常用CS测量的质量及适用性实证研究报告

作 者:北京迪纳市场研究院  阅读次数:6306次  发布日期:2004-04-14


AN EMPIRICAL STUDY ON THE QUALITY AND

CONTEXT-SPECIFIC APPLICABILITY OF COMMONLY USED

CUSTOMER SATISFACTION MEASURES

 

Meng Chung Lee and Jochen Wirtz

18 May 2000

Meng Chung Lee is a Research Executive with Research International Asia Pte. Ltd.

Jochen Wirtz is Associate Professor with the Department of Marketing, The NUS Business School, National University of Singapore, 17 Law Link, Singapore 117591. Tel: +65-8743656; Fax: +65-779 5941; E-mail: fbawirtz@nus.edu.sg, http//www.nus.edu.sg


AN EMPIRICAL STUDY ON THE QUALITY AND

CONTEXT-SPECIFIC APPLICABILITY OF COMMONLY USED

CUSTOMER SATISFACTION MEASURES

ABSTRACT

Many different measures have been used in customer satisfaction research, but few

studies have examined their quality and context-specific applicability. 

The first objective of this paper is to investigate this issue by assessing the affective and cognitive content of commonly applied satisfaction scales.

This study addressed this issue by examining whether satisfaction with hedonic benefits would be better captured by measures with affective content, while satisfaction with mostly utilitarian benefits would be better captured by measures with cognitive content. Furthermore, nine commonly used satisfaction measures were tested for their cognitive and/or affective content. In contrast to what was hypothesized, all measures were shown to capture both factors, although with slightly differing amounts.

The study also investigated whether the selection of satisfaction measures should be a function of product benefits. Results disproved this notion. Rather, measures which were shown to be of good quality (i.e. have good reliability and show low error variances) were equally applicable for measuring satisfaction, independent of whether the product benefits were hedonic or utilitarian.

INTRODUCTION

In today’s increasingly competitive world of business, satisfying one’s customers has become an essential ingredient for success. Customer satisfaction is an antecedent of customer repurchase intention, customer loyalty and retention, and ultimately the profitability of a firm (Bearden and Teel 1983; Fornell 1992; LaBarbera and Mazursky 1983; Oliver 1980; 1997; Oliver and Swan 1989). Therefore, it is essential for firms to effectively manage customer satisfaction. To be able do this, accurate measurement of satisfaction is needed (Wirtz 2000).

Satisfaction has often been measured using scales anchored in emotive words such as 'pleased', 'delighted' and 'happy'. These measures are generally regarded as affective in content in accordance with the nature of the scale anchors (Hausknecht 1990). However, does this imply that these measures actually capture more of affective rather than cognitive postpurchase evaluation? This has been suggested in the past (Hausknecht 1990), but has yet to be empirically tested. Similarly, if the scale anchors comprise of evaluative statements, do these measures actually provide a yardstick towards cognitive post-purchase evaluation?

Although a myriad of satisfaction measures has been developed, to date, there has been no empirical research on differentiating the use of these measures. Measures that have been shown to have high validity and reliability in one or several contexts, have been implicitly assumed to be equally applicable in other contexts.

It seems possible however, that the selection of satisfaction measures should be dependent on at least three factors: (1) purpose of measurement, (2) product characteristics, and (3) respondent characteristics (Lee and Wirtz, 1997). Stauss and Hentschel (1992) demonstrated empirically that the critical incident technique is superior to attribute-specific satisfaction measures in identifying potential areas for service improvement. The finding demonstrates that one type of measure is superior to another depending on the research objective. Our paper investigates the second issue of whether the selection of appropriate satisfaction measures should be a function of product characteristics. We advanced that the content of measures should be matched to the product benefits. Specifically, it is suggested that affective satisfaction measures would better capture satisfaction with products that have mostly hedonic benefits, while cognitive measures would be better for products with mostly utilitarian benefits. This notion is empirically tested in this paper.

