Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA

[1]  Brady D. Lund,et al.  ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing , 2023, J. Assoc. Inf. Sci. Technol..

[2]  Malik Sallam,et al.  ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns , 2023, Healthcare.

[3]  F. Rahimi,et al.  ChatGPT and Publication Ethics. , 2023, Archives of medical research.

[4]  Anna Y. Q. Huang,et al.  A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022) , 2023, Sustainability.

[5]  Viriya Taecharungroj.  "What Can ChatGPT Do?" Analyzing Early Reactions to the Innovative AI Chatbot on Twitter , 2023, Big Data Cogn. Comput..

[6]  M. Iranmanesh,et al.  Determinants of travel apps continuance usage intention: extension of technology continuance theory , 2023, Current Issues in Tourism.

[7]  Jing Liu,et al.  Study on factors influencing college students’ digital academic reading behavior , 2023, Frontiers in Psychology.

[8]  Dirk Ifenthaler,et al.  Reciprocal issues of artificial and human intelligence in education , 2023, Journal of Research on Technology in Education.

[9]  J. Potter,et al.  Re-examining AI, automation and datafication in education , 2023, Learning, Media and Technology.

[10]  E. Yadegaridehkordi,et al.  Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS , 2023, Journal of Retailing and Consumer Services.

[11]  Yogesh Kumar Dwivedi,et al.  The effects of trust on behavioral intention and use behavior within e-government contexts , 2022, Int. J. Inf. Manag..

[12]  Sindhu Singh.  The Moderating Role of Privacy Concerns on Intention to Use Smart Wearable Technologies: An Integrated Model Combining UTAUT2 Theoretical Framework and Privacy Dimensions , 2022, Journal of Global Marketing.

[13]  Ahmad Fadhil Yusof,et al.  AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach , 2022, Sustainability.

[14]  Catarina Neves,et al.  Consumer's intention to use and recommend smart home technologies: The role of environmental awareness , 2022, Energy.

[15]  Der-fa Chen,et al.  Developing an Extended Theory of UTAUT 2 Model to Explore Factors Influencing Taiwanese Consumer Adoption of Intelligent Elevators , 2022, SAGE Open.

[16]  Yeunhee Kwak,et al.  Nursing students' intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. , 2022, Nurse education today.

[17]  Fangfang Yang,et al.  A study of college students' intention to use metaverse technology for basketball learning based on UTAUT2 , 2022, Heliyon.

[18]  Jiyun Chen.  Adoption of M-learning apps: A sequential mediation analysis and the moderating role of personal innovativeness in information technology , 2022, Computers in Human Behavior Reports.

[19]  Dabae Lee,et al.  Developing an AI-based chatbot for practicing responsive teaching in mathematics , 2022, Comput. Educ..

[20]  P. Lally,et al.  Habit and habitual behaviour , 2022, Health psychology review.

[21]  M. Kuhail,et al.  Interacting with educational chatbots: A systematic review , 2022, Education and Information Technologies.

[22]  Chai Ching Sing,et al.  Secondary school students’ intentions to learn AI: testing moderation effects of readiness, social good and optimism , 2022, Educational technology research and development.

[23]  Fahimeh Hateftabar.  Analyzing the adoption of online tourism purchases: effects of perceived tourism value and personal innovativeness , 2022, Current Issues in Tourism.

[24]  G. Zacharis,et al.  Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: an UTAUT2 approach with ‘Learning Value’ , 2022, Education and Information Technologies.

[25]  Mohammed A. Al-Sharafi,et al.  Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach , 2022, Interact. Learn. Environ..

[26]  M. Cukurova,et al.  Teachers' trust in AI-powered educational technology and a professional development program to improve it , 2022, Br. J. Educ. Technol..

[27]  Jennifer G. Whitfield,et al.  Online chat and chatbots to enhance mature student engagement in higher education , 2022, International Journal of Lifelong Education.

[28]  Kwame Owusu Kwateng,et al.  Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic , 2022, Education and Information Technologies.

[29]  Martin Kidd,et al.  Adoption Factors and Moderating Effects of Age and Gender That Influence the Intention to Use a Non-Directive Reflective Coaching Chatbot , 2022, SAGE Open.

[30]  M. Tseng,et al.  Determinants of hotel guests’ pro-environmental behaviour: Past behaviour as moderator , 2022, International Journal of Hospitality Management.

