The influence of satisfaction,trust and switching barriers on

customer retention in acontinuous purchasing setting

Chatura RanaweeraSchool of Business, Wilfrid Laurier University, Waterloo,

Ontario, Canada, andJaideep Prabhu

Judge Institute of Management, University of Cambridge,Cambridge, UK

Keywords Trust, Services, Customer retention, Purchasing

Abstract Adopts a holistic approach that examines the combined effects of satisfaction, trust andswitching barriers on customer retention in a continuous purchasing setting. Argues that such anapproach helps uncover hitherto neglected effects on retention and, in the process, unveils morecost effective ways of retaining customers. Drawing on this framework develops several hypothesesregarding the main and interaction effects of customer satisfaction, trust and switching barrierson retention. Tests these hypotheses on data from a large-scale mail survey of fixed line telephoneusers in the UK, finding that both customer satisfaction and trust have strong positive effects oncustomer retention. Contrary to some assertions in the literature, however, finds that the effect oftrust on retention is weaker than that of satisfaction. Nevertheless, the interaction between trustand satisfaction also has a significant effect on retention, indicating that building both customersatisfaction and trust is a superior strategy to a focus on satisfaction alone. Qualitative evidencefrom the survey offers further support for this finding. Even a “satisfying” service recovery processmight be inadequate to prevent loss of trust, with significant implications for future consumerbehaviour. Finally, the results show that switching barriers have both a significant positive effect oncustomer retention as well as a moderating effect on the relationship between satisfaction andretention. While service providers may be able to retain even dissatisfied customers who perceivehigh switching barriers, argues that ideally, firms should aim at a combined strategy that makesswitching barriers act as a complement to satisfaction.

IntroductionCustomer satisfaction has traditionally been regarded as a fundamentaldeterminant of long-term consumer behaviour (Oliver, 1980; Yi, 1990). Themore satisfied customers are, the greater is their retention (Anderson andSullivan, 1993; Fornell, 1992), the positive word of mouth generated throughthem (Reichheld and Sasser, 1990; Schneider and Bowen, 1999), and thefinancial benefits to the firms who serve them (Fornell et al., 1995). It is nosurprise, therefore, that a fundamental aim of firms is to seek to manage andincrease customer satisfaction.

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Received October 2002Revised March 2003Accepted March 2003

International Journal of ServiceIndustry ManagementVol. 14 No. 4, 2003pp. 374-395q MCB UP Limited0956-4233DOI 10.1108/09564230310489231

However, satisfaction alone does not ensure continued customer patronage(Jones and Sasser, 1995). While satisfaction may be one important driver, trustand switching barriers are also likely to influence retention, both independentlyand in tandem. Furthermore, while the main effects of trust and switchingbarriers on retention are quite apparent, and have indeed been supported in theexisting literature, their interaction effects have rarely been examined. In thispaper, therefore, we attempt to build a more complete model that incorporates themain effects of satisfaction, trust, and switching barriers on retention, togetherwith the interaction effects between satisfaction and trust, and satisfaction andswitching barriers. We believe that such an approach uncovers hitherto neglectedeffects on retention. In particular, it offers a cautionary note on the role ofsatisfaction and trust in service failure and recovery incidents, and also unveilsalternative means of retaining customers. Recent research shows that whilesatisfaction and trust are closely related, they are also conceptually different, havesome distinct antecedents, and also have different empirical effects on retention(see meta-analysis studies by Geyskens et al., 1998; Szymanski and Henard, 2001).Further, some argue that trust is a stronger emotion than satisfaction and that itmay therefore better predict retention (e.g. Hart and Johnson, 1999). Research hasnot, however, adequately examined the relative explanatory power of these twoconstructs. By studying the effects of trust and satisfaction together we seek toshed more light on their relative importance as well as their interaction effects onretention. Doing so can help firms make better decisions about the relative valueof investing in developing satisfaction versus trust among their customers.

Research also shows that switching barriers may have both main andinteraction effects on retention (Gremler and Brown, 1996; Bansal and Taylor,1999; Lee et al., 2001). As a consequence, when switching barriers are high,service firms may continue to retain customers even if they are not highlysatisfied. While the aim of most firms is to offer 100 per cent customersatisfaction, this is often not feasible. For example, the American customersatisfaction index for the second quarter of the year 2002 shows thecross-industry, overall index to be only 73 per cent (ACSI Index, 2002). In thiscontext, identifying alternative means of retaining customers, such as throughswitching barriers, is particularly useful. We argue that examining the role ofswitching barriers could help service firms find a cost-effective alternative topursuing satisfaction alone.

We test our model in a continuous purchasing setting, the fixed linetelephone industry. In a continuous purchasing setting, customers maintainlong-term contractual relationships with service providers. Such settings aretherefore qualitatively distinct from the discrete purchasing patterns found in,say, the retail sector. Examples of such settings include fixed line telephoneservices, cable and Internet service providers, and most utility industries suchas gas and electricity. Such industries are particularly suited to the aims of thisstudy since all three main effects of satisfaction, trust and switching barriers

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are likely to have a strong impact on retention in this context. First,relationships in these sectors are generally long-term, which makes them asuitable context in which to study the effects of trust. Second, in a continuouspurchasing setting, switching is more than simply “walking to another store”.It requires considerable time and effort due to the presence of switchingbarriers, and consequently, the switching decision is made after considerablethought. Finally, these sectors provide an environment of high automation andlow customer-staff contact, phenomena that are increasingly common to othersectors due to the spread of technology. Empirical data from a low customercontact industry, such as fixed line telephone sector, is therefore bound to behelpful in drawing inferences about other sectors as well.

