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Humanities and Social Sciences Communications volume 12, Article number: 561 (2025)
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Machine translation (MT) has emerged as a widely-used foreign language learning tool that could enhance language learning proficiency and productivity. However, the factors influencing college students’ acceptance of MT in foreign language learning remain insufficiently understood. Additionally, the existing literature seems to fail to examine the fitness between MT and foreign language learning tasks. Thus, this study integrates the Unified Theory of Acceptance and Usage of Technology (UTAUT) and Task-Technology Fit (TTF) models to investigate students’ acceptance of MT in foreign language learning. This study adopted a survey-based quantitative research approach, employing a convenience sampling method to collect 313 valid responses. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) to examine the hypothesized relationships. Results showed that performance expectancy, effort expectancy and social influence significantly influenced behavioral intention, and the behavioral intention to use MT had an impact on actual use behavior among students. Moreover, experience has proved to be a moderator that has positively impacted the relationship between performance expectancy and the intention use of MT, and TTF moderated the relationship between performance expectancy and behavioral intention, as well as the relationship between effort expectancy and behavioral intention. The theoretical and practical implications are provided for future researchers and practitioners to enhance students’ effective use of MT in their foreign language learning activities.
The rapid advancement of machine translation (MT) technologies, driven by machine learning and deep learning, has gained increasing attention in foreign language learning. MT offers learners an efficient way to access, comprehend, and produce contents in different languages due to its convenience, immediacy, efficiency, and multilingualism (Lee, 2023). Its widespread adoption has motivated scholars to investigate its effectiveness in various foreign language learning activities, particularly in reading, translation, and writing-related contexts. Research has demonstrated that MT can enhance cognitive development, emotional regulation, and language skills (Omar and Gomaa, 2020). The integration of MT activities into foreign language learning can help cultivate students’ self-regulation and promote the improvement of writing, reading skills, and language awareness (Briggs, 2018; Stapleton and Kin, 2019). For example, MT activities such as post-editing can reduce the cognitive load of lower-level foreign language learners (Lee, 2022), while identifying and correcting MT errors is also conducive to enhancing students’ critical thinking and metacognitive abilities (Yang and Wang, 2023). Furthermore, research has shown that the use of MT can reduce foreign language learning anxiety and enhance learning motivation and confidence, especially for lower-level foreign language learners (Tsai and Liao, 2021).
Despite the benefits of MT being demonstrated in previous studies, there remains limited exploration of the factors influencing college students’ acceptance and behavioral intentions toward MT. These factors are critical not only for optimizing the integration of MT into educational contexts but also for guiding the development of MT systems to satisfy learners’ needs (Rossi and Chevrot, 2019). Moreover, students’ perceptions of MT are often mixed and even contradictory, which could potentially impact their actual use of MT. For instance, Jia et al. (2019) highlighted that although MT could enhance translation efficiency, the quality of the outputs sometimes increased students’ psychological burden and limited their creativity. Similarly, Lee (2023) indicated that while MT could improve learning efficacy, students remained skeptical about the potential of MT to enhance learning effectiveness. Consequently, it is critical to identify factors and the mechanism that influence students’ acceptance of MT in order to optimize its implication in current educational environments.
Understanding students’ acceptance of MT in the context of foreign language learning requires a robust theoretical framework. The Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh et al. (2003), has proved to be a comprehensive model for understanding users’ intention to adopt a specific technology by considering eight key constructs such as performance expectancy, effort expectancy, social influence, and facilitating conditions. In the field of education research, the UTAUT model has proved its critical role in predicting and examining college students’ acceptance of diverse technologies in educational settings, such as mobile learning (Sultana, 2020), online learning platforms (Patil and Undale, 2023), and AI chatbots (Bilquise et al., 2024). However, experience, as a critical moderating variable that directly influences users’ technology acceptance, has been overlooked in UTAUT research, particularly in MT-related studies. Additionally, the existing UTAUT model primarily analyzes users’ perceptions of technology from individual and social perspectives, yet it neglects the influence of objective factors such as task-technology fit (TTF), which can significantly influence technology acceptance. Therefore, it is important to integrate UTAUT with TTF to enhance the explanatory power of the UTAUT model in educational and domain-specific learning contexts (Shirolkar and Kadam, 2023; Du and Lv, 2024).
