Customer Relations [1 ed.] 9781617613692, 9781617612107

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Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

BUSINESS ISSUES, COMPETITION AND ENTREPRENEURSHIP

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

CUSTOMER RELATIONS

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Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

BUSINESS ISSUES, COMPETITION AND ENTREPRENEURSHIP

CUSTOMER RELATIONS

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

VICTORIA J. FARKAS EDITOR

Nova Science Publishers, Inc. New York

Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Customer relations / editor, Victoria J. Farkas. p. cm. Includes index. ISBN  (H%RRN) 1. Customer relations. I. Farkas, Victoria J. HF5415.5.C835 2010 658.8'12--dc22 2010027638

Published by Nova Science Publishers, Inc. † New York

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CONTENTS Preface

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Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

vii Alan P. Fiske’s Relational Models Framework: Applications to Customers’ Relationships with Service Marketers Velitchka D. Kaltcheva, Robert D. Winsor and Anthony Patino

1

Measuring Corporate CRM Strategy: Its Model, Methodology and Application Hyung-Su Kim

37

Inter-Organizational Social Capital As Relationship Investments: An Empirical Investigation of the Effects on Customers’ Relationship Satisfaction Mariachiara Colucci and Manuela Presutti Customer Value Analysis: A Two-Stage Data Mining Approach Ching-Tzu Tsai, Chih-Fong Tsai and Chia-Sheng Hung Customer Relations and Loyalty-Based Segmentation: A B2B Approach in the Tourism Industry Irene Gil-Saura, María-Eugenia Ruiz-Molina and Beatriz Moliner-Velázquez

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95

115

vi Chapter 6

Chapter 7

Contents Pros and Cons of Long-Term Customer Relationships Christina Öberg

129

Involvement as Market Creation - A New Way to Consider Customer Relations Johan Gaddefors and Alistair R Anderson

143

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Index

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PREFACE Customer relations is a broadly recognized, widely-implemented strategy for managing and nurturing a company’s interactions with clients and sales prospects. It involves using technology to organize, automate, and synchronize business processes—principally sales activities, but also those for marketing, customer service, and technical support. This book presents topical research data in the study of customer relations, including how consumers use Alan P. Fiske's relational models framework to construct their relationships with service organizations; measuring corporate Customer Relationship Management (CRM) strategy; and identifying the relational benefits influencing customer loyalty. Chapter 1- Alan P. Fiske’s (1991) Relational Models typology is the latest rigorously developed framework in the social sciences for conceptualizing social interactions. A relational model (class) is a set of schemata, rules, and scripts that people use to construct and construe their interactions with others. Fiske defines six relational models (classes): communal sharing (CS), equality matching (EM), market pricing (MP), authority ranking (AR), asocial (AS), and null. In this chapter, the authors discuss the implementation of Fiske’s Relational Models framework to customers’ relationships with for-profit service marketers. Customer relationship management is especially important for service firms because face-to-face interactions between consumers and company representatives are essential to many service industries. It is therefore imperative that customers’ perspectives on their relationships with service marketers are well understood. This chapter describes how consumers use the relational models in Fiske’s framework to construct their relationships with service organizations. The authors demonstrate that consumers implementing different relational models are likely to react differently to

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Victoria J. Farkas

success and failure encounters with the service marketer. For each relational model, the authors outline strategies that a service organization may use in order to establish the model in its interactions with consumers. The chapter concludes with a discussion of a customer’s use of multiple relational models for the same service marketer. Chapter 2- Customer Relationship Management (CRM) is not the term designating a sub-function of marketing or a part of the corporate information system itself anymore. Rather, it has been increasingly adopted as a core business strategy for continuous organic growth. Then what are the necessary conditions to recognize CRM not as an IT or business function but as a basis of business strategy? One of the key points is that, beyond just analyzing customer data on specific issues, managing customer profiles technically, or planning marketing initiatives like a loyalty program, companies should equip a systematic dashboard to monitor and control regularly, from antecedent to consequence, factors related to every business activity offered to their customers. That is why a systematic CRM performance measurement is important. In other words, CRM performance measurement does not consist in mere analyzing return on investment (ROI) in CRM; it is rather a barometer to align corporate strategies for managing customer relations. This chapter summarizes theories, models, measures, methodologies, and applications of a desirable corporate CRM performance measurement system. In more detail, this chapter addresses first the meanings of diagnosing CRM status in terms of tripartite business implications: assessing outcomes, evaluating CRM capabilities, and grasping opportunity domains. After illustrating theoretical and practical requirements of such a strategic measurement system, a CRM Scorecard is presented as an example of corporate CRM measurement systems which meet such demands. The sidebar included in this chapter describes the implementation process of CRM Scorecard developed through a series of industrial-academic cooperative stages. Next, the details of the four evaluative domains including organizational infrastructure, business process, customer, and organizational performance would be presented by exploring the structure of the CRM Scorecard. Each evaluative domain has its own sub-domains, evaluative factors, and specific measures (quantitative/qualitative and antecedent/consequent measures). In the latter part of this chapter, a practical methodology and notices for CRM diagnosis, and a real business application. Chapter 3- In marketing and strategy research the construct of relationship satisfaction has been considered one of the most important outcome of buyerseller relationships as an increased satisfaction of business partners entails high productivity facilitating the co-ordination of activities. Much of existing

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studies have suggested that more interest should be placed on integrating the satisfaction construct into the larger body of inter-organizational social capital theory. In fact buyers (i.e.,business customers) and sellers are not atomistic entities free to undertake any competitive action within their own resource constraints. Rather, they are embedded in a network of social relationships that can influence their competitive behaviour, according to the idea that social embedded structures can shape the strategic action in business markets. Though a consensus exists regarding the importance of interorganizational social networks for relationship satisfaction, there is no conclusive evidence on which social capital configurations are most beneficial to reinforce relationship satisfaction. By investigating seller–buyers relationships, the authors address the extent to which different dimensions of social capital – relational, structural and cognitive - can affect the customer’s perceived relationship satisfaction, in the business-to-business setting of the Italian retail apparel industry. The authors assume that both strong social ties (i.e., relational and cognitive dimensions) and weak social ties (i.e., structural dimension) are positively related to customers’ relationship satisfaction. As noted in the literature, in fact, the structure of relationships is not all that matters, yet the content of these relationships matters as well. The main empirical contributions of the authors work show, on the one hand, the positive impact of both structural and cognitive dimensions of interorganizational social capital and, on the other hand, a non significant impact of the relational dimension on relationship satisfaction. The authors findings help to address the need to detail and to test different approaches, that refer to inter-organizational satisfaction creation and to social capital, providing evidence of the importance of integrating such articulated constructs. The conceptual novelty of the authors approach resides in developing a framework where inter-organizational social capital is considered a proxy of relationship investments, with the aim to integrate network theory studies with a strategic analysis of a relationship marketing construct. While literature has highlighted the importance of relationship investments of any kind made by sellers on behalf of regular customers, the authors explicitly highlight that customers tend to be more committed and satisfied with sellers who actively make intangible investments toward them in terms of structural and cognitive efforts. Chapter 4- Customer relationship management (CRM) has long been regarded as an important problem to understand and measure the true value of customers. In particular, churn management is one major task of CRM to retain valuable customers since to retain valuable customers is much more

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important than to obtain new (but may not be valuable) customers. However, this leads to a research problem of effectively identifying valuable customers for churn management. As data mining techniques have been widely used in recent literature to discover useful information and/or knowledge from a huge amount of data, this paper considers a two-stage data mining approach to analyze the value of customers. Specifically, this paper takes an automobile parts company as an example, and the first stage uses association rules and decision trees to select representative variables from the chosen dataset respectively. Next, the second stage uses decision trees to develop the customer value analysis model based on the output produced by the first stage. The experimental results by comparing decision trees alone, association rules + decision trees, and decision trees + decision trees show that combining twostage of decision trees not only provides the highest rate of prediction accuracy (81.6%), but also reduces the original 26 variables to 5 representative values. On the other hand, combining association rules and decision trees provides the lowest Type I error, which means that this model has the lowest error rate of recognizing valuable customers into non-valuable customers. Chapter 5- Maintaining customers is increasingly difficult, since many service industries are moving from high personal contact to remote contact via the telephone and Internet. In the current context where the Internet is threatening the tourism service value chain, customer loyalty is more appreciated by service providers than ever. Companies are investing in customer relations in order to establish closer bonds with their buyers. Since customer loyalty has been related with value and this, in turn, with the benefits obtained by customers from their relationships with their suppliers, the present paper aims to identify the relational benefits most influencing customer loyalty in a B2B setting in order to shed light on the link between customer relations and loyalty. A CHAID algorithm is performed, resulting in five segments differing in their level of customer loyalty and their unequal perceptions of confidence and social benefits perceived from their relationships with their main providers. Therefore, the importance of relational benefits differs across customer segments. The design of customer relations policies for each customer segment may result in customer loyalty gains. Chapter 6- This paper deals with the pros and cons of long-term customer relationships. Effects of customer relationships on a long and short-term basis are discussed, with specific focus on how the supplier is affected by a customer relationship. In addition, the paper briefly describes the consequences for customers and other business partners. Three areas given specific attention: (i) revenues versus vulnerability of individual customers,

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(ii) word of mouth versus badwill, and (iii) ideas for development versus risks of lock-in effects. The paper shows that the effects of long-term customer relationships are not only positive. The downsides need to be weighted to the upsides of such relationships, and suppliers and customers need to consider how they act in and think about a relationship and what risks and benefits are associated with the relationship. Chapter 7- In this research project the authors examine an up-market Swedish furniture manufacturer to look at their marketing process. The authors found that they had an innovative approach which involved customers in developing and co-creating the market for their products. Accordingly, this paper describes and conceptualises the novel process in customer relations. The authors case exemplifies how the firm, in engaging with the customer, created the market and the business opportunities. The authors show how the opportunity itself is produced within the interplay of firm and customer. In this process firm and customer jointly established identity, a style of living, and a way of being and becoming. The nature of this coproduction of a market and business opportunity, and how it was constructed in this interplay, is the focus of the authors case. The case reveals how market and business opportunity formation is relationally and communally constituted. The authors make the market concept more relational and show how it is dependent on social interaction.

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Chapter 1

ALAN P. FISKE’S RELATIONAL MODELS FRAMEWORK: APPLICATIONS TO CUSTOMERS’ RELATIONSHIPS WITH SERVICE MARKETERS

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Velitchka D. Kaltcheva, Robert D. Winsor and Anthony Patino Loyola Marymount University, 1 LMU Drive, Los Angeles, CA 90045 USA

ABSTRACT Alan P. Fiske’s (1991) Relational Models typology is the latest rigorously developed framework in the social sciences for conceptualizing social interactions. A relational model (class) is a set of schemata, rules, and scripts that people use to construct and construe their interactions with others. Fiske defines six relational models (classes): communal sharing (CS), equality matching (EM), market pricing (MP), authority ranking (AR), asocial (AS), and null. In this chapter, we discuss the implementation of Fiske’s Relational Models framework to customers’ relationships with for-profit service marketers. Customer relationship management is especially important for service firms because 

tel.: (310) 338-5166; fax: (310) 338-3000; email: [email protected]

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Velitchka D. Kaltcheva, Robert D. Winsor and Anthony Patino face-to-face interactions between consumers and company representatives are essential to many service industries. It is therefore imperative that customers’ perspectives on their relationships with service marketers are well understood. This chapter describes how consumers use the relational models in Fiske’s framework to construct their relationships with service organizations. We demonstrate that consumers implementing different relational models are likely to react differently to success and failure encounters with the service marketer. For each relational model, we outline strategies that a service organization may use in order to establish the model in its interactions with consumers. The chapter concludes with a discussion of a customer’s use of multiple relational models for the same service marketer.

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I. INTRODUCTION Compared to consumers’ relationships with manufacturer brands, customers’ interactions with service marketers typically involve face-to-face encounters with representatives of the service organization. When a consumer interacts with a manufacturer’s brand, s/he typically engages in direct contact with inanimate branded items, but not with the manufacturer of those items (Fournier 1998). In contrast, when a consumer interacts with service brands, s/he usually engages in direct contact with the service organization through face-to-face encounters with its members, such as sales associates and other company representatives (Berry 1995). In fact, in most cases, the quality of the service is crafted through the interaction between the consumer and the company representative (Zeithaml, Berry, and Parasuraman 1988). As Bitner, Brown, and Meuter (2000) argue, the interaction between consumers and service marketers is the moment of truth where consumers and company representatives interact and the service is jointly produced. Because of this critical role that marketer-consumer interactions play in the creation of value in service industries, customer relationship management is especially important to service marketers (Berry and Parasuraman 1991). It is therefore imperative that customers’ perspectives on their interactions with service marketers are well understood. Marketer-consumer interactions in services are social interactions between one or more consumers, on the one hand, and the service organization, represented by one or more of its members, on the other hand (Berry 2002). Because of this true social nature of customers’ interactions with service marketers, we can invoke relational models developed in the social sciences to describe

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marketer-consumer interactions in services, thereby generating new insights that would be helpful to service firms in developing cost-effective customer relationship strategies. In this chapter, we apply Alan P. Fiske’s (1991) Relational Models typology to customers’ relationships with for-profit service organizations. Fiske’s Relational Models typology is eminently suitable for studying marketer-consumer interactions in services because it is widely adopted in the social sciences as a rigorously developed framework for conceptualizing social interactions (Berscheid 1994; Bond and Smith 1996; Goodenough 2003; Rusbult and Van Lange 2003). Fiske argues that people use six relational models (classes)—authority ranking (AR), communal sharing (CS), equality matching (EM), market pricing (MP), asocial (AS), and null—to structure their interactions with others. In this chapter, we discuss how consumers employ the relational models in their relationships with service marketers. In Sections II and III, we demonstrate that the null and AR models have limited relevance to customers’ interactions with for-profit service marketers. Section IV discusses the remaining models—communal sharing (CS), equality matching (EM), market pricing (MP), and asocial (AS)—which are widely implemented in marketerconsumer interactions in services. We describe how consumers use these models to construct their relationships with service organizations. We also demonstrate that consumers implementing different relational models for a marketer are likely to react differently to service encounters, which could be pleasant (service successes) or unpleasant (service failures). Finally, for each relational model, we outline strategies that a service organization may use in order to establish the model in its interactions with consumers. Section V discusses the use of multiple models for the same service marketer. A consumer may use different relational models for the same service firm on different service occasions (such as different visits to the same hotel or store) or in the various phases of an extended service encounter (such as a cruise or tour). We introduce the Personality-Relatedness and Reciprocity (PRR) framework (Kaltcheva and Parasuraman 2009), which defines two continuous relational dimensions representing Fiske’s models, as well as the virtually limitless number of scenarios in which the same consumer employs multiple models for the same marketer.

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II. FISKE’S RELATIONAL MODELS FRAMEWORK Alan P. Fiske’s (1991) Relational Models typology is the latest comprehensively developed framework in the social sciences for conceptualizing social interactions. The Relational Models typology “builds on and meshes with a number of other taxonomies of social relations” (Fiske 1992, p. 710). Each of those previously developed taxonomies was defined from a single methodological perspective in the context of one or two related domains of social life. Fiske compared a number of such domain-specific taxonomies originating in qualitatively different methodological perspectives (such as anthropology, psychology, sociology, and economics) and representing 15 domains of life as diverse as the transfer and distribution of resources, self-definition and motivation, moral ideology and judgment, aggression and conflict, and orientations to land and time. He observed that the different domain-specific taxonomies delineate the same relational models. This convergence across independent theories representing diverse social domains and employing different methodologies led Fiske to conclude that people in every culture use the same fundamental models to structure their interactions with others across all domains of social life. Moreover, because no additional models were defined in the extensive body of literature that he evaluated, Fiske also concluded that the identified fundamental models may compose an exhaustive set. In addition to its extensive theoretical foundations, Fiske’s Relational Models typology has been validated empirically in more than 20 social-cognition and ethnographic studies conducted in several different cultures (for reviews see Fiske 1992, Fiske and Haslam 1996). Fiske’s typology has been found to explain both explicit (accessible to conscious awareness) and implicit (inaccessible to conscious awareness) cognitive representations of social interactions, outperforming alternative frameworks (Fiske, Haslam, and Fiske 1991; Haslam 1994b; Haslam and Fiske 1992). Fiske defines the relational models as schemata, rules, and scripts that people use to construct and construe their interactions with others. People feel obliged to adhere to the relational models and anticipate that others will do the same. “Whatever the context and content, whatever the substance and surface form of the interaction, people’s primary frames of reference in social life are the same elementary relational models. These models are identifiable by the aspects of the interactions that people attend to and the attributes of persons that are meaningful. Certain relational features are meaningful (and others are irrelevant) for the participants’ conception of any given interaction, for their

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intentions, plans, and expectations about it, for their social motivations and emotions, and for their evaluative judgments about it” (Fiske 1992, p. 690). For example (and as described later), the CS relational model involves identification with the other participant(s) to the interaction. Therefore, any attributes that might either facilitate or inhibit identification would be essential for the implementation of this model, and the interacting parties would pay close attention to those attributes. Some consumers, for example, identify with the “green” values of sustainability and conservation endorsed by Patagonia and Starbucks. If these consumers were to discover that the two organizations engaged in practices contrary to their declared ideals, it would lead to a perceived violation of the norms of the relational model and a loss of customer loyalty. Of the six relational models (classes) that Fiske identified, null interactions fall outside the scope of this chapter. The construct of null interactions describes situations in which people “simply ignore others, giving their existence no attention at all” (Fiske 1992, p. 708; Fiske 1995, p. 321). “Most people on earth have a null relationship with most other humans most of the time, simply ignoring them” (Fiske 1992, p. 693). Because the focus of this chapter is on ongoing interactions between consumers and marketers, null interactions are not considered further. Next, we discuss the other five relational models. First we describe the AR model and examine its implementation in service marketer-consumer interactions.

III. THE AUTHORITY RANKING (AR) RELATIONAL MODEL The AR relational model can best be illustrated by the relations between a feudal lord and his fiefs. AR interactions are based on asymmetry among people, who are linearly ordered according to some hierarchical social dimension. The salient social fact in such relations is whether an individual is above or below the social status of any other given individual. High status individuals enjoy prestige, prerogatives, and privileges that their inferiors lack. More specifically, several characteristics define the AR relational model: 1) Preemptive appropriation of resources: High status individuals typically have priority with respect to the allocation of resources. When shared resources are distributed, for example, those occupying

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2)

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3)

4)

5)

a position of high rank, such as rulers and other authorities, can stake a claim to the most valuable items and obtain their share before anyone else. Consequently, on occasions of famine, crop failures, and other forms of scarcity, retainers may be left with an inadequate quantity of resources (or none at all). In bilateral transactions, when resources are exchanged between two individuals, rulers and other higher status people may simply appropriate any desired article or item. Responsibility to take care of retainers: In return for the right to preemptively appropriate valuable resources, authorities typically assume responsibility for the safety, security, and welfare of subordinates and retainers. Rulers are obliged to behave generously and honorably in protecting and supporting the less privileged. The aggregate effect of authorities’ right to preemptively appropriate resources, on the one hand, and their responsibility to take care of retainers and subordinates, on the other hand, often results in a bidirectional flow or redistribution of resources. Central authorities accumulate resources first and then reallocate some of those back to their retainers. Control over behavior: Typically, authorities and other high status individuals have some control over the actions of retainers and subordinates. As a rule, information is channeled from inferiors in the direction of higher-ups, while orders or instructions are handed down through a command chain, as in a military organization. People emulate and defer to leaders, often without questioning. Because people construe authority as carrying a moral responsibility, it is ordinarily sufficient that a leader orders or wills a course of action for it to be considered as right. People are inclined to accept a command as a morally just and valid reason for action. Therefore, challenging, or even questioning, the orders of those in command is typically condemned as inappropriate and disloyal. Status markers: Titles and objects are commonly employed in AR interactions as markers representing the rank or status of the individual displaying such insignia. Examples are the insignia of heads of state and religious leaders; the uniforms, badges, and insignia of military or police officers; and the robes that judges (and sometimes counsel) wear in court. Hierarchical inclusion: The AR relational model involves not only linear rank, but also hierarchical inclusion. The higher the rank or

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status that a leader holds, the greater the number of other people and objects he controls. The leader’s self also includes within itself all those people and objects. On the other hand, the sense of self for retainers and followers is largely derived from recognizing and accepting their place in the hierarchical order. How is the AR relational model implemented in the domain of customers’ relationships with service marketers? All characteristics of the AR model described above are observed in people’s interactions with the government as a service marketer. Government services involve predominantly protection and safety as well as some educational and medical assistance. Police and fire departments are the primary authorities that ensure the protection of citizens’ life and property. Governments may also establish an economic safety net through retirement and medical benefits. Public schools, universities, and libraries offer educational services. Governments fund those services through preemptive appropriation of citizens’ resources by such means as taxation. Laws and ordinances are implemented in order to control how the recipients of the services should behave. Police officers, for example, may issue orders and instructions that citizens are obligated to follow. Violations of laws and ordinances, and refusals to comply with orders or instructions, typically result in the citizen incurring a mandated penalty. In some cases, officers representing the state wear markers and insignia that distinguish their status. There is also hierarchical inclusion: The state encompasses its citizens, and the citizens view themselves as members of the state. Some interactions with for-profit service marketers also involve aspects of the AR relational model. Table 1 shows services in the for-profit sector in which some of the characteristics of the AR model are manifest in customers’ relationships with the marketer. In transportation and tourism services, for example, the captain and crew of boats and aircraft have responsibility to ensure the integrity of the vessel and the safety of its passengers. For this purpose, the members of the crew have a right to issue orders, and the passengers are legally obligated to comply with those orders. The law even empowers members of the crew to preemptively appropriate passengers’ effects, and use or dispose of those effects as needed for protecting the safety of the vessel and the passengers. Uniforms serve as markers distinguishing people in authority (the members of the crew) from passengers. Note that the authority of the captain and crew over customers—and therefore the implementation of the AR relational model—is government-mandated and largely limited to matters of safety.

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Table 1. Implementation of the Authority Ranking (AR) Relational Model in the For-Profit Service Sector Characteristics of the AR Relational Model

Privately-Provided Services Transportation/ Tourism

Medical

Legal

Financial

Customer interactions with…

Crew

Doctors, nurses

Legal advisors

Financial advisers

Preemptive appropriation Responsibility to care

Limited to safety matters Yes

No

No

No

Yes

Yes

Yes

Control over behavior

Commands and orders (typically limited to matters of safety). Customers are legally obligated to comply. Uniforms

Advice and recommendations. Clients may comply voluntarily, but there is no obligation to do so. It is appropriate that consumers exercise their own judgment and/or obtain a second opinion.

No

No

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Status markers

Hierarchical inclusion

Uniforms

No (typically business attire) No

No (typically business attire) No

Aspects of the AR relational model are observed in customers’ relationships with doctors and nurses (medical services), financial advisers (financial services), and legal counsel (legal services). Professionals in all three fields have responsibility for customers’ welfare. Typically, consumers are advised to follow a course of action that, in the expert’s opinion, would be beneficial to the consumer’s health or other interests. Clients, however, are not obligated to comply with the expert’s recommendations and have a right to exercise their own judgment (unless mandated by law) and/or seek another opinion. Status markers are common in the medical services, but their use is limited in the financial and legal services. Typically, the service marketer has no right to preemptive appropriation of the customer’s resources, and there is also no evidence of hierarchical inclusion. In summary, some features of the AR model are found in a limited number of for-profit services. In some services, marketers have responsibility for the

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customer’s safety, health, and/or other interests. However, marketers’ control over the customer’s behavior, the right to preemptive appropriation of the customer’s resources, and the use of status markers are significantly limited. Hierarchical inclusion of customers’ selves into the marketer’s identity is largely unobserved. Because the AR model is not manifest in for-profit services in its entirety, it would be difficult—or even impossible—for a service marketer to implement it as a relational strategy. We therefore do not discuss the AR model further. In the next section (Section IV), we describe the communal sharing (CS), equality matching (EM), market pricing (MP), and asocial (AS) relational models. We show that these four models are regularly employed in customers’ relationships with for-profit service marketers and therefore offer viable strategic opportunities to service firms. Before we turn our attention to the relational models however, we need to outline the structure of Section IV.

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4.0. Overview of Section IV Section IV includes four subsections, each dedicated to one relational model and its implementation in customers’ relationships with service marketers. For each model, we describe: 1) The relational model, 2) Customers’ responses to a completed service encounter (success or failure), and 3) Strategies that a service marketer may execute in order to cultivate the relational model for its interactions with consumers. Relational Model: Each subsection starts with a description of the relational model and its implementation in marketer-consumer interactions. We first describe the relational model because it largely determines customers’ expectations for upcoming service encounters with the marketer. Both the disconfirmation paradigm (Oliver 1997) and the service quality literature (Zeithaml, Parasuraman, and Berry 1991) suggest that consumers form expectations about upcoming service encounters and use those expectations as

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reference points to judge service performance. The relational models involve schemata, rules, and scripts that serve as a benchmark against which customers evaluate a service encounter and determine their responses to it. Customer Responses to Success/Failure: After describing the relational model, we discuss customers’ likely responses to a pleasant (service success) or unpleasant (service failure) encounter with the marketer. Identifying the implications of the relational model for consumers’ likely responses to a service encounter often presents the starting point of strategic customer relationship management. It is vital that service managers understand how the relational models in Fiske’s framework may impact customers’ behaviors. Such insight is essential for management’s ability to articulate and implement relational strategies that are likely to be effective in light of the firm’s overall strategic objectives, strengths, and weaknesses. More specifically, we examine three likely responses to service encounters: (1) repatronage intentions; (2) word-of-mouth communications, which can be either favorable (after a success encounter) or unfavorable (after a failure incident); and (3) feedback to the service firm. We focus on these three customer responses because of their significant impact on a firm’s sales and profitability (Palmatier et al. 2006). For example, customer retention is directly related to company profitability (Reichheld and Sasser 1990) because acquiring new customers typically engages more resources than retaining customers (Zeithaml, Berry, and Parasuraman 1996). Additionally, the profitability of a customer is likely to increase with the longevity of his or her relationship with the marketer (Zeithaml, Berry, and Parasuraman 1996). Word-of-mouth communications are likely to have exponential effects on sales and profitability that transcend those of customer retention (Goldenberg et al. 2007). Customers engaging in favorable or unfavorable word-of-mouth are likely to communicate their views of the marketer to multiple contacts, and those recipients may subsequently modify their patronage behavior in line with the recommendation (Goldenberg et al. 2007). More importantly, the preferred venue for word-of-mouth dissemination seems to have shifted from face-toface networks of limited scope to websites and instant messaging systems, through which virtually limitless numbers of readers can be influenced by other people’s justified or unjustified opinions (Ward and Ostrom 2006). Thus, there is the potential for a “chain reaction” effect in word-of-mouth communications to surpass the impact of an individual customer’s repatronage intentions. Finally, after a service encounter consumers may volunteer feedback to the service firm; they may complain in the wake of a failure incident (Fornell

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and Wernerfelt 1987), and offer suggestions for further improving the service after a success encounter (Bettencourt 1997). Fornell and Wernerfelt (1987) demonstrate that encouraging dissatisfied customers to complain is probably the most effective component to a defensive strategy. One reason for this is that complaining behavior tends to directly reduce customer defection, because problems can be resolved and customer relationships rehabilitated. Another reason is that complaints furnish valuable information to the firm that can be exploited in order to improve the service delivery process and reduce service failures for future customers. Consumers furnish information to the firm not only after service failures, but also after service successes. Bettencourt (1997) shows that delighted customers may volunteer suggestions for service improvement with the intention of helping the organization to enhance its service delivery process. Relational Strategy: Finally, we offer strategies that service marketers may implement to cultivate the relational models in their interactions with consumers. These strategies will be helpful for new service marketers for whom consumers do not have established relational models, as well as for marketers who wish to revise their customer relationship strategy. Marketers for whom consumers already hold a relational model, and who wish to continue implementing the same model, may also use the outlined strategies to nurture and strengthen their relationships with customers. Now that this overview of Section IV is complete, we begin the discussion of the relational models with the communal sharing (CS) model.

