Consumer emotions

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International Journal of Service

Industry Management,

Vol. 12 No. 3, 2001, pp. 234-250.

# MCB University Press, 0956-4233

Received March 2000Revised December 2000Accepted February 2001

The contribution of emotionalsatisfaction to consumer

loyaltyYi-Ting Yu

Monash University, Singapore

Alison DeanMonash University, Churchill, Australia

Keywords Loyalty, Customer satisfaction, Customer loyalty, Higher education

Abstract Many customer satisfaction studies have concluded that there is a significantrelationship between customer satisfaction and loyalty, but this finding has been questioned in

that most of the studies focus on measuring the cognitive component of customer satisfaction.This study includes the cognitive component, but focuses on the affective component. It exploresthe role of emotions in satisfaction, and then compares the predictive ability of the cognitive andaffective elements. Key findings are that both positive and negative emotions, and the cognitivecomponent of satisfaction correlate with loyalty. Regression analysis indicates that the affectivecomponent serves as a better predictor of customer loyalty than the cognitive component. Further,the best predictor of both overall loyalty and the most reliable dimension of loyalty, positive wordof mouth, is positive emotions. Thhe theoretical and practical implications of these findings arediscussed.

IntroductionIt may appear unnecessary to study the relationship between customer

satisfaction and customer loyalty as many studies have confirmed that there isa significant positive relationship between these two variables (see Colgate and

Stewart, 1998; Hocutt, 1998; Patterson and Spreng, 1997). However, many of the

satisfaction-loyalty relationship studies were done when the development of

the satisfaction construct was at an early stage, and customer satisfaction was

still seen as an ``elusive construct'' (Rosen and Surprenant, 1998). More recently,

scholars comment that it is inappropriate to ignore the emotional component of

satisfaction, and hence the reliability findings of the previous studies are

questioned (Liljander and Strandvik, 1997; Peterson and Wilson, 1992; Stauss

and Neuhaus, 1997; Wirtz and Bateson, 1999). Consequently, this paper reports

on a study which aims to:. explore the role of emotions in customer satisfaction; and

. re-test the satisfaction-loyalty relationship when the emotional

component is included.

First, we review the recent literature on customer satisfaction and customer

loyalty, and develop research propositions consistent with the research aims.

The research design is then described, and the results discussed. The paper

concludes with implications and recommendations for future research.

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

http://www.emerald-library.com/ft


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A brief review of customer satisfaction researchIn the early stages of services research, researchers attempted to diminish theconfusion between customer satisfaction and service quality by determiningwhether there is any distinction between them, and by exploring antecedents(Cronin and Taylor, 1992; Oliver, 1993a). Oliver (1993a, p. 76) distinguishesbetween the two constructs by suggesting that satisfaction is ``potentially allsalient dimensions'', it requires experience-dependency and involves emotions.In his study, Oliver reverses the previous notion that satisfaction is theantecedent of quality and claims that quality is the antecedent of satisfaction.Spreng and Mackoy's (1996) study further tests and confirms Oliver'sconceptual model. Lately, customer satisfaction has been commonly acceptedas a different construct from service quality and the emphasis has been onstudying the relationships between them (Shemwell et al., 1998; Taylor andBaker, 1994).

With consistent findings that service quality and satisfaction are differentconstructs, and that service quality leads to customer satisfaction, the researchinterest moved to studying the linkages between customer satisfaction, servicequality and customer loyalty/retention. While the direct relationship betweencustomer satisfaction and loyalty has been shown to be complex andasymmetric (Bloemer and Kasper, 1995; Mittal and Lassar, 1998; Oliver, 1999),and some research has shown that switching behavior and repurchaseintentions are not consistent with satisfaction levels (Stauss and Neuhaus,1997), a number of studies suggest that there is a significant positiverelationship between customer satisfaction and customer loyalty/retention(Anderson and Sullivan, 1993; Cronin, Brady and Hult, 2000; Shemwell et al.,1998; Taylor and Baker, 1994). Hence an overall research proposition issuggested as follows.

Research proposition 1: There is a significant positive relationshipbetween customer satisfaction and customer loyalty.