This study aims to investigate students’ acceptance of MT in foreign language learning by integrating UTAUT with TTF. The motivation stems from two main concerns. First, existing research mainly focuses on exploring the effectiveness of MT in foreign language education, emphasizing its significance for language learners (e.g., Tsai and Liao, 2021; Lee, 2023). However, there were few studies exploring the factors influencing the acceptance and behavioral intention of using MT among college students from behavioral and technical perspectives. Second, while UTAUT has been widely used to predict students’ acceptance of learning technologies (e.g., Budhathoki et al., 2024; Wiangkham and Vongvit, 2024), little attention has been paid to integrating UTAUT with TTF to comprehensively investigate MT adoption in foreign language learning. This study addresses these gaps by providing a more nuanced understanding of how MT can be effectively adopted for foreign language learning by examining how individual, social, and technological factors derived from UTAUT and TTF influence college students’ acceptance of MT in foreign language learning. The main research question is: how do factors derived from UTAUT and TTF models influence college students’ acceptance of MT in foreign language learning? By addressing this question, the current study could provide significant insights for incorporating MT systems into foreign language learning. It further contributes to the expanding research domain on the integration of technology into education from the perspectives of students’ perceptions, especially as artificial intelligence tools become increasingly prominent in foreign language learning.
Existing research has developed several models to explain technology acceptance, such as the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and UTAUT. Among these, the UTAUT model has been widely recognized as the most comprehensive one. As defined by Venkatesh et al. (2003), the UTAUT model integrates eight major technology acceptance models, including the TAM, Social Cognitive Theory (SCT), and TPB, which are commonly employed to determine users’ intentions and behaviors regarding technology adoption. The model posits that users’ intention to use information technologies is mainly influenced by core variables (performance expectancy, effort expectancy, social influence, and facilitating conditions), moderating variables (gender, age, experience, and voluntariness), and outcome variables (behavioral intention to use and actual use behavior).
Research has widely-used UTAUT to explain and predict users’ technology adoption intentions, overcoming the shortcomings of a single technology acceptance model (Dwivedi et al., 2019). Due to its wide applicability, the UTAUT model has been applied to diverse research contexts related to technology adoption. Scholars often combine the UTAUT model with other theories to enhance its explanatory power by incorporating additional external variables. For instance, Li and Zhao (2024) combined UTAUT with the social presence theory to investigate factors influencing students’ intention to use of MOOCs, demonstrating that UTAUT effectively predicts student satisfaction. Wut et al. (2024) explored students’ intention to join blended learning courses in higher education based on the Community of Inquiry framework and UTAUT model, showing that students’ behavior intentions to engage in blended learning significantly influenced their attitude towards blended learning.
Building on the original UTAUT model, this study incorporates user attitude as a core variable and adds experience and TTF as moderating variables to deepen our understanding of MT acceptance among college students. First, user attitude is often regarded as a critical factor that directly influences an individual’s willingness to adopt information technologies in technology acceptance models (e.g., TAM) (Andrews et al., 2021). Numerous studies have demonstrated that attitudes significantly impact the acceptance of specific technologies in foreign language learning (e.g., Li et al., 2019; Almusharraf and Bailey, 2023). In the context of MT, students’ attitudes toward the perceived reliability and the quality of translations may profoundly affect their willingness to use it (Jolley and Maimone, 2022). Consequently, this study incorporates attitude as a core variable to explore college students’ acceptance of MT in foreign language learning.
Second, experience with MT use has been shown to directly affect willingness and motivation to use MT, as well as have an impact on the relationship between other variables such as perceived usefulness and perceived ease of use (Rossi and Chevrot, 2019; Yang and Wang, 2019). Venkatesh et al. (2012) highlighted that most studies have thoroughly explored the effects of UTAUT’s core constructs, often overlooking moderators such as experience that could have impacts on the relationships between different determinants within the model. By including experience as a moderating variable, this study seeks to investigate its impact on shaping students’ perceptions and adoption of MT.
Third, although UTAUT has proved to be effective in explaining psychological and behavioral factors affecting technology use, it fails to account for the specific characteristics of the tasks that users perform or the fit between tasks and the technology itself. This theoretical gap can be effectively addressed through the TTF model, which has been empirically validated as a robust framework for evaluating how well a technology optimally supports users in performing specific tasks (Zhou et al., 2010; Wan et al., 2020). Therefore, the present study incorporates TTF to examine its role in influencing the adoption and behavioral intention to use MT. Finally, this study excludes facilitating conditions from the UTAUT model due to its lack of relevance to the study. In the context of MT, facilitating conditions (e.g., server capacity or technical support) might be assumed to be largely handled by MT service providers, such as mobile app developers or browser extension platforms. Also, the widespread availability and ease of access to MT tools further diminish the degree to which organizational and technical infrastructure support the use of MT (Wang & Wang, 2021). Therefore, this study excludes facilitating conditions in the research model. By integrating user attitude, experience, and TTF into the UTAUT model, the study could provide a more comprehensive understanding of college students’ acceptance of MT in foreign language learning and how well MT fits specific language learning tasks.
Task-Technology Fit (TTF) is defined as the degree to which technology supports users in performing their tasks effectively (Goodhue and Thompson, 1995). This theoretical framework posits task and technology characteristics as two antecedents that function together to promote better task performance (Lin and Wang, 2012). It argues that users are more likely to adopt a technology if its features are in accordance with task requirements and improve their performance. As such, TTF provides a solid foundation for analyzing factors related to technology use and adoption, particularly when combined with models such as TAM and UTAUT (Rahi et al., 2021).
