Supporting Data-Driven Business Model Innovations

Michael Fruhwirth · Christiana Ropposch · Viktoria Pammer‐Schindler

28 citations0/0 files storedJournal of Business Models

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Digital Innovation in Industries Business Strategies and Innovation Big Data and Business Intelligence

Abstract

Purpose: This paper synthesizes existing research on tools and methods that support data-driven business model innovation, and maps out relevant directions for future research. Design/methodology/approach: We have carried out a structured literature review and collected and analysed a respectable but not excessively large number of 33 publications, due to the comparatively emergent nature of the field. Findings: Current literature on supporting data-driven business model innovation differs in the types of contribution (taxonomies, patterns, visual tools, methods, IT tool and processes), the types of thinking supported (divergent and convergent) and the elements of the business models that are addressed by the research (value creation, value capturing and value proposition). Research limitations/implications: Our review highlights the following as relevant directions for future research. Firstly, most research focusses on supporting divergent thinking, i.e. ideation. However, convergent thinking, i.e. evaluating, prioritizing, and deciding, is also necessary. Secondly, the complete procedure of developing data-driven business models and also the development on chains of tools related to this have been under-investigated. Thirdly, scarcely any IT tools specifically support the development of data-driven business models. These avenues also highlight the necessity to integrate between research on specifics of data in business model innovation, on innovation management, information systems and business analytics. Originality/value: This paper is the first to synthesize the literature on how to identify and develop data-driven business models, and to map out (interdisciplinary) research directions for the community. Keywords: Business model innovation, data-driven business models, research agenda. Article classification: Literature review

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References

2002Analyzing the Past to Prepare for the Future: Writing a Literature Review 2010Business Model Generation : A Handbook for Visionaries, Game Changers, and Challengers 2002The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies 2002Why business models matter.2004The business model ontology a proposition in a design science approach 2007The fit between product market strategy and business model: implications for firm performance 2015Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda 2017Debating big data: A literature review on realizing value from big data

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