A Quantitative Performance Evaluation Model Based On a Job Satisfaction-Performance Matrix and Application in a Manufacturing Company

Abstract

In this study, we propose a performance management model based on employee performance evaluations. Employees are clustered into 4 different groups according to a job satisfaction-performance model and strategic plans are derived for each group for an effective performance management. The sustainability of this business process improvement model is managed with a control mechanism as a Plan-Do-Check-Act (PDCA) cycle as a continuous improvement methodology. The grouping model is developed with a data mining clustering algorithm. Firstly 4 different performance groups are determined with a two-step k-means clustering approach. Then the clustering model developed is testified with an Artificial Neural Network (ANN) model. Necessary data for this study are collected with a questionnaire application composed of 25 questions, first 13 variables measuring job satisfaction level and last 12 variables measuring performance characteristics where evaluators are employees themselves. With the help of model developed, human resources department is able to track employees' job satisfaction and performance levels and strategies for different performance groups are developed. Application of the model is conducted in a manufacturing company located in Istanbul, Turkey.