A new rational IPA and application to cruise tourism

At least two versions of IPA, namely the simple IPA and the asymmetric IPA, are available in tourism literature (Albayrak and Caber, 2015; Pritchard and Havitz, 2006). The simple IPA involves asking customers their perceptions relating to importance of various performance criteria and how the firm has performed in terms of these criteria. The simple IPA assumes a symmetric relationship between performance in terms of various criteria and customer satisfaction. The asymmetric IPA or AIPA (Albayrak and Caber, 2013; Caber et al., 2013) recognizes that these relationships could be asymmetric and uses the three-factor theory of customer satisfaction (Matzler and Sauerwein, 2002) to argue that criteria could be basic, excitement or performance criteria and uses regression analysis. While AIPA is an improvement over IPA, AIPA calculations take into account only the magnitude of regression coefficients but not their level of significance. Further, figure 3 of Albayrak and Caber (2015) uses performance in Xaxis but impact asymmetry, not importance, in Y-axis. It is not clear why impact asymmetry should be considered synonymous to importance. In this research note, we propose a variation of AIPA and call it Rational IPA (RIPA). RIPA involves the following steps. Step 1. Collect relevant data. Step 2. Run two sets of regressions with overall customer satisfaction as the dependent variable, and performance in terms of various service criteria as dependent variables. The first set of regressions is called low performance regressions where only ratings below median levels for each criterion are considered. In contrast, the second set of regressions is called high performance regressions. As highlighted in previous studies (Hartline et al., 2003; Ramanathan and Ramanathan, 2011; Silverman and Grover, 1995), the criteria are classified based on the results of the two sets of regressions. 1 a. A critical criterion remains significant in all regressions (except for low performance in terms of the criterion). b. A desirable criterion is significant both for high performance and low performance in terms of the criterion. c. A satisfier criterion is significant for high performance regression in terms of the criterion but not significant for low performance. d. A dissatisfier criterion is not significant for high performance regression but significant for low performance in terms of the criterion. e. All other criteria are neutral criteria. Step 3. Prepare IPA matrix with the importance of criteria on the X-axis and performance (mean ratings) in the Y-axis. Step 4. Conduct IPA based on the criterion classification (importance) and achievement (performance). We demonstrate RIPA in the following steps using publicly available online data on customer ratings of cruise operations.