The empirical evidence on negating turnover intentions among academicians

  • Received October 5, 2021;
    Accepted November 10, 2021;
    Published November 18, 2021
  • Author(s)
    Shaha Faisal
  • DOI
  • Article Info
    Volume 19 2021, Issue #4, pp. 270-282
  • Cited by
    1 articles

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

Employee turnover is a major concern for organizations. Specifically, among private business schools, it is proved to be one of the major impediments in carrying out academic activities. This phenomenon creates a conundrum for both college administrations and students. Therefore, each academic unit must work to minimize employee turnover. This study aims to identify the elements that influence academicians’ turnover intentions and the ways to negate them. It used a random sample of 236 academicians (professors, assistant professors, associate professors, and lecturers) from various business schools in India. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to analyze the sample. In addition, the structural equation modeling (SEM) approach was used to examine the hypotheses. All variables studied had high loadings of 0.50 or more in CFA. The research model was shown to be fit on three important absolute fit indices: absolute, incremental, and parsimonious. The regression weights of hypotheses were also determined to be significant. The findings indicate that organizational support, compensation, and personnel management had a detrimental effect on turnover intentions at business schools. These results can be used by college administration and management in devising interventions that will assist them in retaining existing talented staff and avoiding the negative repercussions of future turnover.

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    • Figure 1. Research model
    • Figure 2. Standardized estimates for the model
    • Table 1. Demographics of the sample (N = 236)
    • Table 2. Construct validity and reliability
    • Table 3. The goodness of fit statistics
    • Table 4. Regression weights of estimation
    • Conceptualization
      Shaha Faisal, Mohammad Naushad
    • Data curation
      Shaha Faisal, Mohammad Naushad
    • Formal Analysis
      Shaha Faisal, Mohammad Naushad
    • Investigation
      Shaha Faisal, Mohammad Naushad
    • Methodology
      Shaha Faisal, Mohammad Naushad
    • Software
      Shaha Faisal, Mohammad Naushad
    • Supervision
      Shaha Faisal, Mohammad Naushad
    • Validation
      Shaha Faisal
    • Visualization
      Shaha Faisal
    • Writing – original draft
      Shaha Faisal, Mohammad Naushad
    • Writing – review & editing
      Shaha Faisal, Mohammad Naushad
    • Project administration
      Mohammad Naushad