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Enablers and inhibitors of AI assimilation in hiring: mitigating the effects of inhibitors through human–AI collaboration.

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Title: Enablers and inhibitors of AI assimilation in hiring: mitigating the effects of inhibitors through human–AI collaboration.
Authors: Hina, Maryam (AUTHOR) maryam.hina@lut.fi, Azad, Nasreen (AUTHOR) nasren.azad@lut.fi, Islam, Najmul (AUTHOR) najmul.islam@lut.fi
Source: Information Technology & People. 2025, Vol. 38 Issue 8, p73-96. 24p.
Subjects: Artificial intelligence, Human-computer interaction, Employee selection, Personnel management
Abstract: Purpose: Most prior studies have primarily investigated AI adoption, with less attention given to AI assimilation in human resource management (HRM). Additionally, prior studies often lack empirical verification of the extent to which human–AI collaboration might alleviate challenges and promote AI assimilation in the HRM context. Thus, this study aims to explore AI assimilation in recruitment with a balanced view that identifies both enabling and inhibiting factors while examining the role of human–AI collaboration in mitigating the effects of inhibiting factors. Design/methodology/approach: We used a mixed-method approach. Using an open-ended survey questionnaire approach and collecting data from 26 HR professionals, we identified five factors, namely, AI competency, recruitment agility, AI opacity, AI empathy and human–AI collaboration, potentially impacting AI assimilation. Thereafter, drawing from the enabler–inhibitor perspective, we theorize that AI competency and recruitment agility are the enablers, whereas AI opacity and AI empathy are the inhibitors of an organization's efforts to assimilate AI in recruitment practices. We tested our proposed model by collecting data from 309 HR professionals. Findings: The findings showed that both enablers, AI competency and recruitment agility, significantly influence AI assimilation; however, both inhibitors, AI opacity and AI empathy, are non-significant for AI assimilation. While looking into the reasons for these non-significant effects, we observed that the interaction term between AI empathy and human–AI-collaboration as well as between AI opacity and human–AI-collaboration both had significant effects on AI assimilation. These interaction effects suggest that human–AI collaboration mitigates the constraining impact of both inhibitors. Originality/value: Drawing from the enabler–inhibitor perspective and by empirically testing our proposed model, this paper significantly contributes to the IS literature. Our study not only identifies factors that promote and inhibit AI assimilation in the context of HRM practices but also reveals how human–AI collaboration may mitigate the effects of inhibitors. Our findings suggest that organizations should have a collaborative recruitment environment where AI handles repetitive tasks, and humans focus on roles requiring emotional intelligence. This approach enhances the integration of AI-powered tools, addresses AI assimilation inhibitors and optimizes recruitment effectiveness. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Purpose: Most prior studies have primarily investigated AI adoption, with less attention given to AI assimilation in human resource management (HRM). Additionally, prior studies often lack empirical verification of the extent to which human–AI collaboration might alleviate challenges and promote AI assimilation in the HRM context. Thus, this study aims to explore AI assimilation in recruitment with a balanced view that identifies both enabling and inhibiting factors while examining the role of human–AI collaboration in mitigating the effects of inhibiting factors. Design/methodology/approach: We used a mixed-method approach. Using an open-ended survey questionnaire approach and collecting data from 26 HR professionals, we identified five factors, namely, AI competency, recruitment agility, AI opacity, AI empathy and human–AI collaboration, potentially impacting AI assimilation. Thereafter, drawing from the enabler–inhibitor perspective, we theorize that AI competency and recruitment agility are the enablers, whereas AI opacity and AI empathy are the inhibitors of an organization's efforts to assimilate AI in recruitment practices. We tested our proposed model by collecting data from 309 HR professionals. Findings: The findings showed that both enablers, AI competency and recruitment agility, significantly influence AI assimilation; however, both inhibitors, AI opacity and AI empathy, are non-significant for AI assimilation. While looking into the reasons for these non-significant effects, we observed that the interaction term between AI empathy and human–AI-collaboration as well as between AI opacity and human–AI-collaboration both had significant effects on AI assimilation. These interaction effects suggest that human–AI collaboration mitigates the constraining impact of both inhibitors. Originality/value: Drawing from the enabler–inhibitor perspective and by empirically testing our proposed model, this paper significantly contributes to the IS literature. Our study not only identifies factors that promote and inhibit AI assimilation in the context of HRM practices but also reveals how human–AI collaboration may mitigate the effects of inhibitors. Our findings suggest that organizations should have a collaborative recruitment environment where AI handles repetitive tasks, and humans focus on roles requiring emotional intelligence. This approach enhances the integration of AI-powered tools, addresses AI assimilation inhibitors and optimizes recruitment effectiveness. [ABSTRACT FROM AUTHOR]
ISSN:09593845
DOI:10.1108/ITP-06-2024-0808