{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T15:38:17Z","timestamp":1773675497485,"version":"3.50.1"},"reference-count":85,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2015,3,9]],"date-time":"2015-03-09T00:00:00Z","timestamp":1425859200000},"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":[[2015,3,9]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 e-Commerce recommenders have positive benefits both for consumers and the online stores using them. The focus of research in this topic has mostly been technical (e.g. design, type of recommenders, inputs, or outputs). However, a prior key question is what leads a consumer to use an e-vendor\u2019s recommender. The consumer process of adoption and use of such recommenders involves subjective factors which need a psychological approach. This perspective has been neglected so far. The purpose of this paper is to discuss and validate an integrative model which adapts various theories and models \u2013 i.e. the original formulation of the technology acceptance model (TAM), the integrated trust-TAM model, and the theory of planned behavior (TPB) \u2013 in order to explain such a process. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 The field study consisted of a simulated online shopping process undertaken by a valid sample of 300 internet users with a recommender at a real online store (Amazon). Many of the constructs\u2019 measurement scales have been adapted from previously validated scales never before applied to this study\u2019s context, and the authors have consequently rigorously validated them here too; this also constitutes one of the research\u2019s valuable contributions. Detailed exploratory and confirmatory factor analyses are applied to assess the empirical validity of the model. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The model\u2019s core structure and its relationships are proved to be valid for explaining a consumer\u2019s intention to use an e-vendor\u2019s recommender In particular, trust and perceived usefulness of the recommender stand out as the determining factors of its use, though the consumer\u2019s attitude toward the recommender and others\u2019 opinion of its use also have significant influence too. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 The consumer\u2019s psychological angle has been overlooked by previous studies on the adoption and use of online stores\u2019 recommendation systems. To the best of the knowledge, this is the first attempt to validate a model aimed at comprehensively approaching the consumer\u2019s adoption of an e-vendor\u2019s recommender. The findings provide several theoretical contributions and implications for practitioners.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/imds-10-2014-0306","type":"journal-article","created":{"date-parts":[[2015,2,12]],"date-time":"2015-02-12T09:57:17Z","timestamp":1423735037000},"page":"284-310","source":"Crossref","is-referenced-by-count":23,"title":["Psychological factors explaining consumer adoption of an e-vendor\u2019s recommender"],"prefix":"10.1108","volume":"115","author":[{"given":"Francisco Jos\u00e9","family":"Mart\u00ednez-L\u00f3pez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene","family":"Esteban-Millat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia C.","family":"Cabal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles","family":"Gengler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122601395520000_b1","doi-asserted-by":"crossref","unstructured":"Ajzen, I.\n                (1991), \u201cThe theory of planned behavior\u201d, \n                  Organizational Behavior and Human Decision Processes\n               , Vol. 50 No. 2, pp. 179-211.","DOI":"10.1016\/0749-5978(91)90020-T"},{"key":"key2020122601395520000_b2","doi-asserted-by":"crossref","unstructured":"Aljukhadar, M.\n                and \n                  Senecal, S.\n                (2011), \u201cUsage and success factors of commercial recommendation agents: a consumer qualitative study of MyProductAdvisor.com\n               \u201d, \n                  Journal of Research in Interactive Marketing\n               , Vol. 5 Nos 2\/3, pp. 13-152.","DOI":"10.1108\/17505931111187776"},{"key":"key2020122601395520000_b3","doi-asserted-by":"crossref","unstructured":"Aljukhadar, M.\n               , \n                  Senecal, S.\n                and \n                  Daoust, C.-E.\n                (2012), \u201cUsing recommendation agents to cope with information overload\u201d, \n                  International Journal of Electronic Commerce\n               , Vol. 17 No. 2, pp. 41-70.","DOI":"10.2753\/JEC1086-4415170202"},{"key":"key2020122601395520000_b4","unstructured":"Anderson, C.\n                (2006), \n                  The Long Tail: Why Future of Business is Selling Less of More\n               , Hyperion, New York, NY."},{"key":"key2020122601395520000_b5","unstructured":"Anderson, J.\n                (1985), \n                  Cognitive Psychology and Its Implications\n               , W. 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