Before proceeding with further literature, we now briefly discuss the termssatisfaction and loyalty and define them for the context of our study. Drawingon the work of leading researchers in the services field for over 20 years, Roestand Pieters (1997) developed a nomological net to distinguish service qualityand customer satisfaction. In doing so, they define satisfaction, as a relative

concept that involves both cognitive and affective components, is consumer-related (rather than product-related), mainly transactional, and incorporatingan appraisal of both benefits and sacrifices. However, Roest and Pieters alsostate that ` . . . eventually, satisfaction may become or influence productattitude, which may be regarded as an aggregated but not relativistic constructinvolving a readiness to act'' (1997, p. 345). In summary, we note the distinctionbetween transaction-specific and overall satisfaction, and we adopt the broaderdefinition of satisfaction whereby the overall measure is an aggregation of allprevious transaction-specific satisfaction, and involves both cognitive and


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affective components. Recently, the overall measure has been shown to be abetter predictor of repurchase intentions (Jones and Suh, 2000).

Loyalty is interpreted as true loyalty rather than repeat purchasingbehaviour, which is the actual rebuying of a brand, regardless of commitment

(Bloemer and Kasper, 1995). True loyalty, in this context, encompasses a non-random, behavioural response which results from evaluation processes thatresult in commitment (Bloemer and Kasper, 1995). This is in contrast tospurious brand loyalty which is a function of inertia. However, loyalty is amulti-dimensional construct and includes both positive and negative responses(Zeithaml et al., 1996). In this study, loyalty is to an educational provider and istherefore service loyalty, rather than brand loyalty as has been developed inrelation to goods. In comparison to brand loyalty, service loyalty studies areunder-represented in the literature (Bloemer et al., 1999; Javalgi and Moberg,1997).

Having established our overall research proposition as above, weacknowledge that it would be meaningless to re-test the same propositionwithout incorporating recent developments in the satisfaction literature. Inparticular, it is argued that satisfaction includes both cognitive and emotionalcomponents. The cognitive component refers to a customer's evaluation of theperceived performance in terms of its adequacy in comparison to some kind ofexpectation standards (Liljander and Strandvik, 1997; Oliver, 1980; Wirtz,1993). The emotional component consists of various emotions, such ashappiness, surprise and disappointment (Cronin et al., 2000; Liljander andStrandvik, 1997; Oliver, 1993b; Stauss and Neuhaus, 1997).

It is important to note that the emotional component is a form of affect, and

is a response to service delivery. In this study, ``consumption emotions are theaffective responses to one's perceptions of the series of attributes that composea product or service performance'' (Dube and Menon, 2000, p. 288). Suchemotions are usually intentional (that is, they have an object or referent) andare different to the concept of mood, which is a generalised state induced by avariety of factors, and is usually diffused and non-intentional (Bagozzi et al.,1999). Emotions and mood (and attitudes) are all elements of a general categoryfor mental feeling processes, referred to as ``affect'' (Bagozzi et al., 1999). Theemotional component in the satisfaction judgment is therefore independentfrom the overall affective sense present in the respondent at the time of theservice (de Rutyer and Bloemer, 1998). The cognitive and emotional

components of satisfaction are now considered separately.

Reflections on the cognitive component in customer satisfactionstudiesAs indicated above, expectancy disconfirmation theory is the dominatingmodel for measuring customer satisfaction (Brookes, 1995). That is, satisfactionis determined by the confirmation or disconfirmation of expectations withperceptions of the perceived performance on various service items (Danaherand Haddrell, 1996). The multi-item disconfirmation model has been applied in


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many customer satisfaction studies, and has been proven to be very useful(Danaher and Haddrell, 1996; Wirtz and Bateson, 1999). Further, whencompared to other approaches, its benefits outweigh its main shortcoming,which is its conceptual overlap with service quality. Such benefits includehigher reliability, convergent and discriminant validity, face validity,managerial value and a lower skewness problem (Danaher and Haddrell, 1996).

