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International Journal of Bank Marketing

Measuring effectiveness of customer relationship management in Indian retail banks

• C. Padmavathy, M.S. Balaji, V.J. Sivakumar,

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• C. Padmavathy, M.S. Balaji, V.J. Sivakumar, (2012) "Measuring effectiveness of customer relationship

management in Indian retail banks", International Journal of Bank Marketing, Vol. 30 Issue: 4, pp.246-266, https://doi.org/10.1108/02652321211236888

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Measuring effectiveness of customer relationship management in Indian retail banks

• C. Padmavathy

Department of Management Studies, National Institute of Technology, Tamil Nadu, India

M.S. Balaji

Department of Marketing and Strategy, IBS Hyderabad, Hyderabad, India, and

V.J. Sivakumar

Department of Management Studies, National Institute of Technology, Tamil Nadu, India

Abstract

Purpose – The purpose of this paper is to develop a multi-item scale for measuring the customer relationship management effectiveness (CRME) in Indian retail banks and to examine its relationship with key customer response variable. Design/methodology/approach – This research adopts two different studies to develop and validate the scale for CRME. In study 1, responses obtained from 197 Indian retail banking customers were used to identify key dimensions of CRME. In study 2, nomological validity for the CRME scale was provided using a new sample of 261 actual bank customers. Furthermore, the relationship between CRME dimensions and customer behavioral outcomes such as customer satisfaction, loyalty and cross-buying were examined. Findings – The results of factor analyses revealed five dimensions for CRME, namely, organizational commitment, customer experience, process-driven approach, reliability and technology-orientation. Organizational commitment, process-driven approach and reliability were found to positively affect customer satisfaction. Reliability was found to have direct association with customer loyalty and both customer satisfaction and loyalty-influenced cross-buying. Research limitations/implications – The identification of the dimension will help bank managers to implement an effective customer relationship management (CRM) that enhances customer satisfaction, loyalty and provides opportunities for banks to cross-sell other related and unrelated products to its customers. Originality/value – This paper provides a robust scale for measuring CRME in the Indian banking

• context. It examines the relationship between CRM efforts and relational outcomes of satisfaction,

loyalty and cross-buying. Keywords Customer relation management, Indian banks, Customer satisfaction, Customer loyalty, Services marketing, Cross-buying Paper type Research paper

• 1. Introduction

Banks have a significant role to play in the economy of a country. In the past decade, the global banking environment has undergone a remarkable transformation. The changing regulatory, structural and technological factors have produced a level of

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0265-2323.htm

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Received 19 June 2011 Revised 3 November 2011 Accepted 16 January 2012

International Journal of Bank Marketing

• Vol. 30 No. 4, 2012
• pp. 246-266

q Emerald Group Publishing Limited 0265-2323 DOI 10.1108/02652321211236888

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competition and disintermediation that few in the banking industry would have predicted a decade ago (Klein, 2005). The Indian banking sector is of no exception to this changing landscape as it faces unprecedented challenges following deregulation, liberalization and globalization of the economy. According to Mr K.V. Kamath, the non-executive chairman and board member of ICICI bank:

[. . .] the winner in the Indian banking sector will be the player who can understand the customer, fulfil customer needs, and achieve high levels of customer retention, leveraging technology, knowledge and human resources to provide quality products and services, and manage risks and returns, thereby delivering value to all stakeholders (Kamath et al., 2003, p. 85).

Therefore, adopting customer-centric strategies aimed at maintaining and enhancing relationship with existing customers is crucial for the survival of Indian banks (Roy and Shekhar, 2010). In the past years, many Indian banks have invested heavily in customer relationship management (CRM) technologies to develop and nurture a long-term and mutually benefiting relationship with the customers (Uppal, 2008). At the core of these customer-centric initiatives was the belief that by understanding customer needs and delivering greater value, banks can enhance their competitive position and generate superior shareholder returns. Despite this enormous investment in CRM systems by the Indian banks, critics have remained unconvinced about the effectiveness of such relational efforts in customer share development and meeting desired business results. The reason being that Indian banks often perceive CRM systems as “a specific technology solution project” (Payne and Frow, 2005) rather than integrating customer needs with the organization’s strategy, people and business process (Sharma and Goyal, 2011). Furthermore, research addressing the characteristics of successful CRM strategies and the scale of measurement of such CRM efforts are limited (Boulding et al., 2005; Mithas et al., 2005). Few studies that have put forwarded the measures of CRM effectiveness (CRME) have been from the supplier perspective (Chen et al., 2009). This limits the understanding of how customers perceive CRM and the outcome of such efforts on their behaviors. Understanding the customer perspective is crucial for an

• rganization, since an effective CRM requires the business process and technology

focused towards the customer. Moreover, CRM effectiveness varies depending on the relationship marketing strategy and exchange situation (Palmatier et al., 2006). As cultural differences influence relational perception and behavior, Jham and Khan (2008) recommended measurement of CRM in an Eastern cultural context such as India. In addition, Soch and Sandhu (2008) argued that since different industries exhibit varying levels of performance, it is imperative to measure CRM in the Indian banking industry. The limited scope of application (supplier perspective) and the resulting exchange context significantly reduces utility and generalizability of the existing scales for measuring CRME from a customer perspective in the Indian banking context. Consequently, there is a pressing need to develop a scale that systematically and psychometrically measures CRME, serving as a measurement foundation for the customer perspective. Additionally, there is a lack of agreement on the impact of CRM efforts on key consumer responses (Knox et al., 2003; Boulding et al., 2005). Therefore, the objectives of this study were two-fold. The primary objective was to develop a multi-item scale for measuring CRME from the customer perspective in the Indian banking context. The second objective was to test the relationship of the scale

