VIKALPA • VOLUME 37 • NUMBER 4 • OCTOBER - DECEMBER 2012 47

R E S E A R C H

includes research articles thatfocus on the analysis and

resolution of managerial andacademic issues based on

analytical and empirical orcase research

ExecutiveSummary

Validating Antecedents of CustomerLoyalty for Indian Cell Phone Users

Harsandaldeep Kaur and Harmeen Soch

KEY WORDS

Customer Loyalty

Cell Phone Market

Behavioural Loyalty

Attitudinal Loyalty

Corporate Image

Switching Cost

The Indian telecom industry is one of the fastest growing in the world and second largestmobile market globally. The Indian cell phone market is characterized by a large subscriberbase (545 million connections as on May, 2010, expected to reach 876.6 million in 2013), lowaverage revenue per user (INR 209 as on March 2009) and high churn rates (53.2% in 2009,and is expected to increase to 59.6% in 2013). The Indian consumers are enjoying a wide rangeof services along with the world’s lowest local call rates. The mobile phone operators in Indiaare faced with the challenge of reducing customer churn in the ever-increasing prepaid sub-scriber market. Under such circumstances, the success of mobile phone service providers inIndia mainly depends on developing innovative schemes to increase customer loyalty. Inservice organizations, customer loyalty is considered as prime determinant of long-term fi-nancial performance of firms. One of the crucial issues of today is to understand how or whya sense of loyalty develops in customers.

Loyalty definitions and metrics are mostly in behavioural and attitudinal terms. Consensus isabsent in the marketing literature on how loyalty should be conceptualized and measured inthe service industry. The present study addresses both the conceptual and measurement is-sues related to customer loyalty amongst the cell phone users. Building on existing literature,the antecedents of customer loyalty are reassessed in a service setting namely, customer satis-faction, trust, commitment, corporate image, and switching costs. This paper reports the de-velopment and validation of scales for measuring customer loyalty and its antecedents. Thescale development and validation process was divided into three phases. Phase 1 consisted ofthe item generation process, assessment of content validity, and initial purification of meas-ures. Item analysis and exploratory factor analysis were used on data collected from 250 post-graduate business students of a major university in India for initial purification of theinstrument. In Phase 2, additional data were collected from 855 cell phone users in northernIndia to assess the latent factor structure through confirmatory factor analysis. In Phase 3, thediscriminant validity and convergent validity of the measures were established.

The managerial implications of the major findings of this study are as follows:

• It contributes to the existing literature by developing a theory-based framework for un-derstanding the direct effects of satisfaction, trust and commitment, switching costs andcorporate image on behavioural and attitudinal loyalty.

• By measuring loyalty as composite measure consisting of both behavioural loyalty andattitudinal loyalty, it provides evidence of the multidimensional nature of loyalty con-struct. Using data collected from mobile phone users, the reliability and validity of thescale are established. The newly refined and validated measures can be used by futureresearchers to study customer loyalty and its antecedent factors.

• The loyalty measures developed in this study provides managers with an easy tool toidentify attitudinal and behavioural differences among customers. Marketing managerscan draw meaningful distinctions between customers who buy out of habit and thosewho buy because of emotional attachment to the company.

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INTRODUCTION

Indian cell phone market is the largest and one ofthe fastest growing markets in the world. It is char-acterized by a large subscriber base (545 million con-

nections as on May 2010 expected to reach 876.6 millionin 2013), low average revenue per user (INR 209 as onMarch 2009), and high churn rates (53.2 % in 2009, andis expected to increase to 59.6 % in 2013) (TRAI, 2009;KPMG, 2009; Gartner Report, 2009).

Churn rates have escalated with increased competitionand deregulation. Factors such as launching of mobilenumber portability scheme, introduction of 3G and 4Gservices and entry of new licenses are likely to furtherincrease churn rates and reduce loyalty (TRAI, 2009).The high churn rates adversely affect mobile telecomoperators because they stand to lose a great deal in pricepremium, decreasing profits levels and a possible lossof referrals from continuing service customers (Manero,2008). In the context of such an intensifying competitionin the telecom sector, there is renewed interest in cus-tomer retention, because obtaining new customers iscostlier than serving the existing ones; therefore, it makessense to retain the existing customers (Reichheld, 1996).

Given the high churn rate, immense competition, andhigh acquisition costs, the best core marketing strategyin the cell phone industry now would be to try to retainexisting customers by heightening customer loyalty andcustomer value. Companies recognize the relevance ofmaintaining a solid base of loyal customers for long-termsurvival, growth, and financial performance (Reichheld,1996; Reinartz & Kumar, 2002). Given the importance ofcustomer loyalty to a service firm, it is important formarketers to fully understand the nature and dimensio-nality of loyalty construct (Jones & Taylor, 2007). Conce-ptual definition and measurement of loyalty has receivedmany different definitions and interpretations in the lit-erature (Rundle-Thiele, 2005b). The contradictory viewsabout loyalty are the result of a systematic lack of a rig-orous study of the reliability and validity of the proposedmeasurement instruments (Odin, Odin & Valette-Flor-ence, 2001). Loyalty has largely been defined and meas-ured in behavioural and attitudinal terms. Russell-Bennett, McColl-Kennedy & Coote (2007) argue thatdefining loyalty with reference to attitudinal or behav-ioural components is too simplistic, and that there arepotential gains from advancing a holistic approachwhich incorporates both the components of loyalty.

