When firms adopt sustainable human resource management: A <scp>fuzzy‐set</scp> analysis
Bibliographic record
Abstract
Abstract Sustainable human resource management (HRM) is critical to sustainable corporate development. However, there is little systematic research examining the determinants of sustainable HRM adoption. We fill this void by identifying and introducing a configurational approach to examine when firms adopt sustainable HRM. Based on institutional theory, we develop a typology of institutional contexts associated with sustainable HRM adoption. We posit that institutional conditions in configuration facilitate firms' adoption of sustainable HRM. Thus, we hypothesize a primary institutional configuration where institutional support, institutional quality, and institutional infrastructure combine to promote the adoption of sustainable HRM. We further propose alternative types of configurations conducive to the adoption of sustainable HRM by introducing two organizational conditions: strategic leadership support and resource slack. A fuzzy‐set qualitative comparative analysis on data from 57 cases in China supports our hypotheses. We find that the combination of institutional conditions promotes the adoption of highly sustainable HRM, and the two alternative types provide functional substitutes for the primary type: (a) strategic leadership support substitutes for the combination of institutional support and institutional infrastructure, and (b) resource slack substitutes for institutional infrastructure. We build an institutional configurational model to advance a holistic understanding of the theoretical drivers of sustainable HRM, contributing to the research on sustainable HRM, institutional theory, leadership, and resource slack.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".