[31]  H. Chueh,et al.  Behavioral intention to continuously use learning apps: A comparative study from Taiwan universities , 2022, Technological Forecasting and Social Change.

[32]  Jong-Chao Hong,et al.  Comparing the Taiwanese learning effects of Shaking-On and Kahoot! , 2022, J. Comput. Assist. Learn..

[33]  K. Nikolopoulou,et al.  Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet , 2021 .

[34]  Jaeho Jeon,et al.  Chatbot-assisted dynamic assessment (CA-DA) for L2 vocabulary learning and diagnosis , 2021, Computer Assisted Language Learning.

[35]  M. Tareq,et al.  Moderating effects of personal innovativeness and driving experience on factors influencing adoption of BEVs in Malaysia: An integrated SEM–BSEM approach , 2021, Heliyon.

[36]  Qi Li,et al.  How the live streaming commerce viewers process the persuasive message: An ELM perspective and the moderating effect of mindfulness , 2021, Electron. Commer. Res. Appl..

[37]  K. Salonitis,et al.  Critical success factors for improving learning management systems diffusion in KSA HEIs: An ISM approach , 2021, Educ. Inf. Technol..

[38]  S. Y. Teh,et al.  Extending the social influence factor: behavioural intention to increase the usage of information and communication technology-enhanced student-centered teaching methods , 2021, Educational Technology Research and Development.

[39]  G. Alkawsi,et al.  The Moderating Role of Personal Innovativeness and Users Experience in Accepting the Smart Meter Technology , 2021, Applied Sciences.

[40]  Yunus,et al.  Factors Affecting Teaching English as a Second Language (TESL) Postgraduate Students’ Behavioural Intention for Online Learning during the COVID-19 Pandemic , 2021, Sustainability.

[41]  Jixin Wang,et al.  Factors Affecting College Students’ Continuous Intention to Use Online Course Platform , 2021, SN Computer Science.

[42]  Hossein Olya.  Towards advancing theory and methods on tourism development from residents’ perspectives: Developing a framework on the pathway to impact , 2020, Journal of Sustainable Tourism.

[43]  Matt Bower,et al.  Reasons associated with preservice teachers' intention to use immersive virtual reality in education , 2020, Br. J. Educ. Technol..

[44]  Shegaw Anagaw Mengiste,et al.  Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: using unified theory of acceptance and use technology 2(UTAUT2) model , 2020, BMC Medical Informatics and Decision Making.

[45]  Eunju Ko,et al.  Chatbot e-service and customer satisfaction regarding luxury brands , 2020 .

[46]  T. Goh,et al.  Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance , 2020 .

[47]  I. Oncioiu,et al.  Intention to Use Accounting Platforms in Romania: A Quantitative Study on Sustainability and Social Influence , 2020, Sustainability.

[48]  Ning Wang,et al.  Do cultural differences affect users' e-learning adoption? A meta-analysis , 2020, Br. J. Educ. Technol..

[49]  Kazi Sirajum Munira,et al.  HR Professionals’ Intention to Adopt and Use of Artificial Intelligence in Recruiting Talents , 2020 .

[50]  Ahmed Al-Azawei,et al.  Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries , 2020, Technology in Society.

[51]  Pavel Smutny,et al.  Chatbots for learning: A review of educational chatbots for the Facebook Messenger , 2020, Comput. Educ..

[52]  Ines Brusch,et al.  Exploring the acceptance of instant shopping – An empirical analysis of the determinants of user intention , 2020 .

[53]  Kalyan Kumar Bhattacharjee,et al.  Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling , 2020, Education and Information Technologies.

[54]  Santiago Melián-González,et al.  Predicting the intentions to use chatbots for travel and tourism , 2019 .

[55]  Matt Homer,et al.  The Influence of Values on E-learning Adoption , 2019, Comput. Educ..

[56]  Dima Dajani,et al.  Behavior intention of animation usage among university students , 2019, Heliyon.

[57]  Paulo Duarte,et al.  A mixed methods UTAUT2-based approach to assess mobile health adoption , 2019, Journal of Business Research.

[58]  A. Woodside.  Accurate case‐outcome modeling in economics, psychology, and marketing , 2019, Psychology & Marketing.

[59]  Salihu Ibrahim Dasuki,et al.  Factors affecting the adoption of e-learning technologies among higher education students in Nigeria , 2019 .

