A longitudinal examination ofcustomer commitment and loyalty

Bart LariviereCenter for Service Intelligence, Ghent University, Ghent, Belgium

Timothy L. KeininghamIPSOS Loyalty, Parsippany, New Jersey, USA

Bruce CooilOwen Graduate School of Management, Vanderbilt University,

Nashville, Tennessee, USA

Lerzan AksoySchools of Business, Fordham University, New York, USA, and

Edward C. MalthouseMedill School, Northwestern University, Evanston, Illinois, USA

Abstract

Purpose – This study aims to provide the first longitudinal examination of the relationship betweenaffective, calculative, normative commitment and customer loyalty by using longitudinal panelsurvey data.

Design/methodology/approach – Repeated measures for 269 customers of a large financialservices provider are employed. Two types of segmentation methods are compared: predefined classesand latent class models and predictive power of different models contrasted.

Findings – The results reveal that the impact that different dimensions of commitment have on sharedevelopment varies across segments. A two-segment latent class model and a managerially relevantpredefined two-segment customer model are identified. In addition, the results demonstrate the benefitsof using panel survey data in models that are designed to study how loyalty develops over time.

Practical implications – This study illustrates the benefits of including both baseline levelinformation and changes in the dimensions of commitment in models that try to understand howloyalty unfolds over time. It also demonstrates how managers can be misled by assuming thateveryone will react to commitment improvement efforts similarly. This study also shows how differentsegmentation schemes can be employed and reveals that the most sophisticated ones are notnecessarily the best.

Originality/value – This research provides the first examination of models for change in customerloyalty by employing survey panel data on the three-component model of customer commitment(affective, calculative, and normative) and considers alternative segmentation methods.

Keywords Customer loyalty, Financial services, Longitudinal, Customer commitment,Panel survey data, Segmentation methods

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1757-5818.htm

The authors wish to thank the AMA Frontiers in Service Conference for awarding an earlierversion of this work with the Best Practitioner Presentation Award. Bart Lariviere acknowledgessupport from the Research Foundation – Flanders (FWO) and Bruce Cooil acknowledgessupport from the Dean’s Fund for Faculty Research, Owen Graduate School, VanderbiltUniversity.

Received 6 September 2012Revised 31 January 2013

Accepted 11 February 2013

Journal of Service ManagementVol. 25 No. 1, 2014

pp. 75-100q Emerald Group Publishing Limited

1757-5818DOI 10.1108/JOSM-01-2013-0025

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1. IntroductionMany research papers have studied the effect of customer commitment on customerloyalty. The theoretical grounding for the models of customer commitment proposedby marketing scholars borrowed from extensive research in organizational behavior( Jones et al., 2008, p. 473) in which the multiple components of employee commitment,its interplay and influence on outcomes such as employee turnover have been studiedintensively (Klein et al., 2009). Customer commitment as a driver of customer loyaltybecame popular since the seminal work of Morgan and Hunt (1994) appeared in theJournal of Marketing. The electronic database Web of Science, for instance, revealsmore than 2,600 citations in peer-reviewed journals for Morgan and Hunt (1994) ofwhich two-third of citations cover a first time span of 15 years (1994-2009), andone-third of publications cover the recent contributions in the last three years(2010-2012). Hence, it is clear that the concept of customer commitment has increased inpopularity over time.

One of the most significant developments in the organizational behavior literatureon commitment has been the recognition that it can take different forms (Meyer et al.,2004). Four years before Morgan and Hunt (1994) published their article on customercommitment, Allen and Meyer (1990) proposed a three-component model of employeecommitment including affective, calculative (also known as continuance commitment),and normative commitment. In essence, affective commitment pertains to “wanting” tomaintain the relationship; calculative commitment pertains to “having” to maintain therelationship; and normative commitment pertains to feelings as though you “should”maintain the relationship (Gruen et al., 2000; Kelly, 2004). These different forms ofcommitment can also be described as “emotional”, “rational” or “moral” attachments,respectively, ( Jones et al., 2010). The three-dimensional model of commitment has beenwidely used and empirically supported in the organizational behavioral literature(Klein et al., 2009) and confirmed in other research disciplines. Echoing the research ofAllen and Meyer (1990) and Adams and Warren (1997), for instance, supported theexistence of three similar dimensions of marital commitment. In contrast, theoverwhelming majority of research in marketing, has treated customer commitment asa one-dimensional construct (most commonly operationalized as affectivecommitment). Our investigation uncovered 12 studies in the peer-reviewedmarketing literature in which all three components of customer commitment werelinked to behaviors and/or behavioral intentions associated with customer loyalty andinclude: four published papers between 1997 and 2007 (Barksdale et al., 1997;Gruen et al., 2000; Bansal et al., 2004; Bloemer and Odekerden-Schroder, 2007) anda double number of publications (eight) in the last four years (Cater and Zabkar, 2009;Cater and Cater, 2010; Hur et al., 2010; Jones et al., 2010; Cater et al., 2011; Kim et al.,2011; Melancon et al., 2011; Beatty et al., 2012). Hence, it is clear that the usage of thethree-component model of commitment in the marketing literature is booming.Unfortunately, all of the aforementioned studies use a cross-sectional design despitethe widespread belief that commitment develops over time based on exchanges withthe organization (Meyer and Allen, 1991) and despite widespread recognition that“marketers should recognize changes in [attitudinal measures] over time” whenevaluating their impact on customer behavior (Bolton et al., 2004, p. 277).

In addition, one of the most critical challenges within commitment research iscapturing the complex interplay among the multiple dimensions of commitment,

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especially as they change over time (Klein et al., 2009). In the organizational behaviorliterature, different analytical tools have been proposed to study the joint effect of thedimensions of commitment (Klein et al., 2009). The use of interaction terms inregression models provides one approach (Meyer and Allen, 1991; Meyer andHerscovitch, 2001; also in marketing: Bansal et al., 2004; Gustafsson et al., 2005).Alternatively, one can study the joint effect of the dimensions of commitment byfocusing on customer segments. The best models describing the effects of changes incommitment can then be found separately within each segment. These segments mightbe identified statistically using a latent class analysis or they could be predefinedsegments that are either obviously distinct from a managerial point of view orpredefined in a way that is grounded in theory (Barksdale et al., 1997; Jones et al., 2010).To date, no study has examined the moderators of three-component model of customercommitment as they change over time, by using segmentation methods and/orinteraction terms.

This research contributes to the literature on customer commitment and loyalty intwo important ways:

(1) It provides the first longitudinal examination of the three-component model ofcustomer commitment and customer loyalty by studying how changes in levelsof customer commitment (affective, calculative, and normative) link to customershare development. As such, we illustrate the benefits of including changes inthe dimensions of commitment in models that are designed to explain howloyalty develops over time, as contrasted with models that conceptualize thesedimensions at baseline only.

