Purpose-This paper aims to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study. Design/methodology/approach-To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with thirty-five features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study's sample. Findings-The taxonomy derived from our research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. Practical implications-A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox. Originality/value-This paper develops a basis for understanding how start-ups build business models to capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.
The importance of innovation for firms for gaining competitive advantage has been widely acknowledged. Innovation in services exhibits some particular challenges. In order to support formal service innovation management, several frameworks of capabilities for service innovation have been published in recent years. However, these frameworks often do not support the use of existing information to apply them to a firm's context and to guide managerial decisions. In this paper the authors aim to show that a firm's service innovation capability can be operationally diagnosed with the help of such a framework in a more concrete way, using existing unstructured data. Building on established methods in text mining, the authors are working towards an approach to realise this. The paper outlines the approach and presents the encouraging results from our exploratory study, as well as avenues for further development of the approach and its implementation in a management information system.
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