The traditional importance-performance analysis (IPA) uses the mean ratings of importance and performance to construct a two-dimensional grid by identifying improvement opportunities and guiding strategic planning efforts. The point estimates of importance and performance vary from sample to sample such that the numerical analyses are different based upon different samples. Thus, using point estimates for items might lead the management to make false decisions. This study integrates confidence intervals and IPA to reduce the variability which enables the decision maker much easier to identify the strengths and weaknesses based upon the sample of size used. Moreover, the assumptions of equal and unequal population variances for constructing confidence intervals are discussed.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence