Examining the relationships between big data analytics capability, entrepreneurial orientation and sustainable supply chain performance: moderating role of trust
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
Purpose Using a dynamic capability view, this study examined the relationships between big data analytics capability (BDAC), entrepreneurial orientation (EO) and sustainable supply chain performance (SSCP) by exploring the moderating role of trust among supply chain partners. Design/methodology/approach Questionnaires were collected from 300 manufacturing organizations using snow sampling. The moderating connections and direct relationships were examined using Hays' process macro and structural equation modeling. Findings BDAC was positively related to EO and SSCP. When supply chain partners experienced low levels of trust, an increase in BDAC did not enhance SSCP. As trust increased, the relationship between BDAC and SSCP became more positive, underpinning the moderating effects of trust. Moreover, trust did not moderate the relationship between BDAC and EO. The moderating effect of trust on the relationship between EO and SSCP showed a positive relationship between EO and SSCP when trust was low; however, the relationship became negative when trust was high. Practical implications Developing technology alone may not be sufficient, as supply chain managers need to establish a strong business relationship based on mutual trust. However, they also need to be aware of the dangers of high levels of trust because these may negatively affect performance. Therefore, supply chain managers need to achieve an optimal level of trust that is neither excessive nor insufficient. Originality/value Advances in technology and entrepreneurial drive for supply chain sustainability make it pertinent to examine trust levels among supply chain partners and the varying impact on BDAC, EO and SSCP. The current study shows the negative aspects of too much trust among supply chain partners.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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