ABSTRACT - This paper examines the extent to which heterogeneity in performance perceptions affects customer satisfaction. That is to say, the paper examines if variance between the perceived performance of a set of attributes may contribute to customer satisfaction. Theory on cognitive consistency suggests that a negative association may be at hand, since it is assumed that cognitive balance is a more satisfying state than cognitive imbalance. Empirical data from two different studies indicate that a negative association is indeed at hand. However, when the level of performance is controlled for, heterogeneity contributes only modestly to customer satisfaction.

Citation:

Magnus Soderlund (1998) ,"", in E - European Advances in Consumer Research Volume 3, eds. Basil G. Englis and Anna Olofsson, Provo, UT : Association for Consumer Research, Pages: 293-300.

European Advances in Consumer Research Volume 3, 1998      Pages 293-300

THE EFFECTS OF HETEROGENEITY IN PERCEIVED PERFORMANCE ON CUSTOMER SATISFACTION: DOES "MANAGEMENT OF HETEROGENEITY" MATTER?

Magnus Soderlund, Stockholm School of Economics, Sweden

ABSTRACT -

This paper examines the extent to which heterogeneity in performance perceptions affects customer satisfaction. That is to say, the paper examines if variance between the perceived performance of a set of attributes may contribute to customer satisfaction. Theory on cognitive consistency suggests that a negative association may be at hand, since it is assumed that cognitive balance is a more satisfying state than cognitive imbalance. Empirical data from two different studies indicate that a negative association is indeed at hand. However, when the level of performance is controlled for, heterogeneity contributes only modestly to customer satisfaction.

INTRODUCTION

Customer satisfaction has become the objective of many firms. Usually, the strive for customer satisfaction is based on assumptions that customer satisfaction matters in terms of customer behavior. For instance, a satisfied customer is assumed to be more loyal and to engage in positive word-of-mouth to a larger extent than a dissatisfied customer. Moreover, the behavior of a satisfied customer is assumed to be more profitable than the behavior of a dissatisfied customer (Anderson et al 1994, Fornell 1992, Jones & Sasser 1995, Reichheld & Sasser 1990). Given such assumptions, it is not suprising that many firms, and many marketing researchers, are interested in factors that affect customer satisfaction. This paper explores one potential causal factor which to date has received little attention in empirical research.

The factor is referred to as heterogeneity in performance perceptions. Consider, for example, a customer who has stayed in a hotel. This customer may perceive the performance of all attributes of the ''product’’ similarly (the bed was very good, the bathroom was very good, the breakfast was very good, etc.). If this is the case, a low level of heterogeneity exists in the performance perceptions. However, another customer may perceive the performance of the attributes in a less consistent way (the bed was very good, the bathroom was very bad, the breakfast was acceptable, etc.). If so, with the vocabulary to be used in this paper, a higher level of heterogeneity exists in the performance perceptions than in the first case. The purpose of this paper is to examine the extent to which the level of heterogeneity in perceived attribute performance affects customer satisfaction.

An examination of this issue serves to contribute to our understanding of the extent to which a common managerial practice is meaningful. This practice, ''management of heterogeneity’’, refers to an emphasis on a high level of consistency. It manifests itself in several ways. For instance, the use of logotypes in different contexts (''our logo must not be different when the customer sees it in ads, on packages, on business cards, etc.’’), the dress code of personnel (''the customer should always see us in dark suits’’), and the physical characteristics of units in multi-outlet chains (''every hamburger restaurant in our chain must appear similar’’) are areas in which some firms desire a high level of consistency. The following statement by Jan Carlzon, former CEO of SAS, when he described one important element is SAS’s strategy, is illustrative: ''We shall become one percent better in one hundred details, not one hundred percent better in one detail’’ (Carlzon 1990, p 49). An example of a scholar who stresses the importance of heterogeneity management is Normann (1992, p 70). He argues that the service firm which considers to include additional parts in an offer, or to increase the level of perceived quality in certain parts, must be absolutely certain that consistency among the parts is at hand. Similarly, Zeithaml et al (1985) suggest that the performance of different employees who interact with the same customer is problematic if it is not characterized by a high level of consistency.

