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on Knowledge Management and Knowledge Economy |
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Issue of 2026–10–05
three papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Seula Kim; Jane Olmstead-Rumsey; Honghao Wang |
| Abstract: | Teams of inventors now produce over 80 percent of U.S. patents, up from just 45 percent in 1976. Over the same period, inventor employment has become increasingly concentrated in large firms, rising six times more than overall employment concentration. To explain these trends, we develop a new model of endogenous team formation within firms and embed it in a general equilibrium model of the inventor labor market. Teams increase firms’ returns to scale by increasing the likelihood of productive matches between inventors. The value of these matches rises over time, possibly driven by increasing knowledge specialization. This causes large firms to expand and small firms to shrink in equilibrium, accounting for the observed increase in inventor employment concentration. Because firms do not internalize knowledge spillovers, there is misallocation of inventors across firms. The rise of team production amplifies this misallocation by a factor of six by reallocating inventors away from small firms that generate disproportionately large knowledge spillovers. |
| Keywords: | innovation; R&D teams; inventor allocation; firm dynamics; economic growth |
| JEL: | E23 E24 O31 O32 O33 |
| Date: | 2026–09–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:103815 |
| By: | Esteve Almirall; Christopher Tucci |
| Abstract: | Generative artificial intelligence illustrates a broader organizational puzzle: young, resource-constrained firms can build on knowledge produced across a wider field to address problems that exceed their internal experience. We theorize one mechanism as social imitation: a distributed and recursive process through which communities observe practices and outcomes across organizations, distill recurrent elements into portable strategies, circulate them, and revise the collective repertoire as adoption produces new evidence. This perspective endogenizes the object of imitation. Organizations do not merely copy practices; communities collectively produce what becomes available to copy. We distinguish the source of a candidate (directly observed organizations or a collective) from its mode of adoption (blind or informed), and examine four forms of imitation in an agent-based model of organizational search. Collective sourcing alone does not improve performance. Its value depends on situated evaluation: assessing whether a candidate fits the adopter's configuration. As interdependence increases, informed social imitation improves population performance and the best solution discovered. As problems grow, it becomes more likely to outperform informed imitation from one or several directly observed peers because collective distillation sustains a broader candidate repertoire. Most gains arise from evaluating a small candidate set, and population performance can improve when only a minority of organizations possesses evaluative capability. Social imitation thus reveals a division of cognitive labor: communities expand the strategies organizations can consider, while organizations create value by determining which strategies fit. It explains how firms can mobilize knowledge beyond their boundaries, and why widespread access to collective knowledge does not produce equal benefits. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.28675 |
| By: | Claude Chien Hung Liu (Audencia Business School) |
| Abstract: | The purpose of this study is to investigate how organisational learning enhances performance in big data analytics (BDA)–artificial intelligence (AI) environments by integrating institutional theory and the knowledge-based view. Specifically, it examines the influence of competitive pressure and data-driven culture (DDC) on performance through organisational learning (OL), and whether analytics talent capability moderates the learning–performance relationship. Design/methodology/approach This study surveyed 374 Chinese managers and IT leaders involved in BDA–AI across industries. Data were collected via an online platform using a seven-point Likert scale. The research model was tested with PLS-SEM (SmartPLS 4.0) and 5, 000 bootstraps, controlling for firm size, industry type and common method bias. Findings The study finds that a DDC influences organisational performance only indirectly through OL. Competitive pressure (CP) positively impacts OL, which strongly improves performance. Additionally, BDA–AI analytics talent significantly moderates the OL–performance link, indicating that greater talent enhances an organisation's ability to translate learning into better performance outcomes. Research limitations/implications This study reveals that a DDC improves organisational performance only when mediated by OL, providing important insights for institutional theory and the KBV. It challenges the assumption that culture alone drives performance, emphasising the essential role of learning in converting knowledge into strategic value. Additionally, BDA–AI talent significantly moderates the learning–performance relationship, highlighting the critical influence of human capital. Future research should examine the interplay of institutional pressures, learning capabilities and talent in data-intensive contexts. Practical implications Organisations must foster a DDC, invest in analytics talent and support continuous learning to maximise BDA–AI benefits. Data-based decision-making, upskilling and interdisciplinary training are essential. Without skilled talent, learning processes falter. Aligning culture, learning and talent is key to driving innovation, adaptability and sustained performance in data-intensive environments. Originality/value This study offers a novel perspective by integrating institutional theory and the knowledge-based view to examine how external pressures and internal capabilities jointly influence performance in BDA–AI adoption. It uniquely highlights OL as a core mechanism and introduces BDA–AI talent capability as a key moderator. Addressing a gap in existing research, the paper emphasises that technology alone is insufficient – organisational processes and human capital are essential for realising AI's strategic value, offering both theoretical and practical contributions to the field. |
| Keywords: | organisational performance, knowledge-based view (KBV), organisational learning, big data analytics and AI (BDA-AI), New institutional theory big data analytics and AI (BDA-AI) organisational learning knowledge-based view (KBV) organisational performance, Artificial inteligence, Organisational performance, Knowledge-based view (KBV), Organisational learning, Big data analytics, New institutional theory |
| Date: | 2026–01–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05740653 |