This study developed an integrated performance evaluation framework for rural development Official Development Assistance (ODA) projects in Korea and validated its effectiveness through practical application. Based on the FAO’s SAFA (Sustainability Assessment of Food and Agriculture Systems) Tool, the framework enables a balanced assessment across economic, social, environmental, and governance dimensions. The methodology incorporates the Analytic Hierarchy Process (AHP) and Importance-Performance Analysis (IPA) to measure the importance and performance of objectives and indicators. The developed framework serves as a tool for Project Design Matrix (PDM) development, monitoring, and evaluation throughout the project’s planning, implementation, and completion phases. During planning, it systematically incorporates stakeholder input in setting objectives and indicators. During implementation, it facilitates real-time monitoring for immediate decision-making and resource reallocation. At completion, it supports comprehensive performance evaluation. Application of the framework to the “Rural Development Programme in Tuyen Quang Province, Vietnam” demonstrated its effectiveness in systematizing excessive indicators and clarifying the hierarchical and logical connections between objectives. This performance evaluation framework can enhance project transparency and accountability by overcoming the limitations of current PDM approaches and providing systematic methods for incorporating stakeholder feedback. It is particularly applicable to multi-sectoral rural development programs and is expected to contribute to integrated development in target areas. However, validation through a single case study presents limitations, necessitating future application across diverse regions and project types to verify generalizability.

DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU’s input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

Nowadays, Information Technology(IT) has become one of the most critical managerial decision areas, because its role in the business practice tends to be more important and hence the amount of the investment in growing. Since there is no dominant and prom