In the literature, however, heterogeneity (sometimes referred to as variability) in the supplier’s activities usually refers to the potential for heterogeneity between purchase occasions or between personnel who perform the same activity with respect to different customers (Kotler 1994). As already indicated, this paper is concerned with heterogeneity in performance among the different parts of an offer which the same customer is exposed to.

THEORETICAL FRAMEWORK

Heterogeneity and its potential as a causal factor

Heterogeneity is conceived of here as a bipolar dimension bounded at the lower end by a ''low’’ level of heterogeneity and at the upper end by a ''high’’ level of heterogeneity. To date, empirical research on the effects of heterogeneity (sometimes referred to as diversity, sometimes as variation) has mainly been carried out at the work group level. At this level, the heterogeneity of groups (for instance in terms of age, sex and tenure in top management teams) is operationalized as an aggregate group-level index, and the extent to which heterogeneity is related to other variables is assessed (cf. Eisenhardt & Schoonhoven 1990, Murray 1989, Wiersiema & antel 1992).

Here, however, we are concerned with heterogeneity in performance perceptions at the individual level. In other words, rather than comparing heterogeneity between groups, we are comparing heterogeneity between individuals. At the individual level, then, each individual’s level of heterogeneity is determined by the differences in perceptions of a set of attributes. As already indicated in the introduction, an individual who views the performance of each product attribute similarly (e.g. all attributes are perceived as performing ''good’’) has a lower degree of heterogeneity in his/her perceptions than an individual who views the performance of some attributes as good while others are perceived as performing bad.

Both these levels of analysis share one fundamental assumption: heterogeneity per se is seen as a potential causal factor. Since this is a crucial point of departure in the present study, it needs some elaboration. As a reference point, it is useful to consider the common approach to an examination of associations between variables. Usually, then, the focus is on how the levels of variables are associated. For instance, a typical study of the antecedents of customer satisfaction is based on a collection of data regarding a) the levels of perceived performance of a set of product attributes, and b) the level of customer satisfaction. Given that the level of perceived performance of one particular attribute is more associated with the level of customer satisfaction than other attributes, a common conclusion is that this particular attribute is more strongly causally related to customer satisfaction than the other attributes. Similar approaches to causal reasoning found in many different fields of research. However, a different approach is to examine antecedents in terms of variation rather than levels. Then, variation (captured e.g. by the standard deviation, the coefficient of variation, or measures of distances) is used in assessments of association with a dependent variable. This approach has been used in studies of the effects of heterogeneity in work groups and in dyads of interacting individuals (cf. Eisenhardt & Schoonhoven 1990, Murray 1989, Tsui & O’Reilly 1989, Wiersiema & Bantel 1992).

In the following, we review the traditional (i.e. level-based) framework of antecedents of customer satisfaction. In the next step, we turn to some arguments which point to the fact that a variation-based framework may provide additional insights into the antecedents of customer satisfaction.

Antecedents of customer satisfactionBa level-based framework

The most common approach to an assessment of antecedents of customer satisfaction is based on an assumption that the object of customer satisfaction (a product, a store, a vendor, a supplier, etc.) can be viewed as composed of several dimensions or attributes. For instance, with respect to stores, it is assumed that the customer is faced with several attributes which may affect the customer’s store satisfaction. Examples of store attributes are assortment width, opening hours, inventory levels and the service provided by store personnel (cf. e.g. Lindqvist 1974-1975, MSgi & Julander 1994, Steenkamp & Wedel 1991).

Moreover, it is assumed that the extent to which the attributes are associated with customer satisfaction reflect the importance of each attribute as a causal factor. It should also be noted that consensus does not exist with respect to how the attribute perceptions should be conceptualized. Some argue that it is the gap between a) the performance level expected prior to a purchase and b) the perceived level of performance after a purchase which produces customer satisfaction. However, others argue that the perceived performance after the purchase per se is a better predictor of customer satisfaction. In any case, no matter how the attributes are conceptualized, many studies have shown that the perceived levels of performance of a set of attributes are associated with the level of customer satisfaction (Adaleeb & Basu 1994, Babakus & Boller 1990, Brown et al 1993, Cronin & Taylor 1992, Leuthesser & Kohli 1995).