(2) It examines how the impact of commitment on share development differs acrosscustomer segments, taking a dual approach: using a managerially relevantpredefined segmentation scheme, and comparing it with a model-basedsegmentation technique (i.e. latent classes). In addition, interaction effects aretested to examine the potential interplay among dimensions of commitment,satisfaction, and changes in customer demographics (i.e. situational triggers).

2. Theoretical background2.1 Changes in affective, calculative, and normative commitment and customer sharedevelopmentAlthough there are no longitudinal studies in the marketing literature of changes inany dimensions of commitment, the migration and switching literature might providesome theoretical foundation for their impact on changes in share of wallet (SOW).First, it has been recognized that a lack of alternatives and costs associated withmoving/switching (e.g. financial, emotional, time, effort, and ability) constrain theswitching decision (Bansal et al., 2005; Bolton et al., 2000; Jones et al., 2000; Sell andDe Jong, 1978). Therefore, it is logical to presume that increases in customers’calculative commitment would reduce the likelihood that customers would partially orcompletely defect (i.e. reduce their SOW). Second, the migration and switchingliterature recognizes that normative concerns constrain or facilitate switchingbehaviors (Bansal and Taylor, 1999; Bansal et al., 2005; Gardner, 1981). In particular,the greater the normative pressure to stay in a relationship, the greater the likelihoodthat customers will not defect (partially or completely). Finally, it is plausible that anincrease in the enjoyment of the relationship will make competing offerings less

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attractive, since higher levels of pleasure and positive emotions have been found tostrengthen customers’ loyalty intentions (Baker et al., 1992; Yu and Dean, 2001).

The organization behavior literature has also recognized that the evolution ofcommitment over time is a critical index of the way the relationship between anemployee and his/her employer evolves over time. Although many researchers withinthis stream have investigated the different dimensions of commitment, few haveexamined their impact as they change over time. Even in the case of the most carefullyconducted longitudinal studies, the effects of changes in commitment have not beenexamined in the vast majority of studies (Bentein et al., 2005; Klein et al., 2009).The research by Bentein et al. (2005) forms a notable exception. These authors found asignificant association between the change trajectories, specifically, the steeper thedecline in an employee’s affective and normative commitments across time, the greaterthe rate of increase in that individual’s intention to leave and the greater the likelihoodthat the person actually left the organization over the next nine months[1].

Based on findings regarding the impact of the commitment dimensions onbehaviors and/or behavioral intentions associated with customer loyalty fromnon-longitudinal studies in the marketing literature, the migration and switchingliterature, and the findings in the organizational behavior literature, we propose thefollowing hypothesis:

H1. The change in any dimension of commitment (affective, calculative, ornormative) is positively associated with customer share development.

2.2 The interplay among commitment dimensions: customer segments and interactiontermsIn the organizational behavior literature, different analytical tools are used to study thejoint effect of the dimensions of commitment (Klein et al., 2009). The use of interactionterms in regression models provides one approach (Meyer and Allen, 1991; Meyer andHerscovitch, 2001; also in marketing: Bansal et al., 2004; Gustafsson et al., 2005). In thiscase interactions are considered primarily as moderators in models that already includethe main effects. Alternatively, one can study the joint effect of the dimensions ofcommitment by focusing on customer segments, where individuals in each segmentstart with similar profiles at baseline. In the organizational literature, Vandenberg andStanley (2009) take a similar approach. The best models describing the effects of changesin commitment can then be found separately within each segment. These segmentsmight be identified statistically using a latent class analysis or they could be predefinedsegments that are either obviously distinct from a managerial point of view orpredefined in a way that is grounded in theory (Barksdale et al., 1997; Jones et al., 2010).

In this research, we use and assess all three analytical approaches: interactionsterms, latent classes and predefined classes. In determining the predefined classes,we employ a simple version of the Reinartz and Kumar’s framework (2003, p. 95),in which they distinguish between a low and a high SOW group at baseline. In ourcase, we build models separately for customers who have a SOW below 100 percent atbaseline (Segment 1, 75 percent of the sample), and those who start with a baselineSOW of 100 percent (Segment 2, 25 percent of the sample). From a managerial point ofview, one would want to consider these two segments separately, given that eachpresents different challenges. In Segment 2 the goal is to preserve SOW since it cannot

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increase for this segment, and in Segment 1 to facilitate an increase. Thus, it seemsimportant to study how changes in dimensions of commitment relate to changes inSOW separately in each segment.

The structure of the interplay among the dimensions of commitment is potentiallyvery complex. Vandenberg and Stanley (2009, p. 409) note that:

Commitment research will need to go through a period of informed exploratory research untilwe have gained enough confidence to know and understand how the different dimensionscombine with one another.

Therefore, only a general, and no specific hypothesis regarding the potential interplayamong the commitment dimensions is formulated:

H2. The impact of commitment on share development is different across segmentsof customers.

2.3 Control variables: change in customer satisfaction and situational triggersThe main focus of this paper is on changes in the three-component model of customercommitment and share development while using a longitudinal research design(panel survey data) and testing alternative segmentation schemes. Nevertheless,we build on the prior loyalty literature and also account for factors that have beenidentified to relate to changes in loyalty. These include change in customer satisfactionand customer characteristics that can change over time. For instance, in a longitudinalexamination of the Canadian banking industry, Cooil et al. (2007) found that changes insatisfaction had a positive relationship to changes in SOW. Furthermore, research hasfound that the relationship between customer attitudes and customer intentions and/orbehaviors is moderated by situational characteristics of customers. Implicit in themeasurement and use of customer characteristics is the belief that changes in thesecharacteristics (i.e. situational triggers, Roos, 1999, 2002; Roos and Gustafsson, 2007)will alter the basis of the business-customer relationship. Demographic changes(e.g. marital status, changes in number of children in household, etc.) and changes ineconomic situations (e.g. change of job, etc.) are believed to alter customers’ evaluationsof a product or service (Gustafsson et al., 2005). Triggers are frequently cast as“alarm clocks” which create an impetus for action (Edvardsson and Strandvik, 2000;Gardial et al., 1996). The most extensive study to date regarding the moderatinginfluence of customer characteristics on the relationship between customersatisfaction, commitment and customer behavior is research conducted byGustafsson et al. (2005). In particular, using cross-sectional survey data to gaugecustomer satisfaction and customer commitment (affective and calculative only, andmeasured at baseline period only), Gustafsson et al. (2005) examined the direct andmoderating impact of triggers on actual customer defection rates nine months into thefuture. Their research found no significant direct effect, nor any significant moderatingeffect with the commitment dimensions. In modeling the relationship, however,Gustafsson et al. (2005) used an overall construct to represent the triggers (e.g. “therehas been a recent change in your working conditions, family situation, or livingconditions [. . .]”) as opposed to assessing potential triggers separately. The inability todistinguish among triggers may explain why Gustafsson et al. (2005) could not find asignificant impact although they had postulated one should exist. Clearly, changes ineconomic circumstances could change customers’ levels of calculative commitment.

