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Journal articles on the topic 'AI adoption intention model'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Chuang, Li-Min, and Sheng-Hsuan Huang. "AI-Supported Healthcare Technology Resistance and Behavioral Intention: A Serial Mediation Empirical Study on the JD-R Model and Employee Engagement." Systems 13, no. 4 (2025): 268. https://doi.org/10.3390/systems13040268.

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This study combines innovation resistance theory, the stimulus–organism–response (SOR) framework, and the job demands–resources model to facilitate an in-depth exploration of the barriers faced by healthcare professionals and the psychological responses they exhibit when adopting AI-supported healthcare technologies. We conducted a questionnaire survey and obtained 296 valid responses from healthcare professionals to examine the relationship between resistance to AI-supported healthcare technologies and AI adoption behavioral intentions. Using the SOR framework as a basis, this study validated

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Kim, Youngsoo, Victor Blazquez, and Taeyeon Oh. "Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model." Behavioral Sciences 14, no. 11 (2024): 1035. http://dx.doi.org/10.3390/bs14111035.

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This study addresses the academic gap in the adoption of generative AI systems by investigating the factors influencing technology acceptance and usage behavior in Korean firms. Although recent advancements in AI are accelerating digital transformation and innovation, empirical research on the adoption of these systems remains scarce. To fill this gap, this study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model, surveying 300 employees from both large and small enterprises in South Korea. The findings reveal that effort expectancy and social influence significantly

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Fang, Wang, Meng Na, and Syed Shah Alam. "Usage Intention of AI Among Academic Librarians in China: Extension of UTAUT Model." Sustainability 17, no. 7 (2025): 2833. https://doi.org/10.3390/su17072833.

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This study explores how academic librarians adopt artificial intelligence (AI) technologies, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as its main framework, expanded with elements from Personal Innovativeness in IT (PIIT) and the Technology Readiness Index (TRI). A quantitative approach was applied, gathering data from 340 academic librarians and analyzing them using PLS-SEM. The results indicate that facilitating conditions (β = 0.345, p < 0.001) and effort expectancy (β = 0.123, p = 0.034) significantly influence behavioral intention, while performance expectan

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Patel, Anil. "A Study on Factors Affecting Adoption of AI Tools on Consumers While Shopping Online." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04340.

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Abstract: This research investigates the factors influencing the adoption of AI tools, particularly chatbots, by consumers during online shopping. By integrating the Technology Acceptance Model (TAM) and Behavioral Reasoning Theory (BRT), the study explores how perceptions like ease of use, usefulness, trust, discomfort, and optimism affect consumer attitudes and intentions. Data collected from 182 respondents was analyzed using SmartPLS. The results show that attitude, perceived usefulness, and perceived ease of use significantly influence consumer intention to adopt AI chatbots. Innovation,

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Nguyen, Thi Hai Duong, Xuan Tiep Nguyen, Tran Ha Trang Le, and Quynh Anh Bui. "Determinants influencing the adoption of artificial intelligence technology in non-life insurers." Corporate Governance and Organizational Behavior Review 8, no. 1 (2024): 205–12. http://dx.doi.org/10.22495/cgobrv8i1p17.

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Although artificial intelligence (AI) technology has been widely used in the insurance industry at a global scale, studies examining the adoption of AI technology in emerging markets are few and far between. This paper fills this gap by using Cronbach’s alpha, exploratory factor analysis, confirmatory factor analysis, and structural equation model (SEM) to discover significant factors affecting their behavioral intentions to adopt AI technology in Vietnam, a developing country. Data is collected from nearly 470 employees in Vietnamese non-life insurance firms. Empirical findings show that the

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Chatterjee, Sheshadri, Bang Nguyen, Soumya Kanti Ghosh, Kalyan Kumar Bhattacharjee, and Sumana Chaudhuri. "Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations." Bottom Line 33, no. 4 (2020): 359–75. http://dx.doi.org/10.1108/bl-08-2020-0057.

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Purpose The purpose of this study is to explore the behavioral intention of the employees to adopt artificial intelligence (AI) integrated customer relationship management (CRM) system in Indian organizations. Design/methodology/approach To identify the factors impacting the behavioral intention of the employees to adopt AI integrated CRM system in Indian organizations helps of literature review and theories have been taken. Thereafter, some hypotheses have been formulated followed by the development of a theoretical model conceptually. The model has been tested statistically for validation us

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Gyesi, Kwesi, Vivian Amponsah, and Samuel Ankamah. "Forecasting Ghanaian Medical Library Users’ Artificial Intelligence (AI) Technology’s Acceptance and Use." Biblios Journal of Librarianship and Information Science, no. 88 (April 11, 2025): e004. https://doi.org/10.5195/biblios.2025.1211.