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developed for CRME with the outcome variables such as customer satisfaction, loyalty and cross-buying. The structure of the article as follows. In the next section, we review the literature

• n various perspectives of successful CRM strategy and develop hypotheses. Then, we

discuss the methodology and present the results of the two studies carried out. Finally, we conclude by providing discussion and implications of the study.

• 2. Literature review

Relationship management is a new paradigm distinctive from the marketing management perspective and has received considerable interest amongst practitioners and scholars in the new millennium. Customer relationship management refers to a set

• f relational practices that firms adopt to enhance customer relationships. In the past,

various studies have examined CRM implementation in different kinds of industries such as hotels (Lo et al., 2010), retailing (Minami and Dawson, 2008), financial services (Dimitriadis, 2010), tourism (O ¨ zgener and Iraz, 2006), transport services (Cheng et al., 2008), business markets (Gummesson, 2004) and public services (Pan et al., 2006). In spite of this widespread adoption, there remains a lack of agreement about what constitutes CRM and how to evaluate the effectiveness of CRM (Chen et al., 2009)? 2.1 What constitutes CRM? In the relationship marketing literature, few frameworks were proposed for the successful implementation and evaluation of CRM strategies. For example, Park and Kim (2003) used information processing perspective to propose an integrated framework of CRM consisting of four stages namely relationship initiation, relationship value, relationship positioning, and relationship commitment. According to this approach, the customer information plays a key role in managing, identifying and maintaining a successful relationship with customers across the evolution stages. The authors argued that when firms care for the relationship with these identified customers, some of them will become core customers who are loyal and generate substantial value for the organization through high profit contribution. Thus, through implementation of customer information system, firms can deliver differentiated customer value to enhance their relationship commitment towards the customers. Chan (2005) proposed a similar conceptualization of CRM that integrated business processes,

• rganizational structures, analytical structures and technological representation to

present a unified view of a customer. In another study, Kim et al. (2003) put forwarded a conceptual model of CRME that consists of four customer-centric perspectives namely, customer knowledge, interaction, value, and satisfaction. The authors proposed that CRM involves meeting customers’ individual and unique needs by managing business interactions. By integrating business processes and technology, firms seek to maintain and enhance the relationship with their customers. Based on the functional and organizational capabilities, Reinartz et al. (2004) presented a model for CRM process implementation. As a strategic perspective, CRM was conceptualized in terms of three different phases of relationship namely: (1) initiation; (2) maintenance; and (3) termination.

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The broader finding of this study suggested that implementation of CRM process in relationship maintenance phase would increase business performance. Payne and Frow (2005) extended Reinartz et al. (2004) work by recognizing the importance of business strategy in implementation of CRM process. The authors presented a cross-functional process-oriented CRM framework where firms implemented programs aligning the business strategy with the customer strategy to create value for customer and firm. Arguably, this value co-creation process maximizes lifetime value of profitable customer and key customer segments. In summary, these studies suggested that the successful implementation of CRM requires consideration of business strategy, organizational motivation and IT in establishing and maintaining the relationship with the customers. Such integration and organization of these elements allows firms to realize superior CRM performance by gaining insights into behaviors of profitable customers. 2.2 Customer relationship management effectiveness It is strongly argued that measuring the effectiveness of relational efforts is imperative for determining future financial performance; however, metrics for assessing CRME are inadequate. For the purpose of this research, we defined CRM as:

[. . .] a set of customer-oriented activities supported by organizational strategy and technology, and is designed to improve customer interaction in order to build customer loyalty and increase profits over time.

This definition is consistent with the conceptualization of CRM from the customer

• perspective. Thus, measuring the effectiveness of customer relationship will, in turn,

“measure the relational efforts or activities that impact customer and business performance variables”. Furthermore, operationalizing and measuring customer relationship management and linking it with business performance variables will provide a complete picture of CRME. Despite its practical relevance, only few studies have investigated and measured CRME. Chen et al. (2009) proposed a metric system for measuring CRME from the supplier

• perspective. They defined CRME through an integrated process-oriented perspective

that centers on three elements namely, information technology, relationship management and organizational climate. The customer-focused information technology (CFIT) signified technology and information systems; relationship management denoted relationship activities of the firm; and customer focused

• rganizational climate (CFOT) referred to firm’s focus on customers. The 16-item

three-dimensional CRME was tested and validated using a survey of 231 business firms including several financial services institutions. These dimensions suggested that CRM investments should be directed towards enhancing relationship activities, augmenting IT, and developing an organizational climate that promotes customer interaction and service. From the customer perspective, Jain et al. (2007) proposed a two-dimensional measure of CRME. The first dimension “customization” referred to personal touch, concern for customers, customer centricity, technology orientation and promotion through customers. The second dimension “credence” included elements such as ethical practices, modesty and being proactivity. However, a notable limitation was the methodology adopted for this study. The authors failed to provide a psychometric

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evaluation of the scale developed. Furthermore, questions regarding contribution of these elements to customer benefits and business performance are not addressed. Reinartz et al. (2004, p. 294) conceptualized CRM from the customer perspective as:

[. . .] a systematic process to manage the customer relationship initiation, maintenance, and termination across all customer contact points in order to maximize the value of the relationship portfolio”.