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For example, the loss of a job could make a fee structure that previously wasconsidered minor to now be considered burdensome. Similarly, other changes can alterour affective and normative commitment to a relationship. In particular, psychologistshave long recognized that changes in life stages often result in a re-evaluation ofrelationships and the corresponding attachment towards them (Levinson, 1978).This study also includes information on situational triggers that prior researchsuggests could affect the satisfaction, commitment, and share-of-wallet relationship.The second part of Table I presents the specific situational triggers investigated andthe sources which support the inclusion of each trigger.

3. Research design3.1 Data collectionThe data used in this study was collected from customers of a large financial servicesprovider and comes from two sources:

(1) the company’s internal records of customers’ transactions and accounts; and

(2) the results from customer surveys gathered over two consecutive years.

A large market research company was in charge of the customer survey. Customerswere sampled at the household level (i.e. only one individual per household wasallowed to participate; interviewers always ensured that individuals who were mostknowledgeable on the subject of the study were interviewed). The interviews tookplace in February 2007 (t1) and April 2008 (t2). The first wave of the survey resulted in1,214 completed interviews and 802 usable responses. In the second wave (t2), 301 ofthe 802 households who participated in t1 also participated in t2, of which 269 werefinally retained. To assess non response bias at t2, we examined whether respondentsand non-respondents differed significantly with respect to the variables beinginvestigated in this study. All t-tests revealed insignificant differences( p-values . 0.05) suggesting the lack of non response bias.

To help ensure the validity of SOW information used in this research, all accountinformation reported in the survey was validated using the firm’s internal customerdatabase (customer confidentiality was maintained through the use of numericalidentifiers that were created for this study, so that the identification of households wasnot revealed). This included verification that the stated product possession with thecompany was in correspondence with the actual ownership as determined from thedata warehouse. As a result, 412 households were removed since (part of) their answersdid not reflect actual ownership at time 1 and another 32 households were removed attime 2; resulting in an effective analysis sample of 269 households for which data wereavailable at both time points.

3.2 Share of walletSOW is defined as the percentage of total money deposited in financial institutions(e.g. checking accounts, savings, investments, etc.) allocated to the financial institution.This definition and conceptualization is analogous to that used by Cooil et al. (2007).

3.3 Measures of constructsTo help ensure the validity of the commitment variables under investigation,all constructs were taken directly from prior literature (with only minor wording

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modifications to fit the industry under investigation). Items for affectivecommitment and calculative commitment were adopted from research byGustafsson et al. (2005). Items for normative commitment were adopted fromKelly (2004) (Table I).

Construct or trigger(sources) Measures

Correlationwith index(loading)

Varianceexplainedby index

(%)Cronbach’s

a

Affective commitment(Gustafsson et al., 2005)

1. I take pleasure in being acustomer of Company X

0.84 78 0.90

2. Company X is the provider thattakes the best care of theircustomers

0.90

3. There is a presence of reciprocity inmy relationship with Company X

0.89

4. I have feelings of trust towardsCompany X

0.89

Calculative commitment(Gustafsson et al., 2005)

1. It pays off economically to be acustomer of Company X

0.83 61 0.68

2. I would suffer economically if therelationship were broken

0.70

3. Company X has locationadvantages versus other providers

0.81

Normative commitment(Kelly, 2004)

1. Our attachment to Company X ismainly based on the similarity ofour values

0.94 85 0.91

2. The reason we prefer Company Xto others is because of what itstands for, its values

0.92

3. What Company X stands for isimportant to us

0.90

Situational trigger(unspecified)(Gustafsson et al., 2005)

A dummy variable (1 if yes, 0 if no)expressing whether there has been arecent change in workingconditions, family situation, etc.

NA NA NA

Change in marital status(Doyle, 2002)

A dummy variable (1 if yes, 0 if no)expressing whether there has been arecent change in marital status

NA NA NA

Change in number ofchildren living at home(Doyle, 2002)

A dummy variable (1 if yes, 0 if no)expressing whether there has been arecent change in the number ofchildren still living home

NA NA NA

Income changes(Doyle, 2002; Katonaand Mueller, 1968;reported in Brady, 1970)

A dummy variable (1 if yes, 0 if no)expressing whether there has been arecent income change

NA NA NA

Job change (Roos andGustafsson, 2007)

A dummy variable (1 if yes, 0 if no)expressing whether there has been arecent job change

NA NA NA

Note: NA – not applicable

Table I.Commitment and

situational triggermeasures

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To measure customer satisfaction, respondents were asked to provide a global,cumulative judgment of satisfaction with the financial services provider (Bolton, 1998;Bolton and Lemon, 1999; Cooil et al., 2007; Keiningham et al., 2007; Mittal et al., 1999;Rust et al., 1995a).

All questionnaire items used in the commitment-satisfaction constructs weremeasured on an 11-point scale, anchored by 0 – totally disagree (very dissatisfied) and10 – totally agree (very satisfied). Table I summarizes the structure of the multi-itemconstructs used.

3.4 Reliability and discriminant validity of the multi-item constructs for commitmentFor each of the three types of commitment, we found that the first principal componentof the corresponding set of measures in Table I (from the baseline period, t1) provided areliable index, and that each component also had relatively high discriminant validity.Each component explained more that 50 percent of the total standardized variance,indicating that each would serve as a reliable index (Fornell and Larcker, 1981; Hjorth,1994). The lowest Cronbach’s a value of 0.68 was for the calculative commitmentconstruct but the other constructs had a values at or above 0.90. These results aresummarized in Table I. Also, in the analysis of the sub-items listed in Table I,for satisfaction and each type of commitment, it was only the first principal componentthat explained more variance than any individual measure (i.e. in each case, only thefirst component had an eigenvalue that was greater than 1). Finally the square ofthe correlation between each pair of the three principal components is smaller than theproportion of variance explained (PVE) by each component in that pair. At baseline thelargest squared correlation has a value of 0.30 (0.5482), and it is between the principalcomponents for the affective and calculative commitment. In contrast, the lowest PVE,which is for the calculative commitment construct, is 0.61 (61 percent in Table I),and thus, the PVE for each construct is much greater than the largest squaredcorrelation between constructs. This indicates that each component has sufficientlyhigh discriminant validity (Fornell and Larcker, 1981; Lord and Novick, 1968).Consequently, we will use each of these three principal components as our primarymeasures for each dimension of commitment. Finally, the overall satisfaction measurehas correlations of 0.45, 0.53, and 0.55 with the constructs for affective, calculative andnormative commitment, respectively; the largest squared correlation among these(0.552 ¼ 0.30) is also smaller than the smallest PVE among constructs (61 percent forcalculative commitment) indicating that overall satisfaction is also a distinctlydifferent attitudinal measure. (Baseline correlations, and correlations between baselineand period 2 values for overall satisfaction and each dimension of commitment areprovided in the Appendix).

