Predicting attitude, use, and future intentions with translation websites through the TAM framework: a multicultural study among Saudi and South Korean language learners

Abstract

Machine translation (MT) practice and activity development in education are possible when students with diverse backgrounds contribute to helping define how MT can best be used for language learning. This study employed a questionnaire based on an adapted version of the technology acceptance model (TAM) to gain perspective on the perceptions, attitudes, and use of MT tools like Google Translate among Saudi and Korean university students learning English as a foreign language (EFL). A total of 470 students were recruited from Saudi Arabia and South Korea to complete a questionnaire measuring scales on perceived ease of use, perceived usefulness, attitude, actual use, and intended future use of MT websites. Structural equation modeling analyzed the relationships among the TAM variables. Results found that Saudi and Korean students both reported high levels on the TAM variables, indicating that translation tools were easy to use and useful for studying language. Lower L2 proficiency correlated with greater perceived usefulness and a more positive attitude. Students frequently use MT tools when learning English and plan to continue using them in the future. The model explained 71% of actual use and 42% of behavioral intention to use MT for language learning. Furthermore, Saudi students reported a slightly easier time using MT tools for language learning, while Korean students planned to use MT more often. Other findings, along with practical applications, are presented in this paper.