|
on Knowledge Management and Knowledge Economy |
|
Issue of 2025–11–10
two papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Schilirò, Daniele |
| Abstract: | This paper presents an examination of the knowledge economy, its nature, evolution, and defining features. Such an economy relies on increasing specialization, research, innovation, and continuous learning. Innovation constitutes a fundamental dimension of the knowledge economy; hence, it emerges as the second central theme of this analysis. The findings indicate that the capacity of companies to innovate depends on several factors, including the availability of sufficient human capital with appropriate levels of education and advanced skills, the presence of robust infrastructure, and the role of institutions. In particular, the innovation ecosystem—where stakeholders interact and collaborate—together with the regulatory and legislative framework, serves to foster and sustain innovation. |
| Keywords: | knowledge economy; knowledge; learning; networks; innovation, technological progress; competitiveness |
| JEL: | D83 L1 O30 O32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126380 |
| By: | Xiaoning Wang; Chun Feng; Tianshu Sun |
| Abstract: | Labor mobility is a critical source of technology acquisition for firms. This paper examines how artificial intelligence (AI) knowledge is disseminated across firms through labor mobility and identifies the organizational conditions that facilitate productive spillovers. Using a comprehensive dataset of over 460 million job records from Revelio Labs (2010 to 2023), we construct an inter-firm mobility network of AI workers among over 16, 000 U.S. companies. Estimating a Cobb Douglas production function, we find that firms benefit substantially from the AI investments of other firms from which they hire AI talents, with productivity spillovers two to three times larger than those associated with traditional IT after accounting for labor scale. Importantly, these spillovers are contingent on organizational context: hiring from flatter and more lean startup method intensive firms generates significant productivity gains, whereas hiring from firms lacking these traits yields little benefit. Mechanism tests indicate that "flat and lean" organizations cultivate more versatile AI generalists who transfer richer knowledge across firms. These findings reveal that AI spillovers differ fundamentally from traditional IT spillovers: while IT spillovers primarily arise from scale and process standardization, AI spillovers critically depend on the experimental and integrative environments in which AI knowledge is produced. Together, these results underscore the importance of considering both labor mobility and organizational context in understanding the full impact of AI-driven productivity spillovers. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02099 |