Understanding Business Ecosystem Dynamics

TLDR

Business ecosystems are heterogeneous, continuously evolving networks of interconnected entities, yet no well‑established methodology exists to study their dynamics, and traditional approaches that rely on data from established firms overlook the many activities at individual and entrepreneurial levels. The study proposes a data‑driven visualization approach that uses institutionally and socially curated datasets to triangulate explanatory insights into interorganizational network dynamics and introduces novel layouts to help decision makers identify and compare ecosystems. The authors employ data triangulation and visualization techniques using disconnected data sources—deals and alliance relationships, executive and funding relationships, and public opinion and discourse—to illustrate their approach through three mobile industry case studies. The article concludes with implications for practice and outlines future research opportunities.

Abstract

Business ecosystems consist of a heterogeneous and continuously evolving set of entities that are interconnected through a complex, global network of relationships. However, there is no well-established methodology to study the dynamics of this network. Traditional approaches have primarily utilized a single source of data of relatively established firms; however, these approaches ignore the vast number of relevant activities that often occur at the individual and entrepreneurial levels. We argue that a data-driven visualization approach, using both institutionally and socially curated datasets, can provide important complementary, triangulated explanatory insights into the dynamics of interorganizational networks in general and business ecosystems in particular. We develop novel visualization layouts to help decision makers systemically identify and compare ecosystems. Using traditionally disconnected data sources on deals and alliance relationships (DARs), executive and funding relationships (EFRs), and public opinion and discourse (POD), we empirically illustrate our data-driven method of data triangulation and visualization techniques through three cases in the mobile industry Google’s acquisition of Motorola Mobility, the coopetitive relation between Apple and Samsung, and the strategic partnership between Nokia and Microsoft. The article concludes with implications and future research opportunities.