Additionally, the authors measured objective and subjective performance of CRM across four industries and three countries to ascertain the influence of CRM on business performance. However, they found weak or mixed support for the relationship between CRM efforts and business performance across the relationship stages. While the above studies conceptualized CRME, there lacks a robust scale to measure the same from the customer perspective. Moreover, the context specificity and the varied dimensions proposed in the literature indicate that there exists a scope to develop the measurement of CRME from this alternative perspective. Moreover, Richards and Jones (2008) urged researchers to carry out additional empirical work related to measurement of CRM to establish its usefulness and predict its influence on behavioral outcomes. Therefore, this study intended to develop a measurement scale from the customer perspective to measure CRME in the Indian banking context. The secondary objective of this study was to investigate the relationship of CRME with the customer response behaviors. The hypotheses describing the relationship of CRME with the behavioral outcomes are outlined in the next section. 2.3 CRM research in financial services industry To date few studies have examined the effectiveness of CRM in financial services

• industry. For instance, Abratt and Russell (1999) conducted a study in the context of a

private South African bank exploring the relationship between banks and its customers based on a survey of 118 high net worth individuals. They reported that service excellence, streamlined services, and innovative products are the key factors in the selection of private banks. In-depth interviews with 42 account managers of Hong Kong commercial banks revealed that both social and business activities were effective in facilitating and enhancing the relationship with customers (So and Speece, 2000). The authors argued that Asians place more importance on “strong relationships in business” than the West. They suggested that technology was essential for leveraging human relationships to ensure customer interaction and gain competitive advantage. Sweeney and Webb (2007) extended the above study by investigating the impact of the benefits accrued from such relationship with customers on firm outcomes. Using a survey of 275 business customers drawn from an Australian manufacturing industry, the role of functional, psychological and social benefits in affecting the relationship commitment of customers and firm was investigated. The findings of this study revealed that firm level relationship commitment was influenced by functional benefits while the individual level relationship commitment was affected by psychological and social benefits. Camarero (2007) investigated the impact of service quality and relational benefits on the market and economic performance in the financial and insurance service setting. It was found that the relational efforts such as preferential treatment, communication and adaption to customer needs significantly affected firms’ market performance.

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Furthermore, service quality influenced relationship orientation and market

• performance. This finding corroborates previous studies that providing quality

services are essential in maintaining and enhancing the relationship with the customers (Gurau, 2003). Hennig-Thurau et al. (2002), Molina et al. (2007) and Dimitriadis (2010) used relational benefits approach to examine the impact of special treatment benefits, social benefits, and functional benefits on relationship outcomes such as satisfaction, commitment, loyalty, and word-of-mouth. The results of these studies revealed that banks could enhance customer satisfaction through relational strategies that focus on strong relationship commitment. In a recent study, it was found that CRM implementation improved the ability of the firm to customize the offerings by communicating effectively and providing timely feedback to the customers. As firms learn how to manage CRM effectively over time, they develop a one-to-one relationship with customers, thereby reducing cost efficiency and increasing profit efficiency (Krasnikov et al., 2009). The findings of the above studies provide support to the argument that an effective CRM strategy should include elements of business strategy, organizational motivation and IT to provide relational benefits to customers.

• 3. Hypotheses development

3.1 The effect of CRME on customer satisfaction A considerable body of literature in marketing has studied the effects of CRM on customer satisfaction. CRM efforts enable firms to collect customer information across various interactions and customize the offers to suit individual tastes and preferences. This enhances perception of perceived quality and affects customer satisfaction (Mithas et al., 2005). Using a multi-method approach in a retail context, Srinivasan and Moorman (2005) examined the effects of CRM investments on satisfaction. The results showed that the firms’ strategic commitments of CRM system investments and CRM capabilities were positively associated with increased satisfaction. Using a multibank approach, Ndubisi and Wah (2005) found differences between the bank customers who are satisfied and loyal, and those who are not on key relationship marketing dimensions such as, competence, communication, conflict handling, trust, and relationship quality. In yet another multibank study, Molina et al. (2007) showed that the bank performance measured in terms of customer satisfaction was greater for firms that have a strong relationship marketing strategy in place. As a result, we hypothesize that: H1. The evaluation of CRME dimensions is positively related to customer satisfaction. 3.2 The effect of CRME and customer satisfaction on customer loyalty Customer loyalty is one of the most expected outcomes of successful CRM efforts. It was argued that the CRM efforts lead to a stronger relational bond and intense customer loyalty (Abratt and Russell, 1999; Farquhar, 2004). The relationship efforts directed towards an individual customer influence his/her loyalty towards the firm than when directed towards a group (Palmatier et al., 2006). Also, specific CRM efforts such as expertise, promoting customer dependency, and increasing similarity to customers increase customer commitment and loyalty than relationship investment and interaction frequency. This indicated a differential impact of relationship activities

