{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T10:35:26Z","timestamp":1792319726124,"version":"4.4.0"},"reference-count":67,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EJM"],"published-print":{"date-parts":[[2023,5,30]]},"abstract":"\n Purpose<\/jats:title>\n Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.<\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.<\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \n The results suggest that GSCA with reflective composite indicators (GSCA\n R<\/jats:sub>\n ) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA\n R<\/jats:sub>\n performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.\n <\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n Future research may further assess the methods\u2019 prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.<\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \n When prediction is the primary study aim, researchers should generally revert to GSCA\n R<\/jats:sub>\n , considering its performance for observed and operative prediction together.\n <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.<\/jats:p>\n 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