowledge (Figure 3.3). Literature support Hypothesis

Research questions Market knowledge Figure 3.3: Rationale for developing hypotheses

In case the researcher does not have the backing of literature or market knowledge to convert a research question to a hypothesis, then the research questions should be retained as such. Narrowing the data collection by converting to hypothesis without a basis could lead to collecting wrong information. The final information set could therefore have: • A set of research questions which are not converted into hypothesis Research question: What motivates the salespersons? • A set of hypothesis indicating the possible answer to a research question Research question: Identify the profile of good salespersons Hypothesis: Married sales people perform better than unmarried sales people (based on the research of Ford et al, 1985). The research question would have had to collect information on several variables to identify the profile, whereas the hypothesis has narrowed the scope of data collection. • A set of hypothesis indicating a relationship between two or more research questions Hypothesis: Upgrading skills would motivate married sales people to perform better. The hypothesis combines the above two research questions (this is based on the model suggested by Pingali, 2010). If this hypothesis was not formulated, then the analysis would have had to do various cross tabulations (discussed in Chapter 8) before it could have arrived at this relationship. Thus, the hypothesis helped narrow the data collection specifically to test the relationship between the two variables. The next section discusses how this information needs to be collected.

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34

SUMMARY The process of finalizing the information for the research studies is described in this chapter. Two methods of identifying the research questions are suggested here: the modified 5Ws and 1H method and the behavioural framework. While the 5Ws and 1H method uses a repeated questioning process, the behavioural framework identifies the state of being, state of mind and situational factors to identify information influencing behaviour. The chapter also discusses when hypotheses could be developed.

CASE 3.1 Testing Effectiveness of “Vote for the Right Candidate” Campaign1 A campaign to educate the voter to choose the right candidate “Voice Yourself ” was run all over the country. The campaign focused on the following: • Right to vote as a fundamental right – Laying stress on the fact that the constitution guarantees every citizen of the Union of India an equal opportunity to elect a representative government which acts on their behalf. • Responsibility of a good citizen – Emphasizes the right as an obligation as well communicates in clear terms the significance of voting as an essential component for effective functioning of a democracy. • Do not vote on the basis of caste, creed, money, regional affiliation and associated considerations. • The onus is on you to make a difference! – The overall message delivered is to “rise up to the occasion and make a difference to the country when the opportunity is available to the individual rather than regret on their non-participation later”. A research was conducted to study the effectiveness of this campaign by studying: • Increase in the voter turnout for elections This will be a primary focus of the study as the actual turnout shall reflect the first step for the success of the communication to the target segment. If there is a significant increase in the voter turnout due to the campaign, the objective of the same shall be deemed to be partially successful, as this indicates that the awareness has increased amongst the voters. • Influence people to make an informed choice The campaign should not only persuade people to cast their vote but also to involve them into the process and base the selection on solid foundation and real, genuine issues/factors rather than commonly used factors such as money, caste, religion, etc., which might be detrimental to the interest of the constituency, in specific, and the country, in general. The process of converting the decision problem to the research problem is not discussed as the case is primarily focusing on developing the research questions.

1

Research Questions and Hypotheses

35

Research Objectives The above decision requires an understanding of the voting behaviour of the citizens. Specifically, this would identify: • Factors which influence the voter turnout • Factors which influence the voter’s choice of candidate • Impact of the campaign on the above two factors Research Questions The research questions generated using the modified 5Ws and 1H framework are: WHAT is the importance of voting? • What would be the impact on the society at large and, hence, influence their lives indirectly? • Is the voter aware of his fundamental right to vote? • Responsibility and the importance that his/her vote will play in electing the candidate. • Perceived impact of the electoral results on their lives. WHO votes? • Whether the education levels of voters have an impact on the keenness towards voting. • Finding out the impact of the socio-economic factors in deciding to vote or not to vote. • Finding out whether gender has an effect on the willingness to vote. WHERE is the polling booth? • Distance of the polling station. WHEN does the person vote? • When candidates who are contesting are able to generate interest. • When developmental issues are raised by candidates. • There is security and lack of fear. • There is ease of polling procedure. • Right party representatives contest. • Due to social factors. • Due to affiliation to a particular party. • Lured by the agenda put forth by the candidate. • Allured by monetary benefits. • Due to efficacy of the previous candidates’ agenda and its implementations. • Influenced by political campaigning by the candidate. • Due to caste and religious affiliations. • Influenced by campaigns run by government and NGOs in creating awareness, and eagerness to vote. HOW has it been translated into action? • Did the voter actually read the candidates manifesto/agenda? • Did the voter evaluate the candidate on his previous performance? • Did the voter check the track record of the candidate?

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Marketing Research: A User’s Perspective

• Did the voter evaluate the pros and cons of electing a particular candidate? • Did the voter contrast and compare the various candidates or was it a biased decision?

CASE 3.2 Overspeeding A study was conducted to find out the reasons for overspeeding in a factory premise. The responses obtained are given as follows: • Overspeed for fun • Overtake the vehicle in irritation • Want to show off • Seeing no vigilance camera around, they speed up • Late to start from home • Delays due to the problems in vehicle • Power problem at home resulting in restless sleep • Late night parties • Delay due to traffic jam • Had to get vehicle repaired • Thinking about a problem in the factory • Thinking about the payment of installment of house loan • Disturbed while driving because of the bitter relationship with wife • Not getting on well with colleagues • Thinking about meeting scheduled with boss • Stressed for incompletion of annual target • No proper road indications • Delayed because of filling up petrol on the way to office • The speedometer of vehicle is not in working condition • Did not get vehicle serviced regularly • Do not accept the speed limit • Do not believe that speed limit has any positive impact • The speed limit is for the losers • I drive safely and others do not know how to drive attitude While the above responses provide some insight about the reasons for overspeeding, using a behavioural framework would provide a greater meaning for the decision maker. Classifying the above responses into the behavioural framework (Figure 3.4) helps the decision maker clearly identify that overspeeding is due to two different sets of state of mind influences. This can help prepare suitable campaigns. But with the current information, the decision maker cannot identify who are the people who constitute the different groups (that is there is no state of being information). So, in addition to the above information the research should have collected state of being information to classify people based on their “state are of mind”. This would help the decision maker know how the corrective action could be taken.

Research Questions and Hypotheses

Preoccupied mind therefore overspeed Seeing no vigilance camera around, they speed off

Start late from home therefore overspeed

Behaviour

Situational factors

37

Knowledge related: • Meeting scheduled with boss • Stressed for completion of annual target • Not getting on well with colleagues • Thinking about the payment of installment of the house loan • Disturbed while driving because of the bitter relationship with wife Attitudes: • Does not accept the speed limit • Does not believe that speed limit has any positive impact • The speed limit is for the losers • “I drive safely and others do not know how to drive” attitude

Motives: • Over-speed for fun • Want to show off

State of mind

Figure 3.4: Classification of reasons for overspeeding using the behavioural framework

Section 2 Methodology Section 2 describes the methodology to be adopted for the research. This section has five chapters. Chapter 4 develops a framework for identifying the appropriate research design based on the type of information to be collected and the purpose for which the information is being collected. While Chapter 5 describes the research designs for gaining insights, Chapter 6 describes the survey research method and how a questionnaire has to be developed. Chapter 7 describes the different methods of sampling and suggests the sampling methods suited for different research designs. Chapter 8 describes the different types of analysis and suggests guidelines for choosing the method of analysis to be used. This chapter also discusses the types of error while testing hypothesis. The chapters in this section are highlighted in the overview diagram.

Research brief Research problem Research questions/ hypothesis Purpose of study

Type of information Research design

Type of analysis

Instrument design Sampling design Data analysis Interpretation

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Identifying the Appropriate Research Design

Learning Objectives At the end of the chapter, the reader would be familiar with: Different types of information from the respondent’s perspective Purpose of collecting information from the researcher’s perspective Framework for identifying the appropriate research design Special topics covered in the chapter: Validity of information State-of-mind information

The problem definition stage determines the information to be collected. The next stage of the research is to design an appropriate methodology (research design) to collect the information. Burns and Grove (2003) define research design as “a blueprint for conducting a study with maximum control over factors that may interface with the validity of the findings”. The appropriate research design would therefore facilitate collection of information which is valid. In general, people associate marketing research with conducting a survey, but as would be discussed in the next section, survey may not always give valid responses. This chapter describes the different types of information from the respondent’s perspective and the different purposes of the research study from the researcher’s perspective and develops a framework to identify the appropriate research design. VALIDITY OF INFORMATION Validity is the extent to which a research measures what it claims to measure. Piercy (1980) states that if marketing decisions are to be based on

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42

research, then the user (of research) has to check the validity of data before using it for decision making. Take, for instance, the responses of parents for a study to identify the Top 5 indoor activities (apart from TV and computer games) of their children (Table 4.1).

Table 4.1: Top 5 Indoor Acivities Survey response summary Age Group

3–7 Years Boys

S.No

8–12 Years

Girls Boys Girls

Total Boys

Girls

Total Kids

3

11

# Responses 1

Studying

2

0

6

3

8

2

Reading newspaper, magazine

1

0

4

0

5

0

5

3

Science project

0

0

5

1

5

1

6

4

Playing with toys like blocks, cars, etc.

13

3

3

0

16

3

19

5

Playing fun games like ludo

8

3

5

1

13

4

17

6

Playing mental games like chess

7

2

3

1

10

3

13

7

Painting

12

7

9

6

21

13

34

8

Crafting

3

3

4

4

7

7

14

9

Carom

5

1

4

1

9

2

11

10

Skating

1

2

1

0

2

2

4

11

Dancing

6

7

6

6

12

13

25

12

Playing instrumental music

5

0

2

0

7

0

7

13

Listening to music

1

3

0

3

1

6

7

14

Singing

2

2

3

5

5

7

12

15

Practising skit or drama

0

0

4

1

4

1

5

16

Playing cards

0

0

1

0

1

0

1

17

Writing letters

0

0

0

1

0

1

1

18

Calling friends

0

0

4

2

4

2

6

19

Decorating house

0

1

0

2

0

3

3

20

Helping mom in kitchen

0

1

2

6

2

7

9

21

Cooking

0

0

0

1

0

1

1

22

Taking care of pet

0

0

0

0

0

0

0

23

Gardening

0

0

2

0

2

0

2

24

Organising things in the house

0

0

1

1

1

1

2

25

Yoga/aerobics/skipping

0

0

1

0

1

0

1

26

Playing Table Tennis

0

0

0

0

0

0

0

Identifying the Appropriate Research Design

43

The study shows that: • For 13 boys between 3 and 7 years “Playing with Toys” falls in the Top 5 activities, whereas it falls in the Top 5 only for 3 girls. • Painting emerged in the Top 5 indoor activities for 21 boys and 13 girls up to the age of 12. • Helping in kitchen emerges in the Top 5 activities for 6 girls in the age of 8–12. • Playing instrumental music was found to be preferred by boys only. • Taking care of pet and indoor high physical activities were selected by none. Are the responses correct? Could the responses be overstated (e.g. response to painting) or understated (e.g. helping in household chores). The error (if any) is because the questions could “force” a respondent to give “acceptable responses” (i.e. the responses have a potential of getting overstated or understated to gain acceptability). Vinten (1995) found charity, library usage, seat belt usage to be overstated and use of alcoholic beverages, traffic violations to be understated. Thus, as Burns and Grove (2003) state that there is a need to identify an appropriate research design. As seen in the above study, certain type of information has a tendency of being overstated or understated. A researcher should therefore examine the type of information to identify issues relating to obtaining valid responses before deciding on how to collect it (research design).

TYPES OF INFORMATION The problem definition stage of the research process identifies the information required to help the decision maker. Information to be collected from marketing research studies could be classified as: • Sensitive information • Information leading to socially desirable response • Motives • Information not subject to bias (unbiased responses) Sensitive information: Personal opinions on controversial topics such as private and/or health information, information relating to unidentifiable individual’s private activities or practices are sensitive information (e.g. treatment history, financial information, etc.). People do not give accurate responses to such information. Thus, methods which mask the sensitivity and allow the respondent to provide such sensitive information would be required. Socially desirable response: Socially desirable response occurs when the answers to questions tend be showing the person as exhibiting “good behaviour”, though he/she may not be behaving in that manner. Seale (1998)

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Marketing Research: A User’s Perspective

observes that what people say in research interviews is not necessarily what they do in practice. Aggregating such information across the respondents would tend to show a majority as exhibiting socially accepted practices. For example, questions relating to patriotism and purchase of goods manufactured in the country may show that people are patriotic and prefer purchasing locally produced goods, while the reality would be different. Socially desirable responses are governed by the social norms prevailing. Social desirability would therefore vary from one society to another. Crane (1999) considers that social desirability bias is one of the biggest problems in researching ethics. Research has to be therefore designed to take account of social desirability bias or compensate for it, otherwise it could lead to results that lack validity (Boddy 2007). Motives: As mentioned in Chapter 3, motives could make a person behave in a particular manner, as motives are psychological states that influence a person to act towards a desired goal (The Free Dictionary.com 2010). Gable (2006) also defines motives as the underlying wishes and desires that people possess and these will influence behaviour. Though motives govern behaviour, they tend to be “below the level of verbalization” as the person may not be associating behaviour to the motive (or would try to rationalize his/her behaviour to assume social desirability). Oppenheim (1992) states people are frequently unaware of their own motives and so cannot give correct answers. Mitchell and Davies (1995) found that the purchase of certain highly priced products was because the consumers want to “show off ” (showing off being a motive). If they are probed into why they purchased the expensive brand, they would typically state that the purchase is made because of the quality of the brand (i.e. they rationalise their behaviour). Green et al. (1993) have noted that respondent’s may not give truthful information to questions relating to motives (if asked directly) as the respondent is afraid of losing prestige or being embarrassed by disclosing truthful information. Nevertheless, such information is essential for understanding behaviour and has to be collected by eliminating the bias. Unbiased responses: Information which is not governed by any motive, social desirability and is not sensitive in nature and is likely to elicit a response without bias when asked directly are classified as unbiased responses (normal information). Thus, a researcher should examine the type of information before deciding on the research design. The next section discusses some broad guidelines to identify the type of information.

RESEARCH QUESTIONS AND TYPES OF INFORMATION For purpose of generalisation and developing the framework, the research questions identified by the behavioural framework (Chapter 3) would be used.

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Behaviour In general, people want to project themselves as behaving in a socially desirable manner. Thus, socially desirable responses are possible when direct questions are asked regarding behaviour of respondents. Overstating or understating would increase if the behaviour cannot be verified. For example, faculty reading habits. As part of a class exercise, the author asks the students to design a study to understand the reading habit of the faculty. Some of the questions typically listed by the students are given in Table 4.2. Acting as a respondent, the responses given in the class by the author are also listed in Table 4.2. Alongside, the nature of information and the validity are given. Table 4.2: Questions and responses for a class exercise on reading habit of the faculty Question

Response

Type of information

Verifiable/ unverifiable

Validity

Type of books you read

All latest books in the discipline

Socially desirable response

Unverifiable

Doubtful

Name some of the List some recent books you have read titles

Socially desirable response

Unverifiable

Doubtful

Are you a member of any library

Yes

Unbiased

Verifiable

Correct

How many library books you get issued from the library on an average?

10–15

Unbiased

Verifiable

Correct

How many hours do you spend in the library?

On an average 10 hours a week

Socially desirable response

Unverifiable

Doubtful

When do you read?

Especially when I am traveling

Unbiased

Verifiable

Correct

How many hours do 3–4 hours a day you read in a day?

Socially desirable response

Unverifiable

Doubtful

How many books do you purchase in a year?

Unbiased

Verifiable

Correct

5–6 books

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46

As seen in Table 4.2, the possibility of a socially desirable response increases if the behaviour is not verifiable. State of Mind As mentioned in Chapter 3, state-of-mind information consists of preferences, opinions, knowledge, perceptions/attitudes and motives. These stateof-mind information can be mapped to the three levels of conscious state (adopted from McLeod 2009). The top most layer, the conscious state, stores information which is easily accessible and the consumer is said to be aware of the information. This part consists of knowledge, preferences and opinions. For example, a consumer may be aware of certain features of a car and based on which he/she would have formed an opinion/preference for that brand. The subconscious state contains information not currently aware but can be brought into conscious state with some probing (e.g. basis of forming perceptions and attitudes). The subconscious state has a direct influence on the conscious state. The perceptions are converted as opinions in the conscious state (e.g. opinion regarding quality of goods manufactured by private and public sector companies). The unconscious state is below the level of verbalization (e.g. motives), but has a direct influence on behaviour. This information may not be obtained even by probing. As mentioned earlier, they may be unaware of their motives and their responses could be socially desirable (e.g. environment-friendly acts). The diagrammatic representation of the state-of-mind information is shown in Figure 4.1.

• Conscious state – Opinions/knowledge • Subconscious state – Formation of perceptions/attitudes • Unconscious state – Motives

Figure 4.1: Diagrammatic representation of the state-of-mind information

The type of information for each of these conscious states along with examples during the purchase of a vehicle is discussed next.

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Conscious State • Preference is state-of-mind information prior to behaviour. Depending on the nature of the behaviour, preferences could either elicit unbiased information or socially desirable responses. For example, opting to plant 12 trees for making driving around 500 kms per month carbon neutral could elicit a socially desirable response. On the other hand, preference for different SUV car brands may not have the issue of social desirability. • Opinions could also be socially desirable or not depending on the subject. For example, a respondent may not have any problem in expressing his/her true opinion on statements like “Cars have become a necessity today” but opinion on safety issues during driving could get a biased response. • Knowledge consisting of factual information is at the conscious state and is easily expressed and likely to derive unbiased responses. For example, consideration set of cars, features, etc. Subconscious State • Perceptions and attitudes are formed over a period of time (detailed process of how perceptions are formed is discussed in Chapter 9). As would be discussed later, this information has to be collected through attributes influencing perception. Information about attributes is knowledge based and hence can be obtained without bias. Unconscious State • Motives as stated are below the level of verbalization and lead to socially desirable responses. For example, in some cases, owing a high power motor bike indicates masculinity or owing a four wheel drive projects status, but the actual responses would be masked by conscious state and the responses would tend to relate to factors like pick up, speed, etc., or driving in muddy roads, etc. (though these may not be their actual requirement). State of Being State-of-being information could be sensitive or non-sensitive. • The most commonly encountered sensitive information in marketing research is age and income. • Information like family background, education qualifications, etc. could also become sensitive. In other words, a lot of the demographic details could be sensitive. Respondents would like to inflate or deflate

48

Marketing Research: A User’s Perspective

the response for sensitive questions. For instance, respondents would like to “hide” their true age and income. Unlike the socially desirable responses where a respondent can get away by giving wrong responses (as they cannot be verified), in case of sensitive information the respondent becomes defensive as he/she cannot hide the reality beyond a certain level. These questions can affect responses for the subsequent questions also (also refer to Chapter 6). Therefore, the way of ensuring that valid responses are obtained is essential for such sensitive information. • Non-sensitive information would lead to unbiased responses. Since the responses could be erroneous depending on what the respondent wants to project himself/herself, it would be important to use an appropriate method to collect the information (research design) such that true information is captured. In addition to the type of information based on the respondent’s perspective, the purpose of research which takes into account the researcher’s perspective, should also be taken into account, before identifying, the research design.

PURPOSE OF STUDY The purpose of the study from the researcher’s perspective could be for: • Gaining insights • Predictive purposes • Problem solving Gaining insights: These studies form part of the exploratory research studies. If the researcher does not have an understanding of the problem, then exploratory studies should be done. The research is done to get a complete understanding of the phenomenon. While the findings cannot be generalised, the insights got from these studies help in the designing research for predictive purposes. For example, an exploratory study was conducted to identify what motivates the channel members (Pingali 2008). The significance of this study was that it helped identify some motivators (Table 4.3) that were not perceived as motivators by the companies (e.g. consumer schemes, launch of new product). Predictive studies: These research studies are conclusive as the results of the study can be generalised to the target population and help make decisions. While the exploratory studies provide insights, the predictive studies identify the importance of the different variables and their impact on the target group (predictive purpose). Table 4.4 provides the generalisations based on a predictive study for companies planning motivational schemes for channel members (for details refer to Pingali 2008).

Identifying the Appropriate Research Design

49

Table 4.3: Motivators as perceived by the channel members S.No

Scheme

S.No

Scheme

1

Exclusivity

11

Rerouting institutional business

2

Training of channel salespersons

12

Assuring a minimum return on investment

3

Market information

13

Information about new techniques

4

Joint advertising

14

Wide range of products

5

Involvement in target setting

15

New product launches

6

Shop displays

16

Computerization of supply chain

7

Customer education

17

Trade schemes in tune with product life cycle

8

Customer schemes

18

Settlement of complaints

9

Good service to channel

19

Information about company activities

10

Provide soft loans

20

Annual rewards

Source: Pingali (2008)

Table 4.4: Sequence of motivational schemes for channel members Stage Stage of product life cycle

Company objectives

Motivational schemes

1

Product is new

Get channel’s support to stock/display brand in market

Goodwill schemes

2

Growth phase

Help dealers build business

Support schemes

3

Growth- maturity phase (competition picking up)

Build long term relationship with the channel members

Building an alliance schemes

4

Maturity phase

Channel should feel that the company is good to work with

Systems orientation schemes

Source: Pingali (2008)

Since the purpose of this study was for predictive purpose, the results should be generalisable and the method of collecting the data would be different from the earlier studies of obtaining insights. Problem solving studies are causal studies. If the objective is to determine whether there is a cause and effect relationship between variables, causal research must be undertaken. For example, if a company wants to know the impact of a celebrity in their advertisements on brand recall. In order to determine causality, it is essential to hold other variable(s) constant and then measure

Marketing Research: A User’s Perspective

50

the impact of the specific variable. As the impact of the other variables has to be controlled, the method of collecting information would be significantly different from the predictive and gaining insight studies. This type of research is very complex and the researcher can never be completely certain that there are no other factors influencing the causal relationship (Uoguelph.ca 2010).

FRAMEWORK Based on the type of information (respondent’s perspective) and the purpose of the study (researcher’s perspective), the appropriate research design need to be identified. Table 4.5 gives the research designs suitable for different situations. Table 4.5: Framework for identifying the research design Type of information

Purpose of the study Insights

Predictive

Problem solving

Sensitive/leading to socially desirable responses

Case studies

• Observations for studying behaviour • Projective techniques for studying state of mind

Simulated market studies

Motive

Projective techniques

Projective techniques

–

Normal information (respondent aware, not leading to socially desirable responses)

Focus group discussions

Survey

Lab experiment

Thus, from the table it can be seen that survey method (which is generally associated with marketing research as mentioned earlier) is ideal only for predictive studies seeking information which forms part of the conscious state and the respondent is not likely to give socially desirable responses (discussed in greater detail in Chapter 6). The same information (conscious state information not likely to lead to social desirable responses) would require a focus group discussion if the purpose of the study is only for obtaining insights (discussed in detail in Chapter 5). For information which is below the level of verbalization (unconscious state) direct questioning would not obtain valid responses both for insights and for predictive purposes, so indirect means of obtaining the information are required. Here projective techniques are to be used (discussed in greater detail in Chapter 5).

Identifying the Appropriate Research Design

51

For insights into information which is sensitive and likely to lead to socially desirable responses, there is a need to develop rapport with the respondent before gaining insights. Here case studies/in-depth interviews are required (discussed in detail in Chapter 5) for obtaining insights. For predictive studies, for this type of information, observations are ideal for studying the behaviour, and projective techniques would be the ideal method for state-of-mind information. Problem solving studies, which study only behaviour, require control of other variables. These studies can use lab experiments for information from conscious state but not likely to lead to socially desirable responses and simulated market studies for information which is likely to lead to socially desirable responses. Since motives are only state-of-mind information, problem solving studies for motives are not possible. Refer to Case 4.1 for application of the research design framework.

SUMMARY The research methodology should ensure that it is able to generate the required information without a response bias. The methodology is determined by using two dimensions: the purpose of the study (researcher’s perspective) and the type of information (respondent’s perspective). The purpose of study could be for gaining in-depth understanding of the issue or for predictive purposes or problem solving. The type of information is classified based on the possibility of getting biased responses (information which is sensitive, having a possibility of social desirability or the information which is below the level of verbalization). The framework for identifying the appropriate research design is presented using these two dimensions.

CASE 4.1 Designing an Anti-smoking Campaign1 The research problem is to design an anti-smoking campaign. Research questions for the study are developed using the behavioural framework. State of Being • How does the smoking pattern differ for males and females? • Does smoking pattern depend on demographics such as rural/urban, income levels, educational levels, occupation, etc.? • What is the percentage of smokers in the total population? • What is the age at which people start smoking? • What is the quitting ratio for smoking? Also refer to consolidated case on evaluating anti-smoking advertising campaign at the end of the book

1

Marketing Research: A User’s Perspective

52

State of Mind • Why do people smoke? Perceptions about smoking Motives behind smoking • Are people aware of the diseases caused by smoking? • Since how long have people been aware of these ill effects? • Are people aware of the social consequences of smoking? • Have people quit smoking due to family pressure/health problems or antismoking campaign? • Why do people relapse into smoking: peer pressure or weak will? Behaviour • • • • •

How often do people smoke? At what time(s) of the day do people smoke? What type of smoking products people use? What is the average expenditure on smoking? What has been the change in the frequency of smoking of people?

Types of Information • Unaware information (motives) Reasons for starting to smoke Reasons for relapse • Sensitive information Frequency of smoking Occasion/situations for smoking Information on income levels, educational levels, occupation, etc. Types of smoking products Average expenditure by smokers on smoking • Socially desirable responses Change in type of smoking pattern after the campaign Age at which he/she started smoking Reasons for smoking Reasons for quitting smoking • Knowledge Percentage of smokers in the total population Quitting ratio of smokers Period of awareness Awareness about the social consequences of smoking Awareness about the diseases caused by smoking Given the type of information, the ideal research design as per the framework would be projective techniques.

5

Research Designs for Gaining Insights

Learning Objectives At the end of the chapter, the reader would be familiar with: Focus group discussions (FGDs) In-depth interviews (case studies) Projective techniques Special topics covered in the chapter: The role of moderators in conducting in-depth studies Content analysis

Research designs for gaining insights as mentioned in the previous chapter help the researcher to get an in-depth understanding of the concept. The studies for gaining insights help in understanding “why” rather than “how many” (Milena 2008). These studies, however, are not generalisable. These studies can, therefore, be taken up before undertaking a predictive study. The commonly used studies for gaining insights are focus group discussions (FGD), projective techniques and case studies (or in-depth interviews). This chapter describes these research designs. The context where each one has to be used has been identified in the previous chapter (refer to Table 4.5, Chapter 4).

FOCUS GROUP DISCUSSIONS This is an exploratory method useful when the purpose of the study is to get insights and the respondents are aware of the information and the information is not sensitive and not likely to lead to socially desirable responses. Focus group discussions are commonly used in marketing research studies prior to conducting a survey. The FGDs provide necessary input for designing the survey questionnaire.

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Focus group discussion involves getting a group of respondents to discuss about a topic. The objective is to allow the participants to freely discuss about the topic and in turn help the researcher get insights about the problem situation. The primary objective is that a comment from one of the respondents should elicit reactions from others thereby leading to a discussion through which all the dimensions relating to the issue will be identified and help build a better understanding of the topic. The success of an FGD is when the discussion is perceived as non-threatening and the participants feel free to express their opinion, whether or not it is shared by others (Zaharia 2008). The following points are to be considered to get the best results from a FGD: • Method • Role of the moderator • Sample Method There are two approaches for conducting FGDs—the “hour glass method” and “the inverted funnel approach” (Figure 5.1). Hour glass method

Inverted funnel method

Prioritise problems

Identify issues relating to a problem

Identify issues relating to a problem

Figure 5.1: Methods for focus group discussions

The hour glass method is used to serve two objectives. The first is to help identify and understand the importance of the different problems for a given issue and the second is to gain insights into a specific problem. Here the first part of the discussion should be initiated at a broad level and allow the participants to identify all the related problems and prioritise them. The second part should bring the discussion to the specific problem being studied to get complete insights about that one problem. For example, the hour glass approach was used by a bank which wanted to provide services to people who were moving into a city. The FGD started with a broad topic of identifying problems associated with “transfer”. In the

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first part, the objective was to understand the importance of banking-related problems amongst the other problems while a person is transferred. After the importance of banking-related problems during transfer is established in part 1 (Figure 5.1), the discussion has to focus specifically on banking problems to identify services that could be provided to the employees who are being transferred (part 2, Figure 5.1). The challenge with the hour glass method would be when the problem being studied is an insignificant problem, and the moderator is not in a position to bring the group discussion to that topic (e.g. if banking is a minor problem during transfer, the respondents would be more interested in discussing the other problems and the moderator would find it difficult to bring the discussion to banking-related problems). In such cases, the moderator should end the FGD after identifying the importance of the problem amongst the other problems and use an inverted funnel approach to get insights for the specific problem (here banking-related problems during transfer). Thus, an inverted funnel approach (which focuses on the second objective of the hour glass studies) could be used for studies with a specific focus, like understanding what motivates the channel members (Table 4.3, Chapter 4). Role of the Moderator The success of the FGD depends on the moderator. Following are some of the points that the moderator should ensure (adopted from Scholl 2002): • Moderator should create an atmosphere which is free to allow the participants to express their thoughts and feelings. The moderator should not try to dominate the proceedings. • Moderator should ensure that the discussion is focused. While the moderator should not dominate the proceedings, he/she should ensure that the deliberations do not go out of control. The moderator should be able to bring the participants back to the topic. • Moderator should put questions in an open and neutral format to avoid bringing in his opinion into the discussion. • Moderator should put probing questions to get a full understanding. • Moderator must be able to capture the non verbal cues. • The FGDs should be planned for about an hour to keep the participants interested. • It may be advisable for the moderator to record the proceedings to ensure that all points discussed are captured. Sample A sample of about eight to ten persons should be drawn to get the best insights. The group should be “heterogeneous within a homogenous group”. Homogenous group means that the sample should be drawn from a specific target group only so that the participants are free to discuss. However, to get

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the full understanding, the sample should be as diverse as possible within that homogenous group. For example, if a focus group has to be conducted amongst college students, then the heterogeneity should be brought in by drawing the sample from students from different educational backgrounds, regions, etc. The sample should also ensure that the opinion leaders are not included. Opinion leaders could bring in the phenomenon of “group think”1 and this would defeat the purpose of the FGD (Refer to Case 8.1 for implementing an FGD).

CASE STUDIES Case studies (in-depth interviewing) are also conducted to gain a complete understanding of a problem. Unlike FGDs, case studies are conducted for issues which are sensitive or for topics where people would not want to discuss in a group (e.g. to understand the reasons for compulsive behaviour). As case studies are conducted on issues which the respondents are not comfortable in sharing information, the facilitator (interviewer) has to play an important role to obtain the necessary information. The interviewer should always use the hour glass method for the case study. The first part of the interviewing, however, is not to prioritise the problems as was done in the case of FGDs. Here the first part would be to build rapport. Once s/he achieves this and the respondents are comfortable, then the detailed probing into the subject can begin (the second part). Thus, compared to an FGD, the case studies will be more time consuming. Again, the interviewer has to have some basic knowledge about the subject to probe in an appropriate manner (in an FGD, the moderator need not be knowledgeable about the concept). Like the FGD, the sample should be diverse; however, since the nature of information is sensitive, the responses should be collected from other stakeholders or even experts for a complete view on the concept. For example, a study on compulsive behaviour should interview behavioural scientists also to get their views on the topic.

PROJECTIVE TECHNIQUES2 As mentioned in Table 4.5, projective techniques are ideal for gaining an understanding of motives (unconscious state). These methods have been adopted from psychology where the projective techniques are used to understand the personality of individuals. Since the objective of marketing research is to study the impact of marketing activities, projective techniques used for marketing research try to divert the responses to the marketing stimuli by adding some brand-related symbols. Thus, projective The group tends to agree with the opinion leaders views, even though they have different opinions. 2 Author thanks Prof. R K Premarajan for providing valuable insights on this topic. 1

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techniques used for marketing research are sometimes referred to as indirect methods. For example, Figure 5.2, would be a pure projective technique used for understanding the personality if it did not have the airlines logo. In the figure, since the airline logo has been added, the responses are tilted towards the brand. Projective techniques have been used in market research since the 1940s (Chang 2001) to obtain information the person would otherwise be unwilling to admit (because they do not want to) or unable to admit because they are unconscious of that aspect of their personality (Donoghue 2000). Projective techniques can also be used when direct questioning about a product to a respondent is likely to obtain false or misleading answers (Haire 1950). The objective of projective techniques is therefore to free the respondents from self-censorship and get true responses (Boody 2007). In other words, the respondent should reply without the interference of the conscious state which could bias the response. Projective techniques use the principle of “projection” wherein respondents project3 their own feelings, motives and aspirations onto some other object or person, and in doing so reveal their own concerns. For example, if person A hates person B, person A tries to project his/her feelings on to person B by stating that person B hates him/her (though in fact person B has no such feeling against person A). Projective techniques thus needs to provide an external stimulus and allow the respondents to express their feelings as responses of that stimulus, rather than directly asking them to report their beliefs and feelings (Kinnear and Taylor 1991). Types of Projective Techniques The commonly used projective techniques are (examples are provided later in the chapter while discussing the process) as follows. Descriptive/Expressive Technique The participants are asked to respond to a given visual and/or statement made by a person. Collage Participants are given a selection of magazines, newspapers or other pictorial materials and asked to select images which represent something about the product, brand, product user or brand user. Freud conceptualized projection as a defence mechanism by which people protect themselves from anxiety by unconsciously attributing their own negative personality traits to others.

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Word Association Respondents are given some words and asked to identify similar words they can think of. Sentence Completion Respondents are given incomplete sentences and are asked to complete the sentences. Thematic Apperception Test The respondents are given a picture and asked to write a story about “what happened, what is happening and what will happen”. Process Since the objective of projective technique is to allow the respondent to answer without the interference of the conscious state, the process of conducting the study becomes very critical. Two aspects of the process, which ensure that the responses are not “self-censored” are speed and conditioning. • The projective techniques should be conducted at a high speed so that the respondent does not “think” before responding. The response should be spontaneous (thinking means that the respondent would get into a conscious state). • To achieve the above, the respondent should be conditioned to allow the response to come without the interference of the conscious state. Thus, the actual stimulus should be given after conditioning. For example, The above two aspects were used in a study conducted to understand the perception of cars (refer to Case 5.1). The study started with some word associations. The respondents were asked to write all the words they could associate with the given word within a few seconds. Even if the respondent is not able to complete, the next word should be given so that the need to respond without thinking is built. The initial dummy questions are therefore essential to condition the respondent to answer spontaneously without self-censoring. The words given included: Nature Breakfast After few words, the complexity was increased slightly by asking the respondent to complete sentences. I like ____________________________________ In the morning ____________________________

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The respondents were again given only few seconds to respond. Moving to the next level of complexity, the respondents were asked to construct full statements by using descriptive/expressive techniques. One of the pictures showed two women shopping (Figure 5.3). One is shown pushing a trolley with the logo “Bright” and the other pushing a trolley with the logo “Safed”4. The respondents were asked to describe the women and again only a few seconds were given.

Figure 5.2: Airline stimuli

Figure 5.3: Detergent stimuli

Having built up the pace, a thematic apperception test was given. Here the respondents were given a picture of an airplane with Lotus Airlines logo5 standing at an airport (Figure 5.2) and asked to write a story of “what happened, what is happening and what will happen” in 2 minutes. 4 5

Brand names disguised. Brand name disguised.

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After administering some of these tests which were primarily for conditioning, the actual stimulus, the picture of the car breakdown (an expressive technique), was given and the respondent was asked to say what the person in the picture was saying (Case 5.1). Again the respondents were given only few seconds to respond. The exercise ended with another expressive technique. The picture shows two women talking. One woman says “no cereal suits, Rintu” and the respondent has to fill the balloon with a response (Figure 5.4).

Figure 5.4: Cereal stimuli

As can be noticed, the conditioning process was built with two objectives: • To build the complexity from simple words to writing a story. The exercise started with simple association and moved to the more complex thematic apperception tests. • Divert the respondents’ attention from the main theme. Except the car stimuli, all others were dummy to condition the respondent. While designing the exercise, effort was made to ensure that the actual reason of the study (car) was disguised by building a dummy theme6. The dummy theme being breakfast (word association, breakfast, followed by a sentence completion, in the morning, and finally rounding up with an expressive technique stating no cereal suits Rintu). The post-study debriefing indicated that the dummy theme served the objective of diverting the respondents from the main theme and possibly minimising the bias on the primary stimulus. People always have an urge to crack the puzzle. Here in this case, they would try to identify why the study is being done. Thus, a dummy theme was built to help the respondents “crack” the puzzle.