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• n loyalty. Thus, we argue that the impact of CRME dimensions on customer loyalty to

be context specific. Based on the above discussion, we hypothesize that: H2. The evaluation of CRME dimensions is positively related to customer loyalty. Customer satisfaction is one of the main predictors of loyalty. In a banking context, satisfaction with the relationship serves as a basis for loyalty (Bloemer et al., 1998; Licata and Chakraborty, 2009). Leverin and Liljander (2006) found that relationship satisfaction did indeed lead to higher loyalty among customers who were treated with the sales-orientation approach. Similarly, in the context of Australian bank customers, a higher level of satisfaction with relationship activities was found to increase customer loyalty towards the bank (Pont and McQuilken, 2005). Hence, we hypothesize that: H3. Customer satisfaction is positively related to customer loyalty. 3.3 The impact of customer satisfaction and customer loyalty on cross-buying Customer intention to purchase multiple financial products depends on the level of

• verall satisfaction they experience with the bank (Li et al., 2005). In addition,

cross-selling reinforces customers’ relationship with the service provider and thereby influences their future purchase behaviors (Lemon and Wangenheim, 2009). However, various studies have reported a contradictory finding of a weak or insignificant relationship between satisfaction and cross-buying. For example, Ngobo (2004) reported an insignificant relationship between customer satisfaction and cross-buying. While customers might be satisfied with the current offerings of their primary service provider, they may find other products offered by other providers as equal or more

• attractive. Moreover, when faced with the need to buy a new product/service, customer

satisfaction could be less relevant to cross-buying (Gustafsson et al., 2005). However, since overall satisfaction and past purchase history provide opportunities for firms to cross-sell related and unrelated products to existing customers, we hypothesize that: H4. Customer satisfaction is positively related to cross-buying. Though customer loyalty has been a prominent area of research in marketing, remarkably few studies have examined the link between loyalty and cross-buying. Many firms believe that loyalty can result in multiple future purchases of the same product or other products. Nevertheless, the evidence of this relationship is correlational (Gupta and Zeithaml, 2006). Reinartz et al. (2008) investigated the direction and strength of the relationship between loyalty and cross-buying using a Granger causality test. They found that loyalty drives cross-buying. However, it was

• bserved that the level of customer spending differed across different product
• categories. Similarly, Gounaris et al. (2007) proposed that extremely loyal customers

are motivated to buy additional products. Based on these arguments, we hypothesize that: H5. Customer loyalty is positively related to cross-buying. Figure 1 presents the hypothesized model of the relationship between CRME, customer satisfaction, loyalty and cross-buying.

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• 4. Methodology

4.1 Study 1 In Study 1, psychometric properties of CRME scale were assessed following the scale development procedure proposed by Hinkin (1995). The objective of this study was to generate potential items and assess the factor structure of CRME scale based on empirical considerations. The current study was conducted in three separate phases of scale development procedures. An initial pool of scale items was generated in Phase 1. Six subject experts were then selected to assess the content and face validity of these

• items. In Phase 2, a preliminary analysis of reliability and dimensionality of CRME

scale was carried out. In the final phase, a confirmatory factor analysis was conducted to improve the scale dimensionality along with the assessment of discriminant and convergent validities. 4.1.1 Phase 1: item generation, content and face validity. An initial pool of potential items was generated based on in-depth interviews and review of the literature. The in-depth interviews were conducted with a convenience sample of Indian retail banking customers (20 participated in total). During the interview, the participants were asked to focus on customer management practices that might influence them in initiating, maintaining or terminating their relationship with the retail bank. The participants were then asked questions such as, “What is your experience with this bank?” “How satisfied you are with this bank?” “What makes you invest in this bank?” and “How efficient is this bank in dealing with the customer issues?” Then, the interviewer asked follow-up questions further discussing the factors/activities that influence them to commit themselves to a deeper relationship with the bank. From these in-depth interviews, scale items were generated by converting the recurring themes into statements and included in the initial pool of scale items. This was followed by a review of relevant literature using the “key words in context” (KWIC) approach. These procedures resulted in a pool of 43 scale items for measuring CRME. Items were next reviewed for content/face validity and to reduce initial pool of

• items. A panel of six expert judges (with PhDs in Marketing) assessed the

representation of the items to the CRME definition by rating each scale item as “clearly representative,” “somewhat representative,” or “not representative”. Only items that were classified as clearly and somewhat representative by at least four of the six

Figure 1. Hypothesized model testing the relationship between CRME and customer response behaviors

