ARIMA based Forecasting of an Integrated Model of 360-Degree Feedback for Administrative Staff of HEIs

Abstract

This research focuses on enhancing the performance of a novel model called the Integrated Model of 360-degree feedback for Administrative Staff in Higher Education Institutions (HEIs). The study employs a time series approach to analyse historical data from this model to inform future strategic decisions. The selected ARIMA models demonstrated high forecasting accuracy, with Root Mean Square Errors (RMSE) approaching negligible values (0.12 for job performance, 0.04 for change in appraisal satisfaction, and 0.05 for job capability). Specifically, the ARIMA (1, 0, 1) model predicts moderate job performance, the ARIMA (0, 1, 3) model suggests relatively low appraisal satisfaction, and the ARIMA (0, 0, 4) model indicates a moderate job capability level, assuming other factors remain constant. The study explores the interconnectedness of data between appraisal satisfaction, job capability, and job performance, highlighting the potential for improved performance within the 360-degree feedback framework. In summary, this research constructs ARIMA models to forecast job performance, appraisal satisfaction, and job capability, demonstrating their effectiveness in the short term. Utilizing precise ARIMA models tailored to these performance indicators has the potential to significantly enhance forecasting accuracy and subsequently boost employee productivity within the Integrated Model of the 360-degree feedback framework.