Enhancing innovative work behavior through perceived organizational support: The mediating role of person-organization fit

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Recognizing the importance of innovative work behavior in driving organizational success, this study investigates the influence of perceived organizational support to enhance employees’ innovative behavior, with person-organization fit positioned as a mediating factor. In pursuit of this goal, organizations must develop and sustain a competent and adaptable workforce, as human resources play a pivotal role in securing long-term competitive advantage. Consequently, implementing effective and innovative approaches to support employees, particularly by nurturing their capacity for innovation, has become increasingly essential. To accomplish the research objectives, data were collected from 251 employees from different sectors in Indonesia between November 2024 and February 2025. The collected data were subsequently examined using the SEM-PLS analysis tool, and the obtained findings highlighted that perceived organizational support possessed a clear and substantial effect on innovative work behavior (p = 0.012) as well as person-organization fit (p = 0.000). The results also reflected that person-organization fit significantly influenced innovative work behavior (p = 0.000) and partially mediated the relationship between innovative work behavior and perceived organizational support (p = 0.000). The investigation emphasized the importance of organizations providing a conducive work environment for employees and adopting activities that could increase the level of employee satisfaction with their respective jobs in the organization. This emphasis is especially crucial because the proper implementation of favorable work environments and adoption of relevant activities invariably can significantly enhance employees’ innovative work behavior.

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    • Figure 1. Research framework
    • Figure 2. The structural path analysis
    • Table 1. Characteristics of the respondents
    • Table 2. Loading factors
    • Table 3. Cross-loading analysis
    • Table 4. Fornell–Larcker criterion
    • Table 5. R² values
    • Table 6. Path coefficient (Direct and indirect testing)
    • Table 7. Predictive value analysis
    • Conceptualization
      Meily Margaretha, Achmad Sudiro, Desi Tri Kurniawati, Sri Palupi Prabandari
    • Data curation
      Meily Margaretha, Desi Tri Kurniawati, Sri Palupi Prabandari
    • Formal Analysis
      Meily Margaretha, Achmad Sudiro
    • Funding acquisition
      Meily Margaretha
    • Investigation
      Meily Margaretha
    • Methodology
      Meily Margaretha, Sri Palupi Prabandari
    • Project administration
      Meily Margaretha
    • Software
      Meily Margaretha
    • Writing – original draft
      Meily Margaretha, Achmad Sudiro
    • Resources
      Achmad Sudiro, Desi Tri Kurniawati, Sri Palupi Prabandari
    • Supervision
      Achmad Sudiro, Desi Tri Kurniawati, Sri Palupi Prabandari
    • Validation
      Achmad Sudiro
    • Visualization
      Desi Tri Kurniawati, Sri Palupi Prabandari
    • Writing – review & editing
      Desi Tri Kurniawati, Sri Palupi Prabandari