In recent years, TTF has been applied to evaluate the effectiveness of AI-driven tools and their impact on students’ learning performance. For instance, Wang et al. (2023) applied TTF to assess user satisfaction with new online learning spaces, indicating that TTF had significantly impacted user satisfaction and the continuance intentions toward using these spaces. This highlights the importance of task-technology fit in ensuring the efficiency of educational technologies. Recent studies have integrated UTAUT with TTF to provide a more comprehensive model for technology adoption in educational and domain-specific learning contexts (e.g., Tian and Yang, 2023; Du and Lv, 2024). These studies suggest that TTF could significantly influence college students’ perceptions of educational technologies, highlighting the importance of integrating TTF with other theoretical frameworks to provide a more comprehensive explanation for the relationship between TTF and the acceptance of technologies.
TTF has also been applied to translation studies, particularly in evaluating how effectively MT support professional translation tasks. It demonstrated that when MT systems align well with current domain-specific tasks, they can significantly enhance translation satisfaction and performance (Cadwell et al., 2018; Cui et al., 2023). Yang (2024) examined the role of MT fit and machine translation literacy in educational settings. It showed that both technology and task characteristics positively impacted MT fit, highlighting the significance of integrating TTF into MT research for examining the impact of MT in both foreign language learning and professional translation settings. As a widely-used learning tool, MT can support learners with diverse learning requirements, such as reading comprehension, vocabulary acquisition, and text construction (Lee, 2020; Yang et al., 2023). However, the effectiveness of MT depends on its ability to support domain-specific learning tasks. A high task-technology fit implies that MT not only meets the needs of students while performing specific learning tasks, but also results in better learning performance and satisfaction (Yang, 2024). In light of this, it is critical to take into account not just learners’ intentions to use MT, but also how well they align with the specific learning tasks. Thus, this study integrates UTAUT with the TTF to comprehensively explore students’ MT adoption in foreign language learning.
Performance expectancy (PE) refers to the extent to which users believe that a technology will improve their task performance, similar to the concept of perceived usefulness (Venkatesh et al., 2016). Previous research has shown that PE has a positive role in promoting learners’ behavioral intention to adopt various innovative technologies (Abbad, 2021). In this study, PE reflects the degree to which students believe that MT can improve the effectiveness of their foreign language learning. Yang and Wang (2019) posted that the quality of MT played a significant and positive role in affecting users’ willingness to adopt it for language learning tasks. Similarly, Lee (2023) highlighted that learners’ perceived efficacy of MT in enhancing writing performance emerged as a critical predictor of their adoption intentions. When students perceive that MT can enhance their academic performance or learning efficiency, their behavioral intention to use it increases. Thus, the following hypothesis is proposed:
H1: Performance expectancy has a significant positive impact on the behavioral intention to use machine translation.
Effort expectancy (EE) is a key component of the UTAUT model that describes the perceived ease of using a technology, closely related to perceived ease of use (Venkatesh et al., 2003; Tian and Yang, 2023). Specifically, the less effort users expend in using an information technology system, the higher the intention to adopt the technology. In translation studies, the adoption of MT among students is largely influenced by how effectively they generate precise and accurate translations that require minimal post-editing (Lee and Briggs, 2021; Tian and Yang, 2023). Research indicates that factors such as ease of use, speed, and operational convenience influence users’ willingness to use MT (Yang et al., 2021). Accordingly, this study proposes the following hypothesis:
H2: Effort expectancy has a significant positive impact on the behavioral intention to use machine translation.
Social influence (SI) refers to the extent to which individuals’ judgments of technologies can be influenced by the actions and opinions of others around them, which is related to social norms in other technology acceptance models (Venkatesh et al., 2003). In this study, SI represents how much college students are influenced by their peers, teachers, parents, or social media in adopting MT for foreign language learning. Relevant research shows that the attitudes of teachers and peers towards the use of MT have an important impact on their intention to engage with MT (Ducar and Schocket, 2018; Stapleton and Kin, 2019; Wang and Wang, 2021). Additionally, institutional support for using MT in academic or professional settings can further encourage its adoption among translators (Paterson, 2022). Based on this, this study proposes the following hypothesis:
H3: Social influence has a significant positive impact on the behavioral intention to use machine translation.
Attitude (ATT) refers to an individual’s overall evaluation of a technology, including both positive and negative perceptions (Venkatesh et al., 2003). In technology acceptance models, attitude has been identified as an important intrinsic variable directly influencing users’ willingness to adopt technologies (Dwivedi et al., 2019; Andrews et al., 2021). Jolley and Maimone (2022) emphasized that students’ attitudes toward MT are critical for determining their intention to use it. In other words, when students have positive attitudes toward the use of MT, they are more likely to perceive it as useful, which in turn strengthens their intention to use it. Thus, this study proposes the following hypothesis:
H4: User attitude has a significant positive impact on the behavioral intention to use machine translation.
