An SEM-ANN Approach - Guidelines in Information Systems Research

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 iss...

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Veröffentlicht in:The Journal of computer information systems Jg. 65; H. 6; S. 706 - 737
Hauptverfasser: Leong, Lai-Ying, Hew, Teck-Soon, Ooi, Keng-Boon, Tan, Garry Wei-Han, Koohang, Alex
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stillwater Taylor & Francis Ltd 02.11.2025
Schlagworte:
Best practice
Guidelines
Information systems
Literature reviews
Neural networks
Structural equation modeling
ISSN:0887-4417, 2380-2057
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0887-4417
2380-2057
DOI:10.1080/08874417.2024.2329128