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on Knowledge Management and Knowledge Economy |
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Issue of 2025–09–22
three papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Bijan Aghdasi; Abhijit Tagade |
| Abstract: | Do markets price knowledge spillovers? We show that patent grants influence the stock returns of firms that are connected through technological knowledge dependencies. Using directed patent citations among publicly listed companies in the United States, we construct a granular measure of each firm's exposure to new patents granted to its technologically upstream firms. Patents granted to these upstream companies significantly boost its abnormal stock returns during the week of the grant. We find that these financial spillovers are predominantly localized within a firm's immediate technological connections. Additionally, we provide a novel empirical decomposition of financial spillovers generated from patent grants, by distinguishing those spillovers emerging from sources of technological knowledge, from those emerging from product market rivals (negative effect) and suppliers (positive effect). Our findings are robust to alternative specifications and placebo tests, and they suggest that technological knowledge spillovers create important market-priced ties between firms that are not fully captured by traditional product market relationships. |
| Keywords: | innovation, networks, spillovers, patents, stock returns, supply chains |
| Date: | 2025–08–13 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2117 |
| By: | Benjamin Davies; Anirudh Sankar |
| Abstract: | We formalize what it means to have conceptual knowledge about a statistical decision-making environment. Such knowledge tells agents about the structural relationships among unknown, payoff-relevant states. It allows agents to represent states as combinations of features. Conceptual knowledge is more valuable when states are more "reducible": when their prior variances are explained by fewer features. Its value is non-monotone in the quantity and quality of available data, and vanishes with infinite data. Agents with deeper knowledge can attain the same welfare with less data. This is especially true when states are highly reducible. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.09170 |
| By: | Drydakis, Nick |
| Abstract: | The study evaluates the effectiveness of a 12-week AI module delivered to non-STEM university students in England, aimed at building students' AI Capital, encompassing AI-related knowledge, skills, and capabilities. An integral part of the process involved the development and validation of the AI Capital of Students scale, used to measure AI Capital before and after the educational intervention. The module was delivered on four occasions to final-year students between 2023 and 2024, with follow-up data collected on students' employment status. The findings indicate that AI learning enhances students' AI Capital across all three dimensions. Moreover, AI Capital is positively associated with academic performance in AI-related coursework. However, disparities persist. Although all demographic groups experienced progress, male students, White students, and those with stronger backgrounds in mathematics and empirical methods achieved higher levels of AI Capital and academic success. Furthermore, enhanced AI Capital is associated with higher employment rates six months after graduation. To provide a theoretical foundation for this pedagogical intervention, the study introduces and validates the AI Learning-Capital-Employment Transition model, which conceptualises the pathway from structured AI education to the development of AI Capital and, in turn, to improved employment outcomes. The model integrates pedagogical, empirical and equity-centred perspectives, offering a practical framework for curriculum design and digital inclusion. The study highlights the importance of targeted interventions, inclusive pedagogy, and the integration of AI across curricula, with support tailored to students' prior academic experience. |
| Keywords: | Artificial Intelligence, AI literacy, AI Capital, University students, Grades, Academic performance, Employment rates |
| JEL: | I23 I21 J24 J21 O33 O15 I24 J15 J16 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1668 |