Article published In: Translation and Interpreting Studies: Online-First Articles
The predictive effects of user perceptions on the willingness to continue using machine translation
Relevant but less than satisfactory?
Published online: 29 January 2026
https://doi.org/10.1075/tis.25014.man
https://doi.org/10.1075/tis.25014.man
Abstract
Machine translation (MT) users generally express a willingness
to continue using MT systems, despite their often-negative perceptions of MT
output quality. Informed by an extended Technology Acceptance Model, this
questionnaire-based study of trainee translators (N = 273)
investigates this phenomenon by assessing the predictive effects of users’
perceptions on their intention to continue using MT systems. Perception of MT
output quality was found to have no significant direct effect on respondent’s
intent to continue using MT; however, the strongest predictor of continued use
was the perceived relevance of MT output, followed by perceived usefulness and
perceived ease of use. This study suggests that user perceptions of relevance
and usefulness, other than output quality, were more direct determinants of user
intent. The results highlight the importance of user perceptions in translation
technology acceptance, providing a new perspective on MT system research and
development.
Article outline
- Introduction
- Literature review
- Perceptions of machine translation
- Perceptions as proximal determinants of translation technology adoption
- Method
- Participants and the context
- Instruments
- Data collection and analysis
- Results
- Preliminary data analysis
- Measurement model assessment
- Structural model assessment
- Discussion
- Implications
- Conclusion
References
References (48)
Al-Rahmi, Ali Mugahed, Waleed Mugahed Al-Rahmi, Uthman Alturki, Ahmed Aldraiweesh, Sultan Almutairy, and Ahmad Samed Al-Adwan. 2022. “Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education.” Education and Information Technologies 27(6): 7805–7826.
Arenas-Gaitán, Jorge, Patricio Ramírez-Correa, and Javier Rondán-Cataluña. 2011. “Cross cultural analysis of the use and perceptions of web based learning systems.” Computers & Education 57(2): 1762–1774.
Asscher, Omri, and Ella Glikson. 2023. “Human evaluations of machine translation in an ethically charged situation.” New Media & Society 25(5): 1087–1107.
Bywood, Lindsay, Panayota Georgakopoulou, and Thierry Etchegoyhen. 2017. “Embracing the threat: Machine translation as a solution for subtitling.” Perspectives 25(3): 492–508.
Cadwell, Patrick, Sheila Castilho, Sharon O’Brien, and Linda Mitchell. 2016. “Human factors in machine translation and post-editing among institutional translators.” Translation Spaces 5(2): 222–243.
Cadwell, Patrick, Sharon O’Brien, and Carlos Teixeira. 2018. “Resistance and accommodation: Factors for the (non-) adoption of machine translation among professional translators.” Perspectives 26(3): 301–321.
Christensen, Tina, and Anne Schjoldager. 2016. “Computer-aided translation tools — the uptake and use by Danish translation service providers.” JoSTrans: The Journal of Specialised Translation 251: 89–105.
Cohen, Jacob. 1988. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: L. Erlbaum Associates.
Creswell, John W. 2012. Educational research: Planning, conducting, and evaluating quantitative and qualitative research. 4th ed. Boston: Pearson.
Davis, Fred. 1989. “Perceived usefulness, perceived ease of use, and user acceptance of information technology.” MIS Quarterly 13(3): 319–340.
Doherty, Stephen, and Dorothy Kenny. 2014. “The design and evaluation of a statistical machine translation syllabus for translation students.” Interpreter and Translator Trainer 8(2): 295–315.
Fernández Torné, Anna, and Anna Matamala. 2021. “Human evaluation of three machine translation systems: From quality to attitudes by professional translators.” Vigo International Journal of Applied Linguistics 181: 123–148.
Garcia, Ignacio. 2010. “Is machine translation ready yet?” Target 22(1): 7–21.
Guerberof-Arenas, Ana. 2026. “Perspectives on machine translation, post-editing, and automation.” In The Routledge Handbook of the Translation Industry, ed. by Callum Walker and Joseph Lambert, 186–204. New York: Routledge.
Guerberof-Arenas, Ana, Joss Moorkens, and David Orrego-Carmona. 2024. “‘A Spanish version of EastEnders’: A reception study of a telenovela subtitled using MT.” JoSTrans: The Journal of Specialised Translation 411: 230–254.
Hair, Joseph, Tomas Hult, Christian Ringle, and Marko Sarstedt. 2022. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 3rd ed. Los Angeles: SAGE.
Jia, Yanfang, and Sanjun Sun. 2023. “Man or machine? Comparing the difficulty of human translation versus neural machine translation post-editing.” Perspectives 31(5): 950–968.
Kasperė, Ramunė, Jolita Horbačauskienė, Jurgita Motiejūnienė, Vilmantė Liubinienė, Irena Patašienė, and Martynas Patašius. 2021. “Towards sustainable use of machine translation: Usability and perceived quality from the end-user perspective.” Sustainability 13(23): art. 13430.
Kasperė, Ramunė, Jurgita Motiejūnienė, Irena Patasienė, Martynas Patašius, and Jolita Horbačauskienė. 2023. “Is machine translation a dim technology for its users? An eye tracking study.” Frontiers in Psychology 141: art. 1076379.
King, William R., and Jun He. 2006. “A meta-analysis of the technology acceptance model.” Information & Management 43(6): 740–755.
