To see the other types of publications on this topic, follow the link: AI adoption intention model.
Journal articles on the topic 'AI adoption intention model'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'AI adoption intention model.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
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.
Full textAbstract:
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 a serial mediation model with moderating effects, demonstrating that resistance to AI-supported healthcare technologies influenced AI adoption behavioral intentions through job resource, job demand, and levels of employee engagement. Further, this study sought to validate the relationship between age-moderated job resource and job demand in employees exhibiting resistance to AI-supported healthcare technologies and their associated AI adoption behavioral intentions. The results indicated that job resources, job demands, and employee engagement serially mediated the relationship between resistance to AI-supported healthcare technologies and AI adoption behavioral intentions. Additionally, age only exhibited significant moderating effects on the relationship between job demands in resistance to AI-supported healthcare technologies and AI adoption behavioral intentions. The findings of this study can aid in promoting the adoption of AI-supported healthcare technologies by healthcare professionals, generating new insights and broadening the scope and vision of existing literature.
2
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.
Full textAbstract:
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 influence employees’ behavioral intention to use generative AI systems. Specifically, effort expectancy plays a critical role in the early stages of adoption, while social influence, including support from supervisors and peers, strongly drives the adoption process. In contrast, performance expectancy and facilitating conditions show no significant impact. The study also highlights the differential effects of age and work experience on behavioral intention and usage behavior. For older employees, social support is a key factor in technology acceptance, whereas employees with more experience exhibit a more positive attitude toward adopting new technologies. Conversely, facilitating conditions are more critical for younger employees. This study contributes to the understanding of the interaction between various factors in AI technology adoption and offers strategic insights for the successful implementation of AI systems in Korean companies.
3
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.
Full textAbstract:
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 expectancy (β = 0.091, p = 0.085) and top management support (β = 0.000, p = 0.997) show limited direct effects. These findings challenge some traditional assumptions of the UTAUT model. Additionally, attitudes were found to mediate the relationship between effort expectancy and social influence on behavioral intentions, while individual readiness and personal innovativeness moderate these relationships (β = −0.069, p = 0.003), highlighting the importance of individual traits. The model demonstrated strong predictive power, with R2 values of 0.677 for behavioral intention and 0.574 for actual behavior, along with Q2 predict values exceeding 0.56. By incorporating PIIT and TRI, this study broadens existing models of technology adoption, offering deeper insights into how organizational factors, personal traits, and readiness interact to influence AI adoption. Practical recommendations include introducing adaptive training programs, personalized support systems, and AI-driven infrastructure enhancements to encourage effective AI integration. Future research should consider longitudinal studies to examine how readiness and innovativeness evolve over time, explore cross-cultural differences, and refine strategies to ensure sustainable AI adoption in diverse academic settings.
4
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.
Full textAbstract:
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, optimism, and complexity also affect perceptions and attitudes. This research contributes theoretical insights and practical strategies for enhancing AI adoption in digital retail. Keywords: AI Chatbots, Consumer Behavior, E-Commerce, TAM, BRT, Perceived Usefulness, Perceived Ease of Use, Digital Adoption
5
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.
Full textAbstract:
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 most important determinant influencing the adoption of AI technology in Vietnamese non-life insurers is attitudes toward adoption. Attitudes toward adoption are positively related to the perceived ease of use and perceived usefulness, consistent with Gupta et al. (2022). Although perceived risk has a negative influence on the behavioral intention to adopt AI technology, it is not a serious issue for insurance companies.
6
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.
Full textAbstract:
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 using a survey by considering 308 usable respondents. Findings The results of this study show that perceived usefulness and perceived ease of use directly impact the behavioral intention of the employees to adopt an AI integrated CRM system in organizations. Also, these two exogenous factors impact the behavioral intention of the employees to adopt an AI integrated CRM system mediating through two intermediate variables such as utilitarian attitude (UTA) and hedonic attitude (HEA). The proposed model has achieved predictive power of 67%. Research limitations/implications By the help of the technology acceptance model and motivational theory, the predictors of behavioral intention to adopt AI integrated CRM systems in organizations were identified. The effectiveness of the model was strengthened by the consideration of two employee-centric attitudinal attributes such as UTA and HEA, which is claimed to have provided contributions to the extant literature. The proposed theoretical model claims a special theoretical contribution as no extant literature considered the effects of leadership support as a moderator for the adoption of an AI integrated CRM system in Indian organizations. Practical implications The model implies that the employees using AI integrated CRM system in organizations must be made aware of the usefulness of the system and the employees must not face any complexity to use the system. For this, the managers of the concerned organizations must create a conducive atmosphere congenial for the employees to use the AI integrated CRM system in the organizations. Originality/value Studies covering exploration of the adoption of AI integrated CRM systems in Indian organizations are found to be in a rudimentary stage and in that respect, this study claims to have possessed its uniqueness.