We do not question the results of these level-based analyses. However, given a view of variation as a potential causal factor, we argue that it is fruitful to examine the extent to which a variation-based approach adds to the explanatory power of the traditional level-based analysis. In the next step, we examine some arguments which suggest that variation per se may be a causal factor in a customer satisfaction context.

Heterogeneity in performance perceptions and customer satisfaction

Basically, the question which this section addresses is as follows: are there any reasons to believe that the degree of heterogeneity in an individual’s perceptions of the performance of a set of attributes is likely to affect customer satisfaction?

The point of departure is the assumption that cognitive balance is a more satisfying state than cognitive imbalance. This assumption is found in several schools of thought. For instance, in a review of theory on consumer behavior, Wilkie (1986, p 312-313) notes that consumers wish to view the world in a consistent manner. That is to say, as consumers, we strive for ''cognitive consistency’’. He also argues that any inconsistencies among our beliefs create psychological tension. The assumption that individuals seek consistency among attitude components is also discussed by Williams (1982, p 152-154). Cognitive dissonance theory is an example of a specific theory in which the consistency assumption is made. For instance, in cognitive dissonance theory, it is contended that inconsistencies or discrepancies among the cognitions (opinions, beliefs, and knowledge) an individual holds create tension and psychological discomfort (ibid., p 156). Similarly, Gestalt theory holds that cognitive equilibrium (e.g. when the world is perceived as a coherent whole) is more satisfying than cognitive disequilibrium (cf. Hergenhahn 1989, p 252).

Attribution theory can also be used in order to deduce arguments about consistency and satisfaction. For instance, Kelley (1973) argues that the ability to explain things is a source of satisfaction. In other words, understanding in causal terms per se may produce satisfaction. Presumably, then, the higher the level of confidence in the explanations (i.e., in the attributions), the higher the level of satisfaction. And one of the factors affecting confidence, according to Kelley, is consistency. That is to say, if I am exposed to a stimulus over time (e.g. supplier behavior, as I am interacting with the supplier), and if I make the same causal attribution every time (e.g. ''the performance of the supplier is good, because this particular supplier cares about me’’), then my confidence in my attributions increases. This, in turn, is likely to increase my satisfaction with my own attempts to understand what is going on. From this follows that a situation in which there is a variation in the performance of the different activities of a supplier is likely to create different attributions, and thus less confidence (and less satisfaction).

It would be expected, then, that a customer who perceives a high level of variation in the performance of product attributes will be subject to cognitive tension to a higher degree than a customer who does not perceive any variation in the attributes’ performance. However, the extent to which the level of variation affects customer satisfaction is not clear from existing research. Moreover, if an association does exist, it seems likely that it is negative. That is to say, we expect a negative association between heterogeneity in perceptions of attributes’ performance and customer satisfaction.

RESEARCH METHOD

The genera design

Data from two different samples of customers were used. In both samples, data were collected with a questionnaire. Moreover, in both samples, data were collected on a) the perceived performance of a set of attributes, and b) customer satisfaction. Multi-item scales were used in both data collections. Cronbach’s alpha was computed in order to assess the reliability of the scales (cf. Peter 1979).

Study 1

This study includes the customers of one particular firm in the transportation business. The core product of this supplier is the physical distribution of goods, and its customers are firms. 130 completed questionnaires were returned. The respondents were asked to assess the performance of the supplier in three dimensions: order handling personnel, drivers and price. The respondents were also asked to assess the supplier in terms of overall customer satisfaction. These variables were measured with multi-item scales, and the specific items in the scales are presented in Appendix 1. Each item was scored on a 10-point scale (1=Do not agree at all, 10=Agree completely). All scales have reliability coefficients exceeding the 0.7 limit; thus a certain level of reliability is at hand in the measurements.

Study 2

In this study, the ''customers’’ participated in a one-year business education program for middle managers. The supplier in this case is an academic institution, and all participants are employed by the same organization. The questionnaire was mailed to the participants after completing the program. 60 completed questionnaires were returned from the population of 150 individuals who have completed the program. The respondents were asked to assess the performance of the program in terms of the extent to which it transferred knowledge to the participants in six dimensions: macro understanding, presentation skills, decision-making, micro understanding, English skills, and cooperation ability. The respondents were also asked to assess the program in terms of overall customer satisfaction. These variables were measured with multi-item scales, and the specific items in the scales are presented in Appendix 2. Each item was scored on a 7-point scale (1=Do not agree at all, 7=Agree completely). All scales have reliability coefficients exceeding the 0.7 limit.