3.5 Operationalization of change variablesCustomer share development is calculated as:

½SOW Change� ¼ ½SOW at Period 2�2 ½Baseline SOW�: ð1Þ

Similarly, change in satisfaction and the change in each dimensions of commitment arealso calculated as the difference between the value at period 2 and the baselinevalue. Satisfaction is measured on an 11-point (0-10) scale. In contrast, the value ofeach dimension of commitment at the baseline is expressed in percentile units.

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For example, a baseline value of 35 for affective commitment would indicate that thecustomer’s measured affective commitment was at least as high as that of 35 percent ofthe sample at baseline (and generally below the other 65 percent of the sample).Similarly, the change in each dimension of commitment is also expressed as theamount by which a customer’s percentile has changed relative to the baseline sample.Thus, a change in affective commitment of þ15 (215) indicates that a customer’saffective commitment has increased (decreased) 15 percentile points on the baselinescale. Consequently, in any linear model of the form:

½SOW Change� ¼ b0 þ b ½Affective Commitment Change� þ · · ·; ð2Þ

the coefficient, b, of the change in affective commitment will represent the change inSOW that is associated with a 1 percentile increase in affective commitment, assumingother predictors in the model are held constant. The coefficients of the change in otherdimensions of commitment would also have the same type of interpretation.

4. Data analysis and findingsOur summary of the analysis and findings is organized as follows:

. We study descriptive statistics for customer attributes, situational triggers andthe baseline and change values for all of the primary variables (each dimension ofcommitment). These values are also compared between the two segments definedearlier: customers who have a SOW below 100 percent at baseline (Segment 1)versus customers with a baseline SOW of 100 percent (Segment 2) (Table II).

. H1 is tested directly by regressing customer share development on each of theprimary variables in partial simple linear regressions where we first correct forall baseline differences among customers (Table III).

. Multiple regression models within segments (nested regressions) are used tostudy the concomitant effects of all the primary change variables. This sectionexplores the potential interplay among commitment dimensions by usingpredefined classes (testing H2).

. The most important interactions among the primary variables (baseline andchange values) are summarized. This section explores the interplay amongcommitment dimensions by introducing interaction terms (testing H2).

. The best latent class model is compared with the predefined class models.This provides an alternative approach to studying the interplay amongcommitment dimensions (testing H2).

. Finally, we study the best models for customer share development that use onlybaseline information and change in commitment information is ignored.

4.1 Descriptive statistics: baseline and change ((period 2) – baseline)Table II provides a statistical summary of baseline and change values for each of thevariables. These are presented for all customers and for the two most manageriallyrelevant predefined segments, those customers who start at a SOW of 100 percent atbaseline (25 percent of all customers) and those who are below 100 percent. Among allcustomers, average baseline SOW is 61 percent, and it decreases by half a percent(20.51) between the two periods, although this change is not significant.

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Segmentation by baseline SOW

Variable All customers

Segment 1:baseline SOW

,100%

Segment 2:baseline SOW

¼ 100%

Class size (%) 100 75 25

Significance ofdifference betweensegments ( p-value)

Baseline means (SD)SOW (%) 61 (33) 48 (28) 100 (0) NA

Percent at SOW ¼ 100% 25 (44) 0 (0) 100 (0) NA

Satisfaction 8.1 (1.5) 8.0 (1.5) 8.5 (1.4) ,0.05

Commitment percentiles

Affective 53 (31) 49 (29) 65 (33) ,0.001

Calculative 51 (29) 48 (29) 60 (30) ,0.01

Normative 52 (30) 49 (28) 62 (31) ,0.01

Customer attributes

Age 52 (11) 51 (11) 54 (12) NS

Income (% above e3,000) 25 (43) 27 (44) 18 (38) NS

Percent with some universityeducation 32 (47) 36 (48) 18 (38) ,0.01

Percent married or livingtogether 86 (35) 87 (34) 82 (38) NS

Children living at home 1.9 (1.3) 2.0 (1.3) 1.5 (1.0) ,0.001

Expertise (0-10) 6 (1.8) 6.1 (1.7) 5.9 (1.9) NS

Time with company (years) 15 (9.1) 15 (9.3) 14 (8.3) NS

Mean change (SD)

SOW (%) 20.51 (30) 4.1 (30) 214 (26) * * * ,0.01

Percent at SOW ¼ 100% 2.2 (42) 13 (34) * * * 231 (47) * * * ,0.001

Satisfaction 20.32 (1.6) * * * 20.38 (1.5) * * * 20.16 (1.7) NS

Mean change in commitment (expressed in baseline percentiles)

Affective 211 (28) * * * 211 (28) * * * 212 (29) * * * NS

Calculative 11 (28) * * * 11 (29) * * * 9.5 (27) * * NS

Normative 211 (30) * * * 211 (29) * * * 210 (32) * NS

Triggers (% with situational change)

Marital status change 9 (29) * * * 11 (32) * * * 2.9 (17) ,0.05

Divorce or death of spouse 4.1 (20) * * * 4.5 (21) * * 2.9 (17) NS

Job status change 30 (46) * * * 27 (45) * * * 38 (49) * * * NS

Became unemployed 0.7 (8.6) 0.5 (7.1) 1.5 (12) NS

Income change 26 (44) * * * 25(44) * * * 29 (46) * * * NS

Decrease in income 11 (31) * * * 12 (33) * * * 7.3 (26) * NS

Change in children at home 17 (38) * * * 18 (39) * * * 15 (36) * * * NS

Notes: Significant nonzero change between time 2 and time 1: *p , 0.05, * *p , 0.01 and * * *p , 0.001,all are two-sided tests; this is based on an analysis of 269 household at each period; the averagecommitment percentiles for all customers at baseline are generally slightly above 50, indicating a slightpositive skew in these distributions; “NA” in the last column indicates that the statistical test is notappropriate because these segment means are significantly different simply because of the way thesesegments are defined

Table II.Descriptive statisticsfor SOW, commitmentdimensions and customerattributes overall and bypredefined segment:baseline and change(time 2-time 1)

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Average satisfaction is 8.1 (0-11 scale) at baseline and falls an apparently small butsignificant 0.32 points ( p , 0.001). Also, there are significant changes in eachdimension of commitment; calculative commitment increases by 11 percentile points,while both affective and normative commitment fall by 11 percentile points ( p , 0.001in each case). Finally, a significantly nonzero percentage of customers are affected byeach of the situational triggers ( p , 0.001 in each case), including changes in jobstatus, although in this case it is not due to a significant proportion of customersbecoming unemployed (only 0.7 percent become unemployed).

