Navigating digital transformation challenges: The role of utilization and exploratory innovation in chinese logistics SMEs
Bibliographic record
Abstract
This study investigates the high-quality development of Chinese logistics SMEs by analyzing the effects of digital technology adoption, organizational resilience, utilization innovation, and exploratory innovation. It explores how digital transformation improves operational efficiency and adaptability, while assessing the mediating role of utilization innovation in connecting technology adoption and resilience to enterprise success. Additionally, the study examines the moderating effect of exploratory innovation on these relationships. A survey of 340 logistics professionals and SMEs within China's supply chain sector was conducted, with hypotheses tested using SPSS and SmartPLS-4. The results reveal that digital technology adoption and organizational resilience significantly contribute to enterprise development, with utilization innovation playing a pivotal mediating role. Furthermore, exploratory innovation moderates the relationship between digital adoption and innovation, highlighting the importance of adaptability in dynamic markets. This study presents a comprehensive framework integrating digital adoption, resilience, and innovation, offering valuable insights into how SMEs can address the challenges of digital transformation. Policymakers and industry stakeholders are encouraged to implement supportive policies, financial incentives, and technological investments to enhance the competitiveness of SMEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".