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ORIGINAL RESEARCH article

Front. Hum. Dyn.
Sec. Digital Impacts
Volume 6 - 2024 | doi: 10.3389/fhumd.2024.1487671

The impact of generative AI (ChatGPT) on recruitment efficiency and candidate quality, with a mediating role of process automation level and a moderating role of organizational size

Provisionally accepted
Dr Sameh Abdelhay Dr Sameh Abdelhay 1*Dalia Hassan Dalia Hassan 1Nadeen Selim Nadeen Selim 1Abdullah Awad Altamimi Abdullah Awad Altamimi 2
  • 1 Umm Al Quwain University, Umm Al Quwain, United Arab Emirates
  • 2 Abu Dhabi Police College, Abu Dhabi, United Arab Emirates

The final, formatted version of the article will be published soon.

    The primary objective of the current paper is to understand the impact of Generative AI Tools on the recruitment process, on their effectiveness in addressing bias, enhancing efficiency, and ensuring accurate candidate evaluation and looking at the moderating role of familiarity and the mediating role of the size of the organization and level of employee. A quantitative survey approach, with 469 professionals participating in an online survey, was used. Structural Equation Modelling (SEM) in Amos SPSS was used in the analysis of the relationships between Generative AI Tools, User Familiarity with AI, and key outcomes in the recruitment process. The study reveals a significant reduction in bias during candidate screening, attributed to the algorithmic objectivity, data driven decision making, and consistency inherent in Generative AI Tools. Efficiency gains and heightened accuracy in shortlisting candidates were also observed. However, User Familiarity with AI emerged as a moderating factor in influencing the relationship between Generative AI Tools and efficiency improvement. As a recommendation, organizations are encouraged to invest in continuous training programs to harness the full potential of Generative AI Tools in optimizing efficiency and ensuring a fair and accurate recruitment process.

    Keywords: Generative AI, ChatGPT, Bias reduction, Screening process, Efficiency, recruitment process, Process automation level

    Received: 29 Aug 2024; Accepted: 22 Nov 2024.

    Copyright: © 2024 Abdelhay, Hassan, Selim and Awad Altamimi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dr Sameh Abdelhay, Umm Al Quwain University, Umm Al Quwain, United Arab Emirates

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.