ANALYSIS AND RESULTS

An examination of the effects of heterogeneity

In order to assess the impact of heterogeneity on another variable, several heterogeneity indicators have been proposed (Allison 1978, Shaw 1981 p 239). Here, two possible indicators have been selected.

The first indicator is the standard deviation. It is recommended by Allison (1978) when an interval scale is at hand in the measurements. It was used as an heterogeneity indicator by Eisenhardt & Schoonhoven (1990) who studied heterogeneity in industry experience in management teams. It was also used as a heterogeneity indicator by Bourgeois (1985) who examined the effects of heterogeneity in perceptions of environmental uncertainty on economic performance. Here, the standard deviation was computed for each respondent with respect to the respondent’s scores on the attributes. In order to illustrate how this indicator behaves in the data at hand, let us use an example from Study 1. In this case, the individual with the lowest standard deviation, 0.12, has the following performance scores on the three attributes: 9.8, 10 and 10. On the other hand, the individual with the highest standard deviation, 3.82, scored as follows onthe attributes: 5.6, 2.4 and 10. Hence, the higher the standard deviation of the performance scores, the higher the level of heterogeneity.

However, according to Allison (1978), if it is reasonable to assume that there is a non-negative ratio scale underlying the interval scale observations, then the coefficient of variation is a preferred indicator. The coefficient of variation is the standard deviation divided by the mean. This measure has been used by several researchers to assess heterogeneity in groups with regard to ratio scaled variables such as age and tenure (Michel & Hambrick 1992, Murray 1989). In the case at hand here, which deals with performance perceptions, it can be argued that a non-negative ratio scale is indeed underlying the measurements. For instance, it is possible to think about the performance of a particular attribute as ''zero’’, i.e. the performance is very low. Therefore, in addition to the standard deviation, we use the coefficient of variation as a second indicator of heterogeneity. For each individual, then, the coefficient of variation was computed with regard to the individual’s scores on the attributes’ performance. In order to illustrate which values this indicator takes on, the individual in Study 1 with the lowest coefficient of variation, 0.1, has the following performance scores on the three attributes (order handling personnel, drivers, and price): 9.8, 10 and 10. On the other hand, the individual with the highest coefficient of variation, 0.64, scored as follows on the attributes: 5.6, 2.4 and 10.

It should be noted that the two indicators of heterogeneity are positively associated; in Study 1 the zero-order correlation is 0.89, and in Study 2 it is 0.91.

Given these two heterogeneity indicators, we used five different models to analyze the effects of heterogeneity on customer satisfaction. In Model 1, the association between heterogeneity and customer satisfaction was assessed by using the standard deviation as a heterogeneity indicator. Similarly, Model 2 assesses the association between heterogeneity and customer satisfaction with the coefficient of variation as a heterogeneity indicator.

Model 3, on the other hand, is a traditional level-based analysis in which only the performance scores on the attributes are used as independent variables.

Model 4 assesses the contribution of heterogeneity in terms of the standard deviation when the performance scores are controlled for. Similarly, Model 5 assesses the contribution of heterogeneity in terms of the coefficient of variation when the performance scores are controlled for. That is to say, in Models 4 and 5, one heterogeneity indicator and the performance scores are included as independent variables. Regression analysis on customer satisfaction was used to highlight the five models. The results are presented in Table 1 and Table 2.

Firstly, with respect to the results obtained with Model 1 and 2, it can be concluded that heterogeneity per se seems to affect customer satisfaction. In both studies, and independent of the heterogeneity indicator (the standard deviation or the coefficient of variation), the sign of the heterogeneity coefficient is negative and significant. Thus, the more heterogeneity in the perceived performance of the attributes, the less the customer satisfaction. This result provides some support for the assumption that individuals who perceive an offer in a consistent manner feel more satisfied than those with less consistency in the perceptions. However, although these models are significant, it should be noted that the R² is modest. That is to say, heterogeneity per se explains only a small part of the variation in customer satisfaction. It can also be noted that the R² is affected by the particular heterogeneity indicator at hand. The difference between the two heterogeneity indicators is particularly salient in Study 1.