7
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.
Full textAbstract:
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, perceived ease of use, and self-efficacy within the extended Technology Acceptance Model (TAM) significantly influence the behavioural intention to utilise AI in an academic library in Ghana. Additionally, the results suggest that perceived usefulness plays a more significant role in influencing behavioural intention compared to perceived ease of use. Furthermore, the study reveals a direct relationship between behavioural intention and use behaviour within TAM. Conclusion. This study underscores the critical factors within the extended Technology Acceptance Model that drive the adoption of AI in academic libraries in Ghana. The results highlight the paramount importance of perceived usefulness in shaping behavioural intention, surpassing the impact of perceived ease of use. Moreover, the direct link between behavioural intention and actual use behaviour reaffirms the model’s applicability in predicting technology adoption. These insights provide a valuable foundation for developing strategies to enhance AI integration in academic libraries, ultimately improving their operational efficiency and service delivery.
8
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.
Full textAbstract:
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 model. Our findings highlight the significant role played by leadership, organizational readiness, as well as the “push-and-pull” effect of competitors and customers in encouraging SMEs to adopt AI technologies. However, in the case of Romania, specific challenges related to a lack of digital skills among employees, a limited understanding of the relative advantage that digitalisation can offer, as well as a lack of marketing efforts from the side of vendors make it difficult for SMEs to consider the implementation of AI technologies. This exploratory study seeks to understand the underlying trends of the phenomenon and serves as a stepping stone for vendors, managers, as well as researchers to better understand the market for AI tools and solutions among Romanian SMEs.
9
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.
Full textAbstract:
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 learning solutions. Through a survey of 263 undergraduate and postgraduate students from Malaysian universities, the study develops an adapted model to assess these adoption factors, contributing unique insights into the integration of AI and mobile learning within the Malaysian higher education context. This model provides actionable recommendations for university administrators, educators, and mobile learning developers, offering practical guidance on promoting the adoption of these technologies to enhance student engagement and learning outcomes. By focusing on real-world application, this study not only bridges theoretical research with practical implementation but also offers valuable lessons for similar educational contexts globally, particularly in emerging markets.
10
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.
Full text
11
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.
Full textAbstract:
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 factors and the intention to adopt AI. The mediation effect was examined through the network operation productivity and network service stability. A survey was conducted targeting network operations and AI professionals to collect data. The analysis results revealed that technological and environmental factors positively influenced the network operation productivity, while only environmental factors positively influenced the network service stability. Furthermore, the findings of this study highlight that environmental factors are the most significant factors that influence network operation productivity and network service stability. Moreover, the direct positive impact of network operation productivity and IT network service stability on the intention to adopt AI underscores their crucial role. In conclusion, when evaluating AI adoption in terms of network operation productivity and network service stability, prioritizing technological and environmental factors over organizational factors is necessary.
12
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.
Full textAbstract:
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 conducted a confirmatory factor analysis and structural equation modeling to analyze research hypotheses. The results indicated that three types of consumer evaluations (i.e., perceived usefulness, perceived ease of use, and importance of exercise) positively influence their attitudes toward AI fitness services. In addition, the positive attitudes regarding AI services positively influenced the intention to use AI services. The results of this research contribute to our knowledge of the consumers’ attitudes and behaviors toward AI services in the sport industry based on the technology acceptance model. Furthermore, this study provided the empirical evidence critically needed to increase our understanding of AI in the sport industry and offered new insights into how sport facility managers can predict their consumers’ intention to use AI services.
13
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.
Full textAbstract:
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 engagement. The article reports valuable insights and relevant implications for public agencies, service providers and researchers.
14
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.
Full textAbstract:
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 importance of the research. The study will be conducted on 100 State University of Jakarta students and Analysis of the data will use Partial Least Square (PLS) Structural Equation Model to explain the connection between variables.
15
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.
Full textAbstract:
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 continuance usage intentions.Design/methodology/approachThis paper uses a survey method to analyze the research model, and a total of 223 responses are collected. The partial least squares (PLS) technique is utilized for data analysis.FindingsThe results show that intelligence and anthropomorphism affect the continuance usage intention of MFA users through their goal difficulty and specificity. Both intelligence and anthropomorphism positively influence goal specificity, whereas they negatively affect goal difficulty. In addition, goal specificity increases users' MFA continuance usage intention, whereas goal difficulty decreases users' continuance usage intention. The findings of this study provide theoretical contributions for AI technology adoption research and offer practical strategies for firms to retain MFA users.Originality/valueBased on goal-setting theory, this study reveals that as two primary AI features of contemporary mobile fitness apps, intelligence and anthropomorphism, can increase comprehension of users' perceptions regarding goal difficulty and specificity in the context of users' continuance usage intentions toward AI-powered MFAs.
