Although extensive research has examined AI integration in education and the UTAUT2 model, few studies have explored ChatGPT adoption with demographic variables in Nigeria and other African contexts. Prior studies have analyzed the impacts UTAUT variables have on students’ behavioral intention and use of ChatGPT, yet little is known about how these relationships differ by demographic profiles. This study addresses that gap by investigating the influence of factors such as age, sex, and programme of study on these associations among (...) Nigerian higher education students. A cross-sectional correlational design was used and data were collected from 8,496 students across various tertiary institutions in Nigeria, using a structured questionnaire. Data were gathered electronically between October 13, 2023, and February 14, 2024. Multigroup analysis within the framework of Partial Least Squares Structural Equation Modelling (PLS- SEM) was performed. Both measurement and structural model metrics are reported for all subgroups. Findings indicate that performance expectancy is associated with higher intention to use ChatGPT, particularly among males, older students, and those with advanced qualifications, while its relationship with actual use varies across age groups. Effort expectancy and social influence showed differing associations with intention and use depending on demographic characteristics. Facilitating conditions were generally linked to greater actual use, and both hedonic motivation and habit were consistently associated with intention and behavior. These results provide empirical evidence of demographic variations in ChatGPT adoption among Nigerian students and suggest that interventions promoting AI tools should consider these differences. (shrink)

Artificial Intelligence, at the forefront of innovation and intelligence, is redefining the pace of life and work, notably within education. This study investigates the determinants influencing Gen Z's behavioral intentions (BI) to integrate AI-powered tools within Indian Higher Educational Institutions (HEIs) by extending the UTAUT2 model with four additional constructs: trustworthiness, personal innovativeness, perceived task excellence, and perceived privacy concern. The data gathered from 430 respondents within Indian HEIs through an online survey following purposive sampling was meticulously analyzed using (...) the structural equation modeling approach in AMOS. The findings validate the applicability of the UTAUT2 model for understanding AI tool integration in the Indian context, with an explanatory power of 34.2%. The study highlights the beneficial impact of hedonic motivation, perceived task excellence, facilitating conditions, and performance expectancy on Gen Z's intention to integrate AI tools. Additionally, the study suggests recommendations for future research and outlines implications based on these findings. (shrink)

In the context of digital monetary market integration, the importance of cross-border digital currency research is receiving prominent attention. This study integrated Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and initial trust factors (ITM) into an integrative framework, which synthetically complemented the objective measures and subjective insights of digital currencies. The results indicated the integrated framework, which verified its robustness predicting the acceptance and recommendation intention of digital currency. By analyzing the two different features of digital (...) currencies, this research puts forward a set of targeted solutions to ensure that users of Chinese and Korean digital currencies make a long-term policy for the sustainability, eventually benefitting the cross-border digital monetary transactions and economic cooperation in Asia, which leads the world to the sustainable development in the digital currencies field. (shrink)

Every emerging technology has its pros and cons; health-conscious users pay more importance to healthy and environment-friendly technologies. Based on the UTAUT2 model, we proposed a comprehensive novel model to study the factors influencing consumers’ decision-making to adopt the technology. Compared to prior studies that focused on linear models to investigate consumers’ technology adoption intentions and use behavior. This study used a Structural Equation Modeling-fuzzy set qualitative comparative analysis approach to account for the complexity of customers’ decision-making processes in (...) adopting new technology. We collected valid responses from 830 consumers, analyzed them, and evaluated them using a deep learning SEM-fsQCA technique to capture symmetric and asymmetric relations between variables. We have extensively incorporated a health-consciousness attitude as a predictor and mediator to understand better the decision-making toward technology adoption, specifically 5G technology. All the factors tested in our model are statistically significant except the economic factors. Health-consciousness attitude and behavioral intention found significant predictors and valid mediators in the process of 5G technology adoption. FsQCA provided six configurations to achieve high 5G adoption. The findings have significant practical ramifications for telecom corporations, advertisers, government officials, and key policymakers. Additionally, the study added substantial theoretical literature to technology adoption, particularly the adoption of 5G technology. (shrink)

This study explores the application of Voice Assistants (VAts) in providing information and guiding passengers at airports, highlighting their potential as a transformative solution to enhance passenger experience. The study examines the major determinants of Voice Assistant (VAts) adoption and use in the airport setting using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model in conjunction with the Information Acceptance Model (IAM). Data were collected from 314 participants in Vietnam, representing various professions and age groups, (...) with the majority being 18 years and older. In addition, many interviews were conducted with people who frequently work in or use airport services to provide qualitative insights. The Structural Equation Model (SEM) using AMOS was applied to analyze the direct and indirect impacts of the factors in the model to identify the accepted factors (Sig < 0.05) aimed at extracting the core content. The findings indicate important predictive factors for the acceptance of VAts in the Vietnamese context, emphasizing that theoretical insights must be thoroughly examined before practical implementation. These insights provide actionable recommendations for airport authorities and technology developers to design user-centered Voice Assistant (VAts) solutions, enhancing service efficiency and passenger satisfaction at airports. (shrink)

