Impact of functional interdependency on employee satisfaction with performance appraisal in the real estate industry

  • Received June 30, 2020;
    Accepted November 26, 2020;
    Published December 10, 2020
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.18(4).2020.19
  • Article Info
    Volume 18 2020, Issue #4, pp. 213-227
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Unbiased performance appraisal tends to bolster the performance of employees. The studies indicate several inadequacies with the current performance appraisal systems. Functional interdependence is one such factor that has been ignored. The study aims to find the factors that can improve the satisfaction with performance appraisal of employees whose deliverables are highly interdependent on other functions. Organizational justice, rater competence, inter-functional conflict, and cohesion are considered the mediating variables. To test the model, the data are collected through a survey using a questionnaire from the executives of Indian real estate companies who have undergone the appraisal process at least once. Firms with more than 500 employees are randomly selected for the list of members of the real estate developers’ associations. The results show that functional interdependency has a negative impact on satisfaction with performance appraisal. Although conflict and cohesion are found to influence satisfaction with performance appraisal, they did not mediate the effect of functional interdependency on satisfaction with performance appraisal. However, the study found that rater competence and organizational justice have a mediating effect. The study provides practical implications to HR managers of real estate companies to train the raters and include the complexities of functional interdependencies in the appraisal system. A grievance mechanism should be created to address the employees’ concerns, ultimately improving satisfaction with performance appraisal.

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    • Figure 1. Conceptual model
    • Figure 2. Results of the path analysis
    • Table 1. Items in the measuring instrument
    • Table 2. Results of reliability tests
    • Table 3. Correlations among latent variables and square roots of AVEs
    • Table 4. Model estimates
    • Table 5. R2 coefficients
    • Table 6. Model fit and quality indices
    • Table 7. Multiple mediators and specific indirect effect
    • Conceptualization
      Elangovan N.
    • Methodology
      Elangovan N., Sridhar Rajendran
    • Validation
      Elangovan N.
    • Visualization
      Elangovan N.
    • Writing – review & editing
      Elangovan N.
    • Supervision
      Elangovan N.
    • Formal Analysis
      Sridhar Rajendran
    • Investigation
      Sridhar Rajendran
    • Resources
      Sridhar Rajendran
    • Writing – original draft
      Sridhar Rajendran