Promoting digital employment intention among students of Chinese higher education institutions

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Digital employment is one of the critical concepts of the digital economy for sustainable development. Promoting digital employment intention of potential employees is indispensable for developing the digital economy. The study aims to explore how digital employment policies predict digital employment intentions and to construct a structural equation model. Based on an online survey of 470 students with digital work experience from Chinese higher education institutions, the data were processed using SPSS 26.0 and Amos 26.0. The results uncover that digital employment policies have a positive impact on digital employability (β = 0.538, p < 0.001), digital employment capital (β = 0.524, p < 0.001), and digital employment intentions (β = 0.257, p < 0.001). At the same time, digital employability (β = 0.216, p < 0.001) and digital employment capital (β = 0.505, p < 0.001) also have a positive impact on digital employment intentions. The structural equation model emphasizes the significant mediating effect of digital employability (0.116) and digital employment capital (0.265). Therefore, the government should actively promote digital policies to encourage and enhance digital employment capabilities, promoting digital employment intentions and behaviors. The support and development of digital employability by the entire society, especially schools, and families, is also significant.

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    • Figure 1. Confirmatory factor analysis
    • Figure 2. Confirmatory factor analysis after model fitting
    • Figure 3. Structural equation model of digital employment intention
    • Table 1. Demographic information
    • Table 2. Reliability statistics
    • Table 3. KMO and Bartlett’s test
    • Table 4. Aggregate validity test
    • Table 5. Differentiation validity test
    • Table 6. Modification indices
    • Table 7. Confirmatory factor analysis model fitting index after deletion and correction
    • Table 8. Aggregate validity test (after deletion and correction)
    • Table 9. Differentiation validity test (after correction)
    • Table 10. SEM path test
    • Table 11. Mediation effect bootstrap test
    • Conceptualization
      Jinshen Yu, Songyu Jiang, Jian Han, Lin Li, Xiaojun Ke
    • Data curation
      Jinshen Yu, Songyu Jiang, Jian Han, Lin Li, Xiaojun Ke
    • Formal Analysis
      Jinshen Yu, Songyu Jiang, Lin Li, Xiaojun Ke
    • Investigation
      Jinshen Yu, Songyu Jiang, Lin Li, Xiaojun Ke
    • Methodology
      Jinshen Yu, Songyu Jiang, Jian Han, Lin Li, Xiaojun Ke
    • Project administration
      Jinshen Yu, Songyu Jiang, Lin Li
    • Software
      Jinshen Yu, Songyu Jiang, Jian Han, Lin Li
    • Supervision
      Jinshen Yu, Songyu Jiang, Lin Li
    • Validation
      Jinshen Yu, Songyu Jiang, Jian Han, Lin Li, Xiaojun Ke
    • Writing – original draft
      Jinshen Yu, Songyu Jiang, Jian Han, Lin Li, Xiaojun Ke
    • Writing – review & editing
      Jinshen Yu, Songyu Jiang, Lin Li, Xiaojun Ke
    • Resources
      Jian Han
    • Visualization
      Jian Han
    • Funding acquisition
      Xiaojun Ke