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Objective. This study investigated the behavioural intentions of medical students in an academic library regarding the use of AI-assisted technologies for research and learning. Method. Employing a survey research design and a quantitative approach, the study sampled 302 respondents using Krejcie and Morgan’s published table. Statistical analyses were conducted using the Statistical Package for Social Sciences (SPSS) version 26, with linear and multiple linear regressions utilised to establish relationships between variables. Results. The results of the study indicate that perceived usefulness

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Apostoaie, Constantin-Marius, Teodora Roman, Alexandru Maxim, and Dumitru-Tudor Jijie. "Determinants of AI adoption intention in SMEs. Romanian case study." Journal of Business Economics and Management 26, no. 1 (2025): 277–96. https://doi.org/10.3846/jbem.2025.23650.

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The paper investigates the drivers and barriers that encourage or hinder the adoption of artificial intelligence (AI) technologies within Romanian SMEs. By using the Technology-Organisation-Environment (TOE) framework, we examined the role of several factors from each TOE dimension in predicting the AI adoption behaviour. The factors were constructed through factor analysis followed by the estimation of a linear regression model. Partial least squares structural equation modelling was then used in order to further explore the relationships and to check the robustness of the linear regression m

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Lakulu, Muhammad Modi, Ayad Shihan Izkair, Mohd Fadhil Abdul Muttalib, and Nur Azlan Zainuddin. "Understanding AI and Mobile Learning Adoption in Malaysian Universities: A UTAUT-Based Model." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 11 (2025): 80–111. https://doi.org/10.3991/ijim.v19i11.52977.

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This study explores the key determinants influencing the intention to adopt artificial intelligence (AI) applications and mobile learning in Higher Education Institutions (HEIs) in Malaysia. As AI technologies and mobile learning increasingly transform the higher education landscape, it is crucial to understand the specific factors driving their adoption. The research identifies five critical determinants—social influence (SI), effort expectancy (EE), hedonic motivations (HM), performance expectancy (PE), and consumer trust (TR)—that significantly impact the intention to use AI-powered mobile

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Jang, Eun-Gyo, and Jin-Myong Lee. "Continuous Use Intention of Fashion AI Recommendation Service Applying Value-based Adoption Model." Journal of Consumer Studies 35, no. 1 (2024): 149–71. http://dx.doi.org/10.35736/jcs.35.1.7.

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Min, Seoungkwon, and Boyoung Kim. "AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model." Digital 4, no. 4 (2024): 947–70. http://dx.doi.org/10.3390/digital4040047.

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As the digital environment evolves, the need to integrate artificial intelligence (AI) technology into corporate IT network operations increases. In this study, the aim was to define the factors that influence AI adoption in the network operations and analyze their impact on productivity and service stability. The technology–organization–environment (TOE) framework was employed for this investigation, focusing on technological, organizational, and environmental factors. In addition, in this study, structural equation modeling was employed to analyze the relationships between these influencing

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Chin, Ji-Hyoung, Chanwook Do, and Minjung Kim. "How to Increase Sport Facility Users’ Intention to Use AI Fitness Services: Based on the Technology Adoption Model." International Journal of Environmental Research and Public Health 19, no. 21 (2022): 14453. http://dx.doi.org/10.3390/ijerph192114453.

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Artificial intelligence (AI) has recently been introduced as a new way of analyzing and predicting sport consumer behavior. The goal of this study was to investigate the relationships among the perceived usefulness, perceived ease of use, the importance of exercise, attitudes towards use, and the behavioral intention to use AI services based on the technology adoption model. The authors recruited 408 participants who participated in an experiment designed to provide a deeper understanding of AI fitness services. After screening, the collected data were screened through assumption tests, and we

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Karippur, Nanda Kumar, Shaohong Liang, and Pushpa Rani Balaramachandran. "Factors Influencing the Adoption Intention of Artificial Intelligence for Public Engagement in Singapore." International Journal of Electronic Government Research 16, no. 4 (2020): 73–93. http://dx.doi.org/10.4018/ijegr.2020100105.

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This study aims at examining the key factors influencing the adoption intention of artificial intelligence (AI)-enabled mobile application for public engagement. Digital technologies such as AI provide the opportunity for public agencies to be inclusive and invite citizens to participate in shaping and reshaping the future of public policies and methods of governance. The authors test the proposed research model and results highlight the significant roles of collaboration, hedonic motivation, reliability, and degree of app savviness in the adoption intention of AI application for public engage

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Ramadhan, Zidane, and Osly Usman. "Technology Adoption and Peer Influence on Student AI Research Tools Purchase Intention." International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM) 3, no. 1 (2025): 852–71. https://doi.org/10.21009/isc-beam.013.59.