We begin the paper by introducing a framework from which we derive anumber of hypotheses linking satisfaction, trust and switching barriers tocustomer retention. We then describe the large-scale, mail survey conducted tocollect data to test these hypotheses. Next we present the results of the studyand discuss their significance. We end the paper with a discussion of theimplications for theory and practice.

Framework and hypothesesBased on a review of the literature, we develop a framework linkingsatisfaction, trust and switching barriers to customer retention (see Figure 1).We define customer retention as the future propensity of a customer to staywith their service provider. While some have used the term “future behaviouralintentions” to describe the construct with this definition (e.g. Zeithaml et al.,1996), we follow Cronin et al. (2000, p. 204) who treat “behavioural intentions”and “customer retention” as synonymous constructs.

Our framework has two main features. First, it examines the main effects ofeach of the three independent variables on retention. Doing so allows us toexamine, among other things, the simultaneous influence of satisfaction andtrust on retention. Second, the framework examines the interaction effects onretention of trust and switching barriers in the presence of satisfaction. We usethis framework to develop our hypotheses as follows.

Figure 1.A holistic framework ofthe influence ofsatisfaction, trust andswitching barriers oncustomer retention

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Customer satisfaction as a driver of customer retentionFollowing Cronin et al. (2000), we conceptualise customer satisfaction to be anevaluation of an emotion, reflecting the degree to which the customer believesthe service provider evokes positive feelings. Numerous studies in the servicesector have hypothesized and empirically validated the link betweensatisfaction and behavioural intentions and behaviours such as customerretention and word of mouth (e.g. Anderson and Sullivan, 1993; Rucci et al.,1998; Bansal and Taylor, 1999; Cronin et al., 2000). Indeed, this link isfundamental to the marketing concept, which holds that satisfying customerneeds and wants is the key to repeat purchase (Kotler et al., 2002). Further, theimportance of satisfaction on retention is so well recognised that some majoreconomies now measure satisfaction at the industry level using large samplesurveys to predict customer retention and future financial performance (seeFornell, 1992; Fornell et al., 1995). In line with previous research we thereforehypothesize that:

H1. The higher the level of satisfaction, the higher the level of customerretention.

Trust as a driver of customer retentionWe conceptualise trust based on Morgan and Hunt’s (1994) interpretation of theconstruct in their seminal study of the commitment-trust theory of relationshipmarketing. Morgan and Hunt conceptualised trust as existing when one partyhas confidence in a partner’s reliability and integrity. Indeed, trust could existat the individual level (see Rotter, 1967) or at the firm level (Moorman et al.,1993). Furthermore, trust, when conceptualised as a dimension of servicequality, could also be thought of as “trust in the service itself” (seeParasuraman et al., 1985, 1988). In the current study, we look at a customer’strust in his/her service provider, and thus, in the firm.

Recent research suggests that, in some cases, service providers may beunable to retain even those customers who are satisfied (e.g. Heskett et al., 1994;Schneider and Bowen, 1999). Thus, satisfaction alone may not be adequate toensure long-term customer commitment to a single provider. Instead, it may benecessary to look beyond satisfaction to other variables that strengthenretention such as trust (Hart and Johnson, 1999). This view is consistent withresearch on marketing channels (e.g. Morgan and Hunt, 1994), which showsthat firms often look beyond satisfaction to developing trust in order to ensureeconomically viable, long-term relationships. Further, this recommendation isbased on the premise that once trust is built into a relationship, the likelihood ofeither party ending the relationship decreases due to high termination costs.

Although the consequences of trust in business-to-business relationships havebeen firmly established, the same cannot be said about trust inbusiness-to-customer relationships. Indeed, in the latter context, the trustconstruct has been used in a somewhat ambivalent manner. Following the lead of

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Parasuraman et al. (1985, 1988), many have used trust (together with assurance)as a dimension of the service quality construct. Gremler and Brown (1996)proposed trust as a conceptual antecedent of customer loyalty. Hart and Johnson(1999) offered anecdotal evidence in support of a similar argument. Gwinner et al.(1998) suggested trust as a relational benefit. More specifically, they proposedtrust as a confidence benefit rated highly by customers in long-term relationalexchanges with service firms. Tax et al. (1998), in their seminal paper on servicerecovery, studied trust in the context of consumer complaint management. Basedon an analysis of both qualitative and quantitative data, they found trust,together with commitment (an indicator of future customer behaviouralintentions) to be a consequence of satisfaction with complaint handling.Levesque and McDougall’s (2000) recent findings, however, indicate that servicerecovery (similar to complaint handling in Tax et al., 1998) could have aqualitatively different impact on trust from that on satisfaction. These numeroususes of the trust construct in business to customer relationships are likely to haveat least partly contributed to the lack of an extensive body of literature on trustas a mediating construct in models of customer retention. However, Garbarinoand Johnson (1999) did look at trust as a driver of customer behaviouralintentions. In a study of theatre customers, they segmented consumers based ontheir relational (purchased season tickets covering a longer period of time) andtransactional orientation. They found that for relational customers, trust, asopposed to satisfaction, mediates the relationship between component attitudesand future intentions. These findings suggest that where customers maintainlong-term contractual relationships (similar to the context of the current study)with their telecommunications service provider, trust is likely to be a strongdriver of customer retention. We therefore, draw upon the Garbarino andJohnson (1999) study and the previously referred to marketing channelsliterature (cf Geyskens et al., 1998) to hypothesise that:

H2. The higher the level of trust, the higher the level of customer retention.