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The diversion from the main stimuli was confirmed when some participants in the debriefing session mentioned that they “guessed” the study was done for a cereal company. Another point to be noted while designing the study is that the drawings should be sketchy and in black and white. Colours have a meaning at an emotional level and hence it could bias the response. Similarly, the images should be sketches without indicating any emotions (unless required for the study). Validity of Responses While it is difficult to check whether the responses are truly reflecting the inner feelings, the research has to draw a conclusion about the usefulness of information based on the responses of some of the dummy questions. The following indicators suggest that the respondents could be responding without a bias (refer to Box 5.1 for responses for detergent and plane stimuli): • Sharply worded responses are not generally used while responding to survey questions: stupid looking (detergent stimuli) pathetic service (airline picture)

Box 5.1: Responses for detergent and plane stimuli Bright detergent

Safed detergent

Lotus airlines

Conservative

Open minded

Delay

Traditional

New generation

Confusion over passenger lists

Lower middle class

Upper middle class

Poor food quality

Obese and old

Young and slim

Loss of confidence

Dominating

Friendly

Pathetic service

Stupid looking

Studies what she buys

Gross inefficiency

Cost conscious

Quality conscious

Poor maintenance

Married

Unmarried

Slow in bringing stairs

Conservator

Experimenter

Overshot runway

Dull

Long legs

Emergency landing

3 children

2 children

Out of fuel

Dutiful

Takes short cuts

Front door fails

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• Statements not relating to the picture: married/unmarried (detergent picture) poor quality food (airline picture) Analysis The responses could contain a lot of irrelevant information. The researcher has to extract the required data and interpret the results. This is done by content analysis. Content analysis requires identifying mutually exclusive, exhaustive and reliable categories (Alden et al. 1999), and then list and count the words under each category (Langer and Beckman 2005). The analysis is shown in the case at end of the chapter (Case 5.1). Interpretation In projective techniques, the interpretation has to be done by the researcher. The researcher cannot re-check with the respondent as it would mean bringing the respondent back to the conscious state. Since the interpretation has to be done by the researcher, care should be taken to ensure that the researcher’s bias does not creep in. This can be reduced by defining as many categories as possible during the content analysis stage and by allowing multiple people to interpret the data. Chapter 6 describes the survey design for predictive studies.

SUMMARY The exploratory studies require research designs for gaining insights. Focus group studies, case studies and projective techniques provide insights depending on the type of information (refer to Table 4.5). Focus group discussions can serve two purposes: one is to prioritise the problems for a given issue and the second is to get an in-depth understanding of a specific problem. The hour glass method is used for studies requiring an understanding on both the purposes, and an inverted funnel method is used to serve only the second objective (getting an in-depth understanding of a problem). Case studies are used for studies which need to capture information which is sensitive or likely to lead to socially desirable responses. Projective techniques aim to collect information which is below the level of verbalization. The role of the moderator and the sampling considerations for in-depth studies are presented. The analysis for projective techniques is discussed along with the case study (Case 5.1).

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CASE 5.1 Mindset of Indian Consumer: Implications for Car Marketers7 Indian Market The liberalization policy (opening of the economy to foreign investments) has created sweeping changes to the Indian economy in the last two decades. For example, during the period from August 1991 to November 1993, the Government of India approved 3,467 foreign collaboration proposals, including 1,565 of foreign equity participation. In terms of actual inflow, foreign direct investment in 1993 was more than twice that of 1992 and four times that of 1991 (Paswan and Dant 1999). As a result of the opening of the economy, rapidly expanding domestic production and international trade and its significant consumer base, the U.S. Department of Commerce identified India as one of the big emerging markets. According to the U.S. Department of States’ 1998 data, India had the sixth largest purchasing power and a generally well-educated middle class (Pysarchik et al. 1999). The Centre for Industrial and Economic Research (CIER) estimated the demand for products and services at $150 billion in 2000–01 and $520 billion in 2009–10. The chairman of CIER predicted that the boom would peak in 2005 (CIER 1998). Indian Car Market The Indian government followed a strict regulation policy for the first 45 years of its independent existence, and restrictions on foreign collaborations led to limitations on the import of technology. As a result, until the 1980s, Indian car market had only two players (Ambassador and Premier). Another player entered the market in 1980. It was only the 1993 decision to de-license the Indian auto industry that resulted in the opening of the market. The industry took off and all major international players entered the Indian market including Mercedes, Hyundai, General Motors and so on. The sales of different cars in India between 1998–99 and 1999–2000 are given in Table 5.1. The objective of the study is to understand the consumer mindset in the changing market scenario. Methodology The study used projective techniques to study the consumer mindset. In a projective test, the subject, presented with a vague stimulus, provides information about the stimulus. The projected information reveals the subject’s unconscious and subconscious attitudes towards the stimulus. Marketing researchers are giving increasing attention to using projective techniques (Miller 1991, Piirto 1990, Fram and Elaine 1991). Projective technique is ideal to study the mindset as it bypasses the people’s inbuilt censoring mechanisms and they help in eliciting honest information (Hollander 1988). Sample Participants attending management development programmes at a management institute in India were administered the test. The participants of these programmes were executives from various companies in India and they formed the major market for the cars in India. The case was prepared by the author for classroom discussion in 2001. The data and the findings pertain to the mid 1990s period. The author also acknowledges the comments of Prof. Premarajan on the earlier draft of the case.

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Table 5.1: Sale of cars in India Car Sales Mercedes Tata Motors

1998–99

1999–2000

1116

893

4732

55776

Daewoo

10121

40217

Fiat

10117

20751

Ford

3233

8023

General Motors

3524

3047

20115

26860

9631

9698

17648

75895

326523

397586

3191

69

411850

638814

Hindustan motors Honda Hyundai Maruti Premier Total Projective Test

The respondents were asked to write their response to a picture. The picture shows a man dressed in a suit standing in front of a car with the bonnet open (Figure 5.5). The surroundings show some huts and trees. The respondents were asked to write “what is the person thinking?” adopted from Hollander 1988. Analysis Content analysis was done by identifying words associated with the following themes: • • • • • •

Background of the protagonist Description of the product Purpose of journey Nature of the problem Attribution of the problem Solution identified

Sample responses are given in Box 5.2. The responses were then checked to identify words associated with these themes (the themes along with the relevant words are listed below): 1. Background of the protagonist • • • • • 8

Upwardly mobile (4)8 Businessman (5) Executive (2) Shaan (show-off in Hindi) (1) Mr. Care F Ree (a creative way of breaking the word carefree) (1)

Figures in the brackets indicate the number of people who cited the phrase.

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Figure 5.5: Car breakdown Though only 30% of the respondents indicated the background of the person, the car is clearly associated with upwardly mobile businessmen or executives. 2. Perception about the product • Modern car (4) • Immense faith in the car (1) • Confident of its performance (1) • Taken aback that the car would come to a halt (1) • Heard some bubbling sound. But kept moving (1) • Kept running it despite some noticeable wear and tear (1) • Left to the mercy of technology (1) • A dream car (1) The responses indicate that the product was considered to be modern and technically superior. In general, the respondents were confident of the product’s performance. They even had faith in the product to continue functioning even when some noticeable weaknesses were visible (as in the case of a respondent who said that he kept running it despite some noticeable wear and tear). 3. Purpose of journey • Visit to his farm house (1) • Set out on business (7) • Important meeting (3) • To his office (4) • Holiday (3) The dominant category emerging under this head is that the car is primarily associated for official purpose (majority of the respondents have indicated this as the purpose). There is however a group which considers it for holiday travels. 4. Nature of the problem • Radiator (12) • Battery (5)

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• • • •

Don’t know (18) Alternator conked off (1) Missing in the engine (1) Engine breakdown (1)

The most common problem identified by the respondents was the radiator. Thirty percent of the respondents specified that the problem was with the heating up of the radiator. The second-most important reason cited was the battery (10% of the respondents mentioned this). Several respondents indicated that it was beyond them and that they were waiting for help which indicates that a service support was needed. Only three persons have associated the problem to the car per se. 5. Attribution • Did not undertake periodic check-ups (5) • Did not service it in time (2) • Did not check the essential requirements of the car before starting (1) 6. Solutions • Need to maintain it regularly (3) • Have periodic check-ups (1) • Service it regularly (1) • Change non-standard battery to a standard one (2) All the responses falling in attributions and solutions category indicate that the person is blaming himself for not taking good care of the car. This indicates that the respondents have attributed the problem to themselves. Discussion The Indian consumers have traditionally been used to associating car to an Ambassador; any design that is not an Ambassador is associated with modern. These modern cars are still perceived to be luxury items affordable only by the upwardly mobile consumers, as the product is mainly perceived to provide esteem value to the owner. The study also indicates that the consumers perceive that the product can be used even if there is some wear and tear. Attribution has mainly been to factors outside the core product, that is the car. The majority of the reasons cited are those which the consumer should have taken good care, e.g. timely service. Only three have attributed it to the manufacturer or the product per se. This clearly indicates that the respondents tend to blame themselves for the happening and consider the products supplied by “standard” companies to be very good. They felt that the product would not have let them down had they taken good care of it or used “standard” accessories.

Box 5.2: Sample responses for the projective test of car breakdown • It is good to have a trendy car. But it is also important to maintain it well, have proper periodic checks and service it regularly. • Shaan initially was taken aback that his modern car could even come to a halt. But he realised later that he could have prevented the mishap if he had taken proper care of the vehicle.

Research Designs for Gaining Insights

• Mr. X decided to visit his farmhouse, which was 180 km away from the town, and he had immense faith in his car. Due to his confidence, he did not check the essential requirements of the car before going out of station. As a result, the water in the radiator tank got overheated. • Right from the beginning, I could feel that the heat under the clutch pedal increasing. It was afternoon so I thought it is because of the weather. I could hear some sound in the engine. I was alarmed but still kept on moving until there was some bubbling sound, large enough for me to hear. There was no water in the radiator right from the beginning and the gasket coil had burnt out. I went to the nearby houses, picked up few buckets of water, allowed the car to cool and restarted. • One upwardly mobile gentleman set out for a business to another city. On the way, his alternator conked out and he is at total loss and is cursing himself for getting stuck. • Mr. Care F Ree started for a long drive to the countryside on a holiday to be alone with the nature. Before starting he did not bother to check the car radiator/battery. In the middle of his journey, his car breakdown. Without any garage at sight, he was left to the mercy of technology. • Mr. Singh was driving his car to his factory. On the way, he realised the engine “missing” a beat. He stopped the car and opened the bonnet and found that the battery of the car was leaking. There was no solution available. Mr. Singh decides to change his non-standard battery with a standard one. • The senior executive was driving his car from his town to another town. He was headed for an important meeting; mid way at a village, the car broke down. He opened the bonnet and since he was unaware of the mechanics of the car he was wondering what to do. He will have to wait till another car passes this way. • The owner of the car did not service the car on time. He kept on running despite some “wear and tear”. The car broke down one day, in the middle of the road. The person went to the nearby shed to get a mechanic. He will have to spend some time and money and will finally reach home, though late.

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6

Survey Research and Questionnaire Development

Learning Objectives At the end of the chapter, the reader would be familiar with: The different types of surveys The considerations for developing a questionnaire to ensure that the response bias is minimised Special topics covered in the chapter: Scale on which information is collected and the response stress Trade-off between stress and boredom while developing questionnaires

A large part of marketing research deals with understanding the consumers and the market to make marketing decisions (Christy and Wood 1999). These studies therefore require information from a cross-section of the target population. Typically, a survey and a questionnaire (part of predictive studies, refer Table 4.5) will help get a snapshot of the current state of affairs for a given group or population (Janes 2001). Surveys rely on self-reported answers obtained from a set of sample respondents in order to generalise to the target population. The answers are obtained via questionnaires which are structured and undisguised (Synodinos 2003) (unlike the case of projective techniques, refer to Chapter 5). Figure 6.1 describes the process of converting the research questions into a questionnaire for a survey research (In Chapter 3, it was mentioned that research questions are different from the questions in the questionnaire). Research questions and types of information were already discussed (Chapters 3 and 4, respectively). Type of analysis would be discussed in Chapter 8. This chapter would discuss the type of survey methods and the consideration for questionnaire construction.

Survey Research and Questionnaire Development

Research questions (Chapter 3)

69

Type of information (Chapter 4)

Types of survey Type of analysis (Chapter 8) Considerations for questionnaire construction

Stress v/s Boredom

Wording

Opening statements

Meaning

Question Placement

Format of questions

Final Questionnaire Figure 6.1: Process of converting research questions into a questionnaire

TYPES OF SURVEY They are different methods of conducting a survey research. Survey administration methods can be classified into interviews and self-administered questionnaires (SAQs). Interviews can be conducted in person or via telephone. SAQs include postal and Web surveys. The factors determining the appropriate survey method are as follows (Owens 2002): • Population to be surveyed • Characteristics of the sample • Type of information • Length and complexity of questions • Response rate • Cost • Time The advantages and disadvantages of the different types of surveys are discussed below. Face-to-face Surveys Janes (2001) states that face-to-face interview method can provide the highest-quality data compared to the other methods. Janes states that when

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a large number of specific questions need to be asked and when people have trouble in understanding the questions; face-to-face surveys are good. Janes also states face-to-face surveys are likely to get a higher response and completion rate and decrease the number of “don’t know/no opinion” type responses. Telephone Surveys Another commonly used method is the telephone interviews wherein the respondents are contacted over telephone. Owens (2002) listed the advantages and disadvantages of telephone survey over other methods as: Advantages • Less expensive than personal interviews • Shorter data collection period than personal interviews • Better control and supervision of interviewers • Better response rate than mail survey Disadvantages • Non-response would be high compared to face-to-face surveys • Questions have to be short • Difficult to administer questionnaires on complex topics Mail Surveys Respondents are contacted through mail and given an option to complete questionnaires at their convenience. Advantages • Generally, lowest cost • Can be administered by smaller team of people (no field staff requirement) • Access to otherwise difficult to locate, busy populations • Respondents have time to think Disadvantages • Most difficult to obtain cooperation • Non-response is very high • Slow in obtaining data Web-based Surveys A Web-based survey involves collection of data through a self-administered electronic set of questions on the Web. Web-based surveying is becoming

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widely used in social science and educational research (Solomon 2001). In an informal search of Yahoo as early as in 1997, Kaye and Johnson (1999) identified over 2,000 Web-based surveys in 59 areas. Web-based surveys are being aided by a growing number of software development systems designed specifically for the purpose. Web-based surveys can be administered both for consumer research or industrial clients. Web-based surveying offers a number of distinct advantages over more traditional mail and phone techniques. The primary advantages being the wide reach and low cost. Global studies targeted at specific individuals can be done through the Web without having to spend on paper, mailing and data entry. The biggest concern in Internet surveying is that the sampled people may not choose to access the survey (Kaye & Johnson 1999) or classify it as junk mail. A comparison of the different survey methods is given in Table 6.1. Table 6.1: Comparison of survey methods Variable

Mail

Phone

Face-to-face

Time for data collection

Slow

Fast

Moderate

Response rate

Low

Moderate

High

Sampling process

Easy to mail to respondent as per sampling plan

Easy to contact respondent as per sampling plan

Difficult to contact respondent as per sampling plan

Stress on respondent

Lowest

High

Moderate

Risk of others answering

Highest

Moderate

Lowest

Length of Questionnaire

Can be long

Has to be short

Can be long

Open-ended responses

Suitable

Not ideal

Best

Questionnaire design

Questions should be easy to comprehend

Moderately difficult questions can be added as there is scope for explanation

Complex questions can be added as there is high scope for explanation

Source: Modified version of Owens (2002)

SOURCES OF ERROR Cui (2010) identified four sources of error while doing survey research. They are sampling error, non-coverage error, non-response error and measurement

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error. These sources of error may make the survey results unacceptable and so steps have to be taken to minimise them. The first three pertain to “who” are being studied and the fourth about “what” is being studied. The sampling chapter (Chapter 7) would address about “who” should be studied to minimise sample-related errors. The next part of this chapter deals with “what” is being studied (questionnaire development) to address the measurement error. Fowler (1995) states that the most critical and preventable threats to the validity of a survey comes from the design of the questions.

DEVELOPING A SURVEY QUESTIONNAIRE Questionnaire construction can be deceptively simple (Birnet al. 1990, Sudman and Bradburn 1982). The perceived simplicity creates many problems because poorly constructed questionnaire can lead to erroneous conclusions. Fowler (1995) and Lockhart and Russo (1994) noted that it is not uncommon for researchers to hold the mistaken belief that questionnaires can be easily written by anyone. Payne (1951) envisioned constructing a good questionnaire as an “art” and it needs to follow certain guidelines. Following are the guidelines for developing the questionnaire: • Minimise the “stress” in answering the question • Minimise “boredom” to avoid random answers • Ensure appropriate wording of questions to communicate intended meaning • Ensure correct placement of questions • Ensure proper administering of questionnaire Stress If the stress in answering a question is high, then the respondent may opt out as he/she has no stake. This would increase non-response bias. Synodinos (2003) found respondent burden is usually high in many manufacturing and management studies. To minimise stress, questions should be as concise as possible and the respondents should be able to answer them with relatively minimal effort. Tourangeau (2000) suggests questions should ask information that respondents can recall easily, as seeking “not easily accessible” information may put a stress on the respondent. However, the level of stress that the question should put on a respondent should be viewed along with the way the information would be used by the decision maker (discussed below). Stress also depends on the scale on which the data is collected and the response options available to the respondent (refer to Box 6.1 for types of data collection scales).

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Box 6.1: Scales of measurement Nominal scale: Information is classified into definite categories and no further distinction is made within the members of a group. For example, gender, country of origin, etc. Ordinal scale: The information is ordered on a criterion to show their relative position. For example, the brands are ordered as per their preference. The difference between the ranks need not be the same. Interval: The information on an interval scale is collected on a continuum where the difference between two points is equal. For example, the level of satisfaction of a customer is measured on a scale from −2 to +2 (−2 being totally dissatisfied and +2 meaning completely satisfied). Ratio: In the ratio scale, the information can be compared with one another. For example, age. The difference between interval and ratio is that in a ratio scale information assumes a common zero, whereas interval scale need not refer to a common zero. In the above case, if two people rate +2 on the satisfaction scale, it cannot be said that both are equally satisfied as both may perceive satisfaction by their own standards and the ratings cannot be compared. Whereas if the age of one individual is 25 and the other is 50, it is evident that both are referring to the common base and they could be compared.

Scale and Levels of Stress Nominal scale questions should provide all possible response options which are mutually exclusive to reduce stress. Since it may not always be possible to list all options in a nominal scale, adding an “any other option” reduces the stress. However, determining on whether or not to add an “any other option” would depend on how the information is going to be used by the decision maker? If the decision has to be amongst a set of pre-determined options, the study should not have the “any other option” and care should be taken to identify all options in the in-depth study conducted prior to the designing of this study. Of all the scales, ordinal scale (ranking) is the most stressful for the respondent as the respondent has to consider all options at one time and order them. Individuals would generally be familiar with the top 2–3 ranks, and sometimes the bottom 2–3. But ranking of the middle items would be stressful. Therefore, asking the respondent to rank only the top few options reduces the stress in the ranking process. For instance, the following questions were used by a management school to identify the importance of the different information sources for the faculty. 1.

Faculty learns from different sources. Which three sources on this list are most important to you in learning?

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Marketing Research: A User’s Perspective Tick top 3 Journal articles Conferences Peer group discussion Text books Consultancy Research projects Workshops Learning by teaching Faculty development programmes Any other

Since, the objective of the institute was to allocate budget for faculty development, the institute was not interested in ranking all the sources (which would have been stressful). Getting information on the top three was sufficient for the institute to determine the package of benefits. On the other hand, ranking all items would be essential if decision has to be made for all options. For example, a company planning to allocate budget for different media may require ranking of all options based on their effectiveness. Here ranking all options is important because the decision maker cannot drop any medium from the advertisement plan. The ranking would help in deciding the budget that needs to be allocated to the different media based on their importance. In an interval scale, the number of items on a bi-polar interval scale1 determines the stress. A bi-polar odd number scale (5 or 7) providing a mid point with “don’t know,” “no opinion” choices minimises stress as against an even point interval scales without a “don’t know,” “no opinion” choice. Not providing a “don’t know,” “no opinion” choice forces a respondent to take a stand and therefore may increase stress. While using an interval scale, a researcher has to decide to use an even point or an odd point scale depending on whether the respondent has to be forced to take a stand or not. For studies which the target group “must” have knowledge, the researcher has to force the respondent to take a stand by using an even point scale and not providing a “no opinion” option (Synodinos 2003). On the other hand, if the target group need not have knowledge about certain aspects of the concept, then an odd point scale with a “no opinion” option could be given. For example, studies to A bi-polar scale has two opposites as the ends of the scale (e.g. a satisfaction scale would have highly satisfied as one end and highly dissatisfied on the other end), whereas a uni-polar scale is unidirectional (e.g. rating the fragrance of a soap on scale from 0 to 5).

1

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understand the attitude of the students towards a subject should use an even point scale as a student should have an opinion about every dimension of how the subject was taught, and a study to find out the importance of attributes for mobile phones can use an odd point scale as a buyer could be indifferent to some features. Boredom Lengthy questionnaires lead to boredom and respondents tend to give random answers if boredom sets in. Janes (2001) found face-to-face interviews lasting longer than 25 minutes gets tedious and people become restive. Stress v/s Boredom For state-of-mind information, stress versus boredom trade-off becomes essential. As mentioned, ranking of brands is stressful and rating the importance of attributes influencing the perception of a brand (on a rating scale) is less stressful. However, rating would mean that the number of questions would be more thereby increasing the chance of boredom. The choice between stress and boredom would therefore depend on the purpose for which the information is being collected. As a general rule, if the study is to influence the “stateof-mind” information processing (e.g. perceptions, refer to Chapter 9), then a rating scale is required. For example, a study on satisfaction of the working environment used the following question; 2.

Keeping in view your work assignment; how do you rate your satisfaction of working environment with respect to (rate on the following scale): Conducive to work

Tolerable

Affects at times

Affects

Greatly affects

Noise Heat Pollution Cleanliness Layout Seating arrangement Machines Office equipment

On the other hand, if the study is grouping people based on “behaviour” then a ranking scale should be used. For example, a study on the voting behaviour.

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3.

Importance of factors influencing choice of the candidate? (rank by importance) a. Party affiliation b. Caste c. Profile of candidate d. Electoral promises e. Educational qualification f. Gender g. Other reasons

Wording The wording of questions can have a significant impact on the results. Even a small difference in wording may produce substantial response effects (Synodinos 2003). Synodinos (2003) goes on to state that questions should use simple structure with familiar and neutral words. The researchers should avoid leading questions (i.e. suggesting a response to the respondent) or loaded questions. The following are examples of leading questions: 4.

The current anti-smoking campaigns are doing a great job and are largely successful a. Strongly disagree b. Disagree c. Cannot say d. Agree e. Strongly agree

5.

The current anti-smoking campaigns put enough emphasis on the dangers and perils of smoking a. Strongly disagree b. Disagree c. Cannot say d. Agree e. Strongly agree

Loaded questions often contain assumptions, whereby the question is directed in a way that a respondent may accept what the researcher is assuming (Walton 1995). Refer to Question 6 below. 6.

You will remember the anti-smoking campaign one year from now–Yes/No

Again, each question should cover a single issue only. Items that inquire about two or more issues would increase stress (refer to Question 7 below) and therefore must be divided into separate questions.

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7.

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I’d like to have you think of the brand name of the drug which you most recently prescribed for the first time. Where did you happen to get the information which led you to prescribe it?

This is a stressful question as the doctor has to first think about the brand name which he/she prescribed for the first time (it may have happened the previous day or could have happened 6 months back). Then they have to link the drug to the sources which led them to prescribe the drug. Communicate Meaning Questions communicating the same meaning to all respondents are a must for reducing bias. That is, the respondent’s understanding of a question should correspond to the meaning intended by the researcher (Schwarz 1999). Therefore, a question should be as clear and precise as possible so that all respondents interpret it as intended. For instance, a study on teaching methodology had the following question: 8.

Which two or three of the sources listed here do you find most important to you personally in familiarising about new teaching methodologies?

Here the word familiarise could mean: • Knowing (i.e. awareness) • Disseminate information (knowledge) If some responses are based on the respondent’s understanding that the question is about awareness, and some responses on the understanding that the question is about knowledge, then the information obtained would not be valid as the sources of information for creating awareness and generating knowledge are different. Care should also be taken to study the impact of culture on the meaning. For instance, questions developed within a particular cultural context may be meaningless or offensive in another. Synodinos (2003) also suggests that double negative questions should be avoided. Opening Statements A questionnaire should start with an introductory statement. However, care should be taken that the introductory statement does not put the respondent on a defensive. For example, a study to understand the information sources for faculty used the following introductory statement. 9.

So many new theories are being developed today that it is getting harder for a faculty to keep current. This study is to understand the importance of the sources of information…

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Statement as in Point 9 is questioning the capability of the faculty and therefore could make the faculty defensive and force him/her to give socially desirable responses. Introductory statements, whenever used, should be neutral. For the above study on information sources for faculty, the introductory statement could be reworded as: 10. You must be using different sources of information to keep yourself updated. This study is to understand the importance of the sources of information… Placement of Questions Proper sequencing of the questions facilitates questionnaire administration and minimises confusion. The questions should be ordered logically and in a manner non-threatening to the respondents. Synodinos (2003) states that questions that request confidential information (e.g. income-related) can lead to high item non-response. That is, such items may be instrumental for some respondents to forego participation in the survey. Therefore, questions pertaining to respondent and/or organizational characteristics (sensitive information) should be in the last section of the questionnaire. Administration Janes (2001) state that interviewers need to be trained to be familiar with the questions and to ask them as neutrally and consistently as possible, in order to eliminate potential sources of bias in responses. Format of Questioning (Specific versus General) Questionnaires are developed to collect information to understand the general pattern of the respondent. This can be done either by starting with general questions as in the case of the question about faculty sources of information (Question 11) or starting with a specific instance and moving to generalisations. 11. Faculty learns from different sources. Which three sources on this list are most important to you in learning? In studies designed to collect generalised information, the demographic variations can be captured through the analysis. However, these questions would not take into account the state-of-mind variations. That is, the complexity of the subject (in Question 11) for which the response has been given cannot be captured. If different faculty respond for subjects, perceived differently in terms of complexity then it would lead to response bias. Thus, these

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studies should specify the “issue” clearly to avoid state-of-mind variations (in this case, the subjects for which information is being sought should be specified in the question). While this issue is resolved in studies moving from specific to general; care should be taken such that the specific situation used at the beginning of the data collection should be of an “average category” (i.e. neither situations that are on the top of the mind because of some special happening nor situations that are rare occurrences). For example, election campaigns are generally issue driven and each campaign would have its own uniqueness. In such situations, it would be difficult to start with a specific campaign and move to generalisations. However, campaigns for launch of cars have been more or less standardised. Here it is possible to start with a specific brand (to eliminate the state-of-mind variations regarding the type of car) and move to generalisations. Once a questionnaire is developed after taking into account the above considerations (refer Case 6.1), it can be used for data collection. The next chapter describes the process of selecting the respondents (sampling methods) for collecting information.

SUMMARY The different types of survey methods are discussed and the advantages and disadvantages of each are presented. The chapter then discusses the important issues for developing the questionnaire to ensure that unbiased information is obtained. The main considerations for developing a questionnaire include (a) trade-off between stress and boredom for the respondent, (b) the wording of the questions to ensure that all the respondents understand the questions similarly and (c) the placement of questions. The way the opening remarks are worded are also discussed.

CASE 6.1 Brand Decision Process for Televisions A study was done to understand the brand decision process for televisions. As part of the study, a survey methodology was used. Two questionnaires were developed—one to be administered to the dealers and the other to the end consumers. DEALER SURVEY SHOP NAME: ADDRESS:

CONTACT PERSON: CONTACT NUMBER:

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1.

Which brands of televisions do you sell: (Tick the brands)  LG

Samsung

Onida



 Sony

Videocon

Others

2.

Rank the following televisions with respect to their sales (1 given for the highest sales).

Brands

Philips

Ranking

LG Samsung Onida Philips Sony Videocon Others

3.

In your opinion, what do you think is the role of advertisements? ____________________________________________________

4.

Monthly average sales (in units) for each brand:

Brands

Colour televisions (nos.)

LG Samsung Onida Philips Sony Videocon Others

5. Do Customers demand for a particular brand? 

 Yes

No

LCD (nos.)

Survey Research and Questionnaire Development

If yes (Rank the preference: 1 given for the highest preference) Brands

Ranking

LG Samsung Onida Philips Sony Videocon Others 6.

Do you share your sales figures with retail audit agencies? Yes No

7.

Do customers prefer financial schemes provided by different brands? Yes No

8.

Which are the companies focusing on the zero percentage finance scheme? Brands

Ranking

LG Samsung Onida Philips Sony Videocon Others (specify) 9.

Percentage of sales with financial schemes.

10.

Rate the following televisions on the basis of the services they provide.

Brands LG Samsung Onida Philips Sony Videocon Others

Highly satisfied

Satisfied

Neutral

Dissatisfied

Highly dissatisfied

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11.

How do consumers rate the brands on the following attributes? (1 being highest and 5 being lowest) Brands

Price

Aftersales service

Technology

Brand image

Availability

Promotional offers

LG Samsung Onida Philips Sony Videocon Others

12.

How often are gift-based schemes offered for TV purchases? Very regular

13.

14.

Regular

Sometimes Rarely

Never

Which of the following brands provide gifts? (Tick) LG

Samsung

Onida

Sony

Videocon

Others

Philips

Rate the promotional efforts of the brands?

Brands

V Good

Good

Can’t say

Poor

LG Samsung Onida Philips Sony Videocon Others

CUSTOMER SURVEY 1.

Do you own? Colour TV

LCD

Both colour TV and LCD

V Poor

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2.

Which among the following brands of television do you own? LG Sony

3.

Onida Others

Philips

Decision of the brand was made by  Self

4.

Samsung Videocon  Spouse

 Friend

 Relative

 Others

What factors influenced the decision in buying your television?  Brand image

 Technology

 Price

 Availability

 Promotional offers

 After-sales service

 Others............ 5.

State the satisfaction level of your present television. Highly satisfied Dissatisfied

6.

Satisfied Highly dissatisfied

Did you do a market study about televisions before you planned to purchase your TV? Yes

No

If yes, the source(s) of the information is/are Friends and family Advertisements Dealers Others …………….. 7.

Neutral

Online

Please rank the following factors considered important for you before purchasing a television (1 given for the most important)

Attributes

Rank

Price Technology Quality Availability After-sales service Brand image

8.

What impacts your purchase decision the most? (1 being the most important)  Discount  Free gifts

9.

 Prize schemes  Free accessories

Rank the following factors based on their importance when you choose a dealer for purchase of TV (1 given for the most important)

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Attributes

Rank

Scale of operation of the dealer Loan options provided by the dealer Reputation of the dealer After-sales services 10.

According to you, which are the top two premium television brands available in the market (in the order of ranking) a) b)

11.

Colour TV LCD ………………………… ……………………………… ………………………… ………………………………

Which among the following television would you like to buy (if you were to choose to buy another brand now)? LG Sony

Samsung Videocon

Onida Others

Philips

Reason …………………………………………………………………… 12.

Age 18–30

13.

30–45

45–60

Gender Male

Female

14.

Educational qualification  < 10  Higher secondary  Graduation  Post Graduation

15.

Occupation Private sector Self-employed

16.

60 and above

Public sector Others

Annual Income Less than 100,000 500,000–1,000,000

100,000–300,000 1,000,000 and above

300,000–500,000

7

Sampling

Learning Objectives At the end of the chapter, the reader would be familiar with: The different methods of sampling Identifying the sampling methods as per the needs of the research design Special topics covered in the chapter: Sampling frame Quota sampling

Marketing research has to invariably depend on a sample as it would not be possible to collect information from the entire universe (target population). This chapter discusses the process of sampling for marketing research studies.

NEED FOR SAMPLING Earlier people used to say that by looking at one person, conclusions for the entire group could be drawn quoting the saying “one needs to check a single grain to know whether the rice is cooked or not”. This was true when the society was homogenous and the views of the society were shared by all members of the society. However, that is no longer the situation today where people have different tastes and preferences. Thus, the “one grain concept” no longer holds and a research study has to get information from a “cross-section of the population” (sample) and draw conclusions about the entire target group. As mentioned in Chapter 6, the sample (respondents who are providing the information) could form a major source of error. The success of the research would, therefore, depend on whether or not the data is collected from a relevant group of respondents to draw meaningful conclusions.

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METHODS OF SAMPLING Sampling should ensure it matches with the purpose of the research study (refer to Figure 7.1). That is, sampling for in-depth studies should ensure diversity in the sample to allow the researcher get a detailed understanding of the concept. Sampling for predictive studies should ensure the data should Sampling based on purpose of research

Studies for gaining insights

Nonprobabilistic sampling

Problem solving studies

Predictive studies

Probabilistic sampling

Nonprobabilistic sampling

Multi-level sampling

Homogenous samples

Figure 7.1: Methods of sampling

be generalisable to the entire population (called external validity). Sampling for problem-solving studies should ensure that the results are comparable to check the impact of the stimulus under question (called internal validity).

SAMPLING FOR STUDIES FOR GAINING INSIGHTS The purpose of these studies is to provide insights rather than to extrapolate the findings to the target universe. The sample has to therefore ensure that it provides an in-depth understanding on the subject. Non-probabilistic sampling helps achieve this purpose. In non-probabilistic methods, the sample members are chosen on criteria determined by the researcher. Since there is an in-built bias in the sample towards the specific respondents, the information obtained from the sample cannot be extrapolated to the universe. The primary purpose of the methods is to provide a complete understanding of the phenomenon under study. The types of non-probability sampling used in in-depth studies are: Expert Panel Sampling The participants of the sample are identified for their perceived knowledge about the topic. This will allow the researcher to get the required knowledge about the issue. For example, an exploratory study done by the

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87

author to understand the “drop-out rates of girl child from schools as part of a study on universalisation of primary education” used an expert panel sample drawn from NGOs, government officials, village school teachers and village panchayat members. Drake and Davies (2006) and Favies and Drake (2007) refer to it as extreme and critical sampling and they have used an expert panel of informative local authorities and private providers for their study on “commissioned home care”. Expert panel sampling can also be used for exploratory studies which are seeking sensitive or socially desirable information. As mentioned in Chapter 5 (under case studies), views of experts are required to gain a complete understanding of the concept. For example, a study of work pressure in school would require an expert panel. Convenience Sampling Though convenience sampling is used in cases of time and budget constraints (Kumar 1999), convenience sampling is suited when the study requires data from individuals with different characteristics and backgrounds in an attempt to reduce the risk of producing homogeneity in responses (Manaktola and Jauhari, 2007) (here the chosen sample need not be experts as in the case of expert panel sample). Similarly, Diamond (1999) suggests that convenience sampling is used to capture the different issues relating to the research by selecting population with diverse characteristics rather than attempting to achieve representativeness. For example, focus group studies should use a convenience sample with people of varied backgrounds who can give a complete understanding of the topic under study. Thus, in such cases, convenience sampling is more like a judgmental sampling. Snow Ball (or Network) Sampling Where the population of interest is severely limited and difficult to identify, snowball sampling is used (Biernacki and Waldorf 1981, Goodman, 1961). In these cases, one person from the target group is identified and interviewed and subsequently contacts of other members of the target group are obtained from that respondent. This way, the sampling continues by getting other contacts from the respondents themselves. The advantage of this method is that it is a cost-effective way of identification of respondents; the disadvantage being the inability to estimate sampling error (Maru and Prince 1994). From a practical sense, this sampling would be required for studies focusing on professionals from the unorganised sector. For these professionals from unorganised sectors, there is no data base listing them (professionals in the organised sector are listed on websites or are members of associations). Thus, for the professionals from the unorganised sector snow ball sampling is ideal and for professionals from the organised sector, where there is a list, a convenience sampling could be used. A study done in the Marketing Fair

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at XLRI to understand the influence of “masons” (builders) on the purchase decisions for paints during house construction used a snowball sampling. By the process of referrals, 73 masons were identified and interviewed.

