Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. Issue 10 (3rd October 2016)

• Record Type: Journal Article
• Authors: Hartmann, Philipp Max; Zaki, Mohamed; Feldmann, Niels; Neely, Andy
• Is Part Of: International journal of operations & production management. Volume 36:Issue 10(2016)
• Page Start: 1382
• Page End: 1406
• Publication Date: 2016-10-03
• Subjects: Business model -- Big data -- Data-driven business model -- Start-up business model; Production management -- Periodicals; Business logistics -- Periodicals; 658.5
• DOI: 10.1108/IJOPM-02-2014-0098
• Languages: English
• ISSNs: 0144-3577

Abstract

Abstract : Purpose: The purpose of this paper is 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 35 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 the 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 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.