The purpose of this paper is to explain and illustrate the use of the importance-performance map analysis (IPMA) also called importance-performance matrix, impact-performance map. This is a useful analysis approach in PLS-SEM that extends the standard results reporting of path coefficient estimates by adding a dimension that considers the average values of the subconscious variable scores. More precisely, the IPMA contrasts the total effects, representing the antecedentconcepts' importance in shaping a certain target concept, with their average subconscious variable scores indicating their performance (Fornell et al., 1996; Martilla and James, 1977; Slack, 1994). The goal is to identify antecedents that have a relatively high importance for the target concept (i.e. those that have a strong total effect), but also have a relatively low performance (i.e. low average subconscious variable scores).
|Number of pages||7|
|Journal||International Journal of Economic Research|
|Publication status||Published - 01-01-2017|
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Economics, Econometrics and Finance(all)