Importance‐performance analysis for improving quality of campus food service

Abstract

Managing service operations has continued to assume greater importance in most developed economies. This is in view of the ubiquity of service organizations and operations, the associated large workforce, and the substantial contribution of the service sector to the gross domestic product of most of these countries. In order to be successful, it is extremely important for service managers to be able to appraise their activities over time by using pertinent information derived from their customers. This paper demonstrates the use of the importance‐performance analysis framework to assist management of a campus food service organization to improve its services. It also studies the relative effects of implicit and explicit weighting methods on importance ranking as well as the grid‐classification of service quality attributes. Furthermore, analysis of the factor structure of the attributes indicates, like some other reported studies, that the five‐dimensional factor structure postulated for services in the literature was not confirmed for the campus food service scenario studied.

Impact-range performance analysis and asymmetry analysis for improving quality of Korean food attributes

“…Customers evaluate service quality based on its attributes and use this information to develop overall assessments that affect final decisions regarding purchasing or repurchasing. In addition, IPA is an outcome of multi-attribute utility theory, which posits that a utility function of attributes is their ability to describe and collect information for all determining attributes-in fact, then, the customer selects the alternative that maximizes his or her utility (Aigbedo and Parameswaran, 2004).…”

Importance Performance Analysis as a Trade Show Performance Evaluation and Benchmarking Tool

“…Thus, exhibitors' performance expectations of the nine activities were measured using a seven-point Likert-type scale anchored by "not at all important" (1) and "very important" Placing the trade show activities into the four quadrants of the I-P map requires calculating cut-off points (cross hair points) for the performance expectation and the perceived performance variables. We used the scale-means of these two variables to determine the cut-off points which is a standard procedure in the literature (Aigbedo and Parameswaran 2004;Bacon 2003;Yavas and Shemwell 2001). The cut-off point for performance expectation is calculated by summing the performance expectation mean scores for all the nine activities and dividing it by nine which gives 5.2.…”

Using BPNN and DEMATEL to modify importance–performance analysis model – A study of the computer industry

“…Matzler, Sauerwein, and Heischmidt (2003) used IPA to improve bank service quality and development strategy. Aigbedo and Parameswaran (2004) used IPA to improve school canteen service quality. Matzler, Fuchs, and Schubert (2004) used IPA in the automobile industry.…”