4.2 The univariate effects of changes in commitment dimensions (testing H1)Table III provides a summary of simple linear regressions of customer sharedevelopment on changes in each of the primary variables. To adjust for differencesacross customers at baseline, simple regressions and correlations were found betweenthe adjusted customer share development and an adjusted version of each of theprimary variables. To make this adjustment, we used the residuals of the change inSOW and the change in each primary variable after each had been regressed on thebaseline values of SOW, overall satisfaction, and each dimension of commitment, alongwith all the basic demographic variables (i.e. age, tenure with company, experience,education, income, whether the head of household is married or living together,whether there were children at home). When this adjustment is made to offset baselinedifferences among customers, the resulting partial simple linear regression estimatesare all significant ( p , 0.001) (the coefficients of all these regressions are in the secondcolumn of Table III; each cell of the last four columns contain the partial correlations).Thus, changes in affective, calculative and normative commitment each have

Two-way partial correlations (with p-values)

Predictor

Simpleregressioncoefficient

(SE)SOW

change

OverallSAT

change

Affectivecommitment

change

Calculativecommitment

change

SAT change 4.5 * * *

(1.1)0.25

(0.000)Affective commitment change 0.32 * * *

(0.07)0.27

(0.000)0.55

(0.000)Calculative commitment change 0.28 * * *

(0.08)0.23

(0.000)0.44

(0.000)0.65

(0.000)Normative commitment change 0.24 * * *

(0.07)0.21

(0.001)0.38

(0.000)0.51

(0.000)0.47

(0.000)

Notes: Significant at: *p , 0.1, * *p , 0.05 and * * *p , 0.01, all are two-tailed tests; n ¼ 269;the partial simple linear regression coefficients are reported with their standard errors in the secondcolumn; each cell of the last four columns contain the partial correlations; the two-sided p-value of eachcorrelation is in parenthesis; when partial coefficients and partial correlations are calculated,the following baseline variables are conditioned on (i.e. adjusted for): age, tenure with company,experience, education, income, whether the head of household is married or living together, whetherthere were children at home, and the baseline levels of SOW, overall satisfaction, and each dimensionof commitment change in overall satisfaction is measured as change on the 11-point (0-10) scale forthis single-item; change in each commitment dimension is measured as percentile change relative tothe baseline

Table III.How change in

satisfaction (SAT)and change in theeach dimension of

commitment affect thechange in SOW

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a significant positive association with customer share development when one adjustsfor differences at baseline. Based on the squared partial correlations in Table III,changes in each of these types of commitment account for the following proportion ofthe variance in change in SOW (ceteris paribus): affective commitment: 7.3 percent(0.272) ( p , 0.001); calculative commitment: 5.3 percent (0.232) ( p , 0.001); andnormative commitment: 4.4 percent (0.212) ( p ¼ 0.001). H1 is therefore supported.

4.3 Multiple regression models within customer segments: examining the interplayamong commitment dimensions using predefined classesIn this section, we build separate models within customer segments, where thesesegments are defined so that, within each segment, the change in SOW can besummarized adequately in terms of a multiple regression on the variables of Table II.One of the simplest approaches of this type is to use the predefined customer segmentsof Table II, and build models separately for customers who have a SOW below100 percent at baseline (Segment 1, 75 percent of the sample), and those who start witha baseline SOW of 100 percent (Segment 2, 25 percent of the sample). Table IV showstwo multiple regression models within each of these two segments:

(1) a regression on all variables; and

(2) the models that minimize the Bayesian Information Criterion (BIC) within eachsegment among all multiple regression models based on these variables(referred to as the “best” models in Table IV, these models were found bybest-subsets regression analyses).

We chose BIC because it is a consistent criterion under general conditions (Rao and Wu,1989; Woodroofe, 1982), and there is substantial theoretical and empirical work whichshows that it provides a way of selecting the model that best represents the actualrelationship among the variables in many settings (Steyerberg et al., 2001; Rust et al.,1995b), including latent class analyses (Biernacki and Govaert, 1999; Dias, 2004).

In Table IV, affective commitment at baseline and affective commitment changeare primary components of each of the best models within segment, and the coefficientsof each of these variables are not significantly different across segments ( p . 0.2,in each case). These coefficients show that there is a positive association betweenpositive change in SOW and both higher baseline affective commitment and positivechange in affective commitment. Among customers in Segment 2 (SOW is 100 percentat baseline) there is an average decrease of 14 percent in SOW among customers forwhom there is a change in job status, ceteris paribus.

4.4 Using interactions to study the interplay among commitment dimensionsIn conjunction with the best models of Table IV, we also considered all two-wayinteractions among the main effects associated with those models. Within eithersegment, none of the models with interaction provide lower BIC values than the bestmodels of Table IV. Models that minimize BIC are not generally models that minimizethe variance of error, and in this section we consider those models that providea marginal decrease in the variance of error, although in every case they have larger BICvalues than the best models of Table IV.

In Segment 1 (baseline SOW below 100 percent), there is only one model withinteraction that provides a smaller error variance than the best model in Table IV:

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½Change in SOW� ¼10:1 2 0:35 ½Baseline SOW�

þ 0:46 ½Baseline Affective Commitment�

þ 0:31 ½Change in Affective Commitment�

2 0:0034 ½Baseline SOW�*½Baseline Affective Commitment�:

ð3Þ

Segmentation by baseline SOWSegment 1: baseline

SOW ,100%Segment 2: baseline

SOW ¼ 100%75 25

Segment size (%) All predictors Best model All predictors Best model

Intercept 27.22 17.38 * * * 254.83 234.54 * * *

Baseline predictorsShare of wallet 20.50 * * * 20.51 * * * – –Satisfaction 2.47 2.89Commitment percentilesAffective 0.14 0.31 * * * 0.40 * 0.47 * * *

Calculative 0.14 0.35Normative 20.05 20.37Customer attributesAge 20.00 20.21Income . e3,000/month 20.24 28.79Some university education 27.56 22.06Married or living together 7.29 0.55Children living at home 6.77 12.63Expertise (0-10) 21.05 1.02Time with company (years) 0.41 * 20.22

Change predictorsChange in satisfaction 1.78 3.76Commitment change (percentiles)Affective 0.10 0.33 * * * 0.34 * 0.39 * * *

Calculative 0.04 0.18Normative 0.18 * 20.16TriggersMarital status change 5.38 210.64Job status change 0.18 215.32 * * 214.15 * *

Income change 27.64 * 28.73Change in children at home 21.00 7.78Within segment R 2 adj. (%)a 25.3 * * * 23.3 * * * 21.5 * * * 23.2 * * *

Overall R 2 adj. (%)b 25.5 * * * 23.5 * * * 41.7 * * * 42.9 * * *

Notes: Significant at: *p , 0.1, * *p , 0.05 and * * *p , 0.01, all are two-tailed tests; awithin segment R 2

adj. ¼ 100%{1 2 [(variance of residuals)/(total variance of change in SOW within segment)]}; boverallR 2 adj. ¼ 100%{1 2 [(variance of residuals)/(total variance of change in SOW within sample)]}; this isbased on an analysis of 269 household at each period; the “best models” are the ones that minimize BIC(and typically do not minimize R 2 adjusted); the following predictors are indicators (0-1 dummyvariables): “income . e3,000/month”, “some university education”, “married or living together”,“children living at home”; the additional situational changes listed in Table III (divorce or death ofspouse, becoming unemployed, decreases income) were also considered but were not predictors in thebest model

Table IV.Models for change in

SOW: baseline attributes,satisfaction, commitment,

and situational changes

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The coefficients of the main effects in this model are similar to those of the modelwithout the interaction term. Also, the interaction term is not significant ( p ¼ 0.122),and provides a very small downward adjustment in the predicted change in SOW,which only marginally improves the fit (R 2 adjusted increases 0.6 percent in absoluteterms; 23.9 percent versus 23.3 for the main effects model) presumably because of thebounded nature of SOW. The error variance of this model increases when the other twointeraction terms and the three-way interaction term (that correspond to the maineffects in this model) are added individually or in any combination.

