Industrial Management & Data Systems· 2016Q1
Gain more insight from your PLS-SEM results
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- 2016year
Short summary
Importance-Performance Map Analysis (IPMA) enriches PLS-SEM by combining path coefficients (importance) with latent variable averages (performance), enabling prioritization of constructs for improvement.
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Key points
- IPMA integrates importance (path coefficients) and performance (average latent variable values) in PLS-SEM.
- It allows for prioritization of constructs to improve a specific target construct.
- Analysis at the indicator level helps identify specific areas for action.
- The method requires specific measurement scales, variable coding, and assumes linear relationships.
AI-generated from the title and abstract; the full text is not read.
Abstract
Purpose The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLS-SEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies. Findings The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and, thereby, gain additional results and findings. More specifically, instead of only analyzing the path coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent variables and their indicators (i.e. performance dimension). Research limitations/implications An IPMA is tied to certain requirements, which relate to the measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes linear relationships. This research does not address the computation and interpretation of non-linear dependencies. Practical implications The IPMA is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain target construct. Expanding the analysis to the indicator level facilitates identifying the most important areas of specific actions. These results are, for example, particularly important in practical studies identifying the differing impacts that certain construct dimensions have on phenomena such as technology acceptance, corporate reputation, or customer satisfaction. Originality/value This paper is the first to offer researchers a tutorial and annotated example of an IPMA. Based on a state-of-the-art review of the technique and a detailed explanation of the method, this paper introduces a systematic procedure for running an IPMA. A case study illustrates the analysis, using the SmartPLS 3 software.
The authors' abstract, as published at the source. Industrial Management & Data Systems, 2016 · DOI ↗
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Field: Organizational Behavior and Human Resource Management
Organizational Behavior and Human Resource ManagementBusiness, Management and Accounting