Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator

Duong, Cong Doanh; Vu, Trong Nghia; Ngo, Thi Viet Nga

doi:10.1016/j.ijme.2023.100883

Cited by 42 publications

(18 citation statements)

References 76 publications

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“…Nevertheless, the literature has examined the adoption of AI technology in service industry and had proposed new adoption models, for instance, Task-oriented AI Acceptance (T-AIA) model [ 10 ] and Artificially Intelligent Device Use Acceptance model (AIDUA) [ 11 ]. Researchers has also investigated the acceptance of generative AI technologies—such as ChatGPT—by extending TAM model [ 12 – 15 ], UTAT Model [ 16 , 17 ], and AIDUA Model [ 19 ] as the research frameworks. The technology adoptions were empirically investigated from the perspectives of Students [ 12 – 16 ], knowledge workers [ 17 ], healthcare professionals [ 18 ], and customers [ 19 ].…”

Section: Research Design and Methodologymentioning

confidence: 99%

Study protocol for factors influencing the adoption of ChatGPT technology by startups: Perceptions and attitudes of entrepreneurs

Gupta,

Yang

2024

PLoS ONE

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Background Generative Artificial Intelligence (AI) technology, for instance Chat Generative Pre-trained Transformer (ChatGPT), is continuously evolving, and its userbase is growing. These technologies are now being experimented by the businesses to leverage their potential and minimise their risks in business operations. The continuous adoption of the emerging Generative AI technologies will help startups gain more and more experience with adoptions, helping them to leverage continuously evolving technological innovation landscape. However, there is a dearth of prior research on ChatGPT adoption in the startup context, especially from Entrepreneur perspective, highlights the urgent need for a thorough investigation to identify the variables influencing this technological adoption. The primary objective of this study is to ascertain the factors that impact the uptake of ChatGPT technology by startups, anticipate their influence on the triumph of companies, and offer pragmatic suggestions for various stakeholders, including entrepreneurs, and policymakers. Method and analysis This study attempts to explore the variables impacting startups’ adoption of ChatGPT technology, with an emphasis on comprehending entrepreneurs’ attitudes and perspectives. To identify and then empirically validate the Generative AI technology adoption framework, the study uses a two-stage methodology that includes experience-based research, and survey research. The research method design is descriptive and Correlational design. Stage one of the research study is descriptive and involves adding practical insights, and real-world context to the model by drawing from the professional consulting experiences of the researchers with the SMEs. The outcome of this stage is the adoption model (also called as research framework), building Upon Technology Adoption Model (TAM), that highlight the technology adoption factors (also called as latent variables) connected with subset of each other and finally to the technology adoption factor (or otherwise). Further, the latent variables and their relationships with other latent variables as graphically highlighted by the adoption model will be translated into the structured questionnaire. Stage two involves survey based research. In this stage, structured questionnaire is tested with small group of entrepreneurs (who has provided informed consent) and finally to be distributed among startup founders to further validate the relationships between these factors and the level of influence individual factors have on overall technology adoption. Partial Least Squares Structural Equation Modeling (PLS-SEM) will be used to analyze the gathered data. This multifaceted approach allows for a comprehensive analysis of the adoption process, with an emphasis on understanding, describing, and correlating the key elements at play. Discussion This is the first study to investigate the factors that impact the adoption of Generative AI, for instance ChatGPT technology by startups from the Entrepreneurs perspectives. The study’s findings will give Entrepreneurs, Policymakers, technology providers, researchers, and Institutions offering support for entrepreneurs like Academia, Incubators and Accelerators, University libraries, public libraries, chambers of commerce, and foreign embassies important new information that will help them better understand the factors that encourage and hinder ChatGPT adoption. This will allow them to make well-informed strategic decisions about how to apply and use this technology in startup settings thereby improving their services for businesses.

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Section: Research Design and Methodologymentioning

confidence: 99%

Study protocol for factors influencing the adoption of ChatGPT technology by startups: Perceptions and attitudes of entrepreneurs

Gupta,

Yang

2024

PLoS ONE

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“…Higher education is experiencing a merging of different digital tools, with M-learning and AI-driven platforms such as ChatGPT gaining prominence (Duong et al, 2023;Gozalo-Brizuela & Garrido-Merchan, 2023). M-learning has developed an established reputation by providing university students with the advantage of accessing educational materials according to their needs and time on and off campus Qashou, 2021).…”

Section: M-learning Chatgpt and Academic Performancementioning

confidence: 99%

“…Still, ChatGPT can provide immediate clarifications, responses to inquiries and personalized explanations based on the learner's speed and comprehension level (Alneyadi & Wardat, 2023). This combination enhances the learner's overall understanding of the material (Duong et al, 2023). The utilization of ChatGPT as a means of facilitating a customized learning experience has the potential to reduce the intermittent character of M-learning successfully.…”

