Big Data capability and sustainability oriented innovation: The mediating role of intellectual capital
Bibliographic record
Abstract
Abstract The past decade has seen the rapid emergence of research on Big Data and its implications in our society, yet research on its impact on organizations' sustainability challenges is still at an early stage. Based on the organizational learning theory, we develop a framework to investigate the effects of Big Data capability (BDC) and further intellectual capital on a firm's sustainability‐oriented innovation performance (SIP). Using a multi‐source data collected from 358 Chinese firms, we tested our proposed hypotheses using the partial least squares‐structural equation modeling (PLS‐SEM) and the fuzzy‐set qualitative comparative analysis (fsQCA). The results show that BDC is positively related to SIP and economic innovation performance (EIP), mediated by intellectual capital. The fsQCA results further reveal that three configurative combinations, that is, “external capability‐internal resource promotion,” “external capability and relationship building help,” and “external relationship collaboration compensation,” can lead to high SIP. This study provides new insights for firms to develop sustainability‐oriented innovation and to coordinate sustainability innovation and economic innovation practices in order to create balanced outcomes.
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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 | 0.004 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".