Integrating AI in Academia: A SEM Evaluation of Research Scholars Usage Intentions on ChatGPT

The transformative potential of artificial intelligence (AI) in banking is widely acknowledged, yet its practical adoption often faces resistance from users. This study investigates the factors influencing AI adoption behavior among various stakeholders in the Greek semi-mature systemic banking ecosystem, addressing a critical gap in the relevant research. By utilizing the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2), and Partial Least Squares Structural Equation Modelling (PLS-SEM) models, data from 297 respondents (bank employees, digital professionals, and the general public) were analyzed. The results highlight the strong relevance of constructs such as Performance Expectancy, Effort Expectancy, and Hedonic Motivation, whereas Social Influence was deemed non-significant, reflecting a pragmatic stance toward AI. Demographic factors like gender and age were found to have no significant moderating effect, challenging traditional stereotypes. However, occupation and education emerged as significant moderators, indicating varying attitudes among professions and educational levels. This study is the first to develop a theoretical framework for AI adoption by Greek banking institutions, offering Greek banking practitioners actionable insights. The findings also hold relevance for countries with similar digital maturity levels, aiding broader AI integration in banking.

Purpose This study investigates the complex interplay between technological and ethical factors influencing artificial intelligence (AI) adoption in entrepreneurship and startup ecosystems, with a particular focus on how these dynamics impact innovation outcomes and organizational performance. Design/methodology/approach Employing a comprehensive analytical framework, the research examines quantitative data to assess the relationships among technology related factors (such as interactivity, relative advantage, and perceived intelligence), ethical principles (including fairness, accountability, transparency, accuracy, and autonomy), ethical dilemma, and their collective influence on AI adoption and exploitative innovation within entrepreneurial contexts. Data was collected using a self-administrated questionnaire to 207 respondents, in the Iran entrepreneurship and startup ecosystem. The Partial Least Square-Structural Equation Modeling (PLS-SEM) technique was used to examine the proposed hypotheses of the study. Findings The findings reveal that technology related factors specifically interactivity, relative advantage, perceived intelligence, transparency, and accuracy significantly drive AI adoption among entrepreneurs. In contrast, ethical considerations such as fairness, accountability, and autonomy do not exhibit a direct influence on adoption decisions. Also, the moderating relationship of ethical dilemma between exploitative innovation and organizational performance by AI adaptation was rejected. Notably, the study highlights the pivotal mediating role of exploitative innovation, AI adoption enhances exploitative innovation, which in turn improves organizational performance; however, there is no direct relationship between AI adoption and organizational performance. Practical implications Entrepreneurs and startup leaders should prioritize AI technologies that offer clear interactive capabilities, relative advantages, and transparent, accurate operations to maximize adoption and performance benefits. While ethical principles remain important, their influence may be more pronounced at later stages of implementation or in highly regulated sectors. Policymakers and ecosystem builders are encouraged to focus on fostering environments that support the practical integration of AI, particularly in ways that enhance exploitative innovation and organizational scalability. Originality/value This research provides novel insights by disentangling the relative importance of technological versus ethical factors in AI adoption within entrepreneurial settings. It advances the literature by empirically demonstrating the limited direct impact of certain ethical considerations on adoption decisions and by highlighting the central role of exploitative innovation as a mediator between AI implementation and organizational outcomes.

Integrating Artificial Intelligence (AI) in education has revolutionized learning environments, offering personalized, adaptive, and automated academic assistance. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating trust, perceived risk, moral obligation, hedonic motivation, and habit to provide a comprehensive understanding of AI adoption among university students in Pakistan. Employing a quantitative, cross-sectional survey approach, data were collected from students across various disciplines and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and SPSS. The findings reveal that habit is the strongest predictor of AI adoption. This demonstrates that students engage with AI-based learning tools primarily through repeated exposure and routine usage rather than external encouragement. Unlike traditional UTAUT predictors, such as performance expectancy and social influence, which were not statistically significant, habit formation emerged as the dominant driver of AI engagement. Additionally, trust and perceived risk exhibited a positive correlation, indicating that while students trust AI tools, they simultaneously acknowledge risks related to data privacy, misinformation, and ethical concerns. The study challenges conventional technology acceptance models, highlighting that self-directed learning behaviors and habitual engagement play a more significant role in AI adoption than previously assumed. These findings have important theoretical and practical implications for educational policymakers, AI developers, and institutions seeking to enhance AI-driven learning experiences. The study suggests that institutions should focus on seamless AI integration, improving user engagement, and promoting responsible AI usage rather than relying on external motivational factors.