Turning to Model 3, a traditional level-based analysis, we see that the R² is substantially higher in both studies compared with Model 1 and 2. The results with respect to Model 3 do also rveal that all attributes of an offer are not equally important as antecedents of customer satisfaction. In Study 1, drivers is the most important attribute, whereas the ability of the program to facilitate decision-making is the most important attribute in Study 2. Interestingly, in Study 2, the program’s ability to convey micro understanding is negatively and significantly associated with customer satisfaction. Presumably, one reason is that the participants have a pragmatic view of the benefits of the program, while the academic institution in charge of the program is influenced by academic/scientific ideals in the design of the program.

Finally, with regard to Model 4 and 5, the results indicate that only little additional explanatory power is obtained by including heterogeneity as an independent variable. There is indeed an increase in R² compared with Model 3, but the increase is very modest. Moreover, the heterogeneity coefficient is significant only in the case of Model 4 in Study 2 (that is, when the standard deviation is used as a heterogeneity indicator). This suggests that heterogeneity in performance perceptions offers only little additional understanding of customer satisfaction compared to the traditional level-based analysis.

Some comments regarding the results

A common result in studies of customer satisfaction is that customer satisfaction scores, as well as performance ratings, are positively skewed (Fornell 1992, Peterson & Wilson 1992). This tendency does also exist in the two studies at hand here. This means that the mean score of all variables is ''high’’ rather than ''low’’. For instance, the mean of the satisfaction variable in Study 2, in which a 7-point scale was used, is 6.404. And this means that those individuals who have rated the performance of the attributes ''low’’ have responded with scores further away from the mean than those who have rated performance ''high’’. Consequently, one would suspect that the level of heterogeneity is higher among individuals with low rather than high performance scores. This is indeed the case hereBin both studies. More specifically, in both samples, a discriminant analysis reveals that the individuals with ''high’’ heterogeneity have scored significantly lower on the attributes’ performance than individuals with ''low’’ heterogeneity.

TABLE 1

REGRESSION ON CUSTOMER SATISFACTION IN STUDY 1

TABLE 2

REGRESSION ON CUSTOMER SATISFACTION IN STUDY 2

In order to further examine the effects of heterogeneity in the context of the level of performance, the following operation was made (in both studies). Firstly, the total performance score for all attributes (i.e. the sum of the performance score for each attribute) was computed for each individual. Secondly, individuals with a total performance score below the mean were allocated to a ''low’’ level group, whereas individuals with a total performance score above the mean were allocated to a ''high’’ level group. Thirdly, Model 2 and 4 were estimated againBbut this time at the subgroup level. The resulting regression coefficients for heterogeneity (SD) are presented in Table 3.

Table 3 indicates that the total performance score serves as a moderating variable with respect to the influence of heterogeneity on customer satisfaction. However, the pattern of influence is not the same in the two studies. If anything, it suggests that the influence of heterogeneity may take place either in high performance and low performance cases. Moreover, Table 3 indicates thatBunder certain conditionsBheterogeneity may be positively associated with customer satisfaction. One explanation, which the data at hand here are not able to support, is that the expected negative influence of heterogeneity on satisfaction is reduced by a creative response by the supplier to a failure with respect to one or several attributes. For instance, consider a case in which the performance of one particular attribute is rated ''low’’, at the same time as the performance of the other attributes are rated ''high’’ (which increasesthe heteorogeneity compared with when the performance of all attributes is rated ''high’’). In this case, a creative response (a ''recovery strategy’’) by the supplier with respect to the ''low’’ performance attribute may be able to create satisfaction despite the fact that heterogeneity exists (cf. Hart et al 1990 and Kelley et al 1993 for a discussion of ''recovery strategies’’). Thus, the satisfaction of the customer in this case, who may very well perceive a high level of heterogeneity, would be subject to the influence of the supplier’s response strategyBan intermediate variable not measured in this study.

TABLE 3

REGRESSION COEFFICIENTS FOR HETEROGENEITY (SD)

In any case, with regard to the results in Table 3, it should be noted that the effects of heterogeneity are still modest when the scores of the attributes are controlled for (i.e. Model 4). That is to say, the attributes’ scores continue to dominate the variation in customer satisfaction under conditions of different performance levels.

