Digital Leadership Impacts on a Village-owned Enterprise Performance: A Moderation Effect of Artificial Intelligence
Bibliographic record
Abstract
This study investigates the impact of digital leadership on the performance of village-owned enterprises, or VOEs emphasizing the moderating effect of artificial intelligence, or AI. As digital transformation reshapes the business landscape, effective digital leadership emerges as a crucial factor for enhancing organizational performance, particularly in rural settings. This study employs quantitative surveys and interviews from VOEs across various villages with 192 research sample size. The findings reveal that digital leadership significantly correlates with improved performance metrics, such as profitability, operational efficiency, and community values. Moreover, the integration of AI technologies further amplifies these effects, providing tools for better decision-making, resource allocation, and customer interaction. The moderation analysis indicates that the presence of AI not only enhances the effectiveness of digital leadership but also facilitates innovative practices within VOEs. This research also contributes to the understanding of how digital leadership, coupled with AI, can drive sustainable growth in village enterprises, offering practical implications for policymakers and community leaders aiming to leverage technology for rural development. Future studies are suggested to explore the long-term effects of these dynamics in diverse contexts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".