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ABSTRACT
The application of hybrid Partial-Least-Square-Structural-Equation-Modeling-Artificial-Neural-Network in Information Systems (IS) research has surged over the years. Grounded on a systematic literature review from the list of premier and other promising IS journals, we found several concerns and issues. Hitherto, there are no guidelines for IS researchers for the hybrid PLS-SEM-ANN approach. We unlocked the potential of the hybrid PLS-SEM-ANN in providing better insight and understanding for IS researchers. In addition, best practices and recommendations for the adoption of PLS-SEM-ANN are discussed. The study contributes to advancing IS research by conducting a systematic literature review with Biblioshiny apps from the R studio’s Bibliometrix package and then proposing a comprehensive and robust approach to address the duality nature through the linear-nonlinear and compensatory-non-compensatory relationships. We proposed a guideline and suggested the minimum sample size, best practices and recommendations for reporting the results. We discuss the opportunities and prospects of the hybrid approach.
HIGHLIGHTS
The number of PLS-SEM-ANN applications in IS research is increasing.
Our systematic literature review reveals several concerns and inconsistencies.
We discuss these concerns and recommend the PLS-SEM-ANN best practices.
We present the advanced approaches, purposes, procedure, and key considerations.
We describe new opportunities and prospects for using the PLS-SEM-ANN approach.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplementary data
Supplemental data for this article can be accessed online at https://doi.org/10.1080/08874417.2024.2329128.