| Sumario: | 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. Summary: What is currently known about this topic? UTAUT2 is widely used to explain technology adoption in language learning but has rarely been tested in GenAI‐mediated IDLE.Existing studies have focused on net effects of predictors, overlooking possible configurational pathwaysGenAI literacy is viewed as an emerging competence, yet its power in explaining learners' GenAI use for IDLE remains underexplored.What does this paper add? ○The study extends UTAUT2 by integrating GenAI literacy as a predictor of behavioral intention and actual use, and as a moderator of the links from social influence to behavioral intention and from habit and behavioral intention to actual use.○The integration of GenAI literacy into UTAUT2 in the IDLE context broadens the model's explanatory scope and increases high predictive power for behavioral intention and actual GenAI use.○Uses a dual‐method approach (PLS‐SEM + fsQCA) to reveal both net effects and multiple sufficient configurations.Implications for practice/or policy ○The increased explanatory power of the extended UTAUT2 model indicates that integrating GenAI literacy into the IDLE context is necessary.○The effect of GenAI literacy implies that teacher training and learning support can incorporate GenAI literacy development to ensure learners are prepared to engage critically and effectively with GenAI tools.○The multiple configurations that lead to high GenAI use inform institutions and policymakers of the importance of supporting varied learner pathways rather than relying on a single route to technology uptake.
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