Trust versus satisfaction as a driver of customer retentionHart and Johnson (1999) have asserted that the condition beyond satisfactionthat ensures true customer loyalty is total trust. They argue that the presenceof trust reflects a stronger relationship commitment than satisfaction. Thisconclusion is consistent with other work in the channel relationship context(Morgan and Hunt, 1994). However, as with trust alone, most of the research onthe relative importance of trust versus satisfaction has been done in the contextof business-to-business and not business-to-customer relationships. The formercontext is likely, in most cases, to involve greater risk of supplier opportunismthan the latter (e.g. Williamson, 1985). As a result, firms invest significantresources in building trust relationships with their business partners and thetermination of such relationships often results in considerable loss to bothparties. Such negative consequences clearly illustrate the importance of trust in

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business-to-business relationships. However, in a business-to-customerrelationship, while relationships can last long, sometimes entire lifetimes, thedepth of relationships and the negative consequences of termination are likelyto be lower. As a result, we would expect trust, while important, to be less sothan satisfaction in influencing retention in a consumer services setting. Wetherefore hypothesize that:

H3. Satisfaction has a stronger linear relationship with customer retentionthan trust.

Moderating effect of trustAs we mentioned above, the literature suggests that service providers may beunable at times to retain even those customers who are satisfied (e.g. Heskettet al., 1994; Schneider and Bowen, 1999). If this inability to retain satisfiedcustomers is at least partly due to the absence of trust (Hart and Johnson, 1999),then this suggests that trust may act as a complement to satisfaction instrengthening customer retention. Conversely, the absence of trust may diminishretention even for satisfied customers. Specifically, it is likely that customersmost likely to be retained will be those with high levels of both satisfaction andtrust in their service provider. And, in the absence of one or the other, their levelof retention is likely to be significantly lower. Furthermore, Levesque andMcDougall (2000), based on a study of the hospitality industry, found that in caseof core service failure, even successful service recovery is inadequate to prevent“negative future intentions”. It is plausible that the negative intentions, despitethe positive (or satisfying) service recovery experience are a result of broken trustfrom the initial service failure. Taken together, the above discussion indicatesthat trust and satisfaction are likely to have a significant interaction effect oncustomer retention. Therefore, we hypothesise that:

H4. For a given level of customer satisfaction, the higher the level of trustthe higher the customer retention.

Switching barriers as a driver of customer retentionFollowing Bansal and Taylor (1999), we define perceived switching barriers asthe consumer’s assessment of the resources and opportunities needed toperform the switching act, or alternatively, the constraints that prevent theswitching act. Keaveney’s (1995) critical incident study was one of the first toexamine switching barriers as a determinant of customer switching behaviour.Subsequently, Gremler and Brown (1996) used in-depth interviews to develop amodel that included switching costs as an antecedent of customer loyalty. Theydefined switching costs as the investment of time, money and effort that, incustomers’ perception, made it difficult to switch. Among the examples ofswitching costs they listed were habit, inertia, set up, search, learning,contractual and continuity costs. Since then, Bansal and Taylor (1999) and Leeet al. (2001) among others have tested and confirmed the positive effect of

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switching barriers on customer retention. In line with existing research wehypothesize that:

H5. The higher the level of perceived switching barriers, the higher thecustomer retention.

Moderating effect of switching barriersWhile the main effect of switching barriers on retention has been empiricallyvalidated in a number of settings, including business-to-business (Heide andWeiss, 1995) and employer-to-employee relationships (Weiss and Anderson,1992), few have tested for the moderating effects of switching barriers on the linkbetween satisfaction and retention. While there is theoretical justification forsuch an effect, Lee et al.’s (2001) study of the mobile phone sector was one of thefew studies that found empirical support for it, and that too only among the lowspenders in their sample. Nevertheless, where switching barriers are sufficientlystrong, they are likely to act as a significant constraint to switching. This wouldindicate that service providers are more likely to retain dissatisfied customerswho perceive high switching barriers. Therefore, we hypothesize that:

H6. For a given level of customer satisfaction, the higher the level ofperceived switching barriers the higher the customer retention.

MethodWe tested our framework and hypotheses on customers of fixed line residentialtelephones in the UK. The fixed line telephone industry in the UK hashistorically been closed to new entrants, making the industry a near monopoly,thus giving customers very little or no choice. However, the national monopolyrun by the state was privatised in the 1990s, forcing it to compete with radicallytransformed companies that then entered the market. These new entrants werevastly more competitive than the former monopoly, and offered far greaterchoice to the customer.