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Technological advancement has brought changes into how researchers conduct their research, Artificial Intelligence (AI) is one of the most impactful technologies that change the process. Through the lens of Technology Adoption Model (TAM) Framework, this research is aimed to uncover the relationship between technology adoption factors and peer influence in students’ intention toward buying AI research tools. Previous research is still limited toward explaining technology adoption toward students' purchase intention and neglected social factors such as peer influence, therefore reinforcing the

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Molnár, László, Gábor Béla Süveges, and Kata Horváth. "Modelling the factors influencing customer adoption of financial robo-advisors." Economy & finance 12, no. 2 (2025): 193–214. https://doi.org/10.33908/ef.2025.2.3.

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In this study, we examine the intention to use robo-advisors among potential users by employing an extended UTAUT model. The novelty of this model lies in its incorporation of constructs such as trust and perceived risk. Furthermore, it also builds upon artificial intelligence attributes, including perceived intelligence and anthropomorphism. To test the theoretical model, we conducted an online questionnaire survey in 2024, which yielded 249 valid responses. Structural equation modelling (CB-SEM) was applied to assess the extended model and its associated hypotheses. The findings indicate tha

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Lee, Jung-Chieh, and Rongrong Lin. "The continuous usage of artificial intelligence (AI)-powered mobile fitness applications: the goal-setting theory perspective." Industrial Management & Data Systems 123, no. 6 (2023): 1840–60. http://dx.doi.org/10.1108/imds-10-2022-0602.

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PurposeDue to the popularity of mobile devices and the development of artificial intelligence (AI), AI-powered mobile fitness applications (MFAs) have entered people's daily lives. However, the extant literature lacks empirical investigations that explore users' continuance usage intentions regarding AI-powered MFAs. To fill this research gap, this paper employs goal-setting theory to establish a research model for exploring how AI-enabled features (i.e. intelligence and anthropomorphism) affect users' perceptions of goal difficulties and goal specificities, which in turn affect their MFA cont

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Wang, Yu-Min, and Chei-Chang Chiou. "Exploring factors affecting user intention to accept explainable artificial intelligence." Computer Science and Information Systems, no. 00 (2025): 41. https://doi.org/10.2298/csis241018041w.

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Explainable Artificial intelligence (XAI) represents a pivotal innovation aimed at addressing the ?black box? problem in AI, thereby enhancing users? understanding of AI reasoning processes and outcomes. The implementation of XAI is not merely a technological endeavor but also involves various individual factors. As XAI remains in its early developmental stages and exhibits unique characteristics, identifying and understanding the factors influencing users? intention to adopt XAI is essential for its long-term success. This study develops a research model grounded in the characteristics of XAI

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Weglarz, Dominika, Cintia Pla-Garcia, and Ana Isabel Jiménez-Zarco. "Acceptance of Generative AI in the Creative Industry: Examining the role of Brand Recognition and Trust in the AI adoption." Retos 15, no. 29 (2025): 90–27. https://doi.org/10.17163/ret.n29.2025.01.

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This study explores the factors influencing the adoption of Generative AI text-to-image tools in the creative industry, using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The objective is to assess how brand recognition and trust, alongside performance expectancy, effort expectancy, facilitating conditions, and social influence, shape the behavioral intention to use Generative AI tools. While previous research has emphasized the importance of UTAUT constructs in technology adoption, the influence of brand equity factors remains underexplored. This study bridges

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Ye, Tiantian, Jiaolong Xue, Mingguang He, et al. "Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study." Journal of Medical Internet Research 21, no. 10 (2019): e14316. http://dx.doi.org/10.2196/14316.

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Background Poor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unnecessary referrals, but the application of AI in medical settings is still an emerging field. Objective This study aimed to investigate the general public’s acceptance of ophthalmic AI devices, with reference to those already used in China, and the interrelated influencing factors that shape people’s intention to use these devices. Methods We propose

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Hicham, OUZIF, EL BOUKHARI Hayat, EL ACHABI Maryem, et al. "Analyzing the adoption of artificial intelligence by Moroccan university teachers: Key insights and implications from the UTAUT model." Edelweiss Applied Science and Technology 9, no. 4 (2025): 2722–32. https://doi.org/10.55214/25768484.v9i4.6644.