16
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.
Full textAbstract:
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 and prior technology acceptance studies that consider individual factors. The model was evaluated using data collected from 252 potential XAI users. The validated model exhibits strong explanatory power, accounting for 45% of the variance in users? intention to use XAI. Findings indicate that perceived value and perceived need are key determinants of users' intention to adopt XAI. These results provide empirical evidence and deepen the understanding of user perceptions and intentions regarding XAI adoption.
17
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.
Full textAbstract:
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 this gap and provides insights to enhance adoption strategies. Standardized questionnaires were used, incorporating UTAUT constructs and brand-related variables such as Brand Recognition and Brand Trust. The sample consisted of individuals working in the creative industry in the US and Spain, with 208 valid responses. The survey was distributed through creative online communities. Partial Least Squares Structural Equation Modeling was employed to validate the hypotheses, ensuring reliable and valid results. Key findings indicate that performance expectancy, facilitating conditions, and brand trust positively influence the behavioral intention to use Generative AI tools, while brand recognition negatively influences behavioral intention. Social influence and effort expectancy did not present statistically significant results. These insights contribute to developing effective adoption strategies for Generative AI in the creative industry.
18
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.
Full textAbstract:
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 proposed a model of ophthalmic AI acceptance based on technology acceptance theories and variables from other health care–related studies. The model was verified via a 32-item questionnaire with 7-point Likert scales completed by 474 respondents (nationally random sampled). Structural equation modeling was used to evaluate item and construct reliability and validity via a confirmatory factor analysis, and the model’s path effects, significance, goodness of fit, and mediation and moderation effects were analyzed. Results Standardized factor loadings of items were between 0.583 and 0.876. Composite reliability of 9 constructs ranged from 0.673 to 0.841. The discriminant validity of all constructs met the Fornell and Larcker criteria. Model fit indicators such as standardized root mean square residual (0.057), comparative fit index (0.915), and root mean squared error of approximation (0.049) demonstrated good fit. Intention to use (R2=0.515) is significantly affected by subjective norms (beta=.408; P<.001), perceived usefulness (beta=.336; P=.03), and resistance bias (beta=–.237; P=.02). Subjective norms and perceived behavior control had an indirect impact on intention to use through perceived usefulness and perceived ease of use. Eye health consciousness had an indirect positive effect on intention to use through perceived usefulness. Trust had a significant moderation effect (beta=–.095; P=.049) on the effect path of perceived usefulness to intention to use. Conclusions The item, construct, and model indicators indicate reliable interpretation power and help explain the levels of public acceptance of ophthalmic AI devices in China. The influence of subjective norms can be linked to Confucian culture, collectivism, authoritarianism, and conformity mentality in China. Overall, the use of AI in diagnostics and clinical laboratory analysis is underdeveloped, and the Chinese public are generally mistrustful of medical staff and the Chinese medical system. Stakeholders such as doctors and AI suppliers should therefore avoid making misleading or over-exaggerated claims in the promotion of AI health care products.
19
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.
Full textAbstract:
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 implement targeted training programs to foster AI integration in Moroccan higher education. It contributes to the literature by extending the UTAUT model to an underexplored cultural and educational context while providing practical recommendations for overcoming barriers to AI adoption in developing countries.
20
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.
Full textAbstract:
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) among faculty in higher education. A quantitative correlational research design was used, with data collected through an online questionnaire distributed to faculty members. The results indicate that PEOU significantly predicts PU, reinforcing the importance of usability in AI adoption. However, PU negatively influences ATT, suggesting that while faculty members recognize AI's usefulness, they may have concerns regarding its implications for academic integrity and pedagogical changes. Despite this, ATT strongly predicts IU, indicating that faculty attitudes are the primary driver of AI adoption. These findings underscore the importance of institutional AI training, ethical guidelines, and AI-integrated curriculum strategies to facilitate the responsible adoption of AI. Future research should incorporate qualitative insights and expand to multiple institutions to enhance generalizability and validity.
21
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.
Full textAbstract:
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 technique used is 300 respondents with the last education of SMK/SMA and have used m-payment OVO in the last 6 months and who are domiciled in Surabaya.
 Findings: The results showed that Perceived Transaction Convenience (PCT), Compatibility (COM), Relative Advantage (RA), Government Support (GS), Additional Value (AV), Perceived Risk (PSR), Absorptive Capacity (AC), Affinity (AFFI), and Personal Innovativeness in Information Technology (PIIT) is proven to have a significant effect on Adaptation Intention (AI). In addition, Social Influence (SI) has been shown to have non-significant results on the Adaptation Intention (AI) of OVO mobile payment users in Surabaya.