LITERATURE REVIEW

Customer Satisfaction

Various models of satisfaction have been proposed and most of these models are based on cognitive comparison processes, where consumers evaluate their consumption experiences by comparing perceived performance with some standard. The disconfirmation- ofexpectations model, which uses expectation as the comparison standard, is the most widely applied model (e.g., Churchill and Surprenant 1982; Oliver 1997; Swan and Trawick 1981; Tse and Wilton 1988; Westbrook 1980), and performs well in competitive markets with reasonably knowledgeable customers who are able to match their needs and wants with what they expect from the chosen product (Wirtz and Mattila 2000). Expectation is a customer’s frame of reference, which is determined by his/her experience with a product, the observed experience of other consumers, word-of-mouth and advertising effects. In the disconfirmation-of- expectations model, consumers are hypothesised to bring expectations into an exchange encounter and then compare these expectations with perceived performance, resulting in a disconfirmation judgement (Yi 1990). Since this comparison process requires deliberate processing of information, the role of cognition is an important driver of satisfaction in the disconfirmation-of-expectations model (Jayanti 1995).

Although many of the previous conceptualisations of customer satisfaction have been based on cognitive evaluation, the role of affect has increasingly attracted attention in recent years (e.g., Mano and Oliver 1993; Oliver 1997; Westbrook 1987). It has been argued that the traditional cognitive disconfirmation-of-expectations models are inadequate in modelling satisfaction, especially in the context of services (Dube -Rioux 1990; Jayanti 1995; Liljander and Strandvik 1996). Services are experiential in nature, and therefore affect becomes an important determinant of satisfaction (Wirtz and Bateson 1999). In fact, one of the early studies on value, that is, a trade-off between sacrifice and quality, has already incorporated cognitive and affective aspects into a model of value (Hartman 1967; Hartman 1973). Developing on the model by Hartman, Ruyter et. al (1997) viewed satisfaction as the synthesis of cognitive and affective reaction to the value of a service.

Product Benefits

Consumption experience refers to the subjective consciousness of consumers as they interact with goods and services (Oliver and Westbrook 1993). It includes consciously experienced cognitive phenomena such as thoughts, beliefs and goals, and also the perception of sensory, emotive, imaginal and aesthetic responses to the ownership and usage of products (Hirschman and Holbrook 1982). As research on consumption experiences grows, evidence suggests that consumers purchase goods and services for a combination of two types of benefits: hedonic and utilitarian.

Hedonic Benefits Hedonic benefits of a product are obtained by customers through the intrinsically pleasing properties of a product, such as the pleasure provided by listening to a musical recording. Hedonic benefits are associated with the sensory and experiential attributes of the product (Batra and Athola 1990). Therefore, consumers usually purchase products with mostly hedonic benefits for the sake of pleasure and enjoyment. Mano and Oliver (1993) showed that the evaluation of hedonic benefits is mostly affective. Similarly, Dube-Rioux (1990) advanced that affect would become a key driver of satisfaction with products where the experiential aspect of consumption is important. Given that consumers buy certain products primarily for hedonic benefits and affect during consumption of such products is likely to be the key driver of satisfaction, it seems logical that satisfaction measures high in affective context should perform better than measures low in affective content. Taken together, these studies suggest that for products with mostly hedonic benefits, satisfaction measures high in affective content capture satisfaction better than measures low in affective content. This notion is examined in this paper.

Utilitarian Benefits. Utilitarian benefits of a product are associated with the more instrumental and functional attributes of the product (Batra and Athola 1990). An example would be the ability of an air-conditioner to cool a room. When consumers evaluate their satisfaction with the utilitarian benefits of a product, consumers primarily evaluate whether a product fulfils their instrumental or functional expectations. Mano and Oliver (1993) showed empirically that the evaluation of the utilitarian benefits of a product is predominantly cognitive. Therefore, it seems intuitive that for products with mostly utilitarian benefits, satisfaction measures high in cognitive content capture satisfaction better than measures low in cognitive content. Again, this notion is examined here.