In relation to the overlap between service quality and satisfaction, the multi-item disconfirmation model is very similar to the most famous service qualitymeasurement scale, SERVQUAL. As both scales use disconfirmation ofexpectations, it is doubtful whether the results of some previous satisfactionstudies show satisfaction or service quality. However, the two constructsemploy a different definition of expectations (Zeithaml et al., 1993), and have aconceptual distinction in that satisfaction is an ` experience-dependency''construct and service quality does not require experience (Danaher and

Haddrell, 1996; Oliver, 1993a). If the scale seeks respondents' assessment oftheir ``perceived service experience'', it is alleged that it is essentially measuringsatisfaction rather than service quality (Danaher and Haddrell, 1996). Thisstudy therefore employs the multi-item disconfirmation scale to measure thecognitive component of satisfaction.

Many previous satisfaction studies, which focus on the cognitive component,suggest that there is a positive relationship between satisfaction and loyalty(see Andreassen and Lindestad, 1998; Colgate and Stewart, 1998; Danaher andHaddrell, 1996; Mittal et al., 1998; Taylor and Baker, 1994), and we thereforepropose the following:

Research proposition 1a: There is a significant positive relationship

between the cognitive component of customer satisfaction and customerloyalty.

However, focusing only on the cognitive component of satisfaction neglects animportant element, namely emotions, and may be insufficient to obtain acomprehensive picture of consumer responses. The emotional element is nowpursued.

Affective measures in customer satisfactionAlthough there is still debate about whether satisfaction is itself an emotionalconstruct or a cognitive construct which includes an emotional component (Babin

and Griffin, 1998; Bagozzi et al., 1999; Crooker and Near, 1998), it appears thatemotions may be one of the core components of satisfaction (Dube and Menon,2000; Westbrook and Oliver, 1991). Further, it is suggested that emotions maydistinguish customer satisfaction from service quality (Oliver, 1993a).

Recent studies recognize that emotion is a core attribute in satisfaction andsuggest that customer satisfaction should include a separate emotionalcomponent (Cronin et al., 2000). Stauss and Neuhaus (1997) argue that mostsatisfaction studies only focus on the cognitive component, and that theomission of the affective component is one of the main issues in satisfaction


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research. Their proposition is supported by Liljander and Strandvik (1997),who argue that customer satisfaction includes both affective (or emotional) andcognitive components. Further, Stauss and Neuhaus (1997) suggest that it isinappropriate to assume that consumers experience the same emotions and

cognition when they give the same score for their overall satisfaction level. Wetherefore propose to include a separate emotional component in satisfaction, asthe major contributor to the affective element.

It is proposed that one's emotions have an influence on behavior. This is dueto human nature, in that one responds to an event in certain ways to maintain apositive emotion, such as happiness, and to avoid a negative emotion, such asdepression. Specifically, a person's positive emotions tend to link to his/herdecisions to stay or continue with what he/she has been doing. Conversely,negative emotions tend to link to the opposite decisions, such as to leave anddiscontinue involvement (Bagozzi et al., 1999). Positive emotions may also leadone to share the positive experience with others, while negative emotions mayresult in complaining behavior (Bagozzi et al., 1999; Liljander and Strandvik,1997). Supported by the previous findings that there is a connection betweenemotions and behavior (Bagozzi et al., 1999), and Stauss and Neuhaus' (1997)study, which found that there is a significant relationship between emotionsand loyalty, we propose the following:

Research proposition 1b: There is a significant positive relationshipbetween the affective component of customer satisfaction and customerloyalty.

The better predictor of customer loyalty: cognitive or affective?

While general research conclusions suggest that there is a positive relationshipbetween customer loyalty and both the cognitive and emotional components ofsatisfaction, there is a lack of empirical evidence to determine which of thecomponents serves as a better predictor of satisfaction. This is particularlyimportant, as the cognitive component of satisfaction alone has failed to serveas an effective predictor of customer loyalty (Stauss and Neuhaus, 1997).

