Abstract
Human resources management (HRM) stakeholders must make strategic decisions about artificial intelligence (AI) or they may risk failure if competitors implement faster objective methods to hire best-fit candidates. Big data is one of the major problems HRM staff face in finding relevant talent due to the post-pandemic paradigm shift towards remote work along with the subsequent exponential increase of online-based recruiting. In the current study, a unique action research experiment was constructed based on a pragmatic ideology. The purpose was to answer four research questions (RQ) and one hypothesis. The key RQ was AI could help, or possibly out-perform humans, for recruiting new employees when there was a high volume of real-time internet-based job application big data. A pharmaceutical company was selected for the action research experiment. A job description was created using the hierarchical cluster analysis in machine learning to identify 27 key skills. The analytical hierarchy process (AHP) was used to transform subjective and qualitative data from the hiring manager into objective quantitative candidate selection criteria. An HRM AI was installed in the organizations human resources information system (HRIS). Participants were randomly selected form the action research study company (N = 10) to complete the experiment. The 10 participants competed against the AI to find the best candidate. The human recruiters and AI evaluated approximately the same real-time applicant big data population from the internet (not a test sample). Quality assurance was performed to ensure the evaluations were accurate and there were no outliers. The scores of the best candidates were compared using ANOVA and post hoc Tukey, with an effect size of 44%. The results demonstrated AI could outperform human recruiters, without discrimination, due to using AHP for prioritizing the hiring criteria and not training the HRM AI with best-in-class resumes.
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Strang, K.D. Resistance, diseconomies, and abnormal AI behavior in HRM: a real-time big data action research experiment at a pharmaceutical. Hum.-Intell. Syst. Integr. 4, 35–52 (2022). https://doi.org/10.1007/s42454-022-00046-6
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DOI: https://doi.org/10.1007/s42454-022-00046-6