4.1. The Communal Sharing (CS) Relational Model The CS Relational Model: The interactions characteristic of families and religious communities illustrate the CS relational model. People implementing the CS model treat each other as identical (indistinguishable) on one or more characteristics, while ignoring distinct individual identities. The self is typically defined in terms of the individual’s membership within a social group. The interacting parties identify with each other and share a common identity. Identification usually finds expression in a desire for, or sense of, oneness, similarity, or association with the other (French and Raven 1959). When identifying with another, the individual acts as if some or all aspects of the other are his/ her own and may feel as if s/he were partially fused with the other. People psychologically appropriate three aspects of the other: (1) the

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other’s resources, (2) the other’s perspective, and (3) the other’s traits and personality characteristics (Aron et al. 1991). Because of the psychological appropriation of the other’s resources, under the CS relational model people are likely to consider resources as common. Resources are usually pooled into a common reserve and shared among group members. People contribute to the extent of their abilities to the resource pool, from which supplies are subsequently allocated to individual members depending on the needs of those members. Fiske calls this principle for managing and apportioning resources the distributive norm of equivalence. Experimental research demonstrates that the distributive norm of equivalence is implemented under the CS relational model (e.g., Clark and Mills 1979; Clark, Mills, and Powell 1986; Clark and Waddell 1985). For example, Clark (1984) found that people who hold the CS model refrain from tracking individual contributions into a collective task, and Lamm and Schwinger (1980, 1983) showed that under the CS model resources are allotted depending on need. Consumers use the CS model in their relationships with service organizations. For example, in addition to other sources of self-definition, people may constructs aspects of their self-concept by categorizing themselves as members of a social group, or in other words, by developing social identities (Bagozzi and Dholakia 2002; Tajfel and Turner 1985). Bhattacharya and Sen (2003) demonstrate that people may establish social identities by identifying with organizations. Company identification is an active, selective, and volitional act on the part of consumers the objective of which is the fulfillment of self-definitional needs (Bhattacharya and Sen 2003). An important vehicle for company identification (and implementation of the CS relational model) is customers’ participation in brand communities (Carlson, Suter, and Brown 2008; Muniz and O’Guinn 2001) because through brand communities consumers establish ties with the company and other customers (McAlexander, Schouten, and Koenig 2002; Schau, Muniz and Arnould 2009). In addition to identification, the CS relational model involves a shared use of resources, which in marketer-consumer interactions can take place in either direction (from marketers to consumers and from consumers to the marketer). For example, some service marketers may become part of a consumer’s social support system (Adelman, Ahuvia, and Goodwin 1994; Arnould and Price 1993; McAlexander, Schouten and Koenig 2002). A business, for example, can extend credit to members of the local community who are in need. On the other hand, loyal patrons of family-owned businesses have been known to pool resources in order to rescue these businesses in adversity.

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Next, we review research investigating how customers who hold the CS relational model for a service marketer are likely to react to service successes and failures. Specifically, we outline how the use of the CS model in the wake of a success encounter or failure incident may influence customers’ intentions to continue patronizing the service marketer, engage in favorable or unfavorable word-of-mouth, and offer feedback or complain to the marketer. Customer Responses to Success/Failure: Satisfying service encounters (service successes) often reinforce customers’ confidence in the firm and their expectations for the future, thereby encouraging patronage, strengthening customer retention, and giving rise to favorable publicity regarding the firm (Zeithaml, Berry, and Parasuraman 1996). On the other hand, a service failure incident generally leaves customers dissatisfied and disappointed, likely to defect and engage in uncomplimentary publicity (Singh 1990; Szymanski and Henard 2001; Zeithaml, Berry, and Parasuraman 1996). In order to shield themselves from further frustrations and distress, some consumers resolve to terminate their relationship with the seller after a failure. Additionally, people often caution family, friends, and co-workers against the firm in order to spare others from a similar disappointment. The relational model that customers hold for the service firm can amplify or mitigate the influence of service successes and failures on customers’ responses to the encounter. Consumers who hold the CS relational model and identify with a service marketer are more likely to hold a favorable view of the service organization—its culture and values (Kaltcheva and Parasuraman 2009). Kaltcheva, Winsor, and Parasuraman (2010a,b) argue that, because of their favorable opinion of the company, such consumers are more likely to believe that the service marketer consistently tries to ensure high quality for its customers. Consequently, CS customers are likely to be loyal to the firm and to serve as its advocates, promoting it to other consumers. Additionally, because resources are shared under the CS relational model, customers employing the model are likely to care about the firm’s success, and, therefore, volunteer suggestions and ideas that would benefit the firm. A service success will reinforce these attitudes and behaviors, and, as a result, CS customers will continue to be loyal to the organization, recommend it to others, and offer feedback and ideas that might benefit it. Nevertheless, because these consumers already engage in such behaviors, a success encounter is not likely to result in a marked change of behavior. In the wake of a service failure, customers employing the CS relational model will be less likely to expect that similar failures will recur. Therefore, as Kaltcheva, Winsor, and Parasuraman (2010b) argue, such consumers will be

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more likely to remain loyal after the failure incident and refrain from engaging in unfavorable word-of-mouth. Additionally, partners to a CS relationship generally do not track others’ inputs and outcomes, and refrain from comparing their own inputs and outcomes to those of others (Clark 1984). As such, consumers implementing the CS model in their interactions with a service marketer will be less likely to care if the marketer may be profiting excessively at customers’ expenses (Kaltcheva and Parasuraman 2009). Kaltcheva, Winsor, and Parasuraman (2010b) argue that this limited concern for comparing payoffs leads to a decreased motivation to seek reparation (by complaining to the firm) or retaliation (by engaging in unfavorable publicity). In summary, the CS relational model is likely to mitigate customers’ responses to a service failure incident, but it is not likely to have a significant intensification effect on responses to a service success. Next, we outline strategies that service organizations may use to initiate and nurture CS relationships with customers. CS Relational Strategies: How can a service marketer encourage customers to adopt the CS relational model? First, the organization must establish a viable brand personality with which consumers can identify (Aaker 1997; Beldona and Wysong 2007; Romaniuk 2008). Brand personality is defined as “the set of human characteristics associated with a brand” (Aaker 1997, p. 347). To be viable for the purpose of developing CS relationships with customers, a brand’s personality needs to be: 1) Desirable: There must be a sufficient number of consumers who wish to identify with the brand’s personality. In other words, the segment of likely brand enthusiasts should be large enough so that pursuing a (typically costly) CS customer relationship strategy would be operationally meaningful. If relatively few people feel enthusiastic about the brand’s personality, the service organization may not be able to secure sufficient benefits from its limited customer base in order to justify (or even recover) the expense of developing the brand personality. 2) Differentiated: No other company in the same or similar service industries should claim the same personality. For example, Harley Davidson advertises itself as espousing the values of freedom, patriotism, and machismo (Schouten and McAlexander 1995). If another motorcycle maker should declare the same or similar values, it would have adverse consequences for both competitors: It would substantially deplete their customer bases and elicit uncertainty

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among consumers regarding the authenticity of the claimed personality. 3) Authentic: Consumers must believe that the company genuinely embraces the values and culture inherent in the adopted personality. For example, the Blue Tomato Company, an Austrian retailer of snowboard equipment, communicates its commitment to the snowboarder lifestyle by vigorously publicizing that its employees are enthusiastic boarders (Foscht, Swoboda, and Morschett 2006). “The Blue Tomato Crew lives [the snowboarder] lifestyle and passes the spirit of this lifestyle on to its customers” (Foscht, Swoboda, and Morschett 2006, p. 560). The Italian motorcycle manufacturer Ducati offers another example of how a firm may strengthen the authenticity of its personality. In 1997, the firm successfully reinforced its young and sporty personality with the ad campaign “Ducati People,” which featured photographs of its employees on Ducati bikes. In the words of Federico Minoli, then CEO of Ducati: “The people who make Ducati are the best ambassadors of the brand” (Turpin and Chung 2004, p. 5). After identifying an authentic, desirable, and differentiated brand personality, the service organization must implement strategies to construct and communicate that personality. Bhattacharya and Sen (2003) outline a number of avenues, ranging from more to less controllable, that organizations may use to craft a desired personality. The marketing mix implemented by the firm is probably the primary vehicle for designing and conveying the values and culture of the company. Firms typically exercise a high level of control over their marketing mix, which makes it an especially effective means of identity construction and dissemination. Other controllable means of communication involve formal self-presentation messages, such as ads and official documents (company reports, press releases, etc.). Companysponsored forums and other initiatives offer another vehicle for shaping the organization’s image. Relational partners such as employees, channel members, and customers can also disseminate favorable or unfavorable publicity about the firm. The popularity of web-based and instant messaging networks of virtually unlimited reach has made customer communications an especially powerful source of identity formation. The organization, however, typically has more control over its employees and channel members than it has over consumers. Finally, the representation of the organization in the media

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(over which the firm has little control) can have a significant impact on how the company is evaluated by the public. An effective strategy for strengthening a brand’s personality and its CS relationships with customers is the cultivation of brand communities (Carlson, Suter, and Brown 2008; McAlexander, Schouten, and Koenig 2002; Muniz and O’Guinn 2001). For example, In-N-Out, a chain of fast food hamburger restaurants located primarily in California, features a secret menu on their website knowledgeable to loyal patrons. These non-advertised items create an implicit bond among loyal patrons, promoting identification with other brand users, and ultimately, with the brand (French and Raven 1959). When developing a brand community, it is essential that the service organization maintain its central role as the axis of the community. One effective strategy to accomplish this would be organizing events at which customers can interact with company representatives (Algesheimer, Dholakia, and Herrmann 2005; McAlexander, Kim, and Roberts 2003; Muniz and O’Guinn 2001; Schouten, McAlexander, and Koenig 2007). By encouraging such interactions, the marketer can communicate to consumers that behind “the normal corporate communications are real people who understand and care about their customers” (McAlexander, Schouten, and Koenig 2002, p. 43). For example, Pinkberry, a yogurt company popular in Los Angeles, maintains a brand community on their website with their Pinkberry Groupie Corner, where loyal patrons can connect with fellow “groupies” and share stories. Pinkberry places itself in the center of the discourse by organizing polls in which patrons can vote on new flavors and toppings. Home Depot and Williams Sonoma maintain their status as the axis of their respective brand communities by organizing classes where customers can interact with company representatives.

4.2. The Equality Matching (EM) Relational Model The EM Relational Model: The exchanging of gifts, dinner invitations, or Christmas cards exemplifies the EM relational model. Transactions are carried out in kind and at an appropriate delay. As a rule, the exchanged resources are similar in kind, and in some cultures, and on some occasions, people even return to the donor precisely the same item received from him or her in the first place. What resources can be exchanged for what other resources typically varies from culture to culture. For example, depending on the culture,

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a home-cooked dinner may or may not be an appropriate response to an invitation to a high-end restaurant. The fact that culturally equivalent resources are exchanged led Fiske to conclude that acquiring the resource is not the objective of the interaction. Instead, the exchanged resources serve as tokens of equality in status, conveying that each relational party is acknowledged as a peer in social station and influence. Essentially, an EM relationship represents a means for expressing respect. The insistence on keeping the exchange appropriately delayed reinforces the message: The delay plays the essential role of conveying the sincerity and spontaneity of the offer. It is also extremely offensive to repay in cash for a friendly gift or invitation, because by virtue of this action, the repaying individual essentially declares that s/he refuses to communicate respect. Because the exchanged resources embody the paying of respect to the relational partner, each party meticulously monitors the value of those resources. People would become offended and distressed if offered an article of lesser value than what they had given the other. Such an offer would be disrespectful because it implies an unfavorable valuation of their status: The other is essentially conveying that, in his or her view, the recipient is inferior in status. Conversely, it is very gratifying to be offered more than what we bestowed the other. By presenting an article of higher value, the relational partner is imparting that he looks up to the recipient, perceiving her to be of higher status than he. Thus, any inequality (or equality) in the cultural valuation of the exchanged resources has a rich subtext conveying the status accorded to each of the relational partners. Because each partner privately desires to be offered more than s/he offers the other, the relational partners essentially have opposing preferences. The distributive norm of equality is employed to reconcile those opposing interests (Corfman and Lehmann 1993; Lane and Messe 1971; Leventhal, Michaels, and Sanford 1972; Reis and Gruzen 1976). In experiments, people asked to allocate a joint payoff were found to implement equality if their decision was to be disclosed to their EM relational partner, but allocated more to themselves if their decision was to be kept in confidence (Lane and Messe 1971; Reis and Gruzen 1976). In summary, under the EM relational model, the relational partners closely monitor the cultural value of the exchanged resources, privately hoping to be offered more than everyone else, while publicly implementing the distributive norm of equality. Research and anecdotal evidence demonstrate that the EM relational model is implemented in customers’ relationships with service marketers. For

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example, Sunglass Hut sends customers a $25 discount card for their birthday. After presenting the card at any of the chain’s stores, customers receive $25 off their purchase. By associating promotions with customers’ life events (rather than shared holidays such as Christmas), Sunglass Hut is conveying that it respects every customer as a person. As another illustration of the EM relational model in the marketplace, Price and Arnould (1999) found that consumers who view their hairstylist as a friend spontaneously offer little favors or other small tokens of appreciation to the hairstylist, expecting complimentary benefits such as free extra services in return. Hairstylists, in turn, readily offer extras to clients and often may go beyond their professional role to accommodate the customer’s needs. The hairstylist may even help out in ways unrelated to the core service. One hairstylist, for example, shared that he was knowledgeable about cars, and on occasion, would offer advice and assistance to clients who have car troubles. Customer Responses to Success/Failure: How would consumers employing the EM relational model react to service successes and failures? The EM model is implemented as a means of communicating respect and equality in status. Research shows that it matters from whom we receive acknowledgement of status because its reinforcing quality is closely linked to the identity of the relational partner: receiving respect from another is genuinely gratifying to the extent that we respect the other (Beach and Carter 1976; Teichman and Foa 1975; Turner, Foa, and Foa 1971; for a review see Foa and Foa 1980). As such, consumers would be more likely to implement the EM model for service marketers whom they can respect. Because respecting someone implies that we hold them in high esteem and have a sense of their worth or excellence (Merriam-Webster Online Dictionary, retrieved on May 1, 2010), a consumer would be more likely to employ the EM model when she holds a favorable view of the culture and values of the service organization. Accordingly, similar to those implementing the CS model, consumers employing the EM model would be more likely to think that the service organization is genuinely trying to ensure high quality for customers. EM customers therefore would be loyal to the firm and would be likely to disseminate favorable publicity about it. Unlike the CS model, however, the EM model involves a privately held desire to benefit more than the relational partner. It is therefore unlikely that EM customers would volunteer suggestions and ideas that can benefit the firm. Because a success encounter would be consistent with EM customers’ favorable view of the organization, it is unlikely that service successes would significantly impact their repatronage intentions, word-of-mouth behavior, and

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propensity to volunteer suggestions and ideas to the firm. After a service failure incident, on the other hand, the EM relational model is likely to mitigate customer defection and unfavorable publicity, but amplify complaint behavior (Kaltcheva, Winsor, and Parasuraman 2010b). Because of their favorable view of the service organization, EM customers would be less likely to anticipate failures in the future, and therefore, would hesitate to switch sellers and/or voice a negative judgment of the firm in public. At the same time however, as previously described, when implementing the EM model, relational partners privately desire to receive preferential treatment and publicly adhere to the distributive norm of equality. Therefore, a consumer who uses this model in his interactions with a service organization would be especially sensitive to any suggestion or indication that he is getting less than what he is paying for. Because a service failure implies that the delivered service was below the customer’s expectations (what the customer believed he was paying for), EM customers would strive to restore equality. They may file a complaint with the firm requesting a full refund, another form of monetary compensation, or depending on the nature of the service, that the service be redone for free. For example, if the sound in a movie theater breaks down or is not clear, viewers may demand a full refund of the ticket, a discount toward a future ticket, or to be allowed to see the same or another movie at the theater for free. If the movie theater fails to act adequately on the customer’s complaint, a consumer employing the EM model may seek to even the score by injuring the firm’s reputation with unfavorable publicity. EM Relational Strategies: A service organization can encourage customers to adopt the EM relational model by unambiguously publicizing that it adheres to the norms of that model. More specifically, the marketer must clearly communicate that it respects every customer as a person (Blodgett, Hill, and Tax 1997; Price and Arnould 1999). For example, a no-hassle return policy (such as the one implemented by Nordstrom) communicates that the firm trusts customers, implicitly imparting a sense of respect. Another way to convey respect would be to show that the firm genuinely cares about customers by giving them an opportunity to voice their needs or concerns (Blodgett, Wakefield, and Barnes 1995). For example, sales associates may explicitly ask for feedback (Blodgett, Wakefield, and Barnes 1995) and then respectfully listen to what customers have to say, acknowledging their viewpoints and emotions (Blodgett, Hill, and Tax 1997; Blodgett, Wakefield, and Barnes 1995; Goodwin and Ross 1990). Because each relational party under the EM model privately desires to be offered more than the other, an especially effective way to communicate respect would be to offer extras to

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customers and demonstrate readiness to go beyond the routine service delivery process in order to accommodate their needs (Price and Arnould 1999). It is important however that the marketer offers the same extras to all customers. That would convey that the marketer respects every consumer as a person. Rewards linked to the customer’s value for the firm (such as the dollar amount the customer has spent with the firm) would communicate exactly the opposite—that the marketer values consumers only to the extent that it benefits from them. Such rewards would be extremely damaging to the cultivation of EM relationships.

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4.3. The Market Pricing (MP) Relational Model The MP Relational Model: Of all relational models, the MP model is the one most widely implemented in customers’ relationships with service marketers. In fact, marketplace transactions are often invoked as an illustration of this relational model. A central feature of market pricing interactions is that qualitatively or culturally dissimilar resources are exchanged, such as tools for corn or labor for money. Because the counterparts to the transaction are focused on acquiring the desired resource, the interaction itself is a mere means for the attainment of that ulterior goal. Each party is largely indifferent to the personality, attitudes, and values of the other party; what matters is that the other can deliver the desired resource. This instrumental quality of market pricing interactions is manifest in a maximization motive: Each relational party strives to obtain the desired resource at minimum cost or, in other words, to achieve the best possible outcomes-to-inputs ratio (Walster, Berscheid, and Walster 1973). Because the outcomes of one relational party generally constitute the inputs of the other party, each party closely monitors everyone’s inputs and outcomes (Clark 1984; Clark and Mills 1979; Clark and Waddell 1985). A universal utility metric or value standard (money) is employed to compare and evaluate all costs and benefits (inputs and outcomes), and the distributive principle of equity is invoked to reconcile the opposing interests of the relational partners (Sampson 1969; Walster, Berscheid, and Walster 1973). Each relational party privately desires to get the best possible deal, but publicly adheres to the principle of equity. For example, in an experiment, Reis and Gruzen (1976) asked participants to distribute a joint payoff. Participants allocated the joint resources equitably when their decision was to be disclosed to their market

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pricing counterparts, but withheld disproportionately more for themselves when their decision was going to be kept in confidence. Customer Responses to Success/Failure: Consumers employing the MP relational model seek to obtain the desired resources at an attractive outcomesto-inputs ratio and care little from which service organization they are acquiring the service. They ordinarily do not pay attention to or take into consideration the culture and values of the service organization. For example, a consumer implementing the MP model would readily purchase from a marketer known to engage in some unethical practices rather than from a higher-priced competitor. Compared to consumers employing the CS and EM relational models (who hold a favorable view of the marketer’s culture and values), MP customers are less likely to have formed a confident attitude about the culture and values of the service organization. Therefore, the behavior of MP customers in the wake of a service success or failure is more likely to be influenced by the particular service encounter than by any prior attitudes about the firm. Such customers would be more likely to intensify their patronage of the firm and disseminate favorable publicity after a service success, and more likely to terminate their relationship with the firm and engage in negative word-of-mouth after a failure incident. Additionally, in case of a service failure, the customer may feel that she was charged more than the value of the performed service, thereby perceiving inequity in favor of the firm. Because consumers implementing the MP model publicly adhere to the principle of equity, but privately desire to get the best possible deal from the transaction, any perception of an unfavorable (for the consumer) inequity would inspire attempts to restore equity. Such attempts may involve complaining to the firm and requesting some form of compensation, and/or engaging in unfavorable word-of-mouth with the intention of damaging the reputation and profitability of the firm, thus “getting even” with it (Ward and Ostrom 2006). On the other hand, a success encounter implies the possibility that the transaction may have been inequitable in the customer’s favor. Consumers employing the MP model would be comfortable with such inequity and therefore would not feel obligated to restore equity, for example, by helping the firm with suggestions and ideas. MP Relational Strategies: Firms that wish to establish the MP model for their interactions with customers must typically present a value proposition that focuses on objective economic worth. That is, market pricing transactions are based on shared perceptions of financial cost-benefit estimates (or inputsto-outcomes ratios), rather than on a shared sense of community, ideals, or respect. The foundational principle of market pricing interactions is therefore

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economic exchange, rather than mutual respect or identity. Examples of service marketers that utilize the MP relational model range from Wal-Mart to auction retailers (such as eBay), as these organizations attract customers based on demonstrations of simple economic value (maximization of the customer’s value per unit of expenses), rather than by appealing to community or identification with shared ideals. Firms can also entice consumers with extras, thus improving the customer’s outcomes-to-inputs ratio. For example, Best Buy and American Express offer points for each dollar spent. The points are redeemable for airline tickets, electronics, and other gifts. What differentiates rewards offered under the MP model from those offered under the EM model is that, under the MP model, the value of the reward is linked to the customer’s inputs into the firm—the dollar amount the consumer has spent with the firm. Under the EM model, the same reward is offered to all customers, irrespective of their level of spending. Deviations from this simple value appeal of the MP relational model are likely to dilute the market pricing concepts by which consumers relate to the service firm. If Wal-Mart, for example, attempts to establish EM relationships with customers by providing complimentary valet parking or other free services, customers may come to believe that these extras will increase WalMart’s costs and thus constrain its ability to offer maximum economic value. In this way, efforts to build other forms of relationships may serve to dilute the value perception that initially provided the primary motivation for exchange.

4.4. The Asocial (AS) Relational Model The AS Relational Model: Asocial interactions have two characteristics. First, people view the other party in the interaction as a social entity (not as an object), acknowledging the s/he has values, aspirations, attitudes, and desires, but use the other purely as a means to some ulterior end. In this sense, the AS relational model involves objectifying others. Second, under the AS model, relational norms are not implemented as obligatory standards, but are adhered only to the extent that their implementation furthers the attainment of the ulterior goal. AS interactions may even involve “playing at” another relational model, if that would serve the desired objective. The norms of the other model are not implemented faithfully however. People feel free to violate or modify any norm that may interfere with their goal. Therefore, in AS interactions, typically there are no defined and shared norms governing what the interaction

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is or should be. (Note that “asocial” should be distinguished from “antisocial.” Antisocial behavior involves aggressive and predatory actions, whereas asocial interactions do not necessarily involve predatory intentions or aggression). Kaltcheva and Parasuraman (2009) demonstrate that the AS relational model is essential to customers’ relationships with leisure marketers. Three characteristics define leisure: (1) intrinsic satisfaction—consumers engage in a leisure activity in order to derive pleasure from the activity itself; (2) subjective freedom—consumers engage in a leisure activity on their own free will, the activity is freely chosen; and (3) involvement—while pursuing a leisure activity, consumers “inhabit” a subjective microcosm distinct from daily life (Unger and Kernan 1983). The AS relational model ensures that the three characteristics of leisure are realized in customers’ interactions with leisure marketers. The first aspect of the AR relational model—that relational partners are acknowledged as people but seen as means to an ulterior end—facilitates pursuing recreational activities for the intrinsic satisfaction derived from the activity itself. Because the focus is on enjoying the experience, people would rather elect to engage in such activities with others who can contribute to the fun, whether those others are personally liked or not. For example, chess enthusiasts would rather play with a stranger if that stranger’s skill level matches theirs (thus promising a challenging game) rather than with a valued friend whose skills are significantly above or below their own. The personality, attitudes, and values of the other are of little importance; what matters most is that the other can contribute to a satisfying leisure pursuit. For instance, little attention to the other’s personality is typically observed in consumers’ interactions with dancers, performers, waiters, cruise staff, theme park character actors, and other leisure marketers. The absence of obligatory standards under the AS relational model is essential for the second characteristic of leisure—subjective freedom. For a truly leisure activity, people must feel free to withdraw from the activity or revise its rules at will. Kaltcheva and Parasuraman (2009) illustrate this point with the behavior of the audience at sporting events. As one team falls behind, a spectator identifying with that team (CS) will feel obligated to remain till the end, giving the team as much moral support as she possibly can; in other words, she will feel obliged to adhere to the CS norm of abilities-based resource contribution and need-based resource distribution. In contrast, a spectator pursuing only leisure (AS) is likely to leave the stadium as soon as the game is not fun anymore. The existence of normative standards requiring compliance would be in conflict with the leisure goal.

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Finally, the absence of defined relational norms allows the AS model to accommodate the third characteristic of leisure—that people engage in leisure pursuits within a subjective microcosm in which rules and object meanings are flexible and different from those in the real world (Bateson 1955; SuttonSmith 1997; Unger and Kernan 1983). When someone wishes to invite others to join a play activity, she sends a cue (which can be as subtle as a smile or tone of voice) that, from this point on, all communications and actions, as well as any objects used in those communications and actions, should not be understood to mean what they typically mean, but to represent other communications, actions, or objects; this cue establishes a leisure microcosm separate from the real world (Bateson 1955). The precise meanings and rules governing the leisure microcosm are formed and modified at the moment on an “expediency” principle (whatever will be more fun). The absence of mandatory pre-specified norms allows people to create play microcosms that are under the participants’ control and do not have to conform to the real world (Piaget 1962). In summary, the AS relational model is essential for leisure interactions because the scripts and rules inherent in that model ensure that the three characteristics of leisure—intrinsic satisfaction, subjective freedom, and involvement—are realized in customers’ relationships with service marketers. Customer Responses to Success/Failure: Because consumers employing the AS relational model, as a rule, pay little attention to the personality, attitudes, and values of the service marketer, it is less likely that they would have established a confident attitude about the service organization. Therefore, similar to the MP model, the behavior of AS customers in the wake of service successes and failures would be influenced more strongly by the particular service encounter than by any prior attitude about the firm. After a success encounter, such customers would become more loyal to the firm and more likely to engage in favorable word-of-mouth. After a failure incident, they would be more likely to defect and disseminate negative publicity. Additionally, because the AS model does not involve shared distributional rules, and relational norms are implemented, modified, and abolished according to the expediency principle, it is less likely that customers would care if the transaction is inequitable (Kaltcheva and Parasuraman 2009). They would be less likely, therefore, to complain to the firm or attempt to harm it with unfavorable publicity after a failure incident. On the other hand, because any privately held desire to receive preferential treatment is not as dominant under the AS model as it is under the EM and MP models, consumers who use the AS model would be more likely to volunteer suggestions and ideas that might benefit the firm.

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AS Relational Strategies: As a rule, the AS relational model would be the most appropriate relational strategy for service firms operating in the leisure industries, such as cruise companies and theme parks. In other services (such as stores, restaurants, and hotels), customers’ primary motivations may or may not involve leisure, play, and fun. For example, consumers may visit a store for a purely utilitarian reason (shopping as a chore) or for recreation and fun (recreational shopping). Firms operating in such industries can elect to define themselves as a recreational venue or as a utilitarian brand. If leisure and fun are the primary motivations of a firm’s customers, the marketer is advised to implement strategies encouraging customers to embrace the AS relational model. One such effective strategy would be to create a microcosm separate from everyday life, where consumers can engage in play and other leisure activities. There are numerous examples of shopping malls, stores, restaurants, and other service venues that offer marketer-constructed retail environments that are designed to feel more real than the real world (Firat and Venkatesh 1995). For example, visitors to ESPN Zone stores, with their gigantic TV screens and bar style drinks, share that the stores make them feel in a place removed from the real world, where ringing phones and family members cannot intrude (Kozinets et al. 2004). Some retail environments vicariously transport customers to far-off places and/or times. For example, visitors to Disney’s EPCOT theme park can enjoy the cuisine and architecture of different countries, vicariously experiencing what it would be like to actually tour those countries. The Johnny Rockets restaurant chain takes customers back to the 1940s with its themed furniture, décor, and music (Bellantonio 2003), while the Mars 2112 restaurant in New York takes visitors on a futuristic voyage to Mars (Elan 1998). Belk (2000) argues that themed environments use spectacle and farcical architecture to “subvert the work ethic” (p. 118) and invoke “a playful mood of irreverent disregard for our normal behaviors and sensibilities” (p. 111). Such environments can induce consumers to adopt a role “as if playing bit parts in a movie” (Deighton 1992, p. 369) and result in “the suspension of ordinary social rules” (Kozinets et al. 2004, p. 670). When designing themed environments, it is important to make sure that the environment is shielded from any intrusions by everyday reality (Huisinga 1955). For example, the retail space can be enclosed, preventing visitors from glimpsing the outside world. Amenities and other elements in the environment that are inconsistent with the theme must be camouflaged or located out of sight. Moreover, consumers increasingly demand elaborate and life-like immersive experiences. It is not sufficient that a themed environment

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represents the idea of a place. It must “be” that place. To be successful, therefore, a leisure marketer must achieve a near replication of reality and meticulous attention to detail (McCloud 2000). In Section IV, we described the four relational models that have a wide implementation in customers’ relationships with service marketers. We also discussed how each of those models may influence customers’ responses to service successes and failures, and outlined strategies that service organizations may use in order to implement the models in their interactions with customers. The next section examines situations in which a consumer uses two or more of the models for the same service marketer.

