Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/58736
Title: Performance Prediction Reliability of Computer-aided Work Simulations and Employment Tests: A Case of Selecting Blue-collar Employees for Repetitive Tasks
Authors: Kaya, Tekiner
Keywords: Assessment tools;Blue-collar recruitment;Ordinal linear regression;Repetitive work;Stepwise linear regression
Issue Date: Dec-2021
Publisher: NIScPR-CSIR, India
Abstract: The process of selecting the right candidate may differ based on job content and process dynamics. Computer-aided work simulation assessment (CAWSA) tools and employment tests are typically used in recruitment processes to achieve good Person-Job (P-J) fit. Related to this, the paper aims to determine the effectiveness and reliability of CAWSA processes and employment tests in predicting repetitive work performance amongst blue-collar employees. Additionally, the ability of these tools to predict P-J fit for repetitive tasks is analysed. Stepwise and ordinal linear regression models were used to determine the predictive capacity of CAWSA techniques and employment tests in relation to actual repetitive work performance. The model was applied on a large-scale automotive company in Turkey. A total of 142 blue-collar candidates participated in the designed recruitment process, of whom 106 were recruited in four different shops at the factory wherein they worked on repetitive tasks for six months. The results show that 84% of the variation on actual work performance can be explained by five different types of CAWSA tools, while employment tests are unable to produce the same results. Finally, a strong correlation (71%) between six months of shop performance and related shop-specific CAWSA process performance is observed, indicating that CAWSA processes can ensure effective P-J fit for repetitive tasks.
Page(s): 1096-1106
URI: http://nopr.niscair.res.in/handle/123456789/58736
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.80(12) [December 2021]

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