Identifying Poor Performance Dimensions And Clustering Poor Performers: A Case Study In Tehran University Of Medical Sciences

• Main Authors: Seyed Mohsen Tabatabaei, Masumeh Habibi Baghi, Seyedeh Bahareh Kashian, Mahmood Biglar
• Format: Article
• Language: fas
• Published: Tehran University of Medical Sciences 2016-02-01
• Series: پیاورد سلامت
• Subjects: inefficient employees; employees clustering; tehran university of medical sciences; iran
• Online Access: http://payavard.tums.ac.ir/article-1-5883-en.html
• ISSN: 1735-8132; 2008-2665

Background and Aim: Employees are an organizationchr('39')s greatest assets and organizational performance is dependent to employee’s performance. Presence of inefficient employees can make other employees to be less productive. To improve inefficient employees to high performance level, it is necessary to analyze the performance of employees. This study aims to identify and determine poor performance dimensions and cluster inefficient staffs. Materials and Methods: This study was an analytical and descriptive research. The research made questionnaire developed for data collection and Principal Component Analysis (PCA) and Cluster Analysis (CA) techniques in SPSS used to analyze the research data. Results: The PCA results showed that six poor performance dimensions were behavioral problems, low results, lack of self-efficacy and creativity, sabotage, postponing, and individualism. The CA results declared that poor performers can be classified to five clusters include poor behavior, lazy, jobber, poor ability, marginal, managers believed that root of employees’ in inefficiency attributed jobber, poor ability, and lazy employees to internal causes, and attributed bad behavior and marginal employees to external causes. Conclusion: The type of inefficiency and its dimensions should be identified in order to make effective decisions for inefficient employees. Employees clustering propose a new attitude toward inefficiency differentiation comparing to literature,  and this five group clustering based on empirical data expected to be more applicable in practice.