Leveraging structural empowerment and human capital for organizational innovation
Bibliographic record
Abstract
Purpose This study examines how structural empowerment fosters organizational innovation, with human capital as a mediating mechanism. It also explores the moderating roles of technology-driven organizational change and the strategic importance of human capital in shaping the empowerment-innovation relationship. Design/methodology/approach Leveraging workplace empowerment and human capital resource theories, this study tests a moderated mediation model using data from 434 South Korean manufacturing firms (42,511 managers and employees). Zero-inflated Poisson’s regression analyses and bootstrapping methods are employed to test the hypotheses. Findings The results indicate that structural empowerment enhances human capital, which in turn drives innovation. Furthermore, technology-driven organizational change and the strategic importance of human capital strengthen the positive effects of human capital on innovation, revealing the crucial contextual factors that amplify these relationships. Originality/value This study integrates workplace empowerment theory with human capital resource theory to offer a new perspective on how structural empowerment fosters innovation by transforming individual capabilities into collective resources. By isolating structural empowerment’s unique role, it highlights the critical mediating function of human capital in driving innovation. Furthermore, the introduction of technology-driven organizational change and the strategic importance of human capital as moderators emphasizes how these factors shape the task and social environments, enabling human capital to function effectively as an organizational resource.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".