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V. THE PERSONALITY-RELATEDNESS AND RECIPROCITY (PRR) RELATIONAL FRAMEWORK Fiske (1992, p. 693) argues that “people rarely use any one of these models alone”—different models may be used to construct different phases of an interaction, to structure different facets of an interaction phase, and to make sense of the interaction from different perspectives. In other words, the relational models are basic elements out of which people create complex social interactions. Most social interactions—both simple one-phase interactions (Haslam 1994b) and multi-phase relationships (Haslam 1994a)—often involve more than one relational model. The use of different relational models for the same relational partner is more likely in customers’ relationships with service organizations than it is in person-to-person relationships. In many service industries, consumers are likely to interact with different representatives of the service company in different interaction phases and on different occasions (or even in the same interaction phase and on the same shopping occasion), and those representatives may initiate the use of different relational models. Consider, for example, a cruise. When shopping for a cruise, consumers may view their interactions with the cruise company as a business transaction and employ the MP relational model. In the course of the cruise, however, the same consumers may employ the AS, CS, and/or EM models. Kaltcheva and Parasuraman (2009) demonstrate that a customer’s behavior with respect to a service marketer is influenced by all the relational models the customer uses (or has used) for the marketer. For example, a consumer who is trying to decide what would be an appropriate tip amount at

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the end of a cruise is likely to take into consideration all the different ways in which she has interacted with the service representative. Therefore, Kaltcheva and Parasuraman (2009) argue that continuous relational dimensions representing various combinations of the four relational models would offer a richer insight into customers’ behavior. They define a two-dimensional framework—the Personality-Relatedness and Reciprocity (PRR) relational framework—for conceptualizing customers’ relationships with service marketers. The Personality-Relatedness and Reciprocity framework is depicted in Figure 1. The square-shaped area defines the range of customer relational schemas. (A relational schema is defined here as a broader construct than a relational model: A customer’s relational schema may involve one or more of Fiske’s models.) Fiske’s relational models occupy the corners of the squareshaped area.

Note: Reprinted from Kaltcheva, V. D., and Parasuraman, A. (2009). The PRR framework: A social-psychology-based framework for analyzing retailerconsumer interactions. Journal of Business Research, 62 (June), 601-608. Figure 1. The Personality-Relatedness and Reciprocity (PRR) Framework.

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All remaining points represent hybrid relational schemas, defined as relational schemas that involve two or more of Fiske’s models. The vertical and horizontal axes represent the dimensions of personality-relatedness and reciprocity, respectively. Personality-Relatedness: The dimension of personality-relatedness defines the degree to which consumers place importance on the marketer’s personality. The personality of a marketer is a component of the broader construct of marketer identity. A marketer’s identity is defined as a set of characteristics that are: (1) central (form the organization’s “core”); (2) distinctive (distinguish the marketer from the other marketers); and (3) relatively stable over time (Albert and Whetten 1985). A marketer’s identity incorporates two types of central, distinctive, and stable characteristics: (1) dispositional characteristics such as values, attitudes and culture; and (2) demographic characteristics such as country of origin and company size (Bhattacharya and Sen 2003). A marketer’s personality is defined as the first element of the marketer’s identity—the values and culture of the service organization. The personality of a service marketer does not directly involve demographic characteristics (such as country of origin or company size), but it may include values and elements of the organization’s culture that arise from demographic characteristics. In summary, a marketer’s personality is defined as the collection of central, distinctive, and stable dispositional characteristics (such as values and the organization’s culture) that consumers perceive in a marketer. Consumers who use a relational schema high on personalityrelatedness interact with a marketer because of the values and culture of the service organization. For example, some consumers are loyal to Starbucks because the company is actively promoting humanitarian causes. Consumers whose relational schema is low on personality-relatedness care only about the quality and value of the core service. Those consumers would readily purchase from any marketer who offers similar services. Reciprocity: The reciprocity dimension represents the degree to which consumers place importance on comparative outcomes, defined as the difference between the consumer’s outcome and the marketer’s outcome (Corfman and Lehmann 1993; Oliver and Swan 1989b). Note that the reciprocity dimension does not define the amount of the difference (if any) between the marketer’s and the consumer’s outcomes; rather, it defines the level of importance that consumers place on that difference. Consumers can evaluate comparative outcomes at the conclusion of each interaction or after a series of interactions. Those who hold a relational schema high on reciprocity place very high importance on the comparative outcomes in an interaction, and

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for example, may feel extremely upset if there is any indication that the marketer is “ripping them off.” Consumers whose relational schemas are low on reciprocity are largely indifferent to comparative outcomes. Few marketerconsumer interactions fall at either end, however. Most customers’ relational schemas involve intermediate levels of reciprocity.

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CONCLUSION Developing relationships with customers is a potentially effective strategy for protecting the firm against competitive threats and the fallout of service failures (Berry 1995; Tax, Brown, and Chandrashekaran 1998). When designing a relationship strategy, the service firm must first identify the most appropriate relational positioning in view of its objectives, strengths, and weaknesses. This target relational positioning can be defined as one of Fiske’s relational models or as levels on the personality-relatedness and reciprocity dimensions. The firm’s overall strategic objectives may determine its target relational positioning. For example, let us assume that the service sector in which the firm is operating is fragmented, with a lot of niche sellers. A service organization may want to differentiate itself by targeting a broad customer base with a value appeal. In such a case, the firm would likely pursue a market pricing relational strategy. Alternatively, if market pricing is the predominating relational strategy in the industry sector, the firm would secure a defensible positioning by adopting a relational strategy that is as different as possible from market pricing. The Personality-Relatedness and Reciprocity framework suggests that, in this scenario, the strongest differentiation would be achieved by pursing a CS relational strategy. The implications of the relational models for customers’ responses to service successes and failures can also inform the articulation of a target relational strategy. The service organization must first identify its strengths and weaknesses regarding its capability to reliably deliver high service quality. Firms plagued by variable quality and a high probability of failure should consider implementing a high personality-relatedness (CS or EM) relational strategy, which would be likely to mitigate the negative consequences of failure incidents. On the other hand, firms that have developed processes and mechanisms for effective quality control would do well to exploit these strengths by implementing relational strategies low on personality-relatedness (MP or AS). Such strategies would amplify the impact of success encounters

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on customers’ repatronage intentions, favorable word-of-mouth, and propensity to volunteer feedback helpful to the firm. Once the target relational positioning is determined, the firm needs to evaluate customers’ current perceptions of their interactions with the marketer (the firm’s actual relational positioning). Reliable and valid scales to measure customers’ relational schemas would have to be developed. The PersonalityRelatedness and Reciprocity framework would be especially helpful in this respect because, unlike the discrete relational models defined by Fiske, the relational dimensions allow assessment of the exact distance between the firm’s actual relational positioning and its target positioning. If this assessment reveals a substantial discrepancy, management may review the feasibility of the target positioning, and if necessary, revise the target to make it more attainable. Then the service organization may implement some of the strategies outlined in Section IV to cultivate the desired relational model for its relationships with consumers. Progress should be evaluated regularly by monitoring customers’ perceptions of their relationships with the firm. If necessary, the management may modify and update aspects of the firm’s relational strategy.

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Tajfel, H., and Turner, J.C. (1985). The social identity theory of intergroup behavior. In S. Worchel and W.G. Austin (Eds.) Psychology of Intergroup Relations, (pp. 6-24). Chicago: Nelson-Hall. Tax, S.S., Brown, S.W., and Chandrashekaran, M. (1998). Customer evaluations of service complaint experiences: Implications for relationship marketing. Journal of Marketing, 62 (April): 60-76. Teichman, M., and Foa, U. (1975). Effect of resources similarity on satisfaction with exchange. Social Behavior and Personality, 3(2), 213224. Turner, J., Foa, E., and Foa, U. (1971). Interpersonal reinforcers: Classification, interrelationship, and some differential properties. Journal of Personality and Social Psychology, 19(2), 168-180. Turpin, D. and Chung, R. (2004). Rebuilding a passion brand: The turnaround of Dacati (B). International Institute for Management Development, IMD 5-0667. Unger, L.S., and Kernan, J.B. (1983). On the meaning of leisure: an investigation of some determinants of the subjective experience. Journal of Consumer Research, 9 (March): 381-392. Walster, E., Berscheid, E., and Walster, G.W. (1973). New directions in equity research. Journal of Personality and Social Psychology, 25 (February), 151-176. Ward, J.C., and Ostrom, A.L. (2006). Complaining to the masses: The role of protest framing in customer-created complaint web sites. Journal of Consumer Research, 33 (September), 220-230. Zeithaml, V.A., Berry L.L., and Parasuraman A. (1988). Communication and control processes in the delivery of service quality. Journal of Marketing, 52 (April): 35-48. Zeithaml, V.A., Parasuraman, A., and Berry L.L. (1990). Delivering Quality Service: Balancing Customer Expectations and Perceptions, New York: Free Press. Zeithaml, V.A., Berry, L.L., and Parasuraman, A. (1996). The behavioral consequences of serivce quality. Journal of Marketing, 60 (April), 31-46.

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Chapter 2

MEASURING CORPORATE CRM STRATEGY: ITS MODEL, METHODOLOGY AND APPLICATION Hyung-Su Kim

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Department of Industrial and Management Engineering, Hansung University, Seoul, 136-792, Korea

ABSTRACT Customer Relationship Management (CRM) is not the term designating a sub-function of marketing or a part of the corporate information system itself anymore. Rather, it has been increasingly adopted as a core business strategy for continuous organic growth. Then what are the necessary conditions to recognize CRM not as an IT or business function but as a basis of business strategy? One of the key points is that, beyond just analyzing customer data on specific issues, managing customer profiles technically, or planning marketing initiatives like a loyalty program, companies should equip a systematic dashboard to monitor and control regularly, from antecedent to consequence,t factors related to every business activity offered to their customers. That is why a systematic CRM performance measurement is important. In other words, CRM performance measurement does not consist in mere analyzing return on investment (ROI) in CRM; it is rather a barometer to align corporate strategies for managing customer relations. This chapter summarizes theories, models, measures, methodologies, and applications of a desirable corporate CRM performance measurement system. In more

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Hyung-Su Kim details, this chapter addresses first the meanings of diagnosing CRM status in terms of tripartite business implications: assessing outcomes, evaluating CRM capabilities, and grasping opportunity domains. After illustrating theoretical and practical requirements of such a strategic measurement system, a CRM Scorecard is presented as an example of corporate CRM measurement systems which meet such demands. The sidebar included in this chapter describes the implementation process of CRM Scorecard developed through a series of industrial-academic cooperative stages. Next, the details of the four evaluative domains including organizational infrastructure, business process, customer, and organizational performance would be presented by exploring the structure of the CRM Scorecard. Each evaluative domain has its own sub-domains, evaluative factors, and specific measures (quantitative/qualitative and antecedent/consequent measures). In the latter part of this chapter, a practical methodology and notices for CRM diagnosis, and a real business application.

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1. INTRODUCTION Customer Relationship Management (CRM) is not the term designating a sub-function of marketing or a part of the corporate information system itself anymore [20]. Rather, it has been increasingly adopted as a core business strategy for continuous organic growth. Beyond the key application areas including marketing, sales [2], and customer service [31], CRM is now expanding its scope into all value chains such as acquisition, manufacturing, logistics, finance, RandD, and human resources in terms of SRM (Supplier Relationship Management) [1], mass customization, PRM (Partner Relationship Management) [4], Customer Equity, Consumer-driven NPD (New Product Development), and ERM (Employee Relationship Management) [4] respectively. Moreover, CRM is increasingly adopted into the so-called CRM-incompatible industries such as consumer product manufacturer [1], public institution, and NGO, not to mention the major CRM-compatible industries such as financial, telecommunication, retailer, airline, and lodging industry [20]. It might be a positive evidence of transformation of CRM from a supportive function into the corporate fundamental strategy. In fact, the assertion that CRM should be regarded as an enterprise-wide strategy is nothing new. Since more than 10 years ago, the time that CRM had been regarded as an IT solution or functional program, even IT consultants and vendors have stressed the essence of CRM as an enterprise-wide strategy notwithstanding that their ultimate objectives were to sell their solutions.

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Actually, just bringing its potentials and importance is not sufficient to address CRM as a corporate strategy in substance. Then what are the necessary conditions to recognize CRM not as an IT or business function but as a basis of business strategy? One of the key points is that, beyond just analyzing customer data on specific issues, managing customer profiles technically, or planning marketing initiatives like a loyalty program, companies should equip a systematic dashboard to monitor and control regularly, from antecedent to consequence,t factors related to every business activity offered to their customers [2, 30]. It is because any kind of corporate-wide business strategy should be balanced from the company-wide viewpoint, and managed in terms of planning, doing, and learning. That is why a systematic CRM performance measurement is important to discuss corporate-wide CRM strategy. In other words, CRM performance measurement does not consist in mere analyzing return on investment (ROI) in CRM; it is rather a barometer to align corporate strategies for managing customer relations [5, 38]. In more details, CRM assessment might have four different purposes including evaluating CRM performance, diagnosing current CRM capabilities, monitoring CRM activities, and finding future opportunities [38]. This chapter summarizes theories, models, measures, methodologies, and applications of a desirable corporate CRM performance measurement system. Section 2 summarizes theoretical backgrounds for the framework of CRM performance measurement, and section 3 presents CRM Scorecard as an effective framework for diagnosing/evaluating CRM strategy. Section 4 exemplifies several measurement indices affordable suitable for each evaluative factor of the CRM scorecard, and section 5 discusses the methodology for diagnosing/evaluating CRM strategy. Finally, several brief business cases applied within which a CRM Scorecard was applied are provided in section 6.

2. THEORETICAL BACKGROUND This section derives common criteria for diagnosing and evaluating business strategy from several key theoretical frameworks of business performance measurement, consequently suggesting potential directions to build a CRM performance measurement framework.

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2.1. Theoretical Frameworks for Evaluating Corporate Business Performance Hundreds of business strategy frameworks have been studied for explaining corporate strategy effectively or presenting its future directions. Among the frameworks, a resource-based view (RBV) [13, 31], service-profit chain (SPC) [16], and balanced scorecard (BSC) [18] might be the most popular theories to illustrate corporate business performance. 

Resource-Based View

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The resource-based view [13] has been considered as a fundamental framework for measuring IT performance. In the context of IT performance measurement [31], three underlying assertions have been made: (1) resource heterogeneity, (2) resource immobility, and (3) resource bundling [13]. These three characteristics account for why a company could not bring the same performance even if the company introduced the identical CRM system to its competitor. Surely a company’s CRM performance comes from interactions between the CRM system and the company’s other peculiar resources [3].



Service-Profit-Chain

The SPC framework establishes relationships between profitability, customer loyalty [29], and employee satisfaction and loyalty, and productivity [16]. Since this framework has the cause and effect relationships between the drivers above, it can help managers to decide to what they should focus on. The SPC framework is also meaningful because it weights internal service quality, employee satisfaction and retention, and employee productivity as the internal core competencies which are requisite for operating business strategies [4].



Balanced scorecard

The balanced scorecard (BSC) framework pursues overall optimization of business strategies through a balanced view of various perspectives including financial, customer, internal process, and innovation and learning perspectives [18]. The BSC framework seems to provide the most integrative approach for measuring business performance,. i.e., efficient and effective business

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processes are implemented by internal resources and capabilities [3, 13], the business processes will relate firstly to a customer perspective, and the customer perspective will eventually lead to superior organizational performance. Although these frameworks were mainly discussed in different fields, i.e., information system, marketing, and management strategy respectively, they share the same conceptual mechanism. That is, the quality of internal operating strategy determines the quality of corporate external activity, and the quality of external activity is correlated with customer experience and corporate financial performance.

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2.2. Requirements of Performance Measurement Framework Given CRM as a business strategy rather than IT or analytical technique, it might be a good approach to review previous typical business performance measurement studies to understand the fundamental directions for building CRM assessment framework. Table 2-1 provides an overview of exemplary studies for enterprise performance measurement. Even though the studies treat of various subjects like conceptual methodologies, models, measures’ characteristics, or the extent of measurement, we could extract common implications from these studies which are beneficial to any performance measurements. These studies imply that a performance measurement framework should be equipped with the following components:

   

Causal model embedded in the measurement framework [24] Different perspectives including customer [7]. Antecedent or conditional factors [2, 30, 37]. Qualitative measures [10, 12, 36].

Since a causal model can help trace the causes of the success or failure of a given strategy, any business assessment framework should involve a theoretically-sound causal model [24]. Such causal model could be implemented by measuring antecedent or conditional factors [2]. These kinds of measures allow managers to attain greater depth of understanding.

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Hyung-Su Kim Table 2.1. Previous Studies for Enterprise Performance Study Lebas 1995 [24] White 1996 [37] Cross and Lynch 1988 [7] Kaplan and Norton 1992 [18]

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Kolay and Sahu 1992 [23] Ghalayini and Noble 1996 [12]

Toni et al. 1995 [36]

Flapper et al. 1996 [10]

Key Points Fundamental concepts, guidelines for performance measurement Classification of measures: Competitive capability, Data source, Data type, Reference, Orientation SMART Model : Performance Pyramid including Balanced Scorecard: Financial Performance, Customer, Business Process, and Innovation and Learning Total performance = f (shareholder, consumer, national economy, and society) Comparison traditional performance measurement methods with non-traditional performance measurement methods Classification of quality; Total Quality Offered, Perceived Quality and Customer Satisfaction, and Quality Cost Methodology for development Performance Measurement System

Implication Emphasis on theoretical causal model for performance measurement Measures in different dimensions (Internal/External– Subjective/Objective) Measures should be measured in different unit and people according to those characteristics Four important perspectives for observing strategies Stress on performance other than profitability Introduce new trend of performance measurement (Non-financial measures) Emphasis on customer perspective for performance measurement Stress on the relationship b/w measures Financial and Non-Financial, Internal and External

Meanwhile, since even a single situation could be interpreted differently according to perspectives, it is recommended to have diverse evaluative perspectives (i.e., internal and external) to provide a chain of evidences for the corporate strategy. Finally, Pperceptual factors have also been considered important in recent literature on business performance measurement [37]. Companies should give more attention to perceptual factors like employee and customer satisfaction [11] which are difficult to measure in a quantitative manner.

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3. CRM ASSESSMENT MODEL: CRM SCORECARD

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3.1. Models for CRM Assessment The requirements of measurement framework discussed in section 2.2 could not only be fundamental guidelines for implementing a business performance framework, but also criteria for evaluating previous studies on CRM performance measurement. From this point of view, it is needed necessary to check if the previous CRM assessment studies met such requirements. Although there has been a definite lack of academic effort addressing the issue of CRM performance measurement, several studies could be considered on this issue. For the purpose of conceptualizing fundamental directions for developing a CRM assessment tool, Zablah et al. [39] emphasized both the input and output of the CRM system for evaluation because of the ongoing nature of the CRM process [9, 32]. Lindgreen et al. [26] suggest a CRM assessment tool consisting of ten evaluative elements categorized into three sets of elements: strategic elements such as customer and brand strategy; infrastructural elements such as culture and people; and process elements such as the relationship-management process. Jain et al. [17], deviating from traditional quantitative Key Performance Indicators (KPI), such as sales, acquisition and retention rates, cost reduction, and service time, suggested various behavioral elements such as attitude to serve, understanding of expectations, quality perceptions etc. Meanwhile, Eevaluative structure and methodology rather than evaluative subjects have been more emphasized from a practical perspective (e.g., [5, 22]). As a subset of the Balanced Scorecard (BSC) [18], Kim et al. [22] suggested a customer-centric BSC consisting of customer knowledge [6, 25], customer interaction, customer satisfaction [11], and customer value perspectives [14], and stressed that the four distinct types should be systematically connected when evaluating the effectiveness of corporate CRM initiatives. Brewton and Schiemann [5] stressed the importance of linkage between a firm’s corporate business strategy and its CRM strategy by suggesting a hierarchical structure of the strategic business map. Although the previous studies addressing CRM assessment have their own research objectives and implications, they are deficient to for diagnosing corporate-wide CRM strategy in terms of the basic requirements discussed in section 2.2. Table 3.1 summarizes how well previous CRM assessment studies have met such demands.

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Hyung-Su Kim Table 3.1. Criteria of performance measurement framework

Criteria CRM Assessment Studies Brewton and Schiemann, 2003 [5] Jain et al., 2003 [17] Kim et al., 2003 [22] Lindgreen et al., 2006 [26] Zablah et al., 2004 [39]

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*

Causal relationship

Manifold perspectives

Customer perspective

Antecedent elements

Perceptual factors

●

N.S

○

○

○

N.S

N.S

●

○

●

●

●

●

○

◎

N.S

○

N.S

●

●

◎

○

◎

◎

●

●: fully satisfied; ◎: satisfied; ○: weakly satisfied; N.S: not satisfied.

3.2. CRM Scorecard This section illustrates CRM Scorecard [19] as an example of corporate CRM measurement systems which meet such demands. The sidebar at the end of this section describes the implementation process of CRM Scorecard developed through a series of industrial-academic cooperative stages. CRM Scorecard is a measurement framework of enterprise-wide CRM strategy, which consists of 38 evaluative subjects over four different evaluative domains [19]. While ordinary CRM performance measurements are mainly addressed in the CRM process domain consisting of external CRM activities [32], CRM Scorecard emphasizes that, the level of organizational infrastructure should be considered ahead of measuring the level of CRM process, and the performance of CRM process would not be reflected directly in organizational performance; rather, it is mediated by customer experience [19]. Therefore, CRM scorecard has a balanced view as it consists of infrastructure, CRM process, customer experience, and organizational performance [18]. In other words, it is structured with the mutually exclusive and collective exhaustive evaluative

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domains to evaluate corporate-wide CRM strategy. Table 3.2 shows a holistic view of the CRM scorecard framework.

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Organizational Performance The measures for organizational performance should be able to indicate whether or not a corporate CRM strategy contributes to bottom-line improvement. Therefore, it is preferable to measure the direct economic effect of CRM initiatives resulted in customer equity, the company’s profitability, and the overall value of the company. Customer equity is a composite performance indicator from CRM initiatives in that it is determined by customer equity drivers [35] such as perceived value, brand equity [27], and company-customer relationship [14]. Moreover, it leads to enhanced corporate profitability because the heart of the customer equity strategy is to maximize customers’ financial contributions and to reducecut its marketing costs. And since profitability is a critical determinant for a firm’s cash flow and overall value, all the this closely connects customer equity, profitability, and corporate value (e.g., shareholder value [23]) are closely connected each other. Customer Experience How customers view a firm is perhaps the most important issue for all top management [10]. Customer value [14] in terms of determinants for purchasing has been studied for years to find the answer [28]. Value equity, usually expressed in perceived value [14], has been defined as a “consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given”. Meanwhile, other assertions have been made that different aspects such as brand equity [27] or mutual relationship are also contributory factors. For this perspective, Rust et al. [35] provided an integrated framework for customer equity driven by perceived value, brand equity, and relationship equity. CRM Scorecard adopts the three customer equity drivers [35] as the firstly-influenced factors by corporate activities. Customers are satisfied when their expectations of the value of a product or service, the company brand, and their relationship with the company are met. The positive relationship between customers’ value and satisfaction has been studied in the marketing context (e.g., [16, 28]). Finally, satisfied customers bring strong customer loyalty [29] to the focal company. Customer loyalty has been defined as “an inclination to perform a diverse set of behaviors that signal a motivation to enhance an ongoing relationship with the service provider”. As increased loyalty of existing customers means more customers

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will repurchase in the future;, customer loyalty has a significant effect on organizational profit and growth [29].

CRM Process Strategy Since any corporate business strategy should be implemented by a group of activities leading to desired business outcomes, measuring corporate performance in the process perspective is regarded as imperative. That’s why business performance measurement is most likely conducted in the context of business activities. The same is the case with CRM strategy. Since the process perspective is important in that buyer-seller relationships evolve over time [9], the process of a company’s relationship marketing as a corporate response strategy should be redesigned in terms of maintaining and developing such relationships. In this respect, the CRM process as a corporate strategy has recently been emphasized [32]. CRM Scorecard defines the CRM process as a series of activities for acquiring, retaining, and expanding the relationship with customers [19]. With a CRM process, companies can contact customers prudently and manage the relationships differently in each distinct relationship phase, and such corporate relationship practices lead to augmenting buyers’ trust and ultimately to cooperative relationships. Therefore, it is recommended that companies adopting CRM should prepare the process in terms of managing target relationships effectively at each stage. Meanwhile, CRM Scorecard categorizes common activities such as definition of core customer, customer segmentation, campaign management, VOC (voice of customer) management, and CRM monitoring/assessing, which are needed to accelerate each CRM processes, into a sub domain called common process. Infrastructure As the internal prerequisites to implementing CRM process, infrastructure domain is divided into four sub categories: i.e., CRM System, human resource, organizational alignment, and organizational culture. Since IT is one of the key resources in organizations for sustainable competitive advantage [3], it should be considered as a necessary condition for successful CRM initiatives [31]. When companies measure the level of CRM system [34], they need to assess not only the scope and quality CRM functionality but also whether or not the CRM system support employees’ jobs effectively [8]. The sub category of human resource is to assess the level of top management’s CRM leadership, employee satisfaction [4], employee’s CRM expertise, and employee capability (customer orientation). Top management’s CRM leadership is especially important because it can affect all the resources and capabilities

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relevant to CRM strategy. Organizational alignment refers to organizational structures or CRM-compatible schemes that are appropriate to the CRM strategy, including organizational structure, reward systems, and employee training programs. Many empirical studies have ascertained that such organizational alignments are critical to make CRM resulted not only in shortterm success but also in long-run one. Development process for CRM Scorecard CRM Scorecard has been developed through an industry-academic cooperative research panel consisting of KAIST (Korea Advanced Institute of Science and Technology) and five representative companies from the industries of finance, retailer, pharmaceutical, consumer goods, and business consulting since 2005. The brief explanations for each development process are following. Step 1: Building a Theoretical Causal Map To find a complete set of CRM success factors and construct its causal model, the research team first reviewed a wide range of literatures related to marketing, business strategy, and information systems and then classified the significant factors into one of the four different perspectives (infrastructure, process, customer, and organizational performance) drawn on the balanced scorecard [18]. Step 2: Extracting Hierarchical Map for CRM Success from Practical Perspective Practical perspective based on real experiences as well as theoretical studies is also important to build a framework for measuring CRM performance. With this purpose, the research team conducted in-depth interviews with 13 CRM or marketing managers of the research panel companies, and derived the hierarchy map from the interviews. Step 3: Integrate Both Models From the theoretical causal map and the hierarchy model in practical perspectives, the research team derived an integrated model for CRM scorecard. Step 4: Develop Measurement Instruments Next, the research team developed instruments measuring the evaluative factors in CRM Scorecard. We tried to use validated survey items as subjective (qualitative) measures for each factor from leading academic journals and KPIs as objective (quantitative) measures from various sources including academic and practice journals, companies’ internal archives, and consulting firms’ knowledge bodies. After a series of pilot test with the research panel, the research team concluded that 15 measurement items among the instruments suggested by CRM Scorecard were inconsistent with reality, deciding to remove them from the KPI list consequently. Step 5: Prioritization of CRM Success Factors To provide a basis for the importance level of each factor, we conducted an Analytic Hierarchy Process (AHP) analysis with a group of CRM experts consisting of CRM or marketing managers, consultants, professors, and researchers to evaluate each set of factors of CRM scorecard in a pair wise fashion with respect to each of the perspective. These results from the AHP analysis can be used to provide a conceptual threshold value for each factor when companies diagnose and measure their CRM initiatives with the CRM scorecard.