METHODOLOGY

A survey method and a self-administered questionnaire were used, allowing for the testing of 'real life' satisfaction in different research contexts. A pilot test with sixty subjects and across ten different products was conducted using Batra and Athola's (1990) hedonic and utilitarian measures. Two products were identified that provide either mostly hedonic or mostly utilitarian benefits. They were ice-cream restaurant services for hedonic benefits, and ATM services for utilitarian benefits. Both products were selected as research context for this study.

A convenience sample of 260 university students from a local university was surveyed. 3 questionnaires were incomplete and were therefore dropped, leading to a final sample size of 257. To disguise the objective of this study and to reduce the possibility of demand effects, this study was introduced to subjects as a study on consumer satisfaction with ATM services and ice-cream restaurants. Subjects were told to evaluate their most recent experience with both products. The order of the products, and the order of the satisfaction measures were randomised to minimise potential order effects and to allow for their testing. In total, 12 versions of questionnaires with different orders of products and satisfaction measures were distributed to the subjects randomly.

The hypotheses advanced in this paper require measures that have anchors with different content dimensions, that is, affect and cognition. Therefore, scales with predominantly affective anchors (e.g. the Delighted-Terrible (DT) scale), and scales with mostly cognitive anchors (e.g. the Percentage scale) needed to be included in this study. After reviewing the satisfaction literature, nine satisfaction measures were chosen to be tested (see Table 1). All nine scales are commonly used in applied and/or academic research. Six of the nine measures were single- item overall satisfaction measures, and three of them were multiitem overall satisfaction measures.

Other measures used in this study included Russell’s pleasure and arousal scales, two disconfirmation measures, post purchase behavior scales, and Batra and Athola's (1990) product benefits scale (refer to Table 2 for details).

__________________________

Insert Tables 1 & 2 about here

__________________________

FINDINGS

Investigation into the Content of Satisfaction Measures

A two step method was used to determine the affective and cognitive content of all nine satisfaction measures. First, an exploratory factor analysis was conducted on the six items of the Pleasure scale together with the two one-item Disconfirmation measures. It was expected that two factors would be extracted, each represented by affect and cognition respectively. Specifically, the Pleasure scale should capture the affective dimension, and the Disconfirmation scale, the cognitive dimension of consumption as highlighted in the literature review. Second, a correlation analysis was conducted between the factor scores obtained, and the nine satisfaction measures. This analysis helped to determine the content of the satisfaction measures, that is, whether they are cognitive or affective or both. A similar approach has been used in the past by Westbrook (1983), when he examined the correspondence of consumers’ emotional product usage experiences to consumer satisfaction measures, and by Williams (1988), who examined the shared variance between job satisfaction measures and two factors representing affect and cognition.

Both the samples (i.e., the ice-cream restaurant and the ATM service) were combined for this analysis. An exploratory factor analysis was first conducted on the six indicators of Pleasure (Pleas1, Pleas2, Pleas3, Pleas4, Pleas5 and Pleas6), and the two indicators of Disconfirmation (Disc1 and Disc2) using Direct Oblimin Rotated Component Analysis. This method was used, as it was assumed that the two constructs Pleasure and Disconfirmation would be correlated. The analysis resulted in only two factors with eigenvalues above 1. The two factors explained more than 60% of the variance (37.7% of variance for the first factor, and 22.9% for the second).

Insert Table 3 about here

The second part of the analysis involved examining the correlation of the two factors with each of the satisfaction measures. If measures were highly correlated with the affect factor, then they are affective in content, and likewise for measures that correlated highly with the cognitive factor. Table 4 shows the correlations of the nine satisfaction measures with the affective and cognitive factors.

Insert Table 4 about here

The data showed that the affectively anchored Happy measures had the highest correlation with the affective factor, and that the cognitively anchored Percentage, 7pt Scale, Likert1 and Likert2 scales captured more of the cognitive factor than the affective factor.

Contrary to expectations, it was also found that the affect anchored Pleased and DT measures had roughly the same amount of cognitive and affective content. As for the multiitem scales, both SD1 and SD2 had high correlation with both cognitive and affective factors, with SD1 showing the highest correlation, together with the Happy scale, of all the satisfaction measures, with the affective factor. Contrary to what was expected, it was found that most of the measures examined in this study captured both cognition and affect. 

