The Emerging Technology in Hiring: Insights from Assembly Line Workers and Managers

Kirk Chang () and Kuo-Tai Cheng
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Kirk Chang: Royal Docks School of Business and Law, University of East London, London E16 2RD, UK
Kuo-Tai Cheng: College of Sustainability, National Tsing Hua University, Hsing Chu 300044, Taiwan

Administrative Sciences, 2025, vol. 15, issue 12, 1-16

Abstract: Hiring assembly line workers is often time- and resource-demanding. Following the call for more effective hiring practices, this article describes the design, development, and implementation of an ‘AI-empowered recruitment model’, an emerging technology in hiring employees. The raw data for model building were gathered from the assembly line workers and their managers. The dataset comprised two parts. Part-1 data were the occupational codes and personality parameters of the top performers (provided by the performers), whereas Part-2 data were the employability and fitness parameters of the top performers (rated by the managers of the performers). Top performers were defined as the employees who had the highest output of products with the lowest defect rate. Through the use of repetitive data-matching algorithms, the model gradually learned and identified the signs (patterns) of top performers. After cross-validation and external testing, the model became established. The model was then applied to the employee recruitment practice, in which the model achieved its purpose by selecting the best-fit candidates from the pool of applicants within minutes. The AI-empowered recruitment model saved organizational resources and expenses. As there was no use of human labor, administrative delays and errors were minimized, thus improving the efficacy of the hiring practice. Limitations and suggestions for improvement were addressed.

Keywords: AI; assembly line; employee recruitment; hiring; workers (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2025
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