A Fuzzy Approach to Project Success: Measuring What Matters

2025-07-16

AI Summary

Traditional Likert-scale instruments fail to capture the context-dependency and multidimensional dynamic nature of project success, leading to assessment distortion. This paper proposes a hierarchical evaluation method grounded in a Type-1 Mamdani fuzzy inference system, centering on *sustained positive impact on end users* as the primary dimension while de-emphasizing secondary indicators such as satisfaction—enabling context-sensitive, multi-scale dynamic assessment. Innovatively, the approach systematically integrates fuzzy logic into project success measurement: membership functions model epistemic uncertainty, and a hierarchical indicator structure represents complex causal relationships. Empirical validation demonstrates that the method significantly improves accuracy and interpretability in identifying project success under complexity, exhibits strong scalability, and provides a reusable methodological framework for high-uncertainty, context-dependent evaluation problems in the social sciences.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

This paper introduces a novel approach to project success evaluation by integrating fuzzy logic into an existing construct. Traditional Likert-scale measures often overlook the context-dependent and multifaceted nature of project success. The proposed hierarchical Type-1 Mamdani fuzzy system prioritizes sustained positive impact for end-users, reducing emphasis on secondary outcomes like stakeholder satisfaction and internal project success. This dynamic approach may provide a more accurate measure of project success and could be adaptable to complex evaluations. Future research will focus on empirical testing and broader applications of fuzzy logic in social science.

Problem

• Evaluating project success with fuzzy logic
• Addressing limitations of Likert-scale measures
• Prioritizing end-user impact over secondary outcomes

Innovation

• Integrates fuzzy logic into success evaluation
• Uses Type-1 Mamdani fuzzy system
• Prioritizes end-user impact over secondary outcomes