Overall, it was somewhat surprising to find that all measures were relatively highly correlated to both cognition and affective factors. The notion that certain measures capture predominantly affect, while others capture predominantly cognition in the customer satisfaction process, had to be rejected.

Model Development

To test the two proposed hypotheses, structural equation modelling (SEM) was used. For each product, a separate model was constructed using AMOS. The two conceptual models for the ice-cream restaurant services and ATM services were assessed using maximum likelihood estimation.

Westbrook (1987) first showed that affect contributed directly and independently to satisfaction beyond the cognitive disconfirmation-of-expectations construct. Since then, many studies have shown that affect is a significant predictor of satisfaction judgements (Mano and Oliver 1993; Oliver 1997; Wirtz and Bateson 1999). Therefore, we modeled affect as a direct antecedent of satisfaction in our study.

In this paper, the model also includes satisfaction as an antecedent to post-purchase behavior. Post-purchase behavior includes repeat purchase, loyalty, and word-of-mouth. Word-of-mouth is defined as the extent to which a customer informs friends, relatives and colleagues about an event that has created a certain level of satisfaction. Loyalty is the extent to which the customer intends to purchase the same product again. For reasons of parsimony, five indicators of the pleasure construct were summed to form Pleas, a summated scale.

For the ATM model, the two indicators Recommend1 and Recommend2 of the Post Purchase Behavior construct had high error variances (> 2) and low item reliabilities (< 0.110). In retrospect, this seems intuitively logical as customers would rarely recommend the use of a particular ATM to their friends. Therefore, these two measures were dropped from the subsequent ATM model, and only the repeat usage scales were retained. 

Confirmatory factor analysis was used to examine the reliability and validity of the measures used. Item reliability was used to examine the measurement error in each indicator, whereas scale reliability was used to examine the commonality of all items measuring the same construct. All items were shown to have high reliability.

Discriminant validity for the construct pairs were examined using Fornell and

Larcker’s (1981) test of average trait variance extracted. All construct pairs for both

measurement models demonstrated that the average variance extracted for any two constructs:

i.e., Pleasure and Satisfaction, Disconfirmation, and Post-Purchase Behavior, exceeded the

square of the structural link between any two constructs. Therefore, it could be concluded that

the constructs had discriminant validity.

The results for both models also showed that the item reliabilities for all scales were well above 0.50 for all items. Hence, it could be concluded that all the items of a particular construct measured the same construct. The data also showed that the variances extracted for the constructs were good as they exceeded 60% (Fornell and Larcker 1981). This implied that all the latent constructs in both models were adequately captured by their measurement items.

Figures 1 and 2 show the structural models for the ice-cream restaurant and the ATM services. Both models demonstrated GFI and adjusted GFI (AGFI) indices close to 1, and chisquare values with a statistical significance level above .05. For the ice-cream restaurant, GFI=0.95, AFGI=0.93, and c2 =117.30 (df=64, p=0.07). For the ATM service, GFI=0.96, AFGI=0.93, and c2 =77.9 (df=60, p=0.06). This indicated that the models fit the data well.

Insert Figures 1 & 2

Examination of Satisfaction Measures in the Context of Hedonic Products

The standardised estimates for the various paths and the associated t-values for the icecream restaurant model are provided in Figure 2. All the parameters were found to be statistically significant at p<0.05. Disconfirmation and Pleasure had a significant positive effect on Satisfaction (standardised effects of 0.48, t=8.61 and standardised effect of 0.66, t=12.15, respectively). The effect of Pleasure on Satisfaction was stronger than the effect of Disconfirmation on Satisfaction. This could be attributed to the nature of the product, which contained mostly hedonic benefits, and therefore, the feelings evoked by the service experience can be expected to contribute more significantly towards satisfaction than cognition-based evaluation. It was also observed that the covariance between Pleasure and Disconfirmation was statistically significant at the 0.05 level. This confirms that Pleasure and Disconfirmation co-vary with each other in the formation of Satisfaction, which means that there is some interaction between the two constructs, and this is consistent with past research (e.g., Wirtz and Bateson 1999).

