Career development and turnover intention: Investigating affective commitment’s mediating role among telecommunications employees in Egypt

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Type of the article: Research Article

Abstract
Employee turnover is an expensive problem for telecom firms going through a fast digital transition. This study investigates whether emotional occupational commitment mediates the association between multidimensional career development (career-goal advancement, professional competence development, promotion speed, and compensation growth) and employees’ desire to leave Egypt’s telecom industry. Employees of three major Egyptian operators (Vodafone Egypt, Orange Egypt, and WE) completed a structured online questionnaire between January and March 2025. Of the 600 surveys received, 588 were found to be legitimate and were included in the final study. Reliability testing (Cronbach’s alpha), descriptive statistics, hierarchical linear regression, path (mediation) analysis, and Pearson correlation were used. All structures have strong internal consistency (alpha range 0.776–0.894). Affective commitment and career growth characteristics were positively correlated, but turnover intention showed a negative correlation. Affective occupational commitment mediates the association between career growth and turnover intention (R2 = 0.588), as demonstrated by path analysis and hierarchical regression. The whole model explains 58.8% of the variation in turnover intention. According to the findings, telecom companies can significantly lower employee turnover by implementing organized career-development programs that increase emotional engagement by enhancing workers’ abilities, making promotion tracks clear, and guaranteeing equitable compensation. To evaluate causal direction and generalizability, future studies should use longitudinal designs and cross-industry comparisons.

Acknowledgment
The author declares that no financial support, institutional grant, or external assistance was received for conducting this research, and there are no use of artificial intelligence tools in preparing the manuscript. 

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    • Figure 1. Variables and proposed model
    • Figure 2. Path analysis of the study variables
    • Figure 3. Gender effects
    • Figure 4. Effects of education level
    • Figure 5. Effects of age
    • Figure 6. Effects of monthly income
    • Table 1. Demographic characteristics of respondents
    • Table 2. Reliability and validity
    • Table 3. Descriptive statistics and relative importance
    • Table 4. t-tests
    • Table 5. Hierarchical linear regression model summary
    • Table 6. Regression coefficients for the three models
    • Table 7. HLRM of the three models
    • Table 8. Parameter estimation of the three models
    • Table 9. ANOVA results for study variables and gender
    • Table 10. ANOVA results for study variables and education level
    • Table 11. ANOVA results for study variables and age
    • Table 12. ANOVA results for study variables and monthly income
    • Table 13. Hypotheses results
    • Table A1. Questionnaire
    • Conceptualization
      Amal Abdulmajeed Qassim
    • Data curation
      Amal Abdulmajeed Qassim
    • Formal Analysis
      Amal Abdulmajeed Qassim
    • Funding acquisition
      Amal Abdulmajeed Qassim
    • Investigation
      Amal Abdulmajeed Qassim
    • Methodology
      Amal Abdulmajeed Qassim
    • Project administration
      Amal Abdulmajeed Qassim
    • Resources
      Amal Abdulmajeed Qassim
    • Software
      Amal Abdulmajeed Qassim
    • Supervision
      Amal Abdulmajeed Qassim
    • Validation
      Amal Abdulmajeed Qassim
    • Visualization
      Amal Abdulmajeed Qassim
    • Writing – original draft
      Amal Abdulmajeed Qassim
    • Writing – review & editing
      Amal Abdulmajeed Qassim