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A uses and gratifications approach to examining users’ continuance intention towards smart mobile learning

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  • Biao Gao

    (Jiangxi University of Finance and Economics)

Abstract

Smart mobile learning (SML), an online learning system built on artificial intelligence technology, signifies a key development trajectory for mobile learning. However, the current literature reveals a research deficit in introducing specific constructs that represent the categorical level of gratification towards SML, and a new gratification for the intelligent dimension of SML has yet to be identified. Utilising the uses and gratifications (U&G) framework, this study identifies five categories of user gratification. These are derived from five theoretical perspectives, including the incentive theory of motivation, learning theory, diffusion of innovation theory, self-determination theory, and flow theory. Hence, this research integrates aspects of technology, content, social, utilitarian, and hedonic gratification to examine their influence on users’ continuance intention towards SML. This study focuses on Liulishuo, an SML app, as a typical research object and incorporates data from 495 valid samples. The analysis via partial least-squares structural equation modelling (PLS-SEM) indicates the hierarchical significance of various gratifications influencing continuance intention. The empirical findings suggest that in the realm of SML, users’ expectations surpass basic intrinsic needs in importance. For the first time, this study introduces the intelligence construct to investigate users’ technology gratification concerning SML, thereby empirically establishing the validity of this construct. This study reveals that technology gratification, embodied in the notion of intelligence, is the most critical determinant of continuance intention towards SML, a relationship that has previously remained unexplored.

Suggested Citation

  • Biao Gao, 2023. "A uses and gratifications approach to examining users’ continuance intention towards smart mobile learning," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02239-z
    DOI: 10.1057/s41599-023-02239-z
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    References listed on IDEAS

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    1. Sara Moussawi & Marios Koufaris & Raquel Benbunan-Fich, 2023. "The role of user perceptions of intelligence, anthropomorphism, and self-extension on continuance of use of personal intelligent agents," European Journal of Information Systems, Taylor & Francis Journals, vol. 32(3), pages 601-622, May.
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    4. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
    5. Zhigang Li & Yi Liu, 2023. "Analysis of the Current Situation of the Research on the Influencing Factors of Online Learning Behavior and Suggestions for Teaching Improvement," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
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    Cited by:

    1. Scott, Carol F. & Bay-Cheng, Laina Y. & Nochajski, Thomas H. & Lorraine Collins, R., 2024. "Emerging adults’ social media engagement & alcohol misuse: A multidimensional, person-centered analysis of risk," Children and Youth Services Review, Elsevier, vol. 159(C).
    2. Rui Qiao & Cong Liu & Jun Xu, 2024. "Making algorithmic app use a virtuous cycle: Influence of user gratification and fatigue on algorithmic app dependence," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    3. Xiongkai Tan & Sha Zhang & Ruichen Ge & Hong Zhao, 2024. "Connectivity in crisis: the contrasting roles of mobile and non-mobile Internet on subjective well-being during the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.

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