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judges were retained for further analysis. This process resulted in a set of 36 items. The retained scale items were then assessed for face validity by three subject matter experts in the banking services domain. They were asked to comment on the overall presentation and suitability of scale items. Corrections were carried out based on the comments received. 4.1.2 Phase 2: scale dimensionality and reliability. An online self-completion questionnaire consisting of the remaining 36 items was administered to approximately 500 Indian retail banking customers. A total of 197 completed responses were obtained, for an overall response rate of 39 percent. Participants included 61.4 percent women and 38.6 percent men, ranging in age from 21 to 56 years. Majority of participants (70.9 percent) had earned a post-graduate degree and worked for private organizations (46.7 percent). The participants (52.6 percent) had 1-4 years of banking experience with their primary bank and a majority (97.5 percent) operated at least a savings account in these banks. The Kaiser-Meyer-Olkin measure ðKMO ¼ 0:943Þ and the Bartlett test of sphericity ðx2 ¼ 4839:44; df ¼ 630; p , 0.01) indicated sample adequacy and appropriateness of factor analysis for the data. Following this, an exploratory factor analysis (EFA) using varimax rotation was performed on the 36 items. As a preliminary step, item-to-total correlation criterion (. 0.4) was employed (Hair et al., 2006). Few items did not meet this criterion; however, they were retained for further analysis on the grounds of face

• validity. In the next step, EFAs were carried out with a factor loading of greater than

0.5 and a cross-loading of less than 0.4 as the minimum cut-off (Hair et al., 2006). The results of this iterative process suggested removal of few items. An EFA with the remaining 21 items indicated a five-factor solution that accounted for 65.8 percent of the total variance. The items had communalities ranging from 0.49 to 0.83 with each item loaded strongly on one factor. The first rotated factor represented a composite of

• rganization response and employee behavior and labeled as “organizational

commitment”. A second factor represented “customer experience”, a third factor represented “process-driven approach”, a fourth factor represented “reliability” and a fifth and final factor represented “technology orientation”. The reliability coefficients for each dimension indicated acceptable internal consistency among the items. The combined reliability for the 21-item scale was quite high (0.92). In addition, average variances extracted (AVE) for each dimension was greater than 0.5, indicating a high level of agreement (convergent validity) among the items measuring their respective constructs (Hair et al., 2006). The results of the exploratory factor analysis appeared reasonable and parsimonious. The resulting 21-item CRME scale was subjected to further structural testing and scale purification using confirmatory factor analysis (CFA) in the next phase. 4.1.3 Phase 3: scale validity. In order to improve the psychometric properties of the CRME scale, a series of confirmatory factor models was estimated using AMOS 16.0. A 21-item five-factor confirmatory factor model was estimated using maximum likelihood procedure. The overall fit of this CFA model (x2 ð179Þ ¼ 366:74; GFI ¼ 0:85; CFI ¼ 0:90; TLI ¼ 0:89; RMSEA ¼ 0:073Þ indicated few indices below the acceptable threshold levels. Based on residual values and modification indices, five items were identified as candidates for removal namely “facial expressions of the bank employees makes me feel good”, “my bank gives privileges to me”, “my bank employees are committed to providing superior service to me”, “my bank sends

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greetings to me on special occasions” and “my bank is flexible to accommodate changes in the services offered”. Table I presents the 16-item five-factor CRME scale along with their references adopted in developing the same. This CFA model displayed acceptable fit indices (x2 ð91Þ ¼ 142:87; GFI ¼ 0:92; CFI ¼ 0:96; TLI ¼ 0:95; and RMSEA ¼ 0:054Þ: All parameter estimates were significantly different from zero at p , 0.01. In addition, the item-factor loadings surpassed the 0.05 level supporting the hypothesized structure and convergent validity of the scale (Hair et al., 2006). Evidence of internal consistency was demonstrated through the composite reliability score for each dimension (organizational commitment was 0.84; customer experience was 0.87; process-driven was 0.74; technology orientation was 0.81 and reliability was 0.64). Although it is recommended that the composite reliability exceed 0.70, a moderate criterion of 0.6 is also acceptable (Bagozzi et al., 1998). Moreover, the AVE for each dimension was higher than the squared correlations among the five factors, confirming discriminant

• validity. Given the exploratory nature of this study, we consider the above results as

adequate and support the scale structure reliably. 4.2 Study 2 This study aims to examine the extent to which the CRME dimensions predict customer behaviors of satisfaction, loyalty and cross-buying using structural equation

• modeling. More specifically, a causal model was developed and tested to examine

whether customer satisfaction and customer loyalty mediates the relationship between CRME dimensions and cross-buying. Additionally, we examine the stability of the CRME scale by assessing the scale structure in a new sample of Indian retail banking

• customers. As proposed by Peter (1981), testing of the hypothesized model (Figure 1)

provides strong support for the nomological validity as it is based on the explicit investigation of CRME construct and measure in terms of formal hypotheses derived from theory. 4.2.1 Sample and measures. Data were collected using a questionnaire survey sent to approximately 800 retail banking customers in India. A total of 261 usable responses were received representing a response rate of 32.4 percent. Each participant responded the extent to which the items described the CRM efforts, satisfaction, loyalty and intentions to purchase other products of their primary bank. CRME was measured using the 16-item scale that was developed and validated in Study 1. Customer satisfaction was measured using three-items “I am satisfied with this bank” (CS1), “I am satisfied with the quality of services provided by this bank” (CS2), and “I am satisfied with the way this bank has fulfiled my expectations” (CS3) adopted from Singh (1990). To measure customer loyalty, three items “I say positive things about this bank” (CL1), “I encourage friends and relatives to invest with this bank” (CL2) and “I use this bank for all my investment needs” (CL3) were adopted from Bettencourt (1997). Finally, cross-buying was measured using two items “I have intentions to buy more products from this bank” (CB1) and “There is a possibility of purchasing additional products from this bank” (CB2) from Tam and Wong’s (2001)