Man, Deliang, Aiping Mo, Meng Huat Chau, Mitchell O’Toole, and Charity Lee. 2020. “Translation technology adoption: Evidence from a postgraduate programme for student translators in China.” Perspectives 28(2): 253–270.
Mellinger, Christopher D. 2017. “Translators and machine translation: Knowledge and skills gaps in translator pedagogy.” The Interpreter and Translator Trainer 11(4): 280–293.
Mellinger, Christopher D., and Thomas A. Hanson. 2017. Quantitative Research Methods in Translation and Interpreting Studies. London: Routledge.
. 2018. “Order effects in the translation process.” Translation, Cognition & Behavior 1(1): 1–20.
. 2020. “Methodological considerations for survey research: Validity, reliability, and quantitative analysis.” Linguistica Antverpiensia, New Series: Themes in Translation Studies 191: 172–190.
. 2024. “Psychometric properties of survey translations: A simulation study.” Translation, Cognition & Behavior 7(1): 159–185.
Mo, Aiping, and Deliang Man. 2017. “The ecosystem of translator workstation: Learning electronic tools in a training program for professional translators in China.” Babel 63(3): 401–422.
Moorkens, Joss. 2017. “Under pressure: Translation in times of austerity.” Perspectives 25(3): 464–477.
Nitzke, Jean, Carmen Canfora, Silvia Hansen-Schirra, and Dimitrios Kapnas. 2024. “Decisions in projects using machine translation and post-editing: An interview study.” JoSTrans: The Journal of Specialised Translation 411: 127–148.
O’Brien, Sharon. 2024. “Human-centered augmented translation: Against antagonistic dualisms.” Perspectives 32(3): 391–406.
Popel, Martin, Marketa Tomkova, Jakub Tomek, Łukasz Kaiser, Jakob Uszkoreit, Ondřej Bojar, and Zdeněk Žabokrtský. 2020. “Transforming machine translation: A deep learning system reaches news translation quality comparable to human professionals.” Nature Communications 11(1): art. 4381.
Ragni, Valentina, and Lucas Nunes Vieira. 2021. “What has changed with neural machine translation? A critical review of human factors.” Perspectives 30(1): 137–158.
Rico Pérez, Celia. 2024. “Re-thinking machine translation post-editing guidelines.” JoSTrans: The Journal of Specialised Translation 411: 26–47.
Rossi, Caroline, and Jean-Pierre Chevrot. 2019. “Uses and perceptions of machine translation at the European Commission.” JoSTrans: Journal of Specialised Translation 311: 177–200.
Saroia, Asher Irfan, and Shang Gao. 2018. “Investigating university students’ intention to use mobile learning management systems in Sweden.” Innovations in Education and Teaching International 56(5): 569–580.
Scherer, Ronny, Fazilat Siddiq, and Jo Tondeur. 2019. “The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education.” Computers & Education 1281: 13–35.
Tian, Sha, and Wenjiao Yang. 2024. “Modeling the use behavior of interpreting technology for student interpreters: An extension of UTAUT model.” Education and Information Technologies 29(9): 10499–10528.
van Egdom, Gys-Walt, and Mark Pluymaekers. 2019. “Why go the extra mile? How different degrees of post-editing affect perceptions of texts, senders and products among end users.” JoSTrans: Journal of Specialised Translation 311: 158–176.
Venkatesh, Viswanath, and Hillol Bala. 2008. “Technology Acceptance Model 3 and a research agenda on interventions.” Decision Sciences 39(2): 273–315.
Venkatesh, Viswanath, and Fred Davis. 2000. “A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies.” Management Science 46 (2): 186–204.
Venter, Peet, Mari Jansen van Rensburg, and Annemarie Davis. 2012. “Drivers of learning management system use in a South African open and distance learning institution.” Australasian Journal of Educational Technology 28(2): 183–198.
Vieira, Lucas Nunes. 2020. “Machine translation in the news: A framing analysis of the written press.” Translation Spaces 9(1): 98–122.
Wang, Xiangling, Tingting Wang, Ricardo Muñoz Martín, and Yanfang Jia. 2021. “Investigating usability in postediting neural machine translation: Evidence from translation trainees’ self-perception and performance.” Across Languages and Cultures 22(1): 100–123.
Wang, Yuxi, Liping Chen, and Jiayin Han. 2024. “Exploring factors influencing students’ willingness to use translation technology.” Education and Information Technologies 29(13): 17097–17118.
Yang, Xiaodong, Bing Song, Liang Chen, Shirley S. Ho, and Jin Sun. 2025. “Technological optimism surpasses fear of missing out: A multigroup analysis of presumed media influence on generative AI technology adoption across varying levels of technological optimism.” Computers in Human Behavior 1621: art. 108466.
Yang, Yanxia, and Xiangling Wang. 2019. “Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model.” Computers & Education 1331: 116–126.
Zhao, Junfeng, Xiang Li, and Hong Liao. 2025. “A context-specific analysis of translation technology usage behavior among college EFL students: Insights from the UTAUT2 model.” Education and Information Technologies 30(12): 16515–16549.
Zheng, Jianwei, and Wenjun Fan. 2022. “Multivaried acceptance of post-editing in China.” Pragmatics and Society 13(4): 644–662.