The predictive effects of user perceptions on the willingness to continue using machine translation

References

1. 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(): –. 10.1007/s10639‑022‑10934‑8
   https://doi.org/10.1007/s10639-022-10934-8 [Google Scholar]
2. 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(): –. 10.1016/j.compedu.2011.03.016
   https://doi.org/10.1016/j.compedu.2011.03.016 [Google Scholar]
3. Asscher, Omri, and Ella Glikson
   2023 “Human evaluations of machine translation in an ethically charged situation.” New Media & Society(): –. 10.1177/14614448211018833
   https://doi.org/10.1177/14614448211018833 [Google Scholar]
4. Bywood, Lindsay, Panayota Georgakopoulou, and Thierry Etchegoyhen
   2017 “Embracing the threat: Machine translation as a solution for subtitling.” Perspectives(): –. 10.1080/0907676X.2017.1291695
   https://doi.org/10.1080/0907676X.2017.1291695 [Google Scholar]
5. Cadwell, Patrick, Sheila Castilho, Sharon O’Brien, and Linda Mitchell
   2016 “Human factors in machine translation and post-editing among institutional translators.” Translation Spaces(): –. 10.1075/ts.5.2.04cad
   https://doi.org/10.1075/ts.5.2.04cad [Google Scholar]
6. Cadwell, Patrick, Sharon O’Brien, and Carlos Teixeira
   2018 “Resistance and accommodation: Factors for the (non-) adoption of machine translation among professional translators.” Perspectives(): –. 10.1080/0907676X.2017.1337210
   https://doi.org/10.1080/0907676X.2017.1337210 [Google Scholar]
7. 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: –. 10.26034/cm.jostrans.2016.301
   https://doi.org/10.26034/cm.jostrans.2016.301 [Google Scholar]
8. Cohen, Jacob
   1988 Statistical power analysis for the behavioral sciences. 2nd ed.Hillsdale, NJ: L. Erlbaum Associates.
   [Google Scholar]
9. Creswell, John W.
   2012 Educational research: Planning, conducting, and evaluating quantitative and qualitative research. 4th ed.Boston: Pearson.
   [Google Scholar]
10. Davis, Fred
    1989 “Perceived usefulness, perceived ease of use, and user acceptance of information technology.” MIS Quarterly(): –. 10.2307/249008
    https://doi.org/10.2307/249008 [Google Scholar]
11. Doherty, Stephen, and Dorothy Kenny
    2014 “The design and evaluation of a statistical machine translation syllabus for translation students.” Interpreter and Translator Trainer(): –. 10.1080/1750399X.2014.937571
    https://doi.org/10.1080/1750399X.2014.937571 [Google Scholar]
12. 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: –. 10.35869/vial.v0i18.3366
    https://doi.org/10.35869/vial.v0i18.3366 [Google Scholar]
13. Garcia, Ignacio
    2010 “Is machine translation ready yet?” Target(): –. 10.1075/target.22.1.02gar
    https://doi.org/10.1075/target.22.1.02gar [Google Scholar]
14. Guerberof-Arenas, Ana
    2026 “Perspectives on machine translation, post-editing, and automation.” InThe Routledge Handbook of the Translation Industry, ed. byCallum Walker and Joseph Lambert, –. New York: Routledge.
    [Google Scholar]
15. 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: –. 10.26034/cm.jostrans.2024.4724
    https://doi.org/10.26034/cm.jostrans.2024.4724 [Google Scholar]
16. 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.
    [Google Scholar]
17. Jia, Yanfang, and Sanjun Sun
    2023 “Man or machine? Comparing the difficulty of human translation versus neural machine translation post-editing.” Perspectives(): –. 10.1080/0907676X.2022.2129028
    https://doi.org/10.1080/0907676X.2022.2129028 [Google Scholar]
18. 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(): art. 13430. 10.3390/su132313430
    https://doi.org/10.3390/su132313430 [Google Scholar]
19. 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: art. 1076379. 10.3389/fpsyg.2023.1076379
    https://doi.org/10.3389/fpsyg.2023.1076379 [Google Scholar]
20. King, William R., and Jun He
    2006 “A meta-analysis of the technology acceptance model.” Information & Management(): –. 10.1016/j.im.2006.05.003
    https://doi.org/10.1016/j.im.2006.05.003 [Google Scholar]
21. 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(): –. 10.1080/0907676X.2019.1677730
    https://doi.org/10.1080/0907676X.2019.1677730 [Google Scholar]