SAMPLING FOR PREDICTIVE STUDIES Unlike the studies for gaining insights, the findings of the predictive studies have to be generalised to the target universe (have external validity), so the sample should be a true representation of the target population. The issues of sample bias, sample error should therefore be addressed for predictive studies. The different sampling methods are described first and then guidelines for choosing the sampling techniques for predictive studies are suggested. Probabilistic Sampling Methods Probabilistic sampling methods adopt a statistical basis for identifying the respondents, and the researcher has to interview only the specified respondent to ensure true representation. Random Sampling Random sampling is used when all the members of the target population have equal probability of being selected. This requires a complete listing of the target population. For example, a study on the women senior management in Australia used the Who’s Who in Business in Australia, an annual publication that includes the names of prominent senior executives from the top 500 public and private companies nationwide to draw the random sample (Rindfleish 2000). Another study on safe egg use in the catering industry first enumerated the caterers and then drew a random sample from the list (Taylor 2004). A complete list of the target population would ensure that all members have equal probability of being chosen. However, unlike the above cases, it is not always possible to develop or obtain a complete listing of the population universe, so a researcher has to use a sample frame to draw a random sample. A sample frame is an existing list which to a large extent represents the members of the target population. That is, the researcher has to identify a sampling frame (list) which “more or less” represents the total population. For example, telephone directory (as a sampling frame) is appropriate for a study focusing on the level of satisfaction of landline phone versus mobile phones (here the assumption is that a person with a landline also has a mobile phone). However, telephone directory is not appropriate for a study interested in understanding the television buying behaviour of people in Jamshedpur as the study would exclude those who do not have a landline telephone. Care should therefore be taken to ensure that the sampling frame can represent the target population. Figure 7.2 shows the appropriateness of the sampling frame. Again, voters list is a sampling

Sampling

89

frame. However, it is appropriate only for studies which look at people above 18 years age. Voters list is used as a sampling frame for National Readership Survey which is looking at Indians above the age of 15 years.

Population Inappropriate sampling frame

Population Inappropriate sampling frame

More or less represents the population Appropriate sampling frame

Figure 7.2: Appropriateness of a sampling frame

To identify an appropriateness of a sampling frame, a researcher should also question: • Why the list was developed? (objective behind developing the list) • Who developed the list? (trustworthiness of the agency/person developing the list) • Recency of the list (when was the list developed) • Cost of obtaining the list Census data can be an appropriate sampling frame for some studies. For example, the National Sample Survey frequently uses the list of census villages as a frame for several social and economic investigations in rural areas of India (Government of India 2010). Though census is done once every 10 years, the list of villages would not change during the intermediate period (even if there is a change it would be marginal). Moreover, the list is prepared by a reliable agency and can be easily obtained. Also refer to Box 7.1. In general, in developing countries like India, for many marketing research studies, a sampling frame is neither readily available nor can it be prepared as developing new frames is an expensive proposition (NCAER, 2002). Dhar (2006) while studying the sampling issues in India identified (a) non-availability of sampling frame and (b) continuously changing sampling frame as problems leading to non-response and missing information. Thus, quite often random sampling or stratified random sampling (discussed below) becomes difficult in the Indian context. Stratified Random Sampling Stratified sampling, another probability method, is superior to random sampling because it reduces sampling error. In a stratified random sampling, the population is divided into strata based on some criteria and then random sampling is drawn from each stratum. As in the case of random sampling, a sampling frame is required for drawing a random sample for each stratum.

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For example, a stratified random sampling method was used to select 278 librarians working in university libraries in Nigeria. Here all the universities were categorised into five groups, consisting of: • Federal universities (conventional) • Universities of agriculture • State universities • State universities of technology • Federal universities of technology Then a simple random sampling method was used to select universities from each of the five groups for the study (Eden and Lawal 1999). Similarly, another study selected 814 employees from three strata, first line, middle and senior managers of hospitals using a stratified random sampling (Ali and Yarmohammadian 2006). It should also be noted that random sampling and stratified random sampling assume the population to be normally distributed (refer to Figure 8.5). Systematic Sampling Systematic sampling, another probabilistic sampling method, is possible when there is a random flow of the target group at some location. For example, a person entering a supermarket. In a systematic sample, the first respondent is chosen at random and then the subsequent respondents are drawn at regular intervals. Supermarkets could be used to identify sample for studies looking at behaviour of middle and upper income households in India. The process of sampling would be as follows: 1. The supermarkets catering to the target group need to be identified 2. A sample of the supermarkets need to be selected 3. The time for drawing the sample should be fixed (ideally the time should be when the flow of the target group is present) 4. Choose a point where the sample would be selected (a place where all the people have to cross). In case of supermarkets, the sample could be drawn at the cash counter. This would be convenient for the respondent also as they would have completed their shopping and are waiting in the queue for their turn. 5. The first respondent is randomly chosen 6. Subsequent sample should be drawn at a regular interval till the end of the time period decided for sampling Similarly, arrival of people at a railway station can provide an opportunity for drawing a systematic sample. A study on the perceived differences between national, international and private brands was done outside the main Taipei railway station by interviewing every tenth person (Cheng et al. 2007). Lawal and Jibowo (2006) also adopted a systematic sampling to study food security in Nigeria.

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91

For a systematic sample, the population should satisfy the Poisson distribution1. Unlike the random sampling methods, the systematic sample can be drawn without a sampling frame. Non-Probabilistic Sampling The only non-probabilistic sampling that could be used for predictive studies is quota sampling. Quota Sampling Quota sampling is similar to stratified sampling, except that here the respondents from the different strata are not identified based on any statistical basis. Sekaran (1992, p. 236) defines quota sampling as “a form of proportionate stratified sampling, in which a predetermined proportion of people are sampled from different groups, but on a convenience basis”. This method of sampling enables representation of subgroups, while minimising budgetary and time constraints. The success of quota sampling depends on the categories used. Therefore, the groups should be identified to help get the desired variation in the behaviour of the target group. Table 7.1: Quota sampling grid for study on awareness of anti smoking campaign Age 21–25

Males

Urban Semi urban Rural

Females

Urban Semi urban Rural

26–30

31–35

36–40

41–45

46–50

Literate Illiterate Literate Illiterate Literate Illiterate Literate Illiterate Literate Illiterate Literate Illiterate

Atkin and Freimuth (1989) used gender, age, socio-economic status and place of residence to define the quota sampling grid for their study. A study to examine people’s awareness to an adult focused anti-smoking advertising Poisson distribution is used to model the number of random events occurring in a given period of time.

1

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campaign used age, geography and literacy rates for designing the sampling matrix (Table 7.1). The respondents for each quadrant were drawn using a convenience sample. Multi-Level Sampling In this method, different sampling techniques are used at different levels before the final respondents are identified. National Sample Surveys used multi-level sampling (refer to Box 7.1). Also refer Case 7.1. Guidelines for Sampling for Predictive Studies 1. Study the population and decide whether sampling can be done at one level or a multilevel sampling has to be done. 2. Study the population characteristics and identify variables discriminating behaviour. Divide the population into strata if the behaviour varies significantly across strata. 3. Identify possible sampling frames. 4. Check for the validity of the frames. 5. If sampling frames are appropriate use random/stratified random sampling as the case may be. 6. If sampling frame is not available, check whether there are locations where the population flow happens (random arrival at a specific location, e.g. shopping centre) to draw a systematic sample. 7. If probabilistic sampling is not possible, then use the quota sampling. 8. Define quota classification and decide on the number of respondents that would be chosen from each cell. Box 7.1: National Sample Survey Round 61, 2004–2005 A stratified multi-stage design has been adopted for the 61st round survey covering all states of India. At the first level in each state/UT, two basic strata have been formed: (i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, if there were one or more towns with population of 10 lakhs or more, as per population census 2001 in the district, then they will also form separate strata for sampling. Identification of villages and blocks: For the rural sector, the list of 2001 census villages (panchayat wards for Kerala) constituted the sampling frame. For the urban sector, the list of latest available Urban Frame Survey (UFS) blocks has been considered as the sampling frame. Identification of households: The ultimate stage of sampling was identification of the households in both the sectors. A random sampling using the electoral roles was used to select an individual. This individual’s house would be used for survey. International Household Survey Network (2006), India, 2005, National Sample Survey Round 61, 2004–2005 (NSS-R61) http://surveynetwork.org/home/index. php?q=activities/catalog/surveys/ihsn/356-2005-001 downloaded 4 December 2010

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SAMPLING FOR PROBLEM-SOLVING STUDIES Problem-solving studies aim at studying the impact of a specific stimulus and the studies involve two or more groups depending on the research design. Since the responses of the groups have to be compared to identify differences between groups, the samples for these studies should be homogenous to ensure that the differences in the findings are not due to the differences in the sample. Thus, a homogeneous sample (Patton, 2001) is one in which the researcher chooses participants who are similar, that is participants who belong to the same subculture or have similar characteristics. Thus, for problem-solving studies, non-probabilistic convenience sampling is used. Based on the above discussion, the suggested sampling plan mapped to the research designs framework (Table 4.5) is given in Table 7.2. Table 7.2: Suggested sampling plan mapped to the research design framework Type of information

Purpose of the study Insights

Predictive

Problem solving

Sensitive/leading to socially desirable responses

Expert and judgmental

Systematic for observations. Convenience for state of mind

Homogenous convenience sampling

Motive

Convenience

Convenience

—

Normal information (respondent aware, not leading to socially desirable responses)

Judgmental

As per guidelines Homogenous for predictive convenience studies sampling

The next chapter deals with the different analysis for marketing research studies.

SUMMARY The probabilistic and non-probabilistic methods of sampling are described and the sampling methods to be used for different methodologies are discussed. For studies for gaining insights non-probabilistic sampling methods are appropriate and for predictive studies the appropriateness of the sampling method depends on the availability of an ideal sampling frame (a list which more or less represents the target population). In case a sampling frame is available, then a random sampling can be used, otherwise a systematic sampling or a quota sampling has to be applied depending on whether there are locations where there is a random flow of the target population or not. Convenience sampling to draw homogenous samples is used for problem-solving studies.

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CASE 7.1 Choosing Appropriate Representative to the Parliament (2009) Research Objectives The research objective of the study is to understand the voting behaviour of Indians. Here only the sampling design is discussed. Sample Design There are a total of 35 states and union territories in India with 543 parliamentary constituencies. The behaviour and attitude of the electorate varies from state to state and constituency to constituency. Also many states are located in geographically remote locations and are not easily accessible. A multi-level sampling was used to identify the cross-section of the population. In the first step, the states and union territories were classified into three categories based on their turnout in the last election (2004): • Low turnout states (35–52.5%) • Marginal states (52.5–62.5%) • High turnout states (>62.5%) The states under each category are given in Table 7.3. Table 7.3: States divided based on voter turnout (2004 general elections) Category

States

Total no. of seats

Low turnout states

Jammu & Kashmir, Gujarat, Delhi, Uttaranchal, Madhya Pradesh, Uttar Pradesh, Rajasthan, Chandigarh, Chattisgarh

190 (36%)

Marginal turnout states

Meghalaya, Maharashtra, Jharkhand, Arunachal Pradesh, Bihar, Goa, Himachal Pradesh, Tamil Nadu, Punjab, Mizoram

165 (30%)

High turnout States

Andaman & Nicobar, Karnataka, Haryana, Orissa, Tripura, Manipur, Assam, Andhra Pradesh, D&D, Kerala, Pondicherry, Sikkim, West Bengal, Lakshadweep, Nagaland

180 (34%)

Source: Compiled by Author from http://pib.nic.in/ archieve/others/2014/apr/d2014041101.pdf Next, from each region a quota sample was used to accommodate the age, demographic and gender differences of the electorate (as the variables were perceived to differentiate the voting behaviour). The break-up of the quota variables is as under: •

Age group Young (18–30) (55%) Middle aged (30–55) (30%) Old (>55) (15%)

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• Location Class 1 city (16%) Class 2 city (10%) Remaining urban (21%) Rural (53%) • Gender Male (52%) Female (48%) A convenience sampling was used to get representation from each of the quadrants in Table 7.4. Table 7.4: Quota sampling grid for studying the voters’ behaviour Location

Young Male

Urban Rural Class 1 Class 2

Female

Middle aged Male

Female

Old Male

Female

8

Analysis

Learning Objectives At the end of the chapter, the reader would be familiar with: Details of some commonly used descriptive and inferential analysis Need for identifying the appropriate data analysis technique Special topics covered in the chapter: Type I and type II errors Comprehensive research proposal

Analysis is a process of resolving data into its constituent components, to reveal its characteristic elements and structure (FAO 1997). In short, analysis helps draw meaningful information from the data set. This chapter discusses the commonly used data analysis and suggests basis for choosing the method for analysing the data. The commonly used methods for hypothesis testing are also discussed. The chapter only provides an overview of the different analysis. The detailed statistical calculations are not discussed as the “user” only needs to check whether the appropriate method has been used and also whether the necessary conditions of the test are met. Moreover, today there are several computer packages available and one need not know about the mathematical calculations.

TYPES OF ANALYSIS The types of analyses are shown in Figure 8.1. Broadly the analysis could be divided as descriptive and inferential.

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Type of analysis

Descriptive

Graphical representation

Inferential

Summary statistics

Cross tabulations

Parametric

Nonparametric

Figure 8.1: Types of analysis

DESCRIPTIVE METHODS The descriptive methods summarise the main features of the data. This allows simple interpretations of the data. These include graphical representation, summary statistics and cross tabulations. Graphical Representation The graphical representations are widely used though they do not provide statistical significance. Hooley and Hussey (1995) found that simple data representation (data tables, graphical representation and summary tables) are more commonly used as compared to statistical analysis (regression, cluster, discriminant, factor and conjoint analysis). Types of Graphical Representation of Data There are many forms of representing data graphically. A graph provides a visual indication of the size of the different data sets or shows the trend whether it is increasing, decreasing, fluctuating, etc. Graphical representation can be through: • Bar graphs: A bar graph is a graphical representation of frequency distributions of ungrouped data. For example, the time spent during the month for different secondary activities by people within age of 25–35 years (Figure 8.2). • Histograms: Similar to the bar graph, a histogram is a graphical representation of a continuous frequency. For example, a graphical representation of the grade wise distribution of students for the Marketing Research course. • Frequency polygons: Instead of depicting the graph as columns, a frequency polygon is obtained by joining the values for the different

Marketing Research: A User’s Perspective

98 80 70

Hours/months

60 50 40 30 20 10 0

Friends

Family Hobbies Shopping TV

Health Finances Parties

Figure 8.2: Time spent for different secondary activities (hours/months) 35 30

Percent users

25 20 15 10 5 0

Highly satisfied

Satisfied

Can't say

Not satisfied

Highly dissatisifed

Figure 8.3: Tractor purchase: satisfaction levels in percent

frequencies. For example, level of satisfaction with the service provided by a tractor company (Figure 8.3). • Pie chart: The information is depicted as a circle divided into sections, wherein each section depicts the proportion for that variable. For example, the preference for different brands of tractors is shown in Figure 8.4.

Analysis

14%

99

2% 17% Ford Escort Mahindra Sonalika

22% 18%

Eicher Tafe

27%

Figure 8.4: Preference for different brands of tractors Source: Pingali 2004

Summary Statistics Summary statistics are measures of central tendencies and dispersion to test their statistical properties. Measures of central tendencies summarise the information of a population. The commonly used measures are mean, median, mode, standard deviation and variance. Mean is the average value for the observations. Median is the middle value of the observed data and mode is the most frequently occurring value. Suppose, the marks obtained by a group of students are 23, 23, 24, 22, 22, 22, 25, 24, 22, 21, then the mean is 22.8, median is 23 and the modal value is 22. Standard deviation (SD) is a measure of dispersion of the data. Standard deviation indicates the extent to which the individual values differ from the mean. For example, the SD of marks for the above group of students is 1.3 (i.e. the students do not vary much in their level of knowledge). In a normal distribution, 68% of values fall within one SD of the mean and 95.5% of values fall within two SDs of the mean (Figure 8.5).

–2 sd –1 sd

μ

+1 sd

+2 sd

Figure 8.5: A typical normal distribution curve

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Cross Tabulation Cross tabulation is a simple method of tabulating data across two or more discrete variables (Table 8.1). Cross tabulations can also help identify relations between variables not earlier expected (especially beneficial in exploratory studies). Table 8.1: Gender-wise classification of students opting for different courses Medical

Engineering

Others

Male Female

Since it is easy to do cross tabulations, a researcher can develop cross-tabulations with different variables to see if there is a pattern or association between the variables. For example, Table 8.1 can show if there is a variation in the choice of courses by gender. Cross-tabulations not only provide a count for different cells, they can also be used to find out the average value for each sub group (average time spent in library), or the total value for each sub group (total money spent on books), etc. Lea and Worsley (2005) used cross-tabulations to understand organic food beliefs by socio-demographics. Cross-tabulations can be easily done using an Excel spreadsheet (refer to Annexure 2).

INFERENTIAL ANALYSIS Inferential statistics help to make inferences about the population from the sample data. Inferential methods are more rigorous and they need to satisfy the underlying population distribution, variable conditions and sampling conditions to make valid inferences. Inferential methods could be divided into “parametric” or “non-parametric” methods. Parametric methods assume that the data follows a normal distribution and that the spread of the variance is uniform. Non-parametric methods are used when the data does not satisfy the normality condition. Parametric and non-parametric tests could be univariate tests (involving a single dependent variable) or multivariate tests (involving more than one dependent variable). Multivariate techniques can be categorised as either dependence or interdependence methods. In dependence methods, there is a clear distinction between the dependent variable and the independent variables. For example, sales (dependent variable) is a function of the advertising budget (independent variable). In the interdependence methods, the variables in the data set are correlated and there is no designated dependent variable. For example, the marketing

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mix variables used by the company could be interrelated and none of the marketing mix variables can be used as a dependent variables for segmentation and positioning studies (discussed in greater detail in the next section). Types of Error and Testing of Hypothesis Inferential data analysis requires testing the statistical significance of the results to check whether the finding can be generalised to the entire population or not. Tests, such as F, T, or χ2 compare observed to expected effects and determine whether or not to accept the results of the test and attest the sampleto-population generalisability. While accepting or rejecting a finding there are two types of errors possible. An analysis may reject a correct sample to population generalisation as it fails the test. This is termed as type I or alpha error. On the other hand, the test may accept a wrong sample to population generalisation. This is termed as type II or beta error. Refer to Figure 8.6 for a graphical representation of type I (alpha) and type II (beta) errors. Assume the test is studying whether the sample represents group 1 or not. If the sample mean has a value greater than X (cut off ) and the test rejects the sample to population generalisation even though the sample was actually drawn from group 1, then that is type I error. On the other hand, if the sample mean is less than X (cut off ) and the test accepts the sample population generalisation, even though the sample is drawn from group 2, then that is type II error (Figure 8.6).

Type II

μ1 Group 1

Type I x

μ2 Group 2

Figure 8.6: Types of errors

The power of the test is the probability that selected sample is correct and mathematically calculated by (1-beta error). As can be seen from Figure 8.6, if type I error is reduced, it would increase type II error and vice-versa. The choice of which error needs to be reduced depends on criticality of errors. If the criticality of choosing a wrong sample is high, then type II error should be minimised, otherwise, type I error should be minimised. For example, a study on the effectiveness (performance) of a fire extinguisher model should reduce the type II (beta) error. While a study on the preference for a television could reduce type I (alpha) error.

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In general, performance-related studies could minimise type II error and state-of-mind (preference) related studies can minimise type I error. Some of the commonly used measures are chi-square test, T-test, ANOVA and Z-test. Chi-Square Test Chi-square is used to test whether there is significant difference between the observed data with the expected data. For example, if an institute wants to compare whether the performance of the current batch is similar to that of the previous batches, then a chi-square could be performed by testing whether there is a significant difference between the batches (Table 8.2). If the differences in the number of passed and failed in the current batch and the previous batch is insignificant (based on the chi-square test) then it could be said that the performance of the current batch is similar to the earlier batches. If the difference is significant then it can be said that the current batch is different from the revious batch. Table 8.2: Comparison of batches 2009 and 2010 Batch 2009

Batch 2010

Pass percentage Fail percentage

T-test T-test is meant to test if the means of the two groups are statistically similar. t=

X1 – X2 SX X 1

2

— — Where X1 and X2 are means of the two groups S–X ––X standard error of the difference between treatment groups 1 and 2 1

2

ANOVA Analysis of variance (ANOVA) is a commonly used test to check whether the means of two groups are the same by checking whether the differences are due to the variability within the groups or due to a random factor. For two samples, T-test and ANOVA will produce the same results. While T-test studies the similarity of mean by looking at the spread of variability, ANOVA studies the similarity of means by partitioning the variance and the test statistic used is F ratio. T-test can study differences between two variables or two groups, whereas ANOVA can compare more than two groups.

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Z-test Z-test checks whether the sample mean and population mean are similar. Z-test requires the knowledge of the population mean and standard deviation. Z=

X– μ σ √n

— X is the mean of the sample μ is the mean of the population σ is the standard deviation of the population n is the sample size Z-test requires that the population to be normally distributed and the sample size should be greater than 30 (if the sample sizes is less than 30, a T-test should be used). t=

X– μ s √n

— X is the mean of the sample μ is the mean of the population s is the standard deviation of the sample n is the number of sample size Z-test can also be used to check the differences of proportions of two groups.

IDENTIFYING THE APPROPRIATE DATA ANALYSIS TECHNIQUE There are several methods for data analysis. With the availability of computer software packages, analysing any volume of data using multivariate techniques is no longer a constraint. However, as a user of research checking whether the appropriate method is used or not is important. Using an inappropriate method would provide an output and an interpretation could be made, but the findings may not be valid (refer to “Marketing Research and Error” in Chapter 1). It is therefore essential to be aware of the appropriateness of method applied (Hornby 2002).The user need not know the formula for doing the test (as the analyses is done by some computer package) but he/ she should know when a particular test can be used, and what are the assumptions on which the analysis is done. The choice of the data collection method depends on the scale on which the data of the independent and dependent variables are being collected. This is shown through some of the commonly used correlation methods.

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Pearson’s correlation is an association between two variables which are on interval (or ratio) scale. For example, if the company wants to see the correlation between the advertising spent and the volume of sales, a Pearson’s correlation is used. The correlation values could vary from −1 to +1. Values close to zero indicate low correlation and higher absolute values indicate high correlation. Positive and negative signs only show the direction of the relationship. Spearman’s rank correlation tests whether there is a correlation between two sets of ranking (ordinal) data. For example, if the government wants to check the influence of the “buy fuel efficient cars” campaign, a Spearman’s rank correlation can be applied. Here the correlation between the preferences (rank) before and after the campaign can be checked (Table 8.3). If there is a high correlation, then it could be said that there is no influence of the campaign. If there is a low correlation, then it can be that the campaign has had an impact. Table 8.3: Ranking of cars before and after the campaign Ranking before the campaign

Ranking after the campaign

Car brand 1 Car brand 2 Car brand 3 Car brand 4 Car brand 5 Car brand 6

The point-biserial correlation coefficient is used when one variable is quantitative and the other variable is dichotomous and nominal. Biserial correlation coefficient is similar to the point biserial, but compares quantitative data against ordinal data. The rank-biserial correlation coefficient is used for dichotomous nominal data vs rankings (ordinal). While it is beyond the scope of the book to go into other statistical techniques, the user should ensure that the appropriate test is being applied.

DETAILED RESEARCH PROPOSAL The research process discussed so far describes the “what” and “how” of the research process and all these should combine to form part of the research proposal (refer to Cases 8.1–8.3 at end of this chapter). In general, the research agency is expected to present the research proposal to the client (user) so that the client can “critically evaluate” the problem definition and the methodology stages before the data collection starts. The next section first describes the conceptual model for the application of interdependence methods and then the subsequent chapters describe the multivariate analysis used in MR.

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SUMMARY The chapter presents the classification of the types of analysis and provides some of the analysis commonly used. The chapter also discusses the type I  and type II errors. It suggests the basis of deciding when to reduce type I error and when to minimise type II error. Testing of hypothesis is also discussed. The chapter concludes by stating the importance of identifying the appropriate analysis based on the scales of the dependent and independent variables.

CASE 8.1 Research Study on Choice of Career in Medical Transcription1 Scope of the Study This study focused on determining the key factors influencing a graduate’s selection of a career option in medical transcription. The survey has been designed to collect the following information: • Attributes important to a job • Key influencers in decision making for a career • Important criteria for making a career in medical transcription Target respondents were graduates who were in their final year of their academic course. Sample was selected from the following degree colleges in Hyderabad. • A.V. Degree College • Aurora PG & Degree College  • Badruka College of Commerce & Arts  • Bhavans New Science College  • Bhavans Vivekananda Degree College • Koti Womens College • Little Flower Degree College • Nizam Degree College • Pragati Maha Vidayalaya Degree College • Sai Sudheer Degree & P.G. College • St. Anns Degree College • Stanley Degree College For Women • Villamarie Degree College • Wesley Degree College • Indian Institute of Management and Commerce Degree College Focus Group Discussion An exploratory study using FGDs (refer to Table 8.4) was used to identify the variables influencing career choice.

1

Study done in 2006

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Table 8.4: Focus group characteristics Characteristics of Focus Group Group size Time duration Group composition Moderator

8 40 minutes Heterogeneous within target group Two

Eight persons were chosen from different educational backgrounds, including B.Com, Microbiology and Computers Sciences for the focus group. The Focus Group Process The focus group was scheduled on 3rd May 2006. A moderator and a facilitator were present to conduct the focus group. The moderator was responsible for the smooth flow of the session, and also to ensure that specific issues were discussed, while the other member recorded the inputs from the respondents. A tape recorder was also used to record their inputs to ensure that the data was not missed out. Prior to the discussion, the group was informed about the objective and purpose of meeting and the line of discussion to be adopted. The sequence of events in the FGD is as follows: 1. Introduction of moderators 2. Briefing the group about the purpose and objective of the discussion 3. Introduction of each participant to the group (introduction includes name, qualification, experience and family background) 4. Everybody was given a chance to share their views and opinions 5. Each participant’s views were listed on the whiteboard, in order to stimulate further conversation on the topics and encourage views of others Questions asked • What were their plans after completion of graduation? • Why didn’t they go for further studies? • Factors influencing career choice. • Factors influencing to join the medical transcription industry. Results of the Focus Group The important factors emerged out of focus group session are: • Company reputation The participants said that the reputation of the company matters most when it comes to joining any industry. According to them, the reputation and standing of the company gives them a better recognition in society and therefore improves their social status. • Salary It was unanimous with the group that salary was an important factor for their decision to join a company. They felt that it was one of the factors which drive them to work.

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• Environment The group felt that the working conditions, including the facilities, infrastructure, work timings and culture of the organisation were important to them to consider working for a company. • Growth The group concurred that they would look to companies that offer them an opportunity to grow in the organisation. • Flexible timings and day job Some members of the group said that they were interested only in the companies which provide flexible working hours. They felt that the employees should be given permission to leave the company as soon as they complete the work. • Location A few of the focus group members felt that the location of the office was also an important factor but perhaps not as important as other factors. • References/advices References and advices play a vital role in making a decision. The factors emerging from FGD include variables influencing the different stages of the decision making process: • Awareness/information providing factors • Interest generating factors • Evaluation-related factors. These inputs were considered while designing the survey instrument. Data and Variables The variables that were covered in the survey instrument (given below) included demographic data in addition to variables derived from the results of the focus group. • • • • • • • • • • •

Age Gender Stream in graduation Proximity to home Town of posting Office facilities Parents advice Peer circle Company reputation Career growth Reference from friends

• • • • • • • • • • •

Stress Qualification Ambiance Remuneration Growth Recognition Social status Awareness Remuneration Flexitimings Market visibility

Questionnaire We thank you and appreciate you for allocating your precious time to participate in our survey. Your opinions and responses are important to us for carrying out our research work. We will assure you that the information you provide will be kept confidential and solely used for academic work.

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1. Name: ___________________________ Age: ___ Sex:___ Qualification:_____ 2. Rate the importance of the following attributes of a job to you Not at all Not Some what Important Very important important important important Proximity to home

1

2

3

4

5

Town of posting

1

2

3

4

5

Office facilities

1

2

3

4

5

Stress in job

1

2

3

4

5

Work environment/ ambience

1

2

3

4

5

Remuneration

1

2

3

4

5

Career growth

1

2

3

4

5

Flexitimings

1

2

3

4

5

3. What are the key factors that you would rely on while choosing a career?

Parents’ advice Market visibility Reputation of industry Publicity/Media Peer circle

Not at all Important 1 1 1

Not Important 2 2 2

Somewhat Important 3 3 3

Important 4 4 4

Very Important 5 5 5

1 1

2 2

3 3

4 4

5 5

4. Are you aware of medical transcription as a career option? (Yes/No) If yes, please describe what you know about medical transcription _____________________________________ 5. Would you like to make a career in medical transcription? (Yes/No) If no, why not? _____________________________________ (If no, then terminate the survey. If yes, then proceed to the next question) 6. If you were made an offer to join a medical transcription company, rank the below mentioned criteria in terms of importance to you. 1. 2. 3. 4.

Company reputation Remuneration Work environment Career growth

5. Flexitimings 6. Location of the office 7. Reference from friends, relatives or acquaintances

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Detailed Analysis Knowledge of medical transcription industry Only 49.5% of the respondents claimed to know about the industry. Table 8.5: Knowledge of ITES vs gender Not aware

Aware

Female

65%

35%

Male

36%

64%

Knowledge of the industry is higher among male respondents than female respondents (Table 8.5). There was no significant difference in awareness levels based on their educational qualifications. Figure 8.7 and Table 8.6 shows that parents’ advice is still the most important source of information (almost three-fourth depend on parents’ advice). This is followed by peer group which is used by almost half the respondents. Media also was found to be playing a significant role, with almost 40% using this source. Table 8.6: Importance of information sources for a career choice (in percent) Not at all Not Some what important important important

Important

Very important

Parents’ advice

4.2

2.1

20

38.9

34.7

Peer circle

2.1

13.7

31.6

45.3

7.4

Publicity/Media

9.5

25.3

24.2

26.3

14.7

50

Percent

40 30

Parents’ advice Peer circle Publicity/Media

20 10 0

Not at all Not Some what Important Very Important Important Important Important

Figure 8.7: Importance of information sources for a career choice

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Overall, people seem to be using different sources for knowledge, but interestingly parents’ advice still dominates even in the technology dominated era. As expected, market visibility and reputation of the company were important for joining a career (Table 8.7 and Figure 8.8). Table 8.7: Importance of factors influencing interest for a career choice

Market visibility Reputation

Not at all important 3.2 5.3

Not important 6.3 4.2

Some what important 20 16.8

Important 49.5 40

Very important 21.1 33.7

50

Percent

40 30

Market Visibility Reputation

20 10 0 Not at all Important

Not Important

Some what Important

Important

Very Important

Figure 8.8: Importance of factors influencing interest for a career choice Table 8.8 and Figure 8.9 shows career growth is most important factor while selecting a company, with over 95% considering it as important or very important. This is followed by remuneration (about 80%) and work environment (75%). Table 8.8: Importance of factors during evaluation of choice of company

Proximity to home Town of posting Office facilities Stress in job Work environment/ ambience Remuneration Career growth Flexitimings

Not at all important 17.9 9.5 4.2 11.6 2.1 2.1 0 2.1

Not important 29.5 15.8 6.3 23.2 6.3 3.2 1.1 7.4

Some what important 23.2 15.8 25.3 31.6 16.8 13.7 2.1 24.2

Important 21.1 42.1 48.4 18.9 44.2 58.9 24.2 45.3

Very important 8.4 16.8 15.8 14.7 30.5 22.1 72.6 21.1

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Percent

80 60

Not at all Important

40

Not Important

20

Some what Important Important

ing s tim Fl e x i-

row th Ca ree rg

on

Re m un er a ti

env iro nm e nt

job ess in

Wo rk

Str

e fa

cili ti

es

g Offi c

of p os t in To wn

Pro x im it y

to h om e

0

Very Important

Figure 8.9: Importance of factors during evaluation of a choice of company Conclusion The study shows that only about 50% of the potential target group is aware of medical transcription as a career option. It is also interesting to note that parents are still the most important source of information for career choice. While market reputation is important for choice of a career, career growth is important for choice of the company.

CASE 8.2 Comprehensive Research Proposal: Broadband BROADBAND Household broadband Internet connections2 became available in the UAE in early 2000. Though Internet-capable personal computers which have a broadband connection have become popular worldwide and are cheaper, the penetration rate in UAE was very low (2.5%) compared to that of 14% of U.S. homes. This represents that the market has great potential. Moreover, worldwide the prices of broadband have been decreasing as is the case of all technology-oriented products. Benefits of a Broadband Service • Fast access and downloads: With the help of broadband, one can watch movies online, chat with friends, download music, listen to Internet radio and play online games. • Uses a different connection: It uses a different telephone line and hence the normal phone lines need not be engaged for the period of Internet usage. • Wireless connection that is always on: With the help of broadband, one can “wirelessly” roam and surf around the house, there is no need to connect/disconnect and also the Internet can be used by multiple computers. 2

Study done in 2004

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• Online shopping and learning: With the help of broadband one can shop and pay bills from home. Access to online courses is easier with broadband. • Home security and surveillance: With the help of broadband, one can maintain surveillance of the house with web cams. Management Dilemma (Decision Problem) Broadband service was available in UAE for over five years, during which period it was observed that the growth of this service was very low. The service providers have achieved only 2.5% of the penetration in the UAE market. The service providers wish to increase the penetration level to 10% in the next two years. The decision problem is converted into the research problem using a decision tree approach (Figure 8.10).

Internet usage Profile customers

Use Internet outside house

Own dial up facility

Profile customers

Own broadband

Satisfaction level

Usage suits broadband

Usage does not require broadband

Aware of benefits of broadband

Aware of benefits of broadband

Reasons for not using broadband

Potential to upgrade to broadband

Figure 8.10: Decision tree for usage of broadband RESEARCH OBJECTIVE • Potential for broadband (Internet needs, usage patterns and knowledge of different Internet options of different consumer segments). • Identify the satisfaction level of the existing broadband customers.

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RESEARCH QUESTIONS The research questions for this study developed using the behavioural framework are as follows: • Behaviour Current usage of Internet by different groups Where do they use the Internet? Who have Internet connections in the house Timing of Internet usage • Situational factors Availability of Internet cafes Availability of telephone connections in the house Telephone usage • State of mind Knowledge Reasons for using the Internet Reasons for using a specific Internet option Speed of connection for different options Awareness of broadband services and its benefits Attitudes/Perceptions Satisfaction level of broadband users Motives Status associated with Internet options • State of being Demographic profile of different segments Methodology Most of the information can be classified as “normal information” as per the research design framework (aware, non-sensitive and not likely to lead to a socially desirable response, refer to Table 4.5). A survey methodology is designed taking into account the type of information. This study would be conducted in a phased manner. In the first phase, the existing data with service providers will be analysed to understand demographics of the existing users. Based on this, the potential customers will be identified using the following characteristics: • Users who look for vital information from the Internet, download research materials, educational materials, etc. • Who uses the Internet for long distance chat, on-line magazines • Who use the Internet for on-line videos, music, games, chat and other services Next phase will cover the exploratory study to understand the awareness/knowledge of customers about broadband. Focus groups will also identify expectations from the Internet and how the potential of broadband actually makes it appealing to each group. Phase III will cover the conclusive research based on findings of Phase II. Sampling Method A systematic sampling using the list of dial-up and broadband users can be drawn using the existing data base. Alternatively, a quota sampling could be used for the conclusive study using the matrix given in Table 8.9.

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Table 8.9: Quota sampling format for the conclusive study Gender

Education level

Male

High school

Internet usage Dial up users

Broadband users

Graduates Post graduates and more Female

High school Graduates Post graduates and more

Questionnaire Design A self-administered questionnaire will be prepared for securing the feedback of customers of all categories. Pilot test: The questionnaire will be tested with small numbers of customers. Based on this, the final questionnaire will be developed. Logistics: The questionnaire will be sent by bills, e-mail to the sample if a systematic sample is used. If quota sample is used, investigators would be hired to administer questionnaire to the respondents satisfying sample criteria. Questionnaire 1. Internet usage Rarely

Sometimes

Frequently

2. Where do you use the Internet? Office Internet Café

House

3. What do you use the Internet for? 1. _________ 2. _________

3. _________

Very Frequently

4. _________

4. Do you have Internet connectivity in the house? 5. What type of connectivity? Dial up Broadband 6. How many hours a day do you spend on the Internet 0–1hr 1–2hrs 2hrs and above 7. What is the usage of the Internet in the house and outside? 8. How important is speed of connectivity for you while browsing? Very Important Not Important

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9. Please specify your industry Teaching Banking Aviation IT Student Others 10. If you are using broadband, how long have you been using broadband? 1 or less yrs 1–2 yrs 3–4 yrs 5yrs and above 11. What is your approximate household annual income? < 50,000 50,000–100,000 100,000–200,000

> 200,000

The following questions are only for those who are availing broadband services: 1. Are you happy with the present broadband service? 2. Would you recommend to your friends and relatives to use broadband? 3

Is the present level of customer service helpful when you face problems with your broadband connection?

