🤖 AI Summary
This paper addresses modeling challenges associated with Complex Performance Indicators (CPIs)—multidimensional composite metrics such as customer satisfaction and sustainability indices—in their design, interpretation, and dynamic evolution. Following the PRISMA-ScR framework, we conduct a systematic scoping review employing thematic coding, modeling-feature extraction, and cross-framework comparative analysis. We propose, for the first time, a comprehensive taxonomy of CPI modeling features and quantitatively assess the coverage of these features by mainstream Model-Driven Engineering (MDE) frameworks. The study bridges a critical gap in the literature: the absence of a systematic MDE-oriented review for CPI modeling. It identifies essential modeling capabilities required to ensure CPI understandability, maintainability, and evolvability. As a key outcome, we deliver a reusable MDE-CPI modeling guideline, providing both theoretical foundations and methodological support for academic research and industrial practice.
📝 Abstract
This paper addresses challenges of designing and managing Complex Performance Indicators (CPI), which amalgamate individual indicators to measure latent, yet crucial business factors like customer satisfaction or sustainability indices. Despite their significant value, designing and managing CPI is intricate; they evolve with rapidly changing business contexts and present comprehension and explanation challenges for end-users. Model-Driven Engineering (MDE) emerges as a potent solution to overcome these hurdles and ensure CPI adoption, though its application to CPI remains an understudied research area. While prior efforts targeted specific CPI modeling objectives, a comprehensive overview of literature advancements is lacking. This study addresses this gap by conducting a scoping review yielding dual outcomes: (1) a comprehensive mapping of modeling features in the literature and (2) a comparative analysis of the coverage offered by the modeling frameworks. These outcomes enhance CPI understanding in academic and practitioner circles and offer insights for future MDE CPI advancements.