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on Knowledge Management and Knowledge Economy |
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Issue of 2026–01–19
five papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Yueyuan Ma |
| Abstract: | Using firm-level data from the US Census Longitudinal Business Database (LBD), this paper exhibits novel evidence about a wave of specialization experienced by US firms in the 1980s and 1990s. Specifically: (i) Firms, especially innovating ones, decreased production scope, i.e., the number of industries in which they produce. (ii) Innovation and production separated, with small firms specializing in innovation and large firms in production. Higher patent trading efficiency and stronger patent protection are proposed to explain these phenomena. An endogenous growth model is developed with potential mismatches between innovation and production. Calibrating the model suggests that increased trading efficiency and better patent protection can explain 20% of the observed production scope decrease and 108% of the innovation and production separation. They result in a 0.64 percent point increase in the annual economic growth rate. Empirical analyses provide evidence of causality from pro-patent reforms in the 1980s to the two specialization patterns. |
| Keywords: | specialization, production scope, R&D, intellectual property rights, patent trade, endogenous growth |
| JEL: | E23 L22 O32 O34 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-77 |
| By: | Anna Nesvijevskaia (ISI 4C - HEG - Haute Ecole de Gestion de Genève, HEG - Haute Ecole de Gestion de Genève) |
| Abstract: | This paper explores the perpetuation of practitioners' tacit knowledge in the context of projects aimed at designing Artificial Intelligence (AI) uses in organizations. By comparing an interdisciplinary review of the state of the art on tacit knowledge with an observational field study of 7 application cases in France and Switzerland, this article sheds light on the dynamics of capturing practitioners' tacit knowledge during the design and operation of AI models and highlights three areas for consideration: (1) the emergence of new devices for translating practitioners' know-how into data models and capturing tacit knowledge through the maieutic carried out in the design phase, (2) the difficulty of taking unconscious tacit knowledge into account when judging AI in use, revealing issues of interpretability, cognitive bias and trust, and (3) the capture of knowledge, including tacit knowledge, as the primary goal of Data Science projects. But this capture may not be desired by the practitioners or even introduce an intermediation that prevents the development of further tacit knowledge derived from real-life experience in favour of that linked to the use of AI. These considerations lead to the improvement of tacit knowledge perpetuation devices, as long as their legitimacy is justified, and the risks are mitigated. |
| Abstract: | Cet article explore la pérennisation des savoirs tacites des acteurs métier dans le cadre des projets visant la conception d'usages d'Intelligence Artificielle (IA) dans les organisations. À travers la confrontation entre un état de l'art interdisciplinaire sur les savoirs tacites et un terrain d'observation de 7 cas d'application en France et en Suisse, cet article met en lumière les dynamiques de capture des savoirs tacites des acteurs métier lors de la conception et de l'exploitation des modèles IA et révèle trois pistes de réflexion : (1) l'émergence de nouveaux dispositifs de traduction des connaissances métier en modèles de données et de capture de savoirs tacites à travers la maïeutique réalisée en phase de conception, (2) la difficulté à tenir compte des savoirs tacites inconscients dans l'évaluation de l'IA à l'usage, révélant des enjeux d'interprétabilité, de biais cognitifs et de confiance, et (3) la capture des savoirs, y compris tacites, comme finalité première de projets de science de données au service de leur pérennisation. Mais cette capture peut ne pas être souhaitée par les acteurs métier, voire introduire une intermédiation empêchant le développement ultérieur de leurs savoirs tacites issus de l'expérience du réel au profit de ceux liés à l'usage de l'IA. Ces pistes mènent au perfectionnement des dispositifs de pérennisation de savoirs tacites, à condition de justifier leur légitimité et de maitriser des risques de dérives. |
| Keywords: | Information Behaviour, Trusted AI, Tacit Knowledge, Knowledge Management, Data Science Project, Artificial Intelligence, Interdisciplinarity, Skills, Human Resources, Comportement Informationnel, IA de Confiance, Ressources Humaines, Compétences, Interdisciplinarité, Intelligence Artificielle, Science de données, Gestion des connaissances, Savoirs tacites |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05413113 |
