A work-applied framework of dynamic resource management in the industrial value chain: Evidence from Thailand

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Type of the article: Research Article

Abstract
Industrial value chains in emerging economies require integrated organizational capabilities to sustain adaptability and applied knowledge under technological and structural change. This study examines the structural relationships among dynamic resources, employee centricity, integrated knowledge, and practical knowledge management and tests a work-applied dynamic resource management framework within Thailand’s industrial value chain.
A quantitative research design employing structural equation modeling was used, based on data collected from 600 executives and business owners across industrial sectors in Thailand. The results showed that the final model fit the empirical data well, with CMIN-p = 0.089, CMIN/DF = 1.118, GFI = 0.962, and RMSEA = 0.014. All hypothesized relationships were statistically significant at p < 0.001. Dynamic resources significantly influenced employee centricity and practical knowledge management, while employee centricity strongly reinforced integrated knowledge, representing the strongest structural path in the model (β = 0.93). The model explained substantial variance in employee centricity, integrated knowledge, and practical knowledge management (R² = 0.80, 0.86, and 0.93, respectively). New S-Curve industries also exhibited the highest overall dynamic resource management construct levels, reflecting stronger adaptive and knowledge-integrative orientations.
The findings indicate that organizational adaptability in industrial value chains is strengthened when adaptive resources, employee-centered governance, and integrated knowledge processes are aligned within a coherent capability structure.

Acknowledgments
The authors gratefully acknowledge the participation of 600 executives from Thai industrial enterprises whose contributions made this research possible. The authors also thank the academic and industry experts for their valuable insights and constructive feedback, which helped improve the rigor and quality of the study.

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    • Figure 1. Conceptual framework
    • Figure 2. Structural model
    • Table 1. Theoretical and empirical foundations of the research hypotheses
    • Table 2. Characteristics of the sampled industrial enterprises
    • Table 3. Comparison of DRM constructs across industry types
    • Table 4. Structural path estimates from SEM
    • Table 5. Highest-scoring observed variables
    • Table 6. Results of hypotheses testing
    • Table 7. Direct, indirect, and total effects in the structural model
    • Data curation
      Kanyarat Sukhawatthanakun, Chatchai Poungsuwan
    • Investigation
      Kanyarat Sukhawatthanakun, Chatchai Poungsuwan
    • Methodology
      Kanyarat Sukhawatthanakun, Chatchai Poungsuwan
    • Resources
      Kanyarat Sukhawatthanakun
    • Supervision
      Kanyarat Sukhawatthanakun
    • Validation
      Kanyarat Sukhawatthanakun
    • Writing – original draft
      Kanyarat Sukhawatthanakun, Chatchai Poungsuwan
    • Writing – review & editing
      Kanyarat Sukhawatthanakun, Chatchai Poungsuwan
    • Conceptualization
      Chatchai Poungsuwan
    • Formal Analysis
      Chatchai Poungsuwan
    • Project administration
      Chatchai Poungsuwan
    • Software
      Chatchai Poungsuwan
    • Visualization
      Chatchai Poungsuwan