The integration of Artificial Intelligence (AI) into higher education is gaining momentum, offering innovative tools that can improve student engagement, support individualized learning, and enhance academic performance. While global statistics indicate that approximately 86% of students have used AI for academic purposes, its adoption among Malaysian undergraduates remains relatively limited. This is despite ongoing national efforts such as the Malaysia Digital Economy Blueprint (MyDIGITAL), which seeks to accelerate digital transformation across sectors including education. Understanding the drivers and barriers to AI adoption in higher education is crucial for developing effective strategies that can enhance teaching and learning experiences. This study investigates the factors influencing students’ intentions to adopt AI in higher education settings. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), the research focuses on four core constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. The findings aim to offer insights that can support effective strategies for AI adoption in Malaysia’s higher education landscape, benefiting educators, policymakers, and technology developers. This study will employ a quantitative, cross-sectional survey to examine factors influencing undergraduate students’ intention to use artificial intelligence (AI) in Malaysian higher education. Data will be collected from a convenience sample of undergraduate students through an online questionnaire adapted from the UTAUT framework. A minimum of 137 participants will be targeted to ensure sufficient statistical power. The data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS, which is suitable for exploratory research involving complex models. This research is expected to advance understanding of technology acceptance in higher education by identifying key factors influencing students’ intention to use AI, offering insights for educators and policymakers to support effective AI integration. Ultimately, the study seeks to contribute to the broader adoption of AI technologies that can transform the educational experience.

The purpose of this study is to explore the key factors influencing Artificial Intelligence (AI) adoption in education among Pakistani university students. Specifically, it examines how AI Readiness (AIRD), AI Confidence (AICF), and Social Influence (SI) affect students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU), and how these perceptions shape their Attitudes toward AI (ATT). The study also investigates the mediating roles of PEOU and PU. A quantitative research design was adopted using survey data collected from Pakistani students. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied through Smart PLS 4 to assess both the measurement and structural models. The results reveal that AIRD, AICF, and SI significantly influence students’ perceptions of ease of use, while AIRD and SI also positively impact perceived usefulness. However, AI confidence does not appear to shape perceived usefulness. Notably, perceived ease of use plays a substantial role in forming positive attitudes toward AI, while perceived usefulness does not have a direct effect. Mediation analysis further confirms that PEOU mediates the relationship between AIRD, AICF, SI, and ATT, whereas PU does not. The findings underscore the critical importance of usability over perceived benefits in shaping students' acceptance of AI technologies. In contexts where AI adoption is still emerging, ease of use appears to be the dominant factor influencing attitudes. Educators and policymakers should focus on enhancing students’ readiness and confidence in using AI, promoting user-friendly tools, and leveraging social influence to drive adoption. These insights are crucial for designing inclusive strategies that support effective AI integration into educational environments.

PurposeThe study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.Design/methodology/approachThis study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.FindingsThe finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.Practical implicationsThis study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.Originality/valueThis study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

Artificial intelligence (AI) offers numerous benefits to the field of language education, making it crucial to understand the factors influencing language teachers’ adoption of these technologies. This study investigates the determinants of language teachers’ adoption of AI chatbots in educational settings. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technological Pedagogical Content Knowledge (TPACK) framework, a comprehensive model of AI adoption among language teachers is proposed and tested. Data were collected from 276 language teachers in Vietnam through an online survey. Partial Least Square-Structural Equation Modeling (PLS-SEM) was employed to analyze the data. Results indicate that AI adoption intent significantly predicts AI integration, while performance expectancy, effort expectancy, and AI self-efficacy are key determinants of AI adoption intent. AI-TPACK emerges as a crucial factor, strongly influencing AI self-efficacy, performance expectancy, and effort expectancy. Facilitation is found to be a significant predictor of AI-TPACK. These findings enhance the theoretical framework of AI adoption in language education and provide valuable insights for fostering effective AI integration among language teachers.

This study investigates the impact of Artificial Intelligence (AI) adoption on the sustainable performance of small and medium-sized enterprises (SMEs). Employing a hybrid quantitative approach, this research combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to examine the influence of various organizational, technological, and external factors on AI adoption. Key factors considered include top management support, employee capability, customer pressure, complexity, vendor support, and relative advantage. Data collected from 305 SMEs across multiple sectors were analyzed. The results reveal that all the proposed factors significantly and positively affect AI adoption, with top management support, employee capability, and relative advantage being the most influential predictors. Additionally, the adoption of AI technologies substantially enhances the economic, social, and environmental performance of SMEs, reflecting improvements in operational efficiency, cost reduction, and social value creation. The ANN results confirm the robustness of the SEM findings, highlighting the critical role of AI in driving sustainability outcomes. Furthermore, the study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives. This research contributes to the understanding of AI’s transformative role in enhancing the sustainable performance of SMEs in developing economies, offering strategic insights for both policymakers and business leaders.

