Evaluation Of Korea’s Rural Development Oda Projects In Kyrgyzstan Using Neural Network Analysis: Focusing On Local Residents’ Perceptions
DOI:
https://doi.org/10.56294/dm2025858Keywords:
Rural Development, Official Development Assistance (ODA), Neural Network Analysis, Residents' Perception, KyrgyzstanAbstract
This study conducted a mid-term evaluation of Korea’s Integrated Rural Development Project (IRDP) implemented in 30 villages of Osh and Batken regions in Kyrgyzstan since 2021, focusing on local residents' perceptions. Particularly, to overcome the limitations of conventional descriptive statistical methods frequently used in previous studies, this research applied neural network analysis to better capture complex and nonlinear relationships among influencing factors. The results indicated that residents generally perceived the Korean rural development ODA project as significantly contributing to local economic development, with the most influential factor being the village-level characteristics. Furthermore, demographic characteristics such as marital status, age, education level, and occupation of residents also had significant effects on their perceived outcomes of the project. This study confirms the usefulness of neural network analysis as an effective method for evaluating ODA project outcomes based on residents’ perceptions and provides meaningful policy implications for enhancing the effectiveness of future Korean rural development ODA projects.
References
Anríquez, G., & Stamoulis, K. (2007). Rural development and poverty reduction: Is agriculture still the key? Journal of Agricultural and Development Economics, 4(1), 5–46.
Asian Development Bank (ADB). (2017). Rural Development and Poverty Reduction in Asia: Lessons from the Past and Prospects for the Future. Asian Development Bank.
Brown, L., & Green, P. (2020). Recurrent neural networks for speech recognition: Advances and challenges. IEEE Transactions on Audio, Speech, and Language Processing, 28, 789–802.
Chen, H., & Wang, S. (2021). Emotion recognition using deep neural networks: A review. IEEE Transactions on Affective Computing, 12(2), 345–360.
Demissie, A., & Legesse, B. (2021). Evaluating the impacts of rural development projects using neural network models: Evidence from Ethiopia. Journal of Development Effectiveness, 13(2), 181–199. https://doi.org/10.1080/19439342.2021.1906084
European Commission. (2015). EU Support to Rural Development: A Review of the Evidence. European Commission.
Food and Agriculture Organization (FAO). (2018). The State of Food and Agriculture 2018: Migration, Agriculture and Rural Development. Food and Agriculture Organization.
Garcia, M., & Patel, R. (2022). Neural network approaches to handwriting recognition: A survey. Pattern Recognition Letters, 150, 45–60.
International Food Policy Research Institute (IFPRI). (2014). Global Food Policy Report 2014. International Food Policy Research Institute.
International Fund for Agricultural Development (IFAD). (2019). Rural Development Report 2019: Creating Opportunities for Rural Youth. International Fund for Agricultural Development.
Kumar, S., & Lee, J. (2020). Neural networks for facial expression recognition: Recent advances. Journal of Visual Communication and Image Representation, 71, 102–115.
Nguyen, T., & Tran, D. (2022). Application of neural networks in gesture recognition: A systematic review. IEEE Access, 10, 12345–12360.
O'Connor, E., & Murphy, K. (2021). Neural network models for human activity recognition: A comparative study. Sensors, 21(5), 1789.
OECD Development Assistance Committee (DAC). (2021). Evaluation Criteria: Adapted Definitions and Principles for Use. OECD Development Assistance Committee.
Sabates-Wheeler, R., & Devereux, S. (2021). Social protection and rural development: A synthesis. World Development, 139, 105–127. https://doi.org/10.1016/j.worlddev.2020.105127
Singh, A., & Gupta, R. (2020). Deep learning approaches for speech emotion recognition: A review. International Journal of Speech Technology, 23(4), 567–582.
Smith, J., & Doe, A. (2021). Application of convolutional neural networks in image recognition tasks. International Journal of Computer Vision, 129(3), 456–478.
United Nations Development Programme (UNDP). (2016). Human Development Report 2016: Human Development for Everyone. United Nations Development Programme.
Wang, Y., & Liu, H. (2022). Neural networks in natural language processing: Applications and challenges. Journal of Computational Linguistics, 48(1), 89–110.
World Bank. (2016). Community-driven development: Lessons from the World Bank experience. The World Bank Group.
World Bank. (2020). World Development Report 2020: Agriculture for Development. The World Bank Group.
Zhang, Y., & Li, X. (2022). Deep neural networks for sentiment analysis: A comprehensive review. Journal of Artificial Intelligence Research, 75, 123–156.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Young-Chool Choi , Yanghoon Song , Ki Seo Kong , Ahyoung Lee (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