In recent years, there has been a growing interest in de-termining the factors that influence the development ofloyalty. Despite this, there is no consensus among re-searchers as to the factors that help a firm maintain andincrease customer loyalty. Previous empirical researchhas focused primarily on satisfaction, trust, and com-mitment as the key ingredients for successful long-termrelationships (Fornell, 1992; Anderson & Sullivan, 1993;Garbarino & Johnson, 1999; Pritchard, Havitz & Howard,1999; Gustafsson, Johnson & Roos, 2005). Very little re-search has been done on corporate image and switchingcosts as the key antecedents of customer loyalty. An in-vestigation of these issues is important because corpo-rate image and switching costs significantly affectcustomer retention either directly or indirectly throughtheir association with the customer relationship percep-tions (like satisfaction, trust, or commitment) and cus-tomer loyalty (Nguyen & LeBlanc, 1998; Bloemer, Ruyter& Peeters, 1998; Burnham, Frels & Mahajan, 2003; Jones,Mothersbaugh & Beatty, 2002).

OBJECTIVES OF THE STUDY

There is lack of research specifically on examining theantecedents of customer loyalty in the Indian context.The telecom market is highly competitive with 10-12players operating in each telecom circle and the entry ofnew licensees is expected to further intensify the com-petition (KPMG, 2009). In the context of such an intensi-fying competition in the telecom sector, customerretention is crucial to increased profitability and mar-gins. Thus, empirical investigation aimed at measuringand validating antecedents of customer loyalty is of ut-most importance for telecom operators to develop strat-egies to increase margins.

The purpose of this study is to describe the develop-ment and refinement of a scale for measuring customerloyalty and its antecedents for cell phone users. Specifi-cally, the key objectives are: (a) to provide a conceptuali-zation of customer loyalty and its antecedents; (b) todevelop a scale for measuring customer loyalty and itsantecedents; and (c) to assess reliability and validity ofthe measures.

CONCEPTUAL FRAMEWORK

Customer Loyalty

The importance of loyalty has been widely recognized

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in the marketing literature. Three conceptualizations ofcustomer loyalty have been identified in literature: be-havioural loyalty, attitudinal loyalty, and compositemeasure of loyalty (Uncles, Dowling & Hammond,2003). Recent studies (Bennett & Rundle-Thiele, 2002;Rundle-Thiele, 2005a) indicate that a multidimensionalview of customer loyalty is important to understanddimensions and measures of loyalty. Dick & Basu (1994)argue that both behavioural and attitudinal loyalty areimportant, when emphasis is both on understanding pastbehaviours as well as predicting future patronage by thecustomer. The present study considers both behaviouraland attitudinal dimensions of loyalty and studies theeffects of potential drivers of loyalty on both forms.

Behavioural Loyalty

The behavioural approach suggests that the repeat pur-chasing of a brand over time by a consumer reflects hisloyalty. Behavioural approaches operationalize loyaltyin four ways, through measures based on the (a) buy-er’s expenditure within a product category that is spentwith a particular supplier (Palmer, McMahon-Beattie &Beggs, 2000; Shukla, 2004); (b) proportion of purchasein the product category (Ehrenberg, 1964); (c) purchas-ing sequence behaviour of consumers (Tucker, 1964); and(d) amount of brand switching among consumers in aproduct category (Javalgi & Moberg, 1997).

The advantage of behavioural measures are that theymeasure observable behaviours and thus help market-ers to understand how people buy primarily in marketswhere data is readily available (Odin, Odin & Valette-Florence, 2001; Rundle-Thiele, 2005b). Another advan-tage of behavioural measures is that they are not incide-ntal as they are based on purchasing behaviour over aperiod of time (Mellens, Dekimpe & Steenkamp, 1996).The major shortcoming of loyalty measures based onrepeat purchase behaviour is that they do not distinguishbetween true loyalty and spurious loyalty (Day, 1969).It is difficult for companies to influence repeat purchas-ing behaviour because behavioural measurements can-not differentiate between the various buying situationsand personal motives that may induce consumers to buythe same brand (Amine 1998; Palmer, McMahon-Beattie& Beggs, 2000; Odin, Odin & Valette-Florence, 2001).Finally, behavioural measures are sensitive to short-termfluctuations like stock-out, lack of choice, habit, low in-come, convenience, etc. (Mellens, Dekimpe & Steen-

kamp, 1996; Hart et al., 1999).

Attitudinal Loyalty

To overcome the limitations of behavioural approach,researchers have proposed measuring loyalty by meansof an attitudinal dimension in addition to the behaviouraldimension (Day 1969; Jacoby & Kyner, 1973; Srinivasan,Anderson & Ponnavolu, 2002). The attitudinal perspec-tive assumes that consistent buying of a brand must becomplemented with a positive attitude towards thebrand to ensure that repeat behaviour will be pursuedfurther (Amine, 1998). Attitudinal loyalty refers to thelevel of consumer’s psychological attachments andattitudinal advocacy towards the supplier (Chaudhuri& Holbrook, 2001).