An examination of the error variances and factor loadings of the measures for Pleasure, Disconfirmation and Post-Purchase Behavior (Table 5) revealed that most of the measures for the three constructs were good, with high factor loadings and medium to low error variances.

Insert Table 5 about here

The satisfaction measures are examined in Table 6. It can be seen that the error variances of multi-item satisfaction measures (SD1, SD2, Likert 2) were lower than those of single-item measures. All three multi-item measures also had higher item reliabilities and factor loadings than any of the single-item measures. This indicates that multi-item measures are better in capturing satisfaction than single-item measures, a view voiced by many researchers (e.g., Bearden and Teel 1983; Churchill and Surprenant 1982; Yi 1990). Two single-item measures, Percentage and Delighted-Terrible (DT), emerged as relatively good measures (as compared to the other single-item measures). They had the lowest error variances among all single-item measures. They also had high item reliabilities and high factor loadings, with Percentage scoring higher than DT on both. Likert1 performed the worst among all the measures, that is, it had the lowest item reliability and factor loadings, and the highest error variances.

Insert Table 6 about here

Table 6 provides a comparison between the affective content of each satisfaction measure and its factor loading. This facilitates the examination of the notion that for products with mostly hedonic benefits, satisfaction measures high in affective content capture more of satisfaction than measures low in affective content.

Happy and SD1 measures were those with the highest affective content (as shown in Table 6), while Percentage, Likert1 and Likert2 had low affective content. SD2 had the highest factor loading (0.96) among all the satisfaction measures, but only moderate affective content compared to some other satisfaction measures (such as Happy and SD1). Likert2 had low affective content in this study, but yet had a high factor loading (0.90), and the factor loading for Happy (0.85) was less than that of Percentage (0.90), even though Happy had a higher affective content than Percentage. 

The above results reject the notion that for products with mostly hedonic benefits, satisfaction measures high in affective content capture more of satisfaction than satisfaction measures low in affective content.

Examination of Satisfaction Measures in the Context of Utilitarian Products

For the ATM service model, the standardised estimates for the various model paths and the associated t-values are provided in Figure 3. All the parameters were found to be statistically significant at the 0.05 level. Disconfirmation and Pleasure had significant positive effects on Satisfaction (standardised effect of 0.54, t=8.06 and 0.52, t=7.70, respectively). The relative impact of Disconfirmation on Satisfaction was higher in the ice-cream restaurant model. That is, in the ice-cream restaurant model, Pleasure was the stronger driver of satisfaction, whereas in the ATM model, both Disconfirmation and Satisfaction were approximately equally strong. This finding may be attributed to the nature of the ATM service, which provides mostly utilitarian benefits. Therefore, its evaluation should be strongly influenced cognition. However, services are experiential in nature, and therefore affect should even in an utilitarian still be an important driver of satisfaction, as a past study in an online banking context has shown (Wirtz and Bateson 1999).

An examination of the error variances and factor loadings of the Pleasure, Disconfirmation and Post-Purchase Behavior measures (Table 7) revealed that all measures were acceptable with high factor loadings and medium to low error variances.

Insert Table 7 about here

Table 8 shows that the error variances of multi-item satisfaction measures were lower than those of single-item measures. These measures also had higher item reliabilities and factor loadings than almost all of the single-item measures. This replicated the findings of the ice-cream restaurant study. Similar to the results in the ice-cream restaurant study, SD2 emerged as the best satisfaction measure with the highest item reliability and factor loading, and the lowest error variance. Also similar to the results of the ice-cream restaurant study, Percentage and DT scales had the lowest error variances among the single-item measures.

Insert Table 8 about here

Table 8 provides a comparison between the cognitive content of each satisfaction measure and its factor loading. This serves to examine the notion that for products with mostly utilitarian benefits, satisfaction measures high in cognitive content should capture more of satisfaction, than satisfaction measures low in cognitive content.

