{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T21:10:32Z","timestamp":1777410632778,"version":"3.51.4"},"reference-count":82,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2016,2,1]],"date-time":"2016-02-01T00:00:00Z","timestamp":1454284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,2,1]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 This paper provides updated guidelines of how to use PLS and how to report and interpret its results.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/imds-09-2015-0382","type":"journal-article","created":{"date-parts":[[2016,1,7]],"date-time":"2016-01-07T07:49:43Z","timestamp":1452152983000},"page":"2-20","source":"Crossref","is-referenced-by-count":5293,"title":["Using PLS path modeling in new technology research: updated guidelines"],"prefix":"10.1108","volume":"116","author":[{"given":"J\u00f6rg","family":"Henseler","sequence":"first","affiliation":[]},{"given":"Geoffrey","family":"Hubona","sequence":"additional","affiliation":[]},{"given":"Pauline Ash","family":"Ray","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020121822051960000_b1","doi-asserted-by":"crossref","unstructured":"Aguirre-Urreta, M.\n                and \n                  R\u00f6nkk\u00f6, M.\n                (2015), \u201cSample size determination and statistical power analysis in PLS using R: an annotated tutorial\u201d, \n                  Communications of the Association for Information Systems\n               , Vol. 36 No. 3, pp. 33-51.","DOI":"10.17705\/1CAIS.03603"},{"key":"key2020121822051960000_b2","doi-asserted-by":"crossref","unstructured":"Aguirre-Urreta, M.I.\n                and \n                  Marakas, G.M.\n                (2013), \u201cResearch note \u2013 partial least squares and models with formatively specified endogenous constructs: a cautionary note\u201d, \n                  Information Systems Research\n               , Vol. 25 No. 4, pp. 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research: a review of past practices and recommendations for future applications\u201d, \n                  Long Range Planning\n               , Vol. 45 Nos 5\/6, pp. 320-340.","DOI":"10.1016\/j.lrp.2012.09.008"},{"key":"key2020121822051960000_b30","doi-asserted-by":"crossref","unstructured":"Hair, J.F.\n               , \n                  Sarstedt, M.\n               , \n                  Ringle, C.M.\n                and \n                  Mena, J.A.\n                (2012b), \u201cAn assessment of the use of partial least squares structural equation modeling in marketing research\u201d, \n                  Journal of the Academy of Marketing Science\n               , Vol. 40 No. 3, pp. 414-433.","DOI":"10.1007\/s11747-011-0261-6"},{"key":"key2020121822051960000_b31","doi-asserted-by":"crossref","unstructured":"Henseler, J.\n                (2010), \u201cOn the convergence of the partial least squares path modeling algorithm\u201d, \n                  Computational Statistics\n    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