In Segment 2 (baseline SOW is 100 percent), two interaction terms are marginallysignificant when added to the best model of Table IV, and the model with both of theseinteraction terms has an R 2 adjusted that is 6.3 percent larger (29.5 percent versus23.2 percent for the main effects model):

½Change in SOW� ¼2 28:6 þ 0:32 ½Baseline Affective Commitment�

þ 0:83 ½Change in Affective Commitment�

2 35:3 ½With Change in Job Status�

þ 0:0069 ½Baseline Affective Commitment�*

½Change in Affective Commitment�

þ 0:33 ½Baseline Affective Commitment�*

½With Change in Job Status�

ð4Þ

(the two-tailed p-values for the last two interaction terms are 0.018, and 0.052,respectively). In this model, both change in affective commitment and change in jobstatus have coefficients of the same sign as in the main effect model of Table IV,but each coefficient is now more than twice as large in absolute terms. The interactionof these two effects indicates that when there is a change in job status, an additionalpositive adjustment of 0.33 in change in SOW accrues for every 1 percentile increase inbaseline affective commitment (ceteris paribus). The small positive coefficient of theinteraction between the two affective commitment variables indicates their effect isslightly super-additive. The other two-way interaction among main effectsand th three-way interaction are not significant and do not provide anyreduction in the variance of error when added to this model individually or in anycombination.

4.5 Using latent classes to study the interplay among commitment dimensionsA latent class regression analysis (Vermunt and Magidson, 2005, pp. 33-36; Cooil et al.,2007, p. 79) was also used to search for alternative customer segmentation andwithin segment regression models for customer share development. These models relyon covariate variables that are used to determine how customers should be classifiedto segments (through logistic regressions) and on a set of regression predictorvariables that are used in the within segment regression model for change in SOW.As covariates for identifying the customer segments, we considered the baseline levelsof satisfaction, each dimension of commitment, and the baseline SOW value(including an indicator for customers who were at a baseline level of 100 percent).As within-segment regression predictors for changes in SOW, we considered the samebaseline variables along with the change in satisfaction, the change in each dimension

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of commitment, and situational triggers. Starting with all candidate covariates andpredictors, we used each stage of the following procedure to find models with the lowestBIC values:

(1) given a selection of predictors, covariates are eliminated in a stepwise procedureand the corresponding optimal number of segments was reselected at each step;and

(2) given a set of covariates and a fixed number of segments, predictors within eachsegment were eliminated in a stepwise fashion.

To find models with even lower BIC values, we continued with successive iterations ofthis two-stage procedure. The same procedure was used separately to find the besttwo-segment model (without reselecting the number of classes in Stage 1). In the finalstage, we tried forward steps, where each excluded predictor and covariate was addedindividually to see if lower BIC values were possible. A similar procedure is describedby Cooil et al. (2007, p. 79). We used Latent Gold 4.5 for the analysis, with multiplestarting points to estimate each model.

The best models invariably included the identification of the segment of customerswho have a SOW of 100 percent in period 2 (27.5 percent of the sample). Technically, we donot have a regression model within this segment, since the change in SOW isdeterministically the difference between 100 percent and whatever the value of SOW atbaseline (e.g. 0 percent for those customers that start at 100, or 25 percent for those whostart at 75 percent, etc.), so that there is no error variance within this segment.This analysis also showed that when more than two segments were used, it was generallydifficult to classify customers accurately to segments, and even the best models with morethan two segments provided correct classification probabilities at or below 73 percent.Nunally and Bernstein (1994, pp. 264-265) suggest that 70 percent would be an appropriateminimum reliability only “in the early stages of predictive or construct validationresearch”. Consequently, if we require a model that correctly classifies customers tosegments with probabilities that are substantially above these minimal levels, only one- ortwo-segment models are relevant from a managerial standpoint.

Among potential one- and two-segment models, the model that minimizes BIC isa two-segment model and this prescribes the simplest version of the model describedabove: it defines segments on the basis of whether or not customers are at 100 percentSOW in period 2. Among those who are at 100 percent share in period 2, 64 percent alsohave a SOW of 100 percent at baseline, so this is a different segmentation from that ofTable IV, although there is substantial overlap. The two covariates used to identify thissegment are calculative commitment at baseline, and an indicator for whether thecustomer’s baseline SOW is 100 percent; the log of the ratio of probabilities of being in thesmaller Segment 2 (those at 100 percent share in period 2) relative to all other customers inSegment 1 is:

loge ðP½Segment 2�=P½Segment 1�Þ ¼ 22:74 þ 2:59 ½Indicator for Baseline SOW¼ 100%�

þ 0:0166 ½Calculative Commitment Percentile at Baseline�:ð5Þ

In the sample, the relative sizes of Segments 1 and 2 are 72.5 and 27.5 percent, respectively.Using the two covariates in equation (5), the model correctly classifies customers to these

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segments with 82 percent probability (l is 35 percent, i.e. the correct classification rate of82 percent represents a 35 percent reduction relative to the accuracy of randomclassifications to segments of the same size).

Allowing for misclassification, this two-segment model has a slightly lower overallR 2 (adjusted) than the best segmentation model of Table IV, which was based onwhether or not share is 100 percent at baseline (R 2 is 28.1 percent versus 28.3 percentfor the model of Table IV). Also, the model within the larger latent class segment(Segment 1, 72.5 percent of the sample) is very similar to the model of Table IV forcustomers with a baseline share value below 100 percent (the predictors are identicaland the absolute difference in coefficient values is never greater than 10 percentrelative to the values in Table IV).

4.6 Putting it all together: the influence of customer segments (testing H2)The examination of predefined and latent classes reveals that commitment is linkeddifferently to SOW at baseline and to share development across segments. Higherbaseline levels in any type of commitment are associated with the predefined segmentthat has the highest SOW at baseline (Segment 2). Also baseline SOW in combinationwith baseline calculative commitment are identified as good predictors to classifycustomers to one of the two latent classes, of which the smallest segment is veryintriguing. More precisely, in Segment 2 of the latent class model (27.5 percent of thesample) customers stayed or increased their SOW to 100 percent, presumably becauseof the high initial levels of calculative commitment at baseline. Hence, for this segmentchanges in commitment dimensions have no influence on share development, ratherthe high initial level of calculative commitment can be considered as the main reasonwhy these customers allocate more money to this preferred provider until it finallybecomes the single provider. In contrast, in Segment 2 of the predefined segmentationmodel (25 percent of the sample; starting with a baseline SOW of 100 percent)changes in affective commitment relate to share development. Segments 1 and 2 of thepredefined segmentation scheme both reveal that changes in affective commitment arelinked to share development. However, the investigation of interaction effects revealssome significant differences across these two predefined segments. More precisely,two interaction terms are responsible for an increase in R 2 adjusted of 6.3 percent forSegment 2, whereas these interactions do not exist in the other segment. In sum,the findings indicate that the interplay among commitment dimensions and itsinteraction with other variables differ across both segments. H2 is therefore supported.

