Importance and performance in PLS-SEM and NCA : introducing the combined importance-performance map analysis (cIPMA)

This research offers a novel approach that extends the application of importance-performance map analysis (IPMA) in partial least squares structural equation modeling (PLS-SEM) by incorporating findings from a necessary condition analysis (NCA). The IPMA comprises assessing latent variables and their indicators' importance and performance, while an NCA introduces an additional dimension by identifying factors that are crucial for achieving the desired outcomes. An NCA employs necessity logic to identify the must-have factors required for an outcome, while PLS-SEM follows an additive sufficiency logic to identify the should-have factors that contribute to high performance levels. Integrating these two logics into the performance dimension is particularly valuable for prioritizing actions that could improve the target outcomes, such as customer satisfaction and employee commitment. Although the combined use of PLS-SEM and NCA is a recent suggestion, this study is the first to combine them with an IPMA (i.e., in a combined IPMA; cIPMA). A case study illustrates the combined use of PLS-SEM and an NCA to undertake a cIPMA. This innovative approach enhances researchers' and practitioners' decision making, enabling them to prioritize their efforts effectively.

Subjects

cIPMA

Importance-performance map analysis

IPMA

NCA

Necessary condition analysis

Partial least squares

PLS

PLS-SEM

SEM

Structural equation modeling

DDC Class

330: Economics

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Importance and performance in PLS-SEM and NCA : introducing the combined importance-performance map analysis (cIPMA)