4. Do you find the service to be expensive? 5. Broadband provides faster access to the Internet? Agree Don’t Know Disagree 6. Broadband is more convenient than dial-up connection. Agree Don’t Know Disagree 7. Broadband reduces time spent on the Internet. Agree Don’t Know Disagree 8. Broadband enables you to watch movies, listen to songs on-line. Aware Somewhat Aware Unaware 9. Faster connection of broadband makes your computer more vulnerable to security threats. Agree Don’t Know Disagree 10. Broadband service comes in three different choices of speed. I Know I Don’t Know 11. The process of upgrading to broadband from dial-up is Very Simple Average Very Complicated

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12. Broadband connection is a status symbol Agree Don’t Know Disagree Data Analysis Analysis would also be done to find the reasons for low rate of growth of broadband and also know the reasons that could motivate the customers to migrate to broadband. Schedule The following is the tentative schedule for the research study: Exploratory research Literature review Client interviews Focus group studies Analysis of exploratory research Conclusive Research Prepare questionnaire Pilot testing Data collection Analysis

10 days 2 days 30 days 15 days

Total

90 days

4 days 4 days 10 days 15 days

Expenses which are part of the research proposal are not included here.

CASE 8.3 Comprehensive Research Proposal Impact of Advertising on the Use of Helmets Background The rapid and unplanned urbanisation has resulted in an unprecedented growth of motor vehicles. This had led to an increase in road traffic and accidents. Worldwide, the number of people killed in road traffic crashes each year is estimated at almost 1.2 million, and the number injured could be as high as 50 million. According to a study conducted by the World Bank, the annual number of road traffic deaths is projected to increase by 83% in the low-income and middle-income countries by 2020. India has the second largest road network in the world with over 3.3 million km of roads with approximately 40 million vehicles. It is estimated that over 100,000 persons die in the traffic crashes annually, over 1.5 million are injured seriously and about 300,000 disabled permanently. Nearly 30–40 % of road deaths and injuries in India are among riders and pillions of motorised two-wheeler vehicles. Nearly half of these deaths and injuries are due to injuries to brain and the nervous system. Helmets have been proven to reduce deaths, severe injuries, skull fractures, neurological disabilities, extent of hospitalisation. Section 159 of the Motor Vehicle Act states:

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“Every person driving or riding (other than in a side car, on a motor cycle of any class or description) shall, while in a public place, wear a protective headgear conforming to the standards of Bureau of Indian Standards”. Despite this, helmet usage rates among twowheeler riders and pillions are low. Though people feel the necessity of usage of safety devices while driving/riding, many of them do not practice that because of a number of factors like inconvenience, “nothing can happen to me” attitude, etc. There is, therefore, a need to increase awareness and usage of helmets amongst the two wheeler riders. Problem Definition The Government of India launched a nationwide public road safety awareness campaign to promote the wearing of helmets among two wheeler riders. This nationwide campaign constituted road signs, television infomercials and print ads. The Government of India wishes to study the effectiveness of this campaign on the usage of helmets. Research Objective Whether the “wear helmet” campaign increased the awareness and usage of “helmets”. Research Questions 1. What are the demographic and psycho graphic characteristics of the motor vehicle users? What are their media habits? • Collect information on age, sex, occupation, marital status, number of children, reading habits, amount of travel, media consumption 2. What is their level of awareness regarding the importance of helmets? Has it improved after the awareness campaign? • Collect information on knowledge of road safety, usage of helmets, knowledge of the campaign 3. Can the customer’s decision be influenced to a great extent by the regulations? • Collect information about people’s reasons for using helmets. 4. Is there a high correlation between people using helmets and those who have seen the ads? • Collect information about influence of helmet usage because of the campaign 5. Whether there is any linkage between one’s age and marital status to one’s road safety standards? • Collect information regarding one’s age, marital status and usage of helmets 6. Whether people who have seen/involved in accidents exhibit frequent usage of helmet? • Collect information about personal experiences, if any which had an impact on helmet usage 7. What type of messages can increase usage of helmet? • Collect messages and approaches which could help increase helmet usage

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Research Design Since non-usage of helmets is not seen so much as an offence by the peer group, the information to be collected (other than actual usage of helmet due to the campaign) could be treated as normal information (knowledge based, non-sensitive and not leading to socially desirable responses). Information regarding usage of helmets after seeing the campaign could lead to socially desirable responses. However, this information would be relevant only if the campaign has been able to create awareness and was able to provide knowledge about the need for helmets. Thus, initially a survey would be conducted. If the study shows a substantial increase in awareness and knowledge then a second phase could consider adopting observations to study the actual usage. Exploratory Survey Focus group discussion was conducted to gain a better understanding of safety concerns among people who use two wheelers. The FGD was conducted with 10 people, approximately in the age group of 20–30 years. The purpose of the FGD was to get an understanding of: • The general perception of people regarding helmet usage • Arguments against helmets • The influential messages and suggested approaches that would encourage helmet usage Findings of Exploratory Study • Regular usage of helmets was less than 50% • People generally do not use helmets at all times • Most people felt that helmet is required but they generally do not wear it because it is not strictly enforced • Some people felt that they are safe riders and hence do not really feel the need for wearing helmets. • You are less likely to hit your head at low speeds • Two respondents said that they felt helmets increase the probability of accidents • One respondent felt that helmet was stylish • One respondent said that helmet was highly “un-cool” • There is a general tendency of people wearing helmet more regularly as they grow older • People start using helmet more regularly once they have seen a wreckage or have been personally involved in some accident • Some felt that helmets will mess up the hair • Helmets are very heavy • Helmets are hard on the neck • The respondents felt there should be freedom of choice • Hassles of carrying helmets around Based on the exploratory study, the survey questionnaire was developed. Questionnaire for Phase 1 1) Your name 2) Your Age 3) Your occupation

Analysis

4) 5) 6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) 17) 18) 19) 20) 21)

119

Your marital status Do you have children? Which newspapers/magazines do you most often read? How many hours of TV do you watch daily? Do you own a vehicle? If yes, what type? How many kilometers do you travel daily? What best describes your knowledge about road safety? How often do you use a helmet while riding? What are the reasons for using one? Have you ever seen or been involved in a road accident? Have you heard of the “wear a helmet” campaign? How did you learn about the campaign? What is the central message of the campaign? Have you shared the information with anyone? Have you seen the road signs on “wear a helmet”? Did the campaign actually convince you to use a helmet while driving? Overall, how will you rate the effectiveness of the campaign? What factor/element do you think, if added, would have significantly increased the effectiveness of the campaign?

Sampling Plan The target population will consist of people who have two wheelers. Since most families use a two wheeler, the universe would encompass all households. Sampling technique: A multi-level sampling to identify households as done in the NSS study could be used (refer to Box 7.1). Data Analysis The primary purpose of the analysis is to profile the respondents based on vehicle usage, current behaviour of helmet use and whether they are aware of the campaign; cross-tabulations could be done to analyse this information. Time frame Data collection, analysis and report writing

90 days

Section 3 Using Multivariate Analysis for MR Studies Section 3 describes some of the multivariate analysis used for marketing research studies. Chapter 9 discusses how the interdependence analysis can be used for marketing decisions, taking segmentation and positioning as examples. The subsequent chapters describe the model or the logic for the respective analysis and focus primarily on how the data should be collected, formatted for computer packages and how the output needs to be interpreted (the mathematical calculations have not been discussed, except where required). Factor analysis is described in Chapter 10, multi-dimensional scaling in Chapter 11, discriminant analysis in Chapter 12, conjoint analysis in Chapter 13 and cluster analysis in Chapter 14. The section concludes with Chapter 15, which provides a checklist for users of “Using MR studies”. The chapters in Section 3 are highlighted in the overview diagram.

Research brief Research problem Research questions/ hypothesis Purpose of study

Type of information Research design

Type of analysis

Instrument design Sampling design Data analysis Interpretation

9

Interdependence Techniques for Marketing Decisions

Learning Objectives At the end of the chapter, the reader would be familiar with: Application of interdependence data analysis for marketing decisions Techniques for exploratory and confirmatory marketing decisions Special topics covered in the chapter: Marketing mix and consumer information processing model

Chapter 8 discussed about the dependence methods. This chapter discusses the application of interdependence techniques for marketing decisions using segmentation and positioning analysis as an example. The chapter briefly describes the marketing concepts (segmentation and positioning) and then identifies the analysis to be used by conceptualizing the information processing method. Subsequent chapters in this section will discuss the multivariate analysis commonly used for MR.

SEGMENTATION Market segmentation is one of the most important components to develop successful marketing strategy (Kotler 1997). Segmentation involves dividing a market into distinct groups of buyers who might require separate products and/or marketing mixes. That is, sorting of individuals according to their preference for products/brands and services. Companies use two different ways to segment the market and develop profiles of the resulting market segments. One of the most frequently used methods for segmenting a market has been demographic segmentation. Demographic segmentation consists of dividing the market into groups

124

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based on demographic variables such as age, gender, family life cycle, income, occupation, education, religion, race and nationality (Bowen 1998). Quinn et al. (2007) have expressed concerns of predictive validity for this form of segmentation as markets are becoming fragmented and consumer behaviour is changing. The objective of second type of segmentation, taking care of the concerns of Quinn et al. 2007, is to define groups with a homogeneous response to marketing stimuli (Frank et al. 1972). Techniques for Segmentation An investigation into the practices of U.K. companies showed that, segmentation was, after forecasting, the second most used statistical analysis (Hussey and Hooley 1995). The tools available for segmentation range from summary statistics and graphical representation to advanced multivariate analytical procedures. Goller et al. (2002) recommend cluster analysis, factor analysis, discriminant analysis and structural equation modeling for identifying valid segments.

POSITIONING Product positioning is a common task faced by managers in marketing. Positioning evolved from market segmentation, targeting and market structure changes during the 1960s and the early 1970s (Sekhar 1989). Ries and Trout (1986) changed the perspective of positioning and mentioned that it is not what is done to the product/service, but rather what is done to the mind of the prospect, they stated “…positioning shifts the emphasis of marketing from the product to the battle for your mind.” Arnott (1992) states that positioning is the process of modifying the consumer perceptions to a marketable object. Thus, two steps are required in the successful positioning process of a brand (Darling et al. 2009): 1. Establishing the brand in the minds of consumers; and 2. Differentiating the brand from the offerings of competitors Techniques for Positioning Perceptual mapping techniques are frequently used to aid managers in making product positioning decisions (Kohli and Leuthesser 1993). The resulting perceptual map provides the company with the position of their brand in the perceptual space (Figure 9.1). Also refer Case 9.1. A variety of methodologies have been proposed for constructing perceptual maps (e.g. multidimensional scaling, factor analysis, discriminant analysis, etc.) (Ghose 1994).

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Uno

Style

Zen Indica Matiz

Santro Indianisation

Figure 9.1: Relative position of selected car brands

The next part describes a conceptual model to identify the appropriate multivariate analysis for these marketing decisions.

CONCEPTUAL MODEL The marketing executive uses marketing mix variables to achieve a successful market penetration (Day 1999). The way in which marketing mix variables increase market acceptance can be understood by studying the consumer information processing. The marketing mix variables influence brand perceptions and attitudes of the consumers and these brand perceptions differentiate the competing brands. Based on the perceptions of different brands, the preference and purchase decisions are made (Figure 9.2). The perceptions and attitudes form the “black box” of information processing. Companies would be interested to know how the marketing mix variables interact and form perceptions). As Julian and Morris (2003) state that different consumers could have different perceptions and these perceptions could lead to different behaviour and therefore there is a need to group consumers into clusters based on their perceptions towards the category. Thus, marketers need to identify homogenous groups (segments) having similar perceptions and understand their brand perceptions (positioning) to improve market share. Application of Techniques Using the Conceptual Model Different techniques can help identify homogenous segments and understand the perceptions/attitudes by working from the marketing mix variables or working back from the preferences (refer to Figure 9.1).

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Techniques using the understanding of how marketing mix variables interact include: • Exploratory segmentation techniques such as cluster analysis • Exploratory analysis for identifying positioning dimensions such as factor analysis Techniques using preference data include: • Profiling different segments using discriminant analysis • Identifying the relative position of the brands using multi-dimensional scaling • Utility for different attributes using conjoint analysis Detailed application of the factor analysis, multi-dimensional scaling, discriminant, cluster and conjoint analysis are discussed in the subsequent chapters. Variable 1 Variable 2

??

Variable 3

...

Brand differentiation

Preference/ Purchase

??

Variable n

Perceptions/Attitudes-Unknown (Focus of marketers) State of mind variations

Figure 9.2: Consumer information processing and marketing decisions

SUMMARY The application of interdependence analysis for marketing decisions is explained using the concepts of segmentation and positioning. To identify the suitable multivariate analysis, a conceptual model of information processing is developed. Analyses for identifying segments or factors differentiating brands can be done by understanding the way marketing mix variables influence perceptions. The analysis can also use preferences to identify the relative position of the brands, utility of different attributes and their levels or help profile customer segments.

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CASE 9.1 Perception of Oil Companies Petroleum retail sector in India is undergoing a change in the way business is being done. The sector has moved away from being primarily government controlled, a move that has brought new levels of competitive threat and customer focus. Thus far, the three public sector companies (PSU)–Indian Oil Corp. Ltd. (IOCL); Bharat Petroleum Corp. Ltd. (BPCL); and Hindustan Petroleum Corp. Ltd. (HPCL)–dominate the petroleum retail sector in India with about 93% market share between them. With the deregulation of the sector in 1999, and the ongoing process of divestment of the government stake in the business, the sector is exposed to new market forces. The competitive threat is bigger, with private players entering the fray. Significantly, the existing players have recognised the need for them to quickly evolve to be market driven. The retail experience, which until recently, at the gas station being nothing more than a place to fill fuel with cash payment only is seeing a complete facelift, with new multi-fuel dispensers, better trained attendants and service elements. The outlet itself is expanding to include grocery stores, cafes, bank ATMs, internet kiosks, etc., giving the customer more than just fuel. Credit cards, debit cards and loyalty cards are also widely accepted. The outcome of these changes is that the consumer is getting a different experience at the petrol pump that is translating into higher service expectation. Consumers are being given reasons to build preference among the three companies (IOCL, BPCL and HPCL) and their brands. As consumers have begun to express their preferences, the companies have entered an inevitable battle for business through improved service quality and relationship building initiatives. This study is done to understand the perception of the oil companies and how they could improve their services and compete with future competition. Research Objective • To identify the factors which affect the customer satisfaction? • To find the level of satisfaction of the customers for the three PSU oil companies. Research Design Since the kind of the information that is being obtained is not sensitive, FGDs was adopted for exploratory studies and a survey questionnaire for descriptive research. Based on the results of the exploration study, a survey questionnaire was designed. Pilot Testing The survey was pre-tested on selected small samples and suggestions were incorporated. The proposal including the questionnaire was critically examined by experts before use. Sampling Plan and Size At each retail outlet, a systematic sampling plan was used where every 20th customer was selected for survey. This was tried even though there were problems as some customers were in a hurry and did not have time. Data Analysis Service Improvement Grid A service improvement grid was developed to understand the perceptions of the different companies. The service improvement grid gives the degree to which expectations of the customer are met along with the importance of the attributes.

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The matrix can be divided into four quadrants (Table 9.1). The top right hand quadrant indicates “the good work” quadrant (which indicates the service levels provided by the company are meeting consumers’ expectations). The top left hand quadrant indicates that the company is spending more on the services which are not perceived to be important for the consumers (the overkill quadrant). The bottom right hand quadrant indicates that the company needs to “concentrate effects” on these services, as the attributes are important but the perceptions of the company on these services is low. The bottom left hand is the “low priority” quadrant where the attributes are not perceived important and the company also need not spend effort on these services. Table 9.1: Service improvement grid Company satisfaction score

Low

Attribute Importance High

High

Overkill

Good work

Low

Low priority

Concentrate effort

Results and Discussion Table 9.2 shows the importance of the attributes and the perception of the companies on the attributes (names of the companies are disguised). Table 9.2: Perception scores of oil companies Attribute

Attribute importance

Company 1

Company 2

Company 3

Convenience

High

High

High

Low

Ambience

Low

Low

High

Low

Attendant interaction

High

Low

Low

Low

Facilities

Low

Low

Low

High

Quickness of service

High

Low

Low

High

Reputation

High

Low

Low

Low

Figure 9.3 shows the perception plotting of the companies along the importance of the attributes.

Interdependence Techniques for Marketing Decisions

Company satisfaction score

Overkill

129

Good work 2

3 3 1

Low priority 1 2

3

2 1

1 2 2 1 3 3 1

3 Concentrate effort

2 Attribute importance Figure 9.3: Service improvement grid for oil companies Good work quadrant: Company 2 has two attributes falling in this quadrant. However, company 1 and 3 have only one attribute falling in this quadrant. Concentrate effort quadrant: The perception of three attributes for companies 1 and 3 and two attributes of company 2 are falling in this quadrant, indicating that consumers’ satisfaction on these important attributes is less than required. Overkill quadrant: Except for one attribute for company 3, none of the attributes fall in this quadrant, indicating that there is no excess service provided to the consumers. Low priority quadrant: Two attributes of companies 1 and 2 and one attribute of company 3 fall in this quadrant. While the oil companies are focusing on improving services, it is found that there is a need to redirect their effort in some cases to improve their service quality. Company-wise analysis is detailed below.

Company satisfaction score

Perception and Recommendations for Individual Companies Company 1: Company 1 is doing a good work only on one attribute and needs to improve on three attributes (Figure 9.4).

Overkill

Good work 1

1

1

Low priority 1

Concentrate effort 1

1

Attribute importance Figure 9.4: Service improvement grid for oil company 1

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Company satisfaction score

Company 2: Company 2 is doing a good work only on two attributes and needs to concentrate on two attributes (Figure 9.5).

Overkill

Low priority

Good work

2

2 2 2 Concentrate effort

2 2 Attribute importance

Figure 9.5: Service improvement grid for oil company 2

Company satisfaction score

Company 3: Company 3 is doing a good work only on one attribute and needs to concentrate on three attributes (Figure 9.6). In addition, this company is spending more on one attribute (overkill quadrant).

Overkill 3

Good work 3

3 Low priority

3

3 3

Concentrate effort

Attribute importance Figure 9.6: Service improvement grid for oil company 3 Overall, company 2 is performing better than the other two companies. All the companies, however, need to improve their services to compete with the private players.

10

Factor Analysis

Learning Objectives At the end of the chapter, the reader would be familiar with: The different stages in factor analysis Adapting the mathematical process of factor analysis for marketing research Interpreting the factor analysis output Special topics covered in the chapter: Identifying how a specific marketing mix variables influences perceptions

Factor analysis is a commonly used technique in marketing research as a lot of studies focus on understanding the attitudes and perceptions which have a direct influence on purchase (refer to Figure 9.2). Factor analysis is primarily a data reduction tool, where in, the analysis removes duplication from a set of correlated variables and represents the data as a smaller set of “derived” variables (factors) that are relatively independent of one another (refer to Figure 10.1, also to Figure 9.1 of Chapter 9). Marketing research process has to adopt this mathematical technique to help make marketing decisions. This chapter describes the steps in the factor analysis and its adaptation for marketing research purposes.

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Initial data set V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14

Derived set F1 F2 F3

Figure 10.1: The initial data set reduced to a smaller number of derived factors

MATHEMATICAL BACKGROUND Mathematically, factor analysis reorganises data into as many independent factors as the number of input variables. The analysis assumes that the input variables are correlated. This can be represented as: V1 = a11F1 + a12F2 + a13F3 + …a1mFm V2 = a21F1 + a22F2 + a23F3 + …a2mFm V3 = a31F1 + a32F2 + a33F3 + …a3mFm … Vm = am1F1 + am2F2 + am3F3 + … ammFm

Matrix 10.1

where Vi are the variables aij are factors loading (described below) Fj are factors The mathematical terms in the factor analysis are mentioned below. Factor Loadings The coefficients (aij)1, known as factor loading, determine the variance of a variable that is contributing to a factor. Factor loading could take the value between −1 and +1. This value can be interpreted like a correlation coefficient. The absolute values determine the extent to which a variable is associated to a factor and the sign indicates the direction of association. aij = variance of ith variable on jth factor. For example, a11 is variance of variable 1 on factor 1.

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Factor Analysis

133

Eigen Value The sum of squares of the factor loading for a factor, known as eigen value (ei), indicates the total variance captured by the factor. For example, for factor 1, the eigen value is calculated by the sum of squares of the loadings ai1 (Equation 10.1). e1 = a112 + a212 + a312 + … + am12

(Equation 10.1)

Eigen value can take any value, subject to a maximum value which is equivalent to the number of variables. The sum of all possible eigen values would be equivalent to the number of variables in the study. Thus, the percent variance explained by a factor can be determined by dividing the eigen value for a factor by the total number of variables and multiplied by 100 (Equation 10.2). (Eigen value/number of variables) × 100

(Equation 10.2)

Communality The variance of a variable captured by the factors in the analysis is known as communality (hj). This is calculated by the sum of squares of the factor loadings for a variable across the factors (Equation 10.3). h1 = a112 + a122 + a132 + … + a1m2

(Equation 10.3)

If all the factors are present, the communality would be 1. In case some factors are removed, as would be done as part of the analysis, the communality would be less than 1. Therefore communality could be between 0 and 1.

TYPES OF FACTOR ANALYSIS Factor analysis could be of two types: (a) exploratory factor analysis and (b) confirmatory factor analysis. Exploratory factor analysis assumes that any variable may be associated with any factor. This is the most common method of factor analysis used for MR studies and it is not based on any prior theory. Confirmatory factor analysis (CFA) tests an existing theory. It is used more for scale development and theory building. CFA is beyond the scope of this book. Factor analysis can be used to group the variables (known as r factor analysis) or group the respondents (known as q factor analysis). Since r factor analysis is commonly used, factor analysis generally refers to r factor analysis.

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APPLICATIONS OF FACTOR ANALYSIS 1. Identification of Underlying Factors Factor analysis reorganises information into independent dimensions from a set of variables by grouping variables which are correlated. 2. Surrogate Variables Factor analysis identifies surrogate variable(s) to represent the factor. This application is useful when the factors have to be used for subsequent analysis (especially dependence analysis, like regression). 3. Identifying Positioning Dimensions Factor analysis (R-type) identifies the dimensions that consumers use to differentiate the brands. 4. Clustering of Respondents Q-factor analysis helps identify groups of respondents who have similar perceptions. This would help in segmenting the population.

STEPS IN PERFORMING FACTOR ANALYSIS The different steps for conducting factor analysis are: 1. Identifying all the lower level variables 2. Developing a questionnaire with the variables identified in the first step 3. Collecting data from a representative sample 4. Formatting data for computer 5. Studying the initial statistics 6. Identifying the number of factors 7. Rotating the factor matrix 8. Identifying the significant loadings 9. Naming the factors 10. Using the factors for managerial decisions The process of conducting factor analysis is explained (the mathematical calculations are discussed only if they are required, otherwise the logic is discussed) using a study to identify the dimensions buyers use to differentiate 4BT engines. 4BT engines are four cylinder turbo diesel engines useful for industrial, construction and agricultural purposes. Identify all the Lower Level Variables The success of factor analysis is to identify all the lower level variables that are likely to influence the phenomenon (here purchase of 4BT engines). A focus group discussion (FGD) or any exploratory research can be done to identify the lower level variables (refer to Chapter 5).

Factor Analysis

135

The following variables were identified through the exploratory research (abbreviation of variables, if any, used in the analysis are given in parenthesis): Variable 1: Company history Variable 2: Cost Variable 3: Reliability Variable 4: Power Variable 5: Availability (avail) Variable 6: Life Variable 7: Relationship with salesperson (relationship) Variable 8: Culture of company (cultural) Variable 9: Location Variable 10: Word of mouth (WOM) Variable 11: Serviceability Variable 12: Service support Variable 13: Technology Variable 14: Environment friendly The appropriateness of the variables for factor analysis recommended by Tobias and Carlson (1969) is the Bartlett’s test of sphericity. This test is conducted to find if the correlation matrix comes from a population of variables which are independent. A large value of the test statistic suggests that the variables are correlated and factor analysis can be used. Develop Questionnaire The variables identified in the exploratory research need to be converted into a questionnaire. It should be noted that for factor analysis the data has to be collected on an interval scale. A questionnaire has to be developed to identify the consumer’s perception about the importance of the different variables during the purchase of the product. As the objective of the study is to identify the features differentiating the brands, the questionnaire should be product specific and not brand specific. In this case, the question could be framed as follows: Rate the importance of the following variables during the purchase of 4BT engines (some variables as questions are indicated below) Company history 5 Very important

4

3

Important

Neutral

2

1

Not important Not at all important

Cost 5 Very important

4 Important

3 Neutral

2

1

Not important Not at all important

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Sample Size The questionnaire is to be administered to a representative sample (refer to Chapter 7 for details of sampling). Factor analysis requires a large sample size to be effective. As a general rule, there should be at least four or five times as many respondents as there are variables (Hair et al. 1987) subject to a minimum of 200. Comrey and Lee (1992) describe the effectiveness of factor analysis for different sample sizes: • • • • • •

50 = very poor 100 = poor 200 = fair 300 = good 500 = very good 1000+ = excellent

Data Formatting For R-type factor analysis, the data should be formatted in a rectangular matrix where the columns should have the variables in the study and the rows should have ratings for the variables given by the respondents. A model table for filling the data is given in Table 10.1. Table 10.1: Data format for R-factor analysis V1 Company history

V2 Cost

R1

4*

3*

R2

3*

3*

V3

V4

V5

V6

V7

R3 R4 R5

R200 * Rating given by the respondents

Studying the Initial Statistics The analysis starts by developing a correlation matrix for the variables and then extracting the eigen values (since the mathematical process is taken care by the package and the user would not require it to evaluate the study, it is not discussed). The statistical packages provide the eigen values (Table 10.2) under the head of initial statistics.

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Table 10.2: Initial eigen values Factor

Eigen value

% of variance

Cumulative % of variance

1

3.342

23.872

23.873

2

2.345

16.747

40.620

3

2.120

15.146

55.766

4

1.764

12.598

68.364

5

1.240

8.854

77.218

6

0.859

6.134

83.352

7

0.690

4.931

88.283

8

0.551

3.938

92.221

9

0.456

3.256

95.477

10

0.262

1.873

97.350

11

0.163

1.164

98.514

12

0.142

1.016

99.530

13

0.047

0.462

99.993

14

0.00024

0.007

100.00

As mentioned, the number of factors would be equal to the number of variables and the cumulative variance explained by all the factors would be 100%. The initial communalities for all variables would be equal to 1 (Table 10.3), since all the factors would be there in the initial stage. Table 10.3: Initial communality Variable

Communality

1

1.000

2

1.000

3

1.000

4

1.000

5

1.000

6

1.000

7

1.000

8

1.000

9

1.000

10

1.000

11

1.000

12

1.000

13

1.000

14

1.000

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138

Identifying Number of Factors to Retain The next step of factors analysis is to identify the number of factors to retain in the analysis. This is a decision that has to be made by the researcher. While there is no statistical way of deciding the number of factors, there are some general rules for identifying the number of factors to be retained in the analysis. Retain the Number of Factors with Eigen values > 1 In general, for principal component analysis, only those factors with an eigen value greater than 1 are considered to be significant. The eigen value rationale is that a factor should account for at least the variance of a single variable if it is to be retained for interpretation. Mitchell (1997) state that the eigen value approach would be reliable when the number of variables is around 20. In this study, the eigen value criteria would identify four factors accounting for 68.364% of the variance. Scree Plot Method The scree-tail test (Figure 10.2) is another approach which can be used to identify the number of factors to be extracted (Cattell 1966). The scree test is derived by plotting the eigen values against the factor number and use the shape of the resulting curve to determine the significant factors. In general, Scree Plot

4.0 3.5

Eigen value

3.0 2.5 2.0 1.5 1.0 .5 0.0 1

2

3

4

5

6 7 8 9 Component Number

Figure 10.2: Scree plot method

10

11

12

13

14

Factor Analysis

139

a scree-tail test will result in one or two additional factors being considered significant than the eigen value method (Cattell 1966). A researcher has to use the visual display to identify the number of factors. The number of factors to be retained would be based on the slope of the graph. The cut-off is decided when the slope starts flattening. This indicates that the variance explained by the subsequent factors is not significant. In this study, from the sixth factor, the successive factors are accounting for smaller and smaller amounts of the total variance. Thus, the scree plot test would suggest that six factors accounting for 83.352% of the variation could be retained for analysis. Cumulative Variance Method Here the researcher needs to decide the amount of variance that should be used for the analysis. If 80% of the variance has to be used, then six factors need to be used for the analysis. If around 75% variance is found acceptable to the researcher then five factors can be retained (refer to Table 10.2 for percent of variance explained by the factors). For discussion here, the 4BT study has used the eigen value criterion for identifying the number of factors. Four factors accounting for 68% of the variance is used for developing the factor matrix. Since it is easy to run factor analysis using the computer packages, the researcher can rerun the analysis with different number of factors and see which analysis gives the best interpretation (discussed later). Initial Factor Matrix Based on the number of factors used in the analysis the initial factor matrix giving the factor loading of the variables on the factors would be developed. The initial factor matrix is given in Table 10.4. Once the number of factors has been extracted, the communalities would also change as some part of the variance is lost due to reduction in the numbers factors (Table 10.5). The sum of squares of the final communalities would also be equal to the variance explained by the number of factors retained in the analysis. In this case, the sum of squares of the communalities would also account for 68.364% variance. Communality of a variable less than 0.5 indicates that the variable is not explained sufficiently by the factors in the analysis (Stream.com 2011). This may suggest that another factor has to be added to get a better explanation of the variable (refer to Case 10.1). Thus, communality is also another check to identify the number of factors.

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Marketing Research: A User’s Perspective Table 10.4: Initial factor matrix Component Matrixa Component 1

Company

0.814

2

3

–0.126

0.143

4 –4.06E-03

Cost

0.710

–0.126

–0.262

0.201

Reliability

0.702

–3.91E-03

–0.254

–1.10E-02

Power

0.656

0.317

–5.79E-03

0.354

Availability

0.565

–0.149

0.494

0.263

Life

0.557

–4.70E-02

–0.497

0.254

Relationship

0.314

0.771

0.205

0.141

Cultural

–0.237

0.755

0.317

0.327

Location

–3.77E-02

–0.578

–0.510

0.237

Word of mouth

0.277

0.485

–2.68E-02

–0.461

Servicability

0.386

–0.220

0.660

–0.426

Service support

0.319

–0.510

0.593

–8.93E-02

0.142

–3.62E-02

–0.110

–0.777

–0.344

–0.380

0.523

0.524

Environment friendly Technology

Extraction Method: Principal Component Analysis 4 components extracted

a.

Table 10.5: Communalities Initial

Extraction

Service support

1.000

0.722

Reliability

1.000

0.557

Company history

1.000

0.699

Technology

1.000

0.811

Word of mouth

1.000

0.526

Availability

1.000

0.654

Cultural

1.000

0.834

Serviceability

1.000

0.815

Cost

1.000

0.629

Life

1.000

0.624

Relationship

1.000

0.756

Power

1.000

0.656

Location

1.000

0.651

Environment friendly

1.000

0.638

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141

Rotation Rotation of the factor matrix is used for interpreting the factors. The initial factor matrix is not used for interpretation as in the principal component method the first factor describes most of variability, followed by the second, third and so on. Logic of Rotation Mathematically, the maximum variance is extracted and put into the first factor, then a residual matrix is developed (after variance explained by the first factor is deducted). From the residual matrix, the second eigen value is extracted and so on. This can be shown graphically in Figure 10.3. Thus in the initial analysis, eigen value 1 will be greater than eigen value 2. Eigen value 2 will be greater than eigen value 3 and so on.

Initial matrix

F1

First residual matrix

F2

Second residual matrix

F3

Third residual matrix

F4

And so on

Fi are factors

Figure 10.3: Logical depiction of extraction of factors

Rotation helps in identifying axis which maximises the variance across the factors (see Figure 10.4). Graphical Representation of Rotation The initial matrix would place the variables on a Euclidean space with the initial factors denoted as the axis. Figure 10.4a shows a hypothetical situation with the placement of six variables on two factors. Rotation is the process of shifting the axis without changing the position of the variables (denoted by the arrow in Figure 10.4a). The final rotated matrix is given in Figure 10.4b (a)

(b) V1

V2

Factor 2 (F2)

V3 V4

V3

V5 V6

Factor 1 (F1)

V1

V2

V4 F1

Figure 10.4: Graphical depiction of rotation of factors

V5 V6 F2

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Marketing Research: A User’s Perspective

After rotation, it becomes easy to interpret the axis by using the variables falling on that axis. Rotation by Computer Packages Heuristics are used to redefine factors such that a variable is loaded on only one factor. That is, rotation tries to ensure that the loadings of a variable on a factor tends to be very high (−1 or 1) or very low (0). Intuitively, this would mean that after choosing number of factors to retain, rotation would spread variability more evenly among factors to make the factors more meaningful and help in their interpretation. Rotation could be of two types, orthogonal or oblique. Each type uses different heuristics for rotation. • Orthogonal: shift the axis by retaining the independence of the factor (i.e. retaining the 90o angle between the axes). The different heuristics for orthogonal rotation are: ◦ varimax: maximise squared loading variance across variables (sum over factors) ◦ quartimax: maximise squared loading variance across factors (sum over variables) • Oblique: Factors are not kept independent and so the angle between the factors is not maintained at 90o. Types of oblique rotation are: ◦ oblimin: minimise squared loading covariance between factors ◦ promax: simplify orthogonal rotation by making small loadings even closer to zero Orthogonal rotation should be used when the objective is to reduce a large set of variables to a smaller number of uncorrelated variables which can then be used in positioning studies or subsequent predictive techniques such as regression or discriminant analysis (Hair et al. 1987). Varimax is suggested as the best rotation for marketing research studies and it is most common rotation procedure used (Dielman et al. 1972, Gorsuch 1974). The rotated matrix for the 4BT engine study is given in Table 10.6. This matrix is used for interpretation. Identifying Significant Factor Loadings There is no statistical basis for determining the significance of factor loadings. Here again a thumb rule of an absolute value of 0.4 is used to identify the variables which are significantly loaded on a factor. The variables loaded under each factor for the 4BT study are highlighted in Table 10.6. There could be situations when a variable is significantly loaded on more than one factor. In this case, “company history” is loaded on factor 1 and 2. There are two views for interpreting such situations.

Factor Analysis

143

Table 10.6: Rotated component matrixa Component 1 Cost

2

3

4

0.780

8.09E+00

–0.107

1.60E-02

Life

0.752

–0.194

–0.145

1.33E-02

Reliability

0.701

0.101 –3.14E-02

0.235

Power

0.689

Company history

7.11E-02

0.411 –8.61E-02

0.652

0.507

4.12E-02

0.122

–5.03E-02

0.865

5.76E-02

0.249

2.54E-02

0.820

–0.180

–0.128

Availability

0.392

0.626

0.183

–0.272

Relationship

0.246 –3.63E-02

0.828

9.03E-02

Servicability Service support

Cultural

–0.214

–0.217

0.827

–0.237

Location

0.232

–0.247

0.692

–0.240

0.295 –7.18E-02

–0.784

Technology Environment friendly Word of mouth

–0.323 –9.04E-02

0.163

–0.188

0.753

0.102

1.24E-02

0.375

0.612

Associated Value

Customer Care

Relationship

Contemporary

Extraction Method: Principal Component Analysis Rotation Method: Varimax with Kaiser Normalisation

• Use the higher loading: Here only the higher factor loading is used for interpretation. In this case, the variable “company history” would be used only under factor 1 for interpretation. • Use all significant loadings: Here a variable is considered for all the factors where the loading is significant. Thus, “company history” would be used for interpretation of both factors 1 and 2. While retaining the higher loading is suitable for scale development, retaining for all the factors would be relevant for marketing decisions. Retaining a variable under all factors is mathematically and logically sound. Mathematically, varimax is retaining independence of factors, so retaining all significant loadings for interpretation should be acceptable. Logically also it should be acceptable as a marketing mix variable could influence more than one factor (positioning dimensions). For example, technology could influence the perception of quality as well as price. Interpretation (Naming of Factors) Using the variables loaded on each factor, the factor names are assigned (i.e. a common theme among the variables has to be identified to label the factors). The naming of factors for 4BT study is as follows.

144

Factor 1: Factor 2: Factor 3: Factor 4:

Marketing Research: A User’s Perspective

The Associated Value of the Product representing: Cost, life, reliability, company history, power. Customer Care representing: Company history, serviceability of product, service support and availability. Corporate Relationship representing: Relationship, culture of the company and location. Contemporary Product Image representing: Technology, environment friendly and word of mouth.

