The interplay of self-efficacy and workplace support in reducing turnover intention: Evidence from Indonesia’s logistics sector

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Type of the article: Research Article

Abstract
Employee turnover affects operational efficiency and service quality in the logistics industry, particularly in rapidly growing urban economies. This study investigates how self-efficacy and workplace support influence turnover intention, with job satisfaction and affective commitment as mediating variables. Addressing a gap in the literature, the study emphasizes the joint influence of psychological and organizational factors on employee retention. Data were collected in 2024 from 215 employees of medium to large logistics firms in Solo Raya, Indonesia, an emerging logistics hub that reflects broader workforce challenges in similar economies. Using structural equation modeling (SEM), results show that self-efficacy (β = 0.410; t = 6.111; p < 0.001) and workplace support (β = 0.427; t = 6.667; p < 0.001) significantly enhance job satisfaction, which in turn reduces turnover intention (β = −0.201; t = 2.386; p = 0.017). Both self-efficacy (β = −0.186; t = 2.094; p = 0.037) and workplace support (β = −0.182; t = 2.175; p = 0.030) also have direct effects on lowering turnover intention. Mediation analysis reveals the role of affective commitment between satisfaction and turnover intention (β = −0.162; t = 2.303; p = 0.022), and of satisfaction between self-efficacy (β = −0.083; t = 2.110; p = 0.035) and support (β = −0.086; t = 2.395; p = 0.017) in influencing turnover. These findings underscore the strategic importance of psychological and organizational support in retaining employees through enhanced job satisfaction and emotional commitment, particularly in dynamic and labor-intensive sectors such as logistics.

Acknowledgments
This study was supported by Sebelas Maret University. The authors express gratitude to logistics professionals and policymakers who participated in the study.

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    • Figure 1. Conceptual framework
    • Table 1. Respondent characteristics
    • Table 2. Measurement of the outer model
    • Table 3. R squared
    • Table 4. F squared
    • Table 5. Fornell-Larcker criterion
    • Table 6. Heterotrait-monotrait ratio (HTMT)
    • Table 7. Direct effect hypothesis test
    • Table 8. Indirect effect hypothesis test
    • Conceptualization
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri, Purwati Purwati
    • Data curation
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri
    • Formal Analysis
      Rohmawan Adi Pratama, Purwati Purwati
    • Investigation
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri, Purwati Purwati
    • Methodology
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri, Purwati Purwati
    • Project administration
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri
    • Supervision
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri
    • Validation
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri, Purwati Purwati
    • Visualization
      Rohmawan Adi Pratama, Purwati Purwati
    • Writing – original draft
      Rohmawan Adi Pratama, Hunik Sri Runing Sawitri, Purwati Purwati
    • Writing – review & editing
      Rohmawan Adi Pratama, Purwati Purwati
    • Resources
      Purwati Purwati
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