The attitudinal measures based on the stated preferencesand commitment, distinguish loyalty from repeat buy-ing and are also less sensitive to short-run fluctuations(Mellens, Dekimpe & Steenkamp, 1996). Attitudinal loy-alty measures help brand managers to understand rea-sons for customer’s purchase of their brands as well asthose of competitors and also help to identify strengthsand weaknesses of their brands (Bandyopadhay &Martell, 2007). Attitudinal measures are not an accuraterepresentation of reality as they rely on consumer dec-laration and not on observed behaviour and it is possi-ble that consumers may not provide true information(Mellens, Dekimpe & Steenkamp, 1996; Odin, Odin &Valette-Florence, 2001). Another disadvantage of atti-tudinal measurements is that while operationalzingattitudinal loyalty, researchers use either antecedents orconsequences of loyalty (Odin, Odin & Valette-Florence,2001).

Composite Loyalty

For understanding long-term relationships, both behav-ioural and attitudinal dimensions of loyalty are impor-tant (Dick and Basu, 1994). The composite approach toloyalty considers customers’ favourable attitudes, inten-tions, and repeat purchasing as measures of true loyalty(Shoemaker & Lewis, 1999; Rundle-Thiele, 2005b). Dickand Basu (1994) define loyalty as the strength of the re-lationship between an individual’s relative attitude andtheir repeat patronage. Research findings support thetwo-dimensional measures as better predictors of cus-tomer’s loyalty (Chaudhuri & Holbrook, 2001; Ganesh,Arnold & Reynolds, 2000; Rauyruen & Miller, 2007; Yi

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& Jeon, 2003). Therefore, in line with various research-ers, this study proposes a composite measure of cus-tomer loyalty incorporating both behavioural andattitudinal dimensions. For this study, behavioural loy-alty is seen as the readiness of customers to repurchasethe services of the service provider and to maintain arelationship with the firm. Attitudinal loyalty is definedhere as the level of customer’s psychological attach-ments, willingness to recommend the service provider,and engagement in positive word-of-mouth (WOM)communications.

The framework presented in Figure 1 shows the rela-tionship between the antecedents of customer loyalty.This model considers behavioural and attitudinal loy-alty as dependant variables, which are affected by cus-tomers’ overall satisfaction, trust, commitment, corpo-rate image, and switching costs.

Antecedents of Customer Loyalty

Customer Satisfaction

Marketing literature consistently identifies customersatisfaction as a key antecedent to loyalty and repur-chase behaviour. Customer satisfaction is understoodas the customer’s emotional or empathic reaction to aperceived difference between performance appraisal andexpectations (Hennig-Thurau & Hansen, 2000). The lit-erature on customer satisfaction research recognizes twobasic conceptualizations of satisfaction: transaction-spe-cific and cumulative satisfaction (Johnson et al., 2001;Johnson, Herrmann & Gustafsson, 2002). Transaction-specific approach defines satisfaction as a customer’sevaluation of his or her experience with and reactionsto a particular product transaction, episode, or service

encounter (Olsen & Johnson, 2003). Cumulative satis-faction describes the customer’s overall consumptionexperience with a product or service over time (Fornell,1992; Johnson, Nader & Fornell, 1996). Cumulative sat-isfaction measures are also better predictors of futurebehaviour (customer retention) and firm performance(profitability) (Anderson, Fornell & Lehmann, 1994;Johnson, Nader & Fornell, 1996; Gustafsson, Johnson &Roos, 2005).

Customer satisfaction with a company’s products orservices is the key to customer retention, company’ssuccess, and long-term competitiveness (Hennig-Thurau& Klee, 1997). High customer satisfaction indicates in-creased loyalty, lower costs of future transactions, lowercosts of attracting new customers, and an enhanced repu-tation for firm (Fornell, 1992; Anderson, Fornell &Lehmann, 1994). It is widely agreed that customer satis-faction leads to customer retention (Anderson &Sullivan, 1993), purchase intentions (Anderson & Mittal,2000), and word-of-mouth (Anderson, 1998)

Trust

Trust is the most critical element in the development ofsuccessful relationship between a service provider andits customers (Morgan & Hunt, 1994; Garbarino &Johnson, 1999). Berry (1995) asserts that trust is criticalto the formation of service-based relationships becauseof intangibility of services. In marketing, trust has beendefined in various ways. Moorman, Deshpande &Zaltman (1993) define trust as “the willingness to relyon an exchange partner in whom one has confidence”.Morgan & Hunt (1994) see trust as existing when oneparty has confidence in exchange partner’s reliability andintegrity.

Figure 1: The Proposed Conceptual Model of Antecedents of Customer Loyalty

CustomerSatisfaction

Trust

AffectiveCommitment

CalculativeCommitment

BehaviouralLoyalty

AttitudinalLoyalty

CorporateImage

SwitchingCost

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Urban, Sultan & Qualls (2000) suggest that trust-basedstrategies are an essential element in building positiverelationships with customers and increasing marketshare and profits of companies. Chaudhuri & Holbrook(2001) found that trusted brands are purchased moreoften and evoke a high degree of attitudinal loyalty.Other authors such as Garbarino & Johnson, (1999),Srideshmukh, Singh & Sabol, (2002), and Ndubisi (2007)also provide empirical evidence that a relationship ex-ists between trust and future purchase intentions andintention to stay in the relationship.