• study. All the items were measured on a five-point Likert scale anchored by “1”

(strongly disagree) to “5” (strongly agree). 4.2.2 Analysis and results. Table I shows the demographic information of the

• respondents. About 42 percent of the respondents had their primary banking

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experience with State Bank of India (SBI). Only 12 percent of the respondents used their primary bank for more than 10 years, and only a few (28 percent) used a single bank for all banking transactions. An EFA of the 16-item CRME scale extracted a five-factor structure consistent with Study 1. Following this, CFA was carried out to confirm the stability of the scale

• structure. As shown in Table II, the results of the CFA confirmed the model fit of the

five-factor CRME scale. Factor loadings were greater than 0.5 and significant at p , 0.01, ensuring internal consistency for each dimension. Additionally, the AVE of each dimension was higher than the squared correlations between the dimensions, confirming discriminant validity (see Tables III and IV).

Items n % Gender Female 97 37.2 Male 164 62.8 Age (years) Less than 20 5 1.9 21-30 141 54.0 31-40 62 23.8 41-50 34 13.0 More than 50 19 7.3 Education Undergraduate 53 20.3 Post-graduate 104 39.8 Professional Degree 84 32.2 Others 20 7.7 Occupation House wife 6 2.3 Private sector 140 53.7 Public sector 30 11.5 Self-employed 16 6.1 Student 65 24.9 Others 4 1.5 Primary bank HDFC 44 16.9 ICICI 34 13.0 KarurVysya Bank 8 3.1 Punjab National Bank 8 3.1 SBI 110 42.1 Others 57 21.8 Relationship with primary bank (years) Less than 1 48 18.5 Between 1-4 88 33.7 Between 4-7 63 24.1 Between 7-10 30 11.5 More than 10 32 12.2 Table I. Demographic information for the 261 respondents

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4.2.3 Hypotheses testing. Following the validation of the five-factor CRME scale, structural equation modeling was used to estimate the model proposed. Figure 2 presents the path coefficients of the hypothesized model. The measurement model was found to have an adequate fit to the data (x2 ðdf ¼ 222Þ ¼ 329:95; p , 0.01, GFI ¼ 0:91; AGFI ¼ 0:88; CFI ¼ 0:95; TLI ¼ 0:94 and RMSEA ¼ 0:043Þ: The item loadings were greater than 0.5 and significant at p , 0.01 level. As shown, organizational commitment ðb ¼ 0:55; p , 0.01), process-driven approach ðb ¼ 0:25; p , 0.05) and reliability ðb ¼ 0:28; p , 0.05) dimensions of CRME positively and significantly affected on customer satisfaction. This provides partial support for H1. In case of customer loyalty, only reliability dimension had a positive influence ðb ¼ 0:36; p , 0.05), partially supporting H2. However, an indirect effect of CRME dimensions on customer loyalty was observed through customer satisfaction. Customer satisfaction had a positive and significant impact on customer loyalty ðb ¼ 0:50; p , 0.01), supporting H3. The indirect effect of organizational commitment and process-driven approach on customer loyalty were 0.27 and 0.12 respectively. The total effect of reliability on customer loyalty was 0.50 [0.28 (reliability-customer satisfaction) * 0.5 (satisfaction-loyalty) þ0.36 (reliability-loyalty)]. Further, as predicted, customer satisfaction ðb ¼ 0:52; p , 0.01) and customer loyalty ðb ¼ 0:37; p , 0.05) positively

Factors and variables References Organizational commitment – OC This bank regularly uses personal information to provide customized products/services (OC1) Mithas et al. (2005); Rygielski et al. (2002) Employees of this bank often interact with customers to assess service performance (OC2) Jayachandran et al. (2005) This bank assess customer satisfaction regularly (OC3) Developed by authors This bank carefully evaluates customer evolving needs (OC4) Jain et al. (2007) Customer experience – CE This bank attends customer complaints promptly (CE1) Cho et al. (2003) This bank takes genuine interest in customer problems (CE2) Developed by authors This bank effectively communicates to customers (CE3) Rygielski et al. (2002) This bank is co-operative (CE4) Developed by authors Process-driven approach – PD This bank delivers services at the earliest (PD1) Developed by authors This bank designed their service processes to satisfy the customer (PD2) Chen et al. (2009) Conducting transactions correctly and rapidly is very common with this bank (PD3) Chen et al. (2009) This bank provides value-added information along with its products/services (PD4) Ko ¨rner and Zimmermann (2000) Reliability – REL This bank maintains consistent service standards (REL1) Chen and Popovich (2003) This bank provides reliable services (REL2) Developed by authors Technology orientation – TO This bank uses latest technology (ATMs, mobile banking and internet banking) to offer quality services (TO1) Yim et al. (2004) This bank makes effective use of ATMs, mobile banking and internet banking to enhance customer service (TO2) Developed by authors Table II. Questionnaire and references adopted for developing CRME dimensions

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influenced cross-buying. This shows that the higher the customers are satisfied and loyal, the higher the opportunity for banks to cross-sell other related and unrelated

• products. This provides support for H4 and H5.
• 5. Discussion

With the need to be more customer-centric, various organizations including banks have taken efforts to design and implement CRM as a step towards success and achievement in their business. Although CRM has become a vital and an essential business strategy for the Indian banks in the new millennium (Rahman, 2006), limited research has been conducted to evaluate its effectiveness (Mithas et al., 2005; Chen et al., 2009). Thus, the present study aims to propose an instrument for measuring the effectiveness of CRM in Indian retail banks. In addition, we examined its relationship with customer satisfaction, loyalty and cross-buying.