As part of the empirical stage of the study, we first interviewed 40 customersrepresenting different demographic categories (based on five age categories)with a view to gaining qualitative support for our framework as well asdeveloping items for the switching barriers construct. While the conceptualdefinition of switching barriers holds across different contexts, as noted byFornell (1992), the actual item descriptions will depend on the industry studied.The interviews helped identify the key barriers commonly perceived by fixedline telephone customers.

Following these interviews, we developed a questionnaire with itemsmeasuring all the key variables in the framework. The questionnaire containedboth structured questions, with Likert-type scales, and open-ended questionsspecifically aimed at ascertaining the impact of critical incidents on satisfactionand trust. In the open-ended questions, respondents were asked to describe the

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nature of critical incidents that led to either dissatisfaction or loss of trust. Thiswas a probing question that encouraged respondents to provide a briefdescription of the events and the outcomes as they remembered them, in theirown words. Since the question asked the respondent to describe the “incidentthey remembered most”, this ensured that the event itself was of a criticalnature from the respondent’s perspective. This type of question also givesrespondents time to collect their thoughts. Thus the approach we followedaimed at improving the reliability of this type of data, consistent with theprocedure recommended by Keaveney (1995) and Bitner (1990).

The questionnaires were mailed to 2,850 customers randomly selected fromthe customer database of a major UK service provider. These customers werefrom two adjacent towns in the South Eastern region of England. Allrespondents used the service of either one of the two main service providers inthe region. The same geographical region was selected to limit the possibility ofbiases entering the data.

MeasuresCustomer retention, customer satisfaction, trust and switching barriers were allmeasured using multiple item, seven-point Likert-type scales, based onvalidated scales from the extant literature. Following Ruyter and Bloemer(1999), we employed exploratory factor analysis to confirm the underlyingstructure of the measures. The results of this analysis confirmed the reliabilityof these previously validated measures (see the Appendix for the items andtheir corresponding reliability coefficients). We discuss each of these scales indetail below:

. Customer retention (CR). We define customer retention as the futurepropensity of a customer to stay with the service provider. Accordingly,we measured retention by adapting a three-item formative scale used byMorgan and Hunt (1994) to measure “propensity to leave” in abusiness-to-business relationship. Customer retention has often beenconceptualised and operationalised as a dimension of the customerloyalty construct (e.g. Boulding et al., 1993; Zeithaml et al., 1996).However, due to the inherent difficulties in linking attitudes to behavioursin cross sectional survey research, it has been common to measure futureintentions as indicators of actual behaviours. There have been somecriticisms of this approach, specifically, that behavioural intentions aresometimes not good indicators of actual behaviour. As a result, thisconstruct has often been referred to as, among others things, a customerbehavioural intention (e.g. Zeithaml et al., 1996), or as customer switchingintentions (Bansal and Taylor, 1999). However, based on a longitudinalstudy, Bansal and Taylor (1999) also showed that in purchasing settingscharacterised by long-term contractual relations, switching intentions area good predictor of switching behaviour. We rely on these findings in

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support of our approach. Our measures are also consistent with those inthe business-to-business literature where the propensity of terminating anexisting relationship has been used as an indicator of relationshipcommitment (see Anderson and Sullivan, 1993; Cronin and Taylor, 1992;Morgan and Hunt, 1994). The three items we used measured thelikelihood of the respondent leaving the service provider at three differentperiods in the future: six months, one year and two years respectively.The overall score was a summate of the three weighted items, reversecoded. Following Morgan and Hunt’s approach, the first item wasweighted four times, the second two times, and the third item was leftunweighted.

. Customer satisfaction (CS). We measured customer satisfaction using athree-item, seven-point Likert-type scale with anchors “strongly agree”and “strongly disagree”. The items were adapted from the satisfactionmeasure developed by Cronin et al. (2000). They drew upon the definitionof satisfaction used consistently over time as “an evaluation of anemotion” (Hunt, 1977). Rust and Oliver (1994) confirmed this view andsuggested that customer satisfaction reflects the degree to which aconsumer believes that the possession or use of a service evokes positivefeelings. Cronin et al.’s (2000) multiple item measure of customersatisfaction consisted of two categories of measures. They adapted a setof emotion-based measures from Westbrook and Oliver (1991), built theirown evaluative set of measures based on previous work by Oliver (1980)and called it a cumulative or overall satisfaction measure. Consistent withthis approach, one item in our study reflected the emotional category andtwo items reflected the evaluative category.

. Trust. We measured trust using Morgan and Hunt’s (1994)operationalisation of the construct in their seminal study of thecommitment-trust theory of relationship marketing. Specifically,following their approach, we measured the consumer’s trust in theservice provider or firm. Morgan and Hunt (1994) used a seven-item,seven-point Likert-type reflective scale to measure trust, which gave aCronbach’s alpha value of 0.947. They reported three of the seven itemsand we adopted these three items in the current study.

