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. |
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| 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] |
| Copyright of Information Technology & People is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187463017 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enablers and inhibitors of AI assimilation in hiring: mitigating the effects of inhibitors through human–AI collaboration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hina%2C+Maryam%22">Hina, Maryam</searchLink> (AUTHOR)<i> maryam.hina@lut.fi</i><br /><searchLink fieldCode="AR" term="%22Azad%2C+Nasreen%22">Azad, Nasreen</searchLink> (AUTHOR)<i> nasren.azad@lut.fi</i><br /><searchLink fieldCode="AR" term="%22Islam%2C+Najmul%22">Islam, Najmul</searchLink> (AUTHOR)<i> najmul.islam@lut.fi</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Information+Technology+%26+People%22">Information Technology & People</searchLink>. 2025, Vol. 38 Issue 8, p73-96. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Employee+selection%22">Employee selection</searchLink><br /><searchLink fieldCode="DE" term="%22Personnel+management%22">Personnel management</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Information Technology & People is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1108/ITP-06-2024-0808 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 73 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Human-computer interaction Type: general – SubjectFull: Employee selection Type: general – SubjectFull: Personnel management Type: general Titles: – TitleFull: Enablers and inhibitors of AI assimilation in hiring: mitigating the effects of inhibitors through human–AI collaboration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hina, Maryam – PersonEntity: Name: NameFull: Azad, Nasreen – PersonEntity: Name: NameFull: Islam, Najmul IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09593845 Numbering: – Type: volume Value: 38 – Type: issue Value: 8 Titles: – TitleFull: Information Technology & People Type: main |
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