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This study examines the factors influencing the adoption of artificial intelligence (AI) by Moroccan university teachers, using the UTAUT model. A questionnaire was distributed to 75 professors at Sidi Mohamed Ben Abdellah University in Fez, and the data were analyzed using structural equation modeling (SEM). The results show that facilitating conditions and social influence are the primary determinants of AI adoption intention. In contrast, performance expectancy and effort expectancy had no significant impact. This research highlights the need to enhance technological infrastructure and impl

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Arifin, M. Jainal, Christina Rahardja, and Dudi Anandya. "FACTORS AFFECTING THE ADOPTION INTENTION MOBILE PAYMENT OF OVO IN SURABAYA." Interdisciplinary Social Studies 1, no. 5 (2022): 624–33. http://dx.doi.org/10.55324/iss.v1i5.132.

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Background: Provide an adequate background covering the literature review and the gap of the research with other relevant former research works. Aim: This study aims to determine the Factors Affecting Adoption Intention to OVO Mobile Payment Users in Surabaya. Method: This type of research is basic business research using a quantitative approach with data analysis in the form of SEM (Structural Equation Model). Management of data in this study using AMOS 22.0 for windows which are used in testing the Measurement Model (Outer Model) and Structural Model (Inner Model). The sampling t

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Tony, Robinson. "Generative artificial intelligence in higher education: Understanding faculty adoption through the technology acceptance model." i-manager's Journal of Educational Technology 22, no. 1 (2025): 18. https://doi.org/10.26634/jet.22.1.21796.

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Generative artificial intelligence (AI) is increasingly transforming higher education by enhancing teaching methodologies, automating administrative tasks, and supporting research initiatives. Faculty adoption of generative AI is crucial for maximizing its potential benefits; however, its acceptance remains inconsistent due to factors such as usability, perceived usefulness, and ethical concerns. This study employs the Technology Acceptance Model (TAM) to investigate the relationships between Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude (ATT), and Intention to Use (IU) amo

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Gajić, Tamara, Dragan Vukolić, Jovan Bugarčić, et al. "The Adoption of Artificial Intelligence in Serbian Hospitality: A Potential Path to Sustainable Practice." Sustainability 16, no. 8 (2024): 3172. http://dx.doi.org/10.3390/su16083172.

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This study investigates the perceptions of employees in the hotel industry of the Republic of Serbia regarding the acceptance and importance of artificial intelligence (AI). Through a modified UTAUT model and the application of structural equation analysis (SEM), we investigated the key factors shaping AI acceptance. Research results show that behavioral intention and habit show a significant positive impact on AI usage behavior, while facilitating conditions have a limited but measurable impact on behavioral intention. Other factors, including social influence, hedonic motivation, performance

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Soliman, Mohamed, Reham Adel Ali, Imran Mahmud, and Tawat Noipom. "Unlocking AI-Powered Tools Adoption among University Students: A Fuzzy-Set Approach." Journal of Information and Communication Technology 24, no. 1 (2025): 1–28. https://doi.org/10.32890/jict2025.24.1.1.

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This study examines, from a post-pandemic theoretical perspective, university students' continuous intention (CI) to utilise AI-powered tools for educational purposes. AI-powered tools are new and underutilised in higher education. The fact that students and teachers need knowledge to use these apps in the classroom compounds the issue. Despite this technology's recent academic introduction, nothing is known about its impacts. In order to investigate the variables that influence the continual intention to employ artificial intelligence, this study discusses the possibility of integrating the s

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Na, Seunguk, Seokjae Heo, Wonjun Choi, Cheekyung Kim, and Seoung Wook Whang. "Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model." Buildings 13, no. 10 (2023): 2518. http://dx.doi.org/10.3390/buildings13102518.

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The research has chosen the workers in construction-related companies in South Korea and the United Kingdom (UK) as research subjects in order to analyse factors that influence their usage intention of Artificial Intelligence (AI) based technologies. The perceived usefulness had a positive impact (+) on technological satisfaction and usage intention in terms of the commonalities shown by the construction industry workers in both countries, South Korea and the UK, in adopting AI-based technologies. Moreover, the most remarkable differences were personal competence and social influence when choo

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Baroni, Ilaria, Gloria Re Calegari, Damiano Scandolari, and Irene Celino. "AI-TAM: a model to investigate user acceptance and collaborative intention inhuman-in-the-loop AI applications." Human Computation 9, no. 1 (2022): 1–21. http://dx.doi.org/10.15346/hc.v9i1.134.

