Comparative influence of gender, age, industry and management level on communication

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The protracted COVID-19 pandemic repeatedly demonstrates the necessity of effective communication inside and outside organizations. However, a deficient comprehensive study of factors able to affect managerial communication limit further progress in the improvement of such business interactions. The research fills in the knowledge gap about the comparative influence of various factors on managerial communication and particularly the impact of individual and organizational characteristics of managers on communication. The paper aims to determine the significance of the relationships between managerial communication and age, genders, managerial levels, and industries in private companies from the energy, education, trade, service, extraction, construction, and production sectors. Within the organizational study, 224 subordinates from Kazakhstan firms reflected on their supervisors’ communications through a multivariate closed questionnaire. The obtained data was further processed and examined through correlation coefficients and dispersion analysis. The research results identified the considerable relationship between communication practices and managers’ age (R2=0.9637), managerial level (R2=0.9640), and industry (R2=0.9653). The study reveals the weak relationship between manager’s gender and communication practices (R2=0.1535): women insignificantly outperform men in this linking process. The research postulates that effectiveness of managerial communication considerably varies by managers’ age, managerial level, and industry, and insignificantly by gender. The paper lays the groundwork for gender-unbiased practices of human resource management and contributes to the idea of building diverse management teams.

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    • Figure 1. Distribution of managers within the levels of communication practices
    • Table 1. Reliability and validity
    • Table 2. Dispersion analysis of the equation (2) parameters
    • Table 3. Dispersion analysis of the equation (3) parameters
    • Table 4. Dispersion analysis of the equation (4) parameters
    • Table 5. Estimation of regression equations of the independent variables
    • Conceptualization
      Anastassiya Lipovka, Maigul Nugmanova
    • Investigation
      Anastassiya Lipovka, Aizhan Salimzhanova
    • Methodology
      Anastassiya Lipovka, Maigul Nugmanova
    • Writing – original draft
      Anastassiya Lipovka
    • Writing – review & editing
      Anastassiya Lipovka, Maigul Nugmanova
    • Data curation
      Natalya Korolyova
    • Formal Analysis
      Natalya Korolyova, Aizhan Salimzhanova
    • Software
      Natalya Korolyova
    • Validation
      Natalya Korolyova
    • Visualization
      Natalya Korolyova
    • Project administration
      Aizhan Salimzhanova