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. • Presents a supervised LDA (sLDA) approach to quantify firm perceived innovativeness. • Uses firm R&D intensity to guide topic modeling in analyst reports. • Captures innovation signals overlooked by traditional metrics. • Validates sLDA scores against R&D intensity and patent-based innovation metrics. • Provides a benchmarking tool for managers and researchers.

This paper integrates insights from the literature on invention networks, gender, and the sociological literature to analyze differences in how firms participate in man-led and woman-led invention networks. We contribute to the current debate on whether clustering or boundary-spanning network properties are more important for invention by introducing gender as an important factor. We empirically test our hypotheses on a sample of more than 30,000 firms from around the world over time using OECD REGPAT global patent data. Our findings indicate that different network properties are important for firm invention in woman-led and man-led innovation networks. In man-led invention networks, firms strongly benefit from being in a boundary-spanning position and are negatively affected by clustering, whereas in woman-led invention networks, boundary spanning has a less pronounced positive effect, and clustering has a positive rather than negative effect. Our findings have substantial implications for firms and policymakers interested in invention and contribute to the studies of gender and invention networks. • Investigates how gender shapes the impact of network positions on invention outcomes. • Finds that clustering and boundary spanning affect invention differently across team genders. • Shows that cohesion can enhance invention performance depending on team composition. • Constructs innovation networks from global patent data. • Provides guidance for firms aiming to design inclusive, innovation-oriented collaborations.

As crises, both natural and man-made, continue to escalate in frequency and complexity, the need for effective and timely humanitarian interventions has become increasingly critical. Artificial intelligence (AI) has emerged as a transformative tool in enhancing humanitarian aid, addressing all stages of the crisis management cycle. Despite growing interest in AI's application within the humanitarian field, the existing literature remains fragmented, with limited synthesis of its overall impact. This study adopts a systematic literature review approach to provide a comprehensive analysis of AI's utilization in humanitarian aid across the crisis cycle, as well as its role in broader humanitarian settings outside of immediate crisis contexts. Based on 60 selected studies, the findings reveal that AI applications in both the pre- and post-crisis phases can be grouped into four specific categories, and that AI's role in broader humanitarian contexts can similarly be divided into four focus areas. Specifically, the categories in the pre-crisis phase include site selection, medical services enhancement, early warning, and information flow, and the categories in the post-crisis phase include distribution and delivery, damage assessment, online and textual insights, and routing optimization. The review highlights AI's significant potential to enhance the effectiveness and efficiency of humanitarian efforts, offering valuable insights for organizations seeking to harness AI's transformative power. • This study provides an overview of the current state of knowledge on the AI-humanitarian aid nexus and outlines a roadmap for future research. • Two research questions guide this review: exploring how AI is applied during crises, and examining its broader use in the humanitarian context. • Understanding the AI-humanitarian link is critical considering how heavily the world relies on aid being effective every time a crisis occurs. • This is the first comprehensive synthesis to examine AI's role in humanitarian aid across multiple crisis types and phases.