[60]  Donya Rooein,et al.  Data-Driven Edu Chatbots , 2019, WWW.

[61]  Krishna Moorthy,et al.  Behavioral Intention to Adopt Digital Library by the Undergraduates , 2019 .

[62]  Mary Helen Fagan,et al.  Factors Influencing Student Acceptance of Mobile Learning in Higher Education , 2019, Computers in the Schools.

[63]  Hongfang Liu,et al.  A clinical text classification paradigm using weak supervision and deep representation , 2019, BMC Medical Informatics and Decision Making.

[64]  H. Woods.  Asking more of Siri and Alexa: feminine persona in service of surveillance capitalism , 2018, Critical Studies in Media Communication.

[65]  Keng Siau,et al.  Factors Influencing the Adoption of Smart Wearable Devices , 2018, Int. J. Hum. Comput. Interact..

[66]  Pervaiz Akhtar,et al.  Understanding behavioural intention to use information technology: Insights from humanitarian practitioners , 2017, Telematics Informatics.

[67]  Brandford Bervell,et al.  Validation of the UTAUT Model: Re-Considering Non-Linear Relationships of Exogeneous Variables in Higher Education Technology Acceptance Research , 2017 .

[68]  Yogesh Kumar Dwivedi,et al.  Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust , 2017, Int. J. Inf. Manag..

[69]  Anastasios A. Economides,et al.  Mobile-based assessment: Investigating the factors that influence behavioral intention to use , 2017, Comput. Educ..

[70]  Lauren E. Sherman,et al.  Smartphones and Cognition: A Review of Research Exploring the Links between Mobile Technology Habits and Cognitive Functioning , 2017, Front. Psychol..

[71]  S. Pandey,et al.  Impact of Social Influence and Green Consumption Values on Purchase Intention of Organic Clothing: A Study on Collectivist Developing Economy , 2017 .

[72]  A. Tarhini,et al.  Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) , 2017, Educational Technology Research and Development.

[73]  Rex B. Kline,et al.  Statistical and Practical Concerns With Published Communication Research Featuring Structural Equation Modeling , 2017 .

[74]  Mehwish Waheed,et al.  The influence of learning value on learning management system use , 2016 .

[75]  S. Zailani,et al.  Barriers to green innovation initiatives among manufacturers: the Malaysian case , 2016 .

[76]  Jan Dul,et al.  Identifying single necessary conditions with NCA and fsQCA , 2016 .

[77]  Adel Al Khattab,et al.  The Effect of Trust and Risk Perception on Citizen's Intention to Adopt and Use E-Government Services in Jordan , 2015 .

[78]  J. Harackiewicz,et al.  What if I can’t? Success expectancies moderate the effects of utility value information on situational interest and performance , 2015 .

[79]  Liu Fan,et al.  Why do users switch to a disruptive technology? An empirical study based on expectation-disconfirmation theory , 2014, Inf. Manag..

[80]  Tiago Oliveira,et al.  Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application , 2014, Int. J. Inf. Manag..

[81]  Raffaele Filieri,et al.  E-WOM and Accommodation , 2014 .

[82]  Y. Don,et al.  Preservice Teachers' Acceptance of Learning Management Software: An Application of the UTAUT2 Model. , 2013 .

[83]  Ned Kock,et al.  Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations , 2012, J. Assoc. Inf. Syst..

[84]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[85]  Peer C. Fiss.  Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research , 2011 .

[86]  Hans van der Heijden,et al.  User Acceptance of Hedonic Information Systems , 2004, MIS Q..

[87]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[88]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[89]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[90]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[91]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[92]  William R. Darden,et al.  Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value , 1994 .

[93]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[94]  Richard G. Netemeyer,et al.  Measurement of Consumer Susceptibility to Interpersonal Influence , 1989 .

[95]  David Baidoo-Anu,et al.  Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning , 2023, SSRN Electronic Journal.

[96]  Ángel Francisco Villarejo-Ramos,et al.  Acceptance and use of big data techniques in services companies , 2020 .

[97]  Manuela Aparicio,et al.  Grit in the path to e-learning success , 2017, Comput. Hum. Behav..

[98]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[99]  Charles C. Ragin,et al.  Redesigning social inquiry , 2008 .

[100]  B. Shao,et al.  The impact of voice assistants’ intelligent attributes on consumer well-being: Findings from PLS-SEM and fsQCA , 2022, Journal of Retailing and Consumer Services.