To date there are very few affective/emotional scales specifically developedto measure customer satisfaction emotions. Stauss and Neuhaus (1997)developed a scale, originally with four dimensions, namely, optimism/confidence, steadiness/trust, disappointment/indecision, and protest/opposition. Stauss has now extended this to five with the addition of

indifference/resignation (personal communication, 1999). In 1997, Liljander andStrandvik (1997), based on the previous literature, developed a morecomprehensive emotional scale that includes seven emotional attributes:happy, hopeful, positively surprised, angry, depressed, guilty and humiliated.Liljander and Strandvik also suggest that customer satisfaction emotions canbe divided into two groups: positive emotions and negative emotions. Positiveemotions include happy, hopeful and positively surprised, while negativeemotions include angry, depressed, guilty and humiliated. Although there is noapparent consensus about the best way to measure the emotional component,


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in traditional literature, positive and negative emotions are often used tocompare effects (Crooker and Near, 1998).

There is consensus among researchers that loyalty is a complex construct,evident in the variety of perspectives that have been used to study it (Javalgi

and Moberg, 1997). These perspectives include behavioral, attitudinal andcognitive processes, however, the early customer loyalty studies focus mainlyon the behavioral perspective and then later shift to an attitudinal approach (deRuyter et al., 1998). Based on the attitudinal approach, customer loyalty can bestudied via its dimensions, such as word-of-mouth, complaining behavior andpurchase intention. However, there are different findings in relation to loyaltydimensions, even when the same loyalty scales are employed. Parasuraman etal. (1994) developed a loyalty scale and found that loyalty consists of loyalty tocompany, propensity to switch, willingness to pay more, external response toproblem and internal response to problem. De Ruyter et al. (1998) later adoptedthe same scale but found that loyalty consists of three dimensions: preference,price indifference and dissatisfaction response. However, the same authorssuggest that the necessary elements to operationalise loyalty are captured inthe ` behavioral intentions battery'', refined by Parasuraman and his co-workers. (Bloemer et al., 1999; Zeithaml et al., 1996). This study thereforeadopts and customises this scale to explore the relationship between customersatisfaction, including both cognitive and emotional components, and loyalty.

MethodologySampleThe subjects in this study were on-campus undergraduate students in business

and economics at a large university in Australia. Convenience sampling wasemployed and self-administered surveys were used to collect the data. A totalof 320 surveys were distributed and 122 valid returns were obtained, giving aresponse rate of 37.5 per cent. The average age of respondents was 24. Therespondent profile was female (55.8 per cent), average age 24 years, andcomprising 57.9 per cent Australian and 42.1 per cent international students.This cohort was considered representative by the head of the school.

Scales employed: customer satisfaction cognitive componentSeven-point Likert scales were employed to measure both customer satisfactionand loyalty. The scale employed to measure the cognitive component of

customer satisfaction focused on educational service attributes, and wascustomised from the instrument developed by Dean (1999). It is based on themulti-item disconfirmation model, and uses a single column format. The scaleincludes six groups of service attributes: course structure, teaching, lecturer'sinteraction and support, administration support, feedback and assessment, andphysical presentation. A typical item reads, ` The written feedback onassignments . . . 1 (failed to meet my expectations) . . . to 7 (far exceeded myexpectations)''. At the end of each group, an overall value for satisfaction withthe focus of the items was obtained.


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Scales employed: customer satisfaction affective componentTo gain insights into the affective component, we used the emotional scaledeveloped and tested by Liljander and Strandvik (1997). The scale was adoptedin its entirety. A typical item reads ``So far, my overall studying experience at(the provider institution) makes me feel

. . .

happy''. Responses are on a Likertscale from 1 (never) . . . to 7 (often). Liljander and Strandvik were unable tostudy the relationship between the customer satisfaction emotional componentand customer loyalty because, in the industry that they used, customers do nothave the choice to switch to another brand or another service provider. Ourstudy therefore builds on their work in pursuing the relationship.

Scales employed: customer loyaltyIn terms of customer loyalty, Parasuraman et al.'s (1994) ` Reconfiguredbehavioral-intentions battery'', subsequently refined by its authors (Zeithaml et

al., 1996) and also used by de Ruyter et al. (1998), and Bloemer et al. (1999) wasadopted and customised for this study. The original scale had 13 items(discussed in relation to Table II), and five components: loyalty to company,external response to problem, propensity to switch, willingness to pay more,and internal response to problem. In their paper, Parasuraman et al. (1994) notethat their scale requires refinement particularly for the latter three of thesecomponents, but that the ``loyalty'' component demonstrated excellent internalconsistency while the ` external response'' was adequate according to thecriteria of Nunnally (1978).