Commitment

Commitment is recognized as an essential ingredient forsuccessful long-term relationships (Dwyer, Schurr & Oh,1987; Morgan & Hunt, 1994). Commitment is defined as“implicit or explicit pledge of relational continuity be-tween exchange partners” (Dwyer, Schurr & Oh, 1987).Moorman, Zaltman & Deshpande (1992) see relation-ship commitment as an enduring desire to maintain avalued relationship. Prior research suggests two formsof commitment – affective commitment and calculativecommitment. Affective commitment is the extent towhich a customer likes to maintain relationship with aservice provider on the basis of their affective attach-ment and identification with the service provider(Amine, 1998). It is based on a sense of liking and emo-tional attachment to the partnership (Wetzels, Ruyter &Birgelen, 1998). Calculative commitment represents aconstraint-based force binding the consumer to the serv-ice provider and stems from a cognitive evaluation ofthe instrumental worth of a continued relationship withthe organization (Wetzels, Ruyter & Birgelen, 1998;Bansal, Irving & Taylor, 2004). Previous empirical re-search has demonstrated a positive relationship betweencommitment and future purchase intentions (Garbarino& Johnson, 1999; Gustafsson, Johnson & Roos, 2005),intention to stay in the relationship (Wetzels, Ruyter &Birgelen, 1998; Pritchard, Havitz & Howard, 1999), cus-tomer advocacy intentions, and willingness to pay more(Fullerton, 2005).

Corporate Image

Marketing literature identifies corporate image as animportant factor in evaluation of the service and the com-pany (Gronross, 1984; Andreassen & Lindestad, 1998).Nguyen (2006) defines corporate image as consumer’s

response to total offerings and is related to businessname, architecture, variety of products/services, tradi-tion, ideology, and to the impression of quality commu-nicated by each person interacting with the organization.According to Ngobo (2004), image in service industriesis conveyed by the service concept (i.e., benefits offered),delivery system (i.e., personnel and technology), and tar-get markets (i.e., types of clients served).

Image has two principal components: functional andemotional. The functional component is related to tan-gible attributes that can be easily measured, while theemotional component is associated with psychologicaldimensions that are manifested by feelings and attitudestowards an organization (Nguyen, 2006). Image as afunction of accumulation of purchasing/consumptionexperience over time, is used by organizations to attractnew customers and retain existing customers (Andrea-ssen & Lanseng, 1997). Previous empirical research hasshown that corporate image positively influences cus-tomer loyalty (Andreassen & Lindestad, 1998; Nguyen& LeBlanc, 2001; Johnson et al., 2001).

Switching Costs

Research increasingly suggests that switching costs arean important factor impacting a customer’s decision toremain with a service provider (Jones, Mothersbaugh &Beatty, 2000). Switching costs are the sacrifices or pen-alties consumers feel they may incur in moving fromone provider to the next (Jones et al., 2007). Burnham,Frels, & Mahajan (2003) proposed a switching cost ty-pology with three types of switching costs: proceduralswitching costs, financial switching costs, and relationalswitching costs. Procedural switching costs represent theconsumer perceptions of the time and effort involved inseeking out information about available alternatives andevaluating their viability prior to switching (Jones,Mothersbaugh & Beatty, 2002). Financial costs consistof benefit-loss costs and monetary-loss costs and involveloss of financial quantifiable resources (Burnham, Frels& Mahajan, 2003). Relational switching costs relate tothe potential loss of a personal bond or friendship witha service provider if the customer switches (Jones et al.,2007).

Dick & Basu (1994) suggest that switching costs are acommon strategy to increase loyalty and prevent cus-tomer defection. Switching costs deter customers from

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switching to a competing firm (Fornell, 1992). Lam et al.(2004) observe that switching costs in the form of mon-etary expenses, time, and psychological effort, encour-age customers to recommend the provider to othercustomers. Various studies have examined a positive re-lationship between switching costs and repurchase in-tentions (Fornell, 1992; Anderson & Sullivan, 1993; Jones,Mothersbaugh & Beatty, 2000; Burnham, Frels, &Mahajan, 2003).

RESEARCH METHODOLOGY

In order to develop, refine and validate measures forvarious constructs, scale development procedures wereused. The methodology used to develop measures fol-lowed recommendations of Churchill (1979), Gerbing &Anderson (1988), and Saxe & Weitz (1982).

Item Generation

Prior to item generation, an extensive literature surveywas carried out for conceptualizing constructs and speci-fying their domain. An exploratory study among cellphone mobile users was undertaken to better understandthe key antecedents of customer loyalty. For this pur-pose, we conducted open-ended interviews with fifteencustomers. The literature review and in-depth interviewswith customers suggested a pool of 50 items to measurethe various constructs. In order to avoid response setbias, approximately 30 per cent of the items in each sub-scale were stated negatively (Churchill, 1979). A ques-tionnaire was developed in which each item wasmeasured on a 7-point Likert Scale ranging from “verystrongly agree” to “very strongly disagree”. The selecteditems are shown in the Appendix.

Assessment of Content Validity

The assessment of content validity serves as a pretest,permitting the deletion of items that are deemed to beconceptually inconsistent (Hinkin, 1998). Content valid-ity is ensured to the extent that expert judges agree thatitems are reflective of the overall construct and that thesejudges agree that the items are representative of thedomain and facets of the construct (Netemeyer, Bearden& Sharma, 2003). A panel of three marketing judgesevaluated the items for content validity and suggestedthe removal of some items they considered to be redun-dant, double-barreled, and ambiguous. This process re-sulted in the elimination of 8 items, leaving a pool of 42

items for further analysis.

