Role of proactive behavior in entrepreneurial alertness: A mediating role of dynamic capabilities

  • Received May 23, 2022;
    Accepted October 10, 2022;
    Published October 27, 2022
  • Author(s)
  • DOI
  • Article Info
    Volume 20 2022, Issue #4, pp. 127-137
  • Cited by
    2 articles

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

The role of entrepreneurial alertness has been increasing to cope with the current fiercely competitive market. Entrepreneurial alertness enables companies to benefit from unseized opportunities and gain a competitive advantage. Therefore, the study explores the role of proactive behavior in enhancing the entrepreneurial alertness of organizations through the mediating role of dynamic capabilities. The study focuses on the Iraqi telecommunication market because of the intense competition between three major telecom companies. Data were collected through a 5-point Likert-scale questionnaire distributed among employees of mobile telecommunications companies in Iraq. The sample aimed to cover different levels in the business hierarchy with a particular focus on the leading positions. Therefore, it included 299 members of boards of directors, branch managers, department heads, and chief supervisors (middle managers). Structural equation modeling (SEM) was used to analyze the responses. Results revealed the positive effect of adopting proactive behavior (e.g., problem prevention, innovation, and holding responsibilities) on entrepreneurial alertness (e.g., scanning, searching for, and evaluating opportunities). The results also approved the mediating role of dynamic capabilities in enhancing the relationship between proactive behavior and entrepreneurial alertness. Therefore, Iraqi governmental telecommunication enterprises are recommended to benefit from the dynamic capabilities of employees (e.g., sensing, learning, integrating, and coordinating capabilities) to maximize the positive influence of proactive behavior on entrepreneurial alertness and, accordingly, improve the prediction of production risks and threats.

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    • Figure 1. Research model
    • Figure 2. Structural model
    • Table 1. Demographic profile of respondents
    • Table 2. Confirmatory factor analysis and Cronbach’s alpha
    • Table 3. Mean, standard deviations, and correlations between main variables
    • Table 4. Path coefficients, SE, CR, and P-values
    • Formal Analysis
      Ahmed Abdullah Amanah
    • Investigation
      Ahmed Abdullah Amanah
    • Resources
      Ahmed Abdullah Amanah, Sahar Abbas Hussein, Dheyaa Falih Bannay
    • Software
      Ahmed Abdullah Amanah
    • Validation
      Ahmed Abdullah Amanah
    • Funding acquisition
      Sahar Abbas Hussein
    • Visualization
      Sahar Abbas Hussein, Dheyaa Falih Bannay
    • Writing – original draft
      Sahar Abbas Hussein
    • Writing – review & editing
      Sahar Abbas Hussein
    • Conceptualization
      Dheyaa Falih Bannay
    • Data curation
      Dheyaa Falih Bannay
    • Methodology
      Dheyaa Falih Bannay