Understanding college students’ acceptance of machine translation in foreign language learning: an integrated model of UTAUT and task-technology fit

Machine translation (MT) has emerged as a widely-used foreign language learning tool that could enhance language learning proficiency and productivity. However, the factors influencing college students’ acceptance of MT in foreign language learning remain insufficiently understood. Additionally, the existing literature seems to fail to examine the fitness between MT and foreign language learning tasks. Thus, this study integrates the Unified Theory of Acceptance and Usage of Technology (UTAUT) and Task-Technology Fit (TTF) models to investigate students’ acceptance of MT in foreign language learning. This study adopted a survey-based quantitative research approach, employing a convenience sampling method to collect 313 valid responses. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) to examine the hypothesized relationships. Results showed that performance expectancy, effort expectancy and social influence significantly influenced behavioral intention, and the behavioral intention to use MT had an impact on actual use behavior among students. Moreover, experience has proved to be a moderator that has positively impacted the relationship between performance expectancy and the intention use of MT, and TTF moderated the relationship between performance expectancy and behavioral intention, as well as the relationship between effort expectancy and behavioral intention. The theoretical and practical implications are provided for future researchers and practitioners to enhance students’ effective use of MT in their foreign language learning activities.