Pre-Testing

The purpose of this stage was to purify the measures. Itwas used to further reduce the pool of items and to testinternal consistency of the scale. Data was obtainedthrough self-administered questionnaires from 250 post-graduate business students of a major university in In-dia. Convenience sampling was used to collect data. Thesample consisted of 132 (52.8%) females and 118 (47.2%)males.

The measures were refined using item analysis at fourstages. First, the correlation of each item with the totalscore for each construct was computed and items withlow correlations with the total score were eliminatedfrom the scale (Ruekert & Churchill, 1984). In the sec-ond stage, items in each construct were correlated withthe total score of other constructs. Items that did not havestatistically significant higher correlations with the di-mension to which they were hypothesized to belong incomparison to item correlations with remaining dimen-sion total scores were deleted from the analysis(Ruekert & Churchill, 1984; Bearden, Netemeyer & Teel,1989).

Thirdly, corrected item-to-total correlations were exam-ined for each construct. Items with high corrected item-to-total correlation have more variance relating to whatthe items have in common and add more to the test’sreliability than items with low corrected item-to-totalcorrelation (Nunnally & Bernstein, 1994). Items that hadcorrected item-to-total correlations more than or equalto 0.35 were retained and the rest were deleted (Saxe &Weitz, 1982). In order to minimize skewness and maxi-mize variance, items with both larger means and largervariances were retained and the rest deleted (Kumar &Beyerlien, 1991; Thomson, MacInnis & Park, 2005). Theseanalyses resulted in a reduced scale with 22 items tomeasure various constructs.

The internal consistency of the items belonging to eachconstruct was then examined using Cronbach alpha andexploratory factor analysis at the subscale level (Ruekertand Churchill, 1984; Flynn, Schroeder & Sakakibara,1994). Principal component analysis was used to exam-ine whether items in each measure loaded on one factoror not. Table 1 indicates that items in each subscale loadon one factor. All the obtained eigen values exceed the

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criterion of 1.0 (Flynn, Schroeder & Sakakibara, 1994;Hair et al., 1998).

Cronbach alpha is concerned with the degree of interre-latedness among a set of items designed to measure asingle construct (Netemeyer, Bearden & Sharma, 2003).Table 1 depicts the reliability alphas for various con-structs. As can be seen, the coefficient alphas for all theeight subscales are above 0.60 which is an acceptablelimit for early stages of basic research (Nunnally &Bernstein, 1994).

Table 1: Reliability and Construct Validity of Measures

Construct Item Eigen Factor Cronbach VarianceLabel Value Loading Alpha Explained

Satisfaction SAT1 2.213 0.818 0.816 73.783

SAT2 0.875

SAT3 0.882

Trust TR1 1.720 0.732 0.781 57.330

TR2 0.715

TR3 0.820

Affective AC1 1.979 0.838 0.803 65.952Commitment AC2 0.832

AC3 0.764

Calculative CC1 1.425 0.844 0.652 71.230Commitment CC2 0.844

Corporate CI1 2.020 0.872 0.861 67.338Image CI2 0.798

CI3 0.790

Switching SC1 1.713 0.745 0.686 57.110Cost SC2 0.709

SC3 0.810

Behavioural BL1 1.159 0.761 0.692 57.951Loyalty BL2 0.762

Attitudinal AL1 1.965 0.811 0.887 65.494Loyalty AL2 0.797

AL3 0.820

Confirmatory Factor Analysis

Confirmatory Factor Analysis (CFA) was employed toassess the latent factor structure. CFA involves the speci-fication and estimation of one or more hypothesizedmodels of factor structure, each of which proposes a setof latent variables (factors) to account for co-variancesamong a set of observed variables (Koufteros, 1999).Confirmatory factor analysis using AMOS 18.0 was car-ried out to test the measurement model presented inFigure 2.

Data was collected through self-administered question-naires from 1,000 mobile phone users. The survey wascarried out in the northern part of India. A total of 855usable responses were obtained for an overall responserate of 85.5 per cent. The sample included 58.2 per centof males and 41.8 per cent of females. The age profile ofrespondents ranged from 20 years to 45 years with al-most 87 per cent of the respondents in the age groupbetween 20 years and 24 years. Regarding current pro-vider subscription, 38 per cent of the respondents wereusing Airtel followed by Vodafone (22%), BSNL (17%),Spice (15%), IDEA (3%), Reliance Communications(1.7%), and TATA Indicom (1.4 %).

Figure 2: Measurement Model

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Model Fit

Model fit determines the degree to which the structuralmodel fits the sample data. AMOS output yielded a Chi-square value (χ2) of 645.909 with 181 degrees of free-dom. The CMIN/DF (minimum discrepancy divided bydegrees of freedom) ratio was 3.569, which is within therecommended range of less than 5, which is indicativeof an acceptable fit between the hypothetical model andthe sample data (Carmines & McIver, 1981). The good-ness-of-fit index (GFI) was 0.937 and adjusted goodness-of- index (AGFI) was 0.912. The root mean square errorof approximation (RMSEA) was 0.05, which falls withinthe cutoff value of 0.06 (Hu & Bentler, 1999).

The Tucker-Lewis Index (TLI) was 0.939 while the Com-parative Fit Index (CFI) was 0.952. The Bentler-Bonett

normed fit index (NFI) was 0.935 and Bollen’s incremen-tal fit index (IFI) was 0.917. The values for fit indices areshown in Table 2 and all exceed the recommended levelof 0.90, suggesting that the hypothesized model repre-sented an adequate fit to the data.