22
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.
Full textAbstract:
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 expectancy, and effort expectancy, have minimal influence on the examined variables. The analysis reveals the crucial mediating role of behavioral intention, effectively bridging the gap between various predictors and AI usage behavior, thereby highlighting its significance in the broader context of technology adoption in the hotel industry. The primary goal of the study, which closes significant research gaps, as well as the manner in which it uses a specific model and statistical analysis to accomplish this goal, shows how innovative the work is. This method not only broadens the field’s understanding but also offers valuable insights for shaping sustainable development practices in the hospitality sector in the Republic of Serbia.
23
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.
Full textAbstract:
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 choosing AI-based technologies. It was analysed that in the case of South Korea, personal competence had a positive impact (+) on perceived ease of use, whereas the UK had a positive impact (+) on perceived usefulness and perceived ease of use. This study holds particular significance in the domain of cross-cultural research within the construction industry. It conducts an analysis of the factors influencing the adoption of AI-driven technologies or products, with a specific focus on the cultural differences between two nations: South Korea and the UK, which represent Eastern and Western cultural paradigms, respectively.
24
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.
Full textAbstract:
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 answer those questions. We propose a model that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to AI (user trust in AI and perceived quality of AI output, from XAI literature) and collaborative intention (willingness to contribute to AI pipelines).We tested the proposed model with an application for car damage claim reporting with AI-powered damage estimation for insurance customers. The results showed that the XAI related factors have a strong and positive effect on the behavioural intention, the perceived usefulness and the ease of use of the application. Moreover, there is a strong link between the behavioural intention and the collaborative intention, indicating that indeed human-in-the-loop approaches can be successfullyadopted in final user applications.
25
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.
Full textAbstract:
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 self-determination theory (SDT) and technology acceptance model (TAM) with the post-acceptance model (PAM). Three hundred forty university students were solicited to complete a questionnaire to collect data for the proposed model. A dual-stage approach uses both symmetrical assumptions from structural equation modelling with partial least squares (PLS-SEM) and asymmetrical configurations from fuzzy-set qualitative comparative analysis (fsQCA). In order to better comprehend the intricate interplay between the model's inputs and its desired output, this approach is devised. Consideration is given to the fact that various configurations of external constructs exert distinct influences on internal constructs. In Thailand, perceived usefulness (PU) and autonomy predict continued AI-powered tool use. Perceived ease of use (PEOU) did not affect continuing intention. Conclusions drawn from the configurational analysis show that no single factor adequately explains a high CI level. Rather, three distinct configurations were identified as improving CI using AI-powered tools. Overall, theoretical and practical ramifications are addressed.
26
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.
Full textAbstract:
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 processed using SPSS 29.0 and AMOS 29.0 through several steps: reliability testing, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The results reveal that five factors: government involvement (GI), perceived cost (PC), management support (MS), technical infrastructure (TI), and organizational culture (OC) positively influence perceived usefulness (PU). Meanwhile, competitive pressure (CP) and vendor partnership (VP) have a direct effect on the intention to adopt AI (AAI). Notably, perceived usefulness (PU) plays a significant mediating role and has a strong impact on the intention to adopt AI (AAI). These findings confirm the appropriateness of the TAM model in explaining AI acceptance behavior and provide managerial implications for business leaders and policymakers to promote AI adoption in manufacturing enterprises.
27
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.
Full textAbstract:
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 ChatGPT adoption in entrepreneurship and digital entrepreneurial intentions, underlying a moderated mediation mechanism of digital entrepreneurial self-efficacy and technostress. Methods: Drawing on the sample of 1326 respondents in Vietnam using a stratified sampling approach, first, Cronbach’s alpha and confirmatory factor analysis were used to test the reliability and validity of scales; after that, Harman’s single-factor and common latent factor were employed to test the common method bias; finally, the PROCESS macro approach was utilized to test the hypothesized model. Findings & value added: Our findings reveal positive impacts of ChatGPT adoption in entrepreneurship on digital entrepreneurial self-efficacy and digital entrepreneurial intentions. Moreover, digital entrepreneurial self-efficacy is found to significantly mediate the impact of ChatGPT adoption in entrepreneurship on digital entrepreneurial intention. Furthermore, technostress emerges as a significant negative moderator, influencing the impact of ChatGPT adoption in entrepreneurship on both digital entrepreneurial self-efficacy and intentions. This study thus contributes to the literature by advancing our understanding of how AI technologies shape entrepreneurial aspirations, offering valuable insights for scholars and practitioners navigating the transformative landscape of digital entrepreneurship.
28
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.