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Figure 3.2. Framework of CRM Scorecard.

Hyung-Su Kim

49

Finally, organizational culture is likewise one of key success factors of CRM strategy in that CRM-compatible cultural factors [15, 38] can be the foundation for other CRM resources and capabilities in the same way as top management’s CRM leadership.

4. MEASURES FOR CRM ASSESSMENT 4.1. Characteristics of CRM Measures It is not too much to say that good measures complete an excellent assessment framework. When you design the performance measures appropriate to business assessment tool including CRM Scorecard, it is recommended to keep the followings in mind.

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

Quantitative and qualitative measure

A business assessment tool should involve not only quantitative measures but also qualitative ones [12, 36]. It means that, to assess a specific evaluative factor, it is necessary to develop both objective measures calculated automatically from internal databases and perceptual measures (i.e., survey items) [37] to understand the perceptual level of the factor. It is because each type of measures by itself is deficient to express the complete aspect of the factor. It is also caused by the fact that quantitative measures usually called objective ones actually are no more than proxy measures. For example, you may measure the employee defection rate as an objective measure of employee dissatisfaction, but you cannot be free perfectly from an internal validity issue like ‘Does it really mirror our employee’s dissatisfaction level?’. Since most constructs discussed in social science, contrary to natural science or the technology field, are in substance qualitative concepts, there is no other way but to considerably depend on perceptual measures like survey instruments [36, 37].



Antecedent and consequent measures

As discussed in section 2.2, a measurement framework should be constructed with causal relationships [24]. Therefore, if the measures were properly designed to meet the requirement, those must have antecedent-

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consequent relationship [2]. Unfortunately however, most of the up-to-date CRM assessment frameworks have tended to focus on consequential aspect. The four evaluative domains in CRM Scorecard are a causal model itself. Infrastructure is antecedent to CRM process, CRM process is antecedent to customer experience, and similarly customer experience is antecedent to organizational performance [30]. Moreover, the structure of evaluative subjects or factors in an evaluative domain shows a causal relationship either. For example in the infrastructure domain, top management’s CRM leadership might improve CRM-compatible organizational alignment such as education/training program or reward system, such organizational alignments might enhance employee’s satisfaction, and consequently the satisfied employees might make their customer-oriented capability better.

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

Closed-loop measures

Speaking of the microscopic viewpoint of measurement characteristics, it is desirable to build measures to evaluate the closed-loop functions such as ‘plan-do-see’. Although this approach could be applied to all the evaluative factors, it is imperative especially to those in CRM process, which is the evaluative domain to measure corporate CRM activities directly. Hence, the CRM process domain in CRM Scorecard consists of many closed-loop measures in terms of PDOL (Plan-Do-Outcome-Learning), which was expanded from the concept of ‘plan-do-see’. Thanks to the PDOL measures, any corporate CRM activities could be evaluated multilaterally in terms of whether or not analysis and planning for the activity were conducted properly and the activity was executed well according to the plan, how its performance was, and whether or not they found strategic insights from the execution to a better future plan (see Figure 4.1). For example, campaign management, one of major CRM activities, might have the ratio of customer-oriented campaign or the frequency of strategic campaign planning for ‘Plan’ measures, the execution or contact rate against the target customers for ‘Do’ measures, the positive response rate or increased profit by the campaign for ‘Outcome’ measures, and the degree of wrap-up/review report or the number of derived insights from the campaign for ‘Learning’ measures.



Reliability and validity of measures

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Whether or not the results of diagnosing CRM could be believable would be determined by the reliability and validity of the applied measures. The reliability of the measures could be acquired by minimizing random or unsystematic errors in measurement errors to produce consistent measurement outcomes, and those validity could be secured by minimizing nonrandom or systematic error to manifest the target objects exactly.

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Figure 4.1. The concept of PDOL].

Due to the ambivalent natures of measures, quantitative measures, sometimes called objective ones, to diagnose a corporate CRM strategy are often faced with a validity issue because of the limitation as proxy indexes even though the objective ones can induce pretty consistent and reliable results. Similarly, although survey items to seize the perceptual levels do not usually matter in terms of validity because such instruments are usually based on theoretically-sounded backgrounds, it is difficult to secure reliability unless those are measured by sufficient respondents in the company. Therefore, since quantitative and qualitative measures are complementary to each other [36], it is recommended to use both types of measure when diagnosing and assessing a corporate CRM strategy.



Feasibility and applicability of measures

Although the secured reliability and validity of CRM measures are critical, it does not mean that such measures are available in all cases. In fact, it has been frequently occurred that many good potential measures, especially for quantitative, were withdrawn with various reasons including technical or

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political ones. Thus, it is necessary to check the feasibility and applicability of the pre-suggested measures and consider those alternatives in advance. To do this, it is good idea to see stepwise whether or not the suggested measures are being used in the company, whether or not it is possible to adopt those hereafter if not use now, and why they could not adopt those if impossible to do. Surprisingly, you may find that the infeasibility of measures frequently come from political reasons rather than technical ones.

4.2. Measures in CRM Scorecard

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This section introduces measurement instruments applicable to the four evaluative domains in CRM Scorecard. Since survey items for each evaluative factor could be adopted from each relevant previous study, we here discuss only quantitative (objective) measures. One thing to remember is that the quantitative ones below should be regarded as tentative because of its nature as proxy index as addressed before. In my case of consulting for companies, I have adopted or rejected ones among such measures and occasionally customized those to be applicable to the companies’ situations.

1. Organizational performance The domain of organizational performance consists of shareholder value [23], organizational profitability, and customer equity. Although stock price would be a good index for profit-making company’s shareholder value, it is not applicable to unlisted firms. In such case, Tobin’s Q might be an alternative. Speaking of profitability, as it is the most directly-influencing factor for its shareholder value [23], the profitability index should be the one reflecting the firm’s organic growth clearly. Public announcement indexes such as ROA or ROE might be selectable. However, many empirical cases have showed that operating profit percentage is one of the most appropriate indexes for profitability. In the meantime, since a firm’s organic growth is after all driven by customers’ repeated purchasing, customers’ total contributive value, i.e., customer equity, is the measure of organizational performance from customer viewpoint. Although it is quite recommended to adopt the formal customer equity or customer lifetime value model reflecting relationship period and future cash flow, net profit per customer might be acceptable depending on the situation. However, note that it will become more and more important to consider not only customer’s direct contribution but

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also indirect one such as customer referral or prosuming when they design a measure of customer contribution value. Table 4.1. Measures proposed for Organizational Performance Evaluative Factor Shareholder Value Organizational Profitability Customer Equity

Objective Measures Stock Price, Tobin’s Q ROA, ROE, Operating Profit Percentage, Revenue per employee etc. Customer Equity, Customer Lifetime Value, Customer Referral Value, Net Profit per Customer etc.

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2. Customer experience Contrary to other evaluative domains, evaluation from customer experience perspective [10] cannot help relying mainly on perceptual measures (i.e., survey items) [37] rather than objective ones derived from internal database. It is because any objective measures cannot project customer perspective exactly on them. When you survey customers in terms of CRM strategy, extracting average score for each item from anonymous survey are not helpful to derive strategic insights. Rather, measurement and management by segmentation are better; frankly speaking, it would be the best if you know each respondent’s identity.

Table 4.2. Measures proposed for Customer Experience Evaluative Factor Customer Loyalty Customer Satisfaction

Value equity Customer Value Drivers

Brand equity Relationship Equity

Objective Measures RFM Score, NPS (Net Promote Score), Core customer ratio, customer activity duration eat. CS (Customer Satisfaction): subjective/relative, ratio of satisfied customer, customer defection rate, complaints rate from VOC etc. Product Quality Index, Price Superiority, Channel Utilization, Customer Waiting Time, VOC rate of Quality/price/convenience etc. Brand Recognition, Market Share, Brand-related Index (e.g., Brand Power) etc. Accumulated Customer Mileages (points), Customer Longevity, Survival Rate etc.

Even though using quantitative measures for customer experience is restrictive, table 4.2 shows several quantitative measures supplementary to survey items. However, note that even some of these quantitative measures

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must be acquired from survey method (e.g., NPS, CS, brand recognition etc.). Meanwhile, to measure core customer ratio indicating customer loyalty, you need to clarify the operational definition of core customer beforehand. In addition, customer activity duration is not mere longevity, rather is the term of both valid purchasing and sustaining relationship. Finally, with respect to customer satisfaction (CS), it is necessary to assess not only subjective CS but also relative one [11].

3. CRM process strategy

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Evaluation from the viewpoint of CRM process is practically important because its measures are related to concrete CRM activities [32]. One of the key points of diagnosing corporate CRM processes is to measure and manage them by each customer segment or layer (see figure 4.2). For example, although the process of customer acquisition is likely to come under the bottom layer (i.e., Level 1 in figure 4.2), it is quite possible to solicit customers into the upper layers directly. Thus, performance management in terms of CRM process means to manage the status of customer migrations including customer acquisition, retention, defection, upgrade, and downgrade in each customer layer.

Figure 4.2. Monitoring customer migration.

Candidate quantitative measures for CRM process are provided in table 4.3. Note that the most popular indexes such as acquisition rate, retention rate, and upgrade/downgrade rate are omitted here: those are the comprehensive final indexes of three CRM processes, respectively, rather than of specific CRM activities.

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Table 4.3. Measures proposed for CRM Process Evaluative factor Common Process

CRM Activity

Objective Measures

Core Customer Definition

Core customer extraction model (OX), Extent of customer valuation (%), Core customer ratio (%), Core customer contribution value ($), etc. Multidimensional segment model (OX), Homoscedasticity by groups, Customer ratio by groups, Contribution value by groups ($), etc. Execution rate (%), Contact rate(%), Response rate(%), Success rate(%), etc. Response time (min.), VOC handling duration (day), Satisfaction of VOC management, Ratio of complaints resolved (%), Ratio of complaints resolved on 1st call (%), etc. CRM assessment system (OX), Extent of CRM evaluation (%), Diversity of measures, etc. Prospective definition model (OX), No. of valid prospective (#), Ratio of conversion of web visitor into prospective (%), Expected profit from prospective ($), etc. Customer contact rate (%), Leads per channel (#), No. of new customer (#), Success rate of acquisition (%), New customer profit ($), etc. No. of target customers of win-back (#), Contact rate(%), Response rate(%), Success rate(%), Duration elapsed before win-back (day), Survival rate after win-back (%), etc. Ratio of conversion of 1st buyer into 2nd buyer (%), Contact rate(%), Response rate(%), Success rate(%), Duration elapsed before 2nd buying (day), etc. Program penetration rate (%), Lift of individual revenue, Point redemption rate (%), No. of acquired customer knowledge (#), etc. Accuracy of defection prediction (%),Contact rate(%), Response rate(%), Success rate(%), Prevention rate of defection (%), etc. Cross-selling index, Increased customer transaction (%), Customer transaction ($), Share of customer (%), etc. Ratio of customer referring (%), Customer referral value ($), Ratio of captured customer network (%), Reduced acquisition cost ($, %), etc. No. of activated customer suggestions (#), Duration of prosuming program (day), Estimated value from prosuming program ($), No. of product/service based on customer participation (#), etc.

Customer Segmentation Campaign Management VOC Management

CRM Assessment Acquisition

Acq. Of Potential Customer

Acq. Of New Customer

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Customer Winback

Retention

2nd Buying Inducement

Loyalty Program

Prevention of Defection Expansion

Cross/Up Selling

Customer Referral Management Prosuming Policy

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Hyung-Su Kim Table 4.4. Measures proposed for Infrastructure Evaluative Factor Customer orientation Long term perspective Organizational culture

Organization experience Explicit goal Cross-functionality Customer oriented partnership Education / training

Organizational alignment

Reward system

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Organizational structure System configuration Information system

System quality

System-job fitness

CEO leadership

Human resource

CRM expert Employee satisfaction Employee capability

Objective Measures Frequency of customer survey (month), index of customer knowledge creation and application, General CRM test score, etc. Duration of consecutive existence of CRM team (month), ratio of long-term CRM project (%), Application of closed-loop (e.g., PDOL) KPI (OX), etc. No. of CRM projects (#), No. of organizational reform project (#), Duration of CRM team (month), etc. Ratio of quantitative KPI (%), Applicability of CRM KPI, etc. Frequency of cross-functional meeting (week), Constitutional diversity of CRM team (#), etc. Diversity of partners(#), Share of customeroriented KPI with partners (OX), Customer satisfaction index for partner, etc. CRM education/training fulfillment (%), CRM education time per employee (hour), etc. CRM reward system (OX), Ratio of CRMrelated points in performance rating system, etc. Distinct CRM team (OX), Team coverage of customer segments (%), No. of passing-through teams from CRM planning to execution (#), etc. System fulfillment of strategic requirements (%), etc. Coverage of customer information types (%), Accuracy of customer information (%), Integration rate of customer information (%), System stability, etc. System used time per day (hour), CRM job coverage (%), etc. Ratio of CRM budget (%), Ratio of participation in CRM meeting (%), No. of emphasis on CRM in printed matter (%),General CRM test score, etc. Fulfillment of CRM experts required (%), Ratio of permanent customer-facing employee (%), etc. Employee satisfaction, Defection rate of core employee (%), Ratio of satisfied employee (%), etc. Customer satisfaction with respondent employee, No. of calls handled per employee (#), etc.

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Speaking of measures for each CRM activities, it is recommended to equip measures for planning, doing, and learning besides outcome ones listed below. For instance, frequency of planning or analysis cycle, execution rate of planned CRM activities, and the existence or frequency of formal feedback/wrap-up meeting can mirror the degree of planning, doing, and learning for a subjective CRM activity, respectively. Thus, the PDOL measures except outcome ones are somewhat of CRM quality management, which is to control the entire processes of implementing CRM strategy internally.

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4. Infrastructure Measures in infrastructure domain are underdeveloped relatively to other evaluative domains. However, since infrastructural factors indicate a sort of foundations of CRM process, those are in need of periodical examinations with survey from internal employees or quantitative proxy measures exemplified in table 4.4. Although the measures in table 4.4 could be evaluated quantitatively, contrary to other domains’ quantitative measures, those figures are not derived from internal database. Therefore, measuring infrastructural factors requires organizational-initiated intention to manage them. With this point, it is quite recommended to designate a person in charge of managing infrastructural factors.

5. METHODOLOGY FOR CRM ASSESSMENT No matter which CRM assessment system you selected, it should be implemented through a systematic diagnosis methodology. Especially for the measurement systems of corporate-wide customer strategy such as CRM Scorecard, a company should arrange preparatory proceedings and pre-defined diagnostic protocols because such systems usually contain multi-trait and multi-method by multi-layer. This section exemplifies a diagnostic methodology considerable when a company adopts a CRM assessment system of a multiple evaluative domains, subjects, themes, and measures.

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Note that the diagnostic processes addressing here are not necessarily confined to only CRM scorecard.

5.1. CRM Diagnostic Process

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Considering Industrial / Corporate Characteristics The CRM strategy for bank or telecommunication that both of them can get 100 % of customer trading data must be different from the one for manufacturing or retailer that can acquire only partial data. Therefore, the first step to diagnose corporate CRM should be to consider industry or business type that the company belongs to. It can provide an opportunity to customize evaluative factors, measures, and methods into the subject’s peculiar situation.

Figure 5.1. CRM Diagnostic Process.

Selecting Evaluative Factors After considering its own business situation, a company can adopt or reject evaluative domains, subjects, and themes appropriate to the company. For an instance of evaluative domain, manufacturing, non-profit organization, or public service institution might withdraw organizational performance domain from their CRM diagnosis because they can hardly detect the relational dynamics between their CRM strategies and their organizational performance indexes. In the case of a company separating marketing and CRM, they might withdraw the evaluative subject such as customer acquisition from its CRM process assessment because such company usually tends to regard acquiring customer as of traditional mass marketing. Surely, it is desirable to involve all the evaluative factors in CRM scorecard even though the subject company wants to drop one of them.

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Weighting Evaluative Factors Selected evaluative factors now have to get their own weight to calculate the scores of upper level factors. For example, the subject of customer retention in the domain of CRM process would be evaluated in terms of a weighted average of the figures from the themes of 2nd buying inducement, loyalty program, and prevention of defection. Therefore, weighting the factors properly based on the present state of CRM is a precondition to derive a valid result of CRM diagnosis.

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Assigning the Representatives for Evaluative Factors Although a lot of traditional CRM indexes could be measured automatically from databases, still more indexes remain to be measured manually. In the same way as most kinds of business strategy assessment, you may find more the latter than the former when you diagnose CRM. Therefore, it is quite recommended to assign the person in charge of each evaluative factor selected before, who is responsible to gather high-quality and reliable data for it [7]. Adopting Measures and Methods As addressed above, diagnostic measures are varied by measurement methods as well; questionnaires for capturing perceptual evaluation, objective but survey-based measures (e.g., existence of customer defection model), and objective and database-feeding ones (e.g., customer retention rate) [37]. Thus, it is needed to check whether or not the selected measures are feasible in the company, adjust them to its situation when declining the measuring methods, or consider replacing them with alternative ones. For extreme cases of this issue, while some companies often reject to questionnaires for measuring perceptual level as diagnostic measures due to the overcredulity on objective way of measurement, the other ones adopt only survey-based diagnosis due to poor database management or internal political issues. Undertaking Diagnosis When undertaking diagnosis of CRM with the measures adopted before, it is advisable to take survey-based diagnosis with questionnaires in advance if possible. Besides the advantage that it can see the overall status of the company’s CRM quickly, it can also prevent the carry-over effect between two types of measures (i.e., objective and perceptual) for same factor, deriving reliable diagnosis results consequently: the results of objective measurements might influence the results of perceptual measurements, but not vice versa. If a

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serious inconsistency between the different measuring methods for the same factor is found, a close examination with retests or observations is inevitable.

Evaluating and Reporting After analyzing the points scored in individual evaluative factors, the results should be reported and shared with top managements and relevant executives. Note that key implications from the report should be based not only on fragmentary comparisons among evaluative factors but also on causal relationships between them [24]. It is a matter of course that the exact causality should be based on statistical analysis of plenty of data, which would be gathered by many repetitive diagnoses. However, the predefined causal model in CRM measurement system can help you to understand the causality between evaluated factors logically even though it was the first diagnosis.

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5.2. Measurement Scale and Scoring Criteria The characteristics of multi-trait and multi-method to assess corporate CRM bring about various types of measurement scales. The figures scored in each measure should be transformed into the standardized points by predefined scoring criteria, and then interpreted to derive future directions by appropriate rubrics. Here are several types of measurement scales and those scoring criteria for diagnosing corporate CRM. 





Likert scale: It is the most popular interval scale to measure the level of respondent’s agreement on the subjective concepts or facts. Although five or seven point scale is most likely to be used in perceptual measures, it is desirable to consider 10 point scale to be marked on an identical scoring system (e.g., a maximum scale of 10 or 100 points) with other measures. Binary: This scale is for inquiring into the truth of the matter by answering with yes/no or O(true)/X(false) (e.g., existence of the person in charge of customer win-back). Generally speaking, the full score (e.g., 10 on 10-point scale) is given to yes or true, otherwise zero. Ratio: It represents the proportion of the subject to total and is expressed in percentage (e.g., the proportion of VIP contribution to

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total customers). It needs to set scoring criteria by ratio intervals, which might be different according to the contents of the measures. Amount of money: It shows monetary value expressed in currency unit (e.g., average contribution value per customer). It is quite difficult to transform a monetary value directly into a standardized point because the level of the measured figures could be interpreted differently according not only to subjects but also its industries. Therefore, it is desirable to transform it into a percentage-based value after comparing with other relevant monetary measure, and then score it by using interval-based scoring criteria. Quantity (number): This is the measurement scale to show the number of time or person. According to its purpose, various kinds of units such as man, count, time, and frequency, could be adoptable. (e.g., daily frequency of customer contacts). Like monetary scale, relative evaluation through comparing with other relevant measures or interval-based scoring system would be more appropriate because absolute evaluation would not be available for this kind of measure, Multi-choice: Multi-choice questionnaires let respondents to select more than one among the suggested items. (e.g., multiple choices of departments/teams participated in CRM TFT for evaluating its structural cross-functionality). This type of scale can also adopt the interval-based scoring system after calculating the proportion of selected items to total suggested.

Transforming each scale into the standardized score makes the measure comparable not only to other CRM measures but also to the level of identical measure of competitors or best practices.

6. BUSINESS CASES There might be two different purposes of CRM assessment. First, for the companies to start out corporate CRM, it could provide them a guideline or roadmap of CRM implementation, based on the present conditions of their resources and capabilities [3]. Second, for the companies that have driven CRM strategy, it could provide them improvement plans or realignment of their CRM strategies,

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based on the up-to-date results and those causes. A few brief cases in such two purposes are discussed in this section.

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6.1. Starting Point of CRM See the Unseen Customer Group New-derm (pseudonym), listed on NYSE, is a global manufacturer of cosmetics and dietary supplements, which operates its business based on network marketing. Although it has shown a glaring growth during the last decade, they it faced a challenging situation recently caused by a keen competition of the product categories, a recent worldwide depression, and the advance of normal companies into MLM (multi layer marketing) market, hence deciding to initiate CRM strategy. Its Korean branch, designated as a test bed of corporate CRM strategy, first commenced diagnosing its present conditions based on CRM scorecard [19]. The key message from the diagnosis was that they should focus on unseen customer group. They found the fact that, the customers in lowest level graded by their past segmentation criteria, who simply purchase and consume their products but not participate in MLM business, had been the driving force of the past growth and would be getting more importance in the future. They had actually regarded only distributors in MLM business as their customer. They immediately developed an epochal consumer management strategy by the two CRM initiatives. Firstly, they developed a new customer equity model for consumers based on both CLV (customer lifetime value) and CRV (customer referral value), which makes it possible to segment consumers effectively and differentiate strategies for each segment. The other one was to bring in a new index called ‘share of house’, the ratio of the repetitively-used categories in the consumer’s house to total product categories of New-derm. It could let the company monitor the relationship quality between the consumer and New-derm. Its CRM-based viewpoint has improved traditional business processes as well. They changed the function of VOC (voice of customer) from a simple call center into a strategic department to manage customer knowledge [6, 25]. They analyzed the VOCs positively, summarized them into one-paged ‘customer report’, shared the report company-widely, and started reforming the chronic problems every month. Another small win was to develop an algebraic model to predict customer defection, which shows the accuracy increased by more than 200% compared with the existing one. The Korean branch became the winner of the most innovative enterprise of 2009 international business award, and branches

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all over the world including the U.S. headquarter are studying the case of Korean branch in global strategic conferences periodically.

Escape from the Bureaucracy Established in 1979, the Korea Industrial Technology Association (KOITA) is a public institution mandated to lay the foundation for the nation's industrial technology development, and to strengthen the innovative capabilities of Korean companies. Although KOITA that had been under the control of Ministry of Science and Technology, had been blessed with compulsory customer loyalty [29] which is usually found in monopoly businesses, it suddenly faced the question of whether or not it needs to be existed continuously as an independent organization provoked by the merger and abolition of Ministry of Education and Ministry of Science and Technology. In fact, other private organizations have been providing similar but sometime better services with KOITA’s ones, the demand of supporting the establishment of corporate RandD center as the core service domain of KOITA has been apparently diminishing. Therefore, to KOITA, CRM is not an additional strategy but an indispensible choice to validate its continuance in terms of customer viewpoint. The three-months-lasting diagnosis with CRM scorecard [19] since October 2008 let KOITA understand why its members had been getting more churned and rejected rejoining. The biggest change to KOITA was the conversion of recognition about their members, from spontaneously-coming service demanders to real ‘customers’ whom KOITA should provide value-creating services and try to maintain good relationship with. For example in expiration management, they quitted the old overbearing practice as faxing one-way document notifying membership expiration and pressing payment of membership fee; rather, they try to derive membership renewal through stepwise channels such as e-mail, phone, and fax for given periods before expiration [30]. Beyond a passive service provider in the way of request-then-response, KOITA is resuscitating as a customer-oriented institution that practices customer acquisition, retention, and expansion strategies through the services of industrial technology policy support, education and training, RandD support, dissemination of information, survey and research, awards and exhibition, and international cooperation.

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6.2. Realignment of CRM Strategy Employee Satisfaction, the Origin of Customer Satisfaction Like Wells Fargo and Royal Bank of Canada, which are well known for their successful CRM strategies, Fine-Equity Bank in Korea is one of the big players that have implemented their enterprise-wide CRM successfully. Their enterprise-wide CRM system developed from 2003 to 2006 provided the bank with an opportunity to integrate resources and capabilities related to implement corporate customer strategy [3, 21]. However, there were some doubts about whether or not the outcome of the CRM system has been shown in real business performance, consequently having them to diagnose it with CRM scorecard [19] on the whole. The results of the diagnosis showed its current CRM performance scrupulously. Although the performance of customer retention (e.g., customer defection rate) among CRM processes has been improved explicitly, the performances of customer acquisition and expansion process, which demand more employees’ cooperation but have stronger impact upon organizational performance, were disappointed [21]. These results have them recognize that they have been missing another critical factor: the people. The private bankers have rejected to share their customer knowledge through others, and the tellers have been reluctant to use the CRM system when they faced with customers. The in-depth investigations revealed that these situations’ were caused by employee dissatisfaction, which come from inappropriate reward system, incompatible evaluative criteria, and coercive atmosphere of CRM [21]. Hereupon, the second mission of FineEquity CRM project became clearer; it was to enhance the level of employee satisfaction [4]. They restructured its CRM reward system, readjusted teller’s role and responsibility, and developed a systematic education program of CRM. The effects of these initiatives began to emerge quickly as the acquisition rate of new customer and cross-selling index were increased dramatically at the end of 2008. By 2008, Fine-Equity Bank was the first in profitability per customer in Korea and awarded by Euromoney as the best private bank in Korea for four consecutive years [21]. Mark a New Era in Manufacturer’s CRM Strategy Maeil Dairies Co. Ltd. is one of the major dairy manufacturers in Korea. Positioned themselves as a child care service provider earlier compared with competitors, Maeil Dairies has operated their own CRM strategies such as ‘Pre-mom School’, which is a kind of acquisition program for the prospective, and ‘0to7’, which is the biggest portal site of child care in Korea. However,

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there was a critical defect in their strategies. Since the departments in charge of each program were separated, and to make matters worse, they could hardly get customer purchasing data, they could not guarantee the connections between such marketing initiatives [1]. For example, they had no idea if the prospective customers participated in ‘Pre-mom school’ grew into real customers and if their actual customers were visiting the portal and using its services. A systematic diagnosis with CRM scorecard [19] let them focus on the critical issue and build an innovative solution. They found that many people who enjoyed various customer events turned out to be just cherry pickers, and they arrived at the conclusion that they should endeavor to get customer purchasing data directly in any way to solve such problem. The CRM TFT members met to map out a plan over several months to induce infant formula customer to register the product code printed on the can through the web site. However, many executives argued against the plan at that time. It was because a major competitor affiliated with a cash-back service provider had already been accumulating the points of purchased products automatically for their customers without the trouble of registering the product code on the web site by themselves. They doubted about it as, who wants to participate in the program in spite of such trouble even though they could get the points automatically if they purchase the competitor’s product? After many complications, the new loyalty program was settled at last in January 2009. Although the expected coverage of registered product through the program was below 10%, surprisingly it reached over 50% in the first month. Moreover, contrary to the competitor’s one, the customers participated in the program were merely driven by pure spontaneity. It skyrocketed the customers’ perception of program participation, and made a retrenchment in its operating cost beyond comparison with competitors. Since Maeil Dairies now understand the real customers of infant formula, they can start a life-stage marketing ranged from infant to adult by suggesting milk, yogurt, cheese, and soft drink according to their life stages. Although the cases above came from different industries and situations, there is a common point to all. A systematic and rigorous diagnosis on corporate CRM provide organizations with the opportunity to avoid stereotyped CRM activities and find a value creating strategy fitted to their own situations [33].