Item Reliability, Composite Reliability and AverageVariance Extracted (AVE)

Item reliability indicates the amount of variance in anitem due to the underlying constructs rather than to er-ror (Suh & Han, 2002). The item reliability of individualitems can be assessed by squaring their respective stand-ardized factor loadings (Segars, 1997). Indicator reliabi-lities should exceed 0.50, which provides evidence thatitems explain more variance than is explained by the er-ror term (Bollen, 1989; Segars, 1997). Table 3 reveals that

Table 2: Model Fit Indices of the Measurement Model

Index of Fit Chi-Square(df) CMIN/DF GFI AGFI NFI IFI TLI CFI RMSEA

Value 645.909 (181) 3.569 0.937 0.912 0.935 0.917 0.939 0.952 0.05

Table 3: Parameter Estimates, Critical Ratios, R2, Average Variance Extracted and Composite Reliability

Latent Variables ItemLabel Standardized Factor Loading Critical Ratiob R2 AVE Composite Reliability

Satisfaction SAT1 0.803 –a 0.644 0.615 0.827

SAT2 0.813 21.241 0.660

SAT3 0.735 20.257 0.540

Trust TR1 0.769 —a 0.592 0.673 0.860

TR2 0.793 23.494 0.629

TR3 0.894 25.659 0.800

Affective Commitment AC1 0.744 —a 0.554 0.655 0.850

AC2 0.807 22.265 0.652

AC3 0.872 22.953 0.761

Calculative Commitment CC1 0.897 —a 0.805 0.756 0.861

CC2 0.842 18.577 0.709

Corporate Image CI1 0.759 —a 0.577 0.602 0.819

CI2 0.820 28.504 0.672

CI3 0.747 20.781 0.558

Switching Cost SC1 0.911 —a 0.830 0.766 0.907

SC2 0.876 34.792 0.767

SC3 0.838 32.553 0.703

Behavioural Loyalty BL1 0.779 —a 0.606 0.668 0.800

BL2 0.854 19.622 0.729

Attitudinal Loyalty AL1 0.804 —a 0.647 0.642 0.805

AL2 0.745 20.015 0.555

AL3 0.731 19.734 0.534

a Indicates a parameter fixed at 1.0 in the measurement model.

b All Critical Ratios (t-values) are significant at 0.05.

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all the items had R2 values greater than 0.50, which showsthat all variables are significantly related to their speci-fied constructs and thus verifying the posited relation-ships among indicators and constructs (Hair et al., 1998).Composite reliability is a measure of the internal con-sistency of the construct indicators, which depicts thedegree to which the items indicate the common latent(unobserved) construct (Hair et al., 1998). Anderson &Gerbing (1988) state that even a perfectly unidimensionalscale will be of little practical use if the resultant com-posite score has unacceptably low reliability. Highlyreliable constructs are those in which the indicators arehighly intercorrelated, indicating that they are all meas-uring the same latent construct (Koufteros, 1999; Lu, Lai& Cheng, 2007). Table 3 shows the composite reliabilityfor various constructs. All constructs had composite re-liability above the recommended level of 0.70 (Hair etal., 1998).

AVE measures the amount of variance that is capturedby the construct in relation to the amount of variancedue to measurement error (Fornell & Larcker, 1981).Higher values of variance extracted indicate that indi-cators are truly representative of the latent construct(Hair et al., 1998). AVE values greater than 0.50 are con-sidered adequate for any construct (Bagozzi & Yi, 1988;Hair et al., 1998). Table 3 shows that all the constructshave AVE values above the recommended level of 0.50,thus providing further evidence of reliability.

Scale Validation

Once the reliability and the structure of the scale aresupported, the validity of the instrument has to be as-sessed.

Convergent Validity

A measure is said to possess convergent validity if in-dependent measures of the same construct converge, orare highly correlated (Netemeyer, Bearden & Sharma,2003). Convergent validity can be assessed from themeasurement model by determining whether each in-dicator’s estimated pattern coefficient on its positedunderlying factor is significant or not (Anderson &Gerbing, 1988). In the AMOS output file, the t-value isthe critical ratio, which represents the parameter esti-mate divided by its standard error (Netemeyer, Bearden& Sharma, 2003). As can be seen in Table 3 all the factorloadings are significant at 0.05 significance level, which

supports the convergent validity. According to Fornell& Larcker (1991), convergent validity of the construct isalso demonstrated when the average variance extractedis above 0.50. Table 3 shows that the AVEs for all theconstructs are above 0.50, which further supports theconvergent validity of the measures.

Discriminant Validity

Discriminant validity refers to the extent to which meas-ures of theoretically unrelated constructs do not corre-late highly with one another (Brown, Churchill & Peter,1993). The discriminant validity of the measures in thepresent study was established by comparing the aver-age variances extracted with the squared correlationbetween two constructs (Fornell & Larcker, 1981). If thesquared correlation between constructs is less than ei-ther of their individual AVEs, it would suggest that eachof the constructs has more error-free variance thanshared with other constructs (Ping Jr., 2004). From Ta-ble 4, it can be seen that all AVEs are higher than squaredinter-construct correlations. This result provides evi-dence of discriminant validity.