One of the criticisms of factor analysis is that the labels of factors can be force fitted and they can be arbitrary or lack scientific basis. To overcome this, the researcher has to rerun factor analysis with different number of factors and choose the one that gives the best interpretation. Refer to case study of soft drinks (Case 10.1). Managerial Implications Factor analysis can help the marketers modify the marketing mix variables to change perceptions of the consumers. As an illustration, two factors and three variables of the rotated matrix of the 4BT study are given in Table 10.7. Table 10.7: Partial rotated matrix of 4BT study Variable

Factor 1

Factor 2

Cost

0.780

0.098

Company history

0.652

0.507

Service support

0.025

0.820

If a 4BT brand is perceived low on both factor 1 and factor 2 (Figure10.5) and the company wants to improve the perception on factor 2, then the company has to improve the service support so that the perception on factor 2 improves (as service is loaded only on factor 2). If the company wants to improve its perception on factor 1, then it has to change its costing. If the company wants to improve its perception on both the factors (i.e. shift it diagonally up), then the company has to identify a variable which impacts both factors significantly. If the loadings on both the factors are almost equal them the movement would be at 45° (improve perception of both almost equally). Here improving “company history” would impact both factors almost equally. Figure 10.5 shows the influence of the variables to change the brand perceptions on both the factors. The withdrawal of a marketing mix variable would have an opposite effect. For example, withdrawal of service support would reduce “perception on factor 1”. Thus, factor analysis can help identify the marketing mix variables that need to be improved to shift a brand on the perceptual map as per the company’s strategy.

Factor Analysis

145

to shift vertically improve service to shift diagonally up improve Company history Factor 2 (Customer Care)

to shift horizonatally change costing

Brand X

Factor 1 (Value)

Figure 10.5: Influencing the brand perceptions

SUMMARY Factor analysis is a process of identifying the factors that differentiate the brands. The process of factor analysis is discussed by describing the stages through which the analysis is done. The application for marketing research studies is presented by suggesting the basis for identifying the number of factors to be retained, followed by identifying the type of rotation to help interpret the factors (factors that consumers use to differentiate the brands). The interpretation of the factor analysis output to help change perceptions is also presented.

CASE 10.1 Perceptions for Soft Drinks A study was conducted to understand the dimensions on which soft drinks are evaluated by consumers. The following variables were used for conducting the factor analysis: VAR00001

Refreshing

VAR00002

Fizz

VAR00003

Quench thirst

VAR00004

Tingy

VAR00005

Strength

VAR00006

Youthful

VAR00007

Advertisement

VAR00008

Colour

VAR00009

Shape

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146

The factor analysis output is given below: Initial Statistics Factor

Eigen value

Pct of Var

Cum Pct

1

2.84894

31.7

31.7

2

2.09541

23.3

54.9

3

1.24435

13.8

68.8

4

0.97179

10.8

79.6

5

0.70768

7.9

87.4

6

0.48792

5.4

92.8

7

0.31448

3.5

96.3

8

0.19841

2.2

98.5

9

0.13102

1.5

100.0

PC

extracted

3 factors.

Factor Matrix Factor 1

Factor 2

Factor 3

VAR00001

0.30418

−0.02026

0.50903

VAR00002

0.90823

0.07322

−0.27061

VAR00003

0.21878

0.09540

0.73366

VAR00004

0.87747

−0.08848

–0.10345

VAR00005

0.59944

0.18279

0.29655

VAR00006

−0.07811

0.85407

−0.32252

VAR00007

0.01751

0.90356

−0.09285

VAR00008

0.85947

−0.14941

−0.24543

VAR00009

0.09638

0.68638

0.31974

Final Statistics *Variable

Communality

*Factor

Eigenvalue

%

Cum %

VAR00001

0.35204 *

1

2.84894

31.7

31.7

VAR00002

0.90346 *

2

2.09541

23.3

54.9

VAR00003

0.59522 *

3

1.24435

13.8

68.8

VAR00004

0.78849 *

VAR00005

0.48069 *

VAR00006

0.83956 *

VAR00007

0.82535 *

VAR00008

0.82125 *

VAR00009

0.58264 *

Factor Analysis

147

Rotated Factor Matrix Factor 1

Factor 2

Factor 3

0.13195

−0.07115

0.57408

VAR00002

0.94370

0.10796

0.03517

VAR00003

−0.02341

0.01972

0.77090

VAR00004

0.86911

−0.07057

0.16781

VAR00005

0.47021

0.15524

0.48527

VAR00006

−0.00821

0.88253

−0.24624

VAR00007

0.00946

0.90842

0.00547

VAR00008

0.89844

−0.11644

0.02211

VAR00009

−0.03488

0.64984

0.39891

VAR00001

Factor Naming Factor 1: Taste Fizz

0.94

Tingy

0.87

Strength

0.47

Colour

0.89

Factor 2: Youthful Youthful

0.88

Advertisement 0.9 Shape

0.65

Factor 3: Satisfaction Refreshing

0.57

Quench thirst

0.77

Strength

0.48

Three factors have captured 68.8% of the information. On the basis of the significant variables, under each factor, the naming of three factors was done. Results with Four Factors Since only 68.8% of the information was captured by three factors with the eigen value criterion (eigen values greater than one) and also that variable 1 (refreshing) was not adequately captured by the three factors (communality of only 0.35), a four factor output was performed. The four factors accounted for about 80% of the variance and the communality of refreshing (variable 1) increased to 0.91.

148

Marketing Research: A User’s Perspective

Rotated Factor Matrix Factor 1

Factor 2

Factor 3

Factor 4

VAR00001

0.13637

−0.01523

0.04352

0.94444

VAR00002

0.94181

0.11205

0.06156

0.01171

VAR00003

−0.04896

−0.02506

0.85930

0.11446

VAR00004

0.86445

−0.07264

0.18810

0.05480

VAR00005

0.44907

0.11422

0.66339

−0.08168

VAR00006

−0.01064

0.87680

−0.10916

−0.25241

VAR00007

0.00502

0.91312

0.00453

0.02585

VAR00008

0.90164

−0.09925

−0.04708

0.13747

VAR00009

−0.04417

0.65667

0.26292

0.33489

Factor 1 captured the same variables as captured by factor 1 of three-factor analysis. Factor 2 also captured the same variables as captured by factor 2 of three-factor analysis. Factor 3 in the four factor output did not capture “refreshing” which is now captured separately by Factor 4. Since “refreshing” and “quenching of thirst” should go together it was decided to use the three-factor analysis only for marketing decisions.

11

Multi-dimensional Scaling

Learning Objectives At the end of the chapter, the reader would be familiar with: Logic of multi-dimensional scaling Different types of data collection methods and data formatting for MDS Naming of dimensions Special topics covered in the chapter: Preprocessing for MDS

Multi-dimensional scaling (MDS) is used to identify dimensions on which brands are perceived and the relative position of the brands with respect to these dimensions (refer to Figure 9.1). MDS can also be utilised to determine if a new dimension has emerged, or whether consumers have changed their perceptions, as a result of marketing activities (Van Auken and Lonial 1991). As shown in Figure 9.2, MDS can thus be considered to be an alternative to factor analysis (also refer to Statsoft.com 2011). Factor analysis can identify the dimensions on which consumers perceive the brands using the marketing mix variables and MDS can identify the dimensions using the preference data. This chapter first discusses the logic of MDS algorithms and then the data collection methods. Finally, the chapter will discuss how MDS can aid the marketing decisions.

MDS ALGORITHMS MDS uses a set of algorithms that help develop perceptual maps using the similarity and dissimilarity of different brands (Wind and Robinson 1972).

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150

The objective of the analysis is to represent the brands on an Euclidean space such that the position of the brands more or less represents the similarity/ dissimilarity judgments of the consumers. Data Format The input data for conducting an MDS has to therefore be a similarity/dissimilarity matrix (the process of data collection and data formatting is discussed later). Table 11.1 gives the dissimilarities of the television channels as perceived by a respondent. Table 11.1: Similarity/dissimilarity matrix for television channels of 1998 Star Plus Star Plus Sony

Sony

Zee

Sahara

DD Metro Cable TV

0 2

0

Zee

3.5

1

0

Sahara

5.5

3.5

10.5

0

DD Metro

10.5

5.5

8

13.5

0

Cable TV

15

13.5

12

8

8

0

Logic of the Algorithms The process by which the perceptual map is developed is explained using the dissimilarity matrix of the television channels in India (Table 11.1). Different heuristics are available to convert the similarity/dissimilarity data set into a perceptual map. Each package may have its own algorithm but they all start with a two-dimensional matrix. Here a heuristic using a random starting and an iterative process to arrive at the final position of the objects is used to explain the logic. In a random starting heuristic, a pair of brands perceived to be the closest are placed randomly on a two-dimensional plane such that the distance between the two brands matches with the consumers dissimilarity perception (In Figure 11.1, Zee and Sony are placed at a distance of 1). Then the second most similar pair is placed on the matrix. If any one of the brands is already on the matrix, the new brand is placed such that the distance matches with the dissimilarity information (in this case Star Plus is placed such that the distance between Sony and Star Plus is 2). After placing the second brand pair, the algorithm checks if the distance between the other pair(s) (Zee & Sony, Zee & Star Plus, Star Plus & Sony) match the consumers’ perceptions (goodness of fit). If the goodness of fit is not satisfied (discussed later), then the brands are adjusted to improve the goodness of fit. Then the next closest pair is placed and adjusted as above to give the best goodness of fit. This way

Multi-dimensional Scaling

151

Step 1 Sony Zee

1

Step 2 Sony Zee

1 2 Star Plus

Figure 11.1: Euclidian distance of brands on the perceptual map

all the brands are adjusted on the perceptual map such that it gives the best goodness of fit. Goodness of Fit The degree to which the distances between brands placed on the perceptual map match with the perceptions of the consumer indicates the goodness of fit. Optimum statistical fit for a multi-dimensional model is indicated by the s-stress value (Ferguson et al. 1997). Mathematically, it is calculated by using the difference between the actual dissimilarity between brands as mentioned by the respondent and the dissimilarity shown on the perceptual map for all pairs of brands. It is calculated by the following formula (Equation 11.1): Stress= [∑ (dij- d^ij) 2 / ∑dij2]1/2

(Equation 11.1)

dij = Actual dissimilarity (distance between brand i and j as mentioned by the respondent). d^ij = Distances between brand i and j as shown on the perceptual map If the distances on the perceptual map match with the actual distances then the stress value would be 0. A higher stress value means that the perceptual map is not matching with the original data. For example, after step 2 (Figure 11.1) the stress value would be 0.23 (Table 11.2 gives the dij and d^ij values). Since the goodness of fit is not acceptable, the brands have to be

152

Marketing Research: A User’s Perspective Table 11.2: Calculating the stress value Brand pair

dij

d^ij

Zee–Sony

1

1

Sony–Star Plus Star Plus–Zee

2

2

3.5

1.5

Table 11.3: Stress improvement table Iteration

S-stress

1

0.13532

Improvement

2

0.11704

0.01828

3

0.10388

0.01316

4

0.09750

0.00638

5

0.09480

0.00271

6

0.09329

0.00150

7

0.09221

0.00108

8

0.09127

0.00094

Iterations stopped because S-stress improvement is less than 0.001000

adjusted to reduce the stress. This is an iterative process and each time a brand is adjusted the stress value is checked. The process continues till there is no further improvement in the stress value (Table 11.3). At this point, the researcher has to check whether the final stress is acceptable or not (also refer Box 11.1 and 11.2). An s-stress value below 0.1 and R2 value approaching unity indicate good fit (Davidson 1983). In case the stress value is high, it indicates that the consumer may be using more than two dimensions to differentiate the brands. Thus, another dimension has to be added as this would allow the brands to be adjusted on three dimensions. Even with three dimensions, if the goodness of fit is not satisfactory, another dimension is added. This process of adding dimensions should continue till the ideal stress value is obtained. Logically, the lowest stress value would be achieved when the number of dimensions in the MDS output are equal to the number of dimensions the consumer is using to differentiate the brands (also see discussion on number of dimensions). The placement of brands on the perceptual map shows the relative distance of the brands on the perceptual space. Number of Dimensions While adding dimensions, care should be taken to achieve meaningful output. As Ferguson et al. (1997) state that the goodness of fit could improve with increasing dimensionality, and thus a balance has to be struck between the

Multi-dimensional Scaling

153

lower stress and fit. That is, a two-dimensional model may have an s-stress of 0.09; a three-dimensional solution based on the same data may have an s-stress of 0.08. In which case, the two-dimensional solution should be accepted as the three-dimensional solution produces only a marginally better fit to the data and this improved fit would be due to the increase in the number of dimensions and not increased psychological fit. Here also a scree plot could be used to identify the number of dimensions. The changes in the stress value for different dimensions can be plotted and the number of dimensions can be identified as done in the case of factor analysis (stop adding dimensions when the slope starts flattening; refer to Figure 10.2). Plotting of the Brands Since, two dimensions provided adequate goodness of fit for the TV channels study, the relative position of the brands can be presented as coordinates on a tabular form or graphically on a perceptual map. Table 11.4: Coordinates for TV channels on two dimensions Dimension 1 1. Star Plus

Dimension 2

1.3052

–0.2289

2. Sony

0.8130

–0.0539

3. Zee

0.8055

0.8088

4. Sahara

–0.1858

–1.4584

5. DD Metro

–0.8198

1.2816

6. Cable TV

–1.9181

–0.3493

Derived Stimulus Configuration Euclidean distance model 1.5

DD Metro

Dimension 2

1.0

Zee

0.5 0.0

Sony Star Plus

Cable TV

–0.5 –1.0 Sahara

–1.5 –2

–1

0 Dimension 1

1

Figure 11.2: Perceptual map for TV channels

2

154

Marketing Research: A User’s Perspective

The final multi-dimensional scaling output for TV channels is given in Table 11.4 (coordinates) and Figure 11.2 (perceptual map). Number of Brands Since the algorithm uses an iterative process, a minimum number of brands are required to achieve the placement of the brands on the perceptual map as per the perception of the consumers. That is, the number of brands should provide sufficient number of pairs for the algorithm to push the brands to a position that would represent the dissimilarity as perceived by the consumers. For an ideal MDS for two dimensions, eight brands are suggested. Eight brands would provide 28 pairs (nC2 pairs) to be compared (SPSS package will give an output with a minimum of 5 brands). Naming of Dimensions The dimensions have to be named based on the placement of the brands on the perceptual map. For example, Dimension 1 of Figure 11.2 would depict “content (local versus national)” and Dimension 2 would denote the “entertainment value”. Unlike factor analysis where the naming uses the variables loaded on a factor; in MDS, the naming has to be done purely by the relative position of the brands. So there is a greater possibility of a researcher’s bias in naming the dimensions. Care should be taken to minimise researcher’s bias or do MDS along with factor analysis (explained below).

FACTOR ANALYSIS AND MDS Since factor analysis and MDS are alternate methods for identifying the dimensions which consumers use for differentiating brands, a study could use both factor analysis and MDS to supplement each other. The output of factor analysis can be used for naming the dimensions, and the relative position of the brands can be obtained by MDS. In addition, this could act as a multimethod validation for the research (the number of factors identified by factor analysis should be same as the number of dimensions identified by MDS).

DATA COLLECTION The data collection and data formatting for MDS is not as simple as it was for factor analysis. As the input format for MDS has to be a dissimilarity matrix, the data collection methods should have the feasibility of being converted into that format. The different methods for data collection for MDS are described below. The data can be collected using the preference data (ordinal scale) or attribute-based data (interval scale).

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Preference-based Data Here the data collection uses the respondents overall preference to develop the similarity/dissimilarity information. The data collection requires the respondent to rank the pairs of brands based on their similarity. They are three methods for collecting preference-based data: • Paired ranking • Conditional ranking • Triads Paired Ranking This process requires the formation of all possible pairs of brands and then obtain the respondent ranks for the pairs based on their similarity. Here there would be nC2 pairs for a respondent to rank. For the above example of TV channels a data sheet for paired ranking is given in Table 11.5. Table 11.5: Paired ranking for TV channels S.No Pairs 1

Respondent ranking based on similarity

Cable TV and DD Metro

7

2

and Sahara

3

and Sony

4

and Star Plus

15

5

and Zee

12

6

DD Metro and Sahara

7

and Sony

8

and Star Plus

9

and Zee

10

Sahara and Sony

11

and Star Plus

12

and Zee

13 14 15

Sony and Star Plus and Zee Star Plus and Zee

7 13

13 5 10 7 3 5 10 2 1 3

The ranking obtained from the respondents can be directly used to form the similarity/dissimilarity matrix (Table 11.1). When respondent gives joint ranks then an average rank should be assigned for the two pairs (e.g. two pairs got rank 3 in the dissimilarity matrix, both the pairs are assigned 3.5 and

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the subsequent ranking starts from 5). If 3 pairs get the same rank, then the average of 3 ranks is given to all the three brands and the next two ranks are skipped (e.g. in Table 11.5 three brands get rank 7, so they are given a score of 8 and the subsequent ranking starts from 10). The computer scales down the ranking before developing the perceptual map (scaling process is similar to the political map drawing process where the  actual distances are scaled down for representing them on the political map). In the paired ranking, the stress on the respondent is high (refer to Chapter  6) and so if the number of brand pairs are high, then it would be difficult to rank. Thus, alternate methods of data collection to reduce stress of the respondent should be used. Conditional Ranking Conditional ranking is a method of reducing the stress of the respondent. While the paired ranking requires a respondent to rank nC2 pairs, a conditional ranking would require a respondent to rank only (n–1) pairs at a time. Here each brand is taken as an anchor brand and all the pairs relating to that brand are presented at a time. The respondent has to therefore rank only (n–1) pairs on their similarity/dissimilarity at a time. After ranking the pairs for one brand, the pairs of the second brand are presented and so on. Unlike in the paired ranking, here the responses have to undergo some additional analysis for conversion into dissimilarity matrix. This is explained using the example of branded shirts. The conditional ranking for branded shirts of a respondent is given in Table 11.6. The brands in the rows denote the anchor brand and the columns denote the pairing brand along with the anchor brand. The values in Table 11.6 give the row-wise ranking for pairs formed with the anchor brand. Table 11.6: Conditional ranking for branded shirts Brand Name

SW

EC

PE

PA

LP

AS

VH

CP

SW

1

2

3

4

5

6

7

8

EC

2

1

2

3

4

5

6

7

PE

3

2

1

2

3

4

5

6

PA

4

3

2

1

2

3

4

5

LP

5

4

3

2

1

2

3

4

AS

6

5

4

3

2

1

2

3

VH

7

6

5

4

3

2

1

2

CP

8

7

6

5

4

3

2

1

Legends—AS—Allen Solly, LP—Louise Philippe, VH—Van Heusen, CP—Color Plus, EC—Excalibur, PA—Park Avenue, SW—Snow White, PE—Peter England

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Creating a dissimilarity matrix from conditional ranking data requires the following preprocessing to be done. Table 11.6 is converted into a pair X pair table (Table 11.7). The coefficients of the table are generated using a rule, “1 is given if the row pair is more dissimilar than the column pair (ranking as in Table 11.6), otherwise 0 is given. While filling Table 11.7, pairs having one common brand are only scored. And for scoring, the ranking given in the row of the common brand is used (i.e. the common brand is treated as the anchor brand). If no common brand is present between two pairs then 0 is assigned to that cell. For example, take the following cells in Table 11.7. Cell row 1, column 2 Row pair 1 (SW & EC) is 2 and column pair (SW & PE) is 3. As row pair is less (more similar) 0 is given. Cell row 7, column 1 Row pair (SW & CP) is 8 and column pair (SW & EC) is 2. As row pair is higher (less similar) 1 is given. Cell row 26, column 2 The pairs are (AS + VH) and (SW + PE). Since no common brand is present, 0 is assigned to the cell. Table 11.7: Pair X pair matrix for conditional ranking Pair 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1

2 0 0 1 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 0 0 1

3 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0

4 0 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0

5 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0

6 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1

9 0 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 0 0 1

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1

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Pair 22 23 24 25 26 27 28

1 1 0 1 1 0 1 0

2 1 0 0 1 0 0 0

3 1 0 0 0 0 0 0

4 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0

8 1 0 1 1 0 1 0

9 1 0 0 1 0 0 0

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 0 0 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 1 1 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Legends—1—SW+EC–.2–SW+PE–.3–SW+PA.4–SW+LP.5–SW+AS.6–SW+VH.7–SW+CP, 8–EC+PE–.9–EC+PA–.10–EC+LP11–EC+AS.12–EC+VH–.13–EC+CP, 14–PE+PA–.15– PE+LP–.16–PE+AS–.17–PE+VH–.18–PE+CP, 19–PA+LP–.20–PA+AS–21–PA+VH–.22– PA+CP, 23–LP+AS–.24–LP+VH–.25–LP+CP, 26–AS+VH–.27–AS+CP–28–VH+CP AS—Allen Solly, LP—Louise Philippe, VH—Van Heusen, CP—Color Plus, EC—Excalibur, PA—Park Avenue, SW—Snow White, PE—Peter England

The sum the rows of Table 11.7 are calculated to develop Table 11.8. Table 11.8: Row totals of conditional ranking pairs Pair No. Brand Name

Row Total

1

SW+EC

1

2

SW+PE

8

3

SW+PA

14

4

SW+LP

19

5

SW+AS

23

6

SW+VH

26

7

SW+CP

27

8

EC+PE

1

9

EC+PA

8

10

EC+LP

14

11

EC+AS

19

12

EC+VH

23

13

EC+CP

26

14

PE+PA

1

15

PE+LP

8

16

PE+AS

16

17

PE+VH

19

18

PE+CP

22

19

PA+LP

1

20

PA+AS

8

Multi-dimensional Scaling Pair No. Brand Name

Row Total

21

PA+VH

14

22

PA+CP

18

23

LP+AS

1

24

LP+VH

8

25

LP+CP

10

26

AS+VH

1

27

AS+CP

7

28

VH+CP

0

159

After obtaining the row totals the pairs are ranked by giving 1 to a pair with the lowest total, 2 to the next lowest total and so on. The ranks obtained by doing so are as shown in Table 11.9 (joint ranks are treated as discussed for Table 11.5). Table 11.9: Rank for the brand pairs Brand Pair

Rank

VH+CP

1

SW+EC

4.5

EC+PE

4.5

PE+PA

4.5

PA+LP

4.5

LP+AS

4.5

AS+VH

4.5

AS+CP

8

SW+PE

11

EC+PA

11

PE+LP

11

PA+AS

11

LP+VH

11

LP+CP

14

SW+PA

16

EC+LP

16

PA+VH

16

PE+AS

18

PA+CP

19

SW+LP

21

EC+AS

21

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Brand Pair

Rank

PE+VH

21

PE+CP

23

SW+AS

24

EC+VH

25

SW+VH

26.5

EC+CP

26.5

SW+CP

28

Using the ranking obtained for the different pairs of branded shirts, the dissimilarity matrix is developed (Table 11.10). Table 11.10: Dissimilarity matrix for branded shirts Brand Name

SW

SW

0

EC

4.5

EC

PE

PA

LP

AS

VH

CP

0

PE

11

4.5

0

PA

16

11

4.5

0

LP

21

16

11

4.5

0

AS

24

21

18

11

4.5

0

VH

26.5

25

21

16

11

4.5

0

CP

28

26.5

23

19

14

8

1

The MDS output for branded shirts is given in Box 11.1.

Box 11.1: MDS for Branded Shirts Iteration history for the 2-dimensional solution (in squared distances) Young’s S-stress formula 1 is used. Iteration 1 2 3 4

S-stress 0.01120 0.00790 0.00607 0.00496

Improvement 0.00330 0.00183 0.00111

Iterations stopped because S-stress is less than 0.005000 For matrix

0

Multi-dimensional Scaling

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Stress = 0.00532 RSQ = 0.99990 Configuration derived in two dimensions Stimulus Coordinates Dimension Stimulus Number 1 2 3 4 5 6 7 8

Stimulus Name SW EC PE PA LP AS VH CP

1

2

1.7532 1.5897 1.0592 0.3387 −0.4149 −1.0871 −1.5345 −1.7043

−0.9133 −0.1250 0.4099 0.7017 −0.6856 −0.2198 −0.3445 −0.6341

Derived Stimulus Configuration Euclidean distance model 1.0

LP

Dimension 2

0.5

PA PE

AS

0.0

EC

VH CP

–0.5

SW –1.0 –2

–1

0 Dimension 1

1

2

Dimension 1 being “value for money” and dimension 2 being “colour/design”.

Triads In triads, the number of options given to a respondent for ranking is reduced to only 3 pairs at a time. nC3 triads need to be formed and each triad is divided into three pairs. For the car study (Figure 9.1), the data collection format is given in Table 11.11. The respondent is asked to rank the three pairs in the triad based on similarity/dissimilarity (+1 is given to the most similar pair and −1 is given to the most dissimilar pair). Table 11.11 also shows the ranking given by one respondent. These rankings are converted into a dissimilarity matrix (refer to Annexure 3 for the process of developing the dissimilarity matrix).

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Table 11.11: Traid method of data collection Triad

Pairs for each triad and their similarity ranking

Santro, Uno, Zen

Santro Uno

+1

Santro Zen

Uno Zen

–1

Santro, Uno, Indica

Santro Uno

+1

Santro Indica

Uno Indica

–1

Uno Matiz

–1

Zen Indica

+1

Santro, Uno, Matiz

Santro Uno

Uno, Zen, Indica

Uno Zen

–1

Santro Matiz Uno Indica

Uno, Zen, Matiz

Uno Zen

–1

Uno Matiz

+1

Zen Matiz

Zen, Indica, Matiz

Zen Indica

+1

Zen Matiz

–1

Indica Matiz

Santro, Zen, Indica

Santro Zen

–1

Santro Indica

Santro, Zen, Matiz

Santro Zen

–1

Santro Matiz

Santro, Indica, Matiz

Santro Indica

–1

Santro Matiz

Uno, Indica, Matiz

Uno Indica

–1

Uno Matiz

+1

Zen Indica +1

+1

Zen Matiz Indica Matiz

+1

+1

Indica Matiz

Attribute-based Data The data collection method using the attribute-based data is called “Distan”. Distan Here rating information on an interval scale for different attributes is collected for all brands in the study to develop the dissimilarity matrix. Unlike the factor analysis where rating information is collected for the product category here attribute rating are collected for each brand (partial data set of a study of soaps is given in Table 11.12).

Table 11.12: Partial data set of attributes for soaps Palmolive Dove Le Sancy Evita Rexona

Aramusk

Park Avenue

Fragrance

7.63

7.36

6.76

8.17

6

8

7.33

Durability

5.94

5.33

8.53

6.83

6.63

6.4

5.66

Freshness

7.06

6.85

6.65

7.67

5.25

7

6.66

Antiseptic

4.06

4.86

5.18

3.67

3.63

4.2

2.66

Masculine image

3.71

2.66

5.09

3

4.75

7.4

6.66

6

8

5.66

8.2

4.25

0

0

Lather

6.81

7.36

6.35

7.67

5.63

7

6.33

Shape

5.06

7

6.24

6.83

5.63

7.8

4

Value

6.44

6.42

7.18

6.33

6.13

7

6

Effect on the skin

7.75

7.36

6.59

8.16

6.5

7.8

6.33

Feminine image

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163

The raw data from Distan can be used directly in SPSS as the computer package would develop the dissimilarity matrix. The MDS for soaps is given in Box 11.2.

MANAGERIAL IMPLICATIONS The primary advantage of MDS is to identify the positioning dimensions and present the relative positioning of the brands on these dimensions. The assumption of MDS, however, is that the respondent has formed perceptions of the brands. In addition, MDS can identify the position of an “ideal brand”. Since consumers can visualise an ideal brand and compare it with the existing brands, the data collection can use “ideal brand” as a brand for data collection. A research study for liquid soaps used “ideal brand” as part of the data collection.

SUMMARY MDS helps develop perceptual maps to show the relative position of the brands. The logic and the basis of identifying the number of dimensions required for developing the perceptual map using the goodness of fit (stress value) are discussed. A minimum of eight brands are required for obtaining a good MDS on two dimensions. The different data collection methods along with the preprocessing required to convert the data into the dissimilarity matrix are also discussed. The chapter also suggests that factor analysis done at the product level along with an MDS study at a brand level will help get the relative position of the brands along with an objective way of naming the dimensions. Box 11.2: MDS for Soaps Iteration history for the two-dimensional solution (in squared distances) Young’s S-stress formula 1 is used. Iteration

S-stress

Improvement

1

0.2114

2

0.17331

0.03809

3

0.17088

0.00243

4

0.17078

0.0001

Iterations stopped because S-stress improvement is less than 0.001000 For Matrix Stress = 0.15059 RSQ = 0.93845 Configuration derived in two dimensions Stimulus Coordinates Dimension

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164

Stimulus Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Stimulus Name Palmolive Dove Le Sancy Dettol Lifebuoy Lux Mysore sandal Liril Cinthol Pears Hamam Camay Margo Evita Rexona Aramusk Park Avenue

1 0.8636 1.5796 0.3022 −1.6166 −2.4182 0.4421 0.5492 0.4035 0.2141 0.7025 −0.5425 1.0195 −0.7787 1.7186 0.064 −1.2753 −1.2277

2 −0.4695 0.0675 0.1408 0.7515 0.6414 0.293 −0.8366 −0.2585 2.0085 0.45 0.7083 −0.084 0.6888 −0.229 −0.6536 −1.5015 −1.7171

Derived Stimulus Configuration Euclidean distance model 3 Cinthol

Dimension 2

2 1

Lifebuoy

Dettol

Hamam Margo

0

Lux

Le Sancy

Pears Camay

Dove

Liril Rexona Palmolive

–1

Evita

Mysore sandal

Aramusk Park Avenue

–2 –3

–2

–1

0 Dimension 1

1

Dimensions not interpreted as stress value not acceptable.

2

12

Discriminant Analysis

Learning Objectives At the end of the chapter, the reader would be familiar with: Developing an equation to differentiate two or more groups of consumers Use of unstandardised coefficients for predictive purposes Special topics covered in the chapter: Corresponding concepts of discriminant analysis in regression analysis

Discriminant analysis is used to determine the variables that discriminate between two or more groups and help profile the members the groups (Klecka 1980). Thus, discriminant analysis can be employed as a predictive method to complement cluster analysis (discussed in Chapter 14) or factor analysis (Wedel and Kamakura 2000). For example, a credit card company can use this analysis to identify variables discriminating between defaulters and regular repayers, and decide whether an applicant should be issued a credit card or not. Thakur et al. (2007) used discriminant analysis to identify factors that contribute to advanced manufacturing technology adoption. This chapter would discuss the process of conducting discriminant analysis.

ANALYSIS Operationally (data collection, formatting and interpretation), discriminant analysis is similar to regression analysis. The difference being that in regression, the dependent variable is measured on a ratio scale, where as in discriminant analysis the dependent variable is a nominal variable. Like the regression analysis, here also a linear equation (Equation 12.1) which would give the discriminant score (Z) is developed. Z = a + b1*x1 + b2*x2 + ... + bm*xm

(Equation 12.1)

166

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where a is a constant and b1 through bm are coefficients for variable x1 to xm. However, discriminant analysis also provides a cut-off value (Z*) which would discriminate between the groups. The graphical representation is given in Figure 12.1.

μ1 Group 1

Z*

μ2 Group 2

Figure 12.1: Graphical representation of two-group discriminant analysis

The steps in conducting the discriminant analysis are discussed with an example of perceptions of Hyundai Santro and Fiat Uno.

IDENTIFYING THE VARIABLES AND DATA COLLECTION A focus group discussion (FGD) was used to identify the variables that buyers use to differentiate the brands. The following variables were identified: • Design • Looks • Purpose • Fuel economy • Esteem value • Maintenance • After-sales service • Suitability • Style • Space • Robust • Comfort • Value • Technology The data collection would be similar to the distan method of multidimensional scaling, where the importance of the variables for the two brands Hyundai Santro and Fiat Uno are collected through a survey.

Discriminant Analysis

167

DATA ENTRY In addition to entering the scores given by the respondents for each attribute (like in the case of factor analysis), an additional data point for group identity is required. For this a “grouping variable” has to be added as another column to the data set (refer to Table 12.1). Each group is assigned a number. Care should be taken that the group identification numbers are consecutive numbers (like 0, 1 or 1, 2 or 11, 12). Table 12.1: Data format showing the inclusion of a grouping variable Respondent

Design

Looks

…

Grouping variable

1

0

2

0

… 50

0

51

1

52

1

100

1

METHOD OF ANALYSIS As in the case of regression analysis, there are two methods: • Enter: Here all the variables are used for analysis at one time. • Stepwise: The variable which has the highest discriminating power is used first followed by the second and so on till all the significant variables are included in the function. As the details of the analysis is not essential for the user, only the SPSS output is discussed. Prior probabilities  Generally, it will be assumed that the groups have equal probability and so a prior probability of 0.5 is given for a 2 group discriminant analysis (0.33 for a 3 group discriminant analysis and so on). The researcher has an option of specifying a different prior probability, if literature or past experience suggests that. Tolerance Level Tolerance level would be relevant for the “enter” method only. The tolerance level is the minimum variance that a variable has to explain to enter into the analysis.

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The default value specified by the computer package is 0.01%. Unlike the stepwise method under the enter method, the process of checking the tolerance level is determined by the order of the variables in the data set. That is, a variable placed higher in the variable list is included in the equation even if the variance explained by it is small but above the tolerance level. Thus, in the enter method, if the first few variables explain 99.99% variance, then the subsequent variables may be dropped from the analysis, even if there are variables with a higher discriminating power than some variables already included. A hypothetical example is given in Table 12.2. Table 12.2: Tolerance test for “enter” method Variable

Variance explained

Cumulative variance explained

Age

23.34

23.34

Income

3.45

26.79

Occupation

31.20

57.99

Marital status

18.51

76.50

Number of children

23.49

99.99

Gender

7.12

Failed tolerance test

Here “gender” would be dropped from the analysis (even though it accounts for higher variance than income) as the earlier variables in the equation already explain 99.99% of the variance. It should be noted that different combination of demographic variables can explain the differences in behaviour (Carmone 1999). Therefore, with several demographic variables in the data set, it could be possible for the cumulative variance explained to be greater than 100. In such situations, the structure matrix (described below) can help reorganise the order of the input data set to improve the predictive power of the analysis. Goodness of Fit The computer packages provide the percent of variance explained, the Wilks’ Lambda and the chi-square value as goodness of fit variables. Refer to Table 12.3 for the goodness of fit for discriminant analysis for Hyundai Santro and Fiat Uno. The significance value of the chi-square value is d | P (G=g | Squared Group P (G=g | dicted G=g) D=d) MahalaD=d) Group p nobis Distance to Centroid

Discriminant Scores

Squared Function 1 Mahalanobis Distance to Centroid

1

1

1

0.318

0.825

0.997

2

0.175

4.096

−0.513

2

1

1

0.944

0.992

0.005

2

0.008

9.566

−1.582

3

1

1

0.678

0.997

0.173

2

0.003

11.820

−1.927

51

2

2

0.482

0.920

0.494

1

0.080

5.382

0.809

52

2

2

0.822

0.980

0.051

1

0.020

7.823

1.286

53

2

2

0.196

1.000

1.669

1

0.000

18.614

2.803

56

2

1**

0.593

0.950

0.285

2

0.050

6.193

−0.977

65

2

1**

0.593

0.950

0.285

2

0.050

6.193

−0.977

66

2

**

1

0.175

0.616

1.837

2

0.384

2.779

−0.156

67

2

2

0.538

0.937

0.380

1

0.063

5.789

0.895

**. Misclassified case P(D|G) is the likelihood of membership in a group given the discriminant score P(G) prior probability P (G|D) indicates the “power of the test”

Table 12.9: Classification results Predicted group Actual group (count)

1.00 (Santro) 2.00 (Uno)

Actual group (percent)

1.00 (Santro) 2.00 (Uno)

1 (Hyundai Santro)

2 (Fiat Uno)

Total

52

0

52

5

47

52

100.0

0.0

100.0

9.6

90.4

100.0

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173

From Table 12.9 it can be seen that only 5 cases have been wrongly classified giving a hit ratio of 95.2%.

MARKETING IMPLICATIONS The analysis provides variables that discriminate between Santro and Uno. Table 12.10 gives the means of the different variables for Santro and Uno. Table 12.10: Mean scores for different attributes for Santro and Uno Variables

Santro

Uno

Design

3.21154

4.4231

Comfort

2.94118

4.2549

Esteem value

3.44231

4.4423

Technology

2.55769

3.8269

Suitability

3.21569

4.1569

Robust.

3.30769

2.4038

Maintenance

3.04

4.04

Fuel economy

2.68627

3.7059

After sales

3.42

4.1

Style

3.65385

4.1731

Value

3.30769

3.7308

Looks

3.63462

3.9423

Space

2.96154

3.2692

Purpose

3.38462

3.5192

The analysis can help in understanding the reasons for the brand perception and its relative position on the perceptual map (Figure 9.2) and the marketing mix variables that need to be improved to change the perceptions. Also refer Case 12.1. The analysis can also suggest the segments the company should focus on (e.g. a less robust model could be targeted for the city commuters).

