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on Knowledge Management and Knowledge Economy |
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Issue of 2026–04–20
four papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| 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. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10529 |
| By: | BELDERBOS, René; SUZUKI, Shinya; OKAMURO, Hiroyuki; ASAKAWA, Kazuhiro |
| Abstract: | Multinational enterprises (MNEs) find it challenging to transfer technological knowledge to their overseas affiliates, as this transfer requires the exchange of tacit knowledge and because technological knowledge may spill over to local rivals. The use of expatriate employees may help MNEs to address these challenges, aiding in the transfer process and ensuring tighter control of this transfer. In this paper we examine under which conditions the assignment of expatriates to foreign affiliates is associated with higher levels of technology transfer by the MNE to those affiliates. Drawing on rich micro data on Japanese MNEs and their foreign affiliates, 2013-2022, affiliate fixed effect analysis confirms that expatriates facilitate knowledge transfer to foreign affiliates in particular for small and medium sized MNEs, MNEs that are technology leaders, and when an affiliate is minority-owned by the MNE or is facing rising geopolitical risks in the host country. |
| Keywords: | Multinational Enterprise (MNE), Small and Medium sized Enterprise (SME), expatriates, foreign affiliates, technology transfer |
| Date: | 2026–03–24 |
| URL: | https://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-160 |
| By: | Noriyuki Yanagawa (The University of Tokyo) |
| Abstract: | This paper examines the framework of corporate governance in family businesses by analyzing its relationship with general governance theories, the unique characteristics of family firms, and succession planning. The study highlights the necessity of distinguishing the "family" system from the "business" system when addressing governance issues. It further argues that clarifying decision-making processes and articulating tacit knowledge are essential. Finally, regarding succession planning, the paper posits that ensuring a smooth transition between principals and fostering consensus among stakeholders, including employees and financial providers, are critical factors. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:jseres:cj125 |
| By: | Shuide Wen; Beier Ku |
| Abstract: | Building on the Agentic ROI framework proposed by Liu et al. (2026), this paper introduces knowledge compounding as a new measurable concept in the empirical economics of LLM agents and validates it through a controlled four-query experiment on Qing Claw, an industrial-grade C# reimplementation of the OpenClaw multi-agent framework. Our central theoretical claim is that the cost term in the original Agentic ROI equation contains an unexamined assumption -- that the cost of each task is mutually independent. This assumption holds under the traditional retrieval-augmented generation (RAG) paradigm but breaks down once a persistent, structured knowledge layer is introduced. We propose a dynamic Agentic ROI model in which cost is treated as a time-varying function Cost(t) governed by a knowledge-base coverage rate H(t). Empirical results from four sequential queries on the same domain yield a cumulative token consumption of 47K under the compounding regime versus 305K under a matched RAG baseline -- a savings of 84.6%. Calibrated 30-day projections indicate cumulative savings of 53.7% under medium topic concentration and 81.3% under high concentration, with the gap widening monotonically over time. We further identify three microeconomic mechanisms underlying the compounding effect: (i) one-time INGEST amortized over N retrievals, (ii) auto-feedback of high-value answers into synthesis pages, and (iii) write-back of external search results into entity pages. The theoretical contribution of this paper is a recategorization of LLM tokens from consumables to capital goods, shifting the economic discussion from static marginal cost analysis to dynamic capital accumulation. The engineering contribution is a minimal reproducible implementation in approximately 200 lines of C#, which we believe is the first complete industrial-grade reference implementation of Karpathy's (2026) LLM Wiki paradigm. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.11243 |