Promotion of rural industrial revitalization through the development of the rural digital economy
Notice bibliographique
Résumé
Introduction The rise of digital technologies has reshaped rural development strategies, offering new opportunities for industrial revitalization in agricultural regions. In China, the rural digital economy—spanning both infrastructure and digital service adoption—has emerged as a critical driver of localized innovation. This study explores the mechanisms through which digital transformation influences rural industrial upgrading. Using a structured survey in a major navel orange production area, the study examines how hardware and software elements of digitalization affect farmers’ innovation intentions, entrepreneurial behaviors, and outcome perceptions. By identifying heterogeneity across business models and farm scales, the study provides empirical insights into the role digital inclusion plays in revitalizing rural economies. Methods This study draws on 1,042 survey responses from a representative navel orange-producing region in China. Key variables reflect three dimensions of rural industrial revitalization: innovation intentions, entrepreneurial action, and perceived outcomes. The independent variables reflect the development of the digital economy through two dimensions: digital infrastructure and service usage. Ordered Probit and OLS models were applied to estimate relationships, with robustness checks performed using instrumental variables to address endogeneity. Instrument relevance and validity were confirmed through standard econometric tests. Heterogeneity was further examined by disaggregating impacts across production types and farm sizes. Results Findings demonstrate that both infrastructure (hardware) and service use (software) aspects of the rural digital economy significantly enhance farmers’ innovation intention, entrepreneurial engagement, and outcome perception. These effects remain statistically significant and become more pronounced after addressing endogeneity. While hardware shows limited effects across different business types, software-related digital adoption significantly benefits most producers. Additionally, the digital economy’s impact on entrepreneurial action and outcomes is more pronounced among medium- and large-scale farms than smaller producers. Three mechanisms—employment, income growth, and improved well-being—mediate this effect. Discussion The results highlight the transformative potential of rural digital economy development in advancing industrial revitalization. Tailored digital infrastructure, training, and inclusive service access are critical to unlocking innovation capacity at the household level. To enhance equitable digital transformation in agriculture, policies should prioritize narrowing digital divides in underdeveloped regions and facilitate the adoption of adaptable digital farming models, including smart production systems and agricultural traceability platforms. Beyond infrastructure, broader institutional, household, and community efforts—ranging from financial literacy to organizational participation—must complement digital investment. Future studies should expand the scope, adopt longitudinal designs, and explore institutional drivers to deepen the understanding of sustainable rural transformation.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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