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CONCLUSION It is not necessary to consider CRM performance measurement if a company regards its CRM as a customer information management system or supportive functions of marketing analytics [20]. CRM performance measurement might prove its worth only when a company adopts CRM as a customer-oriented business strategy and concentrates its resources and capabilities on value-creating activities through customer relations [13, 34]. At this moment, CRM performance measurement would bring the answers for how the present conditions of our CRM are, which what problems it has, and where we should go for advancement. To reach such answers more effectively, it is quite recommended to prepare a corporate CRM measurement framework, making it possible to measure not only CRM activities unfolded to external customers but also the facet of customer experience as the response to the activities and the organizational infrastructures as its internal readiness [16]. In addition to these, if a company wants to recognize its CRM as a substantial strategy supporting its organic growth, it has to monitor its organizational financial performance and try to find a connection with its CRM strategy. Although the practical implications of CRM assessment are getting becoming more important recently, I have found that many organizations were still reluctant to assess their CRM. The crucial reason revealed was that they have misconstrued the assessment of CRM as the evaluation of the CRM team. However, such misunderstanding could be resolved if they understand the essence of CRM exactly. Although data analysis and planning for CRM activity could be done in a CRM team, its execution must be realized through collaborations between many departments that have direct or indirect connections with their customers. That is to say, CRM assessment is not to evaluate the performance of the CRM team itself but to evaluate the enterprise-wide organizational capability. Thus, before measuring performance of corporate CRM, it needs to convince the top management first that the result of CRM assessment should not be a plea of praise and or blame on the CRM team because its responsibility lay with the whole organization of course. In concluding this chapter, there are no organizations as that either doing the perfectnessare doing CRM perfectly or doing nothing for CRM, no matter whether they are using the name termed CRM [20]. It means, as long as they have customers, CRM performance measurement should be regarded as an indispensible indispensable management activity no matter of what their CRM maturity. Business cases of CRM Scorecard wshould be discussed.

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ACKNOWLEDGMENTS 

This chapter is based on o

o

o



Kim and Kim, “A CRM performance measurement framework: Its development process and application,” Industrial Marketing Management 38 (2009) 477–489. Kim et al., “Integration of firm's resource and capability to implement enterprise CRM: A case study of a retail bank in Korea,” Decision Support Systems 48 (2010) 313–322. Kim et al., “CRM Strategy: Its Principles and Applications,” Ch.12. CRM Performance Measurement, SciTech Media Pub., 2009, Seoul, Korea. This work was financially supported by The Korea Sanhak Foundation in the year of 2009.

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[22] Kim, J., Suh, H., and Hwang, H. (2003). A Model for Evaluating the Effectiveness of CRM using the Balanced Scorecard. Journal of Interactive Marketing, 17(2), 5-19. [23] Kolay, M.K. and Sahu, K.C. (1992). International journal of production economics. 28(3), 321-339. [24] Lebas, M. J. (1995). Performance measurement and performance management. International Journal of Production eEconomics, 41(1/3), 23-35. [25] Lin, Y., Su, H.Y., and Chien, S. (2006). A knowledge-enabled procedure for customer relationship management, Industrial Marketing Management, 35(4), 446-456. [26] Lindgreen, A., Palmer, R., Vanhamme, J., and Wouters, J.(2006). A relationship-management assessment tool: Questioning, identifying, and prioritizing critical aspects of customer relationships. Industrial Marketing Management, 35(1), 57-71. [27] Netemeyer, R.G., Krishnan, B., Pullig, C., Wang, G., Yagci, M., Dean, D., Ricks, J., and Wirth, F. (2004). Developing and validating measures of facets of customer-based brand equity. Journal of Business Research, 57(2), 209-224. [28] Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460-469. [29] Oliver, R. L. (1999). Whence Consumer Loyalty? Journal of Marketing, 63(Special Issue), 33-44. [30] Payne, A., and Frow, P. (2004). The role of multichannel integration in customer relationship management. Industrial Marketing Management, 33(6), 527-538. [31] Ray, G., Muhanna, W.A., and Barney, J.B. (2005). Information technology and the performance of the customer service process: a resource-based analysis. MIS Quarterly, 29(4), 625-652. [32] Reinartz, W., Krafft, M., and Hoyer, W. D. (2004). The Customer Relationship Management Process: Its Measurement and Impact on Performance. Journal of Marketing Research, 41(3), 293-305. [33] Rigby, D. K., Reichheld, F. F, and Schefter, P. (2002). Avoid the Four Perils of CRM. Harvard Business Review, 80(2), 101-109. [34] Roh, T. H., Ahn, C. K., and Han, I. (2005). The Priority factor model for Customer Relationship Management system success. Expert Systems with Applications, 28(4), 641-654.

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[35] Rust, R.T., Zeithaml, V.A., and Lemon, K.N. (2000). Driving Customer Equity: How Customer Lifetime Value Is Reshaping Corporate Strategy. New York: The Free Press. [36] Toni, A. D., Nassimbeni, G., and Tonchia, S. (1995). An Instrument for quality performance measurement. International Journal of Production Economics, 38(2/3), 199-207. [37] White, G.P. (1996). A survey and taxonomy of strategy-related performance measures for manufacturing. International Journal of Operations Production Management, 16(3), 42-61. [38] Wilson, H., Daniel, E., and McDonald, M. (2002). Factors for Success in Customer Relationship Management Systems. Journal of Marketing Management, 18(1), 193-219. [39] Zablah, A.R., Bellenger, D.N., and Johnston, W.J. (2004). An evaluation of divergent perspectives on customer relationship management: Towards a common understanding of an emerging phenomenon. Industrial Marketing Management, 33(6), 475-489.

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Reviewed by Chanwook Park The President of Korean Academic Society of CRM CRM Scorecard is a measurement framework of corporate CRM strategy that has been developed through an industrial-academic cooperation of many years. However, this measurement system could be a consulting tool for building CRM strategy as well in that it plays an important role in guiding a company into an optimized CRM strategy. I expect this book can provide relevant academics and practitioners with a direct assist for their research studieses and practices because it illustrates more detailed model, measures, and diagnostic methodology. Hyunchul Ahn Professor of School of Management Information System at Kookmin University Frankly speaking, many prior researches and practical frameworks for CRM evaluation have limitations as they have been still at the level of a concept and provided normative suggestions without theoretical basis, respectively. CRM scorecard seems to overcome such limitations, though. Especially, it is a great idea to equip the framework with the necessary but overlooked evaluative factors such as PDOL measures, which make it possible to control the entire processes of implementing CRM strategy. Nevertheless, such a measurement scheme might have a concern that it has to go through

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somewhat complicated steps to apply a CRM scorecard. Therefore, to make a better practical contribution to industry, it is necessary to build a decision support system to aid assessors in doing a CRM evaluation.

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Chapter 3

INTER-ORGANIZATIONAL SOCIAL CAPITAL AS RELATIONSHIP INVESTMENTS. AN EMPIRICAL INVESTIGATION OF THE EFFECTS ON CUSTOMERS’ RELATIONSHIP SATISFACTION Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

Mariachiara Colucci and Manuela Presutti Department of Management, University of Bologna Via Capo di Lucca, 34 - 40126 Bologna (Italy)

ABSTRACT In marketing and strategy research the construct of relationship satisfaction has been considered one of the most important outcome of buyer-seller relationships as an increased satisfaction of business partners entails high productivity facilitating the co-ordination of activities. Much of existing studies have suggested that more interest should be placed on integrating the satisfaction construct into the larger body of interorganizational social capital theory. In fact buyers (i.e.,business customers) and sellers are not atomistic entities free to undertake any competitive action within their own resource constraints. Rather, they are embedded in a network of social relationships that can influence their 

Tel. (+39) 0512098090 Fax: (+39) 051246411 Email: [email protected]

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Mariachiara Colucci and Manuela Presutti competitive behaviour, according to the idea that social embedded structures can shape the strategic action in business markets. Though a consensus exists regarding the importance of interorganizational social networks for relationship satisfaction, there is no conclusive evidence on which social capital configurations are most beneficial to reinforce relationship satisfaction. By investigating seller– buyers relationships, we address the extent to which different dimensions of social capital – relational, structural and cognitive - can affect the customer’s perceived relationship satisfaction, in the business-to-business setting of the Italian retail apparel industry. We assume that both strong social ties (i.e., relational and cognitive dimensions) and weak social ties (i.e., structural dimension) are positively related to customers’ relationship satisfaction. As noted in the literature, in fact, the structure of relationships is not all that matters, yet the content of these relationships matters as well. The main empirical contributions of our work show, on the one hand, the positive impact of both structural and cognitive dimensions of inter-organizational social capital and, on the other hand, a non significant impact of the relational dimension on relationship satisfaction. Our findings help to address the need to detail and to test different approaches, that refer to inter-organizational satisfaction creation and to social capital, providing evidence of the importance of integrating such articulated constructs. The conceptual novelty of our approach resides in developing a framework where inter-organizational social capital is considered a proxy of relationship investments, with the aim to integrate network theory studies with a strategic analysis of a relationship marketing construct. While literature has highlighted the importance of relationship investments of any kind made by sellers on behalf of regular customers, we explicitly highlight that customers tend to be more committed and satisfied with sellers who actively make intangible investments toward them in terms of structural and cognitive efforts.

THEORY The importance of inter-organizational relationships in marketing and strategy research has received considerable attention (Dyer and Singh, 1998; Yli-Renko et al., 2001; Macintosh and Lockshin, 1997; Molm, 1991; Bagozzi, 1995). Traditionally these studies consider business inter-organizational networks as organizational arrangements, that are intermediate alternatives to hierarchically organized firms and market mechanisms, which have been traditionally proposed as the two main tools to obtain resources (Coase, 1937; Williamson, 1975). In business-to-business settings, in particular, the basic

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assumption of marketing and strategy research is that buyers and sellers play important roles for the performance of each other. This literature suggests that business customers engage in closer relationships with their sellers mainly to manage uncertainty and resource constraints. Therefore, it seems necessary for these players to make mutual adaptations to obtain high reciprocal satisfaction from the on-going business relationships (Hakansson, 1982; Yli-Renko et al., 2001). In this line, researchers look at relationship satisfaction as one of the most important outcome of buyer-seller relationships (Smith, 1998). In fact, in recent years relationship satisfaction has been considered a relevant concept for understanding inter-organizational relationships in business-to-business markets, as an increased satisfaction of buyers and sellers entails high productivity facilitating the co-ordination of activities (Ganesan, 1994; Macintosh and Lockshin, 1997). Anderson and Narus (1990: 66) define relationship satisfaction as “a positive emotional state, resulting from the overall assessment of all aspects of the relationship between the businesses”. Similarly, Molm (1991: 477) states that the satisfaction of one party with the interchange relationship is “an emotional reply to a cognitive assessment based on how good or bad a relationship is judged to be”, and Klein and Roth (1993: 39) describe it as “an emotional state arising from experiences connected to an aim, action or condition”. Although several empirical findings have confirmed the impact of relationship satisfaction on important relational outcomes such as behavioural loyalty (Bolton et al., 2008; Cannon and Perreault, 1999; Sharp and Sharp, 1997 De Wulf et al., 2001), researchers have paid little or no attention to the analysis of potential factors that influence the relationship satisfaction construct (Coyles and Gokey, 2005; Dwyer et al., 1987). Along these lines, studies typically consider satisfaction as a relational phenomenon (Bolton, 1998; Macintosh and Lockshin, 1997), nevertheless much of existing research has mainly focused on segmentation and measurement issues, suggesting that more interest should be placed on integrating this construct into the larger body of inter-organizational relationship theory (Cannon and Perreault, 1999). In fact buyers and sellers are not atomistic entities free to undertake any competitive action within their own resource constraints. Rather, they are embedded in a network of social relationships that can influence their competitive behaviour (Granovetter, 1985; Uzzi, 1997). Within this embeddedness perspective, research about inter-organizational social capital has gained considerable prominence and it has been embraced by management scholars as a potential theoretical basis for the analysis of

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potential antecedents of relationship satisfaction (Nahapiet and Ghoshal, 1998; Burt, 1992). The general tenet is that a more developed inter-organizational social capital, in terms of both the number and the quality of the ties, is more beneficial to reinforce the satisfaction of the relationship between buyers and sellers (Larson, 1992; Lesser, 2000). Further, literature suggests that under conditions of uncertainty and complexity, the adjustments and coordination of actions among buyers and sellers are only possible in the presence of mutual inter-organizational social capital (Lin, 2001; Nahapiet and Ghoshal, 1998). This advances the idea that social embedded structures can shape sophisticated business action (Granovetter, 1985) influencing the strategic and the organizational aims of business partners during the management of their relations. Although academics recognize the importance of inter-organizational social networks for relationship satisfaction, empirical evidence on the nature and the extent of this impact is still scarce, especially in business-to-business markets (e.g., Anderson and Weitz, 1992; De Wulf et al., 2001; Bolton, 1998). In addition there is too little agreement on social capital configurations that are most beneficial to reinforce relationship satisfaction. Extant research has in fact typically adopted an unidimensional approach assuming that social capital always exerts a positive effect on relationship satisfaction (Lin, 2001; Nahapiet and Ghoshal, 1998). By investigating seller–buyers relationships in a business-to-business setting, we intend to explore both at theoretical and empirical levels the extent to which different dimensions of social capital can affect the customer’s perceived relationship satisfaction. The unit of analysis for the study is represented by inter-organizational business relationships between a seller and her/his buyers (i.e., customers) and we measure the satisfaction from the buyer’s point of view as a cumulative effect over the course of her/his relationship with the seller. Our conceptual baseline is that a more developed inter-organizational social capital between buyers and their seller is more advantageous to reinforce the buyers’ relationship perceived satisfaction (Tsai and Ghoshal, 1998; Larson, 1992; Gulati, 1999). The term social capital was originally used to describe those relational resources, embedded in cross-cutting personal ties, useful for the development of individuals in community social organizations (Jacobs, 1965). Recent research has applied social capital theory to analyze the set of relations that a single actor (individual, or firm) has instituted with other people, and to identify the ways in which these relations are exploited to reach personal goals. The general tenet of this literature is to consider social capital as a factor

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influencing in a positive way the action of actors, simplifying the process of tacit knowledge creation (Nonaka, 1991), the spread of relevant information, and the establishment of trust relationships (Moran, 2005; Lesser, 2000). In this chapter we adopt Lin’s view of social capital and define it as the “resources embedded in a social structure of relationships which are accessed and/or mobilized in purposive actions” (Lin, 2001: 22). This definition allows us to focus on social capital as a dynamic inter-organizational relational resource characterized by three different dimensions - structural, relational, and cognitive (Nahapiet and Ghoshal, 1998; Yli-Renko et al., 2001). The structural dimension refers to the overall pattern of connections between actors, that is who you reach (Burt, 1992) determining the extent of resources that are within a business partner’s reach. The cognitive dimension is represented by the development of common goals, norms and reciprocal expectations between partners (Larson, 1992). Finally, the relational dimension concerns the types of personal relationships people have developed through a history of interactions (Granovetter, 1992), by focusing on respect, trustworthiness and friendliness. The novelty of our approach resides in considering inter-organizational social capital as a proxy of investment in a buyer-seller relationship, with the aim to integrate network theory studies (Nahapiet and Ghosal, 1998; Lin, 2001) with a strategic analysis of a relationship marketing construct (Berry, 1995; Goff et al., 1997). In fact, there is evidence of the impact of relationship investment on satisfaction in business marketing relationships (Anderson and Narus, 1990; Ganesan, 1994; Smith and Barclay, 1997; Baker et al., 1999): customers tend to be more committed and satisfied with sellers who actively make efforts toward them. Investments of time, effort and, more generally, of unrecoverable resources in a network of relationships generate psychological bonds that encourage buyers to stay in those strong relations (i.e., based on social capital), and set expectations of reciprocation.

HYPOTHESES Though a consensus exists regarding the importance of interorganizational social networks for relationship satisfaction, there is no conclusive evidence on which social capital configurations are most beneficial to reinforce relationship satisfaction. In this chapter, therefore, we address the extent to which different dimensions of social capital – relational, structural and cognitive - affect the customer’s perceived relationship satisfaction. In

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setting our research hypotheses, we assume that both strong social ties (i.e., relational and cognitive dimensions) and weak social ties (i.e., structural dimension) are positively related to buyers’ relationship satisfaction. As noted in the literature, in fact, the structure of relationships is not all that matters, yet the content of these relationships matters as well (Moran, 2005). As far as the relational dimension is concerned, we focus on interorganizational trust which has become one of the key variables in discussing this dimension of social capital. The positive influence that trust has on relationship satisfaction has been empirically verified in several studies, which have deeply analyzed this link (e.g., Anderson and Narus, 1990; Bolton et al., 2008; Yli-Renko et al., 2001). In a business-to-business setting, Oliver (1997) defines trust as one party’s confidence in an exchange partner’s reliability and integrity which reduces the risk of reciprocal opportunism. This definition is consistent with many others, in marketing and strategic literature (e.g. Jackson, 1985; Dwyer et al., 1987), where trust is typically seen as an indicator of a growing relationship that fosters high levels of commitment. Commitment in turn can be defined as one’s enduring desire to maintain a valued relationship (Oliver, 1997; Jackson, 1985). Thus, business partners, tied by high levels of relational social capital (i.e., inter-organizational trust), rely on relational exchanges to maximize their reciprocal satisfaction over a series of transactions mainly based on informal relations. In particular, we suppose that high levels of relational social capital between a seller and her/his buyer can affect the buyer’s relationship satisfaction in three ways: 1) they reduce the buyer’s perception of risks associated with potential opportunistic behavior of her/his seller; 2) they increase the buyer’s confidence that short-term inequities with her/his seller will be solved over a longer period; 3) they reduce the transaction costs in exchange relationships. Based on these suggestions, we formulate the following hypothesis: Hp. 1: The greater the relational social capital between a buyer and her/his seller, the higher the buyer’s relationship satisfaction.

Our second hypothesis relates to the role of the cognitive dimension of social capital in buyer-seller relations. This dimension of social capital can exert a positive impact on relationship satisfaction, reinforcing role interactions (Ring and Van de Ven, 1994) and intensifying the ability of buyers to recognize and evaluate the quality and value of products furnished by their seller (Cohen and Levinthal, 1990; Lane and Lubatkin, 1998). Examples of benefits associated with a cognitive relationship are feelings of

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familiarity, identification, friendship, and social support (Bolton et al., 2008). The importance of cognitive identification, in particular, for relationship satisfaction in business-to-business markets should not be surprising given that business relationships are ineherently social processes and cognitive identification has been suggested to be the main motivation for customers to visit their sellers (Berry, 1995). Several studies have verified that different perspectives and goals between sellers and buyers can be solved by high levels of reciprocal cognitive identification (Verhoef, 2003; Mittal and Kamakura, 2001). In fact, this allows buyers to better evaluate the seller’s commitment on their relationship (Yli-Renko et al., 2001) and urges the seller to furnish personalized services to her/his customers, such as informing them about new products and designs. Therefore we hypothesize the following:

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Hp. 2: The greater the cognitive social capital between a buyer and her/his seller, the higher the buyer’s relationship satisfaction.

Finally, we investigate the impact of inter-organizational structural social capital between a seller and her/his buyers on buyers’ relationship satisfaction. We advance that a seller, acting as a link to a broad marketplace, is able to connect buyers with other external partners allowing them to access to novel valuable information and significant strategic resources. According to literature, the structural dimension of social capital increases the diversity of potential networks of a business actor, providing access to different sources of new information and knowledge (Lin, 2001). In particular, in business-tobusiness markets high levels of structural social capital with a seller provide greater opportunities for enlranging the buyer's business opportunities reinforcing the level of satisfaction originating from the relationship management. To this respect, several studies have verified that the longer the relationship, the more the satisfaction of a buyer depends on the number and quality of new information, knowledge and resources assured by the relationship with her/his seller. As a consequence, high levels of structural social capital reinforce customers’ perception of the extent to which a seller devotes resources, efforts, and attention intended to maintain or enhance their relationships. This theorizing leads to the following hypothesis: Hp. 3: The greater the structural social capital between a buyer and her/his seller, the higher the buyer’s relationship satisfaction.

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METHOD

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Research Design and Sample We conducted our research in the business-to-business setting of the retail apparel industry in Italy, and targeted the survey at business customers (i.e., buyers). The focus of this study is on inter-organizational business relationships between a seller and her/his buyers, and we measure the satisfaction from the buyer’s point of view as a cumulative effect throughout her/his relationship with the seller. In particular in the study buyers are represented by multi-brand apparel stores, as this is a market characterized by a wide range of competing apparel companies (i.e., many potential sellers), and by opportunities for business customers to switch. The retail apparel context is particularly interesting for the aim of this study because social features of a relationship can be reasonably expected to be important in a setting characterized by a high level of personal contact and advice, where buyers have to decide the right mix and quality of product categories and brands to sell in their stores. The seller in the study is a well known Italian producer - and brand owner - of jeanswear operating in the medium-end segment of the market. In order to identify our sample we decided, coherently with our research, to consider those business customers that had made more than 3 purchase orders with this seller, consisting in at least 50 items, in the last three years. This to exclude occasional buyers for whose is not possible to speak about a proper business relationship with the seller. In fact, in order to capture the level of a buyer’s satisfaction with the seller, we acknowledge that satisfaction is a focal consequence of working partnerships (e.g., Anderson and Narus, 1984; Frazier et al., 1988). The seller thus identified a total of 300 customers localized in Italy who meet these requirements. We tested the hypotheses using survey data. After developing the measurement scales by conducting literature reviews, we developed a questionnaire structured in the closed question-answer form. We personally contacted our population of buyers (identified with the procedure explained before), then the questionnaire was administered through personal interviews, with respondents completing it themselves in the presence of the interviewer. The same researcher administered all questionnaires to minimize the impact of reactive experimenter effects. In total 101 out of the 300 identified buyers agreed to participate in the study, resulting in a response rate of 33.7%, This response rate is analogous to

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other studies that involve business-to-business purchasers (e.g., Kumar, 1995; Boles et al., 1997). In addition a comparison between early and late respondents did not reveal any significant nonresponse bias in the two groups (Armstrong and Overton, 1977). Multi-brand stores cover a wide range of retailers, and our sample include boutiques (22% of cases), wholesalers (55%), national chains in franchising (7%), department stores (16%). As the unit of analysis for this research is the relationship satisfaction between business customers and the seller, we were able to gather a total of 101 usable observations with our survey.

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Measures Measurement scales in the study were all available in the literature and consisted of 7-point Likert questions anchored by “strongly disagree” and “strongly agree”. We used multi-item scales for all constructs to enhance content coverage (Churchill, 1979). After verifying the internal consistency of each construct with Cronbach’s alphas, to ensure data validity we assessed the unidimensionality of the constructs by performing a principal component analysis. This revealed for all measures the presence of the first factor accounting for a large portion of the total variance, moreover each item’s loading exceeded the cut-off point of 0.5. The dependent variable represents an affective state stemming from the positive overall evaluation of the customer’s relationship with the seller (Anderson and Narus, 1984). We also conceptualize the relationship satisfaction as a cumulative effect which displays over the relationship, differently from the construct of satisfaction that is more transaction-specific (Anderson et al., 1997). Three Likert-type statements were then used to measure the degree to which a customer feels she/he has a good relationship with the seller (De Wulf et al., 2001). Basing on the developed conceptual framework, we articulated the social capital construct into its three main dimensions (e.g, Yli-Renko et al. 2001; Tsai and Ghoshal, 1998; Nahapiet and Ghoshal, 1998): relational, structural and cognitive. Therefore we measured three independent variables, described as follows. The relational dimension describes the type of personal relationships that a single actor develop with other external actors through continuous interactions over time, giving particular attention to the feelings of respect and trust (Krackhardt, 1996, 2000; Nooteboom et al., 1997). We used a composite that

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Mariachiara Colucci and Manuela Presutti

basically captures the sharing of common expectations and aims, and the lack of opportunistic behavior. The original measure proposed by Yli-Renko and Colleagues (2001) was here purified by deleting an item, as suggested by the exploratory factor analysis and by the analysis of inter-item correlations. As a result, we measured the relational dimension of social capital with two items. The cognitive dimension reflects the degree of personal, social ties between the two actors in the relationship. In other words, we measured the sharing of codes, languages and paradigms that facilitate the understanding of common goals among partners, and drive actions in the social system (Brass, 1994). This measure, based on Nahapiet and Goshal (1998) and Yli-Renko and Colleagues (2001), is composed of four-items. The structural dimension of social capital typically refers to the development of social/economic ties between different actors and by the location of a single actor in the complex social structure. Accordingly, our measure reflects the degree to which the relationship with the seller provides the customer with the access, or introduction, to a broader set of new profitable business contacts and new valuable information and knowledge (Yli-Renko et al., 2001). Three items were used to assess the presence of this construct. An additional variable was included in the model in the attempt to control for the influence of inter-organizational geographical distance on the relationship between social capital and relationship satisfaction. Several studies advance that a short inter-organizational distance brings together buyers and sellers favoring face-to-face interactions and exchange of reciprocal knowledge, allowing business partners to obtain high reciprocal satisfaction from the ongoing business relationship. As a consequence, the larger the distance between the two partners, the less is the intensity of social capital development, becoming more difficult for a buyer to obtain high levels of satisfaction from her/his relationship with the seller (Torre and Gilly, 2000; Rallet and Torre, 2000; Davenport, 2005; Boschma, 2005; Freel, 2003). The geographical distance was measured as the geographical proximity (in kilometers) between the seller and each business customer. Finally, we also controlled for the size of customers in the sample, by using the annual sales.

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Test of Hypotheses and Results The correlation matrix displayed (see Table 1) does not reveal serious collinearity among predictor variables. However, we checked for multicollinearity in the model by examining the variance inflation factors (Neter et al., 1985). The VIF values ranged from 1.0 to 1.9 (see Table 2) and were well below the upper limit of 2.5 (e.g., Allison, 1999). Table 1. Correlation matrixª Relationship Log Structural Cognitive Relational Log(Sales) Satisfaction (Distance) Relationship Satisfaction

1.000

Structural

0.373 (0.000)

0.511 (= 3.857, and product class is not 11, then the customer value is low If days sales outstanding < 65.800, profit margin < 0.393 or >= 0.848, length of customer relation >= 3.857, and product class is 11, then the customer value is high

80.5%

3

4

5

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6

75.2%

99.4%

55.3%

89.9%

Table 3. The confusion matrix of the DT model Actual Predicted 0 1

0

1

2635

517(16.40%)

47(4.13%)

1092

4.2. Decision Trees + Decision Trees As the five important attributes are identified in the decision rules (c.f. Section 4.1), to develop the two-stage decision tree prediction model, these five attributes in the dataset are used to train and test another C4.5 decision tree. Table 4 lists the decision rules of DT + DT.

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Table 4. The decision rules of DT + DT ID 1

Rule If days sales outstanding >= 65.800, then the customer value is low If days sales outstanding >= 65.800, and 0.393= 0.848, length of customer relation < 3.857, and ordering custom = regular, then the customer value is low If days sales outstanding < 65.800, profit margin < 0.393 or >= 0.848, length of customer relation < 3.857, and ordering custom = not regular, then the customer value is low If days sales outstanding < 65.800, profit margin < 0.393 or >= 0.848, length of customer relation >= 3.857, and product class is not 11, then the customer value is low If days sales outstanding < 65.800, profit margin < 0.393 or >= 0.848, length of customer relation >= 3.857, and product class is 11, then the customer value is high

2 3

4

5

6

Accuracy 89.2% 80.5% 75.2%

99.4%

55.3%

90%

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Table 5. The confusion matrix of the DT + DT model Actual

0

1

0

2635

517(16.40%)

1

47(4.13%)

1092

Predicted

Similar to DT, Rule 4 has the highest rate of prediction accuracy, i.e. 99.4%. The rate of prediction accuracy of Rule 6 in DT + DT is slightly better than the one in DT. On the other hand, Table 5 shows the confusion matrix of DT + DT, in which the Type I and II errors are 16.40% and 4.13% respectively. These results indicate that DT and DT+DT are identical. Although two-stage DT can not improve average prediction accuracy, it does reduce the original variables to 5 important ones. This means that managers only need to consider these five variables to predict customer value instead of the 26 variables.

