The rapid growth of educational technology has made synchronous online learning platforms, such as DingTalk, increasingly prevalent globally. While many studies have explored the factors influencing the adoption of educational technologies, research specifically on DingTalk in the Chinese context is limited. This study addresses this gap by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The UTAUT model, initially proposed by Venkatesh et al. (2003), provides a robust framework for understanding technology acceptance by incorporating key constructs such as performance expectancy, effort expectancy, social influence, and facilitating conditions. However, the original UTAUT model may not fully capture the nuances of technology adoption in specific contexts. Therefore, this study expands the UTAUT model by incorporating additional constructs to better understand the acceptance and use of DingTalk for educational purposes in China. DingTalk's popularity in China makes it a crucial platform to study, as understanding its acceptance and usage can provide valuable insights into the effectiveness of online learning and inform future platform development. The study aims to empirically investigate the factors influencing users' behavioral intention and use behavior concerning DingTalk, focusing on the Chinese educational landscape and the unique characteristics of the platform.

The study's theoretical foundation is the Unified Theory of Acceptance and Use of Technology (UTAUT), initially developed by Venkatesh et al. (2003) and later extended to UTAUT2 by Venkatesh et al. (2012). UTAUT integrates several previous models to provide a more comprehensive explanation of technology adoption. The core constructs of UTAUT – performance expectancy, effort expectancy, social influence, and facilitating conditions – are considered key determinants of behavioral intention and usage behavior. The literature review explores the application of UTAUT in educational contexts, highlighting studies that have extended the model by incorporating additional constructs like hedonic motivation, price value, and habit. Furthermore, the review examines studies investigating the impact of specific constructs within the UTAUT framework on educational technology adoption. This includes examining the role of constructs such as attitude toward behavior, self-efficacy, and received feedback, which are incorporated into the extended model for this study. The inconsistent findings regarding the impact of certain constructs, such as effort expectancy and facilitating conditions, in previous studies, motivated the researchers to investigate these relationships in the context of DingTalk usage.

This quantitative study employed a survey method to collect data from a sample of 856 users of DingTalk for educational purposes in China. Strict inclusion and exclusion criteria were applied to ensure the quality of the data. The questionnaire, developed using a 5-point Likert scale, measured variables derived from the extended UTAUT model. This included traditional UTAUT constructs (effort expectancy, performance expectancy, social influence, facilitating conditions, behavioral intention, and use behavior) and newly added constructs (attitude toward behavior, self-efficacy, and received feedback). The questionnaire also incorporated demographic questions about gender, age, occupation, and experience with DingTalk. The back-translation method was used to ensure the accuracy and clarity of the questionnaire in both English and Chinese. Data analysis was performed using SPSS 23.0 and Amos 24.0. A two-step approach was followed for structural equation modeling (SEM), evaluating the measurement model and then the structural model. Confirmatory factor analysis was conducted to assess the validity and reliability of the measurement scales. Modification indices were used to refine the model fit. The structural model was evaluated using several fit indices, including χ²/df, CFI, SRMR, and RMSEA. Path analysis was performed to test the hypothesized relationships among the variables, and mediation analysis was conducted to explore the mediating effects of attitude toward behavior and behavioral intention.

The study's key findings revealed that several factors significantly influenced users' attitudes toward DingTalk and their behavioral intention and use behavior. Effort expectancy (EE), performance expectancy (PE), facilitating conditions (FC), self-efficacy (SE), and received feedback (RF) significantly and positively impacted attitudes toward behavior (ATB). Social influence (SI), FC, RF, and ATB were significant positive predictors of behavioral intention (BI). FC, RF, and BI significantly influenced use behavior (UB). The extended UTAUT model explained a substantial portion of the variance in users' behavioral intention (R² = 60.9%) and attitude toward behavior (R² = 73.5%), and use behavior (R²=46%). Mediation analyses revealed that ATB played a significant mediating role between PE, RF, and FC and BI. BI mediated the effect of SI on UB. Specifically, 12 out of the 17 hypotheses were supported. The unexpected finding was that effort expectancy negatively influenced attitudes toward behavior, possibly due to the increasing prevalence of technology and improved digital literacy among users. The study also found that performance expectancy, while not significantly predicting behavioral intention, did significantly influence users' attitudes, possibly due to mandatory use in certain educational settings. Facilitating conditions proved highly influential across all aspects of technology acceptance and usage, underlining the importance of technological support and resources. Self-efficacy, while not a significant predictor of behavioral intention, did positively influence attitudes towards usage.

The findings of this study provide valuable insights into the factors influencing the acceptance and use of DingTalk for educational purposes in China. The significant role of facilitating conditions highlights the need for robust technological infrastructure and support in promoting successful online learning. The mediating roles of attitude toward behavior and behavioral intention emphasize the importance of fostering positive user experiences and perceptions. The negative relationship between effort expectancy and attitudes towards behavior warrants further investigation into how the design and usability of online learning platforms can minimize perceived effort. The influence of received feedback underscores the importance of incorporating effective feedback mechanisms within the platform to enhance user engagement. The influence of social influence on behavioral intention highlights the role of social norms and peer influence in technology adoption. The study's findings have implications for both researchers and practitioners in the field of educational technology. For researchers, this study contributes to a more nuanced understanding of technology acceptance models in the context of online education and the Chinese cultural context. For practitioners, the findings provide guidance on designing and implementing effective online learning platforms that enhance user experience, improve engagement, and maximize educational outcomes.

This study extends the UTAUT model to better understand DingTalk adoption in Chinese online education. The findings highlight the crucial roles of facilitating conditions, received feedback, and user attitudes. The study's limitations include the cross-sectional design and potential biases due to the COVID-19 pandemic's impact. Future research could explore longitudinal studies, investigate the moderating effects of other variables, and examine the generalizability of the findings to other platforms and contexts. Practical implications include improving platform usability and incorporating effective feedback mechanisms. The research contributes significantly to the field of educational technology.

The study's limitations include the cross-sectional nature of the data, which limits the ability to make causal inferences. The data collection period overlapped with the easing of COVID-19 restrictions in China, potentially influencing participants' responses. The convenience sampling method may have introduced sampling bias, and the reliance on self-reported data might have led to response bias. The failure to find significant moderating effects, despite attempts at multigroup moderating analyses, also limits the study’s generalizability. Lastly, variations across diverse participant groups were not adequately addressed.