Linking knowledge development with sustainable supply chain performance: mediating effects of innovativeness, proactiveness and risk taking
Bibliographic record
Abstract
Purpose The current study provides new insights into the relationships between knowledge development (KD) and sustainable supply chain performance (SSCP) by exploring the mediating effects of entrepreneurial orientation (EO) in terms of innovativeness, proactiveness and risk taking. Design/methodology/approach Data were collected by questionnaire survey from 242 manufacturing organizations. Structural equation modeling (SEM) was used to test the hypotheses. Findings The results reveal that innovativeness and proactiveness have full mediating effects on the relationship between KD and SSCP. Though KD is negatively related to risk taking and has insignificant indirect effect on SSCP via risk taking, the mediating effect of risk taking remains moderate positive on the relationship between KD and SSCP. Research limitations/implications Given that the current study focuses on manufacturing sector, future research is needed for more comparative studies conducted in different sectors and cultural contexts. The negative link between KD and risk taking also warrants future investigation. Practical implications Organizations may reduce their level of risk taking due to the increase in KD. However, in order to enhance SSCP, risk taking is still needed as it mediates the relationship between KD and SSCP. Originality/value The mediating effects of innovativeness, proactiveness and risk taking on the relationship between KD and SSCP are unknown. Current study aims to address this gap.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".