Penalty-Reward-Contrast Analysis: a review of its application in customer satisfaction research


TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE, vol.24, no.11-12, pp.1288-1300, 2013 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 11-12
  • Publication Date: 2013
  • Doi Number: 10.1080/14783363.2013.776757
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.1288-1300
  • Keywords: Penalty-Reward-Contrast Analysis, dummy variable regression analysis, product attributes, overall customer satisfaction, IMPORTANCE-PERFORMANCE ANALYSIS, ATTRIBUTE-LEVEL PERFORMANCE, ASYMMETRIC IMPACT, EMPLOYEE SATISFACTION, TOURIST SATISFACTION, QUALITY, MODEL
  • Akdeniz University Affiliated: Yes


The objective of the study is to detect the differences in practice of Penalty-Reward-Contrast Analysis (PRCA) by a literature review. The review provides an enhanced understanding of PRCA by categorizing the modes of coding to low and high performance levels and the classification techniques of attributes according their asymmetric impact on overall customer satisfaction. Besides, this study aims to show how differences in practice of PRCA might change to the findings by presenting a case study and its results. By using a tour operator’s service quality measurement, the case study shows that findings of a research might significantly change depending on the techniques adapted for PRCA. The review of PRCA signified three main arguments in its practice; (1) definition of the low and high performance level; (2) decision of the regression coefficients (standardized or unstandardized) for penalty and reward values; and (3) classification of the attributes. Thus, in addition to identification of the techniques and their impacts on research findings by a case study, authors recommended some issues for the future studies.

It is widely observed that researchers use Penalty-Reward-Contrast Analysis (PRCA) to identify the asymmetric influences of product/service attributes on overall customer satisfaction, since it readily adapts to typical customer satisfaction data. However, there is no consensus on its application phases. An extensive literature review of PRCA revealed differences in three main issues: (1) definition of low and high performance levels; (2) decision on the regression coefficients (standardised or unstandardised) for penalty and reward values and (3) classification of the attributes. Therefore, in this study, the authors aim to contribute to the existing literature by proposing a standardised process for PRCA application. Moreover, by using the data on a case study, the authors intended to show that different applications may also produce different findings from the same research data.