journal article Open Access Mar 15, 2026

What Drives GenAI Adoption in Informal Digital Learning of English ( IDLE )?: Structural‐Configurational Modelling of Extended UTAUT2 with GenAI Literacy

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Abstract
ABSTRACT

Background
The use of generative artificial intelligence (GenAI) in informal digital learning of English (IDLE) foregrounds the need to understand the conditions under which learners adopt and continue using these tools.


Objectives
This study integrated GenAI literacy into Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to explain behavioral intention and actual use of GenAI for IDLE. It further examined both net effects and configurational pathways for high GenAI usage.


Methods
We recruited 475 Chinese university students with prior IDLE experience. We used partial least squares structural equation modelling (PLS‐SEM) to test the extended UTAUT2 model and applied fuzzy‐set qualitative comparative analysis (fsQCA) to identify configurations.


Results and Conclusions
Effort expectancy, social influence, and habit significantly predicted behavioral intention, whereas performance expectancy, price value, hedonic motivation, and facilitating conditions were not significant. Habit, facilitating conditions, and behavioral intention predicted actual usage of GenAI for IDLE. GenAI literacy also showed direct positive effects on behavioral intention and actual usage. It negatively moderated the relationship between social influence and behavioral intention, but strengthened the effects of habit and behavioral intention on actual usage. Incorporating GenAI literacy improved the explanatory capacity of the UTAUT2 model in GenAI‐IDLE contexts. fsQCA analysis indicated that high levels of GenAI use can emerge from four configurations. Across them, GenAI literacy, hedonic motivation, and habit appeared as core contributors. These findings provide directions for future research and educational design to support effective informal language learning with GenAI.
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References
61
[3]
Back-Translation for Cross-Cultural Research

Richard W. Brislin

Journal of Cross-Cultural Psychology 10.1177/135910457000100301
[5]
The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age

Peter Cardon, Carolin Fleischmann, Jolanta Aritz et al.

Business and Professional Communication Quarterly 10.1177/23294906231176517
[8]
Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research

Peer C. Fiss

Academy of Management Journal 10.5465/amj.2011.60263120
[9]
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

Claes Fornell, David F. Larcker

Journal of Marketing Research 10.1177/002224378101800104
[10]
Common methods variance detection in business research

Christie M. Fuller, Marcia J. Simmering, Guclu Atinc et al.

Journal of Business Research 10.1016/j.jbusres.2015.12.008
[13]
Hair J. F. (2022)
[17]
Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

Li‐tze Hu, Peter M. Bentler

Structural Equation Modeling: A Multidisciplinary... 10.1080/10705519909540118
[18]
Common Method Bias in PLS-SEM

Ned Kock

International Journal of e-Collaboration 10.4018/ijec.2015100101
[20]
Affective variables and informal digital learning of English: Keys to willingness to communicate in a second language

Ju Seong Lee, Nur Arifah Drajati

Australasian Journal of Educational Technology 10.14742/ajet.5177
[23]
Exploring AI-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation of Chinese EFL learners’ AI adoption and experiences

Guangxiang Leon Liu, Ron Darvin, Chaojun Ma

Computer Assisted Language Learning 10.1080/09588221.2024.2310288
[24]
What Predicts ( AI ‐Mediated) Informal Digital Learning of English in the Global South? The Case of Rural Bangladeshi Students

Guangxiang Leon Liu, Md Kamal Hossain

Journal of Computer Assisted Learning 10.1002/jcal.70196
[33]
Conceptualizing AI literacy: An exploratory review

Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu et al.

Computers and Education: Artificial Intelligence 10.1016/j.caeai.2021.100041
[34]
Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module

Thu Ngan Ngo, David Hastie

English for Specific Purposes 10.1016/j.esp.2024.09.001
[38]
Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing

Ilias O. Pappas, Arch G. Woodside

International Journal of Information Management 10.1016/j.ijinfomgt.2021.102310
[39]
How Big Is “Big”? Interpreting Effect Sizes in L2 Research

Luke Plonsky, Frederick Oswald

Language Learning 10.1111/lang.12079
[48]
The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation

Kuttimani Tamilmani, Nripendra P. Rana, Samuel Fosso Wamba et al.

International Journal of Information Management 10.1016/j.ijinfomgt.2020.102269
[49]
User Acceptance of Information Technology: Toward A Unified View1

Viswanath Venkatesh, Michael G. Morris, Gordon B. Davis et al.

MIS Quarterly 10.2307/30036540
[50]

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Published
Mar 15, 2026
Vol/Issue
42(2)
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Xiaoqi Wang, Lawrence Jun Zhang (2026). What Drives GenAI Adoption in Informal Digital Learning of English ( IDLE )?: Structural‐Configurational Modelling of Extended UTAUT2 with GenAI Literacy. Journal of Computer Assisted Learning, 42(2). https://doi.org/10.1002/jcal.70217