Factors and variables Factor loading Organizational commitment – OC (Cronbach’s a ¼ 0:79; Composite reliability ¼ 0:86; AVE ¼ 0:59Þ This bank regularly uses personal information to provide customized products/ services (OC1) 0.70 Employees of this bank often interact with customers to assess service performance (OC2) 0.82 This bank assess customer satisfaction regularly (OC3) 0.79 This bank carefully evaluates customer evolving needs (OC4) 0.79 Customer experience – CE (Cronbach’s a ¼ 0:81; Composite reliability ¼ 0:87; AVE ¼ 0:64Þ This bank attends customer complaints promptly (CE1) 0.77 This bank takes genuine interest in customer problems (CE2) 0.84 This bank effectively communicates to customers (CE3) 0.78 This bank is co-operative (CE4) 0.80 Process-driven approach – PD (Cronbach’s a ¼ 0:78; Composite reliability ¼ 0:86; AVE ¼ 0:61Þ This bank delivers services at the earliest (PD1) 0.78 This bank designed their service processes to satisfy the customer (PD2) 0.84 Conducting transactions correctly and rapidly is very common with this bank (PD3) 0.74 This bank provides value-added information along with its products/services (PD4) 0.75 Reliability – RE (Cronbach’s a ¼ 0:70; Composite reliability ¼ 0:75; AVE ¼ 0:60Þ This bank maintains consistent service standards (RE1) 0.72 This bank provides reliable services (RE2) 0.83 Technology orientation – TO (Cronbach’s a ¼ 0:73; Composite reliability ¼ 0:77; AVE ¼ 0:60Þ This bank uses latest technology (ATMs, mobile banking and internet banking) to offer quality services (TO1) 0.84 This bank makes effective use of ATMs, mobile banking and internet banking to enhance customer service (TO2) 0.70 Notes: Model fit indices: x2 ¼ 117:6; CFI ¼ 0:981; df ¼ 88; TLI ¼ 0:973; CMIN ðx2=dfÞ ¼ 1: 337; IFI ¼ 0:981; GFI ¼ 0:945; RMR ¼ 0:026; AGFI ¼ 0:915; RMSEA ¼ 0:036 Table III. Confirmatory factor analysis results for Study 2

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Constructs Mean SD Organizational commitment Customer experience Process-driven approach Reliability Technology

• rientation

Customer satisfaction Customer loyalty Cross- buying Organizational commitment 3.50 0.66 0.59 a 0.34 0.27 0.07 0.03 0.31 0.06 0.17 Customer experience 3.72 0.64 0.58 * 0.64 0.29 0.12 0.13 0.24 0.10 0.16 Process-driven approach 3.88 0.57 0.52 * 0.54 * 0.61 0.08 0.09 0.25 0.05 0.12 Reliability 3.87 0.58 0.26 * 0.35 * 0.29 * 0.60 0.23 0.12 0.11 0.07 Technology

• rientation

4.00 0.74 0.17 * 0.36 * 0.30 * 0.48 * 0.60 0.04 0.04 0.03 Customer satisfaction 3.60 0.61 0.56 * 0.49 * 0.50 * 0.35 * 0.20 * 0.59 0.14 0.21 Customer loyalty 3.74 0.63 0.25 * 0.31 * 0.22 * 0.33 * 0.21 * 0.38 * 0.62 0.22 Cross-buying 3.57 0.73 0.41 * 0.40 * 0.35 * 0.26 * 0.16 * 0.46 * 0.47 * 0.50 Notes: * Correlation is significant at the 0.01 level; aDiagonal values indicate AVEs. The upper half of the table indicates the squared correlations, while the lower half indicates the correlation coefficients between the constructs Table IV. Descriptive statistics and discriminant validity

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The results of this study show that the CRME scale conceptualized and developed were both reliable and valid. It consists of 16-items covering five dimensions namely,

• rganizational commitment, customer experience, process-driven approach, reliability,

and technology orientation. These dimensions reveal how effectively CRM is being practiced in the Indian banks. The first dimension “organizational commitment” refers to the efforts by a bank and its employees to serve customers with the desired levels of

• performance. It reflects the bank’s values, attitudes and beliefs in terms of the customer
• rientation and CRM efforts. The understanding and support for CRM are pivotal for

firms to cultivate long-term and mutually benefiting relationships with their customers (Yim et al., 2004). The second dimension “customer experience” describes how well the banks conform to CRM efforts by promptly attending to complaints and efficiently solving problems through proactive customer support. This increases the customer

Figure 2. Structural model testing the relationship between CRME dimensions and customer response behaviors