Full textAbstract:
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 UTAUT model such as performance expectation, effort expectation, social influence and facilitating conditions on attitudes and behavioral intention towards AI adoption in higher education institutions. This study will utilize quantitative research design using Structural Equation Modeling - Partial Least Squares (SEM-PLS) to analyze the data. It becomes central to investigate such AI adoption tendency among students as this will aid the institutions to tap into the potential problems and opportunities that may arise with its adoptions and usage. This study also attempts to clarify the potential linkages by engaging in a discussion prior to conducting empirical testing.
29
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.
Full textAbstract:
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 data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis. Findings As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots. Practical implications This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers. Originality/value This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.
30
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.
Full textAbstract:
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. The quantitative approach was employed using “Partial Least Squares Structural Equation Modeling” (PLS-SEM) to collect data from 200 respondents with experience in AI-driven AI-driven investment tools. The most substantial finding is that the impact of perceived usefulness on behavioural intention was positively correlated but not as strong as the impact on trust and ethics. This research adds to the literature by integrating ethical concerns and social norms into the “technology acceptance model” (TAM) to understand the adoption of AI in the investment sector. Finally, we provide practical implications for financial premises and address AI developers by providing transparent, trustworthy, and ethically sound AI systems for investment management.
31
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.
Full textAbstract:
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 their intention to adopt generative AI tools, which in turn influences actual usage. Designers generally hold positive attitudes toward these tools, and external social influences are crucial to their adoption. Finally, enhancing perceived control may further promote adoption and integration into design practices.
32
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.
Full textAbstract:
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 students’ actual use of AI-based systems, as well as to examine the role of their learning motivations. As a result, perceived usefulness and perceived ease of use of AI-based systems positively impacted students’ attitude, behavioral intentions, and their final, actual use of AI-based systems, while college students’ attitude towards AI-based systems showed an insignificant impact on students’ learning motivations of achieving their goals and subjective norms. Collectively, the findings of the present study could enrich the knowledge of the technology acceptance model (TAM) and the application of the TAM to explain students’ behavior in terms of the adoption of AI-based systems.
33
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.
Full textAbstract:
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 purposive sampling technique. The study's findings revealed that PE (Performance Expectancy), SI (Social Influence), HM (Hedonic Motivation), Hb (Habit), FC (Facilitating Conditions) had significant positive influence on BI (Behavioural Intention) whereas EE (Effort Expectancy) had not significantly influenced BI. On ChatGPT use, H and BI had a positive influence, but FC did not significantly influence ChatGPT use. 67% of the respondents gave priority to learning AI tools in school. In terms of practical implications, this study adds to the current literature on ChatGPT or AI tools in higher education, being useful to education scholars. Also, this study highlights the validation of UTAUT2 model to use ChatGPT among HEI students in Punjab, India. The findings of this study could facilitate discussions among educators working for policies related to the use of AI tools, specifically ChatGPT in India.
34
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.
Full textAbstract:
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 show that research on AI adoption intention has experienced an annual growth of 34.15%, with most publications using the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) approaches. Network visualisation revealed fragmentation in this research, where several clusters of theories develop separately without strong integration. Overlay visualisation showed a shift from technology acceptance model-based studies to exploration of ethical impacts, algorithm transparency, and AI regulation in higher education. Density visualisation confirmed that although technical factors have been widely studied, AI's social and policy aspects are still underexplored. This research provides a more comprehensive conceptual mapping and identifies research gaps that future studies can fill.
35
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.
Full textAbstract:
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 social influence (standardized total effect = 0.550) is the most significant predictor of use intention, followed by effort expectancy (0.480) and perceived usefulness (0.454). Perceived anthropomorphism (0.149) and animacy (0.145) also influence use intention, but with a lower relative impact. By utilizing a probabilistic structural equation model, this study overcomes the linear limitations of traditional acceptance models, allowing for the exploration of nonlinear relationships and conditional dependencies. These findings provide actionable insights for improving generative AI design, user engagement, and adoption strategies.
36
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.
Full textAbstract:
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 empirically tested. According to the results, the S-O-R theoretical framework is suitable for evaluating chatbot adoption intentions. Additionally, the structural model supported the ten hypotheses, validating the suggested directions of substantial impacts. In addition to practitioners and tourism managers, this study also has broad implications for scholars.
37
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.
Full textAbstract:
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 = 0.596, p < 0.001) is the strongest predictor of behavioral intention, followed by effort expectancy (β = 0.241, p = 0.005), while social influence (β = 0.381, p < 0.001) and facilitating conditions (β = 0.217, p = 0.008) play a smaller role. Behavioral intention significantly influences actual usage (β = 0.643, p < 0.001). Gender and age differences emerge, with male students and those aged 21–30 years showing higher acceptance levels. Despite positive attitudes toward ChatGPT, the students report insufficient training for effective use, underscoring the need for AI literacy programs and structured pedagogical strategies. This study calls for further research on AI training programs and their long-term impact on academic performance to foster responsible GenAI adoption in higher education.
