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SERVQUAL Service Quality (July 2014 updated)

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Regression and MANOVA analysis. …

Regression and MANOVA analysis.
Review of Parasuraman, A., Zeithaml, V. A., and L. L. Berry (1988), “SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality,” Journal of Retailing, Vol. 64, No. 1, 12-40.

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  • 1. July 2014 updated Prepared by Michael Ling Page 1 QUANTITATIVE RESEARCH METHODS SAMPLE OF REGRESSION & MANOVA PROCEDURES Prepared by Michael Ling Reference: Parasuraman, A., Zeithaml, V. A., and L. L. Berry (1988), “SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality,” Journal of Retailing, Vol. 64, No. 1, 12-40.
  • 2. July 2014 updated Prepared by Michael Ling Page 2 INTRODUCTION Service quality has been considered as an important attribute to business but yet hard to measure due to its unique features: intangibility, heterogeneity and inseparability of production and consumption. In the absence of an objective measure of service quality, customers’ perception is considered as the standard of measure. This paper contributes to the marketing literature by developing the service quality concept and the derivation of the SERVQUAL scale. The key research question is to search for a universal service quality scale that can be applicable to all service categories. Factor analysis is employed as the data reduction method in the development of SERVQUAL. The paper provides details on how it is developed from initially a 97- item scale across 10 service dimensions into a 22-item scale across 5 service dimensions: tangibles, reliability, responsiveness, assurance and empathy.
  • 3. July 2014 updated Prepared by Michael Ling Page 3 SUMMARY Based on the service literature, the initial SERVQUAL scale consists of 97 items across ten service dimensions: tangibles, reliability, responsiveness, communication, credibility, security, competence, courtesy, understanding/knowing the customer, and access. Each item is represented by two kinds of statements – expectation statements (E’s) that measure customer expectations about the firms in a service category and perception statements (P’s) that measure customer perceptions about the performance of a particular firm in the same service category. Data collection is conducted in two stages. During the first stage, 200 respondents from five service categories are selected and provided with self- administered questionnaires. All responses gathered are pooled for analysis, regardless of their service categories. Based on the disconfirmation model in customer satisfaction literature, a difference score Q = P – Q is formed for each of the 97 items and the coefficient alpha values (α) for the service dimensions range from 0.55 to 0.78. Coefficient α values are then improved through an iterative process of deleting items with low item-to-total correlations to achieve better reliability. The outcome is a reduced set of 54 items, with coefficient alpha values range from 0.72 to 0.83. Finally, the factor structure is reduced to 34 items across 7 dimensions, with coefficient α values range from 0.72 to 0.94. During the second stage, four samples of 200 respondents are selected from each of the four service firms. Again, the respondents are self-administered with questionnaires that made up of 34 items. This time, the data are sorted into the four corresponding groups and analysed. The outcome is a 22-item scale across five dimensions: tangibles, reliability, responsiveness, assurance and empathy.
  • 4. July 2014 updated Prepared by Michael Ling Page 4 CRITIQUE The difference approach The difference approach, Q = P - E, used in the evaluation of SERVQUAL is based on the disconfirmation model in the customer satisfaction literature. The authors argue that the “idea” of a difference score is not new and this approach has been used in role conflict research. Consider the equation Q = P – E, where the same Q value can be obtained from various combinations of P’s and E’s. For example, the case where the difference between P and E is 1 can come from these scenarios: P = 2, E = 1; P = 3, E = 2; P = 4, E = 3; P = 5, E = 4; P = 6, E = 5; P = 7, E = 6. The difference score, Q, will not capture the individual P’s and E’s and valuable information could be left out. A major concern is whether the customers’ perception of service quality is the same regardless of the individual P’s and E’s. The authors have neither discussed this point nor conducted trials to test this possibility. Dimensions of SERVQUAL The final refined SERVQUAL scale consists of five dimensions, which are “designed to be applicable across a broad spectrum of services”. A concern is whether these five service dimensions are sufficient to account for the variations of quality across all service categories. The sample data has been drawn from a limited number (five) of service categories and a limited number (four) of service firms. Is it possible that complex SERVQUAL dimensions (larger number of dimensions) are required in some services such as movie ticketing but not required in other services such as airline ticketing? The authors should address the external validity of
