{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T16:54:16Z","timestamp":1785862456711,"version":"3.56.0"},"reference-count":39,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2020,10,15]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this study is to provide an overview of emerging prediction assessment tools for composite-based PLS-SEM, particularly proposed out-of-sample prediction methodologies.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>A review of recently developed out-of-sample prediction assessment tools for composite-based PLS-SEM that will expand the skills of researchers and inform them on new methodologies for improving evaluation of theoretical models. Recently developed and proposed cross-validation approaches for model comparisons and benchmarking are reviewed and evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results summarize next-generation prediction metrics that will substantially improve researchers' ability to assess and report the extent to which their theoretical models provide meaningful predictions. Improved prediction assessment metrics are essential to justify (practical) implications and recommendations developed on the basis of theoretical model estimation results.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The paper provides an overview of recently developed and proposed out-of-sample prediction metrics for composite-based PLS-SEM that will enhance the ability of researchers to demonstrate generalization of their findings from sample data to the population.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-08-2020-0505","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T13:29:01Z","timestamp":1602768541000},"page":"5-11","source":"Crossref","is-referenced-by-count":189,"title":["Next-generation prediction metrics for composite-based PLS-SEM"],"prefix":"10.1108","volume":"121","author":[{"given":"Joe F.","family":"Hair Jr","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"issue":"7","key":"key2021020414244734500_ref001","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1080\/09544120050135425","article-title":"Foundations of the American customer satisfaction index","volume":"11","year":"2000","journal-title":"Total Quality Management"},{"issue":"10","key":"key2021020414244734500_ref002","doi-asserted-by":"crossref","first-page":"4545","DOI":"10.1016\/j.jbusres.2016.03.048","article-title":"Prediction-oriented modeling in business research by means of PLS path modeling","volume":"69","year":"2016","journal-title":"Journal of Business Research"},{"key":"key2021020414244734500_ref003","article-title":"Sampling weight adjustments in partial least squares structural equation modeling: guidelines and Illustrations","year":"2020","journal-title":"Total Quality Management and Business Excellence"},{"key":"key2021020414244734500_ref005","unstructured":"Chin, W.W. 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