Linking big data analytics capability and sustainable supply chain performance: mediating role of innovativeness, proactiveness and risk taking
Bibliographic record
Abstract
Purpose Drawing on the dynamic capability view (DCV), the current study aims to examine the mediating effects of entrepreneurial orientation (EO), in terms of innovativeness, proactiveness and risk taking, on the relationships between big data analytics (BDA) capability and sustainable supply chain performance (SSCP). Design/methodology/approach Data were collected by questionnaire survey from 300 manufacturing organizations. Structural equation modeling (SEM) was used to test the hypotheses. Findings The findings showed that innovativeness and proactiveness fully mediated the link between BDA capability and SSCP. However, risk taking only partially mediated the relationship between BDA capability and SSCP. There was also a negative relationship between BDA and risk taking. Research limitations/implications Given that the current study focused on the manufacturing sector, future research is needed to compare different sectors and cultural contexts. Further exploration is also needed into the dimension of risk taking in terms of the role of risk taking in linking BDA capability with SSCP in different cultural settings. Practical implications Technology may not increase the risk taking capability. Organizations may be creative and proactive but may remain risk averse despite having access to big data. Organizations need a more balanced approach to dynamically integrate and reconfigure the organizations' BDA and EO capabilities in order to enhance SSCP. Originality/value The role of EO in mediating the relationship between BDA capability and SSCP has not been studied before. The current study aimed to address the gap and contribute to the existing debate on better understanding the factors that are needed by organizations to effectively employ technology to enhance SSCP. Untapped areas for future research are also identified.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".