22. Mellinger, Christopher D.
    2017 “Translators and machine translation: Knowledge and skills gaps in translator pedagogy.” The Interpreter and Translator Trainer(): –. 10.1080/1750399X.2017.1359760
    https://doi.org/10.1080/1750399X.2017.1359760 [Google Scholar]
23. Mellinger, Christopher D., and Thomas A. Hanson
    2017 Quantitative Research Methods in Translation and Interpreting Studies. London: Routledge. 10.4324/9781315647845
    https://doi.org/10.4324/9781315647845 [Google Scholar]
24. Mellinger, Christopher D., and Thomas A. Hanson
    2018 “Order effects in the translation process.” Translation, Cognition & Behavior(): –. 10.1075/tcb.00001.mel
    https://doi.org/10.1075/tcb.00001.mel [Google Scholar]
25. Mellinger, Christopher D., and Thomas A. Hanson
    2020 “Methodological considerations for survey research: Validity, reliability, and quantitative analysis.” Linguistica Antverpiensia, New Series: Themes in Translation Studies: –. 10.52034/lanstts.v19i0.549
    https://doi.org/10.52034/lanstts.v19i0.549 [Google Scholar]
26. Mellinger, Christopher D., and Thomas A. Hanson
    2024 “Psychometric properties of survey translations: A simulation study.” Translation, Cognition & Behavior(): –. 10.1075/tcb.00094.mel
    https://doi.org/10.1075/tcb.00094.mel [Google Scholar]
27. Mo, Aiping, and Deliang Man
    2017 “The ecosystem of translator workstation: Learning electronic tools in a training program for professional translators in China.” Babel(): –. 10.1075/babel.63.3.06aip
    https://doi.org/10.1075/babel.63.3.06aip [Google Scholar]
28. Moorkens, Joss
    2017 “Under pressure: Translation in times of austerity.” Perspectives(): –. 10.1080/0907676X.2017.1285331
    https://doi.org/10.1080/0907676X.2017.1285331 [Google Scholar]
29. 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: –. 10.26034/cm.jostrans.2024.4715
    https://doi.org/10.26034/cm.jostrans.2024.4715 [Google Scholar]
30. O’Brien, Sharon
    2024 “Human-centered augmented translation: Against antagonistic dualisms.” Perspectives(): –. 10.1080/0907676X.2023.2247423
    https://doi.org/10.1080/0907676X.2023.2247423 [Google Scholar]
31. 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(): art. 4381. 10.1038/s41467‑020‑18073‑9
    https://doi.org/10.1038/s41467-020-18073-9 [Google Scholar]
32. Ragni, Valentina, and Lucas Nunes Vieira
    2021 “What has changed with neural machine translation? A critical review of human factors.” Perspectives(): –. 10.1080/0907676X.2021.1889005
    https://doi.org/10.1080/0907676X.2021.1889005 [Google Scholar]
33. Rico Pérez, Celia
    2024 “Re-thinking machine translation post-editing guidelines.” JoSTrans: The Journal of Specialised Translation: –. 10.26034/cm.jostrans.2024.4696
    https://doi.org/10.26034/cm.jostrans.2024.4696 [Google Scholar]
34. Rossi, Caroline, and Jean-Pierre Chevrot
    2019 “Uses and perceptions of machine translation at the European Commission.” JoSTrans: Journal of Specialised Translation: –. 10.26034/cm.jostrans.2019.182
    https://doi.org/10.26034/cm.jostrans.2019.182 [Google Scholar]
35. 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(): –. 10.1080/14703297.2018.1557068
    https://doi.org/10.1080/14703297.2018.1557068 [Google Scholar]
36. 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: –. 10.1016/j.compedu.2018.09.009
    https://doi.org/10.1016/j.compedu.2018.09.009 [Google Scholar]
37. 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(): –. 10.1007/s10639‑023‑12225‑2
    https://doi.org/10.1007/s10639-023-12225-2 [Google Scholar]
38. 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: –. 10.26034/cm.jostrans.2019.181
    https://doi.org/10.26034/cm.jostrans.2019.181 [Google Scholar]
39. Venkatesh, Viswanath, and Hillol Bala
    2008 “Technology Acceptance Model 3 and a research agenda on interventions.” Decision Sciences(): –. 10.1111/j.1540‑5915.2008.00192.x
    https://doi.org/10.1111/j.1540-5915.2008.00192.x [Google Scholar]
40. Venkatesh, Viswanath, and Fred Davis
    2000 “A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies.” Management Science (): –. 10.1287/mnsc.46.2.186.11926
    https://doi.org/10.1287/mnsc.46.2.186.11926 [Google Scholar]
41. 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(): –. 10.14742/ajet.868
    https://doi.org/10.14742/ajet.868 [Google Scholar]
42. Vieira, Lucas Nunes
    2020 “Machine translation in the news: A framing analysis of the written press.” Translation Spaces(): –. 10.1075/ts.00023.nun
    https://doi.org/10.1075/ts.00023.nun [Google Scholar]
43. 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(): –. 10.1556/084.2021.00006
    https://doi.org/10.1556/084.2021.00006 [Google Scholar]
44. Wang, Yuxi, Liping Chen, and Jiayin Han
    2024 “Exploring factors influencing students’ willingness to use translation technology.” Education and Information Technologies(): –. 10.1007/s10639‑024‑12511‑7
    https://doi.org/10.1007/s10639-024-12511-7 [Google Scholar]
45. 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: art. 108466. 10.1016/j.chb.2024.108466
    https://doi.org/10.1016/j.chb.2024.108466 [Google Scholar]
46. Yang, Yanxia, and Xiangling Wang
    2019 “Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model.” Computers & Education: –. 10.1016/j.compedu.2019.01.015
    https://doi.org/10.1016/j.compedu.2019.01.015 [Google Scholar]
47. 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(): –. 10.1007/s10639‑025‑13454‑3
    https://doi.org/10.1007/s10639-025-13454-3 [Google Scholar]
48. Zheng, Jianwei, and Wenjun Fan
    2022 “Multivaried acceptance of post-editing in China.” Pragmatics and Society(): –. 10.1075/ps.19048.zhe
    https://doi.org/10.1075/ps.19048.zhe [Google Scholar]