4.3. Association Rules + Decision Trees

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Ching-Tzu Tsai, Chih-Fong Tsai and Chia-Sheng Hung

Besides using DT as the attribute selection stage, association rules (AR) are considered as the first component in the two-stage approach. Association rules are also used to identify important attributes, and these attributes are used to develop a DT prediction model. In order to find the meaningful association rules, we set the following criteria:

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1. Support＞＝55; 2. The item number in a item set＞＝3; 3. Probability of the rule＞＝0.5; Importance＞＝0.3.Table 6. The 20 association rules Prob.

Importance

1.000

0.31791931

1.000

0.30832923

1.000

0.30832923

1.000

0.31791931

1.000

0.30832923

1.000

0.31791931

1.000

0.31791931

1.000

0.36317790

1.000

0.38323284

1.000

0.34936491

1.000

0.30257691

1.000

0.30771730

1.000

0.37657696

1.000

0.30832923

1.000

0.30832923

Association rule sales in units = 400 - 1260, ordering custom = quarterly -> customer contribution = 1 exchange rate = 32.765 - 33.025, profit margin = 0.2667839663 - 0.5298946923 -> customer contribution = 1 exchange rate = 32.765 - 33.025, length of customer relation = 5.4487506232 - 7.5842678744 -> customer contribution = 1 exchange rate = 32.765 - 33.025, days sales outstanding = 12.526632552 - 54.3913889472 -> customer contribution = 1 Price(in domestic currency)= 2.65 - 6.49, profit margin = 0.1759765096 - 0.42160376015 -> customer contribution = 1 price in domestic currency = 2.65 - 6.49, ordering custom = quarterly -> customer contribution = 1 order number = 400 - 1260, ordering custom = quarterly -> customer contribution = 1 length of customer relation = 5.4487506232 - 7.5842678744, product class = 11 -> customer contribution = 1 length of customer relation = 5.4487506232 - 7.5842678744, profit margin = 0.2667839663 - 0.5298946923 -> customer contribution = 1 length of customer relation = 5.4487506232 - 7.5842678744, profit margin= 0.1759765096 - 0.42160376015 -> customer contribution = 1 days sales outstanding >= 74.8481589888, exchange rate < 30.84 -> customer contribution = 0 currency = TWD, ordering custom = not regular-> customer contribution = 0 Sales in dollars = 205290.329106022 - 844817.106783437, profit margin = 0.2667839663 - 0.5298946923 -> customer contribution = 1 Unit cost = 185.785909090909 - 426.611111111111, product class = 11 -> customer contribution = 1 Unit cost = 185.785909090909 - 426.611111111111, ordering custom = quarterly -> customer contribution = 1

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Customer Value Analysis: A Two-Stage Data Mining Approach 1.000

0.3265841

1.000

0.36317790

1.000

0.34422527

0.962

0.3247031

0.962

0.3247031

109

Profit margin >= 0.42160376015, length of customer relation = 5.4487506232 - 7.5842678744 -> customer contribution = 1 ordering custom = quarterly, profit margin = 0.2667839663 0.5298946923 -> customer contribution = 1 ordering custom = not regular, length of customer relation < 3.2096961244 -> customer contribution = 0 days sales outstanding >= 74.8481589888, exchange channel = customs -> customer contribution = 0 days sales outstanding >= 74.8481589888 -> customer contribution = 0

Consequently, 20 association rules are generated, which are listed in Table 6. Particularly, 8 different attributes are identified in these rules, which are further used to develop the DT prediction model. They are sales in units, exchange rate, price (in domestic currency), order number, length of customer relation, days sales outstanding, currency and order custom. Table 7. The decision rules of AR + DT

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ID 1 2 3

4

5

Rule If days sales outstanding >= 65.800, then the customer value is low If days sales outstanding >= 65.800, and 0.393= 0.848, and length of customer relation >= 3.857, then the customer value is low If days sales outstanding < 65.800, profit margin < 0.393 or >= 0.848, length of customer relation >= 3.857, and ordering custom = regular, then the customer value is low If days sales outstanding < 65.800, profit margin < 0.393 or >= 0.848, length of customer relation >= 3.857, and ordering custom = not regular, then the customer value is low

Table 8. The confusion matrix of the AR + DT model Actual

0

1

0

2032

265 (11.53%)

1

650(21.71%)

2344

Predicted

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Accuracy 89.2% 80.5% 57.8%

75.2%

99.4%

110

Ching-Tzu Tsai, Chih-Fong Tsai and Chia-Sheng Hung

Table 7 lists the five decision rules generated by the DT model over the 8attribute dataset. For predicting customers with the high value, Rule 2 of the DT model provides the highest rate of prediction accuracy, i.e. 80.5%. On the other hand, in Rule 5 to predict low value customers has the highest rate of prediction accuracy, i.e. 99.4%. Table 8 shows the confusion matrix of AR + DT, its Type I and II errors are 11.53% and 21.71% respectively.

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4.4. Comparisons and Discussions Table 9 compares the prediction performances of the three prediction models. As found previously, the results of DT and DT + DT are identical. Hence, their Type I and II errors, and prediction accuracy are almost the same. For AR + DT, although its Type I error rate is the lowest, its Type II error is much higher than the other two models. For prediction accuracy, DT and DT + DT provide 81.6%, which is higher than AR + DT. However, it is interesting to note that since lowering the Type I error can retain more valuable customers, AR + DT is particular suitable for this task although its average accuracy is slightly lower than the other two models. Table 9. Comparisons of DT, DT + DT, and AR + DT Model DT DT + DT AR + DT

Average accuracy 81.6% 81.6% 80.4%

Type I error 16.40% 16.40% 11.53%

Type II error 4.13% 4.13% 21.71%

In short, the two two-stage approach using DT not only can provide similar prediction performances to single DT, but also largely reduce the number of input attributes from the original 26 attributes to 5 attributes. In addition, the results indicate that DT is superior to the association rules to select significant attributes for accurately predicting the value of customers. However, for the Type I error particularly, AR + DT performs the best. Therefore, the 8 variables selected by AR can be regarded as the factors to retain valuable customers. Therefore, AR + DT can be used to predict customers with high value.

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CONCLUSION To achieve successful customer relationship management, it is necessary to effectively analyze valuable customers. Customer value analysis can allow organizations to retain valuable customers. This paper applies the data mining techniques to develop three different customer value prediction models. In particular, a two-stage approach to construct the customer value prediction model is considered, in which the first stage is for performing the variable selection task, and the second stage is to develop the prediction model based on the output of the first stage. This paper takes an automobile parts company as the example, and the decision tree and association rules as the data mining techniques for the experiments. The experimental results show that the single decision tree (DT) model performs the same as the two-stage DT model (i.e. DT + DT) in terms of prediction accuracy and Type I/II errors. However, the difference between them is that the DT model uses 26 attributes for training and testing, but DT + DT only needs 5 attributes since the first stage of DT has identified 5 important attributes for training the second stage of DT. On the other hand, association rules (AR) reduces the attributes from 26 to 8, and allow the DT model trained by the 8 attributes perform the best in the Type I error although its average accuracy is lower than the other two models. Therefore, AR + DT is particularly useful to predict customers with high value only. There are several issues, which could be considered in the future. First of all, different industries can be examined using the two-stage data mining approach for customer value prediction. In addition, other techniques, which can generate useful rules, can also be used for further comparisons, such as fuzzy logic.

REFERENCES Berger, D., and Nasr, N. I. (1998). Customer lifetime value: Marketing models and application. Journal of Interactive Marketing, 12(1), 17–30.

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Blattberg, R. C., Malthouse, E. C., and Neslin, S. A. (2009). Customer lifetime value: Empirical generalizations and some conceptual questions. Journal of Interactive Marketing, 23(2), 157–168. Breiman, L., Friedman, J. H., Olshen, R. J., and Stone, C. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth. Buchanan, R. W. T., and Gillies, C. S. (1990). Value managed relationships: The key to customer retention and profitability. European Marketing Journal, 8(4), 523–526. Chen, Z., and Dubinsky, A. J. (2003). A conceptual model of perceived customer value in e-commerce: A preliminary investigation. Psychology and Marketing, 20(4), 323–347. Cheng, C.-H., and Chen, Y.-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36(3), 4176–4184. Colombo, R., and Jiang, W. (1999). A stochastic RFM model. Journal of Interactive Marketing, 13(3), 2–12. Dash, M. and Liu, H. (1997) Feature selection for classification. Intelligent Data Analysis, 1, 131-156. Foster, G., Gupta, M., and Sjoblom, L. (1996). Customer profitability analysis: Challenges and new directions. Cost Management, 10(1), 5–17. Glady, N., Baesens, B., and Croux, C. (2009). Modeling churn using customer lifetime value. European Journal of Operational Research, 197(1), 402– 411. Guyon, I. and Elisseeff, A. (2003) An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182. Han, J., and Kamber, M. (2006). Data mining: Concepts and Techniques. New York: Morgan Kaufman. Holbrook, M. B. (1994). The nature of customer value: An axiology of services in the consumption experience. In R. T. Rust et al. (Eds). Service quality: New directions of theory and practice. Thousand Oaks, CA: Sage. Hughes, A. M. (2000). Strategic database marketing: The masterplan for starting and managing a profitable customer-based marketing program, 2nd edition. New York: McGraw-Hill. Hwang, H., Jung, T., and Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications, 26(2), 181–188. Kumar, V., and Reinartz, W. J. (2006). Customer Relationship Management: A Data-based Approach. New York: John Wiley and Sons, Inc.

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McCarty, J. A., and Hastak, M. (2007). Segmentation approaches in datamining: A comparison of RFM, CHAID, and logistic regression. Journal of Business Research, 60(6), 656–662. Parasuraman, A. (1997). Reflections on gaining competitive advantage through customer value. Journal of the Academy of Marketing Science, 25(2), 154–161. Quinlan, J. R. (1996). Improved Use of Continuous Attributes in C4.5. Journal of Artificial Research, 4, 77-90. Raphel, N., and Raphel, M. (1995). Loyalty Ladder. New York: Harper Collins Publishers Inc. Tsai, C.-F. and Chen, M.-Y. (2010) Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Systems with Applications, 37(3), 2006-2015. Ulaga, W., and Chacour, S. (2001). Measuring customer- perceived value in business markets. Industrial Marketing Management, 30(6), 525–540. Witten, I.H. and Frank, E. (2005) Data mining: practical machine learning tools and techniques. California: Morgan Kaufman. Woodruff, R. B. (1997). Customer value: The next source for competitive advantage. Journal of the Academy of Marketing Science, 25(2), 139–153. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.

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In: Customer Relations Editor: Victoria J. Farkas, pp. 115-128

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Chapter 5

CUSTOMER RELATIONS AND LOYALTYBASED SEGMENTATION: A B2B APPROACH IN THE TOURISM INDUSTRY Irene Gil-Saura, María-Eugenia Ruiz-Molina and Beatriz Moliner-Velázquez Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

Marketing Department, University of Valencia

ABSTRACT Maintaining customers is increasingly difficult, since many service industries are moving from high personal contact to remote contact via the telephone and Internet. In the current context where the Internet is threatening the tourism service value chain, customer loyalty is more appreciated by service providers than ever. Companies are investing in customer relations in order to establish closer bonds with their buyers. Since customer loyalty has been related with value and this, in turn, with the benefits obtained by customers from their relationships with their suppliers, the present paper aims to identify the relational benefits most influencing customer loyalty in a B2B setting in order to shed light on the link between customer relations and loyalty. A CHAID algorithm is performed, resulting in five segments differing in their level of customer loyalty and their unequal perceptions of confidence and social benefits perceived from their relationships with their main providers. Therefore, the importance of relational benefits differs across customer segments.

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Keywords: Loyalty; customer relations; relational benefits; retail travel agency; B2B.

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1. INTRODUCTION Marketing is defined as “the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large” (AMA, 2007). In comparison to the previous definition (AMA, 2004), where Marketing is consider to focus on the establishment, development and maintenance of continuous relations between buyer and seller as a source of mutual benefits for both parts, the new definition positions marketing as providing long term value rather than narrowly as an exchange of money (short-term) for the benefit of the shareholder/organization. Marketing managers should understand the factors that explain the establishment of long-lasting relations in order to manage their customer portfolio in an effective way (Srivastava et al., 2001). Nowadays, in highly competitive markets, customer segmentation is recommended to effectively target the market (Shani and Chalasani, 1992). This is specially important for the travel agent industry, since the wide use of the Internet is leading to disintermediation and travel agents’ outcomes depend on their abilities to capture the market’s loyalty and ensure access to travel information while providing value-added services (Lewis et al., 1998). Several variables have been considered in this segmentation process. Although descriptive variables have been traditionally considered in customer segmentation, due to the increasing complexity and diversity of customer behavior, these criteria are less and less valid. Thus, following a relational approach, one of the suggested segmentation criteria is based on commitment (Story and Hess, 2006) and loyalty (Yoon and Kim, 2000). In this way, by defining segments of consumers with different assessments of their loyalty to their main suppliers, managers of travel agent providers can design marketing strategies according to the characteristics of each type of customer. Service marketing literature has emphasized the importance of relational benefits perceived by the customer both in B2C and B2B settings as an

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antecedent of customer satisfaction and loyalty towards the service provider (Gwinner et al., 1998; Patterson and Smith, 2001; Hennig-Thurau et al., 2002; Yen and Gwinner, 2003; Park and Kim, 2003; Thao and Swierczek, 2008). In particular, there is evidence of the presence of this type of benefits and their positive influence on business performance of service providers in the context of relationships between travel agents and tourists. Notwithstanding, scarce attention has been paid to the study of this construct and its influence on business results of service providers in the scope of the interorganizational relations in the tourism industry. Thus, from a relational marketing approach, the present paper aims at analyzing the existence of differentiated segments of travel agents regarding their loyalty towards their main suppliers and the influence on this variable of the perceived relational benefits. In this sense, focusing on relationships of travel agents with their main suppliers, the present paper aims to assess the validity of relational benefits as a segmentation variable of travel agents based on their loyalty to their main provider, as well as to identify the relational .benefits most influencing customer loyalty in a B2B setting.

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2. LITERATURE REVIEW In the tourism industry, it has been argued that companies that generate value for the customer, satisfy more effectively their needs and enjoy increased customer loyalty (Dubé and Renaghan, 1999a, 1999b, 2000). Customer value has been defined as the trade-off between the multiple benefits and sacrifices of a supplier's offering as perceived by customers (Ulaga and Eggert, 2002). The benefits that customers receive as a result of their exchanges in their long-term relationships with service providers have been called “relational benefits” (Gwinner et al., 1998; Hennig-Thurau et al., 2002). These benefits refer to any effort actively made by the provider to increase the value perceived by the customer beyond the product or service received (De Wulf and Odekerken-Schröder, 2003). In a B2C setting, three types of relational benefits have been identified, i.e. confidence benefits, social benefits and special treatment benefits (Gwinner et al., 1998; Patterson and Smith, 2001; Hennig-Thurau et al., 2002; Yen and Gwinner, 2003; Park and Kim, 2003). In contrast, research into relational benefits in interorganizational markets is more recent and shows no conclusive evidence. Thus, in B2B markets there

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is not a single conceptualization of the multi-dimensionality of relational benefits construct. In fact, several typologies of relational benefits have been proposed from the definition of two (Homburg et al., 2005), three (Barry and Terry, 2008) and even five types of relational benefits (Ulaga and Eggert, 2003) in the B2B context. In particular, Homburg et al. (2005) distinguish between core benefits and add-on benefits , while Barry and Terry (2008) identify three types - i.e. core offering benefits, sourcing benefits and operations -, and Ulaga and Eggert (2003) point our the existence of five types of relational benefits - i.e. product benefits, service benefits, know-how benefits, social benefits and time-to-market benefits -. None of these proposals have been widely accepted by academics yet, since these typologies seem to be highly context-dependent. In this sense, the meaning of “core benefits” in the former studies varies significantly across individuals and over time. Notwithstanding, in B2C relationships, the classification of Gwinner et al. (1998), that distinguishes confidence benefits, social benefits and special treatment benefits, is widely acknowledged and applied in different contexts. In particular, confidence benefits refer to psychological factors, such as less anxiety and the perception of an inferior risk in the result of the transaction. Social benefits refer to the establishment of personal bonds between customers and store employees, which are translated into familiarity and even friendship. Finally, special treatment benefits combine economic benefits and service customization, e.g. discounts, time savings and additional services not available to other customers derived from the consideration of special client due to the relationship history. In the tourism industry, special treatment benefits have been widely used in order to generate customer loyalty (Palmer and Mayer, 1996; Buhalis, 1998). In this sense, loyalty programs have been widely developed under several formats under a relational marketing approach (Stauss et al., 2001). Notwithstanding, this objective is not always achieved, due to the proliferation of these loyalty programmes (Gilbert, 1996; Meyer-Waarden, 2006) and the customer perception that the benefits received do not compensate the effort required (García de Madariaga and Reinares, 2007). The development of additional benefits is required from the service supplier in order to guarantee customer loyalty. Loyalty has been defined as the combination of a positive attitude and repeat patronage (Dick and Basu, 1994), jointly considering in this way the two traditional attitudinal and behavioural perspectives discussed in the literature. According to the attitudinal perspective, loyalty is defined as an attitude that sometimes involves a relationship with the company. From the

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second perspective, loyalty is considered in terms of behaviour revealed through repeat purchase (Uncles et al., 2003). Customer loyalty concept has been commonly related to commitment. In particular, committed customers do not only show loyal behaviors but also imply themselves emotionally in the continuity of the relationship (Story and Hess, 2006). Loyalty in the service sector is perhaps more difficult to conceptualize than it is for products due to the intangibility, inseparability, variability and perishability linked to services (Bloemer et al., 1998; Mittal and Lassar, 1998), being vital the role of customer relations. In view of the above evidence, it is expected that loyalty and relational benefits will enable segments of heterogeneous consumers to be identified that differ significantly regarding these variables as well as their antecedents and consequences of the relationship with the retailer.

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3. METHODOLOGY In order to achieve the aim of this paper, we perform a personal survey to retail travel agency managers regarding their relationships with their main suppliers (e.g. tour operators, wholesale travel agents, etc.). Table 1 displays the main characteristics of this quantitative research. Table 1. Research Technical Details Universe Geographical scope Sample size Sample design Data collection period Collected information Statistical techniques Statistical software

Retail travel agencies Spain (Madrid, Barcelone and Valencia) 309 retail travel agencies Personal survey to travel agency managers February-March 2009 - relational benefits - loyalty - classification data CHAID Analysis of variance (ANOVA) SPSS version 15.0

The questionnaire include items for measuring relational benefits (16 items adapted from Gwinner et al., 1998) and loyalty (4 items adapted from Zeithaml et al. (1996) and Bloemer et al. (1999)), as well as classification

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variables. All items were rated on a 5-point scale ranging from “strongly disagree” (1) to “strongly agree” (5). Retail travel agencies in the sample have been selected among the largest companies in terms of total assets in the company’s balance sheet following their NACE -National Classification of Economic Activities- and TEA -Tax on Economic Activities- codes obtained from SABI (Iberian Accounting Analysis System), a database that contains the annual reports of the most important Spanish and Portuguese companies. 309 valid questionnaires were obtained from retail travel agency managers in the three main Spanish cities during February and March 2009. Table 2 shows the sample distribution in terms of the main classification variables. From the data gathered through the questionnaire, an Automatic Interaction Detection (AID) is performed considering loyalty as the key variable in the segmentation process. The AID is a nonparametric statistical analysis technique that is used to study the relation of dependency between a dependent variable and several predicting variables (independent or explanatory variables) operating sequentially through analysis of variance in order to detect those independent variables that contribute the most to explaining the variability in the dependent variable (Kass, 1980). Table 2. Sample Descriptors

Main activity of the travel agency: retailer wholesaler Type of provider: - tour operators/wholesale travel agencies - carriers - hotels - other service providers Length of patronage: - 0-5 years - 6-10 years - 11-15 years - More than 15 years

Number

%

278 31

89.97 10.03

216 48 35 10

69.9 15.5 11.3 3.2

81 101 54 73

26.21 32.69 17.47 23.62

In particular, the Chi-square AID (CHAID) procedure subdivides a dataset into exclusive and exhaustive segments that are compared through the chi-

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square statistic (Magidson, 1993). In the present study, CHAID has been used for characterizing travel agent loyalty based on perceived relational benefits. This is expected to provide heterogeneous segments that differ significantly not only in the dependent and independent variables, but also regarding other variables. The resulting segments are compared through an analysis of variance (ANOVA) regarding other variables. In this way, we aim at determining if the subjects belonging to each group constitute a travel agent segment and, thus, also behave in a significantly different way regarding variables that have not been considered for the CHAID. Finally, the distinguishing features of the travel agent segments are identified.

4. RESULTS

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In order to classify retail travel agents based on their loyalty towards their main supplier and relationship benefits, a CHAID algorithm is used considering customer loyalty as the dependent variable, and 16 items referred to confidence, social and special treatment benefits as independent variables. The results are shown in Figure 1.

Risk estimate: 0.635. Standard error: 0.055.

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Figure 1. Classification tree generated by CHAID algorithm.

As can be seen, the CHAID algorithm generates five final segments of travel agents. In order to further characterize each final segment, we test the significance of the differences between segments regarding loyalty and relational benefits. The average values for each segment and the values of the ANOVA test are shown in Table 3. Table 3. CHAID variables: Average values and significant differences 1 Loyalty I am a loyal customer of this provider

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Confidence benefits B1. I believe there is less risk that something will go wrong. B2. I feel I can trust this PROVIDER B3. I am confident the service will be performed correctly by this PROVIDER B4. I have less anxiety when I buy in this PROVIDER B5. I know what to expect from this PROVIDER B6. I get the provider’s highest level of service.

2

3a

3b

3.13 3.67 3.68 4.09 2.93 3.47 3.96 4.03 3.03 3.57 4.02 4.07

3.44 3.66 4.08 4.14

F

4.27 15.01 4.56 119.37 4.45 27.03

4.42 17.87 4.54 34.63

Subgroups for  = 0.05* 1 and 2 and 3a; 3b and 4 1; 2; 3a and 3b; 4 1; 2; 3a and 3b; 4 1 and 2; 3a and 3b; 4

3.09 3.61 3.98 4.08

1; 2; 3a and 3b; 4

1.84 3.00 4.00 4.00

5.00 4826.45 1; 2; 3a and 3b; 4

3.22 3.58 4.02 3.97 2.97 3.43 3.66 3.90

Social benefits

2.53 3.84 2.65 4.00

B7. I am recognized by this PROVIDER’s employees

2.87 3.02 2.90 4.03

B8. I am familiar with the employee(s) that perform the service B9. I have developed a friendship with this PROVIDER’s employees

4

2.66 2.90 2.36 4.24

2.44 2.90 2.64 3.91

4.67 34.52 4.30 19.33 3.57 37.63 3.75 18.82

1; 2; 3a and 3b; 4 1 and 2; 3a and 3b; 4 1 and 2 and 3a; 3b; 4 1 and 2 and 3a; 3b and 4

3.72 42.53

1 and 2; 3a; 3b; 4

3.59 23.69

1 and 2 and 3a; 3b and 4

3.55 27.24

1; 2 and 3a; 3b; 4 1 and 2 and 3a;

B10. They know my name.

2.31 2.80 2.83 4.07

B11. I enjoy certain social

2.34 2.49 2.51 3.75 3.25 23.55

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Customer Relations and Loyalty-Based Segmentation aspects the relationship

3b; 4 1

Special treatment benefits B12. I get discount or deals from this (PROVIDER) that most consumers don’t B13. The prices I get from this PROVIDER are better than those other customers get B14. They do services for me that they don’t do for most customers. B15. I am placed higher on the priority list when there is a line. B16. I get master service than most customers. No. of companies %

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*

123

2

3a

3b

2.68 3.01 3.00 3.71 2.56 2.92 2.98 3.74

4

F

3.54 18.11 3.57 13.77

3.49 10.74 2.55 3.01 3.10 3.69

2.59 3.01 2.95 3.73

2.75 3.04 2.98 3.70

2.78 3.06 3.00 3.70

Subgroups for  = 0.05* 1 and 2 and 3a; 3b and 4 1 and 2 and 3a; 3b and 4 1; 2; 3a; 3b and 4

3.59 14.51

1 and 2 and 3a; 3b and 4

3.52 12.05

1 and 2 and 3a; 3b and 4

3.57 12.37

1 and 2 and 3a; 3b and 4

31 84 59 69 64 10.1 27.4 19.2 22.5 20.8

In order to test the significance of the differences between the types of retailers, the Tukey post-hoc multiple comparison test is used. Only the statistically significant differences between groups at the 5% level are shown.

Regarding the dependant variable for the CHAID algorithm, i.e. travel agent loyalty towards its main supplier, it is observed that the segments 3b and 4 show a significantly higher average value in comparison to the other segments. Travel agent segments with higher levels of loyalty show also higher scores for relational benefits in comparison to the rest of customer segments. Notwithstanding, different profiles are observed for each segment depending on the relative importance of the different types of relational benefits. The first segment is the smallest, and shows the lowest levels of loyalty and relational benefits. In contrast, the second segment is the biggest and, although showing also low levels of customer loyalty, confidence benefits are significantly more appreciated than in the first segment. Confidence benefits make also the difference between the segments 2 and 3a that show similar levels of loyalty towards the main supplier. The two segments with the highest levels of customer loyalty are travel agents included in segments 3b and 4. Notwithstanding, confidence and social benefits distinguish these two segments. In particular, the fourth segment is the

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one showing the highest levels of customer loyalty and relational benefits. Regarding special treatment benefits, there are valued significantly more positively by segments 3b and 4, in comparison to travel agents included in segments 1, 2 and 3b. Additionally, we test the significance of the differences between the different segments in terms of travel agent turnover, company size (measured through number of employees and volume of assets), number of years of commercial relationship with the main supplier, and percentage of purchases. No significant differences across groups have been found. For the sake of brevity, we do not reproduce such results, being available upon request to the authors. Therefore, descriptive variables do not show significant differences, while relational variables such as loyalty and relational benefits allow managers of travel agent suppliers to define segments of customers that differ in the intensity of their perceptions of the benefits provided by the supplier. In general, we distinguish between customers with low levels of loyalty (segments 1, 2 and 3a) that significantly differ in their appreciation of confidence benefits, while highly loyal customers that differs in the importance given to social and special treatment benefits. In this sense, segment 3b could be labeled as “high touch customers”, since they appreciate the social benefits derived from their relationship with their main supplier more than the rest of segments. In general, the higher the evaluation of relational benefits, the higher the loyalty towards the main supplier.

CONCLUSION The literature recognizes the need to segment customers both in B2C and in B2B commerce in order to guarantee the effectiveness of marketing policies in a highly competitive market. From the results obtained in the present paper, we understand that customer loyalty and relational benefits derived from the relationship with the man provider may be two valid criteria for customer segmentation in travel agent markets and to facilitate managers’ decisions regarding their marketing policies. In this way, retailers can design their policies according to the most usual types of customers. In this regard, application of the CHAID algorithm has resulted in five segments of travel agents that differ in their declared loyalty to the service supplier and assessment of relational benefits. This finding allow us to conclude that although a significant part of travel agents show average levels

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of loyalty towards their main suppliers, confidence and social benefits are effective to increase customer loyalty. Notwithstanding, special treatment benefits have not been selected by the CHAID algorithm as one of the most discriminant variables across customer clusters. Managers of travel agent suppliers should concentrate on providing their customers with evidence about confidence benefits and personal contact to generate loyalty, in view of the importance of these variables for segmenting customers depending on their loyalty levels and implement marketing policies according to the segment specifications. Notwithstanding, the present research is a first stage in a study that should be completed with the examination of additional variables. In this sense, behavioral variables, such as patterns of purchase to the service provider should be further examined. Additionally, loyalty has been measured as a declared overall assessment effected by the customer company and, therefore, it might involve a high degree of subjectivity. Triangulation methods may provide further details to this segmentation. In this sense, we understand that the relations between the variables included in our study should be further explored, and thus, the present work opens new research lines. The following step should be the study of additional variables determining customer loyalty, considering as antecedents of this variable customer satisfaction, trust and commitment. Finally, differences between segments across de type of supplier (tour operator, wholesale travel agent, hotel chain, etc.) and market structure (number of alternative suppliers, bargaining power, etc.) should be examined in greater depth in order to further identify the peculiarities of these segments.

