The dynamic capability view in exploring the relationship between high-performance work systems and innovation performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it