Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model

Abstract

Increasing use of MT in translation industry is leading to awareness of its utilization in translation education field. However, the academic literature fails to acknowledge what factors could contribute to students’ intention to use MT, and consequent effects of using MT. The purpose of this study is to develop a comprehensive model in the context of MT adoption based upon Technology Acceptance Model (TAM). The proposed model was empirically validated by using survey data from 109 student translators. The findings indicate that Perceived Usefulness has a stronger effect on Behavioral Intention to use MT and is significantly influenced by Experience. Furthermore, Experience is in turn influenced by Motivation which is affected by Perceived Ease of Use. The enhanced quasi-circular model not only reveals significant factors for MT adoption, but suggests positive effects of using MT. The findings provide significant theoretical and practical implications for translation researchers, teachers, and MT system developers.