DISCUSSION

The main finding in this paper is that heterogeneity in the perceptions of the performance of attributes offers little additional understanding of the antecedents of customer satisfaction. A pattern does emerge, in the sense that a negative association seems to be at hand between heterogeneity and customer satisfaction (which might be expected, given theory on cognitive consistency), but heterogeneity contributes only modestly to customer satisfaction when performance levels are controlled for. However, before the heterogeneity issue is disregarded in future studies of customer satisfaction, some limitations of this study should be stressed.

Firstly, the means of collecting heterogeneity data was indirect, in the sense that heterogeneity scores were created from the respondents’ answers to performance level items. It is possible that other results would have occurred if heterogeneity data had been collected in a more direct way. For instance, respondents can be asked to assess the difference in performance of each pair of attributes. Another option is to ask questions regarding heterogeneity after the rating of the attributes’ performance (e.g. ''Given the attributes which you just have rated, to what extent do you feel that the performance is equally good in terms of all attributes?’’, etc.).

Secondly, since the two different heterogeneity indicators (the standard deviation and the coefficient of variation) give slightly different results, it might be fruitful to explore other measures of heterogeneity. For example, distance measures may be developed (cf. Wagner et al 1984).

Thirdly, the attributes in the two studies reported here, particularly those in Study 1, are somewhat supplier-driven. That is to say, they may represent the suppliers’ views of what the attributes of an offer is composed of to a larger extent than they represent the customers’ view. In other words, future studies should examine heterogeneity in performance when attributes are defined more closely in terms of customers’ needs and benefits sought. This may reveal that heterogeneity is more important than the present study suggests. More specifically, a means-end based model in which e.g. desired consequences in use situations (rather than product attributes) serve as the basis for performance judgements may generate more interesting results (cf. Woodruff 1997).

Fourthly, in this study data on the context of the respondents’ choices (and satisfaction) were not collected. Yet it seems likely that the relationship between heterogeneity and satisfaction may be moderated by the context. Presumably, customer involvement is a factor which may moderate the heterogeneity-satisfaction link. For instance, according to cognitive dissonance theory, post-purchase dissonance is assumed to occur when the customer has been exposed to discrepant information, but the extnt to which discrepant information will arouse dissonance depends upon the level of involvement (Oshikawa 1969). Therefore, it seems likely that similar conditions may be at hand as far as the heterogeneity-satisfaction link is concerned. That is to say, a situation in which the level of involvement is low (e.g. a low level of perceived risk and a low level of perceived need to comply to reference group norms) may result in a high tolerance for heterogeneity. In this case, then, heterogeneity is not likely to have a major effect on customer satisfaction. On the other hand, a high-involvement situation is likely to be more demanding from an information-processing point of view. In this situation a high level of heterogeneity may be less acceptable and thus more strongly related to customer satisfaction.

These limitations should be addressed before the last word is said about the effects of heterogeneity in performance perceptions on customer satisfaction. However, should the results turn out to be similar to those obtained here, one may conclude that efforts devoted to keeping a high level of consistency in a firm’s offer may not pay off in customer satisfaction. It means that ''management of heterogeneity’’ (i.e. a strong emphasis on consistency) in the area of the performance of product attributes is not likely to create substantial benefits for the firm.

Finally, a finding which suggests that ''management of heterogeneity’’ is less likely to produce more satisfied customers may satisfy those who view consistency as boring and potentially dehumanizing (cf. Max Weber’s argument about ''the iron cage of rationality’’). For instance, Ritzer (1996) argues that a predictable, ''McDonaldized’’ world may turn consumption, work and management into a series of mind-numbing routines.

APPENDIX 1

MEASUREMENTS IN STUDY 1

CONCLUSIONS

This paper has examined the extent to which heterogeneity in performance perceptions affects customer satisfaction. That is to say, we have examined if variance between the performance of a set of attributes may contribute to customer satisfaction. Theory on cognitive consistency suggests that a negative association may be at hand, since it is assumed that cognitive balance is a more satisfying state than cognitive imbalance. Empirical data from two different studies indicate that a negative association is indeed at hand. However, when the level of performance is controlled for, heterogeneity contributes only modestly to customer satisfaction.

APPENDIX 2

MEASUREMENTS IN STUDY 2

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