Inter‐platform ecosystems
Bruno Carballa‐Smichowski et al.
Abstract
We extend ecosystem theory to cases in which platforms are complementors to each other: inter‐platform ecosystems. Analyzing web traffic data on 241 European platforms, we identify and characterize demand‐side inter‐platform ecosystems, and propose a theory of why they emerge. We posit that demand‐side inter‐platform ecosystems solve matching problems generated by externalities platforms impose on each other. We describe four strategies platforms implement to solve these problems: network fusion (hosting competitor content), user‐community‐driven interactions (facilitating cross‐posting), meta‐platform (aggregating another platform), and platform concatenation (referring users to another platform for customized complementary services). We link these strategies to the nature of the externalities, the types of platforms involved and their competitive relationship. We conclude with implications for theory and suggestions for further research. Managerial Summary Platforms increasingly act as complementors to each other, creating “inter‐platform ecosystems” to boost cross‐platform interactions and reduce search costs. Using web traffic data on 241 European platforms, we identify four strategies they use to that end: network fusion (hosting competitor content), user‐community‐driven interactions (facilitating cross‐posting), meta‐platform (aggregating another platform), and platform concatenation (referring users to another platform for customized complementary services). These strategies pose three main management challenges. First, because no single platform necessarily orchestrates the ecosystem, platforms must navigate complex multi‐party coordination through bilateral agreements. Second, platforms face coopetition tensions: cooperating multiplies interactions but increases disintermediation risk. Third, pricing structures must account for cross‐platform interdependencies, where a platform's value depends on how its users interact with other platforms’.
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.