Behavioral intention (BI) reflects users’ tendency towards information technologies and the possibility of future use (Venkatesh et al., 2003). UTAUT theory suggests that BI will positively affect their actual usage behavior (Agyei and Razi, 2022). In the context of MT, relevant research has confirmed that students’ behavioral intention to use MT has a significant positive impact on their actual use of the technology (Rossi and Chevrot, 2019; Lee, 2020). The stronger the behavioral intention of students to use MT, the greater the likelihood of students using MT in the future. Therefore, this study proposes the following hypothesis:
H5: Behavioral intention has a direct and positive effect on the use behavior of college students in terms of machine translation.
This study incorporates experience and TTF as moderating variables in the model. First, experience is a core moderating variable in the original UTAUT model (Venkatesh et al., 2003). Previous studies highlight its role in moderating the relationships between UTAUT constructs and behavioral intention in the educational sector (Celik, 2016; Lakhal et al., 2021). Also, experience has been proven to be a key factor in understanding students’ intention to learn and use MT. Greater experience with MT helps students become familiar with the types of translations MT excels at and the common errors it produces, making experience a critical moderator in technology adoption models (Yang and Wang, 2019). Therefore, experience is regarded as a key moderator in technology adoption models. Second, TTF reflects the extent to which technology supports users when performing specific tasks (Goodhue and Thompson, 1995). Previous research has demonstrated the importance of task-technology fit in shaping individuals’ perceptions of the actual utility of the technology and has positive effects on behavioral intention (Zhou et al., 2010; Wan et al., 2020; Wang et al., 2024). Recent studies also confirm that TTF has been proven to act as a significant moderator that effectively improves the explanatory power of the UTAUT model (Shirolkar and Kadam, 2023; Du and Lv, 2024). In the context of MT, TTF evaluates how well MT aligns with translation tasks, assessing whether the MT systems are capable of enhancing translation quality by effectively integrating into the learning environment and satisfying the students’ requirements. The following hypotheses are proposed:
H6a: Experience moderates the relationship between performance expectancy and the behavioral intention to use machine translation.
H6b: Experience moderates the relationship between effort expectancy and the behavioral intention to use machine translation.
H6c: Experience moderates the relationship between social influence and the behavioral intention to use machine translation.
H6d: Experience moderates the relationship between attitude and the behavioral intention to use machine translation.
H7a: TTF moderates the relationship between performance expectancy and the behavioral intention to use machine translation.
H7b: TTF moderates the relationship between effort expectancy and the behavioral intention to use machine translation.
H7c: TTF moderates the relationship between social influence and the behavioral intention to use machine translation.
H7d: TTF moderates the relationship between attitude and the behavioral intention to use machine translation.
Grounded in the theoretical framework of UTAUT and TTF, this study develops a research model as shown in Fig. 1. To explore the factors influencing college students’ intentions to use MT, this study employed a survey-based quantitative research approach, which is widely used for testing proposed hypotheses through statistical analysis (Hair et al., 2019).
This figure shows the research model developed in this study, including the factors that influence the intention to use MT in foreign language learning among college students.
Based on a convenience sampling method, this study recruited 340 translation major students from three comprehensive universities in China. Participants were invited to complete a questionnaire distributed by instructors during class sessions. The instructors briefly introduced the purpose of the survey, and then provided students with a link to the online questionnaire after obtaining their written informed consent. Students who agreed to participate were given time during class to complete the survey voluntarily. Finally, responses from 313 students were used for the analysis, and 27 responses were excluded due to invalid information. According to the widely adopted “10-times rule” method in PLS-SEM, the sample size should exceed 10 times the largest number of formative indicators used to measure a single construct, or 10 times the largest number of structural paths directed at a particular construct in the structural model (Hair et al., 2014). In the present study, there are 13 structural paths, suggesting that the minimum sample size should be considered 130. Therefore, the sample size of 313 participants meets the requirements for data analysis. The sample comprised 179 females and 134 males, with an average age of 23.64 years (range 19–41 years). All participants had the same language background, with Chinese as their first language and English as their second language. They had completed one semester or more of translation training with approximately fifteen years of English learning experience. Thus, the participants generally had basic translation knowledge and language skills, which could ensure that their evaluations of their behavioral intention to use MT were valid (Yang and Wang, 2019). The demographic characteristics of the sample are consistent with prior studies taking MT use as study subjects in foreign language learning contexts (Yang and Wang, 2019; Yang, 2024). The demographic characteristics of the sample are shown in Table 1.
This study employed a questionnaire survey for data collection. The questionnaire was adapted from well-established scales that have been validated in prior studies, with modifications to reflect college students’ perceptions of MT use in foreign language learning. To ensure content validity, we invited three teachers with rich translation experience to propose modifications to the questions and five teaching assistants as interviewees to examine the layout, font size, and language expression of the questionnaire. Based on their suggestions, vague and ambiguous items were removed in order to enhance the readability and validity of the questionnaire. Then, a pilot study was conducted with a small number of students (141 non-participating students in total) to check its reliability and validity. Results showed that all Cronbach’s α values of the constructs exceeded 0.80, suggesting that the scales had a high internal consistency (Hair et al., 2019).
