Exploring the bridge between digital transformation and sustainable supply chain performance: An empirical study based on Yunnan fresh cut flower supply chain
Bibliographic record
Abstract
Rapid global economic expansion and ongoing technological advancements have made digital transformation an essential tactic for businesses looking to boost their competitiveness and accomplish sustainable development. The study aims to explore how digital transformation (DT) indirectly affects the sustainable supply chain performance (SSCP) of fresh-cut flower supply chains in Yunnan Province through organizational readiness (OR) and innovation capability (IC) and to examine the moderating role of technology readiness (TR) in this process. This study adopted the survey method of the questionnaire, and the final valid sample was 354. Based on the RBV and the philosophy of sustainable development, the PLS-SEM approach is used in this study to assess the fresh-cut flower supply chain in Yunnan Province. It was found that OR and IC played a significant mediating role in DT and SSCP. Furthermore, the link between DT and OR, IC and SSCP, was significantly moderated by TR. The empirical results suggest that high technological readiness and innovativeness help firms assimilate and apply new technologies faster and thus achieve better results in digital transformation. This paper provides valuable guidance for firms and policymakers, suggesting active investment in digital technologies and infrastructure development, and focusing on the combination of innovation capability and sustainability to promote the sustainable development of agricultural supply chains.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".