  • 5. July 2014 updated Prepared by Michael Ling Page 5 SERVQUAL by cross-validating their results against a much broader range of service categories. The number of items used for each SERVQUAL dimension is made up of only four to five items. A concern is whether the number of items is sufficient. Is it possible that service quality can be influenced by contextual factors (depending on service categories) which some service categories, due to their complex nature, need to be measured by a larger number of items than others? Reliability The inter-item reliability (coefficient alpha) of the final refined scale ranges from 0.52 to 0.84, where “the reliabilities are consistently high across all four samples” and “the total–scale reliability is close to 0.9”. This is a good outcome. However, given the limited data samples, a concern is whether the reliability can be sustained across all service categories. Again, the issue of external validity of SERVQUAL should be addressed by cross-validating the results against a much broader range of service categories. Amongst the test items, nine pairs of P’s and E’s statements (items #10 to #13, items #18 to #22) are negatively worded, which all come from the Responsiveness and the Empathy dimensions. It is well understood that negatively worded statements are designed to reduce systematic response bias. There are a couple of concerns here. Firstly, the negatively worded items are not spread out across the five dimensions, which should be a better alternative to reduce bias. Secondly, some of the negatively worded items are not straightforward to understand
  • 6. July 2014 updated Prepared by Michael Ling Page 6 and interpret. For example, “It is not realistic for customers to expect prompt service from employees of these firms” (E11). There is potential data quality problem here. The SERVQUAL items are ordinal, which mean that polychoric correlation might be needed to estimate the correlations if the underlying distributions are assumed to be continuous. Questionnaire administration The questionnaire is made up of “97-statement expectations part followed by a 97-statement perceptions part”. There are a couple of concerns here. Why is the expectations part before the perceptions part and not the other way round? Why are the individual items, P’s and E’s, not grouped together? Focus groups should be conducted prior to data collection to find out how the expectation and perception statements should be set up. The 97-statement pairs make the questionnaire lengthy. A concern is that it might cause the respondents to lose interest and attention to answer all the questions. Again, there is a potential data quality problem. Convergent validity Separate one-way ANOVA procedures have been used in the evaluation of the association between SERVQUAL scores (dependent variables) and Overall Q (independent variable) across each of the five SERVQUAL dimensions. Some of the concerns are as below. i. In ANOVA/MANOVA procedures, the dependent and independent variables are interval (or continuous) and categorical respectively. Here, the dependent
  • 7. July 2014 updated Prepared by Michael Ling Page 7 variables (or SEVQUAL scores) are ordinal, not interval, variables. No discussions are provided to explain how this might affect the results. ii. No considerations are taken to distinguish the impact of experiment-wise level of Type I error given that multiple ANOVA procedures are used. In the second data collection stage, six one-way ANOVAs are conducted – one for each of the five SERVQUAL dimension and one for the combined scale. The experiment-wise probability of a Type I error might be be 6 F tests at .05 each or 30 percent. It is important to discuss whether the probability should be set at this level. iii. Why is the MANOVA omnibus test not conducted prior to the ANOVAs? Apart from protecting against inflated error probability of Type I error, the MANOVA procedure also takes into account the intercorrelations among the SERVQUAL dimensions. iv. The assumptions of ANOVAs such as independence, normality and homogeneity of variance for each test group are not tested. No descriptive statistics (such as Skewness and Kurtosis) or Shapiro-Wilk’s statistic is provided. No Levene’s test of homogeneity of variances is reported. v. No effect sizes such as Cohen’s measure is reported. Overall Assessment There is concern that the difference approach, Q = P – E, might be too simplified to have omitted critical information. There is concern whether the five dimensions are sufficient to cover all service categories. There is concern whether the items in the dimensions are influenced by contextual factors. There is concern
  • 8. July 2014 updated Prepared by Michael Ling Page 8 about negatively worded items not spread out. There is concern that the questionnaire is lengthy. There is concern on how the perception-expectation statements are presented. There is concern over convergent validity. The strengths of the paper are the new conceptual framework of SERVQUAL and the high reliabilities achieved. The weaknesses of the paper are the concerns raised above and the applicability of SERVQUAL across all service categories.
  • 9. July 2014 updated Prepared by Michael Ling Page 9 CONCLUSION The contribution of the paper is its development of the service quality scale, SERVQUAL, in the marketing discipline. The final refined scale consists of 22-items across five service dimensions, which is the result of an iterative process of data reduction based on samples drawn from five service categories and four service firms. Though the reliabilities of the measurement scale are consistently high (Cronbach’s value close to 0.9) across the samples, this critique raises concerns over the difference model, Q = P – E, and other areas such as item dimensions, validity, reliability, questionnaire administration and generalization. The research could have improved by addressing the concerns raised in this critique. In particular, closer examination of the difference model should be done to ascertain whether customer perceptions can be summarized by the difference scores, which is a key assumption upon which SERVQUAL is built. Other improvement includes testing whether the reliability coefficients of the SERVQUAL dimensions will hold across a broader range of service categories, and testing the convergent validity of SERVQUAL to increase the rigour of the research method.