The finalized questionnaire consists of two parts. First, it investigates the demographic characteristics of the participants, including gender, age, grade, education level, and experience in using MT. In the second part, it carries out a statistical survey on the eight latent variables in the research model. Specifically, a five-point Likert scale (1 = strongly disagree, 5 = strongly agree) was used to evaluate each variable: three items for performance expectancy (Venkatesh et al., 2003; Yang and Wang, 2019), three items for effort expectancy (Venkatesh et al., 2003; Yang and Wang, 2019), three items for social influence (Venkatesh et al., 2003; Agyei and Razi, 2022), three items for attitudes towards MT usage (Davis et al., 1989; Wang et al., 2021), three items for behavioral intention (Venkatesh et al., 2003; Agyei and Razi, 2022), three items for experience(Yang and Wang, 2019), three items for Task-technology Fit (Wu and Chen, 2017) and three items for use behavior (Venkatesh et al., 2003). The finalized scales are shown in Table 2.
The online survey was distributed to the students through an online platform named Wenjuanxing (https://www.wjx.cn/) from March to June 2024. Before completing the survey, participants were provided with a detailed description of the study’s purpose. Informed consent was also obtained from all participants before completing the survey, and they were free to withdraw at any point during the survey. To ensure the quality and validity of the collected data, several measures were implemented. First, participants were informed that the survey was anonymous and the data would be used only for research purposes. Also, those who completed the questionnaire received a small gift for compensation. Second, the survey platform recorded the time taken to complete the questionnaire, and responses completed in less than the minimum required time were excluded as invalid. Third, two items in the questionnaire were reverse-coded, and participants whose answers were not reverse-coded were considered careless, resulting in the exclusion of their data from analysis. After rigorous screening, a total of 313 valid data were retained for subsequent analysis, while 27 responses were excluded due to invalid information. The response rate was 92.06%. In addition, after completing the questionnaire, 15 participants were randomly selected for semi-structured interviews to further explore their intentions and attitudes regarding the use of MT in foreign language learning.
This study adopted the PLS-SEM approach to analyze the data and test the research model. Compared with covariance-based structural equation modeling (CB-SEM), PLS-SEM estimates structural variables by an iterative process, and does not require sample data to conform to multivariate normal distribution (Hair et al., 2019). Furthermore, it is particularly suitable for evaluating relatively complex models while demanding minimal sample sizes than other statistical methods (Hair et al., 2022). This methodology has gained prominence in various research contexts, including studies on the integration of artificial intelligence in foreign language education (Lin et al., 2020). In view of this, this study adopted PLS-SEM as the data analysis method, using SPSS and Smart PLS software to test the research model. The analysis mainly involved two stages: (1) the reliability and validity test of the measurement model and (2) the evaluation of the structural model to test the proposed research hypotheses.
Specifically, this study first conducted a reliability and validity analysis of the measurements. The reliability test mainly measured the internal consistency of the questionnaire items through the Composite Reliability (CR) value and Cronbach’s α. When the CR value is higher than 0.70, and the Cronbach’s α coefficient is higher than 0.70, it can be considered that the measurement model is within acceptable standard values. In this study, the validity of the measurement model was mainly tested through convergent validity and discriminant validity. In the PLS analysis model, convergent validity is mainly determined by factor loadings and Average Variance Extracted (AVE), with factor loadings to be greater than 0.70 and AVE values to be greater than 0.50 (Hair et al., 2022). The Variance Inflation Factors (VIF) were calculated to ensure that there was no evidence of multicollinearity. All VIF values were lower than the critical value of 5, indicating that there is no significant correlation between predictors in the model (Hair et al., 2019). In terms of the structural model, path coefficients and their significance levels were estimated using 5000 bootstrap subsamples to examine the research hypotheses proposed in this study.
Common Method Bias (CMB) refers to the fact that respondents are susceptible to factors such as the measurement environment and social expectations in the process of completing self-reported scales, resulting in artificial covariation between predictor variables and validity variables (Zhou and Long, 2004). Since CMB has an impact on the validity of research findings, this study controlled and tested CMB through two methods: process control and statistical control. In terms of process control, this study followed the principle of anonymity in scale design and distribution to protect participants’ personal information and tried its best to avoid CMB by balancing the sequential effects of the items and reducing participants’ guessing of the questions. To ensure statistical control, this study employed Harman’s one-way test for all measurement entries, ensuring the eigenvalue was greater than 1 and avoiding factor rotation. The results showed that the variance explained by the first factor of all variables was 25.044%, which was lower than the critical criterion of 40% (Tang and Wen, 2020). Based on the above analysis, it indicated that the effects of CMB were not significant in this study.
