The role of digital transformation and innovation in enhancing resilience and competitiveness of chinese logistics SMEs
Bibliographic record
Abstract
This study explores the high-quality development of Chinese logistics small and medium enterprises (SMEs) by examining the impact of digital technology adoption, organizational resilience, exploratory innovation, and environmental uncertainty. The research investigates how digital transformation enhances operational efficiency, adaptability, and competitiveness while assessing the mediating role of exploratory innovation in linking technology adoption and resilience to enterprise success. Additionally, the study evaluates the moderating effect of environmental uncertainty on these relationships. A survey was conducted among 340 logistics professionals and SMEs operating within China's supply chain sector, focusing on their digital transformation efforts, resilience strategies, and innovation-driven business models. The hypotheses were tested using descriptive analysis in SPSS and structural equation modeling (SEM) in SmartPLS-4. The findings indicate that digital technology adoption and organizational resilience significantly enhance high-quality enterprise development, with exploratory innovation playing a crucial mediating role. Moreover, environmental uncertainty moderates the relationship between digital adoption and innovation, highlighting the need for adaptability in dynamic markets. This study contributes to the existing literature by integrating digital adoption, resilience, and innovation within a unified framework, particularly in the context of Chinese logistics SMEs. The results emphasize the importance of reducing digital adoption barriers, strengthening organizational resilience, and leveraging innovation to enhance long-term sustainability. Policymakers and industry stakeholders are encouraged to implement supportive trade policies, financial incentives, and technological investments to optimize the performance of Chinese logistics SMEs in an increasingly competitive environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".