. Switching barriers (SB).We drew upon Bansal and Taylor (1999), Morganand Hunt (1994) and Fornell (1992) in operationalising the switchingbarriers construct. We measured switching barriers using a five-item,seven-point Likert-type scale. Following Morgan and Hunt (1994), wemeasured perceived switching barriers as opposed to actual switchingbarriers. Further, the items were based on the types of switching barriersidentified in Fornell (1992) and were specifically selected to suit the targetsurvey respondents. This choice was supported by feedback from boththe preliminary interviews as well as discussions with the management of

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a major UK telecommunication service provider. Statements 1-3 reflectedthe facilitating conditions, whereas statements 4-5 reflected theself-efficacy dimension as described in Bansal and Taylor (1999).

Analysis and resultsOf the 2,850 questionnaires mailed, we received 461 responses of which 29 wereincomplete and were discarded. The remaining 432 questionnaires weresubstantially complete and resulted in a valid response rate of 16.2 per cent.There were no significant differences in the response rates for customers of thetwo service providers. Though the response rate was acceptable, we tested fornon-respondent bias as recommended by Armstrong and Overton (1977).Specifically, we compared means for the constructs of early versus laterespondents under the assumption that those who respond in later waves arelikely to be similar to non-respondents. We found no significant differencesbetween the two groups at the 0.05 level, confirming the absence of significantnon-respondent bias.

The questionnaire specifically requested that “the person in the householdmost involved with the decision to switch phone companies” respond to thesurvey. Of the respondents, 66 per cent were male and 30 per cent were female(4 per cent failed to respond to the question relating to gender). The average ageof the respondent was 48 years. Compared to the actual population distribution,older age groups were somewhat over represented. However, there wasadequate representation of all age categories. For further tests, missing itemswere treated as missing at random and were excluded list-wise.

Regression analysisAs expected, some of the data showed deviations from normality. In rectifyingpossible violations of the assumptions for OLS regression analysis, we followedprocedures recommended by Hair et al. (1998). Specifically, the dependentvariable, customer retention, had skewness and kurtosis levels of 21.63 and1.91 respectively. However, the independent variables, namely satisfaction,trust and switching barriers, fell within the limits of ^1. To correct fordeviations from normality, we transformed customer retention using both asquare root and a log transform. Since the log transform gave the best resultsas far as normality (skewness and kurtosis levels of 20.75 and 20.73) andhomoscedasticity were concerned, we used the log-transformed variable forfurther analysis. We left the independent variables un-transformed since theirdeviation from normality was within acceptable limits.

Table I illustrates the simple bi-variate correlations among the keyconstructs. The results suggest significant positive effects of satisfaction,trust and switching barriers on customer retention, with satisfactionexplaining a higher proportion of variance in the dependent variable(R ¼ 0:59) than trust (R ¼ 0:48) and switching barriers (R ¼ 0:26).

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In testing the regression model of customer retention containing moderatingeffects we used the procedure recommended by Aiken andWest (1991) and IrwinandMcClellan (2001). Following recommended practice (see Irwin andMcClellan,2001), we avoided the common heuristics of moderated multiple regressionmodels. Specifically, we changed the origin of each continuous independentvariable though standardisation. Whenever a product term was included, itscomponents were also included irrespective of their relative significance.

Furthermore, as recommended, regression analysis was undertakenhierarchically to test for significant interaction effects over and above themain effects of the independent variables. The resultant models are shown inTable II.

The independent variables were standardised prior to forming theinteraction variable, to prevent the interaction variable from causingunacceptable levels of multicollinearity (see Aiken and West, 1991; Irwin andMcClellan, 2001). The initial model (model 1) contained the simple additiveeffects of satisfaction and trust on retention and had an adjusted R 2 of 37.8 percent. At the next stage, the switching barriers variable was added to the modelto examine the main effects of all three independent variable taken together(model 2). This resulted in an increase in the adjusted R 2 value to 38.6 per cent.

Customersatisfaction Trust

Switchingbarriers

Customerretention

Customer satisfaction 1.000–

Trust 0.674* 1.000(0.000) –

Switching barriers 0.238* 0.169* 1.000(0.000) (0.001) –

Customer retention 0.596* 0.482* 0.266* 1.000(0.000) (0.000) (0.000) –

Note: * Correlation is significant at 0.01 level

Table I.Pearson correlations(sig. two-tailed)

Independent variable Model 1 Model 2 Model 3

Customer satisfaction (CS) 0.503* 0.485* 0.515*Trust 0.148* 0.148* 0.149*Switching barriers (SB) 0.096* 0.108*CS £ SB –0.094*CS £ trust 0.104*R 2 0.381 0.390 0.440Adjusted R 2 0.378 0.386 0.397F 122.300* 84.330* 53.333*

Notes: *a , 0:01. b coefficients have been reported. All changes in R 2 values had a significantF statistic (at 0.05 level)

Table II.Results of OLSregression analysisof drivers ofcustomer retention

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Finally, the two interaction variables were added to the model. As shown inmodel 3, these additions resulted in a further increase in the adjusted R 2 valueto 39.7 per cent. All changes in R 2 values were significant at the 0.05 level.Indeed, while the increases in the R 2 values were not necessarily practicallysignificant, we built the best possible model, which explained the highestvariance in the dependent variable. While a model containing only theinteraction variables would have explained nearly as much variance as theabove model, we specifically avoided this approach, in order to prevent one ofthe common misleading heuristics in regression interaction models describedearlier.

