Advantages and Challenges of AI-Based Personnel Selection: A Scoping Review of Organizational Implications and Human Outcomes

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study is a systematically structured scoping review that examines the organizational and human impacts of AI-based recruitment and selection, with a particular focus on identifying the benefits, risks, and the inherent tensions between them. The article is fundamentally based on the premise that AI cannot be interpreted merely as a technological tool in HR processes, but rather as part of a complex socio-technical system in which algorithms, human decision-makers, and organizational regulatory mechanisms continuously interact to shape the outcomes of selection decisions. The author captures this complexity through a five-dimensional analytical framework that integrates the dimensions of efficiency, bias, transparency, applicant experience, and governance into a single interpretive framework.
The relevance of this topic is undeniable, as the use of AI in recruitment is no longer a future possibility but a widespread practice, particularly in high-volume selection processes.

Organizations are increasingly relying on automated systems to screen resumes, rank candidates, or even analyze video interviews, while the social and legal environment is also setting increasingly stringent expectations regarding the transparency, fairness, and accountability of algorithmic decisions. A particular strength of the study is that it does not merely treat this topicality in a descriptive manner, but also reflects on the fragmentation of the academic discourse, pointing out that different research directions (performance, ethics, candidate experience) often develop in isolation.

One of the article’s greatest strengths lies in its theoretical integration. The author not only summarizes the literature but also develops a socio-technical interpretive framework capable of linking dimensions previously examined in isolation. In this approach, efficiency and legitimacy—that is, organizational performance and social acceptability—are not independent of one another but are structurally interrelated phenomena. This conceptual framework is forward-looking because it contributes to the theoretical consolidation of the field and goes beyond simple cost-benefit type descriptions. Furthermore, it is grounded in strong methodological foundations: the application of the PRISMA-ScR guidelines, the use of the PCC framework, multi-stage screening, and double coding all enhance the transparency and reliability of the analysis.

At the same time, the article has areas for improvement. One of the most important critical observations concerns the limited scope of the literature base. Although the methodology of a scoping review does not necessarily require comprehensive coverage, the final sample of 33 studies can be considered relatively narrow in such a rapidly expanding field of research, especially in light of the explosive growth in publications from 2024 to 2025. This may result in certain biases and limit generalizability. From a methodological perspective, it is also worth noting that although the author employs a quality assessment (MMAT), its role is more supplementary in nature and is not integrated into the analysis with sufficient depth.
From a content perspective, another criticism is that the study is highly conceptual in nature and supports its findings with relatively few empirical examples. “Structural tension” as a central concept is theoretically well-founded, but its practical manifestation—for example, in specific organizational decision-making situations—is less fully developed. Furthermore, key concepts such as fairness, transparency, or trust, although frequently mentioned, are not always operationalized in a consistent manner, which hinders comparability.

In conclusion, the study is a high-quality, well-structured, and theoretically robust work that makes a significant contribution to the academic discourse on AI-based selection. Its greatest value lies in its integrative approach, which is able to link organizational performance with human and ethical considerations within a common framework. At the same time, the study could be further strengthened by a broader and more up-to-date literature review, a deeper empirical foundation, and more precise definitions of certain key concepts. Nevertheless, the article is clearly ready for publication and could be particularly valuable for journals that emphasize interdisciplinary and theoretical contributions.

Author Response

"Please see the attachment."

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript addresses a relevant topic for both Human Resource Management and organizational studies: the growing use of AI in recruitment and personnel selection. Overall, the paper is well structured, clearly written, and methodologically rigorous. The scoping review protocol is transparently presented, the literature coverage is fine, and the proposed socio-technical framework provides an interesting integrative perspective linking performance-oriented and legitimacy-oriented outcomes.

The article makes a solid contribution by synthesizing fragmented streams of literature related to efficiency, fairness, transparency, candidate experience, and governance. The discussion section is quite strong and succeeds in articulating the paradoxical tensions associated with AI-enabled recruitment systems. The manuscript is also well aligned with the scope of Administrative Sciences.

I nevertheless recommend a few minor revisions before publication. First, while the conceptual framework is convincing, the theoretical contribution could be slightly clarified by more explicitly positioning the proposed socio-technical model in relation to existing HRM and socio-technical systems theories. At present, the originality of the framework occasionally appears more integrative than genuinely novel. Second, the methodological section would benefit from additional precision regarding inter-coder reliability and the operationalization of thematic coding. Although the process is described adequately, a few concrete examples of coding decisions or thematic aggregation would reinforce methodological transparency. Third, the discussion on governance and ethics remains somewhat more conceptual than empirical. The paper could be strengthened by briefly acknowledging the practical difficulty organizations face when implementing effective auditing and accountability mechanisms in real recruitment settings.

Finally, a light linguistic revision could improve readability in certain sections where formulations become repetitive, particularly around the recurring opposition between “performance” and “legitimacy”.

Subject to these minor revisions, the manuscript could be considered suitable for publication.

Author Response

 "Please see the attachment."

Author Response File: Author Response.pdf