Results and discussion

The scalesPrior to exploring the findings with respect to the research aims, we includesome discussion about the scales that we used, their reliability and the factorpatterns that they produce.

NormalityIn all cases, negative questions were reverse coded for consistency. Normalitytests, based on the different components in the survey, indicated adequateresults. Of particular interest is the degree of skewness as customer satisfactionstudies tend to be positively skewed (Coakes and Steed, 1999). The overall

scales did indicate some positive skewing (0.22 and 0.23 for emotional andcognitive scales) but, given the nature of the study, these values wereconsidered adequate to continue with the analysis.

ReliabilityThe reliability of the scales was established by utilising Cronbach's alpha. Thecognitive component, emotional component and overall loyalty component hadalpha scores of 0.94, 0.80 and 0.77 respectively, all indicating acceptable values(Nunnally, 1978).


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Factor analysis: emotions scaleIt was intuitively expected that the emotions scale would divide into two factors,representing positive and negative emotions. Principal components analysis withvarimax rotation confirmed this expectation, with the two factors explaining 46.9per cent and 19.1 per cent of the variance respectively (see Table I). However,feelings related to anger loaded more highly with positive emotions than withnegative. This result is not easily explained but it is possible that, as anger isusually directed at someone, its interpretation is confused. Or anger mightrepresent a third dimension of the emotional component and need to be furtherexplored. This possibility is consistent with the model conceptualised by Dubeand Menon (2000) which suggests that negative emotions have three components,``other-attributed'', ``self-attributed'' and ``situation-attributed''. In their model, the``other-attributed'' component is related to anger. Another possibility is that bothpositive and negative emotions can exist at the same time at a high level, thus

anger loads on both factors. It is suggested that customers may have a zone oftolerance for negative emotions and that, within this zone, their negative emotionsdo not affect their positive emotions, so that both positive and negative emotionscan exist at the same time (Liljander and Strandvik, 1997). In this study, a possibleinterpretation is that the respondents have simultaneously experienced highlevels of positive emotions and anger. When anger is dropped from our scale, thereliability only decreases by 0.1, so it is retained in our current analysis. However,its role in customer satisfaction provides an interesting focus for future work onthe emotions scale.

Factor analysis: cognitive scale

The dimensions of the cognitive scale, the educational service attributes havebeen developed and tested previously (Dean, 1999). However, as a number ofitems were added to the original scale, to account for the different educationalcontext (on-campus classes versus distance education), a factor analysis wasperformed. Principal components analysis with varimax rotation identified

TableEmotions scale: fact

loadings and reliabilvalu

Description Factor loading Reliability

1 21. Positive emotions 0.77Happy 0.794

Hopeful 0.778Positively surprised 0.810

2. Negative emotions 0.75Angrya 0.852 0.546Depresseda 0.657Guiltya 0.829Humiliateda 0.771

Note: Factor loadings less than 0.35 have been omitted; a reverse coded


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seven factors with eigenvalues greater than one, accounting for 69.1 per cent of

the variance. The results are shown in Table II.

The factors shown in Table II have loaded essentially as expected, except for

the factor that we had labelled ``teaching'' which split across the factors we hadlabelled ``teaching'' and ``course structure''. On reflection, this result is readily

Table II.Cognitive scale: factorloadings

Description Factor loading

1 2 3 4 5 6 71. Feedback and assessmentClearness of criteria 0.821Written feedback 0.763Assignment return time 0.641Questions and subject aims 0.608Fairness 0.594 0.362 0.372

2. Physical environmentTables 0.879Heating 0.817Chairs 0.811Lighting 0.594

3. Interaction and supportFeedback in class 0.803Interaction with lecturers 0.792Consultation times 0.720

4. Administration

Effectiveness 0.847Efficiency 0.772Time student advisor spent 0.698Friendliness of staff 0.567Visual presentation of lectures 0.464 0.356

5. Learning materialsEase of reading 0.815Learning materials 0.675 0.382Clarity of objectives 0.390 0.630Arrival in timely manner 0.439 0.597

6. Course structure and content

Variety 0.662Flexibility 0.628Ease of understanding 0.623Knowledge or skills taught 0.423 0.602Teaching techniques used 0.415 0.463 0.464

7. TechnologyTechnology in the classroom 0.422 0.755

Note: Factor loadings less than 0.35 have been omitted.


