Decoding the ChatGPT mystery
A comprehensive exploration of factors driving AI language model adoption
Dados Bibliográficos
| ID | 21501002 |
|---|---|
| Autores | Hyerim Jo (0000-0001-7442-4736, HJ Institute of Technology and Management, Bucheon, Republic of Korea, autor correspondente) |
| Ano | 2025 |
| Volume | 41 |
| Fascículo | 3 |
| Páginas | 875-895 |
| Data de publicação | 2025-09-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Information Development (JOURNAL) |
| Identificadores do periódico | ISSN: 0266-6669 • E-ISSN: 1741-6469 |
| Editora | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/02666669231202764 |
| OpenAlex | W4387101356 |
| Idioma | EN |
| Citações recebidas | 18 |
| Referências citadas | 106 |
The increasing ubiquity of Artificial Intelligence (AI) chatbots across a variety of sectors has sparked a burgeoning interest in deciphering the determinants that govern their adoption and usage. This study aims to examine the pivotal factors that influence the practical usage of the AI chatbot, ChatGPT, among a sample of university students. Leveraging the theoretical framework of planned behavior, the research model scrutinizes the interplay between knowledge application, perceived intelligence, usability, attitude, subjective norms, perceived behavioral control, trust, behavioral intention, and actual usage. Data procured from a survey of university students were examined through the lens of structural equation modeling. The outcomes reveal that knowledge application, perceived intelligence, and usability have a positive impact on attitudes towards ChatGPT. Perceived intelligence also influences knowledge application, usability, and trust. Concurrently, attitude and subjective norms notably affect behavioral intention. Contrary to expectations, perceived behavioral control did not significantly influence behavioral intention. Trust emerged as a crucial factor steering behavioral intention, which in turn, positively correlates with the actual usage of ChatGPT. These insights enrich the academic discourse on AI chatbot adoption and provide practical implications for AI developers, educators, and policy makers, striving to enhance user engagement with AI systems in educational contexts
Chatbot · Human–computer interaction · Knowledge management · Structural equation modeling · Technology Acceptance Model · Theory of planned behavior · Usability · World Wide Web · AI in Service Interactions · Applied Psychology · Computer Science · Ethics and Social Impacts of AI · Psychology · Social Psychology · Technology Adoption and User Behaviour · Artificial Intelligence
Danish university policies on generative AI
Developing an integrated model to explore key factors influencing university students' behavioral intentions to use ChatGPT in enhancing higher education in the hail region
ChatGPT Acceptance and Use in Higher Education
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Fostering AI literacy in pre-service physics teachers
ChatGPT Acceptance Among Students
GenAI in hotel operations
Examining the effects of ChatGPT on tourism and hospitality student responses through integrating technology acceptance model
Artificial intelligence attitudes in higher education
The influence of ChatGPT on student engagement
Understanding students’ adoption of the ChatGPT chatbot in higher education
Psychological predictors of financial technology adoption
AI Chatbot characteristics and Gen Z consumers' purchase behavior
Generative AI in Higher Education
What Factors Affect the Adoption Intention and Actual Use of ChatGPT in Higher Education? The Moderating Role of Academic Integrity
Between a Bot and a Hard Place
Online Information Exposure, Knowledge Development, and AI Anxiety
ChatGPT and higher education student well-being
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R
Method Variance and Marker Variables
A SWOT analysis of ChatGPT
Age Differences in Technology Adoption Decisions
ChatGPT and a new academic reality
Modelling technology acceptance in education
Adoption of AI-based chatbots for hospitality and tourism
Eliza in the uncanny valley
Factors Determining the Behavioral Intention to Use Mobile Learning
Acceptability of artificial intelligence (AI)-led chatbot services in healthcare
Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom
Opinion Paper
An updated and expanded assessment of PLS-SEM in information systems research
Technology Acceptance Model in M-learning context
Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions
What if the devil is my guardian angel
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The impact of initial consumer trust on intentions to transact with a web site
Chatting and cheating
PLS-Sem
Understanding Information Systems Continuance
Trust and TAM in Online Shopping
The theory of planned behavior
User Acceptance of Information Technology
Common method biases in behavioral research
A new criterion for assessing discriminant validity in variance-based structural equation modeling
When to use and how to report the results of PLS-SEM
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
A Theoretical Extension of the Technology Acceptance Model
Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach
I, Chatbot
University students’ use of mobile technology in self-directed language learning
The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services
Alexa, what's on my shopping list? Transforming customer experience with digital voice assistants
Moral Uncanny Valley
Artificial intelligence and work
Trust Theory
User Experience of On-Screen Interaction Techniques
Privacy as a Concept and a Social Issue
Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation
| Obras citantes distintas | 18 |
|---|---|
| Citações por ano | 9 |
| Intervalo de citações | 2024 - 2026 (3) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 16 |