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One may measure service quality either with additive models such as SERVQUAL, SERPERF, and two-predictor model of performance and expectation, or with multiplicative models obtained by log-transformation of variables in additive models. This paper analytically compares multiplicative models with additive models. In addition, it empirically compares predictive performance of alternative models on overall service quality and customer satisfaction with a three-way factorial design: 2 forms (additive vs. multiplicative) × 3 types (SERVQUAL vs. SERVPERF vs. two-predictor model) × 2 levels (5 dimensions vs. 22 items). The empirical comparison uses the data gathered to evaluate service quality in five service industries (Hamburger Shop, Pizza Shop, Family Restaurant, Movie Theater, and Bakery Shop) with a standard SERVQUAL questionnaire. The data set includes 1,410 respondents' evaluation of service quality. The analytical and empirical comparison shows that multiplicative models have three distinct advantages. First, multiplicative models are theoretically supported by the prospect theory and the Weber's law. Second, multiplicative models allow one to identify the optimal levels of performance and expectation maximizing overall quality or customer satisfaction. Third, multiplicative models predict overall service quality and customer satisfaction better than additive models do. [ABSTRACT FROM AUTHOR]

This paper addresses two questions regarding models of service quality: single- vs. two-predictor models and items vs dimensions. This paper derives six alternative models specified by a two-way factorial design: 3 types of models (SERVQUAL vs. SERVPERF vs. two-predictor model) × 2 abstraction levels (5 dimensions vs. 22 items). It compares the alternative models with the criteria of predictability, interpretability, and usefulness. It analytically examines the alternative models with the linkages between a theoretical three-predictor model and the alternative models, and empirically compares the alternative models based on data sets collected in five service industries with a convenient sampling technique. The analyses show that the two-predictor model is superior to SERVQUAL and SERVPERF in view of the three criteria. The two-predictor model allows one to examine the effects of expectation and performance on customer satisfaction. This property is important because the management of expectation is as important as the management of performance in managing customer satisfaction. It is possible to recover the two-predictor model as well as SERVPERF from a data set required for SERVQUAL. Thus, it is reasonable to use the two-predictor model as well as SERVQUAL and SERVPERF for gaining more meaningful insights on service quality. [ABSTRACT FROM AUTHOR]

The literature on service quality recommends one to find an efficient direction for revising service quality with a combination analysis of a measurement model of service quality (SERVQUAL or SERVPERF) and the importance-performance analysis (IPA). This paper analytically assesses this recommendation. To this end, it presents a simple rule with which service firms can optimally allocate their limited resources for service quality. The simple rule revises service attributes to be proportional to the normalised demand elasticities with respect to the service attributes. This paper shows that such a combination analysis can efficiently revise service quality based on the simple rule, supporting the recommendation. However, such a combination analysis may not be efficient when the marginal costs of service attributes are not equal. This paper presents an extended rule reflecting different marginal costs of service attributes. The extended rule can be used in an extended combination analysis with a measurement model evaluating the marginal costs. We end this paper with some practical guidance for the combination analysis. [ABSTRACT FROM AUTHOR]

Previous research has analysed service quality mainly with single- and two-predictor models. The single-predictor models indicate the disconfirmation-only model and the performance-only model, whereas the two-predictor models employ a pair of predictors in the set of three predictors (expectation, performance, and disconfirmation). This paper proposes a general three-predictor model nesting the alternative single- and two-predictor models and then shows linkages between the alternative models. However, previous research has used and interpreted the alternative models without considering the linkages between the alternative models. This paper shows that this interpretation may lead researchers to incorrect conclusions on a consumer's evaluation of service quality because different statistical implications may result from the linkages. Accordingly, this paper ends with the recommendation that one should measure service quality with the two-predictor models and interpret the effects of predictors based on the linkages between the theoretical three-predictor model and the two-predictor models. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analytically and empirically show that the dimensions of SERVQUAL are not stable across service industries and even in the same service industry, because customers' evaluations on service quality are heterogeneous. This paper presents a purification model that purifies the measured quality of service attributes through excluding the effects of service types and customer types on the quality levels of service attributes. It also derives SERVQUAL dimensions from the purified quality of service attributes via two alternative approaches (the factor-based approach and the cluster-based approach). Furthermore, based on the two alternative approaches, it analytically and empirically demonstrates that it is more logical to interpret that SERVQUAL dimensions may be heterogeneous across services, and that two grand dimensions (tangibles vs. non-tangibles) can be more stable than the five dimensions of SERVQUAL. This finding implies that there is no need to stick to the five dimensions of SERVQUAL, because they do not represent a customer's psychometric dimensions of service quality. [ABSTRACT FROM AUTHOR]