Big data analytics capability and social innovation: the mediating role of knowledge exploration and exploitation
Bibliographic record
Abstract
Abstract While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".