| By: | Chen, Luoye; Hou, Yun; Xiong, Xueshan |
| Abstract: | We empirically investigate the impact of migration flows induced by the hukou reform on agricultural innovation in terms of quantity and quality. Utilizing the 2014 hukou reform in China as a policy shock, we observe a 23.1% decrease in agricultural patent counts, with no significant effect on disruptiveness. This decline is primarily concentrated in urban areas and is reflected in a reduction in the extensive margin, specifically the number of active innovators. The decrease can be attributed to two interrelated mechanisms: the loss of skilled agricultural workers who possess critical tacit knowledge and a diminished entry of agribusiness due to resource reallocation. The findings highlight the unintended consequences of institutional policies, suggesting that urbanization initiatives may inadvertently impede agricultural technological progress when human capital externalities are insufficiently addressed. |
| Keywords: | Productivity Analysis, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea25:361180 |
| By: | Bishnu, Monisankar; Chingri, Subhrasil; Mondal, Debasis; Prettner, Klaus |
| Abstract: | This paper examines the role of automation in shaping gender inequality among high-skilled and low- skilled workers in the United States. We develop an R&D-based growth model of automation in which we endogenize disparities between men and women and between high-skilled and low-skilled labor through education choices. Automation substitutes for routine, brawn-intensive tasks, while it complements high-skilled, brain-intensive ones. Our framework predicts that automation increases demand for high-skilled workers, raising female participation in knowledge production but also widening within-gender and between-skills inequality. Redistributive transfers to low-skilled workers, financed through robot taxation, reduce high-skilled employment, lower innovation, and slow down economic growth despite compressing within-gender and between-skills inequality. Education subsidies expand the share of skilled workers and foster innovation but come at the cost of greater within-gender and between-skills inequality. Subsidies targeted on women reduce between- gender inequality, but they can raise within-gender inequality and slow down economic growth. Finally, in the case of the presence of norms and institutions that are detrimental to gender equality, female empowerment can reduce inequality and raise economic growth at the same time. |
| Keywords: | Automation; Economic Growth; Education; Gender wage gap; Inequality |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:wiw:wus005:80526059 |
| By: | Zaira García-Tórtola (Department of Economics, Universitat Jaume I, Castellón, Spain); David Conesa (Department of Statistics and Operational Research, Universidad de Valencia, Spain); Joan Crespo (Department of Economic Structure, Universidad de Valencia, Spain); Emili Tortosa-Ausina (IVIE, Valencia and IIDL and Department of Economics, Universitat Jaume I, Castellón, Spain) |
| Abstract: | University rankings predominantly focus on outputs while neglecting the efficiency with which institutions convert resources into outcomes. We contribute to addressing this limitation by analyzing the determinants of university efficiency using a Bayesian stochastic ray frontier model applied to 47 Spanish public universities over the 2016–2021 period. Unlike traditional approaches, our methodology jointly estimates efficiency and its determinants in a single stage. We adopt a multi-output framework encompassing the three university missions: teaching, research, and knowledge transfer. Using backward stepwise selection with the deviance information criterion, we identify key efficiency determinants including the average department size, number of campuses, academic staff characteristics, and multi-province location. Results reveal substantial efficiency variations across universities, with approximately half showing positive efficiency changes over the period. The Bayesian approach provides full efficiency distributions rather than point estimates, enabling robust statistical comparisons. Our findings offer valuable insights for university managers and policymakers seeking to enhance institutional performance beyond traditional output-based rankings. |
| Keywords: | determinants, efficiency, Bayesian, education, stochastic frontier, universities |
| JEL: | C61 J24 R11 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:jau:wpaper:2026/02 |