The results of model 3 show that the main effects of satisfaction(b ¼ 0:515, p , 0:01), trust (b ¼ 0:149, p , 0:01), and switchingbarriers (b ¼ 0:108, p , 0:01) are all significant and positive, thus confirminghypotheses H1, H2, and H5. Second, in support of H3, satisfaction is a strongerdriver of retention than trust (bCS ¼ 0:515 . bTrust ¼ 0:149). Indeed, thisfinding is also supported by model 1 in which the main effects of onlysatisfaction and trust were examined (bCS ¼ 0:503 . bTrust ¼ 0:148).

Third, model 3 provides evidence in support of H4. Specifically, theinteraction effect of customer satisfaction and trust is significant and positiveas hypothesized (b ¼ 0:104, p , 0:01). As illustrated by Aiken and West(1991), the positive sign indicates that higher the level of trust, the greater theslope of retention on satisfaction. This shows that, as we argued, trust acts as acomplement to satisfaction, further strengthening retention. Thus, in theabsence of trust, the impact of satisfaction on retention is likely to be lower.

Finally, model 3 also provides evidence in support of H6. Specifically, theinteraction effect of customer satisfaction and switching barriers wassignificant and negative (b ¼ 20:094, p , 0:01). The negative sign indicatesthat higher the level of switching barriers, the lower the slope of retention onsatisfaction. This shows that switching barriers act as a constraint, limitingthose who are less than satisfied from leaving the service provider. Thisindicates that switching barriers, where appropriate, can be an effective,alternative means of strengthening customer retention.

Additional analysisIt is possible that the results concerning interaction effects above arisespuriously from both an underlying quadratic effect not captured in model 3,and due to the nature of data transformation technique used. As recommendedby Darlington (1990), therefore, we also tested for curvilinear effects of theindependent variables. We found that the quadratic effects of satisfaction, trust,and switching barriers on retention were non-significant at the p , 0:05 level.We also did the regression analysis using alternative data transformations.While the results showed different levels of normality and homoscedasticity, the

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significance levels associated with the independent variables remainedunchanged. These tests provide further support in favour of H4 and H6.

To shed further light on the specific nature of the interaction effects, weconducted the following graphical and statistical analysis as recommended byAiken and West (1991). First, we computed the median scores for customersatisfaction, trust and switching barriers. We then split the entire sample intogroups of respondents who were either above or below the median value on thethree variables and compared the mean retention rates of these groups. Finally,we plotted these means and tested whether they were statistically different.The results of this analysis for customer satisfaction and trust are as follows(see Figure 2). Among highly satisfied respondents (i.e. those above the medianon customer satisfaction), those with low levels of trust (below the median ontrust) were significantly less likely to be retained than those who had highlevels of trust:

Mean retentionLow trust ¼ 0:596 , Mean retentionHigh trust

¼ 0:744; p , 0:05:

This result provides additional support for H4 regarding the interactioneffect of satisfaction and trust. Figure 2 also indicates how trust acts at thehigh end of satisfaction, namely, that it complements satisfaction and furtherstrengthens retention. Whereas, at the low end of satisfaction, trust has nosignificant impact in retaining dissatisfied customers, i.e. there is nosignificant difference in mean retention between those with low andhigh trust.

The results of the same analysis were similar for the interaction ofsatisfaction and switching barriers (see Figure 3). Among dissatisfiedrespondents (i.e. those below the median on customer satisfaction), thosewho perceived high switching barriers (above the median on switching

Figure 2.Mean retention acrossdifferent levels ofsatisfaction and trust

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barriers) were significantly more likely to be retained than those who perceivedlow switching barriers:

Mean retentionLow barriers ¼ 0:406 , Mean retentionHigh switching barriers

¼ 0:523; p , 0:05:

Again, this result provides additional support for H6 regarding the interactioneffect of satisfaction and switching barriers. Figure 3 also illustrates that,unlike in case of trust, the interaction effect of switching barriers is significantat low levels of satisfaction, namely, it restrains dissatisfied customers fromleaving. At the high end of satisfaction, however, switching barriers have nosignificant impact in retaining satisfied customers, i.e. there is no significantdifference in mean retention between those with low, and high perceivedswitching barriers.

Critical incident analysisTo gain a deeper understanding of the relationship between satisfaction andtrust, we also analysed respondents’ descriptions of the critical incidents theyfaced. A categorisation of responses to the open-ended question on criticalincidents revealed the following. There were a total of 419 responses in all. Ofthese, 176 respondents had faced a critical incident relating to service failure. Acomparison of the level of trust among those who had a service failure andthose who did not showed that the former had a significantly lower level oftrust than the latter:

Mean trustHad service failure ¼ 4:24 , Mean trustNo service failure

¼ 5:08; p , 0:05:

Figure 3.Mean retention across

different levels ofsatisfaction and

switching barriers

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Of the 176 who had a service failure, 75 went through a recovery process. Datashowed that those who went through the recovery process had a significantlyhigher level of trust than those who did not have a recovery process:

Mean trustNo service recovery ¼ 3:96 , Mean trustHad service recovery

¼ 4:62; p , 0:05:

These results indicate that service failure in general reduces the level of trustthat customers have in their service provider. Further, while service recoverycan limit the loss of trust, even “satisfying” service recovery was found to beinadequate to prevent loss of trust. Of the 75 who went through the recoveryprocess, 13 described their recovery experience as “satisfying”. Nevertheless,their level of trust in the service provider was significantly lower than thosewho had no service failure in the first place:

Mean trustHad satisfying service recovery ¼ 4:85 , Mean trustNo service failure

¼ 5:08; p , 0:05:

Discussion and implicationsTaken together our results offer strong support for the positive main effects ofsatisfaction, trust and switching barriers on customer retention. The resultsalso support the interaction effects on retention of satisfaction with trust andswitching barriers respectively. We now discuss the implications of theseeffects for services research and for service firms.

