Measuring the Management-Efficiency Dimension of R&D Project Success as a Latent Construct: An AHP-Rasch Decision-Support Framework

Evaluating the success of research and development (R&D) projects is challenging because their outcomes are uncertain, context-dependent and often not immediately applicable in practice. This study focuses specifically on the management-efficiency dimension of R&D project success and aims to develop and test a context-adjusted framework for measuring this dimension as a latent construct, integrating Critical Success Factors (CSFs) and Success Criteria (SCs) within a single decision-support model. A retrospective dataset of 100 completed R&D projects was analysed. Researchers' and managers' judgement was used to weight CSFs through the Analytic Hierarchy Process (AHP). These weights were then combined with observable project Success Criteria (SC). Rasch measurement was then applied to estimate context-adjusted management performance on a common latent scale. The model was calibrated with a training subset and assessed through a holdout subset and sensitivity analysis. The results indicate that organisational role, consortium size, company involvement and technological maturity are the most relevant factors for interpreting R&D project performance. Among the SC, schedule compliance appeared easier to achieve than budget and scope performance. The model also helped to identify atypical projects whose observed performance differed from the expected response pattern. The study contributes a structured AHP-Rasch framework for interpreting the management-efficiency dimension of R&D project success as a context-adjusted latent construct, integrating expert-weighted contextual factors with observable SC to support portfolio management in research organisations.