38
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.
Full textAbstract:
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 influence, as well as the transparency and control of algorithms, using an adapted Path Dependence-Augmented–Unified Theory of Acceptance and Use of Technology model. Methods: This research was conducted through a cross-sectional study, employing a structured questionnaire administered to a sample of 440 healthcare professionals selected using a stratified sampling methodology. Data were collected via specialized platforms and analyzed using structural equation modeling (PLS-SEM) to examine the relationships between variables and the impacts of key factors on the intention to adopt AI-CDSSs. Results: The findings highlight that the perceived benefits of AI-CDSSs are the strongest predictor of intention to adopt AI-CDSSs, while technology effort cost negatively impacts attitudes toward AI-CDSSs. Additionally, social and institutional influence fosters acceptance, whereas perceived control and transparency over AI enhance trust, reinforcing the necessity for explainable and clinician-supervised AI systems. Conclusions: This study confirms that the intention to adopt AI-CDSSs in healthcare depends on the perception of utility, technological accessibility, and system transparency. The creation of interpretable and adaptive AI architectures, along with training programs dedicated to healthcare professionals, represents measures enhancing the degree of acceptance.
39
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.
Full textAbstract:
<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 findings improve the ETAM model and highlight the significance of user-friendly AI interfaces, educational activities highlighting AI's benefits, and institutional support to enhance pre-service teachers' adoption of AI applications in education. Despite its limitations, this study establishes the foundation for further research on AI adoption in educational settings.</p>
40
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.
Full textAbstract:
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 functions such as learning, cognition, and reasoning using the concepts and tools of computer science. This study aims to analyze the impact of AI-based UTAUT2 characteristics on user satisfaction. The research targets general consumers who use AI services, and statistical analysis was conducted using Smart PLS 4.0. The purpose of this study is to analyze the impact of UTAUT2 characteristics in the context of artificial intelligence (AI) on user satisfaction. The research subjects were general consumers who use AI services, and statistical analysis was conducted using Smart PLS 4.0. The results of the study are as follows. First, performance expectancy had a significant positive effect on behavioral intention. Second, effort expectancy had a significant positive effect on behavioral intention. Third, social influence did not have a significant positive effect on behavioral intention. Fourth, facilitating conditions did not have a significant positive effect on behavioral intention. Fifth, trust had a significant positive effect on behavioral intention. Sixth, personal innovativeness had a significant positive effect on behavioral intention. Seventh, behavioral intention had a significant positive effect on actual system use. Eighth, actual system use had a significant positive effect on user satisfaction. Therefore, this study empirically verified the relationships between AI technology’s behavioral intention, actual system use, and user satisfaction based on the UTAUT2 model. Performance expectancy, effort expectancy, trust, and personal innovativeness were identified as significant factors influencing the acceptance intention of AI technology. Furthermore, behavioral intention was shown to lead to actual system use, which, in turn, positively influenced user satisfaction. Based on these findings, AI technology developers and companies should enhance user experience, build trust, and strengthen technical support to maximize AI technology adoption and user satisfaction.
41
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.
Full textAbstract:
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 modeling was applied for data analysis. Findings Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention. Practical implications The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots. Originality/value The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.
42
Ashrafi, Dewan Mehrab. "Managing Consumers’ Adoption of Artificial Intelligence-Based Financial Robo-Advisory Services: A Moderated Mediation Model." Journal of Indonesian Economy and Business 38, no. 3 (2023): 270–301. http://dx.doi.org/10.22146/jieb.v38i3.6242.
Full textAbstract:
Introduction/Main Objectives: This study investigates the determinants of willingness to use financial robo-advisory services. The study aims to identify the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumers’ acceptance of financial robo-advisory services. Background Problem: Fintech and AI-based applications have opened up new prospects for financial management, but studies into the adoption and implementation of robo-advisors are limited and scant. Novelty: The study offers novel insights by exploring the direct and indirect effects of perceived value and risk on consumer decisions around adopting robo-advisory services. The study also identifies other major drivers of robo-advisory service adoption and formulates a comprehensive model. Research Methods: A quantitative method using a deductive approach was applied, with PLS-SEM performed on a sample of 285 respondents from Bangladesh. The sample was gathered using a purposive sampling method. Findings/Results: Findings revealed that while relative advantage and perceived innovativeness positively affected perceived value and adoption intention, complexity negatively impacted perceived value and adoption intention. The findings also highlighted that attitude had a negative effect on perceived risk and intention to adopt robo-advisory services. The mediating impact of perceived value and risk in predicting the relationship between relative advantage, attitude and behavioral intention to adopt robo-advisory services was also identified. Moreover, the study revealed that perceived financial knowledge moderated the relationship between perceived value and behavioral intention. Conclusion: This study contributes to the existing body of literature by showing the intertwined roles of perceived value, perceived risk, and perceived financial knowledge in consumer acceptance of robo-advisory services. The study provides meaningful insights for financial institutions, and policymakers seeking to make robo-advisory services more reliable and acceptable to consumers through innovative service design and positioning.
