Applying Importance–Performance Analysis With Simple Regression Model and Priority Indices to Assess Hotels' Service Performance

Abstract

The purpose of this study was to propose a revised importance–performance analysis (RIPA) grid to improve the weaknesses of self-stated (or implicitly derived) importance and improvement (or resource reallocation) priority for service attributes in importance–performance analyses (IPAs). The importance derived from simple linear regression analyses was used to replace self-stated or implicit importance derived from multiple regression (or partial correlation) analyses in order to construct the RIPA grid, which not only measures attribute importance and performance but also easily and effectively identifies service management strategies. Also, this paper proposes improvement and resource reallocation priority indices to more effectively prioritize the improvement and resource reallocation of service attributes located in the “Concentrate Here” and “Possible Overkill” quadrants, respectively. A case study of tourist hotels is presented to demonstrate the application of the RIPA. The analyzed results show that the simple regression coefficients of all attributes were positive, but some multiple regression coefficients and partial correlation coefficients were negative. It revealed that using multiple regression analysis or partial correlation analysis to estimate attribute importance might be inappropriate, and simple linear regression analysis might be more reasonable in RIPA. The effective and appropriate action scheme for each service attribute can then be acquired using the RIPA approach. The study offers new insights into the attribute importance and improvement/reallocation priority in importance–performance analysis, resolves IPA grid weaknesses, and provides industries with a simple and useful management tool.