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explained as the items left in ``teaching'' are all related to ``learning materials'',and ``course structure'' is really ``course structure and content''. Finally, thesingle item relating to technology split out and formed a factor on its own. Insummary, the seven factors that contribute to the cognitive scale, and thepercentage of variance that they explain, are feedback and assessment (11.2),physical environment (11.1), interaction and support (10.8), administration(10.6), learning materials (10.1), course structure and content (9.7), and

technology (5.5). As the overall cognitive scale demonstrated good reliability(0.94) and was used in its entirety for our analysis, we did not pursue further

implications of the factor structure. However, the results provide a usefulstarting point for further scale development.

Factor analysis: loyalty scaleWhen the items in the loyalty scale were analysed, four factors emerged, as

shown in Table III. These factors (positive word of mouth, complainingbehavior, switching behavior, and willingness to pay more) accounted for 28.9per cent, 19.2 per cent, 10.7 per cent and 9.3 per cent of the variancerespectively. The first of our four factors contains four of the five items inParasuraman et al.'s (1994) ``loyalty to company'' factor. The fifth item, ``Do

Table ILoyalty scale: fact

loadings and reliabilvalu

Description Factor loading Alpha

1 2 3 4 0.941. Positive word-of-mouthSay positive things about the course 0.887

Recommend the course to someone else 0.954Encourage friends to apply for the same course 0.933Consider the same uni. as the first choice if pursuefurther study

0.544 0.479

2. Complaining behavior 0.67Complain to other students if experience problems 0.671Complain to external agencies if experience problems 0.782Complain to school staff if experience problems 0.777

3. Switching behavior 0.72Try to switch to another campus of the sameuniversity if experience problems

0.597 0.611

Try to switch to another university if experienceproblems

0.563 0.646

Study in another uni. If it offers a better price 0.748Try to study fewer subjects at this university 0.622

4. Willingness to pay more 0.45Continue the same course if the price increases 0.635Pay a higher price for the benefits currently received 0.780

Note: Factor loadings less than 0.35 have been omitted


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more business with XYZ in the next few years'' was not included in our studyas we felt it would be confusing to respondents in the second or third year oftheir degree programs. We refer to the first factor as ``positive word of mouth''to indicate its primary focus. Consistent with Parasuraman et al.'s (1994) study,

the best reliability was demonstrated by this factor.In relation to the other factors, ``willingness to pay more'' has the same two

items as Parasuraman et al. (1994) but is not internally consistent, while theitems in ``external response to problem'' and ``propensity to switch'' have beencustomised and are shown in the manner in which they loaded in our study. Wehave changed the names to facilitate clarity.

To conclude the discussion of the scales, we found that they have provensufficiently reliable to work with, and the items have generally loaded asexpected on the various dimensions. Having established that the instrumentwas adequate to pursue the aims of the study, we now report and discuss thefindings with respect to the specific aims.

The role of emotionsTo commence our investigation of the first aim, the role of emotions in themeasurement of customer satisfaction, we first consider the correlationbetween the dimensions of loyalty and the cognitive and emotional componentsof satisfaction (see Table IV). Consistent with research propositions 1a and 1b,Table IV confirms that there is a significant correlation between the two majorcomponents of customer satisfaction (emotional and cognitive) and loyalty.However, there is a higher correlation between overall customer loyalty and theemotional component than the cognitive component, at the 0.01 significancelevel. Further, the emotional component has slightly higher correlationcoefficients for positive word of mouth, switching behavior and willingness topay more when compared to the cognitive component.

