Determinants of employee digital transformation readiness and job performance: A case of SMEs in Vietnam

  • 585 Views
  • 345 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The subject of stimulating employees toward digital transformation has always been one of the most significant topics, attracting the interests of scientists and decision-makers. This study analyzes the impact of critical factors, including self-efficacy, attitude, leadership, and employee characteristics on employee readiness for digital transformation and job performance. The variables were selected based on the extended theory of planned behavior and personal resource adaption models. To achieve the research objective, partial least squares structural equation modeling was employed. Data were collected from a survey of 302 employees of SMEs in Vietnam. Research findings showed that employees attitude (β = 0.1148; p < 0.05), self-efficacy (β = 0.3737; p < 0.05), and characteristics (β = 0.3328; p < 0.05) affected their readiness for digital transformation. Employees self-efficacy and characteristics also demonstrated direct impacts on job performance. Meanwhile, leadership showed no direct impact (p = 0.6430) but an indirect impact on job performance through readiness (β = 0.1360, p < 0.05). Additionally, among the factors, employee readiness is the most substantial predictor of job performance (β = 0.5152). The findings can benefit Vietnamese SME managers and policymakers, as they can better understand employees and develop effective strategies and measures to promote employee readiness and job performance in a digital transformation context.

Acknowledgment
This article is a scientific product of the ministry-level project “Research on factors affecting digital transformation of small and medium-sized enterprises in Vietnam”, code B2022-BKA-22, funded by the Ministry of Education and Training of Vietnam.

view full abstract hide full abstract
    • Figure 1. Conceptual model
    • Figure 2. SEM results
    • Table 1. Construct definition and measurement scales
    • Table 2. Demographic profile of the respondents (N = 302)
    • Table 3. Reliability and validity
    • Table 4. Reliability of scales
    • Table 5. Discriminant validity, according to Fornell-Larcker
    • Table 6. VIF values of variables
    • Table 7. Path coefficients
    • Conceptualization
      Nguyen Thi Thu Thuy, Tran Thi Bich Ngoc
    • Investigation
      Nguyen Thi Thu Thuy
    • Methodology
      Nguyen Thi Thu Thuy, Tran Thi Bich Ngoc
    • Writing – original draft
      Nguyen Thi Thu Thuy, Hong Pham Thi Thanh, Lam Tran Si
    • Writing – review & editing
      Nguyen Thi Thu Thuy, Tran Thi Bich Ngoc
    • Formal Analysis
      Hong Pham Thi Thanh
    • Resources
      Hong Pham Thi Thanh
    • Validation
      Hong Pham Thi Thanh, Tran Thi Bich Ngoc
    • Data curation
      Tran Thi Bich Ngoc
    • Project administration
      Tran Thi Bich Ngoc
    • Supervision
      Tran Thi Bich Ngoc
    • Software
      Lam Tran Si
    • Visualization
      Lam Tran Si