A PLS-SEM analysis was employed to conduct reliability, validity, and hypothesis analysis. As shown in Table 3, the CR values of all latent variables were higher than the threshold of 0.70; all Cronbach’s α values were higher than 0.70, indicating that the measurement items in this study were reliable and internally consistent. Moreover, all factor loadings in this measurement model were higher than 0.70, and the AVE values of all dimensions were higher than the threshold of 0.50, showing that the instruments had good reliability and the measurement items of the scale possess good convergent validity. Furthermore, VIF was tested, with all values below the critical value of 5, suggesting no issues of multicollinearity among the model’s variables (Hair et al., 2022). In terms of the discriminant validity, AVE values for each construct were higher than the correlation coefficients with other constructs in the study model. As demonstrated in Table 4, the Heterotrait-Monotrait (HTMT) ratio was also calculated, with all values less than the threshold of 0.90 (Henseler et al., 2015), indicating that the discriminant validity among constructs was satisfactory.
To further evaluate the model fit, R2, Goodness-of-Fit (GoF) value and the standardized root mean square residual (SRMR) were used to evaluate the predictive ability of the model. First, R2 is an important index to evaluate the predictive ability of the model, and the minimum tolerance of this value is 0.1(Falk and Miller, 1992). In this study, the R2 value for behavioral intention to the model was 0.558, and the R2 value for actual use behavior to the model was 0.392, indicating that the explanatory power of the model was moderate and met the basic requirements (Hair et al., 2019). Second, the values of GoF calculated by this model was 0.595, which is higher than the threshold of 0.360 and demonstrating a high level of overall model fit (Wetzels et al., 2009). Furthermore, the value of SRMR was 0.074 and lower than 0.080 (Henseler et al., 2016), indicating that the model has good fitness.
This study tested the research hypotheses by calculating the path coefficients, t-values, and p values between the latent variables (Table 5), as well as the moderating effect of experience and TTF within these pathways (Table 6). As shown in Table 5, all postulated hypotheses except hypothesis 4 were statistically supported. Specifically, performance expectancy had a significant positive effect on the behavioral intention to use MT(β = 0.310, p < 0.05), and H1 is supported; Effort expectancy had a significant positive effect on the behavioral intention to use MT(β = 0.186, p < 0.05), and H2 is supported; Social influence had a significant positive effect on the behavioral intention to use MT(β = 0.149, p < 0.05), and H3 is supported; Behavioral intention to use MT had a significant positive effect on the use behavior (β = 0.438, p < 0.05), supporting H5. Nevertheless, attitude use failed to significantly affect the behavioral intention to use MT (β = 0.102, p > 0.05), and H4 is not supported.
In terms of the moderating effects of experience and TTF in the research model, results showed that experience exhibited a statistically significant impact on the relationship between performance expectancy and behavioral intention (β = 0.138, p < 0.05). This finding suggests that the positive effect of performance expectancy on behavioral intention becomes stronger with increased experience, thus supporting H6a. Similarly, TTF demonstrated a significant moderating effect on the relationship between performance expectancy and behavioral intention (β = 0.119, p < 0.05). This finding suggests that as task-technology fit improves, the positive influence of performance expectancy on behavioral intention is enhanced, supporting H7a. Additionally, it showed that task-technology fit played an important moderating role in the relationship between effort expectancy and behavioral intention (β = 0.213, p < 0.05), indicating that a higher task-technology fit enhances the impact of effort expectancy on behavioral intention. This result supports H7b and highlights the critical role of TTF in increasing the adoption of behavioral intentions. Figure 2 shows the results of structural model analysis from the SmartPLS software.
This figure represents the results of structural model analysis from the SmartPLS software. It shows outer loadings from the PLS algorithm and t-values following bootstrapping.
This study adopted the UTAUT model combined with TTF to investigate the factors influencing college students’ acceptance of MT in foreign language learning settings. There are some interesting findings worthy of report and discussion.
First, it was found that performance expectancy, effort expectancy, and social influence had a significant positive impact on the behavioral intention to use MT, which is consistent with previous studies (Yang and Wang, 2019; Tsai and Liao, 2021; Lee, 2023). Specifically, performance expectancy directly affected their behavioral intention to use MT, indicating that MT is more likely to be adopted when students perceive it as a valuable tool for assisting in learning tasks and enhancing learning efficiency. Also, effort expectancy was a significant predictor of behavioral intention to use MT, which supports the UTAUT theory (Venkatesh et al., 2012) and is consistent with prior research in foreign language contexts (Lee and Briggs, 2021; Tian and Yang, 2023). It should be noted that performance expectancy was found to be the most significant determinant of behavioral intention, compared with effort expectancy. This may be due to the fact that students nowadays have gained more experience in using MT, leading them to believe that they can use it without devoting too much effort to it. In addition, social influence had a significant positive impact on the intention to use MT. On the one hand, students in collaborative learning situations are easily influenced by their peers’ attitudes towards MT (Wang and Wang, 2021). On the other hand, the degree of teachers’ support for MT will also have an impact on students’ behavioral intention to use it (Ducar and Schocket, 2018; Stapleton and Kin, 2019). This highlights the importance of teacher support and peer collaboration in promoting MT use. In this regard, teachers should concentrate on guiding students to enhance their higher-order thinking skills, thereby enhancing their academic performance, while incorporating MT into language learning activities. Meanwhile, peer collaboration activities such as online peer feedback should be encouraged to further improve MT’s effectiveness.
