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To better promote the adoption of AI technology in the construction domain, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, delves into the key factors influencing the adoption of AI technology in the construction industry. By introducing personal-level influencing factors such as AI anxiety and personal innovativeness, the UTAUT model is extended to comprehensively understand users\u2019 attitudes and adoption behaviors towards AI technology.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODOLOGY:<\/jats:title>\n            <jats:p>The research framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with the added constructs of artificial intelligence anxiety and individual Innovativeness. These data were collected through a combination of online and offline surveys, with a total of 258 valid data collected and analyzed using structural equation modeling.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS:<\/jats:title>\n            <jats:p>The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors. Specifically, Performance Expectancy (PE) (\u03b2=\u200a0.266, 95%), Effort Expectancy (EE) (\u03b2=\u200a0.262, 95%), and Social Influence (SI) (\u03b2=\u200a0.131, 95%) were identified as significant predictors of UB. Additionally, Facilitating Conditions (FC) (\u03b2=\u200a0.168, 95%) also positively influenced UB.Moreover, the study explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>PRACTICAL IMPLICATIONS:<\/jats:title>\n            <jats:p>This study lie in informing industry stakeholders about the multifaceted dynamics influencing AI adoption. Armed with this knowledge, organizations can make informed decisions, implement effective interventions, and navigate the challenges associated with integrating AI technology into the construction sector.<\/jats:p>\n          <\/jats:sec>","DOI":"10.3233\/jifs-240798","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T11:17:00Z","timestamp":1715080620000},"page":"564-581","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["An extended UTAUT model study on the adoption behavior of artificial intelligence technology in construction industry"],"prefix":"10.1177","volume":"49","author":[{"given":"Xiongyu","family":"Wu","sequence":"first","affiliation":[{"name":"School of Transportation Engineering, Changsha University of Science and Technology, Changsha, China"}]},{"given":"Yixuan","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Changsha University of Science and Technology, Changsha, China"}]},{"given":"Wenxi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Changsha University of Science and Technology, Changsha, China"}]},{"given":"Nina","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Changsha University of Science and Technology, Changsha, China"}]}],"member":"179","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2021.103299"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103441"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2006.10.003"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107836"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2020.1752488"},{"key":"e_1_3_2_7_1","doi-asserted-by":"crossref","unstructured":"RaoT.GaddamA.KurniM.SarithaK.Reliance on Artificial Intelligence Machine Learning and Deep Learning in the Era of Industry 4.0. 2021. p. 281\u2013299.","DOI":"10.1002\/9781119792253.ch12"},{"key":"e_1_3_2_8_1","doi-asserted-by":"crossref","unstructured":"YaoX.ZhouJ.ZhangJ.Bo\u00ebrC.R.editors From intelligent manufacturing to smart manufacturing for Industry 4.0 driven by next generation Artificial Intelligence and further on 2017 5th International Conference on Enterprise Systems (ES); 2017 22\u201324 Sept. 2017.","DOI":"10.1109\/ES.2017.58"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.01.113"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1108\/CI-02-2017-0013"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.03.022"},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.08.008"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12541-010-0075-3"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)ME.1943-5479.0000650"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2019.100868"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.24200\/sci.2016.2163"},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.122843"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matt.2020.06.011"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","unstructured":"Liu Jiemei Research on user acceptance model of artificial intelligence products based on UTAUT model [D] Southwest University of Finance and Economics 2021. 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