All the satisfaction measures were either moderately or highly correlated with the cognitive factor (see Table 8). SD2 was the measure that had the highest correlation with the cognitive factor (0.57) followed by Percentage (0.54). An examination of the results showed that most satisfaction measures, which had high correlations with the cognitive factor, also had high loadings of the Satisfaction construct. These include the SD2, Percentage, SD1 and 7pt Scales. Most satisfaction measures, which had low correlations with the cognitive factor, also had low factor loadings of the Satisfaction construct. These include Pleased, Happy and DT.

However, the findings do not conclusively show that satisfaction measures high in cognitive content capture more of satisfaction, than measures that are low in cognitive content for two reasons. First, the Likert2 scale had the second lowest loadings (0.76) on Satisfaction although its correlation with the cognitive factor was the third highest among all the satisfaction measures. This finding contradicts this notion.

Second, a comparison of both structural models revealed that two measures had consistently low error variances (< 0.30), high item reliabilities (> 0.70), and high factor loadings (> 0.83). They were the SD1 and SD2 measures. In our study, both measures loaded highly on the cognitive factor. However, SD1 had higher correlation with the affective factor than SD2. Not surprisingly both measures were multi-item measures. Among the single-item measures, Percentage emerged as the best measure in both studies with the lowest error variances (< 0.40) and the highest factor loadings. Again, these findings rejected the notion that different measures perform better for measuring satisfaction with different types of product benefits.

Rather, our findings imply that good measures, i.e. measures with high reliability and factor loadings together with low error variances, perform well irrespective of whether they are used in a context of mostly hedonic or utilitarian benefits. Therefore, the notion that product benefits and measure content should be matched had to be rejected.

DISCUSSION

All nine satisfaction measures tested in the study capture both cognitive and affective content. These results are consistent with work in the job satisfaction literature (Brief and Robertson 1989; Williams 1988). Some measures in this study captured equal amounts of both cognitive and affective factors. These results were not surprising because cognitive consistency theories and their supporting evidence have established the inter-relationship between cognition and affect (Organ and Near 1985: 246). 

The results of this study also show that most of the satisfaction measures captured more of the cognitive than the affective factor. This may be due to the type of measures used in this study. All the measures tested were verbal measures, which might not adequately capture affective dimensions (Zajonc 1980). Zajonc (1980) proposed that the communication of affect follows more easily non-verbal than verbal channels, and that the processing of affect is more similar to the acquisition and retention of motor skills than that of word lists. Hence, word lists may not be that effective in capturing affective dimensions.

Furthermore, past studies have shown that satisfaction measures of global well-being reflect primarily cognitive dimensions (e.g., Campbell 1976, Andrews and Withey 1976; McKennell 1978). As some of these measures have been adopted in customer satisfaction research, our results of this study are not surprising. It seems that the wording and format of these measures typically entail a conscious evaluation of one's satisfaction.Furthermore, most of the measures used provide a means of anchoring respondent's judgements by some kind of comparative or relativistic standard, which again encourages more cognitive processing.

It was found that measures which were shown to be of good quality were equally applicable for measuring satisfaction, independent of whether the product benefits were hedonic or utilitarian. In particular, all multi-item measures tested performed best for both products having higher satisfaction loadings and lower error variances than all single-item measures used in this study.

The content of satisfaction measures (whether high/low in affective content or high/low in cognitive content) did not seem to influence their satisfaction loadings for different product benefits. Perhaps, as suggested by Crooker and Near (1998), cognitive and affective measures might not be distinct from each other in the studies of subjective wellbeing. As such, it seems more important for satisfaction measures to have good psychometric properties (i.e. to have good reliability, convergent and discriminant validity, and show low measurement error), than to be either anchored in affective or cognitive scales. Taken together, the findings reject the notion that the selection of appropriate satisfaction measures should be a function of product benefits.

