Towards a new conceptualisation of evidence-based human resource management
Bibliographic record
Abstract
Purpose Notwithstanding that evidence-based human resource management (EBHRM) is gaining more ground in governmental institutions, it is still lacking a clear and research-driven conceptualisation (Marler and Fisher, 2013). Therefore, this study seeks to establish a fundamental clarifying concept of EBHRM by using a systematic literature review. Design/methodology/approach This method builds on an intensive scanning of 2,584 (interdisciplinary) articles, collected from Web of Science and Scopus. Eventually, 50 articles met the predetermined inclusion criteria and were analysed. The most recent conceptualisation of evidence-based management in the literature has served as a guideline to compare the review results and further scrutinise the differences and similarities (Barends et al., 2014; Barends and Rousseau, 2018; Rynes and Bartunek, 2017). Findings This has enabled us to elaborate a comprehensive conceptualisation. The articles were divided into two groups, one group (n = 31) has Rousseau et al. as a reference, the other (n = 19) did not, and used various definitions. Three themes were identified: evidence-based research methods (n = 30), specific skills (n = 36) necessary to apply an evidence-based strategy and a link with the academic-practice gap (n = 25). Practical implications Based on the results, we recommend adding two dimensions to strengthen the current conceptualisation: a first dimension referring to how evidence-based management can be established (i.e. which methods and skills are necessary) and a second dimension referring to the why of evidence-based management in an organisation (reducing the academic-practice gap). Originality/value This paper starts from a systematic review approach unlike previous research in the field to contribute to the further conceptualisation of EBHRM (Rynes and Bartunek, 2017).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.204 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.041 | 0.024 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.035 | 0.044 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".