Managing the Dynamics of Platforms and Ecosystems

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    Coopetition Balance and Coopetition Capability in Platform Ecosystems: Complementors’ Perspective
    ( 2020-01-07) Yoo, Dong ; Roh, James ; Cho, Sunyoung ; Yang, Ma Ga
    In a platform ecosystem, complementors can utilize various resources from a platform owner that receives a variety of products/services via complementors for better customers’ choice and satisfaction. The literature has focused on the cooperative nature of the platform ecosystem. Less attention, however, has been given to coopetition (i.e., simultaneous strategic use of cooperation and competition). Drawing upon paradox theory, this study develops a research model that explains the individual and joint impact of coopetition balance and coopetition capability on relationship performance in a platform ecosystem. Based on survey data from 365 complementors to Amazon, this study illustrates that coopetition balance and coopetition capability have a significant impact on relationship performance. Additionally, coopetition capability moderates the relationship between coopetition balance and relationship performance. In particular, results show that coopetition capability is the most critical variable to enhance relationship performance. Theoretical and practical implications are discussed in this paper.
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    Modular Change in Platform Ecosystems and Routine Mirroring in Organizations
    ( 2020-01-07) Seidel, Stefan ; Grisold, Thomas ; Berente, Nicholas
    Organizational routines involve modular digital technologies that are part of larger platform ecosystems that often transcend organizational boundaries. Change in organizational routines is thus interwoven with innovation and associated change in digital platforms. To get at this “embedded” routine change, we use the concept of modular operators to conceptualize how changes to digital technologies in platform ecosystems are mirrored in changes in the organizational routines in which these technologies are implicated. We distinguish between enabling and constraining impacts and develop a set of propositions to move towards a theory of “routine mirroring.” We use the Industrial Internet of Things (IIoT) as a base example.
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    Discovering Business Models of Data Marketplaces
    ( 2020-01-07) Fruhwirth, Michael ; Rachinger, Michael ; Prlja, Emina
    The modern economy relies heavily on data as a resource for advancement and growth. Data marketplaces have gained an increasing amount of attention since they provide possibilities to exchange, trade and access data across organizations. Due to the rapid development of the field, the research on business models of data marketplaces is fragmented. We aimed to address this issue in this article by identifying the dimensions and characteristics of data marketplaces from a business model perspective. Following a rigorous process for taxonomy building, we propose a business model taxonomy for data marketplaces. Using evidence collected from a final sample of twenty data marketplaces, we analyze the frequency of specific characteristics of data marketplaces. In addition, we identify four data marketplace business model archetypes. The findings reveal the impact of the structure of data marketplaces as well as the relevance of anonymity and encryption for identified data marketplace archetypes.
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    On the Heterogeneity of Digital Infrastructure in Entrepreneurial Ecosystems
    ( 2020-01-07) Schulte-Althoff, Matthias ; Schewina, Kai ; Lee, Gene Moo ; Fürstenau, Daniel
    Digital infrastructure represents for startups in entrepreneurial ecosystems an important asset but also a major risk. Drawing on studies about digital entrepreneurship and ecosystems, we examine the determinants of the heterogeneity of startups’ tech stacks in ecosystems. Using publicly available data from the data aggregators Stackshare and Crunchbase, we identify popular endogenous categories in startups’ tech stacks. Then we conduct a visual network analysis and a multivariate regression analysis, utilizing the identified technology categories to measure the heterogeneity of the startups’ tech stacks. The analysis supports the propositions that firm age and increased funding are positively associated with tech stack heterogeneity, whereas funding rounds are negatively associated with tech stack heterogeneity. Implications of our findings on digital entrepreneurship and ecosystems are discussed.
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    Understanding Ecosystem Data
    ( 2020-01-07) Basole, Rahul
    There is a growing body of empirical studies on business ecosystems. Driven by different questions these studies typically employ a wide variety of data sources – ranging from open to proprietary, structured to unstructured – that contain a broad range of entities, relationships, activities, and issues of interest. Individually, these data sources offer the ability to investigate very targeted business ecosystem questions. However, when linked and combined these data sources can potentially open up many new lines of inquiry. The purpose of this study is to provide an overview of the scope and complexity of the business ecosystem data landscape, discuss what type(s) of information is captured in them, identify how data sources overlap and differ, discuss strengths and weaknesses, and suggest new types of analyses that could be generated when combined. In doing so this study aims to help researchers and practitioners with the data identification and selection process and stimulate novel data-driven ecosystem intelligence. The study concludes with theoretical and managerial implications.