Enablers and Barriers to the Integration of AI in Recruitment: Insights from HR Practitioners
Bibliographic record
Abstract
This article researches into the integration and potential impact of Artificial Intelligence (AI) in recruitment processes, using organizational theories to interpret perceptions and insights from practitioners in the field of Human Resource Management. The methodology centers on an empirical study incorporating 28 semi-structured interviews, conducted in a broad variety of sectors with practitioners employed in recruitment process outsourcing (RPO) firms and HR departments of large companies. An inductive framework guides the research, utilizing the Gioia methodology to develop concepts, themes, and categories that reveal the complex dynamics inherent in recruitment. Two main categories emerge: AI enablers – trends and features of recruitment processes that constitute fertile ground for AI to improve them – and AI barriers to its adoption. Informed by a socio-technical lens, this study contributes to theory by identifying four main socio-technical managerial tensions resulting from contradictions between enabling factors and barriers: information-processing gaps, mass personalization, delegation ethics, and human-AI complementarity. An agenda with open questions for future research is provided to researchers, and practical implications and directions are suggested to HR managers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".