Factors Influencing the Acceptance of ChatGPT in High Education: An Integrated Model With PLS-SEM and fsQCA Approach

被引:5
作者
Zhao, Yipeng [1 ]
Li, Yan [1 ]
Xiao, Yuyao [2 ]
Chang, Haodong [1 ]
Liu, Bo [1 ]
机构
[1] Chengdu Univ Technol, Chengdu, Peoples R China
[2] Southwestern Univ Finance & Econ, Chengdu, Peoples R China
关键词
ChatGPT; college students; higher education; technology acceptance; hybrid methods combined PLS-SEM and fsQCA; INFORMATION-TECHNOLOGY; PERCEIVED USEFULNESS; USER ACCEPTANCE; PARADIGM; SATISFACTION; BEHAVIORS; EXTENSION;
D O I
10.1177/21582440241289835
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
The swift incorporation of artificial intelligence (AI) into higher education has significantly propelled the digital transformation of education. This advancement is crucial for educators aiming to augment teaching quality through AI technologies, such as ChatGPT. However, the acceptance of ChatGPT among college students remains underexplored. This paper aims to clarify the determinants influencing college students' acceptance of ChatGPT and to facilitate its widespread adoption in higher education. To achieve this, we integrate the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) to develop a novel research framework. Employing a mixed-method approach that includes PLS-SEM and fsQCA, we analyze data from 298 Chinese college students. Our findings indicate that discomfort and insecurity adversely affect Perceived Ease of Use (PEU) and Perceived Usefulness (PU) in the context of ChatGPT adoption. Additionally, both PEU and PU positively impact attitudes, which, in conjunction with Subjective Norms (SN) and Perceived Behavioral Control (PBC), bolster the intention to accept ChatGPT. Insights from fsQCA reveal that the acceptance of ChatGPT among students is not driven by a singular factor but by an amalgamation of these elements, underscoring the complex nature of technology adoption. The paper concludes with practical recommendations for educators and designers to refine curriculum design and teaching methodologies, boost student engagement and learning efficacy, and promote the broader adoption of educational technology. Factors influencing the acceptance of ChatGPT in high educationThis study contributes to the existing literature by amalgamating the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) into a novel research model to identify significant factors that influence Chinese college students' acceptance of ChatGPT via a mixed approach that combines PLS-SEM and fsQCA within higher educational settings.
引用
收藏
页数:16
相关论文
共 86 条
[41]   ChatGPT- Reshaping medical education and clinical management [J].
Khan, Rehan Ahmed ;
Jawaid, Masood ;
Khan, Aymen Rehan ;
Sajjad, Madiha .
PAKISTAN JOURNAL OF MEDICAL SCIENCES, 2023, 39 (02) :605-607
[42]   Merging the norm activation model and the theory of planned behavior in the context of drone food delivery services: Does the level of product knowledge really matter? [J].
Kim, Jinkyung Jenny ;
Hwang, Jinsoo .
JOURNAL OF HOSPITALITY AND TOURISM MANAGEMENT, 2020, 42 :1-11
[43]  
Kline RB., 2023, Principles and Practice of Structural Equation Modeling, V5th
[44]  
Kuo K.-M., 2013, BMC Medical Informatics and Decision Making, V13, P14
[45]   The 'smart' classroom: a new frontier in the age of the smart university [J].
Kwet, Michael ;
Prinsloo, Paul .
TEACHING IN HIGHER EDUCATION, 2020, 25 (04) :510-526
[46]   User acceptance of You Tube for procedural learning: An extension of the Technology Acceptance Model [J].
Lee, Doo Young ;
Lehto, Mark R. .
COMPUTERS & EDUCATION, 2013, 61 :193-208
[47]   Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model [J].
Lee, Ming-Chi .
COMPUTERS & EDUCATION, 2010, 54 (02) :506-516
[48]   Why do people use information technology? A critical review of the technology acceptance model [J].
Legris, P ;
Ingham, J ;
Collerette, P .
INFORMATION & MANAGEMENT, 2003, 40 (03) :191-204
[49]  
Lin C.-W., 2021, Journal of Function Spaces, V1, P8
[50]   Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model [J].
Lung-Guang, Niu .
COMPUTERS & EDUCATION, 2019, 134 :50-62