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The present study seeks to determine the key factors influencing chatbot adoption, as well as attempts to validate the unified theory of acceptance and use of technology (UTAUT) in the context of AI chatbots adoption among research scholars. The data for this study were collected through purposive sampling using a cross-sectional survey. The population of the study comprised research scholars enrolled in three public sector universities in Pakistan. The eight-factor proposed measurement model was estimated using confirmatory factor analysis (CFA) based on 30 valid items. The goodness of fit indices suggest a favourable fit for the model \u03c7\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20091.710, DF\u2009=\u2009381;\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    \u2009=\u20090.000; IFI\u2009=\u2009.902; TLI\u2009=\u20090.886, CFI\u2009=\u20090.900, RMSEA\u2009=\u20090.056. Our research affirms that social influence, trust, and facilitating conditions play pivotal roles as primary predictors of behavioural intentions for AI chatbots adoption among scholars. The study suggests that the perceived risks associated with using AI chatbots due to their potential misuse can be minimized by effectively implementing AI user guidelines, and developing AI literacy among scholars. Information professionals and ethical libraries can play an important role in \u201cbuilding the bridge\u201d between cutting-edge technology capabilities and information users\u2019 needs and rights. The proposed eight-factor AI chatbots adoption model holds substantial potential in understanding the influence of performance expectancy, effort expectancy, social influence, trust, perceived risk, and facilitating conditions on behavioural intention to AI chatbots adoption. This study contributes to the limited body of research investigating the factors influencing AI chatbot adoption among research scholars using the UTAUT model with additional constructs for trust and perceived risk.\n                  <\/jats:p>","DOI":"10.1177\/09610006241269189","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:16:00Z","timestamp":1724048160000},"page":"1205-1223","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":33,"title":["Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT)"],"prefix":"10.1177","volume":"57","author":[{"given":"Guanhua","family":"Chen","sequence":"first","affiliation":[{"name":"Dongshin University, Korea"}]},{"given":"Jiamei","family":"Fan","sequence":"additional","affiliation":[{"name":"Suzhou University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0069-2249","authenticated-orcid":false,"given":"Mehreen","family":"Azam","sequence":"additional","affiliation":[{"name":"Government of the Punjab, Pakistan"}]}],"member":"179","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CHB.2015.11.036"},{"key":"e_1_3_4_3_1","article-title":"AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization","author":"Afzal A.","year":"2024","unstructured":"Afzal A., Chalumattu R., Matthes F., Espuny L. 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