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<p>This study examines the impact of student aspiration factors (expectations, motivation, and enjoyment) on university students’ actual usage of mobile learning systems. The researchers conducted a structured questionnaire survey among 518 randomly selected university students from three universities in Hainan Province to measure their self-reported responses to seven constructs (expectations, motivation, enjoyment, perceived usefulness, perceived ease of use, attitudes, and actual use). A sophisticated two-stage structural equation modelling-artificial neural network approach was applied, and this study revealed non-compensatory and nonlinear relationships between the predictors and the actual usage of university students. The results revealed that all hypotheses were supported except for the direct effect between motivation and perceived usefulness, which was nonsignificant. Furthermore, according to the normalized importance derived from the multilayer perceptron, perceived usefulness (100%), attitude (73.0%), enjoyment (33.5%), motivation (30.8%), perceived ease of use (25.1%), and expectations (12.2%) are significant predictors of the actual usage behaviour of university students. Finally, this study presents theoretical and practical implications for the actual usage behaviour of mobile learning systems among university students.</p>

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Analysing students’ aspiration factors that impact actual use of mobile learning systems: a two-stage SEM-ANN approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月20日

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  • Henan Wu,
  • Xiaoping Que,
  • Ling Pan

摘要

This study examines the impact of student aspiration factors (expectations, motivation, and enjoyment) on university students’ actual usage of mobile learning systems. The researchers conducted a structured questionnaire survey among 518 randomly selected university students from three universities in Hainan Province to measure their self-reported responses to seven constructs (expectations, motivation, enjoyment, perceived usefulness, perceived ease of use, attitudes, and actual use). A sophisticated two-stage structural equation modelling-artificial neural network approach was applied, and this study revealed non-compensatory and nonlinear relationships between the predictors and the actual usage of university students. The results revealed that all hypotheses were supported except for the direct effect between motivation and perceived usefulness, which was nonsignificant. Furthermore, according to the normalized importance derived from the multilayer perceptron, perceived usefulness (100%), attitude (73.0%), enjoyment (33.5%), motivation (30.8%), perceived ease of use (25.1%), and expectations (12.2%) are significant predictors of the actual usage behaviour of university students. Finally, this study presents theoretical and practical implications for the actual usage behaviour of mobile learning systems among university students.