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Performance Prediction Reliability of Computer-aided Work Simulations and Employment Tests: A Case of Selecting Blue-collar Employees for Repetitive Tasks

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Title Performance Prediction Reliability of Computer-aided Work Simulations and Employment Tests: A Case of Selecting Blue-collar Employees for Repetitive Tasks
 
Creator Kaya, Tekiner
 
Subject Assessment tools
Blue-collar recruitment
Ordinal linear regression
Repetitive work
Stepwise linear regression
 
Description 1096-1106
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.
 
Date 2021-12-27T10:27:47Z
2021-12-27T10:27:47Z
2021-12
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58736
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(12) [December 2021]