Satisfaction, trust and retentionA considerable body of research has tested the main effect of satisfactionon retention (e.g. Anderson and Sullivan, 1993; Rucci et al., 1998; Bansaland Taylor, 1999; Cronin et al., 2000). Previous research has generallyfound a significant positive effect of satisfaction and our study confirmsthis finding. We show that in a low customer contact, mass service setting,satisfaction is the strongest driver of customer retention. In contrast,previous research has provided limited empirical insight into the impact oftrust on customer retention. Our results confirm the expected positive effectof trust on retention. However, unlike in business-to-business relationships,we find trust to be a weaker predictor of retention than satisfaction. Thiscontradicts some recent literature which suggests that trust is a strongeremotional response than satisfaction, and that firms must therefore gobeyond satisfaction to build trust to increase commitment and create trueloyalty (see Hart and Johnson, 1999). We find no evidence that trust, as adirect determinant of retention, is more important than satisfaction, noteven in a continuous purchasing setting more suited to trust relationships.This is likely to be a result of the low relationship termination costs

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associated with the business-to-customer context. This fact does not,however, diminish the value of trust as a driver of retention. Specifically, inaddition to the main effect of trust, its significant interaction effect withsatisfaction suggests that, in the absence of trust, satisfaction will have lessimpact on retention, and thus, may not be adequate to retain customers.Therefore, companies may need to employ a combined strategy aimed atincreasing both satisfaction and trust simultaneously. As discussed before,Figure 2 illustrates this phenomenon graphically: it shows that evenrespondents with high levels of satisfaction are significantly less likely tobe retained when their level of trust in the service provider is low.

Traditionally, satisfaction and trust have been thought of as similarconstructs. Some have even suggested that these constructs be combined tobuild constructs reflective of cumulative customer evaluations. For example,Crosby et al. (1990) combined satisfaction and trust into a single latentconstruct of relationship quality. Others have suggested that the inclusionof similar customer evaluations of services lead to redundancy (e.g. Croninand Taylor, 1992). The study by Garbarino and Johnson (1999) was animportant exception, where they argued and empirically showed thatsatisfaction and trust can have distinct impacts on future behaviouralintentions. Our finding of a significant interaction between satisfaction andtrust suggests the possibility that customers may be satisfied despitesimultaneously having low levels of trust. This is consistent with theargument that satisfaction and trust, taken together, need not necessarilybe redundant.

The results from the critical incident analysis offer additional support for thesignificant interaction effects of satisfaction and trust. The results show howtrust can be lost in the event of service failure, despite a satisfying servicerecovery experience. Further, the analysis shows that even consumers who hada satisfying service recovery experience had a significantly lower level of trustthan those who experienced no service failure in the first place. Taken together,these findings have important implications for services research. With a fewexceptions (e.g. Zeithaml et al., 1996; Levesque and McDougall, 2000), thegeneral trend in the recent literature has been to suggest that successful servicerecovery is the panacea to all service problems. Researchers have argued boththat service recovery can restore customers to a satisfied state or make themdelighted (Johnston and Fern, 1999) and that customers who experiencesuccessful service recovery can sometimes be even more satisfied than thosewho did not experience a service failure in the first place (e.g. Brown, 2000).While both these points may be partly true, the full impact of service failureand recovery on trust appears to be different from that on satisfaction.Customers appear to be willing to accept the apology and compensation offeredby the service provider as part of the recovery process, but may be less willingto trust their service provider to offer a trouble free service in the future. These

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findings are consistent with assertions made by Levesque and McDougall(2000) that in some service failure and recovery situations, customers mayforgive the service provider, but not forget the bad experience of failure.

In sum, therefore, while satisfaction may be an important driver of retention,trust is another important and distinct driver of retention. The implication ofthis finding for service firms is that they may need to pursue a combinedstrategy aimed at managing satisfaction and trust, both independently as wellas simultaneously, if possible.

Satisfaction, switching barriers and retentionOur results also show that the main and the interaction effects of switchingbarriers on retention were highly significant. Taken together these findingssuggest that, in an industry with switching barriers, service providers arelikely to be able to retain even those customers who are less than satisfied. AsFigure 3 illustrates, the mean retention rate of customers with low levels ofsatisfaction is significantly higher for those who perceive high than low levelsof switching barriers. Further, as reflected in the switching barriers scale, thesebarriers are mostly to do with the time, money and effort needed to switch.Thus, for firms mainly interested in maintaining market share, switchingbarriers may be an effective means of retaining customers. In a climate ofstrong competition, where firms are faced with the challenge to find alternative,cost effective means of retaining customers, switching barriers can be a usefulalternative to pursuing higher and higher levels of customer satisfaction fortheir own sake.

