Human Resource Management in India: Performance and Complementarity
Bibliographic record
Abstract
This is a study of the relationship between HR practices and organisational performance of large‐scale enterprises in India. The main survey yielded 252 usable replies from the HR directors. Results show that mutually supportive sets of HR practices do not yield disproportionately superior outcomes than limited and focused individual practices. This highlights the limitations of strategic HRM in an Indian context. It seems there is little immediate benefit in developing sophisticated mutually supporting HR systems if particular firm or regionally relevant interventions yield clear benefits on their own right. These results highlight the limitations in national level institutions made for a general lack of complementarities, and/or that firms do not want to take the risk of over‐relying on a specific institutional feature that may be subject to change. We also find that innovative firms are not in any way more likely to adopt best HR practices to a greater degree than their less innovative counterparts. India's weak and uneven institutional coverage may open up more opportunities for HR innovation, but the lack of systemic support means that there are fewer opportunities for the latter to realise its fullest potential.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".