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comfort levels and ensures their needs and requests are satisfied on a continual basis. The third dimension describes the “process-driven approach” to customer service. This approach enables the banks to fulfil customer requests with speed and astuteness. The “reliability” dimension refers to the extent to which the Indian banks consistently and accurately deliver services to its customers as promised. This dimension is consistent with the findings of Zineldin (2005) that reliability is one of the necessary conditions for banks to deliver superior customer value than competitors and thereby fulfil customer expectations and requirements. The last dimension, “technology orientation”, addresses the operational performance of banks with the use

• f the latest technology such as automatic teller machines, internet banking and mobile
• banking. The bank’s CRM technology allows collection of customer information across

various points of contact and thereby delivers personalized services to maximize customer value. Further, CRM technologies augment the customer information processes and enables effectiveness of relational efforts. We also provide nomological validity of CRME scale by evaluating the consequences of CRME on customer satisfaction, loyalty and cross-buying. As far as the influence of CRME dimensions on customer satisfaction is concerned, the results revealed that only three dimensions, organizational commitment, process-driven approach and reliability showed a significant impact on satisfaction. This is consistent with prior research on the relationship between CRM dimensions and customer satisfaction (Vries and Brijder, 2000; Jun et al., 2004). It could thus be proposed that

• rganizational commitment is crucial in implementing CRM as it enables the Indian

retail banks to deliver a more personalized customer service. Furthermore, only reliability dimension was significant in influencing customer loyalty. This implies that the ability of the banks to deliver high quality customer service as promised can potentially lead to customer loyalty. The findings show that it is essential for banks to develop and nurture strong relationships with their customers before engaging in cross-selling. Thus, the objective should be to increase customer satisfaction and loyalty through relational practices, which leads to cross-selling opportunities. This recommendation is in line with the findings of Bolton (1998) that the satisfaction levels are critical for provider-customer relationships, and banks have to strive for customer satisfaction before they try to cross-sell their products.

• 6. Managerial implications

The study findings have significant implications for bank managers. We identified the key dimensions of CRME that should be implemented to enhance the business

• performance. The five dimensions namely, organizational commitment, customer

experience, process-driven approach, reliability and technology orientation measure the effectiveness of CRM efforts in Indian banks. The identification of these dimensions enables bank managers to design an effective CRM that fosters enduring relationships with customers. Further, these dimensions emphasize that CRM efforts should focus on key areas such as process, technology, management and people (Sin et al., 2005). Thus, bank managers should focus on orchestration of all the five dimensions to maximize CRM effectiveness. The finding, that of the five dimensions of CRME, only organizational commitment, process-driven approach and reliability positively influence satisfaction with the bank help managers ascertain priorities, at least in terms of satisfaction drivers. Thus,

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executing customer transactions correctly and delivering services rapidly as promised are important in enhancing customer satisfaction. This scale can also be used as a tool by the banks to identify the aspects of CRM where improvements are needed to increase the levels of customer satisfaction and loyalty. Periodic assessment of the effectiveness of CRM using this scale could help bank managers track changes in CRM dimensions that impact customer satisfaction and loyalty over time. Another implication from this study is that the satisfaction and loyalty from relational perspective provide opportunities for banks to cross-sell other banking products/services. Thus, managers should focus on relational practices that enhance satisfaction and loyalty as the customers are likely to spread their purchases across different categories offered by the bank. Moreover, this implies that the bank managers should pay attention to customers who are at a specific point of relationship with the bank for cross-selling of other banking products.

• 7. Limitations and future research directions

This study has some limitations. First, the sample features (i.e. convenience sample) warrant caution before generalizing the results beyond the population studied and require replication of the scale to other industries and nations for generalizability. Second, various other factors can also be studied as outcome indicators of CRME. For example, customer trust and commitment are some of the variables (Kassim and Abdulla, 2006). Third, moderating influence of relationship duration between CRME and customer satisfaction would be intriguing as the buyer-seller relationship goes through different phases (Verhoef, 2003). Fourth, this research considered customer perceptions towards CRME and future research should examine employee perceptions towards relationship practices and its outcomes on business performance. Finally, this study offers a cross-sectional view. CRM efforts can be regarded as an ongoing process to keep abreast of the changing customer preferences. Thus, future studies should use longitudinal framework to provide further insights on the dimensions studied over time.

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• C. Padmavathy is a Doctoral Candidate in the Department of Management Studies at the

National Institute of Technology, Tamil Nadu, India. Her teaching interests include CRM, banking and insurance and HR counseling. Her research interests outline CRM, mobile banking,

• nline banking and consumer behavior. C. Padmavathy is the corresponding author and can be

contacted at: cpadma85@gmail.com M.S. Balaji is the Assistant Professor of Market at the Department of Marketing and Strategy, IBS, Hyderabad, India. His teaching interest includes services marketing, brand management, marketing management and marketing research. Research interest outlines branding services, sports consumption and experience, sensory marketing and customer relationship management. V.J. Sivakumar is the Associate Professor of Marketing at National Institute of Technology, Tamil Nadu, India. Having possessed a rich experience in industries, he stepped down to academia on the grounds of his passion towards teaching. His teaching interest includes CRM, IT consulting in retailing and sales management. His research interest rests in CRM, banking, retailing and store loyalty.

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