This study investigates the factors influencing English as a Foreign Language (EFL) learners’ adoption of AI chatbots by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The research incorporates two additional constructs, Core Self-Evaluation (CSE) and Learning Value (LV), to enhance the model’s predictive power in the context of language learning technology. A quantitative approach was employed, collecting data from 362 English-major undergraduates at a prominent university, using structured survey questionnaires. The data were analyzed (...) using partial least squares structural equation modeling (PLS-SEM) to evaluate the relationships within the augmented UTAUT2 model. The results reveal that performance expectancy, facilitating conditions, habit, CSE, and LV significantly influence EFL learners’ behavioral intentions and actual use of AI chatbots. Effort expectancy, social influence, and hedonic motivation were found to have no significant impact on adoption intentions. These findings underscore the importance of aligning AI chatbot functionalities with learners’ educational goals and supporting their self-evaluative beliefs to promote technology acceptance in language learning. The study advances the UTAUT2 model by demonstrating the relevance of CSE and LV in predicting EFL learners’ adoption of AI chatbots. The findings offer insights for educators and developers to enhance chatbot design, meeting learners’ pedagogical needs and expectations. (shrink)

Purpose The rapid integration of generative artificial intelligence (AI) tools, such as ChatGPT, into higher education presents both new opportunities and ethical challenges. This study aims to examine university students’ perceptions and ethical readiness toward the use of generative AI tools in the educational context, with a comparative focus on Science, Technology, Engineering, and Mathematics (STEM) and non-STEM disciplines. Design/methodology/approach The research uses the Ethical Adoption Model of Generative AI, an extension of the unified theory of acceptance and use of (...) technology 2 (UTAUT2) framework that integrates ethical constructs, including ethical awareness (EA), perceived ethical risk (PER) and AI ethical anxiety (AIEA). Data were collected from 372 university students in Indonesia. Quantitative analyses, comprising t-tests, analysis of variance and partial least squares structural equation modeling (PLS-SEM), were used to examine both group differences and predictive relationships among constructs. Findings Results revealed significant differences between STEM and non-STEM students across several dimensions, particularly hedonic motivation, facilitating condition, social influence and AI ethical anxiety. Habit emerged as the most distinctive factor among STEM students, indicating deeper integration of AI into their academic routines. The PLS-SEM results identified performance expectancy, ethical awareness, habit and AI ethical anxiety as significant predictors of behavioral intention, with AI ethical anxiety exerting a negative influence. The findings underscore that STEM and non-STEM students experience distinct ethical concerns and adoption patterns, suggesting the need for differentiated institutional strategies, emphasizing technical empowerment for STEM students and ethical guidance for non-STEM students, to promote responsible and equitable AI adoption in higher education. Originality/value This study integrates UTAUT2 and ethical decision-making constructs to reveal an ethical mediation chain (Ethical Awareness → Perceived Ethical Risk → AI ethical anxiety) shaping students’ behavioral intention. (shrink)

Under the background of global cross-border mobile commerce integration, the importance of cross-border payment research is becoming increasingly prominent and urgent. The important value of this study is to empirically research the influence power of key elements in using two different mobile payment platforms in Korea. The extended unified theory of acceptance and use of technology has been widely applied in various studies because of its strong interpretive power. In Korea, there are a few empirical studies on Chinese users. Based (...) on a survey of 908 Chinese participants in Korea, this study is one application extending UTAUT2 by incorporating multi-group and multi-model constructs: UTAUT2, information system success model, and an initial trust model, considering a multi-group analysis with some mediating variables. By comparing the two different payment platforms’ characters, this manuscript provides a set of targeted measures to ensure Chinese WeChat Payment platform decision-makers create effective long-term strategic policies for cross-border m-payments in Korea, and eventually, benefit cross-border m-commerce and economic cooperation in Southeast Asia. (shrink)

This study examines the ethical use and potential misuse of ChatGPT among business students who have been trained in ethics and decision-making. Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the study incorporates perceived ethical norms and ethical AI usage behavior. A mixed-methods design was employed, combining a survey of 400 purposively sampled business students with key informant interviews with business faculty members. Partial Least Squares Structural Equation Modeling (PLS-SEM) results indicate that facilitating conditions, performance (...) expectancy, and social influence have a significant influence on students’ intentions to use ChatGPT. In contrast, effort expectancy and perceived ethical norms show non-significant effects, revealing a disconnect between ethical awareness and actual usage behavior. Qualitative findings further highlight concerns related to plagiarism, data privacy, and overreliance on AI. Overall, the study highlights the importance of establishing clear institutional policies, ethical guidelines, and digital literacy initiatives to promote responsible AI use and maintain academic integrity in higher education. (shrink)

With the increasing use of mobile devices and new technologies, electronic payments, such as near field communication mobile payments, are gaining traction and gradually replacing the currency-based cash payment methods. Despite multiple initiatives by various parties to encourage mobile payments, adoption rates in developing countries have remained low. The purpose of this research is to explore the prime determinants of NFC mobile-payment adoption intention and to develop a model of mobile payment adoption that includes perceived risk as one of the (...) major elements by extending the UTAUT2 theory components. An online survey was used to acquire data from 370 NFC mobile payments users for the current study. To validate the components and their correlations, structural equation modelling was implemented. According to the findings, performance expectancy, hedonic motivation, social influence, and facilitating conditions have substantial impacts on the consumers’ intentions to adopt NFC mobile payments. Effort expectancy and PR were reported to have no considerable effects on the adoption intention. In addition, INFC is revealed to be a major mediator between the associations of the actual adoption of NFC mobile payment with PE, HM, and SI. The findings of the study would assist providers and marketers in better understanding of the consumers’ behavior, designing effective marketing strategies to enhance the consumers’ positive intentions, and achieving the mass adoption of NFC mobile payments in different environmental contexts. (shrink)