Second, attitudes towards MT did not have a significant impact on the behavioral intention to use MT, different from the findings of Andrews et al. (2021) that users’ attitudes towards technology use had a significant effect on their behavioral intention. Results of the interviews found that despite 87.64% of students expressing dissatisfaction with MT quality, most of the students (98.73%) acknowledged its role in reducing foreign language learning anxiety, and they were still willing to use MT to assist their foreign language learning in their actual studies. It is indicated that students’ attitudes toward MT did not have a direct effect on their willingness to use it. This supports Dwivedi et al. (2019) argued that the willingness to use a technology is mainly affected by the technology itself (e.g., speed, quality, product interface, etc.), while attitude has a relatively small impact on the willingness to use the technology. Additionally, the behavioral intention to use MT significantly positively influenced actual MT use behavior, supporting Rossi and Chevrot’s (2019) finding that students’ behavioral intentions influenced their actual MT use behavior. In line with the UTATU model, those with high levels of behavioral intentions had high levels of usage of the technology (Dwivedi et al., 2019). As most participants have long experience using MT and a strong intention to use it, their long-term familiarity likely reinforced their intention to continue utilizing MT in their language learning.
Third, this study reveals that experience as a moderator had a positive impact on the relationship between performance expectancy and the behavioral intention use of MT, which is consistent with prior research that experience acts as a strong factor influencing the perceived usefulness of MT (Yang and Wang, 2019).Students with increasing experience in MT can become more familiar with the performance of different text types across various MT systems, making it possible to identify errors more quickly in MT, thereby maximizing its advantages and increasing translation productivity (Daems et al., 2017; Niño, 2020). Therefore, students are more likely to accept and proactively integrate MT into their foreign language learning with their increasing experience in using MT. Furthermore, it was found that a high level of task-technology fit was associated with both perceived performance expectancy and effort expectancy, which in turn contributed to the development of positive behavioral intentions. Specifically, TTF moderated the relationship between performance expectancy and the behavioral intention to use MT, which echoes previous studies that the fit between students’ requirements and the service provided by MT directly promotes their perceived performance, thereby promoting the behavioral intention to use it (Yang, 2024). The higher the fit degree is, the more useful the MT is perceived. Also, the moderating role of TTF on the relationship between effort expectancy and the behavioral intention to use MT was proved in this study. This finding supports previous study that the usability of MT may vary depending on the translation tasks (Wang et al., 2021). As MT becomes more capable of meeting the demands of completing different translation tasks, the influence of effort expectancy on behavioral intention increases. Teachers should consider the requirements of specific learning tasks and the fitness of the technology when integrating MT into foreign language training. By designing tailored tasks that align with the capabilities of MT systems, teachers can improve both students’ learning performance and MT literacy.
The findings of this study offer both theoretical and practical implications. Theoretically, this research enriches the literature on the factors influencing college students’ acceptance of MT by incorporating the UTAUT with the TTF model. While a limited number of studies have combined these frameworks, our research model provides a more comprehensive understanding of MT acceptance, not only from the perspective of user perceptions but also by examining the moderating effects of TTF and experience on behavioral intentions. This insight is especially important for tasks that require specialized tools, such as translation technologies, where the functional fit between the task and technology is essential for achieving successful task performance. In addition, the study highlights the critical roles of experience and TTF in predicting the acceptance of MT in foreign language learning. TTF was analyzed as a moderating variable in the present study compared with previous research that considered it as an independent one in technology acceptance models. This study emphasizes the importance of individual and technological factors, suggesting adapting the technology acceptance model to accommodate the characteristics of the task and user experience in educational and professional translation environments. For instance, the requirements for MT in foreign language learning practice may differ from those in daily translation practice settings, thereby deepening our understanding of how technology perceptions and task fit interact to shape technology acceptance in domain-specific contexts.
