The impact of capacity utilisation on product innovation in emerging economies: The moderating effects of firm ownerships
Bibliographic record
Abstract
While it is acknowledged that higher capacity utilisation results in efficient resource allocation, which could improve firms' productivity and innovation in emerging markets, research on which firms maximise their capacity to improve their innovation is unexplored. We draw insights from the resource-based view theory to develop and test a theoretical model to examine how various ownership structures (i.e., domestic, foreign, and state) moderate the relationship between capacity utilisation and product innovation. The empirical model is based on a sample of 80,587 firms from numerous emerging markets. Results reveal that capacity utilisation negatively influences product innovation, considering the specific context of emerging economies. Furthermore, we find that (i) domestic ownership positively moderates the relationship between capacity utilisation and product innovation, such that any increase in domestic ownership weakens the negative effect of capacity utilisation; (ii) foreign ownership negatively moderates the relationship between capacity utilisation and product innovation, such that any increase in the extent of foreign ownership strengthens the negative effect of capacity utilisation; (iii) state ownership negatively moderates the relationship between capacity utilisation and product innovation, such that any increase in state ownership strengthens the negative effect of capacity utilisation. Some implications for theory, practice, and policy are further discussed. • This research examines how capacity utilisation impacts product innovation. • Domestic ownership weakens the negative effects of capacity utilisation on product innovation. • Foreign ownership strenghtens the negative effect of capacity utilisation on product innovation. • State ownership strenghtens the negative effect of capacity utilisation on product innovation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".