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More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities to provide advanced features; on the other hand, human-in-the-loop approaches are on the rise to involve people in AI-powered pipelines for data collection, results validation and decision-making.Does the introduction of AI features affect user acceptance? Does the AI result quality affect people willingness to use such applications? Does the additional user effort required in human-in-the-loop mechanisms change the application adoption and use?This study aims to provide a reference approach to

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Bui, Huy Nhuong, and Cong Doanh Duong. "ChatGPT adoption in entrepreneurship and digital entrepreneurial intention: A moderated mediation model of technostress and digital entrepreneurial self-efficacy." Equilibrium. Quarterly Journal of Economics and Economic Policy 19, no. 2 (2024): 391–428. http://dx.doi.org/10.24136/eq.3074.

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Research background: In the rapidly evolving milieu of digital entrepreneurship, the integration of artificial intelligence (AI) technologies, exemplified by ChatGPT, has witnessed burgeoning prominence. However, there remains a dearth of understanding regarding the relationships between ChatGPT adoption in entrepreneurship and individuals’ cognitive career processes of digital entrepreneurship. Purpose of the article: The primary aim of the research is to adopt the Social Cognitive Career Theory and a moderated mediation model to unravel the intricate dynamics that characterize the impact of

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Lan, Giao Thi Phuong, and Pham Ngoc Khanh. "Factors affecting the perceived usefulness and intention to adopt artificial intelligence in manufacturing enterprises in the Southeast region of Vietnam." Edelweiss Applied Science and Technology 9, no. 6 (2025): 894–911. https://doi.org/10.55214/25768484.v9i6.7973.

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This study aims to explore “Factors affecting the perceived usefulness and intention to adopt artificial intelligence in manufacturing enterprises in the Southeast region of Vietnam.” Based on an integrated framework combining the Technology Acceptance Model (TAM) and the Technology–Organization–Environment (TOE) model, this study analyzes how organizational, technological, and environmental factors influence the adoption of artificial intelligence (AI). By employing a mixed-methods approach—comprising expert interviews and a quantitative survey of 435 manufacturing enterprises—the data were p

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Mohsin, Farhana Hanim, Norhayati Md Isa, Khairunnisa Ishak, and Hatijah Mohamed Salleh. "Navigating the Adoption of Artificial Intelligence in Higher Education." International Journal of Business and Technopreneurship (IJBT) 14, no. 1 (2024): 109–20. http://dx.doi.org/10.58915/ijbt.v14i1.433.

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With the emergence of Education 4.0, Artificial Intelligence (AI) is increasingly being used and integrated in higher education institutions in recent years. It is hardly surprising as we are living in the era of digital technologies and a transformational shift in the educational system. This conceptual article proposes a study on adoption of artificial intelligence (AI) in higher education among undergraduate students. Drawing from the Theoretical model of The Unified Theory of Acceptance and Use of Technology (UTAUT), this study aims to examine the influence between the key variables in the

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Satrio Tegar Sadewo, Shinta Ratnawati, Axel Giovanni, and Ipuk Widayanti. "The Influence of Personal Innovativeness on ChatGPT Continuance Usage Intention among Students." SATESI: Jurnal Sains Teknologi dan Sistem Informasi 5, no. 1 (2025): 88–98. https://doi.org/10.54259/satesi.v5i1.4117.

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The rapid advancement of generative AI technologies, such as ChatGPT, has introduced significant innovations across various sectors. However, the factors influencing the continuance usage intention of these technologies remain underexplored, particularly among university students in Indonesia. This study investigates the role of Personal Innovativeness within the framework of the Technology Acceptance Model (TAM) in determining students' intentions to continue using ChatGPT. The study used a quantitative method, involving a survey of 252 Indonesian university students. The survey measured Pers

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Pillai, Rajasshrie, and Brijesh Sivathanu. "Adoption of AI-based chatbots for hospitality and tourism." International Journal of Contemporary Hospitality Management 32, no. 10 (2020): 3199–226. http://dx.doi.org/10.1108/ijchm-04-2020-0259.

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Purpose This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables. Design/methodology/approach To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview

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Richard Immanuel, Vinitha K. "Significance of Behavioral Intention to Adopt Articficial Intelligence in Investment Strategies." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 472–85. https://doi.org/10.52783/jisem.v10i18s.2933.

