The Need for Absorptive Capacity Alleviates the Free-Rider Problem in Knowledge Production
Jerker Denrell et al.
What the paper says
Knowledge sharing is central to strategy and organizational learning, yet its effect on knowledge production remains underexplored. When knowledge diffuses too easily, individuals may free ride on others’ costly knowledge production, creating a suboptimal equilibrium in which knowledge sharing persists but the average payoff is no greater than if everyone produced knowledge independently—the so-called Rogers’ paradox. We develop a game-theoretic model to re-examine this puzzle. In our baseline model, we reproduce Rogers’ paradox; frictionless sharing does not increase performance beyond individual knowledge production alone. We then extend the model to incorporate absorptive capacity—the need for prior investment in one’s own knowledge before learning from others. Absorptive capacity discourages pure free riding. Counterintuitively, although it introduces frictions in knowledge sharing, absorptive capacity increases collective knowledge production beyond the level attainable through individual knowledge production alone. This explains why extensive knowledge sharing in domains such as academia does not erode incentives for knowledge production. It also contributes to knowledge-based theories of the firm by showing that although hierarchical control may be crucial in contexts where knowledge is simple and codifiable, it may not be necessary where knowledge is complex and tacit. More broadly, our analysis illustrates how formal modeling can uncover overlooked mechanisms and integrate insights across cultural evolution, organizational learning, and strategy. This paper was accepted by Alfonso Gambardella, business strategy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024.06471 .
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.