DISCRIMINANT ANALYSIS FOR THREE GROUPS A two group discriminant analysis would have one discriminant function. A  three-group discriminant function would have two discriminant functions. That is, the number of discriminant functions would be one less than the number of groups. The three-group discriminant analysis for Santro, Uno and Matiz is shown in Tables 12.11–12.16.

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Eigen Values The eigen values indicate the proportion of variance explained by each function (Table 12.11). The magnitudes of the eigen values are indicative of the functions’ discriminating abilities (Oliver.efos.hr 2011). Table 12.11: Eigen value for three-group discriminant analysis S.No

Eigen value % of variance

Canonical correlation

1

1.403

77.4

0.764

2

0.410

22.6

0.539

Table 12.12: Test of significance for a three-group discriminant analysis Test of Function(s)

Wilks’ Lambda

Chi-square

df

Sig.

1 through 2

0.295

171.509

28

0.000

2

0.709

48.308

13

0.000

The first test presented in the first row of Table 12.12 tests both canonical correlations (1 through 2) and the second test tests the second canonical correlation alone (UCLA Academic Technology Services 2011). While the first Table 12.13: Standardised canonical discriminant function coefficients for a three-group analysis Variables

Function 1

2

Design

−0.607

0.666

Looks

0.370

−0.707

Purpose

0.317

0.236

Fuel economy

−0.013

−0.138

Esteem value

−0.258

0.091

Maintenance

0.135

0.639

After sales

−0.142

−0.091

Suitability

−0.239

0.157

Style

0.042

−0.357

Space

−0.183

−0.114

Robust

1.011

0.323

Comfort

−0.118

0.288

Value

−0.193

0.061

Technology

−0.094

−0.183

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Table 12.14: Structure matrix for a three-group discriminant analysis Function

Variables

1

2

0.591

*

0.446

Esteem value

−0.429

*

−0.040

Comfort

−0.366*

0.321

Style

−0.304

−0.187

Looks

*

−0.175

−0.120

Maintenance

−0.179

0.509*

Suitability

−0.205

0.502*

Design

−0.365

0.400*

Value

−0.027

0.385*

Technology

−0.269

0.379*

Fuel economy

−0.236

0.317*

0.043

0.310*

After sales

−0.202

0.217*

Space

−0.020

0.168*

Robust

*

Purpose

Table 12.15: Group centroids for a three-group discriminant analysis Cut-off values

Function 1

2

1.00

0.626

−0.830

2.00

−1.643

0.122

3.00

1.017

0.709

Table 12.16: Classification matrix for a three-group discriminant analysis Predicted Group Membership 1.00 Original

Count

%

1.00 (Santro)

2.00

3.00

37

3

2.00 (Uno)

3

42

7

52

3.00 (Matiz)

10

3

39

52

1.00 (Santro)

12

Total 52

71.2

5.8

23.1

100.0

2.00 (Uno)

5.8

80.8

13.5

100.0

3.00 (Matiz)

19.2

5.8

75.0

100.0

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function maximises the differences between the values of the dependent variable, the second function is orthogonal to it (uncorrelated with it) and maximises the differences between values of the dependent variable, controlling for the first factor (Faculty Chass NSCU.edu 2011). As in the group 2 case, the standardised canonical discriminant function coefficients (Table 12.13) provide the two discriminant functions and the structure matrix (Table 12.14) gives the importance of the variables in discriminating between the groups. The group centroids (Table 12.15) help identify the cut-off values for the two discriminant functions and Table 12.16 gives the hit ratio. The predictive power of the three group (Santro, Uno and Matiz) test is 76%.

SUMMARY Discriminant analysis is useful to discriminate between two or more groups. Like regression analysis, discriminant analysis has “enter and stepwise” methods. The detailed interpretation of the analysis and the goodness of fit tests for the equation are discussed. Standardised and unstandardised equations are also discussed. Corresponding concepts of regression analysis are also mentioned.

CASE 12.1 Deodorant Soap for Men Shaw Wallace launched Aramusk the first deodorant men’s soap in India in the mid 1980s. The industry leaders at that time praised the unique positioning and predicted that the soap would be a success. However, the soap was not successful until the mid 1990s. Analysing the failure, research showed that the soap purchase decision (during 1980s) was made by the housewife and that most households used the same soap for the entire family. Subsequently, men’s grooming segment in India started growing and companies are launching brands specific for the “male grooming segment”. In this context, a research was conducted to understand the factors differentiating the preference of soaps by men and women. A discriminant analysis was done to identify the discriminating factors. The output of the discriminant analysis is given below.

Eigen values Function 1

Eigen value

% of variance

Cumulative %

Canonical correlation

4.136

100.0

100.0

0.897

Discriminant Analysis

177

Wilks’ Lambda Test of Function(s) 1

Wilks’ Lambda

Chi-square

df

Sig.

0.195

104.724

8

0.000

Standardised canonical discriminant function coefficients Masculine fragrance

1.000

Creamy

−0.056

Softness

0.620

Long lasting

−0.121

Value

−0.291

Shape

0.072

Colour

0.279

Deodorant

0.435

Structure Matrix Value

−0.514

Masculine fragrance

0.486

Softness

0.458

Long lasting

−0.380

Creamy

−0.162

Colour

0.107

Germ protection

−0.078

Deodorant

−0.010

Shape

0.000 Pooled within groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function. Importance of Attributes for Different Groups Female

Male

Value

5.26

2.89

Masculine fragrance

2.8

6.00

Softness

2.23

4.29

Long lasting

7.69

5.69

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Canonical Discriminant Function Coefficients Masculine fragrance

0.608

Creamy lather

−0.047

Softness

0.554

Long lasting

−0.092

Value

−0.253

Shape

0.132

Colour

0.324

Deodorant

0.314

(Constant)

−6.955

Unstandardised coefficients

Functions at Group Centroids Group

Function

0.00

−2.004

1.00

2.004

Unstandardised canonical discriminant functions evaluated at group means

Prior Probabilities for Groups Prior Group

Cases Used in Analysis Unweighted

Weighted

0.00

0.500

35

35.000

1.00

0.500

35

35.000

Total

1.000

70

70.000

Discriminant Analysis

179

Case-wise Statistics Case Actual Number Group

Highest Group

Second Highest Group

Discriminant Scores

Original

Predicted P (D>d | P (G=g | Squared Group P (G=g | Squared Function 1 Group G=g) D=d) Mahalanobis D=d) Mahalanobis Distance to Distance to p df Centroid Centroid

1

0

0 0.301

1 1.000

1.068

1 0.000

25.428

-3.038

2

0

0 0.428

1 1.000

0.630

1 0.000

23.063

-2.798

3

0

0 0.501

1 1.000

0.452

1 0.000

21.918

-2.677

4

0

0 0.328

1 1.000

0.958

1 0.000

24.879

-2.983

5

0

0 0.978

1 1.000

0.001

1 0.000

16.299

-2.033

6

0

0 0.618

1 1.000

0.249

1 0.000

20.318

-2.503

7

0

0 0.301

1 1.000

1.068

1 0.000

25.428

-3.038

8

0

0 0.167

1 0.924

1.908

1 0.076

6.905

-0.623

9

0

0 0.581

1 0.997

0.305

1 0.003

11.949

-1.452

10

0

0 0.328

1 1.000

0.958

1 0.000

24.879

-2.983

11

0

0 0.978

1 1.000

0.001

1 0.000

16.299

-2.033

12

0

0 0.618

1 1.000

0.249

1 0.000

20.318

-2.503

13

0

0 0.350

1 0.987

0.872

1 0.013

9.456

-1.071

14

0

0 0.657

1 1.000

0.197

1 0.000

19.828

-2.448

15

0

0 0.370

1 1.000

0.804

1 0.000

24.064

-2.901

16

0

0 0.201

1 0.948

1.636

1 0.052

7.453

-0.725

17

0

0 0.350

1 0.987

0.872

1 0.013

9.456

-1.071

18

0

0 0.657

1 1.000

0.197

1 0.000

19.828

-2.448

19

0

0 0.370

1 1.000

0.804

1 0.000

24.064

-2.901

20

0

0 0.201

1 0.948

1.636

1 0.052

7.453

-0.725

21

0

0 0.581

1 0.997

0.305

1 0.003

11.949

-1.452

22

0

0 0.504

1 1.000

0.446

1 0.000

21.872

-2.672

23

0

0 0.240

1 0.965

1.379

1 0.035

8.037

-0.830

24

0

0 0.775

1 1.000

0.082

1 0.000

18.444

-2.290

25

0

0 0.354

1 1.000

0.859

1 0.000

24.362

-2.931

26

0

0 0.496

1 0.995

0.464

1 0.005

11.076

-1.324

27

0

0 0.504

1 1.000

0.446

1 0.000

21.872

-2.672

28

0

0 0.240

1 0.965

1.379

1 0.035

8.037

-0.830

29

0

0 0.775

1 1.000

0.082

1 0.000

18.444

-2.290

30

0

0 0.354

1 1.000

0.859

1 0.000

24.362

-2.931

31

0

0 0.496

1 0.995

0.464

1 0.005

11.076

-1.324

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180

Case-wise Statistics Case Actual Number Group

Highest Group

Second Highest Group

Discriminant Scores

Original

Predicted P (D>d | P (G=g | Squared Group P (G=g | Squared Function 1 Group G=g) D=d) Mahalanobis D=d) Mahalanobis Distance to Distance to p df Centroid Centroid

32

0

0 0.254

1 0.970

1.302

1 0.030

8.225

-0.863

33

0

0 0.699

1 0.998

0.149

1 0.002

13.126

-1.618

34

0

0 0.652

1 0.998

0.203

1 0.002

12.659

-1.554

35

0

0 0.699

1 0.998

0.149

1 0.002

13.126

-1.618

36

1

1 0.773

1 1.000

0.083

0 0.000

18.472

2.293

37

1

1 0.615

1 0.998

0.254

0 0.002

12.288

1.501

38

1

1 0.875

1 0.999

0.025

0 0.001

14.839

1.848

39

1

1 0.550

1 0.996

0.357

0 0.004

11.641

1.407

40

1

1 0.357

1 0.987

0.847

0 0.013

9.541

1.084

41

1

1 0.615

1 0.998

0.254

0 0.002

12.288

1.501

42

1

1 0.875

1 0.999

0.025

0 0.001

14.839

1.848

43

1

1 0.550

1 0.996

0.357

0 0.004

11.641

1.407

44

1

1 0.357

1 0.987

0.847

0 0.013

9.541

1.084

45

1

1 0.205

1 0.951

1.606

0 0.049

7.517

0.737

46

1

1 0.208

1 1.000

1.582

0 0.000

27.741

3.262

47

1

1 0.019

1 1.000

5.535

0 0.000

40.470

4.357

48

1

0(**) 0.240

1 0.965

1.379

1 0.035

8.037

-0.830

49

1

1 0.550

1 0.996

0.357

0 0.004

11.641

1.407

50

1

1 0.357

1 0.987

0.847

0 0.013

9.541

1.084

51

1

1 0.205

1 0.951

1.606

0 0.049

7.517

0.737

52

1

1 0.208

1 1.000

1.582

0 0.000

27.741

3.262

53

1

1 0.773

1 1.000

0.083

0 0.000

18.472

2.293

54

1

1 0.357

1 0.987

0.847

0 0.013

9.541

1.084

55

1

1 0.205

1 0.951

1.606

0 0.049

7.517

0.737

56

1

1 0.208

1 1.000

1.582

0 0.000

27.741

3.262

57

1

1 0.773

1 1.000

0.083

0 0.000

18.472

2.293

58

1

1 0.614

1 1.000

0.255

0 0.000

20.372

2.509

59

1

1 0.126

1 1.000

2.341

0 0.000

30.682

3.535

60

1

1 0.773

1 1.000

0.083

0 0.000

18.472

2.293

61

1

1 0.614

1 1.000

0.255

0 0.000

20.372

2.509

62

1

1 0.126

1 1.000

2.341

0 0.000

30.682

3.535

Discriminant Analysis

181

Case-wise Statistics Case Actual Number Group

Highest Group

Second Highest Group

Discriminant Scores

Original

Predicted P (D>d | P (G=g | Squared Group P (G=g | Squared Function 1 Group G=g) D=d) Mahalanobis D=d) Mahalanobis Distance to Distance to p df Centroid Centroid

63

1

1 0.649

1 1.000

0.208

0 0.000

19.933

2.460

64

1

1 0.208

1 1.000

1.582

0 0.000

27.741

3.262

65

1

1 0.119

1 0.856

2.436

0 0.144

5.994

0.444

66

1

1 0.250

1 1.000

1.323

0 0.000

26.615

3.155

67

1

1 0.208

1 1.000

1.582

0 0.000

27.741

3.262

68

1

1 0.119

1 0.856

2.436

0 0.144

5.994

0.444

69

1

1 0.250

1 1.000

1.323

0 0.000

26.615

3.155

70

1

1 0.943

1 1.000

0.005

0 0.000

15.501

1.933

** Misclassified case

Classification Results Predicted Group Membership

Original

Group

0.00

1.00

Count

0.00

35

%

0

Total

35

1.00

1

34

35

0.00

100.0

0.0

100.0

1.00

2.9

97.1

100.0

98.6% of original grouped cases correctly classified. The goodness of fit indicators are significant in suggesting that the two groups are distinct in their preference for soaps. The classification table shows that only one respondent was wrongly classified after applying the discriminating equation indicating a 98.6% hit ratio. The variables having the highest discriminating power are fragrance, effect on skin and value for money. Companies promoting soaps for these segments need to position their soaps on these attributes.

13

Conjoint Analysis

Learning Objectives At the end of the chapter, the reader would be familiar with: Defining the model for conjoint analysis Developing the design for data collection for conjoint analysis Designing the concept cards Calculating the utility values Special topics covered in the chapter: Identifying the interaction effect

Conjoint analysis, while taking into account the consumer’s preference for the product/service as a whole, estimates the value (utility) consumers place on the different levels of an attribute or feature. In other words, conjoint analysis goes beyond identifying the importance of the attributes. Hobbs (1996) state that conjoint analysis has become a popular method for identifying and understanding the combined effects of product attributes on preferences for a product/service. Conjoint marketing research studies have been used in various business sectors, e.g. credit card (Kara et al. 1994), financial services (Arias 1996), etc. This chapter discusses the application of conjoint analysis for marketing decisions.

FEATURES OF CONJOINT ANALYSIS Conjoint analysis looks at the preference for a product as a whole and identifies the trade-offs the customers are likely to make in choosing a product and service (Toombs and Bailey 1995). Levy (1995) states that conjoint analysis engages the respondents in a more realistic judgment than other research methods.

Conjoint Analysis

183

Conjoint measurement procedures can be used for studying both quantitative and qualitative attributes. Specifically conjoint analysis is useful for: • New product formulations • Predicting the possible market share given the competitive offering. • Studying the value consumers place on qualitative features like brand name, colour, etc. • The trade-off between price and other attributes • Identify segments having similar preferences (Ness and Gerhardy 1994) Gil and Sanchez (1997), however, state that conjoint analysis is based on the assumption that the consumers evaluate the product/service by combing the utility of individual attributes.

METHODS There are two general approaches for conjoint—the two-factor-at-a-time tradeoff method and the full-concept method. The two-factor-at-a-time trade-off method studies the preferences by presenting two attributes at a time. The full concept is more realistic as all factors are considered and evaluated at the same time (Koo et al. 1999). Here only the full-concept method is discussed.

MODEL The success of conjoint analysis is in defining the model. The model could be a simple additive model or an additive model with interactive effect. Simple Additive Model In a simple additive model, it is assumed that the total utility is the sum of the individual utilities (Equation 13.1). Ut = Ua + Ub + Uc… + Un

(Equation 13.1)

Where “a to n” are the attributes Ua to Un are the utilities for each attribute Ut is the total utility Interactive Effects Model If two or more variables have an interaction effect, then the effect of the variables on the dependent variable would rely on the presence of the other variable. The interaction effect could either positively enhance the value for the product or reduce the value. For example, a study on soaps found shape and colour to have interaction effect. People associate rectangular-shaped soaps with strong colours and oval-shaped soaps with softer colours. Therefore, an oval-shaped soap with a strong colour could have a reduced utility value.

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184

The model has to, therefore, account for the interaction effect, if any (Equation 13.2). Ut = Ua + Ub + Uc… + Un + Ua*b

(Equation 13.2)

where, “a to n” are the attributes and “a and b” have an interaction effect Ua to Un are the utilities for each attribute Ut is the total utility Ua*b is the utility of the interaction effect Hair et al. (1984) state that interaction effects are more likely for intangible attributes and they state that interaction effect would require additional concept cards (discussed later) for the respondent to evaluate. Thus, the researcher has to identify the interaction effect before designing a full study (Sawtoothsoftware.com 2011). Identifying Interaction Effect A simple analysis can be used to identify the interaction effect. Table 13.1 gives the ranking of concept cards for soaps for two segments (details of concept cards is discussed in the later part of this chapter). Table 13.1: Ranking of concept cards for soaps for two segments. S.No

Concept cards: Attributes and levels

Ranking for

Lather

Fragrance

Effect on skin

Segment 1

Segment 2

1

High

Perfume

Soft skin

1

1

2

High

Perfume

Normal skin

2

2

3

High

No perfume

Soft skin

5

5

4

High

No perfume

Normal skin

6

6

5

Low

Perfume

Soft skin

3

3

6

Low

Perfume

Normal skin

4

4

7

Low

No perfume

Soft skin

7

7

8

Low

No perfume

Normal skin

8

8

Table 13.2: Segment 1—Lather X fragrance Fragrance Lather

Perfume

No Perfume

High

3

11

Low

7

15

Conjoint Analysis

185

To identify the interaction effect, the total ranking obtained for the different levels for each pair of attributes are calculated (Tables 13.2–13.7). If the sum of the diagonal cells is equal then there is no interaction effect between the pair of attributes. However, if the sum of the diagonal cells is different, then it can be said that the two variables have an interaction effect. No interaction effect between lather and fragrance for segment 1 as the sum of the diagonal cells is equal (Table 13.2). Table 13.3: Segment 1—Fragrance X effect on skin Fragrance

Soft skin 4 12

Perfume No Perfume

Effect on skin Normal skin 6 14

No interaction effect between fragrance and effect on skin for segment 1 as the sum of the diagonal cells is equal (Table 13.3). Table 13.4: Segment 1—Lather X effect on skin Lather High

Soft skin 6

Low

10

Effect on skin Normal skin 8 12

No interaction effect between lather and effect on skin for segment 1 as the sum of the diagonal cells is equal (Table 13.4). Thus for segment 1, there is no interaction effect and the model would be a simple additive model. Table 13.5: Segment 2—Lather X fragrance Lather

Perfume

High

8

Low

10

Fragrance No Perfume 8 10

No interaction effect between lather and fragrance for segment 2 as the sum of the diagonal cells is equal (Table 13.5). Table 13.6: Segment 2—Fragrance X effect on skin

Perfume

3

Effect on Skin Soft Skin Normal Skin 15

No Perfume

7

11

Fragrance

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186

The sum of diagonals in Table 13.6 is not equal (14 and 22), so perfume and effect on skin interact for segment 2. Table 13.7: Segment 2—Lather X effect on skin Effect on Skin Lather

Soft Skin

Normal Skin

High

4

12

Low

6

14

No interaction effect between lather and effect on skin for segment 2 as the sum of the diagonal cells is equal (Table 13.7). For segment 2, there is an interaction between perfume and effect on skin. The model should therefore take this into account the interaction effect (Equation 13.3). Usoap = Ufrag + Ulather + Ueffect + U (effect*frag)

(Equation 13.3)

U denotes the utility value

CONDUCTING THE CONJOINT ANALYSIS STUDY The process of conducting a conjoint analysis is explained using the example of cars. Attributes and Levels The first stage involves identifying the attributes and levels. The attributes and levels can be identified through a FGD or from literature. If the analysis is done in conjunction with factor analysis then surrogate variables could be used to represent the factors. Here again the levels for the attributes have to be identified through FGD or from literature. Table 13.8 gives the attributes and levels for the car study. Table 13.8: Attributes and levels for the car study Attributes

Levels

Brand

Brand 1

Brand 2

Brand 3

Engine

1.2 litre Diesel

1.2 litre Petrol

1.5 litre Diesel

1.5 litre Petrol

Colour

New York Blue

London Rose

Belgian Chocolate

Tokyo silver

Price

Rs. 0.43 million

Rs. 0.55 million

Rs. 0.67 million

Conjoint Analysis

187

Concept Cards As this analysis is used more for new product development and the product is still at the concept stage, the respondent is presented with cards which describe the proposed product. These cards are called concept cards. The concept cards should be presented such that the respondents can visualise the proposed product. The card could have a description of the product highlighting the attributes and levels or it could have a pictorial representation (for example refer Table 13.9). Table 13.9: Concept cards based on Latin square design Card number

Concept card

1

1.2 L Diesel, Brand 1, New York Blue colour

2

1.2 L Diesel, Brand 2, London Rose

3

1.2 L Diesel, Brand 3, Belgian Chocolate

4

1.2 L Petrol, Brand 1, London Rose colour

5

1.2 L Petrol, Brand 2, Belgian Chocolate

6

1.2 L Petrol, Brand 3, New York Blue

7

1.5 L Diesel, Brand 1, Belgian chocolate colour

8

1.5 L Diesel, Brand 2, New York Blue

9

1.5 L Diesel, Brand 3, London Rose

Study Design For a full concept study, the respondents have to be presented with sufficient product variants (as concept cards) to identify the utility values. For the study on cars (Table 13.8), the total number of combinations possible is 144 (3 × 4 × 4 × 3). As it would be impossible to expect a respondent to rank 144 concept cards, the number of cards should be reduced such that there is no loss of information for calculating the utility values for different levels of an attribute. A researcher can use factorial designs (like Latin square design) or orthogonal arrays to reduce the number of cards without losing the information relevant to calculate the utility values. Latin square design is used when there are as many levels for each attribute as the number of attributes. For example, a subset of Table 13.8 is given in Table 13.10. Here there are three attributes each with three levels so a Latin square design can be used.

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Table 13.10: A subset of attributes and levels for cars Levels Brand

Brand 1

Brand 2

Brand 3

Engine

1.2 litre Diesel

1.2 litre Petrol

1.5 litre Diesel

Colour

New York Blue

London Rose

Belgian Chocolate

Three attributes each at three levels would require a total of 27 cards. With Latin square design the number of cards can be reduced to 9. For the Latin square design, a 3 × 3 matrix is developed with the rows labeled with the levels of one attribute and the columns labeled with the levels of another attribute. The levels of the third attribute are arranged in the cells such that a level is shown only once in a column. The Latin square design for these attributes is given in Table 13.11 and the concept cards in Table 13.9. Table 13.11: Latin square design for three attributes and three levels Brand 1

Brand 2

Brand 3

1.2 litre Diesel

New York Blue

London Rose

Belgian Chocolate

1.2 litre Petrol

London Rose

Belgian Chocolate New York Blue

1.5 litre Diesel

Belgian Chocolate

New York Blue

London Rose

An orthogonal array gives the lowest number of combinations for conducting the study. Orthogonal array reduces the cards by eliminating repetitive combinations and retaining a representative set of combinations. SPSS generates orthogonal plans. The orthogonal design for the attributes and levels identified in Table 13.8 is given in Table 13.12. Table 13.12: Orthogonal plan for car conjoint analysis Brand

Engine

Colour

Price

Status

Card Number

1.00

1.00

1.00

1.00

0

1

3.00

3.00

1.00

3.00

0

2

1.00

4.00

1.00

1.00

0

3

2.00

2.00

1.00

2.00

0

4

2.00

4.00

3.00

1.00

0

5

1.00

3.00

2.00

1.00

0

6

3.00

2.00

4.00

1.00

0

7

1.00

4.00

4.00

3.00

0

8

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Brand

Engine

Colour

Price

Status

Card Number

1.00

1.00

4.00

2.00

0

9

1.00

3.00

3.00

2.00

0

10

2.00

3.00

4.00

1.00

0

11

2.00

1.00

2.00

3.00

0

12

3.00

1.00

3.00

1.00

0

13

3.00

4.00

2.00

2.00

0

14

1.00

2.00

3.00

3.00

0

15

1.00

2.00

2.00

1.00

0

16

Brand (1—Brand 1; 2—Brand 2; 3—Brand 3) Engine (1—1.2 L Diesel engine; 2—1.2 L Petrol engine; 3—1.2 L Diesel; 4—1.5 L Diesel engine) Colour (1—New York Blue; 2—London Rose; 3—Belgian chocolate; 4—Tokyo Silver) Price (1—Rs. 0.43 million; 2—Rs. 0.55 million; 3—Rs. 0.67 million)

The orthogonal design has reduced the 144 combinations into 16 concept cards. Each combination is defined in terms of the levels in the attributes. For example the first card is defined as level 1 for all the attributes (Brand 1, 1.2 litre diesel engine, New York Blue colour and prices at 0.43 million). The column of “status” in Table 13.12 would be analysed while discussing hold out cards. Hold Out Cards In addition to the cards required as part of the study design, sometimes some additional concept cards are used in the data collection. These cards, known as hold out cards, are used while obtaining the ranking of the respondents during data collection but not used for the calculation of the utility values. The hold out cards are used to cross-check the results. Once the utility values are calculated, the total utility for all the cards including the hold out cards are calculated and ranked (higher utility is given rank 1 and so on). If the rank of the hold out cards matches with the ranking given by the respondent, then the utility values can be considered reliable. Computer packages providing orthogonal array design also have an option of identifying the hold out cards. The column “status” defines the study design cards and hold out cards. Cards that are part of the study design would be denoted with a 0 and the hold out cards would be identified by 1 (Table 13.13). There is no statistical basis for inclusion of hold out cards, so the number of hold out cards required has to be specified by the researcher.

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Table 13.13: Orthogonal plan with hold out cards Brand

Engine

Colour

Price

Status

Card Number

4.00

2.00

3.00

3.00

0

1

2.00

1.00

2.00

3.00

0

2

4.00

1.00

4.00

2.00

0

3

4.00

1.00

1.00

1.00

0

4

1.00

2.00

2.00

1.00

0

5

3.00

2.00

4.00

1.00

0

6

3.00

1.00

3.00

1.00

0

7

4.00

3.00

2.00

1.00

0

8

3.00

3.00

1.00

3.00

0

9

1.00

1.00

4.00

3.00

0

10

2.00

1.00

3.00

1.00

0

11

3.00

1.00

2.00

2.00

0

12

1.00

3.00

3.00

2.00

0

13

1.00

1.00

1.00

1.00

0

14

2.00

3.00

4.00

1.00

0

15

2.00

2.00

1.00

2.00

0

16

2.00

1.00

1.00

1.00

1

17

3.00

1.00

3.00

3.00

1

18

Details of levels are same as Table 13.12

Data Collection Data collection could be either through ranking the full concept or rating them on a scale. While ranking gives better results, rating method is easy for respondents to use when a large number of cards have to be evaluated. Calculating Utility To explain the utility calculation, three attributes (colour, engine and price) at two levels each is used. The eight concept cards for three attributes at two levels each and their ranks are given in Table 13.14.

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Table 13.14: Ranking of car on three attributes, each at two levels S No Colour

Concept cards for cars: Attribute and levels Engine

Price

Ranking of respondent

1

New York Blue

1.2 L Diesel

0.55 million

1

2

New York Blue

1.2 L Diesel

0.67 million

2

3

New York Blue

1.2 L Petrol

0.55 million

5

4

New York Blue

1.2 L Petrol

0.67 million

6

5

Tokyo Silver

1.2 L Diesel

0.55 million

3

6

Tokyo Silver

1.2 L Diesel

0.67 million

4

7

Tokyo Silver

1.2 L Petrol

0.55 million

7

8

Tokyo Silver

1.2 L Petrol

0.67 million

8

Conjoint analysis calculates the utility of each level and the relative importance of the attribute. The calculation of utilities is based on studying the deviation of average rank of each level from the overall average ranking (the detailed calculations are shown in Table 13.15). Table 13.15: Calculation of utilities for cars (three attributes at two levels each) Attribute level

Average rank of the attribute level

New York Blue

3.5

Tokyo Silver

Coefficient**

Utility reverse sign***

–1

0.76

+0.76

5.5

+1

0.76

–0.76

1.2 L Diesel

2.5

–2

1.5 L

+1.5 L

1.2 L Petrol

6.5

+2

1.5 L

–1.5 L

Rs. 0.55 million

4.0

–0.05

0.38

+0.38

Rs. 0.67 million

5.0

+0.5

0.38

–0.38

* **

Deviation*

Deviation from the overall average rank which is 4.5

(Deviation)2  number of levels  (Deviation)2

Number of levels = 6, Sum of squares of deviations = 10.5 *** Since rank 1 is preferred over rank 2 and so on, the initial sign of the deviation column has to be reversed to obtain utility value

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The analysis also provides utility values for qualitative dimensions, like brand and colour. It should however be noted that conjoint analysis calculates the utility values only for the specified levels of the qualitative attributes. For quantitative attributes, like price, the analysis would provide a continuous utility function. While the utility values will be calculated for each individual, the utility value for the group can also be calculated. The individual utilities can be used for segmentation purposes (group people with similar utility values). Identify Importance of Attributes and Levels The relative importance of the attributes and levels can be seen by graphically representing the utility values of each attribute and its levels (Figure 13.1). The attribute with the higher slope indicates that the importance of that attribute is more. That is, the utility value would change significantly by changing the levels of that attribute. This information would be helpful to modify the product at the concept stage using the simulation exercise (discussed below). In this case, the type of engine has the highest slope, followed by colour and price. 2 1.5 1

Utility

0.5 0 -0.5

1

2

Colour Engine Price

-1 -1.5 -2

Levels 1 and 2 for each attribute (refer Table 13.15 for attribute levels)

Figure 13.1: Importance of attributes and levels

Redefining the Product Using Simulation The utility values can be used to simulate the market and suggest changes in the product concept. The new product description along with the description of the existing products can be incorporated into the simulation model to obtain predictions of how buyers will respond to the new product concepts given the competitors’ offerings. By this process, it would be possible to modify the concept and identify the most appealing offer and the likely market share for the new concept (refer to Case 13.1).

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193

SUMMARY Conjoint analysis is useful for new product development. The analysis should start by defining the model for the analysis (a simple additive model or a model with interaction effects). Then the study design should be finalised by minimising the number of concept cards using factorial designs. The detailed process of developing the concept cards, the data collection process and the analysis are presented. The application of market simulation to modify the product concepts is also discussed. CASE 13.1 Conjoint Analysis for Salt Brands Decision Problem Whether a company should go in for branding the salt it is planning to sell, that is how relevant is brand name in the purchase of salt as compared to other features like price and functionality. Research Objectives 1. To ascertain whether consumers perceive a significant difference between branded and unbranded salt. 2. To ascertain which attributes hold the highest utility among consumers (One of the attributes being brand name). Research Questions 1. What are the utility values for attributes of salt and its levels? 2. Are functional benefits more important than brand name to a consumer? 3. Can a new brand with the ideal combination of attributes compete against already established brands? Focus group discussions were conducted to find out the significant attributes that a consumer will look for during purchase of salt. Based on the FGD, the following attributes and the different levels were selected: Attributes

Levels

Price

6.5, 10.5, 14

Health

Plain, iodized

Free Flow

Yes, No

Brand

Brand 1, Brand 2, Brand 3 (brand name disguised), Sagar (fictitious), Local (unbranded)

Method • Ranking-based conjoint analysis was used. • No weights were given to the respondents and the attributes. • Except price none of the attributes were partially or completely ordered.

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Results of the Analysis The group statistic analysis gives the utility function for salt as: U = (0.0659*PRICE) + (0.2194*HEALTH) + (0.1324*FREE FLOW) + (0.5823*BRAND) Importance level for the attribute: BRAND is the highest at 58.23%. The least important is PRICE at 6.59% (Figure 13.2). 80 58.23

Percentage

60 40 21.94

20

13.24

6.59 0

Price

Health

Free Flow

Brand

Features Figure 13.2: Relative importance of features Within brands, Brand 1 was given the highest utility value of 4.902, which is more than double of the next closest brand Brand 3 with a utility value of 2.228. The relative importance of the different brands is given in Figure 13.3. 6

4.902

4

Utility value

2 0

Brand 1

2.082

2.228

Brand 2

Brand 3

Sugar Sagar

Local

–2 –4 –6

–3.783 Brand Figure 13.3: Utility of brand

–5.428

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195

The unknown brand (Sagar Salt) and unbranded salt have low utility. It is interesting to note that a fictitious brand has higher utility than an unbranded salt. This could suggest the importance of brand value. 0.6

Utility value

0.4

0.517 0.136

0.2 0

Medium

High

Low

–0.2 –0.4 –0.6

–0.653

–0.8

Price Figure 13.4: Utility of price

Utility value

Figure 13.4 shows the utility of PRICE, low price has a higher utility than high price. However in the overall utility function (as seen previously), price has the lowest importance. 2.5 2.0 1.5 1.0 0.5 0 –0.5 –1.0 –1.5 –2.0 –2.5

1.946

Iodised

Non iodised

–1.946 Health Figure 13.5: Utility of health

The attribute health has been given the second highest importance by the households. Iodized salt has a higher utility as compared to non-iodized salt (Figure 13.5).

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1.5

1.174

Utility value

1.0 0.5 0

No

Yes

–0.5 –1.0 –1.174

–1.5

Free flow Figure 13.6: Utility of free flow

Households prefer free flow salt as against the normal salt which is hygroscopic (Figure 13.6). The distribution of preferred levels (%) is given in Table 13.16. Table 13.16: Distribution of preferred levels Price Low

Medium

High

41.07

26.79

32.14

Health Iodized

Plain

92.86

7.14

Free flow Yes

No

78.57

21.43

Brand Brand 1

Brand 2

Brand 3

Sagar

Local

60.71

19.64

16.07

1.79

1.79

Conjoint Analysis

Simulation To increase the preference for a new brand (Sagar) three simulations were done. Simulation Run 1 Product 1 Price: Health: Free flow: Brand:

Medium Iodized No Brand 1

Product 2 Price: Health: Free flow: Brand:

Low Iodized Yes Sagar

Result 1 First choice (%) Product 1: 78.57 Product 2: 21.43 Simulation Run 2 Product 1 Price: Health: Free flow: Brand:

Low Iodized Yes Local

Product 2 Price: Health: Free flow: Brand:

Low Iodized Yes Sagar

Result 2 First choice (%) Product 1: 33.93 Product 2: 66.07 Simulation Run 3 Product 1 Price: Health: Free flow: Brand:

Medium Iodized Yes Brand 2

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198

Product 2 Price: Health: Free flow: Brand:

Low Iodized Yes Sagar

Result 3 First choice (%) Product 1: 83.93 Product 2: 16.07 The study clearly shows that brand name is important to market salt. The study also shows that strengthening functional attributes (e.g. free flow) can increase the utility more than reducing the price.

14

Cluster Analysis

Learning Objectives At the end of the chapter, the reader would be familiar with: Hierarchical and non-hierarchical clustering Application of cluster analysis for segmentation

Cluster analysis is used for groupings of objects. As a market research tool, cluster analysis is often employed in preliminary analysis to find market segments. Churchill and Iacobucci (2007) suggest that cluster analysis is a useful market segmenting tool to identify groups with similar preferences. Christopher (1969) states that cluster analysis enables segmentation to be made on many dimensions simultaneously. This chapter discusses the application of cluster analysis for segmentation. The aim of cluster analysis is to divide the data into groups, or clusters such that homogeneity within each cluster is high and heterogeneity across groups also is high. In cluster analysis, the individuals are grouped on the basis of state-of-mind information. Punj and Stewart (1983) suggest the following considerations for conducting cluster analysis: • Method to use: Since there are different algorithms to group the objects, different grouping are possible by different methods. The choice of the algorithm has to best satisfy the requirements of the research and the analysis. • The number of clusters formed: As no standard objective criteria exist to determine the number of clusters (Hair et al. 1987) the researcher can run the analysis with different number of clusters and decide on the most ideal one.

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• Selection of variables: Carmone (1999) states that the selection of the variables for clustering is important or else the results can be misleading. Punj and Stewart (1983) suggest that the problem can be addressed by having a firm theoretical base for identifying variables.

METHODS Broadly, there are two methods for clustering (a) hierarchical clustering and (b) non-hierarchical clustering. Hierarchical Clustering Hierarchical clustering is a process where the clustering starts by combining two or more items based on an algorithm (e.g. nearest neighbour) and goes on combining the other items till all the items are combined into one group. In hierarchical clustering, there are different algorithms for combining the observations into clusters. These include single linkage, complete linkage, average linkage the centroid method and Ward’s method (Hair et al. 1984). Everitt et al. (2001) and Ketchen and Shook (1996) state that Ward’s has the fewest inherent biases.

