A framework of importance-performance analysis based on the multiple determination coefficient

Jiunn I. Shieh, Hsin Hung Wu

Research output: Contribution to journalArticlepeer-review


This study uses derived importance based on the multiple determination coefficient to replace self-stated importance for importance-performance analysis. The traditional importance-performance analysis assumes that there are no interactions among the survey items. Without considering the interactions among the survey items, some items might be either underestimated or overestimated in terms of importance for quadrant classifications, which might result in misunderstanding the major strengths (weaknesses) to minor strength (weaknesses) and vice versa. Thus, the improvement efforts might be in vain. In this study, the proposed framework based on the multiple determination coefficient considers the items interactions to be under the other items influence. A case is illustrated to show how this framework differs from the traditional importance-performance analysis when interactions among the survey items are taken into consideration.

Original languageEnglish
Pages (from-to)463-467
Number of pages5
JournalIAENG International Journal of Computer Science
Issue number3
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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