The dynamic capability view in exploring the relationship between high-performance work systems and innovation performance
Bibliographic record
Abstract
In this study, we develop and test a framework that theorizes how high-performance work systems (HPWS)—a set of interrelated HR practices—build dynamic capabilities (i.e. learning, integration, and reconfiguration capabilities), which in turn lead to innovation performance. We also hypothesize that organizations with a stronger innovation culture, where employees share a common understanding of the value and importance of innovation, will be better able to convert capabilities into innovation performance. We test our hypotheses using time-lagged, multisource data from 173 companies in the Iranian pharmaceutical industry, a knowledge-intensive, high-velocity environment highly dependent on HRs to innovate. Our results show that the relationship between HPWS and innovation performance is mediated by dynamic capabilities (DCs). Further, alongside finding support for the moderating effect of innovation culture in the relationship between DCs and innovation performance, we find that innovation culture moderates the indirect effect of HPWS on innovation performance via DCs such that innovation culture strengthens the mediated relationship. The theoretical and practical implications of our findings are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".