Predictive factors for inter-agency partnership success

Area of Science:

• Development Cooperation
• Partnership Evaluation
• Project Management

Background:

• Development cooperation partnerships are crucial for collective capacity building.
• A significant failure rate (20%) exists in achieving partnership objectives.
• A deeper understanding of success/failure factors is needed for effective ex-ante evaluation.

Purpose of the Study:

• To systematically identify and classify factors influencing development cooperation partnerships.
• To develop a semi-automated method for classifying these factors by importance.
• To streamline partnership planning and evaluation processes.

Main Methods:

• Systematic identification and classification of partnership influence factors.
• Development of a dataset of 750 factors.
• Visualization of factors along operational, governance, and project quality axes.
• Grouping factors into ten distinct clusters.

Main Results:

• A comprehensive dataset of 750 factors influencing partnership success.
• Factors categorized into three main axes: operations/country context, governance/management, and project quality.
• Factors organized into ten clusters for easier analysis and application.

Conclusions:

• The identified factors and methodology can aid evaluators and planners in multi-stakeholder discussions.
• The approach facilitates information gathering and the use of Natural Language Processing (NLP) and Machine Learning (ML).
• Future work should focus on standardizing factors and incorporating automated context/behavioral analyses.