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Artificial Intelligence (AI) is revolutionizing the investment management industry, promising enhanced decision-making, improved efficiency, and optimized portfolio management. Despite this, the adoption of AI in finance services is hindered by concerns of trust, ethics, and the risks they might pose. The primary constructs of interest include perceived trust (PT), ethical concern (E), perceived usefulness (PU), social norms (SN) and behavioural intention (BI) regarding how so-called artificial AI tools are adopted by investors in the investment management process, which this study explores. T

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Kim, Jhonghee, and Hyungjoon Kim. "The use of generative AI tools in design work: Motivation and decision-making process of users." Edelweiss Applied Science and Technology 9, no. 4 (2025): 74–82. https://doi.org/10.55214/25768484.v9i4.5939.

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As generative AI technologies evolve, more designers are integrating these tools into their workflows. While existing research has examined the use of generative AI in design, few studies have conceptualized user engagement within an integrated model of motivational and behavioral factors. This study explores key constructs—attitudes, subjective norms, perceived behavioral control, intention to use, and actual usage—through the Uses and Gratifications Theory (UGT) and the Theory of Planned Behavior (TPB). Results indicate that designers' attitudes and subjective norms significantly affect thei

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Li, Kang. "Determinants of College Students’ Actual Use of AI-Based Systems: An Extension of the Technology Acceptance Model." Sustainability 15, no. 6 (2023): 5221. http://dx.doi.org/10.3390/su15065221.

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Acceptance of, behavioral intention towards, and actual use of AI-based systems or programs has been a topic of growing interest in the field of education. A considerable number of studies has been conducted to investigate the driving factors affecting users’/students’ intentions regarding certain technology or programs. However, few studies have been performed to understand college students’ actual use of AI-based systems. Moreover, the mediating effect of students’ learning motivation was seldom considered. Therefore, the present study was conducted to explain factors contributing to college

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Singh, Sukhjit, Pooja Singh, and Vismaad Kaur. "Understanding ChatGPT Adoption among Higher Education Students in Punjab, India: An Application of UTAUT2 Model." Innoeduca. International Journal of Technology and Educational Innovation 11, no. 1 (2025): 5–28. https://doi.org/10.24310/ijtei.111.2025.20219.

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This study examines Indian Higher Education students' behavioural intention to use ChatGPT in their learning. Unified Theory of Acceptance, and Use of Technology 2 (UTAUT2) model is used to investigate the impact of the eight UTAUT2 factors on the students' behavioural intention towards using ChatGPT. A pilot study on 100 students was done to check the reliability and validity of the instrument based on the UTAUT2 model. Using a quantitative research approach, data was gathered from 362 students of Punjab (A North region State), India (313 students’ data was included in final analysis) using p

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Kim, Cheong. "Understanding Factors Influencing Generative AI Use Intention: A Bayesian Network-Based Probabilistic Structural Equation Model Approach." Electronics 14, no. 3 (2025): 530. https://doi.org/10.3390/electronics14030530.

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This study investigates the factors influencing users’ intention to use generative AI by employing a Bayesian network-based probabilistic structural equation model approach. Recognizing the limitations of traditional models like the technology acceptance model and the unified theory of acceptance and use of technology, this research incorporates novel constructs such as perceived anthropomorphism and animacy to capture the unique human-like qualities of generative AI. Data were collected from 803 participants with prior experience of using generative AI applications. The analysis reveals that

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Wicaksono, Teguh, Rizka Zulfikar, Purboyo Purboyo, Farida Yulianti, and Lamsah Lamsah. "Tracing The Digital Transformation: A Bibliometric Investigation Of Artificial Intelligence Adoption In Higher Education." Applied Business and Administration Journal 4, no. 02 (2025): 93–110. https://doi.org/10.62201/abaj.v4i02.217.

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This study aimed to track the digital transformation journey through the lens of AI adoption in higher education from 2010 to 2025. Using a data-based bibliometric method from Scopus, this study identified the dominant theories used in AI adoption intention studies and conceptual structures. The literature selection process was carried out systematically using the PRISMA method to ensure transparency and accuracy in document selection. Data analysis used bibliometric techniques to analyse the research landscape quantitatively and was conducted using VosViewer Software. The analysis results sho

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Kamsin, Intan Farahana. "Teacher’s Acceptance and Intention to Use Artificial Intelligence Technology in Teaching and Learning Based on the UTAUT Model." International Journal of Information and Education Technology 15, no. 7 (2025): 1428–35. https://doi.org/10.18178/ijiet.2025.15.7.2344.

