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on Economics of Strategic Management |
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Issue of 2026–05–04
four papers chosen by João José de Matos Ferreira, Universidade da Beira Interior |
| By: | Schilirò, Daniele |
| Abstract: | This paper offers a conceptual review of the relationship between the knowledge economy and innovation, challenging simplistic, linear assumptions about how new ideas are generated. Given their increasing global significance, the study focuses on Fourth Industrial Revolution (4IR) technologies. Specifically it examines the nature, evolution, and defining features of the knowledge economy. Such an economy relies on increasing specialization, research, innovation, and continuous learning, with learning and experience being its most critical sources. Furthermore, the paper argues that innovation constitutes a fundamental dimension of the knowledge economy, noting that knowledge production is strictly related to innovations. Rather than a sequential chain, innovation is presented here as a complex, systemic process characterized by multiple feedbacks and loops. This analysis highlights the systemic, non-deterministic nature of innovation and its strong relationship with knowledge. However, the capacity of companies to innovate depends heavily on the innovation ecosystem—the framework where stakeholders interact and collaborate—as well as the regulatory and legislative framework. Consequently, several factors, including the availability of sufficient human capital with appropriate education and advanced skills, the presence of robust infrastructure, and the role of institutions, are necessary to make companies' innovations effective. While this paper does not claim to provide definitive answers, it seeks to offer new insights for future research. |
| Keywords: | knowledge economy; knowledge; learning; networks; innovation; technological progress; competitiveness |
| JEL: | D83 L10 O30 O32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128049 |
| By: | Felipe Aguilar; Roberto Alvarez |
| Abstract: | In this paper, we use data for more than 5, 000 Chilean companies to investigate whether participation in business association increases the probability of R&D investment. Dealing with the endogeneity of participation through a bivariate Probit model with an exclusion variable that captures the trust environment among firms, we find that this probability increases by about 27%. This effect is heterogeneous across firms. Participation increases the probability of R&D investment by 30.8% for SMEs and by 43.9% for those companies with severe financial constraints. Our evidence is consistent with the idea that associativity may help SMEs to close the innovation gap and/or to alleviate financial problems. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1064 |
| By: | Thomas Cornelissen; Christian Dustmann; Uta Schönberg |
| Abstract: | Exposure to better peers in the workplace can influence career trajectories through two opposing channels: positively, via knowledge spillovers, and negatively, through competition for advancement. We disentangle these effects by studying untrained labor market entrants and distinguishing between coworkers in the same occupation with whom they are likely to compete versus those with whom they are unlikely to compete. We find robust evidence of persistent knowledge spillovers but also identify countervailing competition effects of comparable magnitude. Both effects are more pronounced for men than for women. |
| Keywords: | Knowledge Spillovers, Peer Effects, Competition |
| JEL: | J13 J24 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:25080 |
| By: | Giuseppe Pulito; Mariola Pytlikova; Sarah Schroeder; Magnus Lodefalk |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cer:papers:wp818 |