Factors influencing students' listening learning performance in mobile vocabulary‐assisted listening learning: An extended technology acceptance model.

Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning performance (LLP) through vocabulary learning performance (VLP) in the context of mobile vocabulary‐assisted listening learning by using two mobile learning tools. Methods: A total of 129 college students with a pre‐intermediate level of English were recruited as participants, and a 10‐week mobile vocabulary‐assisted, listening‐learning course was conducted in 2022. In each task of this course, the students had to learn target words from a listening passage on Quizlet and then engage in listening activities on Randall's ESL Cyber Listening Lab. Quantitative responses obtained through an online questionnaire were analysed through partial‐least‐squares structural equation modelling. Results: The analysis results indicated that BI significantly predicted LLP through VLP. Perceived ease of use (PEU) and perceived usefulness (PU) were significant antecedents of BI. However, PEU did not significantly predict PU because of the difficulty of navigating between the two technological tools used in this study. The extended model demonstrated its effectiveness in explaining listening learning performance, as evidenced by an explained variance (R2) of 69%. Conclusion: The extended model validates the influence of BI on learning performance and it can also draw teachers' focus toward the significance of enhancing students' BI to improve their listening learning performance. Pedagogical implications based on the results are provided in this paper. Lay Description: What is already known about this topic?: TAM was used to study learners' acceptance of mobile‐assisted language learning.TAM incorporates latent variables to explore mobile‐assisted language learning.Investigating factors influencing BI is a primary research focus in extended TAM literature.Mobile tools could improve listening learning and vocabulary retention. What this paper adds to that: Learning performance was considered as a dependent variable in an extended TAM.BI might predict students' learning performance in vocabulary and listening in an extended TAM.Teachers used two mobile tools to design mobile vocabulary‐assisted listening tasks.Pre‐learning the target words facilitate students' listening learning performance. Implications for practice and/or policy: We show the importance of BI on predicting listening learning performance.The impact of BI on other factors became another focus of TAM research.Results highlight pre‐learning target words' importance for better listening performance.Existing mobile tools improve listening performance, avoiding new system development. [ABSTRACT FROM AUTHOR]