Practically, findings of this study have valuable implications for MT designers, universities, and teachers in promoting the effective use of MT in foreign language learning. Specifically, our results suggest that MT designers should tailor language services to enhance students’ positive perceptions and integrate MT features with diverse tasks to promote continued usage. MT systems, for example, must be user-friendly and perceived as beneficial by teachers and learners, and effective in accomplishing specific tasks such as grammar correction, vocabulary building, and cultural nuances in translation. Also, regularly updating MT services based on user feedback (students, teachers, and professional translators) can help achieve a better task-technology fit in educational and professional settings. Additionally, universities should invest in infrastructure and training, such as providing free MT resources and community-based support services, to enhance students’ experiences with MT. For teachers, results indicate that the adoption of MT in educational settings is more than merely introducing the technology to the classroom. Teachers must ensure that MT systems are well-aligned with the specific language learning tasks students perform. Moreover, attention should be given to facilitating conditions, such as providing adequate training and resources to enable students to utilize MT systems effectively. They should train students to assess when MT is appropriate critically, promote the development of technological literacy, and foster higher-order thinking skills in collaborative and problem-solving contexts. This would not only enhance students’ proficiency with MT but also encourage more strategic and efficient use in their foreign language learning processes. In addition, teachers can improve the effectiveness of incorporating MT into foreign language teaching design by focusing on students’ individual factors (e.g., experience and technology perception) and task-technology fit. For example, teachers can construct various machine translation training scaffolds based on students’ experiences to enhance the effectiveness of students’ use of machine translation in aiding foreign language learning. Students can also be encouraged to focus on collaborative learning when applying machine translation for language learning. These approaches can provide practical insights to improve the design and practice of MT as a foreign language learning tool.
This study integrates the UTAUT with the TTF model to investigate the factors influencing students’ acceptance of MT in foreign language learning. By examining how TTF and experience moderate traditional UTAUT variables, such as performance expectancy and effort expectancy, this research seeks to provide a more comprehensive framework for understanding technology adoption in educational contexts. Results indicate that performance expectancy, effort expectancy and social influence significantly influenced behavioral intention, and the behavioral intention to use MT had an impact on actual use behavior among college students. Moreover, experience has proved to be a moderator that has positively impacted the relationship between performance expectancy and the intention use of MT, and TTF moderated the relationship between performance expectancy and behavioral intention, as well as the relationship between effort expectancy and behavioral intention. The study contributes to the broader discourse on educational technology by emphasizing the influence of task relevance and user experience on the adoption of MT, which has implications for both pedagogical strategies and future technology design.
Despite the contributions, the present study has several limitations. First, the sample was restricted to translation students from Chinese universities, which constrains the generalizability of the findings. Future studies can include a more diverse range of participants from various regions and countries, and academic disciplines to enable comparative analyses. Second, this study focused on a narrow set of factors influencing user acceptance based on the UTAUT. Additional theoretical frameworks, such as social cognitive theory and expectation confirmation theory, along with individual factors like translation self-efficacy, perceived trust, and enjoyment, may also affect user acceptance of MT worthy of exploration. Furthermore, the present study considers TTF as a whole to examine its impact on the behavioral intention to use MT in foreign language learning. Future research could further explore the specific task and technology characteristics of MT, along with other supplementary factors, to develop a more comprehensive understanding of MT adoption. Third, this study used cross-sectional data, which failed to account for the potential changes in students’ perceptions over time with the advancement of technology. Future research could benefit from longitudinal studies to better assess how these factors impact MT use behavior over extended periods.
The datasets analyzed in the current study are not publicly available since they are concerned with individual participants, and we made it clear in the informed consent form that their confidentiality would be ensured. But the data are available from the authors upon reasonable request.
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This work was supported by the Fundamental Research Funds for the Central Universities (2024SK13); Research Project for Applied Research in Social Sciences in Jiangsu Province: Foreign Language Topics (24SWB-05); Research Project on Philosophy and Social Sciences in Jiangsu Universities.
China University of Mining and Technology, Xuzhou, China
Lu Sha, Xiaoyue Wang & Tingting Liu
Center for Translation and Cross-cultural Studies of China University of Mining and Technology, Xuzhou, China
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LS conceived the original idea, carried out the experiment and wrote the manuscript. XW contributed substantially to the writing and revising of the manuscript. TL provided critical feedback to the improvement of manuscript. All authors read and approved the final manuscript.
Correspondence to Tingting Liu.
The authors declare no competing interests.
Ethical approval was obtained from China University of Mining and Technology Institutional Review Board of School of Foreign Studies (No. 0124102) on 2nd January, 2024. This study was conducted in compliance with the 1964 Helsinki declaration and its later amendments or comparable standards.
The survey was conducted through an online platform named Wenjuanxing from March to June 2024. Before initiating the data collection process, the authors provided all participants with written informed consent. All the participants were informed of the purpose of the research, their rights as participants, and the scope of their consent. All participants were willing to participate in this research and provided responses only for the purposes of academic research. The authors also ensured that all the responses would be confidential and only used for academic research purposes.
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Sha, L., Wang, X. & Liu, T. Understanding college students’ acceptance of machine translation in foreign language learning: an integrated model of UTAUT and task-technology fit. Humanit Soc Sci Commun 12, 561 (2025). https://doi.org/10.1057/s41599-025-04888-8
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