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TABLE 1

SATISFACTION MEASURES EXAMINED IN THIS STUDY

No.  Symbol  Operationalisation  Source

1

7pt Scale

One Item 7-Point Very Satisfied-Very Dissatisfied Bipolar Scale

Oliver and Bearden (1983)

 

Percentage 

 

One Item 11-Point Not At All Satisfied-Completely Satisfied Scale

Oliver and Bearden (1983)

 

Likert1 

 

One Item 7-Point Likert Type Scale Strongly Disagree-Strongly Agree Scale

Westbrook and Oliver (1981)

 

Pleased 

 

One Item 7-Point Pleased-Displeased Bipolar Scale

Crosby and Stephens (1987)

5

 

 Happy 

 

One Item 7-Point Extremely Happy-Extremely Unhappy Bipolar Scale

Crosby and Stephens (1987)

 

DT 

 

One Item 7-Point Delighted-Terrible Bipolar Scale

Andrews and Witheys (1976)

7

 

 SD1 

 

Four Items 7-Point Bipolar Scale Satisfied/Dissatisfied Favourable/UnfavourablePleasant/Unpleasant I like it very much/I didn't like it at all

Eroglu and Machleit (1990)

 

SD2 

 

Six Items 7-Point Bipolar Scale Pleased me/Displeased me Contented with/Disgusted with Very satisfied with/Very dissatisfied with Did a good job for me/Did a pooer job for me Wise choice/Poor choice Happy with/Unhappy with

Oliver and Swans (1989)

 

Likert2 

Twelve Items 7-Point Likert Type Scale This is one of the best experience I had in <product> This experience of dining at <product> is exactly what I need This experience of <product> hasn’t worked out as well as I thought it would I am satisfied with the experience I have mixed feelings with the experience My choice was a wise one. If I could do it over again, I would not have <product>  I truly enjoyed <product> I feel bad about my decision to <product> I am not happy that I decided to<product> <Product> has been a good experience. I’m sure it was the right thing for me to <product>

Westbrook and Oliver (1981)

 

TABLE 2

OPERATIONALISATION OF VARIABLES

Variables  Symbol  Operationalisation  Source
1. Arousal

 

Arous1 

Arous2 

Arous3

Arous4 

Arous5 

Arous6 

Six Items Semantic Differential Scale:

Calm/Excited

Jittery/Dull

Aroused/Unaroused

Relaxed/Stimulated

Wide awake/Sleepy

Sluggish/Frenzied

Mehrabian and Russell (1974)

2. Pleasure

 

 

 

Pleas1

Pleas2

Pleas3

Pleas4

Pleas5

Pleas6

Six Items Semantic Differential Scale:

Pleas1 Bored/Relaxed

Pleas2 Hopeful/Despairing

Pleas3 Happy/Unhappy

Pleas4 Melancholic/Contented

Pleas5 Pleased/Annoyed

Pleas6 Unsatisfied/Satisfied

Mehrabian and Russell (1974)

3. Disconfirmation

 

Disc1

Disc2 

 Disc1 One Item Multichotomous Question using 5-point scale anchored by Worse than Expected and Better than Expected

 

Disc2 One Item Multichotomous Question using 5-point scale anchored by Too Low: It was better than you expected and Too High: It was worse than you expected

Oliver (1980)

Churchill and Suprenant (1982)

4.Post -Purchase Behavior

Repurchase1

Repurchase2

Recommend1

Recommend2  

One Item 7-Point Likely -Unlikely Bipolar Scale

One Item 7-Point Possible-Impossible Bipolar Scale

One Item 7-Point Likely -Unlikely Bipolar Scale

One Item 7-Point Possible-Impossible Bipolar Scale

 Dabholkar (1995)

5. Utilitarian Benefits 

 

Utilitarian

 Four Items 7-Point Semantic Differential Scale

useful/useless

valuable/worthless

beneficial/harmful

wise/foolish

Batra and Athola (1990)

6. Hedonic Benefits 

 

Hedonic 

Four Items 7-Point Semantic Differential Scale

pleasant/unpleasant

nice/awful

agreeable/disagreeable

happy/sad

Batra and Athola (1990)

TABLE 3

AFFECTIVE AND COGNITIVE FACTOR LOADINGS FOR PLEASURE AND

DISCONFIRMATION MEASURES

    Factor Loadings 
Constructs  Indicators  Affective Factor  Cognitive Factor
  Pleas1  0.70  -0.02
  Pleas2  0.69  0.01
  Pleas3  0.81  -0.01
  Pleas4  0.63  0.08
  Pleas5  0.80  0.21
  Pleas6  0.63  0.46
Disconfirmation Disc1  0.07  0.89
Disc2  0.05   0.89

Note: The boxes grouped those measures with high affective and cognitive loadings separately.