REFERENCES American Marketing Association (AMA) (2004): Marketing Definitions: A Glossary of Marketing Committee on Definitions, AMA, Chicago. American Marketing Association (AMA) (2007): Definition of Marketing. www.marketingpower.com . Barry, J. and Terry, T.S. (2008). Empirical study of relationship value in industrial services, Journal of Business and Industrial Marketing, 23 (4), 228–241. Bloemer, J.; De Ruyter, K. and Wetzels, M. (1999). Linking perceived service quality and service loyalty: a multidimensional perspective. European Journal of Marketing, 33 (11/12), 1082-1106.

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Bloemer, J.; de Ruyter, K.; Peeters, P. (1998). Investigating drivers of bank loyalty: the complex relationship between image, service quality and satisfaction. International Journal of Bank Marketing, 16 (7), 276-286. Buhalis, D. (1998). Strategic use of information technologies in the tourism industry. Tourism Management, 19(5), 409–421. De Wulf, K. and Odekerken-Schröder, G. (2003), “Assessing the impact of a retailer’s relationship efforts on consumers’ attitude and behaviour”, Journal of Retailing and Consumer Services, 10 (2), 95-108. Dick, A.S. and Basu, K. (1994). Customer loyalty: toward an integrated conceptual framework”, Journal of the Academy of Marketing Science, 22 (2), 99-113. Dubé, L. and Renaghan, L. M. (1999a). Building customer loyalty - guests’ perspective on the lodging industry’s functional best practices (part I). Cornell Hotel and Restaurant Administration Quarterly, 40, 78–88. Dubé, L. and Renaghan, L. M. (1999b). How hotel attributes deliver the promised benefits—guests’ perspective on the lodging industry’s functional best practices (part II). Cornell Hotel and Restaurant Administration Quarterly, 40, 89–95. Dubé, L. and Renaghan, L. M. (2000). Creating visible customer value. Cornell Hotel and Restaurant Administration Quarterly, 41, 62–72. García de Madariaga, J. and Reinares, P.J. (2007): Mejora de la gestión de los programas de fidelización multisponsor: una propuesta operativa basada en las preferencias de los consumidores. Proceedings XIX Encuentro de Profesores de Marketing, Vigo 20-21 September. Gilbert, D.C. (1996). Relationship marketing and airline loyalty schemes. Tourism Management, 17 (8), 575-582. Gwinner, K.P.; Gremler, D.D. and Bitner, M.J. (1998). Relational benefits in services industries: The customer's perspective. Journal of the Academy of Marketing Science, 26 (2); 101-114. Hennig-Thurau, T.; Gwinner, K.P. and Gremler, D.D. (2002). Understanding relationship marketing outcomes: An integration of relational benefits and relationship quality. Journal of Service Research, 4 (3), 230-247. Homburg, C.; Kuester, S.; Beutin, N., and Menon, A. (2005). Determinants of Customer Benefits in Business-to-Business Markets: A Cross-Cultural Comparison. Journal of International Marketing, 13 (3), 1-31. Kass, G.V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics 29, 119-127.

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Lewis, I.; Semeijn, J.(J.) and Talalayevsky, A. (1998). The impact of information technology on travel agents. Transportation Journal, 37 (4), 20-25. Meyer-Waarden, L. (2006): The effects of loyalty programs on customer lifetime duration and share of wallet. Journal of Retailing, 83 (2), 223236. Mittal, B. and Lassar, W. (1998): Why do customers switch?: The dynamics of satisfaction versus loyalty. Journal of Services Marketing, 12 (3), 177194. Palmer, A.J., and Mayer, R. (1996). Relationship marketing: A new paradigm for the travel and tourism sector. Journal of Vacation Marketing, 2 (4), 326-333. Park, C.H. and Kim, Y.-G. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail and Distribution Management. 31 (1), 16-29. Shani, D. and Chalasani, S. (1992). Exploiting niches using relationship marketing. Journal of Service Marketing, 6, 43-52. Srivastava, R. K., Fahey, L. and Christensen, H. K. (2001). The resourcebased view and marketing: The role of market-based assets in gaining competitive advantage. Journal of Management, 27 (6), 777–802. Stauss, B.; Chojnacki, K.; Decker, A., and Hoffman, F. (2001). Retention effects of a customer club. International Journal of Service Industry Management, 12 (1), 7-19. Story, J. and Hess, J. (2006). Segmenting customer-brand relations: beyond the personal relationship metaphor. Journal of Consumer Marketing, 23 (7), 406-413. Thao, H.T.P., and Swierczek, F.W. (2008). Internet use, customer relationships and loyalty in the Vietnamese travel industry. Asia Pacific Journal of Marketing and Logistics, 20 (2), 190-210. Ulaga, W. and Eggert, A. (2002), Customer perceived value: A substitute for satisfaction in business market. Journal of Business and Industrial Marketing, 17 (2–3), 107-118. Ulaga, W., and Eggert, A. (2003). Developing a Standard Scale of Relationship Value in Business Markets: Development of a Measurement Scale. Working Paper 2/2003. Working Paper Series. Institute for the Study of Business Markets (ISBM) at the Penn State University, University Park.

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Uncles, M.D.; Dowling, G.R. and Hammond, K. (2003). Customer loyalty and customer loyalty programs. Journal of Consumer Marketing, Vol. 20 No. 4, pp. 294-316. Yen, J.R. and Gwinner, K.P. (2003). Internet retail customer loyalty: the mediating role of relational benefits. International Journal of Service Industry Management, 14 (5), 483-500. Yoon, S.J. and Kim, J.-H. (2000). An empirical validation of a loyalty model based on expectation disconfirmation. Journal of Consumer Marketing,17 (2), 120-136. Zeithaml, V; Berry, L.L. and Parasuraman, A. (1996). The Behavioral Consequences of Service Quality. Journal of Marketing, 60 (April), 3146.

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Chapter 6

PROS AND CONS OF LONG-TERM CUSTOMER RELATIONSHIPS Christina Öberg

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Linköping University Department of Management and Engineering SE-581 83 Linköping, Sweden

ABSTRACT This paper deals with the pros and cons of long-term customer relationships. Effects of customer relationships on a long and short-term basis are discussed, with specific focus on how the supplier is affected by a customer relationship. In addition, the paper briefly describes the consequences for customers and other business partners. Three areas given specific attention: (i) revenues versus vulnerability of individual customers, (ii) word of mouth versus badwill, and (iii) ideas for development versus risks of lock-in effects. The paper shows that the effects of long-term customer relationships are not only positive. The downsides need to be weighted to the upsides of such relationships, and suppliers and customers need to consider how they act in and think about a relationship and what risks and benefits are associated with the relationship.



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INTRODUCTION Marketing research has increasingly come to focus on relationships (Achrol and Kotler, 1999; Anderson, Håkansson and Johanson, 1994; Morgan and Hunt, 1994). Based on a literature search on customers and relationships it is concluded that the number of publications on customer relationships doubled between 1993 and 2003 (Öberg, 2004), and the theoretical development continues. From the supplier’s perspective, it is often stated that it is more profitable to retain than to continuously contract new customers (Ford and Håkansson, 2006). Customers are also expected to contribute to business development through their involvement in innovations, for instance (Baldwin, Hienerth and von Hippel, 2006). From the customer perspective, potential arises from products and services that better fit the customer’s needs. Risk is also considered to decrease as a result of the relationship. But, are customer relationships only positive for involved customers and suppliers, and in the wider perspective of other business parties? Based on the idea of disequilibrium shaping development (Kirzner, 1973), for instance, customer relationships may mean that companies continue as previously rather than develop. This paper discusses and illustrates pros and cons of long-term customer relationships. The paper focuses on customer relationship and its consequences for the supplier, yet also briefly discusses how customer relationships affect the customer and other business partners.

CUSTOMER RELATIONSHIPS Customer relationships refer to long-term connections with individual buyers. Such relationships were early acknowledged (Alderson and Cox, 1948; Coase, 1937), but have been given increased focus in recent literature (Öberg, 2004). Stability between customers and suppliers challenges ideas on price as the coordinator for market activities (Richardson, 1972) and also that the market consists of actors that meet only on a temporal transaction basis (Håkansson, 1982). In literature, the customer relationship idea is often underpinned by customers as active parties contributing in various ways to development of the relationship (Håkansson, 1982). This further assumes that customer relationships are closely connected to cooperation between firms. An individual customer may also represent a major part of a company’s revenues, and thus be a valuable asset for the company.

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Explanations for customers and suppliers engaging in long-term interaction differ between various schools of thought: transaction cost theory refers to the costs of constantly looking for new options (Williamson, 1979). Resource dependence theory describes the longevity by attributes of power and dependence (Emerson, 1962; Pfeffer and Salancik, 1978). Social aspects have to various extents been referred to in the literature (Homans, 1958; Sweeney, 1972). And, commitment and trust between firms also suggest that companies would continue to work with each other (Ford, 1980).

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THE CUSTOMER IN A CUSTOMER RELATIONSHIP In a customer relationship, the roles of the customer may extend beyond being the party buying and using the product or service offered (Webster and Wind, 1972). Literature on word of mouth (Kumar, Petersen and Leone, 2007) describes the customer as a co-marketer, and much service marketing literature portrays the customer as a co-producer (Gummesson, 1994; Vargo and Lusch, 2008; Wikström, 1996) or ‘prosumer’ (Normann, 1991; Toffler, 1980), which indicates that the customer is active in the production of services. According to literature on innovations, customers help in developing new ideas (Athaide, Meyers and Wilemon, 1996; von Hippel, 1977, 1978) and providing information (Fang, 2008; Lengnick-Hall, 1996). This indicates that the customer contributes in various ways to the supplying company.

THE GOOD AND BAD OF LONG-TERM CUSTOMER RELATIONSHIPS The above all indicates that customers positively contribute to a supplier company and its development: Based on that relationships include customer retention (Ahmad and Buttle, 2001; Olkkonen, 1996) and also decrease risk (Axelrod, 1984; Verdoorn, 1956), customer relationships would expectedly contribute to revenues in a more prominent way than transaction-based exchanges. Through the customer’s involvement in development and, through word of mouth, in marketing, the customer also contributes to long-term development. However, these aspects come with a down side: risks related to that single customers account for much of a supplying company’s revenues, make the supplier vulnerable. Spread of rumors through word of mouth would

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hurt a supplier, and may do so extensively more than those positive signals sent by customers promoting a product or service (Hocutt, 1998; Kumar et al., 2007). Adjusting to a single customer, may lead to that the supplier’s products do not as well fit with other customers’ requirements, and co-producing and codevelopment indicate that much efforts are placed with single customers, which may be costly and also lead to that development is taken in a direction that does not fit the supplier’s competences or intentions. Table 1 summarizes these positive and negative effects of customer relationships, with a focus on whether the effects are immediate or long-term. The various aspects are discussed in further detail below. Table 1. Pros and cons of customer relationships

Positive

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Negative

Short-term Revenues

Losses of individual customers may severally hurt the supplier financially Each customer becomes a great investment

Long-term Added customers (through word of mouth) Development of ideas, products and company Badwill (through word of mouth) Risks for losses of other customers/inability to establish relationships with other customers based on adjustments to single customers

REVENUES VERSUS VULNERABILITY OF INDIVIDUAL CUSTOMERS From the supplier perspective, the most obvious reason for customer relationships is related to revenues. This can be argued in two ways: (i) customer relationships guarantee a flow of revenues, and (ii) it is cheaper to sell to an existing customer than to acquire a new one. In the sense that customer relationships assure an inflow of money and customer retention reduces the outflow in terms of marketing expenses (Ahmad and Buttle, 2001; Thomas, 2001), there would thus be an advantage in terms of monetary profitability of customer relationships. And this would apply both in the longand short-run perspective.

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The other side of the coin relates to risks. While long-term relationships are expected to reduce risks (Emerson, 1962; Pfeffer and Nowak, 1976; Pfeffer and Salancik, 1978), the risk per customer actually increases. It is often stated that twenty percent of a company’s customers represents eighty percent of its revenues. Should one important customer decide to dissolve its relationship with the supplier (Giller and Matear, 2001; Tähtinen, Matear and Gray, 2000), this would severely harm the supplier financially, and further, through reputation, also otherwise. This latter has been shown in how individual customers base their relationship on that other customers associate with the supplier (Öberg, 2008, 2009; Öberg and Grundström, 2009). Thus, when one customer leaves, others may choose to abandon the supplier as well. Related to the financial aspects of customer relationships is also that while efforts need not as much be placed on finding new customers, there is the cost of maintaining a relationship, and the sunk costs of investments made on a specific customer and adjustments to that specific customer. Thus, the financial aspects of a customer relationship do not only relate to lower costs of marketing and greater revenues based on retention, but include costs of maintaining and adjusting to a customer, risks with a single customer representing a major part of the supplier’s revenues, and risks related to reputation.

WORD OF MOUTH VERSUS BADWILL The value of a customer is by Kumar et al. (2007) stated as the life time revenues less costs of acquiring and keeping the customer, added with profits from customers attracted through word of mouth by the first customer, that would otherwise not had become customers of the supplier. Thus, word of mouth is important, not only to spread goodwill about the company, but also to increase the number of customers and thereby revenues. This in turn is similar to the situation of how individual customers make a supplier more attractive for other customers (Öberg, 2008, 2009; Öberg and Grundström, 2009). Word of mouth thus does not only decrease marketing expenses (as is the way it is often treated in social marketing), but may also prove to be important in the long run. The other side of the coin is negative word of mouth (Kuokkanen, 1996). There is a clear tendency that customers more often make negative than positive remarks. What is more, a customer would be more inclined to listen to negative comments than to positive ones, and further: it is easier for a potential

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customer to resist from establishing a business relationship, than to actually start one. Thus, the risk of badwill communication is larger than positive word of mouth. If a customer is dissatisfied with a business relationship, there is thus also the risk that this spreads to other relationships. This can be argued twofold: the customer would have a tendency to depict the supplier negatively. And, if the customer chooses to leave, this increases the risk that other customer companies do the same, or resist from turning to the supplier in the first place. Taken together, the customer could be seen as a representative of the business relationship. The customer as well as the supplier affects the relationship and the relationship in turn affects these parties. This representation and how the relationship becomes a representative of the (customer and) supplier means that other parties will judge the supplier on activities of the relationship and also on how individual parties talk about the relationship. Word of mouth thus becomes an effective marketing tool, yet research shows that there are larger risks with negative word of mouth than are the benefits of positive one. Thus, risks are that a customer that is not satisfied with the relationship will negatively harm other relationships and further negatively impact the acquisition of new customers. Word of mouth and badwill can have immediate effects, but also impact the supplier long-term. If word of mouth helps to establish other long-term relationships and badwill decreases this ability, this will have long-term consequences for the supplying company.

IDEAS FOR DEVELOPMENT VERSUS RISKS OF LOCK-IN EFFECTS In the even longer run than customer acquisition or losses based on word of mouth and badwill, is that of how customers contribute to development of the supplier company (Baldwin et al., 2006; Thomke and von Hippel, 2002; von Hippel, 1977). Specifically in research on innovations, the customer’s role as co-developer has been stressed (Fang, 2008; Lengnick-Hall, 1996). The customer contributes with ideas or even work on solutions in collaboration with the supplier (von Raesfeld and Roos, 2008). Furthermore, through the testing of ideas, their commerciality may be captured, which benefits the supplier (Baldwin et al., 2006). Literature on open innovations (Piller and Walcher, 2006) increasingly stresses customers as contributing parties in

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innovations. Apart from open innovations, customers mostly work on ideas that will benefit the specific customer. Open innovations, on the other hand, includes that customers participate in innovations to benefit others. But also in the literature where this is not explicitly the case, ideas developed in collaboration with one customer would expectedly be transferred to other customer relationships and products. What is more, the development of innovations and products would be assumed to also develop the supplier company as such. The company remains up to date and able to launch new products and services; its innovativeness increases. The other side of the coin is that when a supplier commits to develop its products (or even organization) to fit the customer’s requests, other ideas may need to be abandoned. The more specialized the products become, the less probable that they will fit with other customers’ wants and needs. In addition, the customer may just provide the ideas, whereas it is not certain that the customer will actually be attracted by the product developed and buy it. This is specifically the case with open innovations, where contributors of ideas may interact with the supplier only for the fun of figuring out and designing new ideas. What is more: radically new products are seldom developed in collaboration with the customer. Instead, it is often the supplier that has to convince the market that a highly advanced product has its benefits. There is also the risk that co-development projects lead to many adjustments and small, but costly development steps, that neither becomes innovations that are attractive for other customers, nor are very effective in terms of invested money. Within the frames of a customer relationship, there is further a risk that development is resisted. Researchers have drawn attention to how disequilibrium put larger focus on developments than times of stability (Kirzner, 1973). A customer relationship could be seen as a stable constitution, which also means that the supplier does not find it important to develop new ideas. If taking this further, long-term customer relationships may counterforce entrepreneurship and innovativeness. Taken together, customer relationships are increasingly associated with the customer as contributing party for development. However, the customer relationship also forms a stability that may actually counterforce development. Ideas developed together with customers may allow for improvements that benefit also other customers, but may also be that customer-specific that they only benefit one single customer. Risks associated with development are that these idea may not be top of the line and may not be radical, but consist of a time-consuming, continuous adjust to an individual customer’s requests.

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CONCLUDING DISCUSSION This paper discussed and illustrated pros and cons of long-term customer relationships. These positive and negative effects can be seen as short-term or immediate effects, and effects that affect the supplier long term. The paper focused on effects that were short and long-term and showed that in each dimension where a customer relationship could be seen as an advantage, there were also factors that counter-forced the positive sides. More specifically, three areas were considered: (i) revenues versus vulnerability of individual customers, (ii) word of mouth versus badwill, and (iii) ideas for development versus risks of lock-in effects. Revenues were considered as an immediate effect, while word of mouth and badwill and customers’ impact on development affect the supplier company also long-term. The three areas focused are interlinked to various extents. Word of mouth and badwill, and ideas for development versus risks of lock-in effects, affect revenues. Badwill increases risks of dissolution, which in turn negatively impact the finances of the supplier company. Company development based on customers’ participation equally impact revenues and the tendency for other customers to consult the supplier. And word of mouth would attract new customers that in turn may contribute with ideas to the supplier. Revenues versus vulnerability of individual customers, word of mouth versus badwill, and ideas for development versus risks of lock-in effects, point at how the supplier is affected by customer relationships. But what about the customer? If looking into these three dimensions, the customer would benefit, or be harmed, strongest by the last one. If product development is not optimized, this will negatively impact the customer. On the other hand, the customer may well consult other parties for better products or services. While it takes both a customer and supplier to establish a relationship, it is dissolved as soon as one party decides to leave (Alajoutsijärvi, Möller and Tähtinen, 2000; Hocutt, 1998). This, and the fact that customers and suppliers are affected differently long- and short-term of the relationship, mean that customers and suppliers will not necessarily treat and consider the relationship in the same manner. And this will in turn impact how they contribute to the relationship. For other business partners, based on the network idea (Anderson et al., 1994; Easton and Håkansson, 1996; Gadde, Huemer and Håkansson, 2003; Johanson and Mattsson, 1994), they would be affected by a single relationship in various ways. If taking into consideration revenues versus vulnerability of individual customers, word of mouth versus badwill, and ideas for

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development versus risks of lock-in effects, the following could be expected: the revenue aspect ultimately means that the customer chooses the specific supplier and thereby does not buy that amount from a competing supplier. The supplier reaching that income, means that the supplier buy more from other parties: sub-suppliers and the like, and also have more money to invest in development and growth. The downside: vulnerability of individual customers, means that the supplier will spend less (or in the worst case: go bankrupt) as a result of a lost customer. For competitors, this may on the other hand be positive, as it allows them as alternatives. Word of mouth would potentially involve additional companies as customers to the supplier, and badwill will mean that no such new relationships are shaped. For competitors to the supplier, this will work the other way around: badwill may lead to customers choosing to turn to them instead, while word of mouth attracts potential customers away from competitors. However, competition has several levels (Lehmann and Winer, 2008), and badwill directed at a single company may actually spill over to competitors, as may word of mouth. Especially new products, that when these are talked about, often help competitors. Thus in certain aspects, that customers start taking about a specific product or need may lead to customers not necessarily turning to the same supplier, but them realizing the potential of a product and starting looking for suppliers of that product. Ideas for development versus risks of lock-in effects, lastly, also might affect other companies. A supplier that develops a product or service together with a customer, or the customer giving information about the products or needs and wants, may exclude other customers from that supplier. They are not given the same attention as would be the case if the supplier did not work as closely with the specific customer to fulfill the specific customer’s needs. On the other hand, those improvements developed together with the customer would often come to other customers’ advantage. Thus, both the lock-in effects and development aspects may affect other customers. In the broader perspective, the constitution of the long-term relationship may lower all over development, which negatively impacts the customer, the supplier and other parties. Taken together, this paper shows that it is not only positive to establish long-term customer relationships. The downsides need to be weighted to the upsides of such relationships, and suppliers and customers need to consider how they act in and think about a relationship and what risks and benefits are associated with the relationship.

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REFERENCES Achrol, R.S. and Kotler, P. (1999). Marketing in the network economy. Journal of Marketing, 63, 146-163. Ahmad, R. and Buttle, F. (2001). Customer retention: a potentially potent marketing management strategy. Journal of Strategic Marketing, 9 (1), 29-45. Alajoutsijärvi, K., Möller, K., and Tähtinen, J. (2000). Beautiful exit: How to leave your business partner. European Journal of Marketing, 34 (11/12), 1270-1290. Alderson, W. and Cox, R. (1948). Towards a Theory of Marketing. The Journal of Marketing, 8 (2), 137-152. Anderson, J.C., Håkansson, H., and Johanson, J. (1994). Dyadic business relationships within a business network context. Journal of Marketing, 58, 1-15. Athaide, G.A., Meyers, P.W., and Wilemon, D.L. (1996). Seller-buyer interactions during the commercialization of technological process innovations. Journal of Product Innovation Management, 13 (5), 406-421. Axelrod, R. (1984). The evolution of cooperation, New York: Basic Books Inc. Baldwin, C., Hienerth, C., and von Hippel, E. (2006). How user innovations become commercial products: A theoretical investigation and case study. Research Policy, 35, 1291-1313. Coase, R. (1937). The Nature of the Firm. Economica, 4 (386), 386-405. Easton, G. and Håkansson, H. (1996). Markets as networks: Editorial introduction. International Journal of Research in Marketing, 13, 407413. Emerson, R. (1962). Power-dependence relations. American Sociological Review, 27, 31-41. Fang, E. (2008). Customer participation and the trade-off between new product innovativeness and speed to market. Journal of Marketing, 72, 90104. Ford, D. (1980). The development of buyer-seller relationships in industrial markets. European Journal of Marketing, 14 (5/6), 339-354. Ford, D. and Håkansson, H. (2006). The idea of interaction. The IMP Journal, 1 (1), 4-27. Gadde, L.-E., Huemer, L., and Håkansson, H. (2003). Strategizing in industrial networks. Industrial Marketing Management, 32, 357-364.

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Williamson, O. (1979). Transaction Cost Economics: The Governance of Contractual Relations. Journal of Law and Economics, 22, 233-261. von Hippel, E. (1977). Has a customer already developed your next product? Sloan Management Review, 18 (2), 63-75. von Hippel, E. (1978). Successful industrial products from consumers' ideas. Journal of Marketing, 42 (1), 9-49. von Raesfeld, A. and Roos, K. (2008). How should a small company interact in its business network to sustain its exchange effectiveness? Creativitiy and Innovation Management, 17 (4), 271-280.

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Chapter 7

INVOLVEMENT AS MARKET CREATION A NEW WAY TO CONSIDER CUSTOMER RELATIONS Johan Gaddefors1 and Alistair R Anderson2 1

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Department of Economics Swedish University of Agricultural Sciences Uppsala, Sweden 2 Aberdeen Business School, Robert Gordon University Aberdeen, UK

ABSTRACT In this research project we examine an up-market Swedish furniture manufacturer to look at their marketing process. We found that they had an innovative approach which involved customers in developing and cocreating the market for their products. Accordingly, this paper describes and conceptualises the novel process in customer relations. Our case exemplifies how the firm, in engaging with the customer, created the market and the business opportunities. We show how the opportunity itself is produced within the interplay of firm and customer. In this process firm and customer jointly established identity, a style of living, and a way of being and becoming. The nature of this coproduction of a 

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Johan Gaddefors and Alistair R Anderson market and business opportunity, and how it was constructed in this interplay, is the focus of our case. The case reveals how market and business opportunity formation is relationally and communally constituted. We make the market concept more relational and show how it is dependent on social interaction.

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INVOLVEMENT AS MARKET CREATION - A NEW WAY TO CONSIDER CUSTOMER RELATIONS In this paper we argue that marketing, or at least in some instances, involves an increasing closeness to the customer. In this way customer relationships have changed over time; such that the most recent forms involve the customer in helping to create the market (Gaddefors and Anderson, 2008). Zwiick et al (2008:164) make this point boldly, suggesting, “the increasingly popular proclamations of the demise of old marketing, characterized by control over brand and demand, is superseded by almost activist-style declarations that new marketing in the 21st century requires the fundamental realization that customers are in charge”. We agree with the thrust of Zwiik et al’s argument, but propose that the “old” style is not quite dead. Brand and demand remain important, but not paramount. Instead what we see is an increasing customer involvement; such that brand and demand are co-created by customers and producers alike. As Arvidsson (2008:326) suggests that, “One of the most important and fundamental trends in contemporary consumer society is the progressive inclusion of consumers in the processes where value is produced around products and brands.” In fact this idea has been around for some time. Gummesson (2007) points out that the futurist Alvin Toffler (1980) first coined the terms “prosumer” and “prosumption” as composites of producer, consumer and consumption. The nature of customer relationships has changed over time. Grönroos (1999) argued that perceptions of marketing as a discipline have undergone a paradigmatic shift. From the foundation of the marketing mix and the embedded 4Ps, the sea change has turned to, among others, relationship marketing (Zontanos and Anderson, 2004). Anderson and McAuley (1999) argued that the concept of the marketing mix and 4Ps marketing was gradually becoming obsolete and being replaced by more interactive relationships. In these more recent approaches it has been suggested that exchanges, although still important, are facilitated through interactions between suppliers and customers, and hence interaction becomes a central marketing concept

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(Grönroos, 2006). Gummesson (2007) describes the old style of customer relationships as a legacy from economics with sellers and buyers who enter into exchange relationships. This view considers the seller the active party who persuades a passive customer. Relational approaches to marketing have partially managed to change this view. McKenna (1991) provides a useful overview to present a more strategic view of relationship marketing. He emphasises putting the customer first and shifting the role of marketing from manipulating the customer to genuine customer involvement. Gardner et al (2000) show how even for the most high tech products increasing customer involvement is important (Chorev and Anderson, 2006). Yet this shift towards a more socialised version of customer relationships is but part of a general shift to recognising the social aspects of business (Jack et al, 2004). Nonetheless, Gummesson (2007) proposes that despite its significance, network is a rare word in general marketing management textbooks, because most are founded on a consumer goods paradigm. By emphasising networkbased marketing theory, the emphasis becomes the interaction between parties. The shift to interactive marketing has been theorised as ‘value co-creation’ by Zwiick et al (2008) to pay particular attention to the ways in which the new consumer subject ‘works’ through the concept of ‘customer relationship’. They contrast this with the language of early market research that had emerged not as a means of seeking consumer input, but as a social process for managing consumers. Thus, (2008;184) “Unlike old-style marketers who cling to Philip Kotler’s celebrated four Ps for effective customer management, the iconoclastic adherents to the logic of co-creation focus on the provision of ambiences that set consumers free to produce and share technical, social, and cultural knowledge”. Cova and Dalli (2009) similarly argue that consumers do work and are active in the value creation process. They explain that the aestheticisation of consumption causes individuals to be on a never-ending identity quest; a quest to define the meaning of their lives. Consequently, consumers go to markets to produce their identity, specifically their self-images. Consumers contribute to the creation of goods and services by not only reacting to companies’ modes of providing, but – more fundamentally – by constructing their consumption objects, both physically and culturally (Keat et al., 1994). Accordingly, firms and customers jointly create market; create sites, for interaction between services, goods and identities (Gaddefors and Anderson, 2008).

