Obtaining sustainable competitive advantage through collaborative dual innovation: empirical analysis based on mature enterprises in eastern China
Bibliographic record
Abstract
In an increasingly competitive market environment, dual innovation which include exploitative innovation and exploratory innovation has become a magic weapon for enterprises to improve performance. This study identifies the mechanism of how collaborative dual innovation influence sustainable competitive advantage, and tests its intermediary role in innovation performance. Using the survey data of 256 mature enterprises in China, this study finds that collaborative dual innovation positively affects the sustainable competitive advantage of mature enterprises through partial mediation of innovation performance. In addition, it shows that the two dimensions of collaborative dual innovation, dual innovation balance (DIB) and dual innovation complementation (DIC), have different impact mechanisms and paths on enterprises’ competitive advantage. While DIB has a direct effect on the competitive advantage, DIC strongly affects the competitive advantage both directly and indirectly through the mediating effect of innovation performance. This study sheds new insight of the interaction between dual innovation and sustainable competitive advantage, and provides a guidance to enterprises how carry out dual innovation effectively to maintain sustainable competitive advantages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".