Risk management practice, alliance management capability, and enterprise resilience: Findings from Indonesian state-owned enterprises

  • Received November 26, 2021;
    Accepted January 25, 2022;
    Published February 7, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.20(1).2022.17
  • Article Info
    Volume 20 2022, Issue #1, pp. 190-202
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This work is licensed under a Creative Commons Attribution 4.0 International License

In the era of high uncertainties, all businesses, including state-owned enterprises, are trying to be resilient, be able to absorb the negative impacts caused by the changes, adjust, rebound, and then thrive and success after the disruptions. This study aims to examine to what extent risk management and alliance management capabilities promote enterprise resilience among Indonesian state-owned enterprises using dynamic capability theory. Analysis was done using SPSS and Structural Equation Model – Partially Least Squares on 322 valid questionnaires that were received via an online survey from the boards of directors and senior management of state-owned enterprises and their subsidiaries. The study discovered that alliance management capabilities have a significant positive effect on enterprise resilience and risk management practice. Furthermore, the findings show that risk management contributes significantly to the formation of enterprise resilience and act as a mediator between alliance management capabilities and enterprise resilience. Thus, enterprise resilience can be developed by having the ability to form and manage alliances effectively and efficiently, as well as practicing risk management, which allows a firm to anticipate and plan mitigation actions in the face of an uncertain and disruptive situation.

Acknowledgment
We acknowledge the Ministry of Higher Education Malaysia for HICoE Research Funding, Accounting Research Institute (ARI), Universiti Teknologi MARA, Malaysia, Universitas Indonesia, Indonesia, for all support and resources.

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    • Figure 1. Conceptual framework
    • Figure 2. Path model
    • Table 1. PLS results for measurement model analysis and model fit indices
    • Table 2. Discriminant validity (HTMT)
    • Table 3. Structural measurement
    • Table 4. Hypotheses testing
    • Data curation
      Purwatiningsih Lisdiono
    • Formal Analysis
      Purwatiningsih Lisdiono
    • Methodology
      Purwatiningsih Lisdiono, Haslinda Yusoff, Ancella A. Hermawan
    • Resources
      Purwatiningsih Lisdiono, Jamaliah Said, Haslinda Yusoff, Ancella A. Hermawan
    • Software
      Purwatiningsih Lisdiono
    • Writing – original draft
      Purwatiningsih Lisdiono
    • Conceptualization
      Jamaliah Said, Haslinda Yusoff
    • Investigation
      Jamaliah Said, Ancella A. Hermawan
    • Supervision
      Jamaliah Said, Haslinda Yusoff
    • Writing – review & editing
      Jamaliah Said, Haslinda Yusoff, Ancella A. Hermawan
    • Funding acquisition
      Ancella A. Hermawan