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THE CASE The objective of our study was to explore how customer relationships worked in this case. Our interest was therefore in studying the nature of relationships and how, and if, identity played a part in relationship marketing. Accordingly, we focused on contextually embedded processes (Chell, 2000; Steyaert and Katz, 2004; Downing, 2005) in this case study. The multiple voices of customers, entrepreneur, managers and workers forming this partial ethnographic work (Alvesson and Deetz, 2000) made it possible for us to compare and evaluate statements from the empirical field (Kärreman and Alvesson, 2001) and to usefully discuss the process of involvement as market creation. Navus (a fictitious name) is a small Swedish firm that designs, builds and sells furniture. They have retail stores in Sweden’s larger cities and in Copenhagen, Denmark. While some final assembly and finishing work is done in Navus’ own workshop, the manufacturing of their furniture is carried out by independent carpentry workshops. The stores sell an assortment of household wares that fit with the philosophy of the furniture. Exploring our case, we collected data from a range of sources (Silverman, 1993), such as observations, interviews, participation in formal and informal meetings and conversations at the head office and in the retail outlets. The interviews and the formal meetings were audio-taped and transcribed. In the other situations, notes were taken and categorised in terms of the themes we observed. Asked to introduce us to the business concept and the furniture’s design, the CEO stresses the concepts of long term vision, authenticity and presence in the concept as a whole. By long term vision, she means that the product range should be lasting, it should transcend trends. Authenticity refers to the furniture being just what they are – well-built, durable and open. Talking about presence she say that everything Navus makes should be exquisite in the minds of the potential customers; they should make it easier for the customer to make an active, conscious choice. They work to create a presence in the buying situation and a presence about the things customers choose to have around them. This way, design and creativity have come together to produce a ‘presence’, a concept which is an imagination of what they want to be. What Navus does cannot be simply seen as customer-oriented, developing products as a response to an existing market need. Elaborating on the particular features of Navus the founder and designer says that: ‘For me, it’s not about the products, but about decorating your home. … Our mindset redirects the customer’s attention.’ There is of course a market, a meeting

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place for transactions, but we can also see how the product exchanged is primarily symbolic; a representation of meaning. The chairs are not just for sitting on, they are icons; the lamps are not just for lighting the room, but illuminate the conjunction of style and identity; the chests are not just for keeping treasures, they are meant to be treasured themselves; all are symbols to enjoy and enjoin the customer and Navus. Notice too, how the founder talks about identity, ‘I sometimes get the feeling that people think they’re buying our mindset, but I think the furniture helps them to focus on what they want themselves. In this way, you could say that material things can assist us in this existential endeavour. Thus, we may see the identities of producers and consumers as interwoven, assisting each other in the existential endeavour. Developing the relationship theme the founder tells about when he talks with sales staff in Navus stores: ‘I tell the people who sell our furniture that they should create a relationship with the customers. We can’t reduce the buying situation to a single consumer purchase. That’s not what it’s all about. If it were – no-one would buy anything, or at least – not very many people would. There’s something bigger in what we’re communicating. It’s a desire they have, a longing for another content, another life. And this is where culture comes in, the art of being human. It’s this that we nudge at with our mindset, and a lot of people understand what we’re doing and are attracted by it. Strictly speaking, it’s probably easier to just sell furniture.’ This quotation show how the products and other communication completely intertwine with the Navus concept. It is not only about designing, producing and selling furniture; neither is it about artificially constructing an image, a hyper-reality. Instead, Navus exemplifies how an idea is developed in concert with customers and how the business opportunity is constructed in this interplay. The involvement between Navus and the customer could be seen as that short moment when the goods are purchased, but we also sense a longer relationship, built both before and after the purchase. Navus’ CEO tells us that: ‘Many of our customers want to discover us, that we stand for something, a kind of guarantee. ... Shopping becomes like a project of sorts. Changeability is one of my fundamental ideas, e.g., getting customers to dare to repaint their chairs. We’re creative and present, our customers are creative and present. We show them how to do it, how they can arrange one thing, and then another and another, and so on. It brings out the customer’s creativity. … Many people feel a longing, but the ones who buy have courage too.’ Thus, the relationship between Navus and the customer cannot be seen as an exchange of furniture and money or reduced to the moment of purchase. Rather, it is mutual and extended in time. The CEO explains, ‘We don’t do any customer surveys.

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Listening is done more in our contact network’, whilst the marketing manager sees this interaction, ‘when they buy something, they recreate their own lifestyle.’ The redirection of attention is based in the relationship with the customer and built on the furniture design and the firm’s other communication. This interaction is constructed in many places; the store, for example, where the design, the choice of other products in the arrangement, even signs and displays bring the concept together. But also at the company’s website, within their advertising, television and editorials, and also where products are placed in public spaces, such as in libraries and hotel lobbies. It is in the becoming relationship, created when the customer with all her senses meets a combination of communication channels, that the redirection of the customer’s attention takes place. Navus’ CEO explains: ‘We use a different type of colour, with a different finish, tone and smell. … I want to make our concept known. I’m not thinking so much about the furniture itself, but more about the whole.’ Accordingly, Navus’ opportunity emerges in interaction with the customer over time and through multiple channels.

UNDERSTANDING INVOLVEMENT AND CUSTOMER RELATIONS The process of consumption, as Navus explains it, appears to be much more interactive than traditional theory suggests. We can say that marketing involves an increasing closeness to the customer and that the very nature of this relationship has changed. As proposed by Cova and Dalli (2009) there is a shift to involvement and value-co-creation when consumers go to market to co-produce their own identity. Accordingly, consumption in our case doesn’t just mean the use of goods, but also the interactive production of identity. In our interpretation of the Navus case, customers and staff alike are all active in this involvement. We saw how Navus was a part in people’s identity construction; the consumers, but we note the involvement of the Navus people as well. People were invited to share Navus’s identity and design, perhaps even their philosophy and existential endeavour. But importantly, they were not directly involved in the design of the furniture. This shows how customer involvement doesn’t imply that the firm has to surrender to the customer. The customer is, and remains, in charge of his or hers own identity construction, but is not in charge of what is happening in the Navus collection. What the

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entrepreneur did was to share in the production of an identity as a brand and as an image of what they want to be. Thus, he has included people in this process, people who are willing to engage in the co-consumption of symbolic artefacts. What the consumer does is to co-create the market by getting involved in the entrepreneurs sharing. This process is clearly relational, but it is a different form of relationship marketing. The customer is not just closely involved before a transaction, but becomes engaged in the transaction. This perception, this engagement, sheds a different light on relationship marketing. Clearly, the image of the market as something that entrepreneurs act upon no longer applies. Instead of a battlefield of products and services competing on objectively based differences, it is an involvement where identities are being created and recreated, based on the play of signs, symbols, images and where the products represent these signs. To sum up, in our perspective the entrepreneurial market didn’t exist as a thing, but as an interactive process between producer and consumer. We saw how the entrepreneur engaged with all people involved. We see how the involvement resulted in an identity process about becoming a firm and becoming a customer. Thus, we discussed the significance of identity. Identity of the brand, identifying with this brand to the extent of co-creation. We noted how Navus and their customers recreated the market. We showed how relations were extended over time and space, embracing the co-production of identity and products.

REFERENCES Anderson, A.R. and McAuley, A. (1999), ‘Marketing landscapes: the social context’, Qualitative Market Research: An International Journal, Vol. 2, No. 3, pp. 176-188. Arvidsson A, 2008, The Ethical Economy of Customer Coproduction, Journal of Macromarketing, Volume 28 Number 4, 326-338. Chorev, S., Anderson, A. R. (2006). Success in Israeli high-tech start ups; Critical factors and process. Technovation, 26(2), 162-174. Cova, B., Dalli, D., 2009, Working consumers: the next step in marketing theory? Marketing Theory; 9(3) 315-339. D.M. Gardner, F. Johnson, L. Moonkyu and I. Wilkinson, (2000) A contingency approach to marketing high-technology products, European Journal of Marketing 34 (9/10), pp. 1053–1077.

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Gaddefors, J., Anderson, A.R., 2008, Market creation: the epitome of entrepreneurial marketing practices, Journal of Research in Marketing and Entrepreneurship, 10(1) 19-38. Grönroos, C. (2006), “On defining marketing: finding a new roadmap for marketing,” Marketing Theory, 6, (4), pp. 395-417. Grönroos, C., 1999.Relationship marketing: challenges for the corporation, Journal of Business Research, Vol. 46 No. 3, pp. 327-35. Gummesson, E. (2007), “Exit services marketing – enter service marketing”, Journal of Customer Behaviour, Vol. 6 No. 2, pp. 113-41. Jack S.L.; Dodd S.D.; Anderson A.R, (2004) .Social structures and entrepreneurial networks: the strength of strong ties, The International Journal of Entrepreneurship and Innovation, 5(2), 107-120. Keat, R., Abercrombie, N. and Whiteley, N., (eds) (1994) The Authority of the Consumer. London: Routledge. McKenna, R. (1991), Relationship Marketing: Successful Strategies for the Age of the Customer, Addison-Wesley, Reading, MA. Toffler, A. (1980), The Third Wave, New York: William Morrow. Zontanos, G., Anderson, A. R. 2004 Relationships, marketing and small business: an exploration of links in theory and practice. Qualitative Market Research, 7(3), p. 228-236. Zwick, D., Bonsu, S.K., Darmondy, A., 2008, Putting Consumers to Work, ‘Co-creation’ and new marketing govern-mentality, Journal of Consumer Culture, 8(2): 163–196.

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INDEX

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A abolition, 72 accounting, 92 accuracy, xiii, 72, 100, 108, 110, 112, 119, 121, 124, 125, 126 acquisitions, 156 adaptations, 85 agencies, 115, 134, 135 aggression, 5, 26 Alan P. Fiske, vii, ix, 1, 3, 4 algorithm, xiii, 112, 114, 129, 136, 138, 140 ambassadors, 17 ANOVA, 134, 135, 137 anthropology, 4 anxiety, 132, 137 apparel industry, xii, 84, 91, 96 AR, x, 2, 3, 6, 7, 8, 9, 10, 26, 117, 118, 122, 123, 124, 125, 126 architecture, 29 articulation, 33 AS, x, 2, 3, 10, 25, 26, 27, 28, 30, 34 Asia, 143 asocial, x, 2, 3, 10, 26 assessment, 34, 45, 48, 50, 51, 52, 57, 58, 63, 66, 67, 68, 71, 76, 80, 85, 111, 140 assets, 134, 139, 142 association rules, xiii, 108, 110, 112, 113, 114, 117, 122, 123, 125, 126, 128 asymmetry, 6

authenticity, 17, 163 authorities, 6, 7, 8 automate, ix automobile parts, xiii, 108, 115, 126

B badwill, xiv, 145, 150, 151, 152, 153 balance sheet, 134 Bank of Canada, 73 bankers, 74 bargaining, 36, 140 behaviors, 12, 16, 29, 40, 53, 133 benefits, ix, xiii, xiv, 8, 17, 20, 23, 89, 108, 111, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 146, 150, 151, 152, 154 bias, 92, 101 blame, 76 bonds, xiii, 88, 129, 132 bridges, 99 business environment, 97 business function, x, 43, 45 business partners, xi, xiv, 83, 86, 89, 94, 96, 97, 98, 99, 100, 145, 146, 153 business processes, ix, 47, 72 business strategy, x, 43, 44, 45, 46, 48, 50, 53, 54, 68, 75 buyer, xi, 53, 64, 83, 85, 87, 88, 89, 90, 91, 94, 96, 97, 98, 99, 100, 101, 102, 130, 155

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Index

buyers, xi, xii, xiii, 53, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 94, 96, 97, 99, 100, 129, 146, 161

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C case study, 77, 79, 127, 155, 162 cash flow, 52, 61 causal relationship, 58, 69 causality, 69 CHAID algorithm, xiii, 129, 136, 138, 140 cheese, 75 childhood, 39 churn management, xii, 107, 108, 109, 110, 115 class, ix, 1, 119, 120, 121, 122, 123 classes, ix, 1, 3, 6, 19, 112 cleaning, 112 clients, ix, 20, 130 clustering, 112 clusters, 140 cognition, 5, 37 cognitive dimension, xii, 84, 87, 88, 89, 93, 96, 97, 98 cognitive effort, xii, 84, 99 cognitive representations, 5 color, iv community, 18, 25, 35, 36, 39, 40, 87 compensation, 22, 24 competition, 71, 101, 105, 154 competitive advantage, 54, 102, 111, 128, 142 competitive behaviour, xi, 84, 86 competitive business environment, 109 competitive markets, 130 competitors, 17, 71, 74, 154 complaints, 13, 62, 63 complexity, 86, 102, 130 compliance, 27 complications, 75 composites, 160 conceptual model, 127 conceptualization, 132 configuration, 65 configurations, xi, 84, 86, 88, 100 conflict, 5, 27, 39

congruence, 40 conscious awareness, 5 consensus, xi, 84, 88, 98 conservation, 6 consulting, 54, 60, 82 consumer goods, 54, 161 consumer markets, 101 consumers, ix, x, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 28, 29, 30, 32, 34, 35, 72, 111, 131, 133, 138, 141, 158, 160, 161, 162, 163, 165, 166 consumption, 36, 40, 111, 127, 160, 161, 162, 165 contingency, 166 convergence, 5 coordination, 86 coproduction, xiv, 160 correlation, 94 correlations, 93 cosmetics, 71 cost, 3, 23, 25, 50, 64, 75, 100, 109, 116, 119, 123, 147, 149 counsel, 8, 10 country of origin, 32 creativity, 102, 163, 164 crew, 9 CRM, vii, ix, x, xii, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 78, 79, 81, 82, 107, 108, 109 CRM Scorecard, xi, 44, 46, 50, 51, 53, 54, 56, 57, 58, 60, 66, 76, 82 CS, ix, 2, 3, 5, 10, 13, 14, 15, 16, 17, 18, 21, 24, 27, 30, 33, 34, 62 cultivation, 18, 23 culture, 5, 15, 17, 18, 19, 21, 24, 32, 37, 38, 50, 64, 163 currency, 70, 122, 123 customer data, x, 43, 45 customer loyalty, ix, xiii, 6, 47, 53, 62, 72, 115, 129, 131, 133, 136, 138, 139, 140, 141, 143 customer profiles, x, 43, 45

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Index customer relations, ix, x, xiii, xiv, 3, 11, 13, 17, 44, 45, 52, 76, 78, 80, 81, 101, 103, 105, 108, 110, 112, 115, 125, 129, 130, 133, 143, 145, 146, 147, 148, 149, 151, 152, 153, 159, 160, 161, 162 Customer Relationship Management, ix, x, 43, 44, 79, 80, 81, 128 customer service, ix, 36, 44, 80 customer value analysis model, xiii, 108, 110 customers, x, xi, xii, xiii, xiv, 2, 3, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 38, 43, 45, 52, 53, 58, 61, 62, 64, 70, 71, 73, 74, 76, 83, 84, 85, 87, 88, 90, 91, 92, 94, 96, 98, 99, 100, 107, 109, 110, 111, 112, 115, 116, 119, 124, 125, 126, 129, 130, 131, 132, 133, 138, 139, 140, 142, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 156, 159, 160, 161, 162, 163, 164, 165, 166

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D dancers, 27 data analysis, 76 data collection, 115 data mining, xii, 108, 109, 110, 112, 113, 114, 115, 125, 126 database, 61, 66, 68, 109, 110, 112, 113, 119, 127, 134 database management, 68 datasets, 114 debts, 35 decision trees, xiii, 108, 110, 114, 117 demographic characteristics, 32 demonstrations, 25 Denmark, 162 dependent variable, 92, 135, 136 depression, 71 deviation, 114 dimensionality, 132 disappointment, 15 disequilibrium, 146, 152 dispersion, 114 dissatisfaction, 57, 74

distress, 15 distributive justice, 38 diversity, 65, 90, 105, 130 doctors, 10

E earnings, 111 e-commerce, 127 economic sociology, 103 economy, 49, 154 education/training, 58, 65 educational services, 8 EM, ix, 2, 3, 10, 19, 20, 21, 22, 24, 25, 28, 30, 34 emotional state, 85 empirical studies, 54 entrepreneurs, 165 equality, ix, 2, 3, 10, 19, 20, 21, 22, 39 equipment, 17, 115 equity, 23, 24, 39, 41, 52, 60, 62, 72, 80 evaluative domain, xi, 44, 52, 58, 60, 61, 66, 67 evaluative factors, xi, 44, 54, 58, 67, 68, 69, 82 examinations, 66 exchange rate, 122, 123 exchange relationship, 36, 89, 102, 104, 161 execution, 58, 65, 76 exercise, 9, 10, 18 experiences, 29, 40, 54, 85 expertise, 54 exploitation, 36, 105 exploration, 102, 167 exposure, 97 extraction, 63, 112

F face-to-face interaction, x, 2, 94 factor analysis, 93 fairness, 38 family members, 29 famine, 7 fantasy, 35, 39 fast food, 18

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Index

feature selection, 110, 112, 117, 118, 127 feedback, 12, 15, 16, 22, 34, 66 feelings, 89, 93 financial performance, 47, 76 fitness, 65 Ford, 146, 147, 155 forecasting, 110, 111 formula, 75 foundations, 5, 66 framing, 41 free recall, 37 free will, 26 freedom, 17, 26, 27 friendship, 89, 132, 137 furniture manufacturer, xiv, 159

G

information technology, 108, 142 infrastructure, xi, 44, 52, 54, 58, 66 intangible investments, xii, 84, 99 integration, 39, 80, 142, 157 intellectual capital, 104 interdependence, 103 internal consistency, 92 internal validity, 57 internationalization, 37 Internet, xiii, 129, 130, 143 interpersonal conflict, 39 intrusions, 29 investments, xii, 84, 99, 149 Ireland, 105 IT, x, 43, 45, 46, 48, 54 Italy, 83, 91

K global economy, 104 goods and services, 101, 162 governance, 97, 104 guidelines, 48, 50

knowledge acquisition, 102, 105 Korea, 43, 54, 72, 73, 74, 77, 79

L

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H heterogeneity, 46 hotels, 28, 135 human resources, 44 hybrid, 32 hypothesis, 89, 91, 96, 119

I ideals, 6, 25 identity, xiv, 10, 13, 18, 21, 25, 32, 34, 40, 61, 160, 162, 163, 165, 166 ideology, 4 image, 18, 141, 164, 165 images, 166 imagination, 163 imitation, 39 impacts, 154 independent variable, 93, 135, 136 inequality, 20 inequity, 24 inertia, 98 inflation, 94

landscapes, 166 languages, 93 leadership, 54, 57, 58, 65 learning, 45, 47, 65, 66, 100, 102, 103, 105, 114 learning process, 100 leisure, 26, 27, 28, 29, 41 lifetime, 61, 72, 109, 111, 126, 127, 142 local community, 15 lock-in effects, xiv, 145, 152, 153 logistics, 44 longevity, 12, 62, 147 long-term customer, xiv, 145, 146, 152, 154 loyalty, ix, x, xiii, 6, 43, 45, 47, 53, 62, 68, 72, 75, 85, 100, 104, 105, 115, 116, 129, 130, 131, 133, 134, 135, 136, 138, 139, 140, 141, 142, 143

M machine learning, 128 management, xii, 12, 34, 37, 47, 50, 52, 54, 57, 58, 61, 62, 63, 66, 72, 73, 75, 78, 79,

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Index 80, 86, 98, 99, 107, 108, 109, 110, 115, 155, 156, 161 manufacture, 115 manufacturing, 44, 67, 81, 162 markers, 7, 8, 9, 10 market concept, xiv, 160 market structure, 140 marketing, ix, x, xi, xiv, 18, 35, 37, 43, 44, 45, 47, 52, 53, 54, 67, 71, 74, 75, 79, 83, 85, 88, 89, 97, 101, 102, 103, 108, 109, 112, 119, 127, 130, 131, 133, 139, 140, 142, 147, 148, 149, 150, 155, 156, 157, 159, 160, 161, 164, 165, 166, 167 marketing initiatives, x, 43, 45, 74 marketing mix, 18, 160 marketing strategy, 37 marketplace, 20, 23, 90, 97 Mars, 29, 36 mass customization, 44 material resources, 96 matrix, 94, 119, 120, 121, 124 media, 18 membership, 13, 73 mergers, 156 messages, 18 meta-analysis, 39, 40 metaphor, 143 methodology, xi, 44, 46, 50, 66, 82, 110, 111 microcosms, 27 migration, 63 military, 7 mining, xiii, 108, 110, 112, 113, 114, 127, 128 Ministry of Education, 72 misunderstanding, 76 monitoring, 34, 45, 54 monopoly, 72 motivation, 4, 16, 25, 53, 90 MP, x, 2, 3, 10, 23, 24, 25, 28, 30, 34 multidimensional, 78, 114, 141 multimedia, 128 multiple regression, 95 multiple regression analysis, 95 music, 29

mutual respect, 25

N negative consequences, 34 network theory, xii, 84, 88, 99, 104 networking, 102 nodes, 117 non-valuable customers, xiii, 108 North America, 115 null, x, 2, 3, 6, 98, 119 null hypothesis, 119 nurses, 9, 10

O opportunism, 89 opportunities, xiv, 10, 45, 90, 91, 97, 99, 157, 159 opportunity domains, xi, 44 optimization, 47 organic growth, x, 43, 44, 61, 76 organizational culture, 54, 57 organize, ix organizing, 18

P Pacific, 143 patriotism, 17 performance indicator, 52 performance measurement, x, 44, 45, 46, 48, 49, 50, 51, 52, 53, 75, 77, 78, 79, 81 performers, 27 permission, iv personal contact, xiii, 91, 129, 140 personal goals, 87 personal relations, 87, 93, 143 personal relationship, 87, 93, 143 personality characteristics, 14 personality traits, 39 photographs, 17 play activity, 27 pleasure, 26 police, 7 portfolio, 130 positive relationship, 53

Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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156

Index

postmodernism, 36 prediction models, 110, 117, 118, 119, 124, 126 predictor variables, 94 preferential treatment, 22, 28 prestige, 6 prevention, 68 principal component analysis, 92 prioritizing, 80 probability, 33, 116 problem solving, 112 process innovation, 155 producers, 160, 163 product attributes, 111 productivity, xi, 47, 83, 85, 101 profit, x, 2, 3, 8, 10, 46, 53, 59, 60, 64, 67, 116, 119, 120, 121, 122, 123, 124 profit margin, 116, 119, 120, 121, 122, 123, 124 profitability, 12, 24, 47, 49, 52, 60, 74, 101, 109, 110, 111, 112, 115, 116, 127, 149 project, xiv, 61, 64, 74, 159, 164 proliferation, 133 proposition, 24 prototypes, 38 psychology, 4, 31, 36, 38 public service, 67 purity, 114

Q qualitative concept, 57 quality control, 34 quantitative research, 134 questioning, 7

R reality, 29, 54, 164 recall, 109 reciprocity, 32, 33, 38 recognition, 62, 73 recommendations, iv, 9, 10 recreation, 28 redistribution, 7 regression, 95, 100, 112, 128

regression analysis, 95 reinforcers, 40 rejection, 119 relational dimension, xii, 4, 30, 34, 84, 87, 88, 93, 96, 97, 98 relational model, ix, 1, 3, 4, 5, 6, 8, 10, 11, 13, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 38 relational models, ix, 1, 3, 4, 5, 6, 10, 11, 13, 23, 24, 29, 30, 31, 33, 34, 38 relationship management, x, xii, 2, 3, 11, 78, 80, 81, 90, 105, 107, 108, 125 relationship marketing, xii, 39, 40, 53, 84, 88, 99, 101, 142, 160, 162, 165 relationship quality, 72, 142 relationship satisfaction, xi, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 100 relevance, 3 reliability, 59, 60, 89, 113 remote contact, xiii, 129 reparation, 16 replication, 29 reputation, 22, 24, 149 requirements, xi, 44, 50, 51, 65, 91, 148 resources, 4, 6, 7, 8, 10, 12, 13, 14, 15, 19, 20, 23, 24, 40, 47, 54, 57, 71, 73, 76, 85, 87, 88, 90, 97, 103 restaurants, 18, 28 retail, xii, 28, 29, 38, 77, 79, 84, 91, 96, 130, 133, 134, 136, 143, 162 retaliation, 16 retirement, 8 revenue, 64, 153 rewards, 23, 25, 38 risks, xiv, 89, 145, 148, 149, 150, 152, 153, 154 ROI, x, 44, 45 rubrics, 69 rules, ix, xiii, 1, 5, 11, 27, 28, 29, 103, 108, 110, 113, 114, 116, 117, 119, 120, 121, 122, 123, 124, 126

S sales activities, ix

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Index sales prospects, ix savings, 132 scarcity, 7 schema, 31, 32, 33 schemata, ix, 1, 5, 11, 37 scripts, ix, 1, 5, 11, 27 secondary data, 100 self-concept, 14 self-definition, 4, 14 self-image, 162 self-interest, 39 self-presentation, 18, 39 sellers, xi, xii, 21, 33, 83, 84, 85, 86, 88, 90, 91, 94, 99, 161 senses, 164 service firms, x, 2, 3, 10, 28 service industries, x, xiii, 2, 3, 17, 30, 129 service marketers, x, 2, 3, 8, 10, 11, 13, 14, 20, 21, 23, 25, 27, 29, 31 service organizations, ix, x, 2, 3, 4, 14, 16, 29, 30 service provider, xiii, 39, 53, 73, 74, 101, 129, 131, 132, 135, 140 service quality, 11, 33, 41, 47, 141 shape, xi, 84, 86 shareholder value, 52, 60 signals, 148 signs, 164, 166 Singapore, 156 social benefits, xiii, 130, 132, 139, 140 social capital, xi, xii, 83, 84, 86, 87, 88, 89, 90, 91, 93, 94, 96, 97, 98, 99, 100, 104 social context, 166 social exchange, 39, 97, 104 social group, 13, 14 social identity, 40 social identity theory, 40 social influence, 35 social interactions, ix, 1, 3, 4, 30 social life, 4, 5 social network, xi, 84, 86, 88, 98 social relations, xi, 4, 37, 38, 84, 86, 97 social relationships, xi, 37, 38, 84, 86 social rules, 29 social sciences, ix, 1, 3, 4

social status, 6 social structure, 87, 93, 101, 103 social support, 14, 90 software, 134 Spain, 134 specific tax, 4 specifications, 140 spontaneity, 19, 75 Spring, 39 stock price, 60 structural dimension, xii, 84, 87, 88, 90, 93, 95, 97, 98 style, xiv, 29, 36, 160, 161, 163 subjective experience, 41 subjectivity, 140 substitutes, 96 supplier, xiv, 131, 133, 136, 138, 139, 140, 145, 146, 147, 148, 149, 150, 151, 152, 153 survey, 54, 57, 59, 60, 61, 64, 66, 68, 73, 81, 91, 92, 109, 111, 133, 134 sustainability, 5 Sweden, 145, 156, 159, 162 synchronize, ix synthesis, 128

T Taiwan, 107, 115 taxation, 8 taxonomy, 81 technical support, ix technology, ix, 57, 72, 77, 80, 105, 108, 142, 157, 166 telephone, xiii, 129 tellers, 74 testing, 117, 119, 126, 151 textbooks, 161 threats, 33 total product, 72 tourism, xiii, 9, 129, 131, 133, 141, 142 trade-off, 99, 111, 131, 155 training, 54, 65, 73, 117, 119, 126 training programs, 54 traits, 14 transaction costs, 89

Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

158

Index

transactions, 7, 23, 24, 89, 111, 113, 115, 163 transformation, 45 transmission, 115 transport, 29 transportation, 9 tripartite business implications, xi, 44 trustworthiness, 88 turnover, 139 Type I error, xiii, 108, 119, 125, 126 typology, ix, 1, 3, 4

U UK, 159 universities, 8

valuable customers, xii, 107, 119, 125 valuation, 20, 63 venue, 12, 28 vision, 163 VOCs, 72 vulnerability, xiv, 145, 152, 153

W wants and needs, 151 waste, 119 wear, 8 welfare, 7, 10, 36 wholesale, 134, 135, 140 work ethic, 29 workers, 15, 162

V yes/no, 70

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Valencia, 129, 134 validation, 113, 143

Y

Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Customer Relations, edited by Victoria J. Farkas, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,