Nevertheless, while switching barriers may be a useful tool in the arsenal ofthe strategist, evidence from the employee retention literature suggests thatcustomers could become resentful of switching barriers, especially if thesebarriers lead to a scenario of complete entrapment (e.g. Withey and Cooper,1989) and, in the process, affecting other dimensions of loyalty such as positiveword of mouth. Given that the time and effort required to switch are perceivedto be important switching barriers, service providers may therefore wish tofocus on service features that increase switching costs without necessarilycreating absolute barriers to switching. Indeed, we suggest that an ideal wayfor firms to prevent customer resentment is to create switching barriers thatalso add value to the service. For example, telephone service providers whoalso offer Internet access as part of a package deal would be offering an extraservice that adds value to the overall service bundle, while creating anadditional barrier to switching given that changing internet service providersis often a time and resource consuming endeavour. Therefore, while switchingbarriers could be thought of as an alternative means of retaining customers,they could also be a complementary source of satisfaction through theprovision of added value to the service bundle. Taken together, this suggeststhat, depending on their circumstances, service firms may benefit from

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pursuing a combined strategy of increasing satisfaction and switching barriers,both simultaneously as well as independently, if possible.

ConclusionsWhile past studies have focussed on customer satisfaction as the key driver ofretention, our paper attempts to build a more complete framework of the factorsthat influence retention. In addition to satisfaction, we incorporate the role ofless studied drivers such as trust and switching barriers. Indeed, while themain effects of trust and switching barriers are somewhat apparent, and havebeen supported in the existing literature, we hypothesise and offer evidence ofinteraction effects on retention of both satisfaction and trust, and satisfactionand switching barriers respectively. To the best of our knowledge, no existingstudies have empirically tested for an interaction effect on retention ofsatisfaction and trust. Similarly, while the interaction effect betweensatisfaction and switching barriers has been suggested as plausible,empirical support has been scant. Our study fills these two major gaps inthe literature.

Nevertheless, this study has some limitations that offer opportunities for futureresearch. First, the results are based on cross sectional data, making it hard tomake strong inferences about cause and effect. Second, the results are based onthe survey of a single service industry. Naturally, our findings are most likely tohold for similar, low customer contact, mass service contexts with a continuouspurchasing pattern. However, the high automation and low customer-staff contactof this context also makes our findings relevant for related industries where suchphenomena are increasingly common due to the spread of technology.Nevertheless, the applicability of our findings to other contexts needs furtherresearch. Further, future research may focus on the effects of failure and recoveryon customers’ long-term commitment and loyalty to the service provider. Whileour results provide strong support for the main and interaction effects of trust,satisfaction and switching barriers on customer retention, empirical validation ofthese effects in multiple settings would help shed further light on these andrelated phenomena of vital importance to service firms.

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Further reading

Cronin, J.J. and Taylor, S.A. (1994), “SERVPERF vs SERVQUAL: reconciling performance basedand perceptions – minus – expectations measurement of service quality”, Journal ofMarketing, Vol. 58, January, pp. 125-31.

Gale, B.T. (1998), “Introduction: to customer satisfaction, loyalty, and value”, available at:www.cval.com/Intro.html

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Gremler, D.D. and Brown, S.W. (1998), “Service loyalty: antecedents, components, andoutcomes”, American Marketing Association, Winter, pp. 165-6.

Gremler, D.D. and Gwinner, K.P. (2000), “Customer-employee rapport in service relationships”,Journal of Service Research, Vol. 3 No. 1, pp. 82-104.

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Oliver, R.L. and Satisfaction:, A. (1997), Behavioural Perspective on the Consumer, McGraw-Hill,New York, NY.

Prabhu, J. (1992), “Customer loyalty in service relationships”, working paper, University ofSouthern California.

Singh, J. (1988), “Consumer complaint intentions and behaviour: definitional and taxonomicalissues”, Journal of Marketing, Vol. 52, January, pp. 93-107.

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Appendix

Construct/itemsReliabilitycoefficient

Retention (anchor: very low . . . very high)What do you think are the chances of you totally terminating your relationshipwith your Phone Co.?a

Formativescaleb

1. Within the next six months?2. Within the next one year?3. Within the next two years?

Customer satisfaction (anchor: strongly agree . . . strongly disagree) 0.941. Overall, I am happy with my phone company2. My phone company meets my expectations3. I think I did the right thing when I joined this phone company

Trust (anchor: strongly agree . . . strongly disagree)In our relationship, my phone company . . . 0.781. Cannot be trusted at timesa

2. Can be counted to do what is right3. Has high integrity

Switching barriers (anchor: strongly agree . . . strongly disagree) 0.951. There are technical difficulties associated with changing my phone company2. I am concerned about not being able to keep my phone number whenchanging phone company

3. Changing phone company is costly4. Changing phone company requires a lot of effort5. I might have changed my phone company if I could do so without hassle

Notes: a Item/items reverse coded. b As per standard practice, reliability coefficients have notbeen calculated for the formative scale

Table AI.Measures

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