Obtaining sustainable competitive advantage through collaborative dual innovation: empirical analysis based on mature enterprises in eastern China
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 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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.032 |
| 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 it