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The integration of Artificial Intelligence (AI) technology in teaching and learning is becoming increasingly prevalent, necessitating teacher preparedness for pedagogical reform. This study investigates the factors influencing secondary school teachers’ acceptance of AI technology based on the Modified Integrated Theory of Acceptance and Use of Technology (UTAUT). Specifically, it examines the roles of Performance Expectation, Effort Expectation, Social Influence, and AI Anxiety in shaping behavioral intention, and explores the moderating effects of gender, age, and teaching experience. Data w

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Rafiq, Farrukh, Nikhil Dogra, Mohd Adil, and Jei-Zheng Wu. "Examining Consumer’s Intention to Adopt AI-Chatbots in Tourism Using Partial Least Squares Structural Equation Modeling Method." Mathematics 10, no. 13 (2022): 2190. http://dx.doi.org/10.3390/math10132190.

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Artificial intelligence (AI) is an important link between online consumers and the tourism industry. AI-chatbots are the latest technological advancement that have shaped the tourism industry. AI-chatbots are a relatively new technology in the hospitality and tourism industries, but little is known about their use. The study aims to identify factors influencing AI-chatbot adoption and their use in improving customer engagement and experiences. Using an offline survey, researchers collected data from 530 respondents. Using the structural equation modeling technique, the conceptual model was emp

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Cabero-Almenara, Julio, Antonio Palacios-Rodríguez, Hazel de los Ángeles Rojas Guzmán, and Victoria Fernández-Scagliusi. "Prediction of the Use of Generative Artificial Intelligence Through ChatGPT Among Costa Rican University Students: A PLS Model Based on UTAUT2." Applied Sciences 15, no. 6 (2025): 3363. https://doi.org/10.3390/app15063363.

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The rise in generative artificial intelligence (GenAI) is transforming education, with tools like ChatGPT enhancing learning, content creation, and academic support. This study analyzes ChatGPT’s acceptance among Costa Rican university students using the UTAUT2 model and partial least squares structural equation modeling (PLS-SEM). The research examines key predictors of AI adoption, including performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and actual usage. The findings from 194 students indicate that performance expectancy (β = 0.59

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Marinescu, Șerban Andrei, Ionica Oncioiu, and Adrian-Ionuț Ghibanu. "The Digital Transformation of Healthcare Through Intelligent Technologies: A Path Dependence-Augmented–Unified Theory of Acceptance and Use of Technology Model for Clinical Decision Support Systems." Healthcare 13, no. 11 (2025): 1222. https://doi.org/10.3390/healthcare13111222.

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Background/Objectives: Integrating Artificial Intelligence Clinical Decision Support Systems (AI-CDSSs) into healthcare can improve diagnostic accuracy, optimize clinical workflows, and support evidence-based medical decision-making. However, the adoption of AI-CDSSs remains uneven, influenced by technological, organizational, and perceptual factors. This study, conducted between November 2024 and February 2025, analyzes the determinants of AI-CDSS adoption among healthcare professionals through investigating the impacts of perceived benefits, technological costs, and social and institutional

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Alejandro, Isidro Max V., Joje Mar P. Sanchez, Gino G. Sumalinog, Janet A. Mananay, Charess E. Goles, and Chery B. Fernandez. "Pre-service teachers' technology acceptance of artificial intelligence (AI) applications in education." STEM Education 4, no. 4 (2024): 445–65. http://dx.doi.org/10.3934/steme.2024024.

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<p>We verified a pre-service teachers' Extended Technology Acceptance Model (ETAM) for AI application use in education. Partial least squares structural equation modeling (PLS-SEM) examined data from 400 pre-service teachers in Central Visayas, Philippines. Perceived usefulness and attitudes, usefulness and attitudes, ease of use and attitudes, and intention to use AI apps were significantly correlated. However, subjective norms, experience, and voluntariness did not affect how valuable AI was viewed or intended to be used. Attitudes toward AI mediated specific correlations use. These fi

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Jang, So-Jeong, HENGYU KANG, YANAN CHEN, and Ha-Kyun Kim. "The Impact of UTAUT2-Based Artificial Intelligence Technology on User Satisfaction through Actual System Use." Global Convergence Research Academy 3, no. 2 (2024): 101–13. https://doi.org/10.57199/jgcr.2024.3.2.101.

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Artificial Intelligence (AI) is a computer system that performs perception, reasoning, learning, and language abilities similar to humans, making it a core technology of the Fourth Industrial Revolution. Particularly, generative AI, which creates new content such as text, images, and music through large-scale data learning, has garnered significant attention. This technology utilizes artificial neural networks and machine learning to understand user intentions, learn from data, and generate various types of content. AI is defined as a technology that implements human intelligence-related funct

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Le, Xuan Cu. "Inducing AI-powered chatbot use for customer purchase: the role of information value and innovative technology." Journal of Systems and Information Technology 25, no. 2 (2023): 219–41. http://dx.doi.org/10.1108/jsit-09-2021-0206.