Section: M-learning Chatgpt and Academic Performancementioning

confidence: 99%

Tech‐infused classrooms: A comprehensive study on the interplay of mobile learning, ChatGPT and social media in academic attainment

Amjad,

Aslam,

Tabassum

2024

Euro J of Education

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Mobile learning (M‐learning), ChatGPT and social media are integral to university education, improving accessibility, personalization and interactive engagement in the learning process. This paper aimed to investigate the role of M‐learning, ChatGPT and social media in university students' academic performance. It was a cross‐sectional quantitative investigation, and data were collected from 600 university students through a self‐developed questionnaire having four subscales (M‐learning, ChatGPT, social media and academic performance). Simple linear regression and process macro were deployed to test study hypotheses. The analysis revealed that M‐learning is a significant contributor to students' academic performance at the university level (β = .460, t(598) = 12.663, p < .001). Social media were found to be significantly mediating the relationship between M‐learning and academic performance with coefficient B = .267, SE = .038, t(597) = 7.08, p < .001, 95% CI [0.193, 0.341]. ChatGPT was significantly contributing to academic performance (B = .288, t(596) = 2.50, p = .013), but the interaction term had an insignificant effect on the relationship between M‐learning and academic performance (β = .292, t(596) = −0.43, p = .668). The study suggests that M‐learning can improve students' performance. The influence of social media and the non‐moderating role of ChatGPT indicate the need for strategic integration of these tools to enhance university students' outcomes. It is recommended that university students should judiciously use social media to enhance M‐learning. In contrast, universities should foster this integration to achieve educational aims and explore how AI tools like ChatGPT can be optimally deployed to improve learning.

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“…Previous research, i.e., refs. [ 15 , 21 , 25 , 30 , 31 ], that has measured the adoptability of ChatGPT in education has underscored the substantial impact of FCs on its adoption and real usage. Furthermore, the research conducted by Menon and Shilpa [ 15 ] has shown that FCs, i.e., a mobile device, consistent internet connection, and technical support, are pivotal factors that can profoundly affect the adoption of ChatGPT.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Numerous studies, i.e., refs. [ 15 , 21 , 23 , 31 , 33 , 34 ] showed that when students convey a positive BI towards ChatGPT, signifying their readiness to include it in their routines or tasks, there is a heightened probability this intention being translated into actual use. Furthermore, the more substantial the intention to use ChatGPT, the more likely students are to engage in active interaction with the platform, seek its assistance, and integrate it into their academic activities [ 13 , 29 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Examining Students’ Acceptance and Use of ChatGPT in Saudi Arabian Higher Education

Sobaih,

Elshaer,

Hasanein

2024

EJIHPE

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This study examines students’ acceptance and use of ChatGPT in Saudi Arabian (SA) higher education, where there is growing interest in the use of this tool since its inauguration in 2022. Quantitative research data, through a self-reporting survey drawing on the “Unified Theory of Acceptance and Use of Technology” (UTAUT2), were collected from 520 students in one of the public universities in SA at the start of the first semester of the study year 2023–2024. The findings of structural equation modeling partially supported the UTAUT and previous research in relation to the significant direct effect of performance expectancy (PE), social influence (SI), and effort expectancy (EE) on behavioral intention (BI) on the use of ChatGPT and the significant direct effect of PE, SI, and BI on actual use of ChatGPT. Nonetheless, the results did not support earlier research in relation to the direct relationship between facilitating conditions (FCs) and both BI and actual use of ChatGPT, which was found to be negative in the first relationship and insignificant in the second one. These findings were because of the absence of resources, support, and aid from external sources in relation to the use of ChatGPT. The results showed partial mediation of BI in the link between PE, SI, and FC and actual use of ChatGPT in education and a full mediation in the link of BI between EE and actual use of ChatGPT in education. The findings provide numerous implications for scholars and higher education institutions in SA, which are also of interest to other institutions in similar contexts.

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Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator

Cited by 42 publications

References 76 publications

Study protocol for factors influencing the adoption of ChatGPT technology by startups: Perceptions and attitudes of entrepreneurs

Study protocol for factors influencing the adoption of ChatGPT technology by startups: Perceptions and attitudes of entrepreneurs

Tech‐infused classrooms: A comprehensive study on the interplay of mobile learning, ChatGPT and social media in academic attainment

Examining Students’ Acceptance and Use of ChatGPT in Saudi Arabian Higher Education

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