43
Yang, Heetae. "Continuance Intention to use Generative AI in Office Tasks : Focusing on the Information Adoption Model." Information Systems Review 27, no. 2 (2025): 285–301. https://doi.org/10.14329/isr.2025.27.2.285.
Full text
44
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.
Full textAbstract:
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 intentions in e-learning. It identifies the mediating mechanism underlying the relationship between adoption intentions and its antecedents and examines the moderating effect of self-efficacy. A purposive questionnaire was distributed online among college students. A total of 298 responses were drawn. A quantitative survey methodology included Chi-square analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. The results show that college students' adoption intention determinants are AI-powered e-learning system traits (relative advantage, complexity, observability) and satisfaction. Furthermore, the impacts of AI-powered e-learning system traits on adoption intention are mediated by satisfaction. Self-efficacy positively moderates the impact of innovation traits on adoption intention. The discussion and implications present theoretical advancements in elucidating the mechanism of adoption intention and putting forward instructive recommendations for improving the adoption intention of technology-driven innovations in the digitalized education era.
45
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.
Full textAbstract:
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 concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF). Findings According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI. Practical implications By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education. Originality/value The finding of this study should be considered when successful implementation of AI in education is in progress.
46
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.
Full textAbstract:
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 theorized model has been validated with 476 useable responses.FindingsThe findings revealed that performance expectancy, openness, social influence, hedonic motivations and generativity have a positive impact on entrepreneur's acceptance intention of AI. Additionally, affordance has no direct relationship with AI acceptance intention, but it affects AI acceptance intention through attitude. Inconvenience has a significant negative relationship with the intention to accept AI, while uncertainty was found to be positively affecting the AI acceptance intention. Effort expectancy did not confirm any significant relationship.Research limitations/implicationsBy considering existing theoretical models and concepts the authors contribute to the AI's theoretical progress, specifically in the domain of entrepreneurship. The authors complement and extend existing technology adoption/acceptance theories and digital entrepreneurship theories by developing a theoretical model, AIADE, explaining the entrepreneur's intention to accept AI.Practical implicationsThe practical implications of the study show that performance expectancy (positive), openness (positive), social influence (positive), hedonic motivations (positive), generativity (positive), affordance through attitude (positive), uncertainty (positive), effort expectancy (negative) and inconvenience (negative) are the antecedents for the entrepreneurs to accept AI for digital entrepreneurship. The authors suggest that intentional improvement planning is developed by increasing entrepreneur's positive perceptions of AI affordance and explanation of its generativity and openness, and improving their attitude of using AI for digital entrepreneurship.Originality/valueThis is the first study that reveals the critical antecedents of entrepreneur's intention to accept AI for digital entrepreneurship. Relevant theoretical background, discussion, implications, limitations and future research recommendations are discussed.
47
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.
Full textAbstract:
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 ensure the accuracy of the proposed model. To comprehend the intricate relationships in the model, the authors used the partial least squares structural equation modeling (PLS-SEM) technique. Findings The results revealed that perceived usefulness (PU), subjective norm (SN), tech literacy (TL) and tech simplicity (TS) have a significant impact on students’ intention to use AI applications. The intention to use AI chatbot applications significantly influences the adoption of AI chatbots in higher education. The findings indicated that the intention to use AI chatbots mediates the effect of PU, SN, TL and TS on the adoption of AI chatbots. Tech readiness (TR) moderates the effect of PU and TS on the intention to use AI chatbot applications. Originality/value This research addresses a new insight into AI chatbot adoption within higher education, particularly demonstrating how AI chatbots and tech readiness act as mediators and moderators in shaping students’ perceptions and adoption of AI chatbot applications.
48
Duong, Cong Doanh, Thanh Hieu Nguyen, Minh Hoa Nguyen, Ngoc Su Dang, Anh Trong Vu, and Ngoc Diep Do. "Exploring the role of generative artificial intelligence (ChatGPT) adoption in digital social entrepreneurship: a serial mediation model." Social Enterprise Journal, June 10, 2025. https://doi.org/10.1108/sej-03-2024-0029.