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Purpose This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention. Design/methodology/approach Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation

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Chen, Jiyun, and TsangKai Chang. "Exploring factors influencing AI-powered E-learning system adoption intention: An empirical study on mediation and moderation effects." Environment and Social Psychology 10, no. 3 (2025). https://doi.org/10.59429/esp.v10i3.3551.

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The prevalence of artificial intelligence (AI) technology in modern society has profoundly changed traditional communication and learning methods. As the application of AI technology in e-learning systems becomes increasingly pervasive, there is an urgent need for research on issues related to the behavioral intention of AI-powered e-learning systems. This study employs an integrated framework combining Innovation Diffusion Theory (IDT), the Technology Acceptance Model (TAM), and self-efficacy theory to analyze factors that empirically examine factors influencing college students' behavioral i

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Abdekhoda, Mohammadhiwa, and Afsaneh Dehnad. "Adopting artificial intelligence driven technology in medical education." Interactive Technology and Smart Education, January 29, 2024. http://dx.doi.org/10.1108/itse-12-2023-0240.

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Purpose Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education. Design/methodology/approach This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concernin

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Upadhyay, Nitin, Shalini Upadhyay, and Yogesh K. Dwivedi. "Theorizing artificial intelligence acceptance and digital entrepreneurship model." International Journal of Entrepreneurial Behavior & Research ahead-of-print, ahead-of-print (2021). http://dx.doi.org/10.1108/ijebr-01-2021-0052.

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PurposeThis paper aims to determine the entrepreneur's intention to accept artificial intelligence (AI) and provide advancement in the domain of digital entrepreneurship.Design/methodology/approachExtensive literature review and theories have been considered in the area of technology adoption/acceptance and digital entrepreneurship to identify the factors affecting the intention of entrepreneurs with respect to accept AI for digital entrepreneurship. Further, a model, artificial intelligence acceptance and digital entrepreneurship (AIADE) is theorized after formulating some hypotheses. The the

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Lam, Thomas Yuen Tung, Yue Hu, Y. Yi, et al. "A Model Predicting Artificial Intelligence Use by Gastroenterology Nurses in Clinical Practice: A Cross‐Sectional Multicenter Survey." Journal of Gastroenterology and Hepatology, July 3, 2025. https://doi.org/10.1111/jgh.17042.

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ABSTRACTBackground and AimsNurses' participation during colonoscopy has been demonstrated to significantly improve the detection rate of polyps and adenomas. Nonetheless, the adoption of AI in clinical practice still poses challenges. There is limited understanding of the factors influencing gastroenterology nurses' intentions to use AI in clinical practice. We aimed to examine how gastroenterology nurses' intentions to use AI are affected by perceived usefulness, acceptance of this technology, and perceived risk via a moderated mediation model controlling for nurses' characteristics.MethodsA

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Maldonado-Canca, Luis-Alfonso, Juan-Pedro Cabrera-Sánchez, and Ana-María Casado-Molina. "AI in enterprise management: determinants of purchase intention among CEOs without AI experience." Journal of Enterprise Information Management, June 25, 2025. https://doi.org/10.1108/jeim-03-2025-0230.

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PurposeThis study introduces a novel model of AI adoption focused on purchase intention by CEOs with no prior AI experience – a key shift from traditional usage-based approaches. It addresses how executives make strategic investment decisions under uncertainty, emphasizing trust and perceived value over readiness factors.Design/methodology/approachSurvey data from 252 CEOs were analyzed using PLS-SEM and necessary condition analysis (NCA). The model evaluates the effects of security, perceived value, response costs, organizational compatibility and facilitating conditions, explaining 73.7% of

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Rahman, Muhammad Khalilur, Md Arafat Hossain, Noor Azizi Ismail, Mohammad Shahadat Hossen, and Moniya Sultana. "Determinants of students’ adoption of AI chatbots in higher education: the moderating role of tech readiness." Interactive Technology and Smart Education, April 9, 2025. https://doi.org/10.1108/itse-12-2024-0312.

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Purpose This study aims to investigate the key factors influencing students’ adoption of artificial intelligence (AI) chatbot applications in higher education. It further examines the mediating and moderating role of AI chatbots and tech readiness in determining the effect of perceived usefulness, subjective norms, tech simplicity and tech literacy on the intention to use AI chatbot applications. Design/methodology/approach A survey was conducted at Malaysian universities whereby 430 students participated and 426 responses were deemed valid for analysis. The data was carefully examined to ensu

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