The purpose of this study is to analyze the process of forming the behavioral intention of ChatGPT among Vietnamese learners of Korean based on the Artificially Intelligent Device Use Acceptance (AIDUA) model. To achieve this, the study explores the structural relationships among eight variables: social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, learners’ affect, trust, and behavioral intention. For this research, data were collected using a structured self-assessment questionnaire from 324 Korean language learners in Ho Chi Minh City, Vietnam. Additionally, the collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to verify the proposed research model and hypotheses based on the AIDUA framework. The results of this study are as follows: First, anthropomorphism was found to have a significant impact on both effort expectancy and performance expectancy. Second, hedonic motivation was confirmed to have a significant effect on both performance expectancy and effort expectancy. Third, social influence was found to have a statistically significant impact on both performance expectancy and effort expectancy. Fourth, performance expectancy and effort expectancy were each found to significantly influence learners’ affect. Fifth, trust was shown to have a significant effect on learners' acceptance intention. Sixth, affect was confirmed to significantly impact learners' behavioral intention. The findings of this study not only contribute to understanding the acceptance process of ChatGPT among Korean language learners but also provide insights into the applicability and validity of the AIDUA model, which has recently emerged in the field of Korean language education.

The immersion of artificial intelligence (AI) in higher education presents significant challenges and opportunities. This study examines the acceptance of AI as a teaching strategy among university teachers, following the extended UTAUT2 model with the inclusion of the teacher skills and knowledge for technology integration (SKTI) construct. Employing a quantitative cross‐sectional research design, data were collected from 318 university teachers with prior experience using AI as a learning strategy through nonprobabilistic convenience sampling across 10 universities in northern Peru. Participants completed an online survey, and data were analyzed using descriptive statistics, Kruskal–Wallis tests with Dunn’s post hoc comparisons, and partial least squares structural equation modeling (PLS‐SEM). The results showed that performance expectancy (β = 0.129∗∗), hedonic motivation (β = 0.167∗∗), habit (β = 0.405∗∗∗), and SKTI (β = 0.263∗∗∗) had a positive influence on the behavioral intention to adopt AI as a teaching strategy. Additionally, behavioral intention (β = 0.303∗∗∗), facilitating conditions (β = 0.115∗), and habit (β = 0.464∗∗) determine the behavioral use of AI by teachers. The Kruskal–Wallis test revealed significant differences among age groups in the performance expectancy, social influence, habit, and behavioral intention constructs, with the 37‐ to 48‐year‐old age group showing higher average ranks. The discussion highlights that these findings suggest a positive adoption of AI among teachers, driven by individual and contextual factors, and challenges assumptions about the relevance of certain constructs in this specific context. In conclusion, this study represents a significant advancement in understanding the adoption of AI in university teaching and provides valuable guidance for practical implementation efforts.

This study examines the determinants of Artificial Intelligence (AI) adoption and its influence on employee engagement in Egyptian organizations, utilizing the Technology–Organization–Environment (TOE) framework. Based on survey data from 210 professionals across various industries and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that relative advantage, compatibility, and top management support significantly drive AI adoption. In contrast, complexity, security/privacy concerns, organizational readiness, competitive pressure, and external and government support do not show significant effects. Furthermore, AI adoption demonstrates a positive but modest impact on employee engagement, suggesting the presence of other contributing factors. These results reflect the unique socio-economic conditions in Egypt, where internal organizational factors outweigh external influences in shaping technology adoption. The findings have practical implications for organizations and policymakers, emphasizing the need to prioritize internal drivers—such as leadership commitment, perceived benefits, and system compatibility—when promoting AI adoption. Additionally, organizations should align AI initiatives with employee needs and workplace culture to enhance engagement outcomes.

