The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Gain more insight from your PLS-SEM results: The importance-performance map analysis
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.
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Cites
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Self-citationAn assessment of the use of partial least squares structural equation modeling in marketing research
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Self-citationUsing pls path modeling in new technology research: updated guidelines
Henseler, J., Hubona, G., & Ray, P. A. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-09-2015-0382 · Cited by 7,173
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Self-citationPeng, D. X., & Lai, F. (2012) · Journal of Operations Management · doi:10.1016/j.jom.2012.06.002 · Cited by 1,532
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Self-citationFoundations of the American Customer Satisfaction Index
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Cited by
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
Self-citationPartial least squares structural equation modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-1 · Cited by 3,285
Self-citationHair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0517-x · Cited by 2,712
Self-citationBenitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020) · Information & Management · doi:10.1016/j.im.2019.05.003 · Cited by 1,788
Partial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022) · Handbook of Market Research · doi:10.1007/978-3-319-57413-4_15 · Cited by 1,483
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Metadata
- Authors
- Ringle, C. M., & Sarstedt, M.
- Venue
- Industrial Management & Data Systems
- Publisher
- Emerald
- Published
- 2016-10-17
- Volume
- 116
- Issue
- 9
- Pages
- 1865-1886
- ISSN
- 0263-5577 (ISSN-L)
- OpenAlex
- W2528032239
- Cited by
- 1,491
- References
- 9
- FWCI
- 55.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems. https://doi.org/10.1108/imds-10-2015-0449
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