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.
|Number of pages||5|
|Journal||IAENG International Journal of Computer Science|
|Publication status||Published - 2020|
All Science Journal Classification (ASJC) codes
- Computer Science(all)