Perceived innovativeness: A supervised latent-dirichlet-allocation-based approach

Abstract

Innovation is a crucial driver of competitiveness, yet traditional metrics fail to capture its full scope. Therefore, holistic approaches encompassing a wider variety of innovation types are needed. We conceptualize perceived innovativeness as an externally attributed judgment formed under information asymmetry, where financial analysts act as informed intermediaries translating firms’ disclosures into credible innovation signals. Following this concept, this paper extends an existing LDA method by introducing a supervised approach (sLDA), trained with Research & Development (R&D) intensity. This variation addresses the gap between the dynamic, multifaceted nature of innovation and the limitations of prevailing metrics. We analyzed 1606 analyst reports from 499 European companies to test our approach. Our findings show that, compared to unsupervised approaches, the sLDA approach yields more accurate results in modelling perceived innovativeness. Furthermore, we demonstrate robust results over the period of 2019–2022: the supervised model achieves consistently lower mean absolute rank errors compared to the unsupervised baseline and, in most industries, than R&D-based benchmarks. We also identify influential topics and find fixed-effects-robust associations with patent indicators, with rank-based evaluation reducing sensitivity to outliers. This study contributes to the ongoing discourse on innovation assessment and benchmarking by linking language-based perception with signaling theory and offering a method that captures perceived innovation as expressed in analyst language. It provides managers with a tool to support strategic planning, investors with an additional signal of perceived innovation and regulators with insights into innovation narratives complementing R&D and patent disclosures.