TABLE 4

AFFECTIVE AND COGNITIVE FACTOR LOADINGS FOR

SATISFACTION MEASURES

Measure 

Ranked by Correlation with Affective Factor

Correlation Coefficient with Affective Factor

Correlation with Cognitive Factor 

Ranked by Correlation with Cognitive Factor

Happy  0.56  0.45  9

SD1  

2  0.56  0.54  4
SD2  0.50  0.59  1
Pleased  0.49  0.50  8
DT  0.47  0.52  6
7pt Scale 0.43  0.53  5
Likert2  0.41  0.55  3
Percentage  0.40  0.56  2
Likert1  0.38  0.51  7

TABLE 5

ASSESSMENT OF PLEASURE, DISCONFIRMATION AND POST PURCHASE

BEHAVIOR MEASURES FOR THE ICE-CREAM RESTAURANT SERVICE STUDY

Construct  Indicators  Error Variances  Factor Loadings

Pleasure 

Pleas (summated scale)

0.22 0.86

Disconfirmation 

Disc1  0.13 0.93
  Disc2 0.49 0.70

Post Purchase Behavior

Repurchase1  0.53  0.87
  Repurchase2  0.35  0.89
  Recommend1  0.21  0.96
 

Recommend2 

0.23 0.95

TABLE 6

ASSESSMENT OF SATISFACTION MEASURES FOR THE

ICE-CREAM RESTAURANT SERVICE STUDY

Measure 

Item Reliability 

Error Variances 

Satisfaction

 Loadings

Correlation With

Affect Factor

SD2 

0.91  0.09  0.96  0.50

SD1 

0.87  0.15  0.94  0.56

Likert2 

0.82  0.18  0.90  0.41

Percentage* 

0.81  0.20  0.90 0.40

DT* 

0.76  0.25  0.88  0.47

Pleased* 

0.74  0.32  0.88  0.49

7pt scale* 

0.76  0.40  0.87  0.43

Happy* 

0.69  0.34  0.85  0.56

Likert1* 

0.60  0.51  0.80  0.38

Note: '*' indicates single-item measures.

TABLE 7

ASSESSMENT OF PLEASURE, DISCONFIRMATION AND POST PURCHASE

BEHAVIOR MEASURES FOR THE ATM SERVICE STUDY

Construct  Indicators  Error Variances  Factor Loadings

Pleasure  

Pleas (summated scale)

0.30  0.82
Disconfirmation  Disc1  0.20  0.83
 

Disc2

0.21  0.80
 Post Purchase Behavior Repurchase1  0.28  0.92
  

Repurchase2

0.16  0.94

 

TABLE 8

ASSESSMENT OF SATISFACTION MEASURES FOR THE

ATM SERVICE STUDY

Measure Item  Reliability Error Variances

Satisfaction Loadings

Correlation with Cognitive Factor

SD2

0.77 0.18 0.88 0.59

SD1

0.71 0.28 0.84 0.54

7pt scale*

0.62 0.41 0.80 0.53

Percentage*

0.64 0.36 0.80 0.56

Likert1*

0.59 0.47 0.80 0.51

Happy*

0.59 0.41 0.77 0.45

Pleased*

0.57 0.45 0.76 0.50

Likert2

0.59 0.27 0.76 0.55

DT*

0.52 0.28 0.74 0.52

Note: * indicates single-item measures.

FIGURE 1

MODEL 1 - ICE-CREAM RESTAURANT SERVICE

 

FIGURE 2

MODEL 2 – AUTOMATIC TELLER MACHINE (ATM) SERVICE


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