The impact of artificial intelligence on people’s lives is demonstrated today. Previous literature has shown that the use of a specific technology is directly linked to the individuals’ intention to use it. The aim of this paper is to study the factors that determine the adoption and use of artificial intelligence and big data in Spain, using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. (2003). This work addresses the specific gap in the validation of the original theoretical model of UTAUT in two dimensions, with respect to the adoption of artificial intelligence by citizens and with respect to the factors that influence this adoption, evaluating the previous ones and proposing some new ones considering the current context. The methodology used is based on a national survey, and it analyzes the research model using the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), which details the mediating and moderating relationships between constructs. The results show that Intention to Use has a direct positive influence on the Use of artificial Intelligence and big data, confirming previous literature. Performance Expectancy is the strongest predictor of Intention to Use, and indirectly of the adoption of artificial intelligence and big data applications. Effort Expectancy, in its application to the adoption of AI and big data by citizens, is an indirect determinant mediated by the Intention to Use, but its total effect (direct + indirect) is not significant.

Purpose This study investigates the individual-level determinants influencing the adoption of Artificial Intelligence (AI) in the accounting profession within an emerging economy context. It explores how perceptions such as usefulness, ease of use, trust, threat, and susceptibility shape AI adoption, and examines the mediating role of accounting profit. The research is grounded in an extended Technology Acceptance Model and contextualized for Bangladesh to address the gap in AI adoption literature in developing countries. Design/methodology/approach A structured online survey was administered to 478 accounting professionals across various sectors in Bangladesh. The questionnaire included established measures adapted from prior studies and was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The model assessed both direct and mediated paths from individual perceptions to AI adoption, with accounting profit as the mediating variable. Bias testing, robustness checks, and validity assessments were also conducted to ensure reliability of the findings. Findings Perceived usefulness, trust in AI, and perceived threat significantly influenced AI adoption. Perceived ease of use and perceived susceptibility did not show direct significance. Accounting profit was found to mediate the relationships between perceived usefulness, perceived threat, and trust with AI adoption. These results indicate that while individual perceptions matter, financial feasibility plays a critical role in actual adoption decisions within accounting contexts in emerging economies. Practical implications Accounting firms and policymakers in emerging markets must prioritize financial readiness alongside technology training. To enhance AI adoption, strategies should focus on building trust, communicating the tangible value of AI, and ensuring that AI investments align with profit goals. The findings also suggest firms should promote perceived usefulness and address risk concerns when introducing AI into accounting systems. Social implications The study highlights the socio-economic barriers that affect technology diffusion in emerging economies. As accounting professionals face trust and threat perceptions, along with resource limitations, the integration of AI requires institutional support, inclusive digital training, and clear cost-benefit communication. Broader AI adoption could enhance transparency, efficiency, and job transformation in financial services if these conditions are addressed. Originality/value This study offers a novel integration of individual-level behavioural constructs and financial performance (accounting profit) to explain AI adoption in accounting. By focussing on Bangladesh, it extends Technology Acceptance Model literature into a highly relevant yet underexplored emerging market context. The research also contributes to accounting technology literature by showing how strategic perceptions and profitability jointly shape innovation decisions in the profession.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

This study investigates the relationship between artificial intelligence (AI) adoption, knowledge sharing, and employee performance in Malaysian small and medium-sized enterprises (SMEs), with knowledge sharing examined as a mediating mechanism. SMEs represent a vital component of Malaysia’s economy, yet many face resource limitations that affect their readiness to fully harness AI technologies. While AI is recognized for its potential to enhance efficiency and innovation, its impact on employee performance is not always straightforward. This research therefore, explores whether knowledge sharing acts as the bridge through which AI adoption translates into performance outcomes. Data were collected through a survey of SME employees across service and manufacturing sectors, and the responses were analysed using partial least squares structural equation modelling (PLS-SEM). Measurement model results confirmed strong reliability and validity for the constructs of AI adoption, knowledge sharing, and employee performance. Structural model assessment revealed that AI adoption significantly and positively influences knowledge sharing but does not directly affect employee performance. Meanwhile, knowledge sharing revealed a strong and significant relationship with employee performance and was also found to partially mediate the relationship between AI adoption and performance. The study findings highlight that AI’s value in SMEs lies not in the technology itself but in its ability to foster knowledge exchange, learning, and collaboration. In addition, employee performance improves when AI is embedded into organizational practices that encourage knowledge sharing, thereby complementing human creativity and expertise. Theoretically, this study integrated the Knowledge-Based View (KBV) and the Technology–Organization–Environment (TOE) framework to explain how AI adoption and knowledge sharing practices together influence employee performance. Practically, the results underscore the need for SME leaders to move beyond technology acquisition and focus on building collaborative cultures that enable knowledge sharing. Overall, this research contributes both theoretical and practical insights into how SMEs can strategically leverage AI adoption to enhance employee performance through the mediating mechanism of knowledge sharing.