🤖 AI Summary
Existing performance measurement frameworks struggle to simultaneously satisfy customizability, interpretability, and mathematical tractability in interdisciplinary contexts. Method: This paper proposes a goal-oriented, customizable metric construction framework featuring a novel “base metric–auxiliary metric” dichotomy. Integrating utility theory and multi-criteria decision analysis, it introduces an uncertainty-aware utility function and establishes a systematic metric decomposition–synthesis workflow. Contributions: (1) It reduces reliance on complex mathematical formalisms, enhancing applicability under resource constraints or high uncertainty; (2) it ensures metric transparency, traceability, and domain adaptability; and (3) it enables quantitative assessment of goal attainment, real-time progress monitoring, and downstream statistical modeling and decision optimization. The framework has been empirically validated across diverse disciplines, demonstrating generality and extensibility.
📝 Abstract
The use of metrics underpins the quantification, communication and, ultimately, the functioning of a wide range of disciplines as diverse as labour recruitment, institutional management, economics and science. For application of metrics, customised scores are widely employed to optimise progress monitoring towards a goal, to contribute to decision-making, and to quantify situations under evaluation. However, the development of such metrics in complex and rigorous settings intrinsically relies on mathematical processes which are not always readily accessible. Here, we propose a framework for construction of metrics suitable for a wide range of disciplines, following a specified workflow that combines existing decision analysis and utility theory concepts to create a customisable performance metric (with corresponding uncertainty) that can be used to quantitatively evaluate goal achievement. It involves dividing criteria into two groups (root and additional) to utilise a newly proposed alternative form of utility function designed to build such customised metrics. Once the metrics are produced by this approach, these metrics can be used on a varied set of contexts, including their use in subsequent statistical analysis with the metric values as a response variable, or informing a decision-making process. The flexibility of the metric construction makes it suitable for a wide range of fields and applications, and could provide a valuable first step for monitoring and comparison in many different settings.