Table 4: Discriminant Validity Analysis*

SAT TR AC CC CI SC BL AL

SAT 0.615

TR 0.022 0.673

AC 0.003 0.059 0.655

CC 0.025 0.153 0.030 0.756

CI 0.034 0.443 0.162 0.133 0.602

SC 0.007 0.057 0.011 0.092 0.096 0.766

BL 0.059 0.284 0.105 0.131 0.342 0.132 0.668

AL 0.078 0.113 0.002 0.163 0.255 0.057 0.235 0.642* Based on (Fornell & Larcker, 1981): AVE in the diagonal and squared correlation

off-diagonal.

DISCUSSION

A review of literature revealed that there is no consen-sus among researchers on the definition and measure-ment of loyalty. The earlier studies on antecedents ofcustomer loyalty have focused primarily on customersatisfaction, trust and commitment. The purpose of thisstudy was to examine the effects of customer satisfac-tion, trust, commitment, switching costs and corporateimage on behavioural and attitudinal loyalty. Anotheraim of the study was to describe the development andrefinement of a scale for measuring customer loyalty andits antecedents.

56

First, multi-item measures were developed and purifiedfor measuring customer loyalty using standard scaledevelopment procedures on a sample of 250 mobilephone users.

Confirmatory factor analysis was then applied on datacollected from 855 mobile phone users. Results revealedthat all the indicators had adequate item reliability. Allthe model fit indices were above the recommended cri-teria. Reliability was assessed using coefficient alpha,composite reliability and average variance extracted andall the values obtained were above the accepted range,thus establishing the reliability of the constructs. Con-vergent and discriminate validity was established forall the eight constructs.

MANAGERIAL IMPLICATIONS

The present study makes both academic and practicalcontributions. From an academic point of view, it con-tributes to the existing literature on relationship mar-keting. The study first provides a theory-based frame-work for understanding the direct effects of satisfaction,trust and commitment, switching costs and corporateimage on behavioural and attitudinal loyalty. A signifi-cant contribution that the model makes is the apprecia-tion of the ‘corporate image’ construct and ‘switchingcost’ construct and its influence on customer loyalty.Moreover, by measuring loyalty as a composite meas-ure consisting of both behavioural loyalty and attitudinalloyalty, the study provides evidence of the multidimen-sional nature of loyalty construct. Using data collectedfrom mobile phone users, it establishes reliability andvalidity of the scale. The newly refined and validatedmeasures can be used by future researchers to studycustomer loyalty and its antecedent factors.

From a managerial point of view, the contributions ofproposed study are as follows:

Segmenting of Customers – Customers demonstrate loy-alty in a number of ways. Segmenting loyals can assistin developing an understanding of the nature of loyaltyorientation and can inform appropriate marketing ac-tions (Rowley, 2005). Managers can identify four groupsof loyal customers based on behavioural and attitudinalloyalty levels: true loyalty, latent loyalty, spurious loy-alty, and low loyalty (Dick & Basu, 1994). Customerswith true loyalty are characterized by a high degree ofboth behavioural loyalty and attitudinal loyalty. Man-

agement should attempt to retain customers with trueloyalty because they can be depended upon to makerepeat purchases and engage in positive word-of-mouth.Customers with latent loyalty have a positive attitudeto the service provider but low repeat patronage. In or-der to sustain loyalty of this group, managers shouldfocus in providing better value products and strong re-covery strategies.

Customers with spurious loyalty have a high degree ofbehavioural loyalty but low attitudinal loyalty. Suchcustomers are more likely to switch and also more re-sponsive to promotions from other brands. Strategiesthat aim to build positive attitude like improving serv-ice quality, and better customer care service are key tosustaining loyalty of this group. The low loyalty groupcustomers are low on both behavioural and attitudinalloyalty. Such customers are highly unstable and respon-sive to competitors with alterative offerings. Such cus-tomers require long-term efforts and managers shoulddesign strategies using both behavioural and attitudinaldimensions to build long-term relations.

The loyalty measures developed in this study providesmanagers with an easy tool to identify attitudinal andbehavioural differences among customers. Marketingmanagers can draw meaningful distinctions betweencustomers who buy out of habit and those who buy be-cause of emotional attachment to the company. The serv-ice providers can use behavioural and attitudinal mea-sures to segment their customer base according to thetype of loyalty exhibited by customers and accordinglydevelop strategies to enhance loyalty and hence profits.The segmentation of customers based on their loyaltymay also help managers to direct differentiated com-munications and marketing messages according to theloyalty exhibited by customers.

The scales of affective and calculative commitment alongwith behavioural loyalty can be used to understand cus-tomer types. Marketing managers can draw meaning-ful distinctions between customers who buy out of habitand those who are affectively committed to the brandand repeat purchase. Such segmentation can help toidentify and track those customer segments that arelikely to respond to relationship marketing efforts.

Identifying Differences in Antecedents of Loyalty: Theproposed scale can be used as a tool by marketing prac-

VALIDATING ANTECEDENTS OF CUSTOMER LOYALTY FOR INDIAN CELL PHONE USERS

VIKALPA • VOLUME 37 • NUMBER 4 • OCTOBER - DECEMBER 2012 57

titioners to identify differences in antecedents of loyaltyamong loyalty groups. A thorough understanding offactors that influence customer loyalty is beneficial forcustomer relationship managers to develop and reinforcemarketing strategies to increase retention. Finally, man-agers can use the scale to identify factors crucial for de-velopment of long-term relationships with customers.

