A model for personnel selection with a data mining approach: A case study in a commercial bank

Abstract

Orientation: The success or failure of an organisation has a direct relationship with how its human resources are employed and retained.Research purpose: In this paper, a decision-making tool is provided for managers to use during the recruitment process. The effective factors in employees’ performance will be identified by discovering covert patterns of the relationship between employees’ test scores and their performance at work.Motivation for the study: Large amounts of information and data on entrance evaluations and processes have been kept in organisations. There is a need to discover the pattern in the relationship between employee’s test scores and their performance at work as a tool for use during the recruitment process.?Research design, approach and method: The data mining technique that was used in this project serves as the decision tree. Rules derivation was accomplished by the Quick Unbiased and Efficient Statistical Tree(QUEST), Chi-squared Automatic Interaction Detector (CHAID),C5.0 and Classification And Regression Tree (CART) algorithm. The objective and the appropriate algorithm were determined based on seemingly ‘irrelevant’ components, which the Commerce Bank Human Resources management’s experts describe.Main finding: It was found that the ‘performance assessment’ variable was not considered as the objective. Also, it was concluded that out of 26 effective variables only five variables, such as province of employment, education level, exam score, interview score and work experience, had the most effect on the ‘promotion score’ target.Practical/managerial implication: The database and personnel information of the Commerce Bank of Iran (in 2005 and 2006) was studied and analysed as a case study in order to identify the labour factors that are effective in job performance. Appropriate and scientific employment of staff that were selected from the entrance exams of companies and organisations were of crucial importance.Contribution/value-add: It is of great importance that an extensive use of data mining techniques be applied in other management areas. Whilst this is a low-cost technique, it can help managers to discover covert knowledge in their organisations.