4.7 The best models that use baseline information onlyFinally, in order to demonstrate the benefits of including changes in commitmentdimensions in models that try to understand customer share development, we estimatealternative models that ignore these dynamic effects. The best models that use onlybaseline information (i.e. the models that minimize BIC within segments of Table IV)are not very predictive. When baseline SOW is less than 100 percent (Segment 1),the best model is:

½Change in SOW� ¼ 25:55 þ 0:65 ½Time with Company� ð6Þ

(R 2 adjusted ¼ 3.7 percent, within Segment 1).

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When baseline SOW is 100 percent (Segment 2), the best model is:

½Change in SOW� ¼ 230:24 þ 0:25 ½Baseline Affective Commitment� ð7Þ

(R 2 adjusted ¼ 8.8 percent, within Segment 2).The findings clearly demonstrate the importance of considering changes in

commitment dimensions, since the R 2 adjusted for Segments 1 and 2 that do considerthese dynamic effects are 23.3 and 23.2 percent, respectively, (Table IV) and henceexplain a significant higher proportion of the variance in customer share developmentthan models that treat commitment as static variables only (i.e. R 2 adjusted of 3.7 and8.8 percent, respectively).

5. Discussion, implications, limitations, and future research directionsFor researchers and managers, it is important to gain insight into how loyalty developsover time, and to determine the drivers of change in loyalty. This study advances theempirical research regarding the relationship between the three-component model ofcommitment, and customer loyalty in several ways. First, this study reveals that,overall, the change in any dimension of commitment is positively associated withcustomer share development (supporting H1). Second, the analysis of customersegments and the examination of interaction effects demonstrate that it is misleading toassume that everyone will react in the same way to efforts that are designed to cultivatecustomer commitment (supporting H2). Further, we show that the best models that useonly baseline variables are not very predictive. More precisely, change in commitmentdimensions are needed to enhance the PVE in longitudinal models of loyalty. Finally,this study finds that among situational triggers, only job changes significantly impactshare development, and this is a negative impact that only occurs for a subsample of thestudied population. Still, the existence of such effects in other studies (Doyle, 2002; Roosand Gustafsson, 2007), and the widely used practice of customer life stage segmentation(Swift, 2000) indicate that situational triggers remain an important topic for furtherresearch. It may be that triggers take a longer time to affect SOW than the 14-monthperiod considered here. Also, the changes in a customer’s affective, calculative, and/ornormative commitment levels may already capture the relevant effects that situationaltriggers have on loyalty behavior. Further research on this topic is warranted.

Our research findings have several implications for managers and researchers.First, many companies conduct customer satisfaction surveys (Morgan and Rego,2006) on a regular basis, and often employ independent customer samples that cannotbe linked. Our results demonstrate the importance of using panel survey data in orderto better understand and predict how loyalty develops over time: that is, to betterunderstand the within customer variability in customer loyalty over time. Manycompanies want to become the preferred supplier, implying that loyalty needs to bemonitored and analyzed longitudinally, including its potential drivers. Our studyfindings reconcile with prior research that commitment and satisfaction are importantantecedents of loyalty. However, our study findings also demonstrate the necessity tohave repeated data on a same sample of customers such that not only satisfaction andcommitment at baseline can be conceptualized, but also that changes in thesedimensions over time can be monitored. These findings have also implications forresearchers studying customer commitment and loyalty, since in the case of the mostcarefully conducted longitudinal studies in the existing marketing literature

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(Gustafsson et al., 2005; Verhoef, 2003), customer commitment as a determinant ofloyalty has been treated as a static variable (i.e. conceptualized at baseline only). To thebest of our knowledge, Verhoef (2003) represents the only peer reviewed researchinvestigating the longitudinal impact of commitment (affective only) on customershare development but this study considered customer satisfaction, and affectivecommitment measured at time period 1 only, and did not investigate the impact ofchange in affective commitment nor change in satisfaction. Also Gustafsson et al.(2005) examined the impact of customer commitment (affective and calculative only,and measured at baseline period only) on actual customer defection rates nine monthsinto the future, but were unable to study change in commitment dimensions since theydid not have panel survey data. Most empirical studies in the literature have relied oncross-sectional data; our study findings reconcile with the call by Bolton et al.(2004, p. 277), namely: “marketers should recognize changes in [attitudinal measures]over time” when evaluating their impact on customer behavior.

Second, our study findings offers a viable opportunity for firms to build more directrelationships with customers and to build switching barriers in relation to competitors inorder to enhance customers’ commitment levels (Gustafsson et al., 2005). This is becauseoverall our findings reveal that each increase in affective, calculative or normativecommitment is associated with an increase in customers’ SOW. Nevertheless, thesefinding are population-averaged effects, meaning that the positive impact of suchcompany investments do not necessarily translate into favorable loyalty trajectories forevery single customer. For instance, the segments uncovered in the latent class model ofthis study reveal that for some customers only calculative commitment at baseline isimportant (and this is their primary reason to become more loyal to their preferred supplierover time), whereas for other customers investments in building more direct relationshipsto increase their level of affective commitment seem warranted to enhance their loyaltylevel towards the company. The findings of our paper suggest that managers should focustheir relationship efforts on customers with lower calculative commitment levels,especially if their company is not considered the customers’ preferred supplier.

Finally, our study findings unveil how managers can be misled by assuming thatevery customer will react to commitment improvement efforts similarly and suggestthat easy segmentation methods like predefined segments are viable alternatives totake this customer heterogeneity into account. In this study, a predefined segmentationscheme based on a conceptual classification scheme that was proposed in the scientificliterature (Reinartz and Kumar, 2003) was employed; for managers it will be importantto find and test for alternative ways to form predefined segments and test whether thereturn on commitment improvements differs across these segments. For instance,companies might consider using existing segmentation schemes (e.g. online versusoffline versus multichannel customers; b-to-b versus b-to-c customers; customer withor without a loyalty card, etc.) as a starting point; such existing schemes are easy toimplement and will increase the likelihood of actually accounting for potentialcustomer differences when analyzing customer loyalty data.