Assessing Relationship Quality: Relationship qualitycan be seen as the degree of appropriateness of a rela-tionship to fulfill the needs of the customer associatedwith that relationship (Hennig-Thurau & Klee, 1997). Theconcept of relationship quality is a higher order constructconsisting of several distinct but related components ordimensions, which are: trust, commitment and satisfac-tion (Dwyer, Schurr & Oh, 1987; Rauyren & Miller, 2007).The valid and reliable measures of satisfaction, trust andcommitment (first-order constructs) can be used to as-sess relationship quality (second-order construct) andexamine its effect on customer loyalty.

Directions for Future Research

This paper has introduced and discussed a model thatexamines the antecedents of customer loyalty. The pa-per provides the researchers with a proposed integratedmodel of customer loyalty incorporating attitudinal and

behavioural dimensions. First, future researcher mayempirically examine the proposed model and examinerelationships among antecedents of customer loyalty.Second, further research may incorporate the effect ofvariables like service quality, customer value, price per-ceptions on customer loyalty and examine more influ-ences.

Third, this study develops and validates the measuresof customer loyalty and its antecedents for cell phoneusers. Future studies could consider to what extent themeasures proposed in this study are valid in differentservice industries and what modifications need to bemade in the scale items across different samples andcontexts. Further, personal characteristics like age, in-come, education, and situational characteristics like priceorientation, service recovery, and loyalty card member-ship play an important role in shaping a customer’spurchase intentions. Future research may test the mod-erating effects of personal and situational characteris-tics on the development of customer loyalty. Thisresearch was carried out only in the northern part ofIndia, a vast and heterogeneous country. It would beimportant to test the measures in other parts of India toimprove the reliability and validity of the study.

APPENDIX – MEASURES

Please indicate the extent to which you agree or disagree withthe following statements (1 = very strongly disagree and 7 =very strongly agree)

Customer Satisfaction

• The plan offered by my mobile phone company meets my expecta-tions.

• I am not pleased with the service I get from my mobile phonecompany.

• The overall network quality provided by my mobile phonecompany is excellent.

• I regret my decision to have chosen my current mobile phone com-pany.

• I believe that I did the right thing when I chose this mobilephone company.

• Overall, I am satisfied with my mobile phone company.

Trust

• The promises made by my mobile phone company are reli-able.

• I trust my company to provide me with consistent qualityservices.

• Customers are valued more than profits at this company.

• The company is interested in solving problems that I may have.

• My phone company has a great deal of integrity.

• I do not trust my mobile phone company.

Affective Commitment

• I feel committed to my mobile phone company.

• I feel a strong sense of identification with the phone com-pany.

• I am emotionally attached to the phone company.

• If my phone company were no longer in business, it would be asignificant loss for me.

• I do not intend to maintain a long lasting relationship with mymobile phone company.

Calculative Commitment

• I feel somewhat trapped into using this mobile phone com-pany.

• Right now staying with my current service provider is morea matter of necessity than of choice.

58

Corporate Image

• My mobile phone company is a leading firm in the Indiancellular industry.

• The company gives good impression through its advertise-ments and other media.

• In my opinion, this company has good image in the minds of itsconsumers.

• I believe that my mobile phone company outperforms its competi-tors.

Switching Cost

• To shift to a new mobile connection is a big hassle.

• I will lose benefits of being a loyal customer if I switched toa new mobile phone company.

• Shifting over to a new mobile company isn’t that big a deal.

• I would be concerned about people who would be unableto reach me, if I changed my number.

• If I were to switch to a new connection, it would require a lot oftime.

• If I changed my connection, I would have to learn new ways ofdoing things.

VALIDATING ANTECEDENTS OF CUSTOMER LOYALTY FOR INDIAN CELL PHONE USERS

Behavioural Loyalty

• I intend using my current mobile connection in future.• In future, if I ever need a new connection, I will go for the same

company.• I am very likely to switch to another mobile connection soon.• I intend to reduce usage of services of this phone company in

future.• I would readily pay more for the same service.

Attitudinal Loyalty

• This mobile phone company is as good as others.• I encourage friends and relatives to use services of this com-

pany.• I say positive things about my mobile phone company to

others.• I recommend my mobile phone company to anyone who

seeks my advice.• If I had to do it all over again, I would choose my current mobile

phone company.

Note: Items in italics were the ones deleted during scale refinementand validation.

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Harsandaldeep Kaur is presently working as Assistant Pro-fessor in the Department of Commerce and Business Manage-ment, Guru Nanak Dev University, Amritsar, India. A docto-rate in Marketing, she teaches courses like advertising man-agement and retail logistics. She has presented papers at na-tional and international conferences. Her research interests arein the areas of customer loyalty, customer retention, and ser-vice recovery.

e-mail: harsandaldeep@yahoo.com

Harmeen Soch is presently working as an Associate Profes-sor (Marketing) at Punjab Institute of Technology, the mainuniversity campus of Punjab Technical University in India. Herresearch interests are in the area of customer loyalty, servicerecovery and customer citizenship behaviour. She teaches mar-keting research, strategy and advertising. She has publishedpapers in national and international journals of repute andhas won the best paper awards for her doctoral work at manyconferences. She is currently working on two funded projectsawarded by University Grants Commission (UGC), New Delhiand is involved in cross-cultural research in the area of tech-nology adoption.

e-mail: harmeensoch@ptu.ac.in

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