The correlation coefficients for the positive and negative emotions suggestthat positive emotions are associated with all dimensions of loyalty except``complaining behavior''. This finding may be due to the emotions scale notcovering all the emotions that significantly correlate with the various loyaltydimensions. Intuitively, we expect that positive and negative emotions wouldlink to complaining behavior (Liljander and Strandvik, 1997) but, as well as thethree positive emotions included, there are other positive emotions, such as

Table IV.Correlation analysisresults

Emotionalcomponent

Cognitivecomponent

Positiveemotions

Negativeemotionsa

Overall loyalty 0.534** 0.424** 0.516** 0.404**Positive word of mouth 0.590** 0.517** 0.582** 0.435**Complaining behaviora 0.088 0.153 0.007 0.133Switching behaviora 0.262** 0.145 0.246** 0.206*Willingness to pay more 0.350** 0.313** 0.375** 0.235*

Notes: * p < 0.05; ** p < 0.01; a items reverse coded


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relief, elation and joy (Bagozzi et al., 1999), which were not included in the scale.There is no evidence to show that each emotion has the same influence ondifferent responses, such as complaining behavior, but rather that differentemotions may trigger different behavioral intentions (see Stauss and Neuhaus,

1997).Bagozzi et al. (1999) suggest that emotions influence decision making, and

that positive emotions particularly link to one's intention to maintain anongoing plan and share the outcome of a certain activity/event. This conclusionis consistent with our research findings that positive emotions significantlycorrelate with positive word of mouth (to share the positive experience),switching behavior (negatively correlated) and willingness to pay more (inorder to stay where he/she is).

The association between loyalty and negative emotions (which have beenreverse coded) suggests that they have a significant impact on loyalty as well.Of particular interest is the lack of association between negative emotions andcomplaining behavior. Again, this may be due to the absence of specificnegative emotions from the scale, such as regret and disappointment, which aremore likely to cause complaining behavior (Zeelenberg and Pieters, 1999). Theseven emotions in our emotions scale do not correlate with the respondents'complaining behavior in this study, and it is noteworthy that the findingsabout the relationship between regret and disappointment with complainingbehavior are inconsistent in Zeelenberg and Pieters' (1999) studies. Theseresults emphasise the need to further explore the possible negative emotionsthat may influence complaining behavior. They also indicate that educationproviders need to seek out negative responses as these are unlikely to be

voluntarily provided by students.

The satisfaction-loyalty relationship revisitedThe second major aim of our study was to re-test the satisfaction-loyaltyrelationship when the emotional component of satisfaction is included. Inparticular, we were keen to establish the best predictors of loyalty and so weused regression analyses to explore the possible relationships.

Best predictors of overall loyaltyTo gain a feel for the relative importance of the cognitive and emotionalcomponents in predicting customer loyalty, in the first regression we used

overall loyalty as the dependent variable, with the cognitive and emotionalcomponents as independent variables. The adjusted R2 = 0.336, and F(2, 98) =26.325, sig = 0.000. The standardized beta coefficients are shown in Table V

Table Standardized be

coefficients (dependevariable: over

loyalt

Beta t sig.

Cognitive component 0.179 1.917 0.058Emotional component 0.482 5.149 0.000


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and, while only 33.6 per cent of the variance in loyalty is explained, the findingsindicate that the emotional component is an important factor in explainingloyalty, apparently more important than the cognitive component.

To substantiate the finding that inclusion of the emotional component leadsto better results in explaining loyalty when compared to using the cognitivecomponent alone, we performed another regression with overall loyalty as thedependent variable and only the cognitive component as the independentvariable. In this case, the adjusted R2 = 0.172, and F(1, 101) = 22.151, sig =0.000. The beta value for the cognitive component equalled 0.424, t = 4.706,sig = 0.000. This result suggests that the emotional component is an importantpredictor of loyalty, and is consistent with the suggestions of Liljander andStrandvik (1997) that customer satisfaction is better explained when emotionsare included.

The next question of interest is the relative effect of positive and negative

emotions. To explore this, another regression analysis was performed, againusing overall loyalty as the dependent variable, but including the factor scoresfor the two types of emotions. While the same variance is explained, positiveemotions emerge as the best predictor of overall loyalty (beta = 0.336, t= 3.153,sig = 0.002) with negative emotions also significant (beta = 0.232, t= 2.488, sig= 0.015) and the overall cognitive assessment no longer significant. One wouldexpect that having positive emotions towards ` my overall studying experienceat service provider'', would result in more loyalty to that service provider. Ourfinding supports this assumption and also indicates that there is a negativeassociation for negative feelings (as responses were reverse coded). Howstudents feelabout their studying experience is therefore highly relevant to the

messages they are likely to give to others, and the personal responses they arelikely to make.