Dendrogram using Average Linkage (Between Groups) Rescaled Distance Cluster Combine Label

CASE

0 5 10 15 20 25 Num +--- -- -- -- +--- -- -- -- +--- -- -- -- +--- -- -- -- +--- -- -- -- +

1 PALMOLIVE LIRIL 8 CAMAY 12 MYSORE SANDAL 7 PEARS 10 3 LE SANCY LUX 6 REXONA 15 DOVE 2 EVITA 14 CINTHOL 9 11 HAMAM MARGO 13 DETTOL 4 5 LIFEBUOY ARAMUSK 16 PARK AVENUE 17

Figure 14.1: Hierarchical clustering for soaps

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201

The output is in a form of a dendrogram which indicates the distance at which the items have been combined (Figure 14.1). There is no statistical method to determine the number of clusters. The number of clusters has to be decided by visual inspection of the dendrogram to form groups (Humphries et al. 2007). The hierarchical clustering for soap brands (Figure 14.1) can be divided into four groups: glamour soaps (Palmolive, Liril, Camay, Mysore Sandal, Pears, Le Sancy, Lux, Rexona, Dove and Evita), health soaps (Hamam, Margo, Dettol and Lifebuoy), freshness soaps (Cinthol) and soap for men (Aramusk and Park Avenue). Non-hierarchical Clustering In the non-hierarchical method, the number of groups have to be pre-specified and the algorithm (K-means) ensures that the groups are formed which maximises homogeneity within the groups and increases heterogeneity across the groups. The non-hierarchical clustering is an iterative process, and unlike the hierarchical clustering the group membership can change with every iteration. The clustering starts randomly by identifying group centroids for each cluster. Then the objects are assigned to either of groups such that the inter group variance is maximised and intra group variance is minimised. Once the initial groups are formed, then the group centroids are recalculated. Again, all the objects are reassigned to the new groups using the same principle of maximising inter group variance and minimising intra group variance (in this process, the group membership may change). Once the new groups are formed, the group centroids are recalculated and new groups are formed. This process continues till the maximum inter group variance is achieved. Carmone et al. (1999) suggest that if the size of the input data matrix increases, K-means partitioning method becomes appropriate. The final output provides the grouping of objects into clusters. The programmes can also provide the distances from each object to the centre of its cluster (Table 14.1). This information can help determine the level of association between an object and a cluster (also refer to Case 14.1). Number of Clusters To determine the optimal number of clusters the number of groups can be varied until there is no significant incremental change in within group sum of squares is observed (Mahajan et al. 1990). Alternatively, a hierarchical clustering could be used to give a rough indication of the clusters and this could be used to define the number of clusters for non-hierarchical clustering (refer to soap example below). K-means clustering is being done for the soaps classified using the hierarchical clustering earlier. Since hierarchical clustering suggested four clusters, the K-means also specified four clusters. Table 14.1 gives the classification of the soaps in the four clusters.

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Table 14.1: Non-hierarchical clustering for soaps Clusters Cluster

Distance

Palmolive

1

1.828

Dove

1

2.852

Le Sancy

1

2.565

Dettol

2

1.739

Lifebuoy

2

3.914

Lux

1

2.543

Mysore sandal

1

2.598

Liril

1

1.522

Cinthol

3

0.000

Pears

1

2.269

Hamam

2

2.495

Camay

1

1.381

Margo

2

2.463

Evita

1

2.885

Rexona

4

3.684

Aramusk

4

3.278

Park Avenue

4

2.660

Table 14.2 shows the comparison of the clustering using the hierarchical and non-hierarchical clustering. Except for Rexona, all other soaps were classified in the same clusters both in the hierarchical and the non-hierarchical clustering. While Rexona was classified under Cluster 1 (Glamour soaps) in the hierarchical analysis, it was also classified along with Park Avenue and Aramusk in the non-hierarchical analysis. Table 14.2: Comparison of hierarchical and non-hierarchical clustering output Cluster

Hierarchical

non-hierarchical

Cluster 1 Glamour soaps

Palmolive, Liril, Camay, Mysore Sandal, Pears, Le Sancy, Lux, Rexona, Dove and Evita

Palmolive, Liril, Camay, Mysore Sandal, Pears, Le Sancy, Lux, Dove and Evita

Cluster 2 Health soaps

Hamam, Margo, Dettol and Lifebuoy

Hamam, Margo, Dettol and Lifebuoy

Cluster 3 Freshness soaps

Cinthol

Cinthol

Cluster 4 Soap for men

Aramusk, Park Avenue

Rexona, Aramusk Park Avenue

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203

The reason for Rexona being clustered with Aramusk and Park Avenue could be due to the perceptions that people have about Rexona or there could some other reason for these three soaps to be grouped together (the study could not probe into it further).

IMPLICATIONS FOR SEGMENTATION For both hierarchical and non-hierarchical clustering, the profiling of the clusters has to be done to make it meaningful for the decision maker. For this, the stateof-being variables and the behavioural characteristics of the groups have to be used. Thus, these variables should be added in the data collection instrument. As different methods give different groupings, cluster analysis is mainly recommended for exploratory segmentation. Cluster analysis could be complemented by discriminant analysis (refer to Chapter 12).

SUMMARY Cluster analysis is used as an exploratory process of segmentation. Two methods are commonly used. One, the hierarchical method uses algorithm where the process starts by combining the observations one by one and continues till all the objects are combined into one group. The number of segments has to be defined based on visual inspection. The non-hierarchical method classifies the objects into pre-specified number of groups by minimising the intra group variance and maximising the inter group variance.

CASE 14.1 Awareness of RTI Act The Government of India passed the Right to Information (RTI) Act in October 2005. Though the Act came out with much fanfare, the Central government has so far not run an all India campaign to promote the same1. Before the government launches a pan-India RTI campaign it would be important to know the awareness levels of the RTI Act in India as of now. Research Objectives 1. What is the level of the awareness campaigns amongst the urban working class? 2. How do the levels of awareness differ across socio-economic classes? Questionnaire 1. Name 2. Gender, Male/Female 1

Study done after one and half years of passing the RTI Act

204

Marketing Research: A User’s Perspective

3. Age a. 16–19 b. 20–25 c. 26–35 d. 35–45 e. 45–60 f. 60+ 4. Place 5. City 6. State 7. Zone 8. Please indicate the type of settlement where you reside a. Village b. Town having population below 1 lakh c. Town having population between 1 and 10 lakh d. City having population above 10 lakh e. Four metros 9. Please indicate your occupation a. Farming b. Agricultural labourer c. Craftsman d. Private business e. Industrial labourer f. Government servant g. Salaried employee in private sector h. Professional 10. Monthly income (in Rs.) a. Below 2000 b. 2000–5000 c. 5000–8000 d. 8000–15000 e. 15000–25000 f. 25000+ 11. Have you heard of the RTI Act? a. YES b. NO 12. If Yes, a. How did you get to know it i. Newspaper ii. TV iii. Colleagues at work iv. Family/Friends v. Campaigns b. What have you heard about the RTI Act i. History of the RTI Act ii. How it came into being iii. When it can be used iv. How it has to be used c. Have you informed others about the RTI Act i. Yes ii. No

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205

13. Please tick which is most appropriate to you, a. I have just heard of the RTI Act b. I know about the RTI Act. I know when it can be used c. I know how to use the RTI Act d. I have used the RTI Act e. I have never heard of the RTI Act A cluster analysis was done to profile the population based on their awareness of Right to Information Act. The cluster analysis output is given in Tables 14.3–14.8. Table 14.3: Initial cluster centres Cluster 1 Age Occupation Income group Type of settlement Zone Awareness level Newspaper TV Colleagues Govt. official Family/Friends NGO campaigns

2 1 9 4 4 3 1 0 0 0 0 0 1

3 2 2 5 4 3 2 0 0 0 0 0 1

6 6 3 4 3 5 0 0 0 0 0 0

Table 14.4: Iteration history Iteration 1 2 3

Change in Cluster Centres 1

2

3

2.766 0.308 0.000

2.171 0.577 0.000

1.750 0.310 0.000

Convergence achieved due to no or small change in cluster centres. Table 14.5: Distances between final cluster centres Cluster

1

1 2

3.599

3

3.773

2

3

3.599

3.773 3.758

3.758

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Table 14.6: Number of cases in each cluster Cluster

1

51.000

2

38.000

3

34.000

Valid

123.000

Missing

0.000

Table 14.7: Partial list of cluster membership Case Number 1 2 3 4 5 6 62 63 64 65 119 120 121 122 123

Cluster 1 1 1 3 2 2 2 2 3 1 1 2 2 3 1

Distance 3.009 0.850 2.818 1.870 3.541 2.597 2.557 1.922 2.021 1.448 2.038 2.557 1.922 2.021 1.448

Table 14.8: Final cluster centres Cluster Age Occupation Income group Type of settlement Zone Awareness level Newspaper

1

2

3

2 7 4 4 3 3 1

3 4 4 4 3 2 1

5 6 4 4 3 4 0

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207

Cluster 1

2

3

TV

0

0

0

Colleagues

0

0

0

Govt. official

0

0

0

Family/Friends

0

0

0

NGO campaigns

1

1

1

The profile of the three groups was as follows: • Cluster 1 includes salaried employees in the private sector mainly in the age group of 20–25. This cluster shows that they are aware of the Act, and know how to use it. Their primary mode of information about the Act is through newspapers and NGO campaigns. • Cluster 2 is composed of people running their own private businesses mainly in the age group of 26–35. The group knows about the Act, but has not actually used it. As in the case of cluster 1, the information sources are newspapers and NGOs. • Cluster 3 mainly includes government servants in the age group of 45–60. They know about the Act and have also used it to their benefit.

Conclusion It is interesting to note that the benefit of the RTI act has been more to the government servants and not the general public. This may suggest the general public is still not aware of the benefits of the Act or they are not sure whether they can use the Act. This study supports the finding of Srivastava (2008) who found that most of people availing the act are from “within the system”. Even the Prime Minister felt that there is a need to strengthen the Act by bringing out a legislation to protect the whistle blowers (India Today 2011). The study also points out that the primary source of information about the RTI Act was through campaigns run by NGOs and newspapers for the general public. The study identifies that the television, which is a powerful medium, has not been harnessed to its fullest extent. Hence, it is recommended that the government should increase television-based information campaign for promoting RTI and encourage people to use the Act.

15

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Learning Objectives At the end of the chapter, the reader would be familiar with: Brief scenario of marketing research (MR) trends, and MR in India Check list for using MR reports Special topics covered in the chapter: Shelf life of MR studies Comprehensive research study

Market research would be effective if it is designed and implemented effectively. This chapter briefly presents the trends in MR, and the MR scenario in India and subsequently the user’s perspective for using the MR studies.

MARKETING RESEARCH: TRENDS Green Book (2013), a comprehensive report on the trends of research worldwide, states that the importance of MR would not diminish. The report however states that: • There would be a shift to smaller and less expensive studies • The clients are not as involved in the research as in the past Again, the Green Book (2013) points out that while the clients are demanding innovative ways of obtaining respondent data (e.g. using mobile communication devices), the researchers, on the other hand, feel they “face challenges” using mobile surveys as they lack representativeness. The researchers also feel that social media as a tool for MR is still at its infancy and tools need to be developed for its use for marketing research purposes.

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Pinto (2012), on the other hand, points out that new trends help companies understand the behaviour of individuals across different countries. Some of the technological innovations include: • Perception analyser, which would facilitate research to do be done in any language. • Cati, a software application driven telephone survey technique. • Eye-trackers which can measure attention and emotional responses.

MARKETING RESEARCH: INDIAN SCENARIO Till few years back, companies did not perceive MR to be essential, but with the Indian economy growing and foreign companies trying to enter the Indian market, the need for MR has been increasing. The MR industry in India in 2012 was estimated at 10 billion with an estimated growth of 15% (Pinto 2012). Sarkar (2010) states that while FMCG contributes 65–70% of the market research studies, new sectors like telecom, insurance, retail and banking are now becoming the high growth areas for most of the research firms. Government agencies and NGOs have also started using MR. The increasing need for MR has changed the industry. From in-house research departments in the 1970s; to using the advertising agencies during the 1980s and moving to specialised MR agencies in the 1990s the India MR agencies main focus has been on (Businessmapsofindia.com, 2011): • Understanding consumers • Market demand • Product development • Positioning • Marketing mix The data collection techniques have also changed with introduction of the Internet and the social media. Market research agencies are also conducting surveys with hand-held terminals (B2Binternational.com, 2007). Sarkar (2010) states that many automobile companies are using social media channels to analyse the behaviour of their potential customers.

USER’S PERSPECTIVE Here it should be noted that while it is good (or trendy) for the clients to demand for innovative ways of obtaining data, the basic requirement of MR  research should not be compromised. Therefore, as mentioned in Chapter 1, the user has to be “critical” and he/she should check whether the research agency ensured that each step was properly planned and executed to minimise errors.

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Thus, output should be the last thing that the user should see/use. The user should check on the following before accepting the report. • Decision area to research problem: Has the research problem been properly conceptualized to capture the information needs to address the decision problem. • Identifying the research questions: Are the research questions sufficient to provide information to understand the research objective and thereby address the decision area. • The methodology adopted: Is the research design appropriate. Here it should be pointed out that a complex research design does not necessarily provide better results. The research design should be appropriate and decided based on the type of information to be collected and the purpose of the study. Supporting the Green Book (2013), going for smaller and less expense studies could be better than complicated and highly analytical studies. • Sample: In the Indian context, the biggest challenge is to get an appropriate sample. Thus, care should be taken to ensure that the sample is appropriate. Question whether an appropriate sampling method was used? Was the sampling frame relevant? If a quota sampling was adopted, check whether the dimensions for drawing the sample differentiate the behaviour amongst the target group. • Questionnaire design: Another challenge in India is the diversity of languages. In addition to checking whether the questionnaire is correctly worded to ensure unbiased responses, it would be important to check if the translation to different languages retains the meaning of the questions. • Data collection: As mentioned earlier, interviewers’ bias is a possible source of error. The user should therefore check about the interviewers and their experiences. In addition, the user should also check whether there were sufficient cross checks done to verify the validity of data collection process. • Data analysis: Was an appropriate data analysis technique used? Were the default options of the statistical packages modified when required? • Interpretation: Are the interpretations valid? Have the inferences been drawn to help the decision area? Refer to case studies for application of the check list (Case 15.1 and 15.2). Marketing Research Report and Decision Making Once the user is satisfied with the report, the information can be used for decision making.

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Some market research reports suggest action plans (decisions), but the user should utilize the report for information only as he/she would be the best person to decide how to modify the marketing mix variables to support the decision. Shelf Life of Marketing Research Report The entire process of research would have taken some time and so there would be a lag between initiation of the research and the final report. Since the market is dynamic and changing; any delay in using the information would make the output outdated. The decision maker should, therefore, use the information as fast as possible. In other words, marketing research is a perishable product and the findings should not be used for future decisions.

SUMMARY The chapter highlights the importance of questioning the MR report before accepting it and using it for decision making. A checklist to identify the validity of a research study is presented (application of the check list is shown through a case (Case 15.1) given as part of this chapter). The chapter also discusses about the use of the MR report for decision making and emphasises that the shelf life of a MR report is very short. A comprehensive research study (Case 15.2) is also given at the end.

CASE 15.1 Attitude of Parents Towards TV Viewing by Children Excessive television viewing is becoming increasingly regularized in today’s society. With more and more working parents, children are left without 24 hour supervision. Children thus have unlimited access to TV. A study found that 93% of children who live in urban areas have regular access to television and watch it for an average of 3 hours a day. This is at least 50% more than the time spent on any other out-of-school activity, including homework, being with friends or reading. In an article Dr. N. Bhaskara Rao, the chairman and founder of Center for Media Science, states that “More than half of television viewers in India today are children of below 15 years. And yet there is hardly any sensitivity about the relevance and impact of what is dished out by various television channels”. Psychiatrists say that the chief drawback of the amount of viewing on TV now-adays is increase in childhood aggression. They tend to demonstrate aggressive behaviour more often in school and at home. This occurs because television programmes often depict violence that is perpetrated by characters that are attractive and can be rewarded by the acts of violence. Psychiatrists recommend that parents limit the amount and type of TV that their children watch. This paper studied the attitude of the parents towards television viewing by children. Hundred working parents with children between 8–14 yrs of age were given a thematic apperception test followed by a questionnaire.

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Excerpts of the research are given below. Questionnaire Thematic Appreciation Test Describe the situation in the picture below?

General Questionnaire 1. How many children do you have? One

Two

Three

2. Do you have ... Baby girl

Baby Boy

Both

3. How old is your child? Less than 4

4–8

8–14

14+

4. Do you allow your child to watch the television programmes with you? Always

Never

Sometimes

Frequently

5. How many hours of television does your child watch in a day? less than ½ hrs

1 hr

2 hrs

3 hrs

> 3 hrs

6. What types of television show does your child watch the most? Comedy Series Cartoons Soap Operas Talk Shows Drama Movies Action Based Programs Sports Music Dance Reality Shows Informative Others 7. Do your children chat about the shows they have watched even after the television is switched off ? Yes

No

8. Do your children imitate any character that he/she watches in television? Yes

No

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9. What type of character does he/she imitate? Comedian Cartoons Action Hero/Heroines Dancer Sports Personality Host/Anchor Romantic Hero/Heroine Singer Others 10. Do you encourage his/her behaviour? Yes

No

11. What’s your reaction to his behaviour? Enjoy Proud

Happy Others

Angry

No reaction

12. Do your children imitate any act that he/she watches in television? Yes

No

13. What type of act does he/she imitate? Action-fighting Cartoons Dancing Sports Host/Anchor WWF wrestler Adventure-jumping Others

Dialogues Rider

14. Do you appreciate his/her act? Yes

No

Name:

Gender:

Occupation:

City:

Analysis Some responses from thematic appreciation test in verbatim are given below: 1. Children are watching TV. Mother is trying to stop them from watching TV. But children are not bothered to listen. 2. Children are watching a TV show. Mother is possibly instructing them to sit back and view from a safe distance. 3. Mother and children are conversing. Most probably mother is telling her son to limit watching TV everyday. Also she is telling her son on pros and cons of watching television programmes with an emphasis on the good side of television watching. 4. Mother and children are having leisure time. 5. Some game is being played on TV. Mom is shouting at the kids and the kids are not bothered & they are glued to the screen with their play station. 6. Mother is asking the children to switch off the TV and go to study. 7. Two kids are playing a video game and their mother wants them to stop playing. 8. Children are watching action movie and their mother is asking them to switch off. 9. Mother is enquiring something but kids are not paying any heed to her. 10. Kids are watching movie and their mother has joined them.

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11. Mother is trying to say something to the kids who are watching television. They are so involved in that they are least bothered to listen to what she is trying to explain. 12. Children are flipping TV channels. Mother is asking them to switch it off and do something worth while, either study or play outdoors. 13. A boy and a girl sitting and watching television. Their mother comes from behind and asks them to switch off the TV. But they were so busy watching television that they couldn’t hear her. Then she gets angry and shouts at them and asks them to immediately go and sit down to study. 14. The children are simply sitting and browsing the channels while the mother looks on. 15. Children are enjoying watching television along with their mother. 16. Children are watching the television by sitting very close to the TV. Mother has come to tell the children to stop watching TV and switch it off. 17. Children are watching TV and not paying any heed to their mother. 18. The distance at which the children are watching TV is not safe. Mother is scolding the kids. She is asking them to switch off the television as their TV time is over. The kids refuse and the mother is upset. 19. The children are helping their mother in dusting the floor with a brush and simultaneously all of them are watching TV. 20. War at home. 21. There is breaking news on TV and the family is watching it in full concentration. 22. Mother is explaining to the children what they are showing on TV. 23. Children have come back from school and want to watch TV. Their mom is forcing them to play instead. 24. Mother and children are enjoying a good movie. 25. A channel is showing the crash of World Trade Center and the family is watching the telecast. 26. There is a huge fight for the TV remote between the kids. Their mother has ventured to stop the fight. She is scolding both of them. 27. The children are quarrelling about which show to watch on TV. The eldest one is screaming loudly at the younger ones and all of them are calling for their dad. 28. Mother of the kids is asking them to hand over the remote immediately as some movie for adults is showing on TV. 29. Looks as if children have been quarrelling amongst themselves for the remote. Mother is threatening them that she will switch off the television if they don’t stop immediately. 30. Elder sister wants to watch her favorite show. She wants the TV. Now she is trying to convince her brother to go out with his friend and leave her alone in the room. Content analysis of the responses from the TAT is given in Table 15.1. Table 15.1: Content analysis of TAT What are the parents saying

What are the children doing

Spend leisure time more usefully

Playing games on TV

Trying to stop children from watching TV

Watching action movie

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What are the parents saying

What are the children doing

Instructing to watch TV at safe distance

Glued to TV screen

Mother is screaming/shouting/ scolding kids

Fight between the kids/children; quarrelling for TV show or remote

Asking to switch off TV and go to study/play

Not paying any heed to mother/not bothered to listen

Limit TV viewing

Browsing or flipping TV channels

Explaining what is being shown

Enjoying together

While there are some positive attitudes most parents have negative attitudes towards TV viewing by their children. On the positive side, they feel the children are enjoying movies, having leisure time with family or the parents are explaining to the kids what is being telecasted on TV. On the negative side, they feel that the children are either quarrelling amongst themselves for the show or remote or they are just glued to the television screen. Parents are trying to prevent their kids from watching TV and some want them to switch it off and go to play or limit their viewing. The parents mention that though they are scolding them or screaming at them in order to make them follow their instructions, the children do not seem to be bothered and are sometimes disregarding them. TAT therefore suggests that the parents do not have a positive attitude to TV viewing. Fights, quarrel, etc. between kids and parents anger depicts the seriousness of the situation. And more importantly TAT suggests that the parents are not able to control the children’s behaviour. Questionnaire Analysis The questionnaire designed to understand the kinds of programs that kids generally watch on TV, their behaviour and the parents’ reactions are summarised next. 80% 70% 60% 50% 40% 30% 20% 10% 0%

1 hr

2 hr 3 hr Hours of TV viewing by children Figure 15.1: Hours of TV viewing by children

>3 hr

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Most of the parents state that they have restricted their children TV viewing to one or two hours per day (Figure 15.1). Very few of them allow their children to go up to 3 hours and above. 9%

5%

6%

1% 13%

5%

11%

9%

31% 9%

1%

13% Commedy Series 31% Cartoons 1% Soap Operas 9% Talk Shows 9% Drama 11% Movies 5% Action Based Programs 6% Sports 9% Music 5% Dance 1% Reality Shows

Figure 15.2: Type of TV programmes watched Though Figure 15.2 shows that cartoons and comedy series are the dominant programs watched, there are children who watch talk shows, dramas and other programs also. Table 15.2: Parent’s perceptions of impact of TV on children (in percent) Yes

No

TV programmes impact children

76

24

Children chat at TV programmes

64

36

Children imitate TV characters

84

16

Seventy six percent of the population believes that television has the capacity to impact their children (Table 15.2). While two-thirds believe that children chat about TV programmes, 84% of the parents have agreed that their kids imitate a character they watch on television. Again from Figures 15.3 and 15.4, it can be stated that these programmes do have an impact on them as they imitate those characters or acts. It can be concluded that the majority of the parents feel that television impacts their children and the parents feel that TV viewing: 1. 2. 3. 4. 5.

Should be reduced/limited Increases aggressive behaviour Has negative effect on kids Should not be encouraged Impacts their lifestyles

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7%

1%

7%

3%

217

19% Comedian 26% Cartoons 15% Action Hero/ Heroines

19%

7%

15%

15% Dancer 7% Sports Personality 7% Host/Anchor 7% Romantic Hero/Heroine 1% Singer 3% Others

26% 15% Figure 15.3: TV characters children imitate 10%

20%

8% 1% 8%

8% 1%

16%

20% 12% 16% 16% 12% 1% 8% 8% 1% 8%

16%

Action-fighting Cartoons Dancing Dialogues Sports Host/Anchor WWF wrestler Rider Adventurejumping

10% Others

Figure 15.4: TV acts children imitate Applying the Checklist The research wants to check the perceptions about television viewing by children and the information required to be collected included both socially desirable responses and information about the parent’s knowledge and opinions of children viewing habits. The information likely to lead to socially desirable responses was captured by a thematic appreciation test and the knowledge and opinions were collected using close ended questions. The information was collected from a sample of working parents with children 8–14 years. Relevant analysis was used to conclude that parents feel that television could have negative impact on the children. The findings of the study can therefore be used for decision making.

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CASE 15.2 Comprehensive Research Study Evaluating Anti-smoking Advertising Campaign Background India has around 200 million active tobacco users. Tobacco is emerging as a major cause of all cancers in India according to the latest cancer registry data compiled by the Indian Council of Medical Research (ICMR). Anti-smoking laws are being implemented in India. The Central government tabled a comprehensive anti-smoking law in Parliament. On 20 July 2003, the Union Cabinet decided to toughen anti-smoking laws, restricting smoking in public places and advertisement of tobacco products. The Department of Health has sent a notification on the 25th February 2004 stating: 1. Prohibition of smoking in a public place (except restaurants under certain conditions)  2. Prohibition of advertisements of cigarettes and other tobacco products 3. Prohibition of sale to minors The Supreme Court (SC) has also directed all states to ban smoking at public places and to ensure compliance of existing provisions on tobacco advertising. (Public places where smoking has been banned include hospitals, health institutes, public offices, public transport, court buildings, educational institutions, libraries and auditoria. However, hotels and amusement parks have been excluded from the ban.) There has been lot of anti-smoking advertisements in recent past. • Cancer Patients Aid Association has focused on ways and means to tackle smoking across the country. They developed campaigns in print, on billboards and through television. • A series of public awareness advertisements appeared regarding passive smoking. • The Rotary Club of Tiruchy launched the “Quit and Win” International smoking cessation campaign in 2004, under the aegis of the World Health Organisation (WHO). On 21 July 2007, the Health Minister has stated that a law would enforce that all workplaces in India will be smoke free. But despite these efforts, there seems to be little or no impact on cigarette reduction/ cessation as: • In India, 90, 000 people die due to tobacco and related diseases each year. • Tobacco contributes to 56.4% and 44.9% of cancers in men and women, respectively. • More than 6.4 million children take to smoking. • It is estimated that India as compared to any other country will have the fastest rise in tobacco-related deaths each year. • India’s tobacco industry is one of the biggest in the world. India’s acreage devoted to growing tobacco is the second largest in the world. Decision Area • Has the ongoing anti-smoking campaign helped in reducing smoking? • Should the government invest more in advertising to spread the message? • Is there a need to change the targeting strategy?

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Conceptualization of the Research Study The effectiveness of a campaign can be measured by: 1. Campaign recall – A measure of aided and unaided recall in terms of how many people are able to recall the campaign. 2. Effectiveness of campaign in smoking cessation and reduction – How far the campaign is effective in reducing the tendency to smoke. 3. Number of smokers – The increase/decrease in the number of smokers after the campaign. The anti-smoking campaign can impact the people in three ways: • Reduction in the number of cigarettes smoked per day • Quit smoking altogether • Preventing non-smokers from beginning to smoke 4. Cigarette sales – The increase/decrease in sales of cigarettes after the campaign can be used as a measure of its effectiveness. Care should be taken to note that other possible causes for decrease in cigarette sales like increase in pricing or change in content are to be taken into consideration. Operationalising these to the research study was done by adapting the health change behaviour models and protection motivation theory. Health Belief Model The health belief model (HBM) predicts healthy behaviours by focusing on the attitudes and beliefs of individuals. The HBM is based on the assumption that a negative health condition can be avoided by taking a recommended action. More importantly, the model assumes that he/she can successfully take a recommended action. The HBM suggests that the perceived threat and net benefits: perceived susceptibility, severity, benefits and barriers would determine the “readiness to act” (Table 15.3). Table 15.3: HBM dimensions Concept

Definition

Perceived susceptibility

Chances of getting affected

Perceived severity

Extent of consequences

Perceived benefits

Reduction in the consequences by taking suggested action

Perceived barriers

Financial and social barriers, influencers

Cues to action

Impact on outer self

Self-efficacy

Ability to stay away from the habit

Source: Adopted from Glanz et al. (2002) Protection Motivation Theory Protection motivation theory (PMT) focuses on the impact of persuasive communication on behavioural change (Rogers 1983).

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The PMT suggests that the intention to protect depends on: 1) The perceived severity of a likely consequence (e.g. cancer) 2) Chances of occurrence (probability of getting cancer) 3) The reduction in the probability of the consequence by changing behaviour (e.g. quitting smoking) 4) Ability to adopt the recommended preventive behaviour. Research Objectives 1. To measure awareness of the anti-smoking campaigns 2. To analyse various attributes of the campaign and assess their effectiveness in communicating anti-smoking messages Research Questions1 1. What are the root causes that lead a person to begin smoking and quit smoking? 2. What is the extent of awareness about anti-smoking laws among the target segment? Does the knowledge impact smoking behaviour? 3. Was the campaign successful in reaching all the targeted audience or it was limited to only a section of the audience. 4. What are the attributes that lead to increased recall of a campaign? Which of these attributes have generated the maximum impact? 5. Has the campaigning been successful in making the viewers informed about the ill-effects of smoking? 6. What is the level of involvement of smokers in the ad campaign? 7. Was the campaign successful in changing the perceptions towards smoking or not? 8. Has it changed their behaviour after exposure to the anti-smoking campaign? 9. What is the extent of correlation between increased recall of an advertising campaign and propensity to quit/reduce/avoid smoking? 10. Influence of the campaign on the influencers. Types of Information Types of information to be collected can be classified as per the behavioural framework: State of being • Demographic details • Frequency of smoking • Smoking situations State of mind (knowledge) • Awareness levels of ill-effects of smoking Respiratory Heart Cancer Skin Other

Also refer to case at end of Chapter 4 for developing research questions for a study to design a anti-smoking campaign

1

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• Awareness levels of anti-smoking campaigns by: TV Newspaper NGO led programmes/seminars Celebrity endorsements Internet • Awareness of anti-smoking laws State of mind (motives) • Feeling of being a smoker Pride Desperation Shame Nonchalance Sense of style • Reasons for starting to smoke State of mind (perceptions) • Perceived effect of smoking on social usage • Perceived effect of smoking on self-esteem • Perceptions of smoking among general public • Reasons for failure of anti-smoking campaigns • Reasons for leaving smoking Look/personal image Financial constraints Constraints from parents/spouse Personal health Pride Anti-smoking campaign Other • Reasons for continuation of smoking Pleasure Social/peer status Admittance into social group Stress reliever Alertness/efficiency increment Style Addiction Other Research Design The type of information could be sensitive, leading to socially desirable responses. This would require the data to be collected indirectly. Here instead of using the projective techniques, this study used the non-smoking peer group to obtain the data. While the data is sensitive, likely to lead to socially desirable responses for the smoking group, the non-smoking peer group would be willing to give their responses about their smoking peer group without bias. Again the awareness of the advertisement campaign and information it is providing is collected from the non-smoking peer group as their level of awareness and understanding of the campaign would indicate whether the message is being communicated or not. The main habits of the non-smoking peer group are assumed to be the same as the smoking group.

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Thus, the data collection would be designed to identify the profile of the smokers and their habits from the non-smoking peer group. Sampling Here it should be noted that the investigators were encouraged to give more importance to identifying non-smoking peer group. However, it is difficult to always identify the non-smoking peer group. Thus, the questionnaire would add two questions regarding smoking habits at the end of the questionnaire. While the questions are added at the end of the questionnaire to avoid offending non-smokers, these questions would be analysed first to remove responses of the smokers from the analysis. Since the sample is looking at non-smoking peer group, a quota sample similar to the profile of the smokers was used. Age, location and literacy rates the important variables influencing smoking behaviour were used to draw a quota sample (Table 15.4). Table 15.4: Sampling design to study effectiveness of anti-smoking campaign Age 21-25

Males

Urban Tier 1/2 Rural

Females

Urban Tier 1/2 Rural

26-30

31-35

36-40

41-45

46-50

Literate Illiterate Literate Illiterate Literate Illiterate Literate Illiterate Literate Illiterate Literate Illiterate

Questionnaire Used Name: Age Group: 45 years

18–25 years

25–35 years

35–45 years

Profession Student

Professional

Own Business

Other [Please specify]

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Time spent on TV (in Hrs) per day: Time spent on Newspaper (in Hrs) per day: Time spent on Magazines (in Hrs) per day: Time spent on Radio (in Hrs) per day: Activity of interest: Reading

Sports

Watching TV/Movies

Traveling

Why do people smoke? Pleasure Peers smoke Useful for networking Stress reliever Alertness Style Addiction Other [Please specify] I have seen an anti-smoking campaign with the last one month Yes

No

Please answer the following on the 5 point scale (where +2 is strongly agree and -2 is strongly disagree) Most people start smoking because of peer pressure The message used in the current anti-smoking campaign is very clear The current anti-smoking campaign puts enough emphasis on the diseases that can be caused by smoking The current anti-smoking campaign is doing a great job and is largely successful Smoking is more prevalent among the young than the old Smoking is difficult to quit The government should ban smoking Many smokers have quit smoking after the campaign The campaign has made people aware of the social consequences of smoking The campaign had suitable personalities to communicate the message The frequency of such campaigns has to be increased The campaign targets the young audience more than the old The campaign focuses on rural areas rather than the urban areas The campaign should have been forceful about the message it was propagating Cigarette smoking is the most harmful of all addictions

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Smoking is most expensive of all addictions People generally get addicted to smoking during their student days What was the media used for the anti-smoking campaign? TV Newspaper Celebrity Endorsements

NGO led Programmes/Seminars Internet Outdoor Advertising

Main “anti-smoking rationale” of the campaign can be classified into: Looks/Personal Image Social Disapproval/Smoking is not cool Constraints from parents/spouse Personal health/Health of immediate family Pride in being able to resist/give up Others [please specify] Do you know anyone who quit smoking? Why did he/she quit? Looks/Personal image Social disapproval/Smoking is not cool Constraints from parents /spouse Personal health/Health of immediate family Pride in being able to resist/give up Others [Please specify] Your friend is a smoker who smokes 10 cigarettes a day. According to you, which of these ailments should he be most worried about due to cigarettes? Respiratory problems Heart problems Cancer Skin-related issues Others [Please specify] Level of association with smoking: I never smoked I smoke only during parties I used to smoke, but have given up I smoke (If response to the above question is other than I never smoked), How much is your frequency of smoking? Less than once a week More than once in a week but not everyday

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More than once a day but less than 5 cigarettes a day More than 5 cigarettes a day but less than a pack (of 10s) More than a pack (of 10s) a day Results And Analysis2 Smoking Behaviour Figure 15.5 shows 28% of the sample smokes. Figure 15.6 shows the frequency of smoking of the smokers in the sample. (It should be noted that this would not represent the percent of smokers in the population as the sample has been titled to non-smoking peer group.) As mentioned, smoking-related questions were added only to reascertain that the respondent is from the non-smoking peer group. Smokers’ responses were deleted from the subsequent analysis.

1%

28%

65% I never smoked 6% I used to smoke but have given up 28% I smoke 1% I smoke only during parties

6%

65% Figure 15.5: Smoking behaviour 8% 23%

19%

8% Less than Once a Week 19% More than once a day but less than 5 Cigarettes a day 50% More than 5 cigarettes a day but less than a pack (of 10s) 23% More than a pack (of 10s) a day

50% Figure 15.6: Frequency of smoking 2

Only part of the analysis and conclusions are discussed here.

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Figure 15.7 shows that 37% of the smokers smoke for pleasure. There are also large number of people who smoke as they are addicted, or perceive it to be a stress reliever or because of peer acceptance. The subsequent campaigns could therefore design campaigns differently for different groups.

40% 35% 30% 25% 20% 15% 10% 5% 0%

Networking

Peers smke

Stress Reliever Addiction

Pleasure

Figure 15.7: Reasons for smoking

Perceptions of Anti-smoking Campaign Figure 15.8 shows 80% of the people have seen the anti-smoking campaign in the last

20%

20% No 80% Yes

80% Figure 15.8: Awareness of anti-smoking campaign one month.

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5%

227

6% 11% 5% 6% 11% 41% 37%

37%

Strongly Disagree Disagree Can not say Agree Strongly Agree

41% Figure 15.9: Communication of the anti-smoking campaign Figure 15.9 shows that an overwhelming majority of 78% either agree or strongly agree that the message delivered is very clear. Sixty one percent of the respondents feel that the anti-smoking campaign does not 18%

16%

18% 43% 15% 8% 16%

8%

15%

Strongly Disagree Disagree Can not say Agree Strongly Agree

43% Figure 15.10: Content of the anti-smoking campaign

put sufficient emphasis on the diseases caused by smoking (Figure 15.10). Hence the campaign needs to be altered to focus more on the diseases caused by smoking. 15% 9%

7% 7% 45% 24% 9% 45% 15%

Strongly Disagree Disagree Can not say Agree Strongly Agree

24% Figure 15.11: Perception on the effectiveness of the anti-smoking campaign

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Supporting the earlier finding, a large number of the respondents feet that the current anti-smoking campaign is not effective (Figure 15.11). Similarly, Figure 15.12 shows that the impact the anti-smoking campaigns had on inducing people to stop smoking has been low. Only 36% agree that the campaign had 1%

35%

23% 23% 33% 8% 35% 1%

8%

Strongly Disagree Disagree Can not say Agree Strongly Agree

33%

Figure 15.12: Perception on the impact of anti-smoking campaign

induced people to stop smoking, 56% disagree that the campaign helped people to quit smoking.

