Understanding Business Ecosystem Dynamics

Author:

Basole Rahul C.1,Russell Martha G.2,Huhtamäki Jukka3,Rubens Neil4,Still Kaisa5,Park Hyunwoo6

Affiliation:

1. Georgia Institute of Technology, Atlanta, Georgia, USA

2. Stanford University, Stanford, California, USA

3. Tampere University of Technology, Finland

4. University of Electro-Communications, Tokyo, Japan

5. VTT Technical Centre of Finland, Finland

6. Georgia Institute of Technology, Atlanta, Georgia

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.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Management Information Systems

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4. B. Alsallakh L. Micallef W. Aigner H. Hauser S. Miksch and P. Rodgers. 2014. Visualizing sets and set-typed data: State-of-the-art and future challenges. EuroVis - STARs R. Borgo R. Maciejewski and I. Viola (Eds.). The Eurographics Association. DOI: 10.2312/eurovisstar.20141170

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