Extending the UTAUT Model of Tencent Meeting for Online Courses by Including Community of Inquiry and Collaborative Learning Constructs

Qin, Ruobing; Yu, Zhonggen

doi:10.1080/10447318.2023.2233129

Cited by 7 publications

(1 citation statement)

References 104 publications

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“…Adjei et al (2021) found a positive relationship between effort expectancy and the intention to use mobile phone-based Interactive Voice Response in a rural area of Ghana [ 58 ]. In contrast, Qin and Yu (2023) identified that effort expectancy negatively influenced the intention to use Tencent Meeting for online courses [ 59 ]. Hence, additional research is needed to these inconsistencies.…”

Section: Literature Review and Theoretical Modelsmentioning

confidence: 99%

Users’ continuance intention towards an AI painting application: An extended expectation confirmation model

Yu,

Yang,

2024

PLoS ONE

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With the rapid advancement of technology, Artificial Intelligence (AI) painting has emerged as a leading intelligence service. This study aims to empirically investigate users’ continuance intention toward AI painting applications by utilizing and expanding the Expectation Confirmation Model (ECM), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and the Flow Theory. A comprehensive research model is proposed. A total of 443 questionnaires were distributed to users with AI painting experiences for data collection. The hypotheses were tested through structural equation modeling. The primary conclusions drawn from this research include: 1) Confirmation plays a crucial role, significantly and positively predicting satisfaction and social impact. 2) Personal innovativeness has a significant effect on confirmation. 3) Satisfaction, flow experience, and social influence directly and positively predict intention, with social influence showing the most significant impact, while perceived usefulness, perceived enjoyment, and performance expectancy show no significant impact on intention. 4) Habit plays a negative moderating role in the association between social influence and continued intention to use. These findings offer valuable insights and inspiration for users seeking to understand the appropriate utilization of AI painting and provide actionable directions for the development of AI painting.

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Section: Literature Review and Theoretical Modelsmentioning

confidence: 99%

Users’ continuance intention towards an AI painting application: An extended expectation confirmation model

Yu,

Yang,

2024

PLoS ONE

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Online teaching model in the context of blended learning environment: Experiential learning and TAM

Lai,

She,

2024

Educ Inf Technol

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The unified theory of acceptance and use of DingTalk for educational purposes in China: an extended structural equation model

Hou,

2023

Humanit Soc Sci Commun

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With educational technology growing by leaps and bounds, synchronous online learning platforms have become a prevalent practice worldwide. Although numerous studies unraveled the behavioral intention of educational technologies with statistical methodology, there is a paucity of that of DingTalk, one of China’s most popular online platforms for synchronous learning. This study aimed to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating new constructs and examining the factors that affect users’ behavioral intention and use behavior of DingTalk. The study collected 856 valid responses from China, which were analyzed using SPSS 23.0 and Amos 24.0. The findings indicated that (1) effort expectancy (EE), performance expectancy (PE), facilitating conditions (FC), self-efficacy (SE), and received feedback (RF) could significantly impact users’ attitudes toward behavior (ATB); (2) social influence (SI), FC, RF, and ATB could be significant predictors of user behavioral intention (BI); (3) FC, RF, and BI were found to have a significant effect on use behavior (UB); (4) the extended UTAUT model could explain 60.9% of the variance of users’ behavioral intention of DingTalk in China; (5) the study identified ATB and BI as joint mediators between certain variables in the model. The study presented a robust theoretical underpinning to explain the acceptance and use of educational technologies in China and provided insights into the future enhancement of E-learning platforms.

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Extending the UTAUT Model of Tencent Meeting for Online Courses by Including Community of Inquiry and Collaborative Learning Constructs

Cited by 7 publications

References 104 publications

Users’ continuance intention towards an AI painting application: An extended expectation confirmation model

Users’ continuance intention towards an AI painting application: An extended expectation confirmation model

Online teaching model in the context of blended learning environment: Experiential learning and TAM

The unified theory of acceptance and use of DingTalk for educational purposes in China: an extended structural equation model

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