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This study investigates predictors of students’ behavioral intentions (BIs) and actual ChatGPT use using a modified UTAUT2 framework. A cross-sectional correlational design surveyed 8,496 students across Nigeria via a ChatGPT utilization questionnaire measuring PE, EE, SI, FCs, HM, HB, BI, and use behavior.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/higher-education-students-chatgpt-use-behavior-structural-equation-modelling-of-contributing-factors-through-a-modified-utaut-model/600498/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/higher-education-students-chatgpt-use-behavior-structural-equation-modelling-of-contributing-factors-through-a-modified-utaut-model/600498.png","ImageObject",300,407,{"name":92,"@type":93},"OmBimo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What gap does the study address regarding ChatGPT adoption in Nigeria?","Question",{"text":112,"@type":113},"It addresses limited understanding of which factors influence ChatGPT adoption among Nigerian higher education students, especially where prior research has not examined these factors comprehensively in this context.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study model the predictors of ChatGPT use?",{"text":117,"@type":113},"It applies a modified UTAUT2 framework and uses structural equation modelling to examine relationships between extended UTAUT variables, behavioral intentions, and actual ChatGPT use behavior.",{"name":119,"@type":110,"acceptedAnswer":120},"Which factors were found to positively or negatively predict behavioral intention and use behavior?",{"text":121,"@type":113},"Behavioral intention was positively predicted by performance expectancy, social influence, hedonic motivation, and habit, while effort expectancy and facilitating conditions were negative predictors. For actual use behavior, facilitating conditions, hedonic motivation, habit, and behavioral intention were positive predictors, while performance expectancy and social influence were negative predictors.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},600498,1792144585,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090893581,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Contemporary Educational Technology, 2025, 17(4), ep592  \n ISSN: 1309-517X (Online)   \n OPEN ACCESS  Research Article  \nHigher education students’ ChatGPT use behavior: Structural equation modelling of contributing factors through a modified UTAUT model  \nValentine Joseph Owan 1,2  \n 0000-0001-5715-3428  \nIbrahim Abba Mohammed 3*  \n 0000-0002-5388-6859  \nAhmed Bello 3  \n 0000-0001-7883-9357  \nTajudeen Ahmed Shittu 3  \n 0000-0002-1559-4839  \n1 Department of Educational Psychology, University of Calabar, Calabar, Cross River State, NIGERIA  \n2 Ultimate Research Network, Calabar, Cross River State, NIGERIA  \n3 Department of Science Education, Federal University of Kashere, Kashere, Gombe State, NIGERIA  \n* [Corresponding author:](Corresponding author: muhammadia@fukashere.edu. ng)[ muhammadia@fukashere.edu. ng](Corresponding author: muhammadia@fukashere.edu. ng)  \nCitation: Owan, V. J., Mohammed, I. A., Bello, A., & Shittu, T. A. (2025) . Higher education students’ ChatGPT use behavior: Structural equation modelling of contributing factors through a modified UTAUT model. Contemporary Educational Technology, 17(4), ep592. [https://doi.org/10.30935/cedtech/17243](https://doi.org/10.30935/cedtech/17243)  \nARTICLE INFO ABSTRACT  \nReceived: 27 Jun 2024  \nAccepted: 13 Aug 2025  \nDespite the increasing interest in artificial intelligence technologies in education, there is a gap in understanding the factors influencing the adoption of ChatGPT among Nigerian higher education students. Research has not comprehensively explored these factors in the Nigerian context, leaving a significant gap in understanding technology adoption in this setting. This study addressed this gap by investigating the predictors of students’ behavioral intentions (BIs) and actual use behavior of ChatGPT through the lens of the unified theory of acceptance and use of technology 2 (UTAUT2) framework. A cross-sectional correlational research design was used to examine the relationships between extended UTAUT variables, BIs, and ChatGPT use behavior. A sample of 8,496 higher education students from diverse institutions in Nigeria participated in the study. The data were collected using the higher education students’ ChatGPT utilization questionnaire, which assessed various factors, such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), hedonic motivation (HM), habit (HB), BI, and ChatGPT use behavior. The findings reveal several significant predictors of students’BIs and actual usage of ChatGPT. PE, SI, HM, and HB were found to be significant positive predictors of BI, while EE and FCs were significant negative predictors. For ChatGPT use behavior, FCs, HM, HB, and BI were significant positive predictors, whereas PE and SI were significant negative predictors. BI mediated the relationships between several factors and ChatGPT usage behavior: positively for some (PE, SI, HM, and HB) and negatively for others (EE and FC) . This study contributes to understanding the adoption of ChatGPT in higher education contexts. The findings highlight the importance of addressing usability issues, providing adequate support and resources, promoting a positive user experience, fostering habitual usage, and leveraging social networks to encourage adoption.  \nKeywords: artificial intelligence, chatbots, GenAI, large language models, technology use  \nINTRODUCTION  \nArtificial intelligence (AI) is a modern technology that has sparked international discussions and debates among scholars, particularly about the development of large-language models. AI refers to intelligent technological devices and software that can reason, absorb information, gather knowledge, interact, control, and distinguish between objects (AlAfnan et al., 2023) . These AI applications have received much attention in the last couple of years. It has been documented that AI enables tutors to personalize learning and promote individualized education (Chen et al.","cbCaimNKWQCAv018","https://ap.wps.com/l/cbCaimNKWQCAv018","pdf",513223,22,"English","# ARTICLE INFO\n## ABSTRACT\n## INTRODUCTION","[{\"question\":\"What gap does the study address regarding ChatGPT adoption in Nigeria?\",\"answer\":\"It addresses limited understanding of which factors influence ChatGPT adoption among Nigerian higher education students, especially where prior research has not examined these factors comprehensively in this context.\"},{\"question\":\"How does the study model the predictors of ChatGPT use?\",\"answer\":\"It applies a modified UTAUT2 framework and uses structural equation modelling to examine relationships between extended UTAUT variables, behavioral intentions, and actual ChatGPT use behavior.\"},{\"question\":\"Which factors were found to positively or negatively predict behavioral intention and use behavior?\",\"answer\":\"Behavioral intention was positively predicted by performance expectancy, social influence, hedonic motivation, and habit, while effort expectancy and facilitating conditions were negative predictors. For actual use behavior, facilitating conditions, hedonic motivation, habit, and behavioral intention were positive predictors, while performance expectancy and social influence were negative predictors.\"}]","Higher education students’ ChatGPT use behavior - Structural equation modelling of contributing factors through a modified UTAUT model | PDF",1792090810,55]