Continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory

Abstract

IntroductionThis study examines the factors that predict Chinese students’ continuance intention to use mobile learning for second language acquisition based on the technology acceptance model and self-determination theory.MethodOne hundred seventy undergraduates have participated in the survey and the structural equation modeling is conducted to assess the validity of the integrated model and hypotheses.ResultsThe findings show that instructor support can significantly predict autonomy, competence and relatedness. Autonomy and competence are positively related to perceived ease of use and continuance intention to use mobile learning for English acquisition. Relatedness significantly correlates with perceived ease of use but is not directly related to learners’ continuance intention. The relationship between perceived ease of use and continuance intention to adopt mobile learning is positive and significant. In addition, the results show that instructor support, autonomy, relatedness, competence and perceived ease of use can predict Chinese students’ continuance intention to use m-learning for second language acquisition, with 70.5% of the total variance in continuance intention being explained by these five variables.ConclusionThese results thus empirically support the integrated model, which can be used as a theoretical framework in future studies on mobile learning in higher education contexts. Moreover, the results of this study have a number of practical implications for universities and instructors.

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“…In addition, our research adds value to the existing corpus by bridging the gap between SDT and large language models, illustrating how a theoretical framework traditionally associated with education can be effectively applied to the rapidly evolving landscape of AI technologies. Notably, they echo past research suggesting that when technology users' psychological needs are met, their engagement with technology is not only more likely but also more sustainable [15][16][17]. This expands upon the understanding of user motivation in technology acceptance models, providing empirical support for a more nuanced approach that incorporates SDT's psychological needs alongside traditional predictors of technology use.…”

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