Knowledge Resources and Innovation Performance: A PLS‑SEM Analysis of European Regions

Abstract

Purpose | The study examines the relationship between knowledge resources and innovation performance at the regional level in the European Union. It focuses on the role of key knowledge-related factors: human capital, research capacity, and knowledge production, in shaping innovation outcomes, using a latent variable approach. Research method | The analysis is based on regional data for 2024 from the Regional Innovation Scoreboard. Partial Least Squares Structural Equation Modelling (PLS-SEM) is applied to estimate the relationship between two reflective latent constructs: knowledge resources and innovation performance. The model is estimated using SmartPLS and evaluated regarding standard reliability, validity, and structural assessment criteria. Results | The findings reveal a positive and statistically significant relationship be¬tween knowledge resources and innovation performance. Knowledge resources explain a substantial share of variance in innovation outcomes. At the same time, ranking results based on latent scores indicate notable regional heterogeneity, suggesting that similar knowledge endowments may lead to different innovation effects depending on regional conditions. Originality / value / implications / recommendations | The study provides empirical evi¬dence on the importance of knowledge resources using a parsimonious PLS-SEM frame¬work. It highlights the distinction between input and output dimensions of innovation and underscores the need for context-sensitive regional innovation policies focused on strengthening knowledge assets and their effective utilisation.