As with all scientific research of this kind, there are limitations to our study thatshould be explicitly noted, and these limitations point to directions for future research.Most significantly, our data come from only one firm in one industry, and one country.Therefore, similar longitudinal studies carried out in other industries and cultures offeran opportunity for further research. For instance, in this study it was shown that there

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exists substantial overlap in cluster membership between the predefined segmentsand the latent class model. For managers and practitioners that acknowledge theimportance of considering customer segments, it is a good thing that such “easier”predefined segmentation models may perform well increasing the likelihood to accountfor heterogeneity issues using segments in real practice. However, further research andmeta-analyses on empirical investigations of different segmentation schemes and indifferent research settings, seem warranted to enhance our understanding of differentsegmentation alternatives. Finally, as mentioned before, an in-depth investigation oftriggers in a longitudinal research design that covers a longer period is needed todetermine whether triggers do have an impact, and how long it takes before such animpact takes effect.

Note

1. Note that Bentein et al. (2005) did not investigate the relationship between changes incalculative employee commitment and changes in employees’ turnover intentions, becauseno change (flat trajectory) in calculative commitment was postulated and observed duringthe time period of their study. In our study, however, we do find evidence of significanttrajectories of change in all dimensions of customer commitment. Consequently, therelationship between the change in all dimensions of commitment (affective, calculative,and normative) and customer share development is examined.

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Further reading

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Aurier, P. and N’Goala, G. (2010), “The differing and mediating roles of trust and relationshipcommitment in service relationship maintenance and development”, Journal of theAcademy of Marketing Science, Vol. 38 No. 3, pp. 303-325.

Baumann, C., Burton, S. and Elliott, G. (2005), “Determinants of customer loyalty and share ofwallet in retail banking”, Journal of Financial Services Marketing, Vol. 9 No. 3, pp. 231-248.

Bowman, D. and Narayandas, D. (2004), “Linking customer management effort to customerprofitability in business markets”, Journal of Marketing Research, Vol. 41 No. 4,pp. 433-447.

Brown, J.R., Lusch, R.F. and Nicholson, C.Y. (1995), “Power and relationship commitment: theirimpact on marketing channel member performance”, Journal of Retailing, Vol. 71 No. 4,pp. 363-392.

De Wulf, K., Odekerken-Schroder, G. and Iacobucci, D. (2001), “Investments in consumerrelationships: a cross-country and cross-industry exploration”, Journal of Marketing,Vol. 65 No. 4, pp. 33-50.

Garbarino, E. and Johnson, M.S. (1999), “The different roles of satisfaction, trust, andcommitment in customer relationships”, Journal of Marketing, Vol. 63 No. 2, pp. 70-87.

Geyskens, I., Steenkamp, J.B., Scheer, L.K. and Kumar, N. (1996), “The effects of trust andinterdependence on relationship commitment: a transatlantic study”, International Journalof Research in Marketing, Vol. 13 No. 4, pp. 303-318.

Gundlach, G.T., Achrol, R.S. and Mentzer, J.T. (1995), “The structure of commitment inexchange”, Journal of Marketing, Vol. 59, January, pp. 78-92.

Jones, T.O. and Sasser, W.E. Jr (1995), “Why satisfied customers defect”, Harvard BusinessReview, Vol. 73, November/December, pp. 88-99.

Magi, A.W. (2003), “Share of wallet in retailing: the effects of customer satisfaction, loyalty cardsand shopper characteristics”, Journal of Retailing, Vol. 79 No. 2, pp. 97-106.

O’Reilly, C. and Chatman, J.A. (1986), “Organizational commitment and psychologicalattachment: the effects of compliance, identification, and internationalization onprosocial behaviour”, Journal of Applied Psychology, Vol. 71 No. 3, pp. 492-499.

Reichheld, R.F. (2003), “The one number you need to grow”, Harvard Business Review, Vol. 81No. 12, pp. 46-54.

Silvestro, R. and Cross, S. (2000), “Applying the service profit chain in a retail environment”,International Journal of Service Industry Management, Vol. 11 No. 3, pp. 244-268.

Tsiros, M., Ross, W.T. and Mittal, V. (2009), “How commitment influences the termination of B2Bexchange relationships”, Journal of Service Research, Vol. 11 No. 3, pp. 263-276.

About the authorsBart Lariviere is an Assistant Professor of service management at Ghent University, where he isFounder and Executive Director of the Center for Service Intelligence. His research focuses on thelink between customer loyalty and various aspects of the service encounter including servicerecovery, servitization, services cape, consumer well-being, and multichannel customermanagement. He intensively collaborates with Belgian companies bridging the gap betweenpractice and academia. His research has been published in Journal of Service Research, Journal ofService Management, Journal of Interactive Marketing, the European Journal of OperationalResearch, and Expert Systems with Applications. He was finalist for best paper in Journal ofService Research, and his research received the Best Practitioner Presentation Award (twice) atthe Frontiers in Service Conference.

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Timothy L. Keiningham is Global Chief Strategy Officer and Executive Vice President atIpsos Loyalty. He is author and/or Editor of eight books, his most recent being Why LoyaltyMatters. He has received best paper awards from the Journal of Marketing (twice), the Journal ofService Research, and Managing Service Quality (twice), and has received the Citations ofExcellence “Top 50” award (top 50 management papers of approximately 20,000 papersreviewed) from Emerald Management Reviews. Tim also received the best reviewer award fromthe Journal of Service Research.

Bruce Cooil is The Dean Samuel B. and Evelyn R. Richmond Professor of Management at theOwen Graduate School of Management, Vanderbilt University. His research interests include theadaptation of grade-of-membership and latent class models for marketing and medical research,estimation of qualitative data reliability, large sample estimation theory and extreme valuetheory. He has also written and consulted on models for mortality, medical complications,medical malpractice, and automobile insurance claims. His publications have appeared inbusiness, statistics and medical journals, including the Journal of Marketing Research, Journal ofMarketing, Psychometrika, Journal of the American Statistical Association, Annals of Probability,Circulation, and the New England Journal of Medicine.

Lerzan Aksoy is an Associate Professor of marketing at Fordham University Schools ofBusiness in New York. She is author and/or Editor of four books, including Loyalty Myths andWhy Loyalty Matters. She has received best paper awards from the Journal of Marketing,and Managing Service Quality (twice), and has received the Citations of Excellence “Top 50”award (top 50 management papers of approximately 20,000 papers reviewed) from EmeraldManagement Reviews. She was awarded finalist for best paper in the Journal of Service Research.Lerzan also received best reviewer awards from the Journal of Service Management and theJournal of Service Research. Lerzan Aksoy is the corresponding author and can be contacted at:aksoy@fordham.edu

Edward C. Malthouse is Sills Professor of Integrated Marketing Communications at theMedill School, Northwestern University. He received the Walter Award for Research Excellencefrom Northwestern University and has received best paper awards from the Journal ofInteractive Marketing, European Advertising Academy, the Direct/Interactive MarketingResearch Summit, and ESOMAR/ARF Worldwide Audience Measurement Conference. He wasawarded finalist for best paper in the Journal of Service Research. Ed has also received severalteaching awards, including the Robert B. Clarke Outstanding Educator Award from the DirectMarketing Educational Foundation.

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Appendix

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Longitudinalexamination

99

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Table AII.Correlations amongprimary variables(baseline and period 2)

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