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on Intellectual Property Rights |
| By: | Claudia Collodoro (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Lucrezia Fanti (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Jacopo Staccioli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Maria Enrica Virgillito (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy) |
| Abstract: | This work provides a comprehensive large-scale analysis of artificial intelligence-based worker management (AIWM) systems from an industry-wide exposure perspective focusing on traditional industries. We begin by examining the knowledge production underlying these workforce management tools and leverage technology patent-classification to identify their dynamics and specific features. For this purpose, we use patent data retrieved from Orbis Intellectual Property covering the years 1975 to 2022, considering patents filed with both the EPO and the USPTO. Furthermore, to identify patents related to AIWM heuristics, we retrieve their full text from Google Patents and conduct a textual analysis using a dependency parsing algorithm. Finally, using the dictionary of human tasks provided by O*NET, we construct a measure of exposure to AIWM systems for individual human tasks and occupations. Linking the technological and labour market domains, we find that the professions most exposed to AIWM systems are those at the top of organisational hierarchies. |
| Keywords: | Artificial Intelligence Worker Management, Sector-level Analysis, Patenting Activity, Techno-organisational Change |
| JEL: | O14 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie5:dipe0056 |
| By: | Hanming Fang; Xian Gu; Hanyin Yan; Wu Zhu |
| Abstract: | We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO’s AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976–2023) and Chinese patents (2010–2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa. |
| JEL: | C55 G14 O31 O33 O34 O57 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35022 |
| By: | Stephen L France (Mississippi State University [Mississippi]); Nebojsa Davcik (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School); Brett J Kazandjian (Towson University [Towson, MD, United States] - University System of Maryland) |
| Abstract: | Measuring brand equity is of vital importance to marketing practitioners and scholars. Academics and practitioners have developed a range of tools and metrics for measuring brand equity, but in the fast-paced and transformational digital era, it may be that current metrics are not sufficient. The authors develop a conceptual understanding of the brand equity paradigm using practitioner and scholarly views. A practitioner-focused analysis is given on how companies can best understand and measure brand performance in a digital environment and take actionable insights, using the share of search, digital brand awareness, and digital brand sentiment constructs. The authors argue that digital brand equity metrics cannot be based only on social media and current digital metrics indicators but also must include a human side of the brand and the technology-consumer nuances. The study proposes a research agenda and highlights important research and policy questions in developing digital brand equity. |
| Keywords: | AI, Social media, Measurement, Digital, Brand equity |
| Date: | 2025–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05568657 |