The Interplay of Competency Diversity and Resilience in High-tech Companies
Bibliographic record
Abstract
This study explores how competence diversity influences the resilience of small and medium-sized enterprises (SMEs) in high-technology industries, including mechatronics, aerospace, and automotive. Based on the theories of dynamic capabilities (Liu, Y., & Wang, J., 2024) and distributed innovation, the study demonstrates that companies with broader, interdisciplinary, and cross-sectoral competencies can better cope with external shocks, recover more rapidly, and respond effectively to changing industrial challenges. The findings indicate a growing need to move beyond linear innovation models, which conceptualise innovation as a unidirectional, internally driven process and adopt distributed competence-based approaches. At the centre of this research is the concept of cross-fertilisation, defined as a practice-driven form of cross-sectoral collaboration that enables the integration of different areas of knowledge to solve complex problems. This rethinking is particularly important for SMEs working in environments with high levels of technological uncertainty, where responsiveness, adaptability and innovation capacity are essential for competitiveness.The study highlights persistent gaps in comprehending how enterprises can effectively manage diverse competence sets and regulate collaborative innovation processes. Moreover, the authors raise new issues about the trade-offs between competence extensions and the structural conditions necessary to ensure sustainable interdisciplinary cooperation. The results of this study highlight practical issues for improving sustainability and innovation performance in industrial sectors.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".