The impact of individual and organizational factors on employee innovative work behavior: Empirical evidence from private companies in Vietnam

  • 27 Views
  • 3 Downloads

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

Employee innovative work behavior plays a vital role in innovation management in private companies, especially in an emerging market like Vietnam. This study investigates the influence of individual factors (including employee creativity and innovative self-efficacy) and organizational factors (constituting innovation climate and organizational support) on innovative work behavior and the impact of innovative work behavior on employee job performance. To test the hypotheses quantitatively, the study uses a two-stage second-order partial least squares structural equation modeling (PLS-SEM) method and a questionnaire-based study with 706 employees from private businesses in Vietnam. The findings indicate that individual factors substantially impact workers’ innovative work behavior (scoring 0.491) compared to organizational factors (scoring 0.395). In addition, all factors, including employee creativity, innovative self-efficacy, innovation climate, and organizational support, positively impact workers’ innovative work behavior. Specifically, innovative self-efficacy exerts the most significant influence on innovative work behavior (with a score of 0.360), followed by organizational support (scoring 0.272) and employee creativity (scoring 0.157). Simultaneously, the innovation climate exerts a minor influence on innovative work behavior, with a score of 0.142. Finally, innovative work behavior directly and positively affects employee job performance, scoring 0.641.

view full abstract hide full abstract
    • Figure 1. Proposed research model (stage 1)
    • Figure 2. Proposed research model (stage 2)
    • Figure 3. Structural model result (stage 1)
    • Figure 4. Structural model result (stage 2)
    • Table 1. Sample characteristics
    • Table 2. Convergent validity, measurement models, and reliability
    • Table 3. Discriminant validity
    • Table 4. Heterotrait-monotrait ratios
    • Table 5. Path coefficients
    • Table 6. Constructing reliability and validity in second-order PLS-SEM model
    • Table 7. Discriminant validity in second-order PLS-SEM model
    • Table 8. Path coefficients in second-order PLS-SEM model
    • Table 9. Aggregate reliability of the constructs after eliminating observed variables
    • Conceptualization
      Tran Hai Yen
    • Data curation
      Tran Hai Yen, Chu Tien Minh, Khuc Dai Long
    • Formal Analysis
      Tran Hai Yen, Nguyen Ngoc Diep
    • Investigation
      Tran Hai Yen, Chu Tien Minh, Nguyen Ngoc Diep, Khuc Dai Long
    • Methodology
      Tran Hai Yen, Chu Tien Minh
    • Project administration
      Tran Hai Yen
    • Resources
      Tran Hai Yen, Chu Tien Minh, Nguyen Ngoc Diep
    • Software
      Tran Hai Yen, Chu Tien Minh, Khuc Dai Long
    • Supervision
      Tran Hai Yen
    • Visualization
      Tran Hai Yen, Chu Tien Minh, Khuc Dai Long
    • Writing – original draft
      Tran Hai Yen, Chu Tien Minh, Nguyen Ngoc Diep
    • Writing – review & editing
      Tran Hai Yen