Shaping Ecosystem Rules: Complementarities, Interdependencies, and Firms' Success in Coordinating Ecosystems Via Standard-Setting

• 成果类型: Article; Early Access
• 署名作者: Ranganathan, Ram; Chen, John S.; Ghosh, Anindya
• 署名单位: University of Texas System; University of Texas Austin; Baylor University; Tilburg University
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2022.16136
• 发表日期: 2025
• 关键词: alliances; COMPETITION; ecosystems; networks; platforms; shaping; STANDARDS

摘要

In ecosystems characterized by distributed authority, each firm competes with others to shape the ecosystem's rules to its advantage while navigating complex interorganizational interdependencies. This creates a coordination problem best exemplified within consensus standards-setting organizations, where different firms present technical proposals and attempt to shape the specifications that dictate how the ecosystem's technological components must work together. In this paper, we examine what factors determine a firm's shaping success in such contexts by integrating two major perspectives, one rooted in interfirm social relations and the other that views an ecosystem as a structure of technological interdependence. In so doing, we identify a socio-technical trade-off between the extent of a firm's relational influence and its efforts to shape core technical proposals (i.e., those that affect many interdependent ecosystem components). A firm with many existing relational ties holds influence that increases its success at shaping, but if such a firm's proposals are at the core, some of its partners will invariably face high adjustment costs because of the proposal's wide-ranging impact. Interestingly, a firm with greater prospects for engaging with future complementary partners does not face this trade-off and indeed benefits from shaping at the core. Additionally, value capture considerations also affect how other firms weigh these socio-technical trade-offs, with support for the firm's shaping attempts dampened among potential future complementors but strengthened among current partners. We provide evidence for these arguments by applying machine-learning techniques on fine-grained data from two prominent wireless telecommunications standards. Our work brings a fresh perspective to a growing ecosystems literature by illuminating both social and technological factors that affect firms' shaping success.