Best predictors of word of mouth behaviorAs indicated in Table IV, positive word of mouth has the highest correlation withthe components of customer satisfaction and the highest reliability of the fourloyalty dimensions. Further, switching costs are high and price regimes aregenerally inflexible in education, so we decided to conduct a further regressionanalysis using the components of satisfaction with positive word of mouth as thedependent variable. As there was no correlation between complaining behaviourand the components of satisfaction, we did not pursue its analysis.

The results in Table VI indicate that positive emotions are an importantpredictor of ` positive word of mouth'', but that the students' cognitive

Beta t sig.

Positive emotions 0.369 3.720 0.000Negative emotions 0.152 1.735 0.086Cognitive component 0.263 2.882 0.005

Table VI.Standardized betacoefficients (dependentvariable: word ofmouth)


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assessment of satisfaction with educational attributes is also a significantpredictor. The adjusted R2 = 0.395 and F(3, 100) = 23.402, sig = 0.000. Further,while the t value for negative emotions is not significant at the 95 per centconfidence level, the result suggests that this is worthy of further investigation.

When a further regression was run, using positive word of mouth as thedependent variable against the cognitive component only, the result confirmedthat, in this study, positive emotions are a better predictor of positive word ofmouth than the cognitive element. In particular, for the latter regression, theadjusted R2 = 0.260 and F(1, 104) = 37.855, sig = 0.000.

In general then, if emotions were not included in the scale, and only thecognitive component used to measure satisfaction, a comprehensive illustrationof satisfaction is not gained. Consequently, it is suggested that an emotionalscale needs to be included as part of customer satisfaction measurement.

Implications of the studyThe main theoretical implication of this study is that the emotional componentof satisfaction, which has not been considered in some of the recent customersatisfaction studies, serves as a better predictor of loyalty than the cognitivecomponent. In particular, positive emotions are positively associated withpositive word of mouth and willingness to pay more, and negatively associatedwith switching behaviour. Similarly, negative emotions are negatively relatedto positive word of mouth and willingness to pay more, and positivelyassociated with switching behaviour. However, as this study represents arelatively small sample in one industry, these results require furtherinvestigation and verification.

As there is a significant relationship between customer satisfaction(especially the emotional component) and customer loyalty, and based on theassumption that it is cheaper to retain existing customers than attract newcustomers (see Alford and Sherell, 1996), it appears that managers need to re-emphasise how customers ``feel'' about their experiences of service delivery. Inparticular, they should try to achieve some balance in their pursuit ofsatisfaction information. It seems feasible that, in endeavouring to adoptmeasurement practices that are scientific and rigorous, managers may notprovide sufficient opportunity for comments with an affective base and,consequently, fail to recognise the power and importance of emotions. Anobvious extension of this is that, in retaining or enhancing customer loyalty,

organizations need to explore and, as far as possible, manage the emotionalcomponents.

Future researchIt is suggested that expectations, perceived performance and/or satisfactionlevel shift over time (Patterson et al., 1998; Peterson and Wilson, 1992). In thisstudy, emotions in customer satisfaction were measured at one specific point intime and, hence, the result is only true for the time of completion of thequestionnaire. As the delivery of higher education is an extended service


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encounter, the endurance of loyalty resulting from satisfaction may bechallenged (Oliver, 1999). Future longitudinal studies could explore this issue.

Demographic backgrounds also need to be considered. In particular, it issuggested that different cultural backgrounds may affect one's beliefs and

behaviors (Hofstede, 1994; Winsted, 1997). That is, future research couldcompare the sample being studied based on their demographic backgrounds tosee if the affective component still serves as a better predictor for customerloyalty.

As in other satisfaction studies, there are still some methodological issuesthat need to be addressed. These include the use of bipolar construct questions,and positively skewed responses (Peterson and Wilson, 1992). Finally, there is aneed to develop and refine the emotion scales. This provides scope for muchinteresting work.

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Consumer emotions