Full textAbstract:
Purpose Using an integrated framework of the Entrepreneurial Event Model and the Stimulus–Organism–Response theory, this study aims to investigate how artificial intelligence (AI)-driven stimulus [Generative AI (ChatGPT) adoption in digital social entrepreneurship] affects individuals’ cognitive processes (perceived feasibility and perceived desirability), which subsequently influence their behavioral intentions (digital social entrepreneurial intention). Design/methodology/approach This research used a stratified sampling method to survey 986 higher education students in Vietnam. Hypotheses were tested using structural equation modeling. Findings The results indicate that GenAI (ChatGPT) adoption in digital social entrepreneurship significantly enhances both perceived feasibility and perceived desirability. These cognitive perceptions are positively associated with intentions to engage in digital social entrepreneurship. In addition, this study finds that GenAI (ChatGPT) adoption in digital social entrepreneurship poses a serial indirect effect on digital social entrepreneurial intention through a perceived feasibility–perceived desirability path. Practical implications The findings provide actionable recommendations for aspiring students (potential future entrepreneurs), educators and policymakers to foster the use of AI technologies in promoting digital social entrepreneurship. Originality/value This study offers substantial theoretical contributions by merging the Entrepreneurial Event Model and the Stimulus–Organism–Response framework. Thus, it advances the extant understanding of the cognitive mechanisms driving digital social entrepreneurial decision-making in the context of AI adoption. This research addresses a critical gap and establishes a foundation for future theoretical advancements in digital social entrepreneurship and AI integration.
49
Byambaa, Oyundari, Chimedtsogzol Yondon, Enkhbat Rentsen, Bayanjargal Darkhijav, and Mahfuzur Rahman. "An empirical examination of the adoption of artificial intelligence in banking services: the case of Mongolia." Future Business Journal 11, no. 1 (2025). https://doi.org/10.1186/s43093-025-00504-y.
Full textAbstract:
Abstract Artificial intelligence (AI) has profoundly impacted banking services, particularly in the context of rapid technological advancements. The success of the banking sector depends on establishing customers’ intention to adopt AI. However, research on AI adoption in Mongolia’s banking sector remains limited, underscoring the need to understand consumer behavior and key adoption factors. This paper seeks to evaluate consumer attitudes toward adopting AI in banking services. To achieve this goal, we surveyed the perceptions of customers from selected banks, yielding 508 participants and 487 valid responses for subsequent analysis. The proposed model was assessed using a partial least squares approach to the technical acceptance model. Our findings indicate that the banks involved in this study have already integrated various AI products. The results demonstrate that perceived usefulness, perceived trust, and attitudes toward AI in banking significantly enhance the adoption of AI-enabled banking services. Additionally, the study examines the partial mediating effect of attitudes toward AI on the intention to adopt AI in banking, identifying ATT as a mediating variable between PEOU and PU with INT. These findings provide practical insights for banks and stakeholders seeking to enhance AI-powered customer service while contributing to the literature on AI adoption in banking from a consumer perspective.
50
Khirfan, Randa, Heba Kotb, Huda Atiyeh, Anas Khalifah, Nahid AlHasan, and Samah Abdelalla. "Exploring the Influence of Transformational Leadership on Nurses' Intentions towards Artificial intelligence Utilization in Non-AI Implemented Hospitals." Research Journal of Pharmacy and Technology, November 18, 2024, 5469–79. https://doi.org/10.52711/0974-360x.2024.00837.
Full textAbstract:
Transformational leadership (TFL) is an inspiring and motivating leadership style and vital change and novel technology-enhancing factor. The lack of research studying the TFL mechanism of influencing nurses’ readiness and intention for artificial intelligence (AI) adoption in non-AI implemented hospitals is the core problem. Thus, the study aimed to examine the relationship between TFL and nurses’ intentions toward AI utilization in Jordanian hospitals - an online questionnaire disseminated to nurses in targeted hospitals where AI technology is not implemented. Method used structured questionnaire grounded on a Multifactor Leadership Questionnaire (MLQ) for measuring TFL, and Theory of planned behaviors (TPB) and Technology Acceptance Model (TAM) for measuring intention are utilized. The analysis process encompasses descriptive statistics, Pearson correlations, and hierarchical regression. The age group 31-40 years old and those with higher educational levels recorded significantly higher intentions to utilize AI. Even with the limitations of self-reporting and cross-sectional design, findings underscore the criticality of TFL, mainly intellectual stimulation's role in structuring nurses' readiness and intention towards AI utilization, and the necessity for targeted leadership strategies to promote AI adoption culture. Despite that, TFL fosters creativity and critical thinking; some organizational factors such as training and support are significant influential factors. Thus, targeted interventions help overcome resistance and create innovation supportive culture. The results revealed a weak positive influence of TFL on nurses' intentions toward AI utilization, and the perceived intellectual stimulation dimension is the strongest intention predictor.