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Exploring factors influencing students' willingness to use translation technology.

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Title: Exploring factors influencing students' willingness to use translation technology.
Authors: Wang, Yu-xi1, Chen, Li-ping1 chenliping@njnu.edu.cn, Han, Jia-yin1
Source: Education & Information Technologies. Sep2024, Vol. 29 Issue 13, p17097-17118. 22p.
Subject Terms: *Factor analysis, Structural equation modeling, Questionnaires, Investors, Product usage segmentation
Abstract: The study of students' willingness to use translation technology can motivate students to use the translation technology and improve their translation efficiency. This paper reports on a survey which collected 716 valid questionnaires from students in the Master-level Program in Translation and Interpreting. Structural equation modeling (SEM) was used to assess the influence of six potential variables on Master of Translation and interpreting(MTI)usage intention. The results show that perceived usefulness (PU), perceived ease of use (PEOU), subjective norm (SN) and translation technology self-efficacy (TTSE) have a significant positive influence on usage intention (UI), and perceived ease of use and perceived usefulness have significant mediating effects. Additionally, self-efficacy has a marginal or no influence at all on perceived usefulness, and flow experience (FE) has no effect on usage intention. Suggestions for future studies in the area of translation technology teaching are proposed based on the results and limitations of this study. These findings contribute to the promotion of the use of translation technology by master of Translation and interpreting at universities, which is expected to provide a beneficial research foundation for the teaching of translation technology. [ABSTRACT FROM AUTHOR]
Copyright of Education & Information Technologies is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Exploring factors influencing students' willingness to use translation technology.
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  Data: <searchLink fieldCode="JN" term="%22Education+%26+Information+Technologies%22">Education & Information Technologies</searchLink>. Sep2024, Vol. 29 Issue 13, p17097-17118. 22p.
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  Data: The study of students' willingness to use translation technology can motivate students to use the translation technology and improve their translation efficiency. This paper reports on a survey which collected 716 valid questionnaires from students in the Master-level Program in Translation and Interpreting. Structural equation modeling (SEM) was used to assess the influence of six potential variables on Master of Translation and interpreting(MTI)usage intention. The results show that perceived usefulness (PU), perceived ease of use (PEOU), subjective norm (SN) and translation technology self-efficacy (TTSE) have a significant positive influence on usage intention (UI), and perceived ease of use and perceived usefulness have significant mediating effects. Additionally, self-efficacy has a marginal or no influence at all on perceived usefulness, and flow experience (FE) has no effect on usage intention. Suggestions for future studies in the area of translation technology teaching are proposed based on the results and limitations of this study. These findings contribute to the promotion of the use of translation technology by master of Translation and interpreting at universities, which is expected to provide a beneficial research foundation for the teaching of translation technology. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Education & Information Technologies is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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