10% 12%

28%

9%

10% 28% 9% 41% 12%

Strongly Disagree Disagree Can not say Agree Strongly Agree

41% Figure 15.13: Anti-smoking campaign’s impact on social consequences

Figure 15.13 shows that majority of the respondents (53%) feel that the campaign was able to highlight the social consequences of smoking. Only 34% of the respondents either agree or strongly agree that the personalities used for the campaign were effective in communicating the message (Figure 15.14). Thus, there is a need to identify different personalities to communicate the message. Another study may be required to identify the profile of the personalities who would be effective in communicating the message of anti-smoking.

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Sixty seven percent of the respondents want the frequency of the anti-smoking campaigns increased (Figure 15.15). 20%

7%

16%

27%

20% Strongly Disagree 16% Disagree 30% Can not say 27% Agree 7% Strongly Agree

30% Figure 15.14: Effectiveness of the personalities used in the anti-smoking campaigns 11% 9%

45%

11% Strongly Disagree 9% Disagree 13% Can not say 22% Agree 13% 45% Strongly Agree

22% Figure 15.15: Perception on the frequency of the anti-smoking campaign 10%

26%

8%

19% 10% Strongly Disagree 19% Disagree 8% Can not say 37% Agree 26% Strongly Agree

37% Figure 15.16: Perception of the target group (age) of the anti-smoking campaign

Marketing Research: A User’s Perspective

230

Sixty three percent respondents feel that the campaigns have focused more on the young audience (Figure 15.16).

9% 8% 5%

49% 29% 49% 5% 8% 9%

Strongly Disagree Disagree Can not say Agree Strongly Agree

29% Figure 15.17: Perception that the anti-smoking campaign has a rural focus

A large portion of the respondents feel that the campaign has focused on urban areas more than on the rural areas (Figure 15.17).

22%

17%

17%

22% 17% 15% 29% 17%

Strongly Disagree Disagree Can not say Agree Strongly Agree

29% 15% Figure 15.18: Perception on the extent of addiction to smoking Close to 46% people agree that cigarette smoking is the most harmful of all addictions (Figure 15.18). Sixty four percent of the respondents agreed to the fact that people get addicted to smoking during their school/college days (Figure 15.19) which suggests that the targeting of the campaign was correct (Figure 15.16). Fifty seven percent respondents feel that people start smoking because of peer pressure (Figure 15.20).

Using the Marketing Research Studies

231

11% 7% 11% Strongly Disagree 7% Disagree 16% 16% Can not say 28% Agree 38% Strongly Agree

38%

28% Figure 15.19: Perception on the timing when people get addicted to smoking

13% 32%

10% 13% 10% 20% 25% 32%

Strongly Disagree Disagree Can not say Agree Strongly Agree

20% 25% Figure 15.20: Smoking: Role of peer group

23%

16%

23% 14% 12% 35% 16%

Strongly Disagree Disagree Can not say Agree Strongly Agree

14% 35%

12% Figure 15.21: Perception on the difficulty to quit smoking

Marketing Research: A User’s Perspective

232

Around 50% of the respondents feel that smoking is very difficult to quit (Figure 15.21) and they feel that people try to quit smoking but they were not successful in it. Impact of Anti-smoking Campaigns

60% 50%

52%

40% 30% 20% 10% 0%

17%

12% 2%

11% 4%

2%

TV Newspaper Magazines Radio

Internet

Outdoor advtg

NGO activity

Figure 15.22: Media and recall of anti-smoking campaigns

Figure 15.22 shows that television campaigns had the highest visibility. Fifty two percent of the respondents recall the campaigns seen on TVs. TV is followed by the Internet

80% 70% 60% 50% 40% 30% 20% 10% 0%

69%

15% 6% Personal/ Parents’/ Self power family health spouse pressure

6% Social disapproval

3% Personal image

Figure 15.23: Recall of message in anti-smoking campaign

(17%), newspaper (12%) and outdoor publicity (11%). The recall of the impact of smoking on personal/family health message is most at 69% (Figure 15.23). Parent’s/spouse pressure at 15% is a distant second.

Using the Marketing Research Studies

233

50% 40% 30% 20% 10% 0%

Personal/ Parents’/ Self power family health spouse pressure

Social disapproval

Personal image

Figure 15.24: Reasons for quitting smoking Figure 15.24 supports the impact of the campaign by showing that “Personal Health/ Health of Immediate Family” as the top reason for quitting (47%) followed by “Parent/ Spouse pressure” (29%). Thus, overall the campaign has been successful in communicating “Personal Health/ Health of Immediate Family” as the reason why people should quit. So campaigns should continue to focus on this angle. In addition, the influence of the spouse/parents should be exploited to the fullest. 28%

28% Cancer 25% Heart Problems 47% Respiratory Problems

47% 25% Figure 15.25: Most dangerous disease due to smoking Respiratory problem was perceived to be the most dangerous disease which smokers need to be wary of (Figure 15.25).Thus, the campaigns should emphasise this in their campaigns. Conclusion and Recommendations Smoking is an addiction, and the youth are the most vulnerable section of the population who get induced into the habit of smoking because of pleasure and peer pressure.

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Marketing Research: A User’s Perspective

While the anti-smoking campaigns are not perceived to be doing enough in reducing the smoking habits, the anti-smoking campaigns have made a dent and helped some people quit smoking. The anti-smoking campaign has been able to target the group vulnerable to smoking (youth) and communicate that smoking could deteriorate their health and their family member’s health. It is recommended to continue with the campaigns focusing on the health issue and continue targeting the youth. Campaigns can also be conducted in schools to educate children about the ill effects of smoking. While the respondents felt that the frequency of the campaigns should be increased, this should be researched more, as over exposure of campaigns focusing on negative consequences could prove to be dysfunctional.

Annexure 1 RESEARCH BRIEFS Company 1: A Biscuit Manufacturer (Understanding the Dynamics at the Point of Purchase for Biscuits) The key issues we want to understand are: A. What role does the housewife have at the point of purchase? • Does the housewife always decide the type and brand of the biscuit that she wants to buy? • Does in-shop activity influence her in any manner – Does she look around the shop shelves, displays (including the natural product displays), make active enquiries, looks around the POP material, etc. • Does she physically check the pack she buys – what are the things she looks for in the pack, how important is the date of manufacture to her? B.

Do children influence the choice of biscuit type or brand when accompanying their mother at the point of purchase?

We need insights into the behaviour of children at the point of purchase – to what extent does their pestering influence the mother switch the brand of biscuits that she wanted to buy (e.g. she came to buy only glucose but also ended up buying cream biscuits). C.

Can the retailer influence the brand choice of the consumers?

We specifically want to know that in the event of the retailer pushing a particular brand of biscuits, does the housewife change her pre-decided brand. How does the retailer go about this? This needs to be examined in the context of specific types of biscuit. That is, retailers’ push in Glucose, Marie and cream biscuits. The research should give clearly answer whether “Point of Purchase Activities” work and give us a strategy to utilize the “Point of Purchase” effectively for pushing our brands. Target Group Housewives in the age group of 25–40 years with children in the age group of 6–12 years belonging to SEC ABC.

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Marketing Research: A User’s Perspective

Company 2: A Bank To understand the potential of health insurance sold along with the saving accounts of XYZ Bank (name disguised), vis-à-vis other health insurance products in the market (stand alone or bundled). Company 3: An Investment Company An investment company wants to reach out to investors in non-metro towns like Jamshedpur. Towards this, the company needs to understand: • Investor profile • Investment pie • Needs for which one invests • Depth of understanding of different investment products • Awareness about mutual funds/brand ranking • Investment process • Who are the key decision makers? • Who/what are the key decision influencers/relative importance? • Role of an investment advisor • Media habits

Annexure 2 ANALYSIS TOOLS USING EXCEL Excel can be used for several types of analysis: • Creating pivot-tables (cross tabs) • Graphs • Calculations • Descriptive analysis • Statistical analysis • Logical analysis • Database analysis Some applications are discussed below. Pivot Tables The following steps can be used to develop pivot tables using Excel: • To create a pivot table, first highlight any cell in the data, then click on the menu Data—Pivot Table. • Excel automatically selects the entire data sheet in which the highlighted cell is present. • The next dialog box asks whether to put the pivot table on the existing worksheet or on a new sheet. • Select “New Worksheet” then click the layout button. • Then click-and-drag the variable icons to define the format of the pivot table (row variable, column variable and the data variable). • Selection of the function to perform (count, sum, average, etc.) • Click OK and Finish. Descriptive SUBTOTAL function can be used to calculate different functions in a list or database. The basic operation is as follows: • SUBTOTAL(function_num, ref1, ref2) For e.g. SUBTOTAL(9,C2:C10) • SUBTOTAL function number (as shown below) can be changed to calculate different functions.

238

Marketing Research: A User’s Perspective Function-num

Function

1

Average

2

Count

3

Count A

4

Max

5

Min

6

Product

7

Stand dev

8

S dev vp

9

Sum

10

Variance

11

Variancep

• =FORECAST(28,O2:O6,P2:P6) – Calculates, or predicts, a future value by using existing values. • =CHITEST(O2:O26,P2:P26) – Calculates the test for independence • =CORREL(O2:O6,P2:P6) – Calculates correlation • =TTEST(C2:C20,P2:P20,2,1) – to determine whether two samples are likely to have come from the populations that have the same mean • =ZTEST(C2:C11,4) – ZTEST returns the probability that the sample is from the population Database Excel can also be used for analyses of a database. Some applications and examples for a mobile service provider are given in the table. Formula

Description (Result)

=DCOUNT (A1:I501,“balance”,A504:J505)

This function counts records of a specific location. For example, it can be used to identify the number of connections requiring recharge between two data periods.

=DAVERAGE (A1:I501,“balance”,A504:J505)

This function averages records of a specific location. For example, this can be used to identify the average usage of the segment.

=DSUM (A1:I501,“balance”,A504:J505)

This function adds records of a specific location. For example, this can be used to calculate the total usage of a segment during two data periods.

Annexure 3 PRE-PROCESSING OF TRIAD DATA Suppose there are four brands (stimuli A, B, C, D), the triads and their respective pairs would be as follows: Triads

Pairs

ABC

AB

BC

AC

ABD

AB

BD

AD

ACD

AC

AD

CD

BCD

BC

BD

CD

Unlike in the case of paired ranking or conditional ranking, only one consolidated dissimilarity matrix can be developed for triads. Process of developing the dissimilarity matrix starts by calculating (Step 1) the proportion of respondents who perceive a pair of brands more similar as compared to the another pair of brands (e.g. Perception of similarity/ dissimilarity of Brand A and Brand B as compared to Brand A, B and C). A generalized table of step 1 is given below. Step 1 Create comparison table for each stimulus. as compared to (in percent) Stimulus A similar to…

A

B

C

D

A

-

-

-

-

B

-

-

PBC*

PBD

C

-

PCB

-

PCD

D

-

PDB

PDC

-

* Stimulus A judged closer to B than C by respondents (in percent)

The subsequent steps in developing the dissimilarity matrix are first listed and then a hypothetical example is used to show the calculation. Step 2 Convert into Z values (using normal tables) and total the column.

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Marketing Research: A User’s Perspective

Step 3 The column average of tables of step 2 are entered as column elements for respective brands in another table. Step 4 Average the values in the row. Step 5 Develop the raw dissimilarity matrix by adding the row average of a stimulus to each entry in the column of the respective stimulus. If the responses are 100% consistent then this matrix would give a dissimilarity matrix. However, there could be some inconsistencies in the responses so the corresponding cells of the matrix are averaged to give the dissimilarity matrix. A Hypothetical Example Step 1 as compared to (in percent) Stimulus A similar to…

A

B

C

D

A

-

-

-

-

B

-

-

0.75

0.6

C

-

0.25

-

0.4

D

-

0.4

0.6

-

as compared to (in percent) Stimulus B similar to…

A

B

C

D

A

-

-

0.65

0.2

B

-

-

-

-

C

0.35

-

-

0.15

D

0.8

-

0.85

-

as compared to (in percent) Stimulus C similar to…

A

B

C

D

A

-

0.35

-

0.45

B

0.65

-

-

0.6

C

-

-

-

-

D

0.55

0.4

-

-

Pre-processing of Triad Data

241

as compared to (in percent) Stimulus D similar to…

A

B

C

D

A

-

0.2

0.45

-

B

0.8

-

0.75

-

C

0.55

0.25

-

-

D

-

-

-

-

Step 2 Z scores along with column totals and averages. as compared to (in percent) Stimulus A similar to…

A

B

C

D

A

-

-

-

-

B

-

-

0.675

0.255

C

-

-1.035

-

-0.255

D

-

0.255

-0.255

-

Total

-0.78

0.42

-.255

Average

-0.26

0.14

-0.09

as compared to (in percent) Stimulus B similar to…

A

B

C

D

A

-

-

0.385

-0.845

B

-

-

-

-

C

-0.385

-

-

1.035

-

D

0.845

-1.035

-

Total

0.46

-0.65

0.19

Average

0.15

-0.22

0.06

as compared to (in percent) Stimulus C similar to…

A

B

C

D

A

-

-0.385

-

-0.125

B

0.385

-

-

0.255

C

-

-

-

-

D

0.125

-0.255

-

-

Total

0.51

-0.64

0.13

Average

0.17

-0.21

0.04

Marketing Research: A User’s Perspective

242

as compared to (in percent) Stimulus D similar to…

A

B

C

D

A

-

-0.845

-0.125

-

B

0.845

-

0.675

-

C

0.125

-0.675

-

-

-

-

-

Total

D

0.97

-1.52

0.55

Average

0.32

-0.51

0.18

Steps 3 and 4 Stimulus averages entered in their respective columns. A

B

C

D

Average

A

0.00

0.15

0.17

0.32

0.16

B

-0.26

0.00

-0.21

-0.51

-0.245

C

0.14

-0.22

0.00

0.18

0.025

D

-0.09

0.06

0.04

0.00

0.0025

Step 5 Raw dissimilarity matrix  

A

B

C

D

A

0

-0.095

0.195

0.3225

B

-0.1*

0

-0.185

-0.5075

C

0.3**

-0.465

0

0.1825

D

0.07

-0.185

0.065

0

* Row average 0.16 added to column of respective stimulus in A column (in this case -0.26). The values 0.16 (row average) and -0.26 (column value of stimulus A) are from Step 3 & 4. ** Similarly, 0.16 added to 0.14.

Dissimilarity Matrix  

A

B

C

D

A

0

B

-0.0525*

C

0.2475**

-0.325

0

D

0.19625

-0.34625

0.12375

0 0

* Average -0.01 and – 0.095. The values of corresponding cells from Step 5. ** Average of 0.3 and 0.195

Since it is a hypothetical example, the variation is high.

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Name Index Adityapur, 23 Ahmed, Zafar U, 3 Ali M M R, 90 Allen Solly, 156, 158 Ambassador, 63, 66 Ambler, T, 20 American Marketing Association, 4 Aramusk, 164, 200–203 Arias, J.T.G, 182 Armstrong, Jon Scott, 5 Arnott, D, 124 Asia, 3 Atkin, C.K, 91 Australia, 88 B2Binternational.com, 209 Bailey G, 182 Beckman, Suzanne C, 62 Bethan M, 87 Bettis, R.A, 3 Biernacki, P., 87 Birn, R., 72 BOC gases, 22 Boddy, Clive R, 57 Bowen, John T, 124 Bowman, E H, 3 Bradburn N M, 72 Brindley Clare, 3, 7 Burns A C , 5 Burns N, 41, 43 Bush R F, 5 Business.com, 32 Businessmapofindia.com, 209 Cable TV, 150, 153, 155 Camay, 164, 200–202 Carlson J E, 135 Carmone Jr., Frank J, 168, 200, 201 Carpenter, Jason M., 18 Cattell, R B, 139 Center for Industrial and Economic Research, 63 Center for Media Science, 211 Chang, J F, 57

Chapman R G, 14, 17, 20, 29, 32 Cheng, Julian Ming-Sung, 90 Christopher, Martin, 199 Christy, Richard, 68 Churchill, G.A. Jr., 199 Cibotti Elaine, 63 Cinthol, 164, 200–202 Clancy K, 6 Color Plus, 156, 158 Comrey, Andrew L., 136 Crane A, 44 Cui, W W, 71 Daewoo, 64 Dant Rajiv, 63 Darling J R, 124 Daves Rachel, 3, 5, 6, 44 Davidson M L, 152 Day G, 125 DD Metro, 150, 153, 155 Dettol, 164, 200–202 Dhar Subrata, 89 Diamond J, 87 Dielman T E, 142 Donoghue S, 57 Dove, 164, 200–202 Drake, Paul R, 87 Dunning J H, 21, 22 Eden, US, 90 Elliot R, 5 Everitt B, 200 Evita, 164, 200–202 Excalibur, 156, 158 Faculty Class NSCU, 176 FAO, 4, 13, 96 Ferguson E, 151, 152 Fiat Uno, 64, 162, 166, 170–175 Ford N M, 33 Ford, 64 Fowler F J Jr, 72 Fram, Eugene, 63 Frank R E, 124

256

Name Index

Freimuth V, 91 Freud S, 57

Krieg P, 6 Kumar R, 87

Gable S L, 44 General Motors, 63, 64 Gerhardy, H, 183 Ghose Sanjoy, 124 Gibson L, 6 Gil, José M, 183 Goller Susanne, 124 Goodman L A, 87 Gorsuch R L, 142 Government of India, 63, 89 Green Book, 208, 210 Green P E, 3, 44 Grieg Ian D, 6 Grove S K, 41, 43

Lafarge cement, 22 Lance Leuthesser, 124 Langer, Roy, 62 Lawal B O, 90 Lea Worsley, 100 Lee, Howard B, 136 Lesancy, 164, 200–202 Levy D S, 182 Lifebuoy, 164, 200–202 Liril, 164, 200–202 Lockhart D C, 72 Lonial, Subhash C, 149 Louise Philippe, 156, 158 Lux, 164, 200–202

Hair J F, 136, 142, 184, 199 Haire M, 57 Halepete Jaya, 22 Hall, W.K, 3 Hamam, 164, 200–202 Hindustan Motors, 64 Hobbs J E, 182 Hollander Sharon, 63, 64 Honda, 64 Hooley, Graham, 97, 124 Hornby, Susan, 103 Hyundai Santro, 63, 64, 166, 170–175

Mahajan Vijay, 201 Manaktola, Kamal, 87 Margo, 164, 200–202 Maru, Karen, 87 Maruti, 64 Matiz, 174, 175 McLeod S, 46 Mercedes Sánchez, 183 Mercedes, 63, 64 Michael Hussey, 97, 124 Michael Wood, 68 Milena Z R, 53, 54 Miller Cyndee, 63 Mitchell Vincent Wayne, 3, 5, 6, 44, 138 Moore, Marguerite, 18 Morris Leigh, 125 Mysore Sandal, 164, 200–202

Iacobucci, D, 199 International Household Survey Network, 92 Jamshedpur, 17, 22–24 Janes Joseph, 68, 69, 75, 78 Jauhari Vinnie, 87 Jibowo A A, 90 Jobber D, 5 Johnson James C, 5 Johnson T J, 71 Julian Bond, 125 Kamakura W A, 165 Kara A, 182 Kaye B.K, 71 Ketchen, D, 200 Kinnear T C, 57 Kipling Rudyard, 29 Klecka W R, 165 Kohli, Chiranjeev S, 124 Kotler P, 123

NCAER, 89 Ness, M.R., 183 Nigeria, 90 Oliver.efos.hr, 173 Olu Olat Lawal, 89 Oppenhiem A N, 44 Owens Linda, 69–71 Palmolive, 164, 200–202 Park Avenue, 156, 158, 164, 200–203 Paswan, Audhesh, 63 Patrick J Robinson, 149 Patton M, 93 Payne Stanley Le Baron, 72 Pears, 164, 200–202

Name Index Peter England, 156, 158 Piercy Nigel, 3, 6, 7, 41 Piirto Rebecca, 63 Pingali Venugopal, 1, 2, 33, 48, 49 Pinto Ashwin, 209 Praxair, 22 Premarajan, R K, 56, 63 Premier, 63, 64 Prince R A, 87 Punj G, 199, 200 Quinn Lee, 124 Rao N Bhaskara, 211 Rexona, 164, 200–203 Ries Al , 124 Rindfleish Jennifer, 88 Ritchie Bob, 3, 7 Russo J R, 72 Sahara, 150, 153, 155 Sanchez-Ubeda E F, 18 Santro, 162 Sarkar Shiladitya, 209 Sawtoothsoftware.com, 184 Schneider K C, 5 Scholl Norbert, 55 Schwarz Shalom H, 77 Scribd.com, 30 Seale C, 43 Sekaran U, 91 Sekhar K M, 124 Shook C, 200 Snow White, 156, 158 Solomon David J, 71 Sony, 150, 153, 155 Star Plus, 150, 153, 155 Stewart D W, 199, 200 Strubler David, 30 Sudman S, 72 Synodinos Nicoloas E, 68, 72, 74, 76–78

257

Taiichi Ohno, , 30 Tata Motors, 22, 64 Tata Power, 22 Tata Steel, 22 Taylor Eunice, 88 Taylor J R, 57 TCS Smart Manager, 25 Telcon, 22 Thakur L S, 165 Thomas, Tsu Wee Tan, 3 Time Management Guide, 3, 18 Timken, 22 Tinplate, 22 Tobias S , 135 Toombs K , 182 Tourangeau R, 72 Trout Jack, 124 Turner, Lisa, 2 U K, 124 UCLA Academic Technology Services, 174 Uoguelph.ca, 50 US Department of Commerce, 63 Van Auken, Stuart, 149 Van Heusen, 156, 158 Vinten Gerald, 43 Waldorf, D, 87 Walton Douglas N, 76 Wedel M, 165 Wind, Yoram, 149 Witt Stephen F, 6 Witt, Christine A, 6 Yarmohammadian M H, 90 Zee TV, 150, 153, 155 Zen, 162

Subject Index 5W and 1H, 29, 34 Action (AIDA), 21 Additive model, 183 AIDA, 20 Algorithm, 200, 203 Alpha error, 101 ANOVA, 102 Attitudes, 47, 131 Attribute based data, 162 Automobile cluster, 22 Average linkage, 200 Awareness (AIDA), 20, 77 Bar graph, 97 Bartlett’s test of sphericity, 135 Behaviour, 44, 45, 47, 51, 52, 75 Behavioural framework, 29, 31, 34 Beliefs, 57 Below level of verbalization, 44, 46, 47, 50, 51, 62 Beta error, 101 Biased response, 51 Bi-polar scale, 74 Bi-serial correlation, 104 Boredom, 72, 75, 79 Brand decision, 31 Brand perception, 125 Business School ranking, 25 Canonical correlation, 169, 174 Canonical Discriminant function, 169, 178 Case studies, 53, 62 Cati, 209 Causal research, 49 Censoring mechanism, 63 Centroid method, 200 Channel members, 48, 49 Chi square, 102, 168 Cluster analysis, 9, 97, 124, 126, 165, 203 Communalities, 133, 137, 139, 140 Complete linkage, 200 Compulsive behaviour, 56 Concept cards, 182, 184, 187, 189, 193

Conceptualization, 8, 16, 20–22, 25, 26, 29, 219 Conditional probability, 172 Conditional ranking, 155–157 Conditioning, 60 Confirmatory factor analysis, 133 Conjoint analysis, 9, 97, 126, 182, 191 Conscious state, 46, 50, 57, 58 Consumer mindset, 63 Consumer perceptions, 124, 150 Content analysis, 53, 62, 64, 214 Convenience sampling, 87, 92, 93 Correlation matrix, 135, 136, 170 Cross tabulation, 97, 100 Cumulative variance, 137, 139 Cut off value (Discriminant analysis), 166 Data analysis, 96, 103, 119, 127, 210 Data formatting, 136 Debriefing, 60 Decision area, 14, 210, 218 Decision maker, 3, 4, 6, 7, 9, 13–15, 17, 18, 28, 31, 43, 72, 211 Decision making, 1–3, 9, 210, 211 Decision models, 3 Decision problem, 13, 14, 16, 18, 22, 112, 193 Decision process, 7 Decision tree, 8, 13, 18, 19, 22, 29 Defense mechanism, 57 Demographic segmentation, 123 Dendogram, 201 Dependence method, 100, 123 Derived variables, 131 Descriptive analysis (technique), 57, 59, 96, 97 Desire (AIDA), 21 Discriminant analysis, 9, 97, 124, 126, 165, 203 Discriminant function, 171, 172, 173, 177 Discriminant score, 166, 169 Discriminating power, 167, 168, 170 Dissimilarity matrix, 150, 155–157, 160–162

Subject Index Distan, 162, 163 Dummy questions, 58 Dummy test, 16, 17, 22 Eigen value, 133, 136–139, 141, 173, 174 Enter method, 167, 170, 176 Environmental consciousness, 18, 20 Euclidean distance, 153 Expert panel sample, 86, 87, 93 Exploratory factor analysis, 133 Exploratory research, 48, 135 Exploratory segmentation, 126 Exploratory studies (survey), 62, 86, 87, 118 Expressive technique, 57, 59, 60 External validity, 86 Eye tracker, 209 Face to face surveys, 69, 70, 75 Factor analysis, 8, 97, 124, 126, 131–133, 135, 144, 149, 153, 154, 162, 163, 167, 186 Factor loading, 132, 133, 142 Factor matrix, 139, 146 Factor naming, 147 Factorial design, 187 Focus group discussion, 50, 53, 54, 56, 62, 105, 134, 166, 186 Forecasting methods, 6 Frequency polygons, 97 Full concept method, 183 Gaining insights, 48, 50, 51, 53, 62, 93 Generalizability, 53, 78, 86, 101 Global studies, 71 Goodness of fit, 150–152, 163, 168, 181 Graphical representation of rotation, 141 Graphical representation, 97, 124 Group centroid, 171, 175, 178, 201 Group think, 56 Grouping variable, 167 Hand held terminals, 209 Health belief model, 219 Heterogeneous group, 55 Heuristics, 150 Hierarchical clustering, 200–203 Hierarchy of effects, 20 Histogram, 97 Hold out cards, 189, 190 Homogeneous group/ sample, 55, 56, 85, 93, 125 Hour glass method (FGD), 54–56 62 Hypothesis, 28, 32, 33

259

In depth studies/ interviews, 51, 53, 56, 86 Indian Auto industry, 63 Inferential analysis, 96, 101 Information processing, 123, 125 In-house research department, 209 Intangible attributes 184 Interaction effect, 182, 185 Interaction method, 8, 13, 16, 22, 23 Interdependence analysis/ methods, 8, 100, 104, 123, 126 Interest (AIDA), 21 Internal validity, 86 Interval scale, 73, 74, 154 Interviewee, 6 Interviewer, 56 Introductory statement, 77 Intuition, 2, 3 Inverted funnel approach (FGD), 54, 55, 62 Iterative process, 152 Judgmental sampling, 87, 93 Kaiser Normalization, 143 k-means, 201 Knowledge, 47, 52, 77 Latin square design, 187, 188 Leading questions, 76 Levels of consciousness, 46 Liberalization, 63 Loaded questions, 76 Logic of algorithm, 150 Logic of rotation, 141 Mahalanobis function, 171 Mail surveys, 70 Market segmentation, 124 Market structure, 124 Marketing activities, 56 Marketing decision, 8, 41, 123, 126, 131, 143 Marketing decisions, 6 Marketing Environment, 1, 2, 14 Marketing Information system,1, 3, 9 Marketing mix, 101, 123, 125, 126, 131, 143, 144, 149, 173 Marketing Planning, 1, 2 Marketing stimuli, 56, 124 Marketing Strategies, 2, 5, 123 MDS algorithms, 149 Mean squared error, 171 Mean, 99

260

Subject Index

Measurement error, 71 Median, 99 Methodology, 51, 63, 104, 113, 210 Mobile surveys, 208 Mode, 99 Moderator, 54, 55, 62 Motive, 44, 47, 93 MR agencies, 209 MR industry in India, 209 MR scenario, 208 Multidimensional scaling, 9, 124, 126, 149 Multi-level sampling, 92 Multivariate analysis/ techniques, 8, 100, 103, 104, 123–125 Naming of dimensions, 154 Naming of factors, 143 National Readership survey, 89 National Sample survey, 89, , 92 Needs, 1 Nominal data/ variable, 104, 165 Nominal scale, 73 Non coverage error, 71 Non parametric analysis, 100 Non probabilistic sample, 86, 91, 93 Non response bias, 71, 72, 89 Non sensitive information, 47, 48 Nonhierarchical clustering, 200–203 Normal distribution, 90, 99 Normalized data, 170 Number of dimensions, 152 Oblimin rotation, 142 Oblique rotation, 142 Observations, 51 Opinion leader, 56 Opinion, 46, 47 Ordinal data/ scale, 73, 104, 154 Orthogonal array (design), 187–189 Orthogonal rotation, 142 Paired ranking, 155, 156 Parametric methods, 100 Pearson’s correlation, 104 Perception analyser, 209 Perceptions, 47, 75, 127, 129, 131, 144, 149 Perceptual map, 124, 149–151, 153, 154, 156, 163, 173 Pie chart, 98 Pilot testing, 127 Plotting of brands, 153 Point–biserail correlation, 104

Poisson distribution, 91 Positioning, 101, 123, 124, 126, 134, 163 Power of the test, 101 Predictive method, purpose, studies, 48–51, 53, 68, 86, 88, 92, 93, 142, 165 Predictive power, 171 Predictive validity, 124 Preference based data, 149, 155 Preference, 46, 47 Prior probabilities, 167 Probabilistic sampling, 88, 92 Problem definition, 8, 13, 41, 43, 104, 117 Problem solving, 48, 49, 51, 86, 93 Product decision, 31 Product development, 193 Projection, 57 Projective technique, 50, 51, 53, 57, 58, 62–64 Promax rotation, 142 Protection motivation theory, 219 Purchase decision, 125 Purchasing power, 63 q factor, 133, 134 Qualitative attributes, 183, 192 Quantitative attributes, 192 Quartimax rotation, 142 Questionnaire, 28, 53, 68, 71, 72, 75, 78, 79, 107, 114, 135, 203, 210, 212, 215, 222 Quota sampling, 91, 93, 210 r factor, 133 Random sampling, 88, 91–93 Rank biserial correlation, 104 Ratio scale, 73, 165 Recruiters’ satisfaction, 25, 26 Regression analysis, 97, 165, 176 Research agency, 23 Research brief, 8, 13–16, 18, 23 Research design, 8, 41, 43, 48, 51, 53, 62, 93, 118, 127, 210, 221 Research objective, 13, 14, 16, 17, 28, 29, 35, 94, 112, 117, 127, 193, 203, 220 Research problem, 5, 8, 13–15, 22, 23, 210 Research process, 7–9, 28 Research question, 14, 28, 29, 31–33, 35, 44, 68, 113, 117, 193, 210, 220 Researcher’s perspective, 41, 48, 50, 51 Researchers’ subjectivity, 7 Residual matrix, 141 Respondent’s perspective, 41, 48, 50, 51 Response bias, 78

Subject Index Response inducing techniques, 5 Response rate, 69–71 Risk perception, 3, 6, 14, 71 Risk reduction, 4 Risk taking ability, 9 Rotated factor matrix, 141, 144, 147, 148 Rotation, 134, 141 Sample bias, 88 Sample mean, 101 Sample size, 136 Sample, 55, 56, 63, 68, 102, 210 Sampling design, plan, 94, 119, 127 Sampling error, 71, 87–89 Sampling frame, 88, 89, 91–93, 210 Sampling, 8, 62, 71, 85, 86, 113, 222 Scale development, 133, 143 Scree plot, 138, 153 Segmentation, 101, 123, 126, 192, 203 Self-administered questionnaire, 69 Self-censored, 58, 57 Sensitive information, 43, 47, 52, 53, 56, 93 Sentence completion, 58 Shelf life of MR, 211 Similarity matrix (see also dissimilarity matrix), 150 Simulated market studies, 51 Simulation, 192, 197 Single linkage, 200 Situational factors, 32 Six Sigma, 30 Snow ball (network sampling), 87, 88 Social media, 208, 209 Social norms, 44 Socially desirable information/ response, 43, 45, 47, 48, 50–53, 78, 87, 93 Source of error, 210 Spearman’s rank correlation, 104 Standard deviation, 99 Standardized canonical discriminant function, 174 Standardized equation, 176 State of being, 32, 47, 51, 203 State of mind, 31, 32, 41, 46, 47, 51, 52, 75, 78, 79, 199 Statistical bias, 88, 91 Statistical significance, 97, 101 Stepwise method, 168, 176 Stratified random sampling, 89, 90–92 Stress (response), 68, 71–73, 75, 77, 79, 156

261

Stress value, 152, 153, 163 Structural equation modeling, 124 Structure matrix, 170, 175–177 Subconscious state, 46, 47 Summary statistics, 97, 99 Surrogate information, 5, 32 Surrogate variable, 134, 186 Survey, 8, 41, 50, 53, 68, 79, 209 Systematic sampling, 90, 91, 93 Target (group), 68, 74, 85, 87, 88 Targeting, 124 Telephone interviews, 70 Test statistic, 135 Testing of hypothesis, 101, 105 Thematic Apperception Test, 58–60, 211 Theory building, 133 Tolerance level, 167 Tolerance test, 170 Toyota’s scientific approach, 30 Trends of research, 208 Triads, 155, 161 t-test, 102 Two factor at a time trade off method, 183 Type I error (see also alpha error), 96, 101, 105 Type II error (see also beta error), 96, 101, 105 Types of error, 101 Unaware information, 52 Unbiased responses, 43, 44, 48, 79, 210 Unconscious state, 46, 47, 50, 56 Uninformed response, 5 Unipolar scale, 74 Unstandardized coefficient, 165, 170 Unstandardized equation, 176 Utility calculations, 190 Utility values, 182, 183, 187, 189, 191, 192 Valid information/ responses, 8, 77, 43 Validity, 7, 41, 44, 61, 210, 211 Varimax rotation, 142–143 Ward’s method, 200 Web surveys, 69–71 Wilks’ Lambda, 168, 169, 174 Word association, 58 Wording of questions, 76 Z test, 103, 102

Author’s Profile Pingali Venugopal has teaching, industry, consulting and research experience in the Marketing function in the Indian context for over 34 years. Venugopal has completed his Postgraduate Programme and Doctorate from the Indian Institute of Management, Ahmedabad. Currently he is the Associate Director, XLRI Center for Global Management and Responsible Leadership. He has been a marketing faculty at XLRI, Jamshedpur since 1994 and the Dean of the institute from 2004 to 2010. He has also been a visiting faculty to leading institutes in India including the Indian Institute of Management in Ahmedabad, Kolkata, Lucknow, Ranchi and Indore, and international institutes including American University of Armenia and Educatis University Switzerland. He has taught courses like Marketing Management, Sales and Distribution Management and Marketing Research at these institutes. Prior to joining academics, Venugopal worked in the Marketing Department of Coromandel Fertilisers, and Nagarjuna Fertilisers and Chemicals Ltd. for a period of 10 years. While at Coromandel, he was also involved in the launch of their cement brand. Professor Venugopal’s research and publications cover different marketing topics and he has authored seven books covering areas of Marketing and Indian Economy. His books, Sales and Distribution Management: An Indian Perspective and Marketing Management: A Decision Making Approach, have been adopted in several business schools including the Indian Institute of Management. Dr Venugopal is a member of the Board of Tata Metaliks; Advisor to Kerzon Consultants and a member of the Academic Committee of National Institute of Technology, Jamshedpur, National Institute of Agricultural Extension Management, Hyderabad and GITAM University, Vishakapatnam. He has also been involved as a consultant with several projects for both the profit and nonprofit sectors. On the invitation of Government of India he prepared the report State of Indian Farmer—A Millennium Study wherein he evaluated the impact of the policy measures on the Indian farmers from Independence to 2000. After the formation of Jharkhand state, he was part of the steering committee of the Jharkhand Development Report prepared for the Planning Commission. Annually he conducts Management Development Programmes on Effective Marketing and Professional Sales Management. In addition, he conducted customised training programmes for companies including the Tata Group, Essar Group, Hulas Steel, Piramal, Videocon and Timken. He has also been inducted as an expert for programmes done by some consulting companies.