Data-Driven Business Model Innovation: Bibliometric Analysis of Past and New Research

Objectives: Business model innovation (BMI) is becoming increasingly important for companies due to digitalization, digital technologies, advances in analytics, and an increasing amount of available data, which is putting pressure on incumbents to reconfigure their current BMs and on startups to design new BMs. In data-driven business model innovation (DDBMI), data-driven tools are either used to support the innovation process or to transform an existing BM into a new, data-driven one. There are different approaches to DDBMI that make an integration necessary. Our objective is to systematize the knowledge or intellectual structure of DDBMI, especially the research field on which DDBMI is built as well as the identification of current trends in research.
Prior Work: Previous research has outlined that data-driven approaches—despite potential disadvantages—can add value to companies and customers by helping to improve customer channels and relationships, optimize cross-organizational processes and value networks, establish value creation mechanisms, and thus create competitive advantages and successful BMs. Nevertheless, research has paid little attention to the application of data-driven tools in BMI, and a user-centric, comprehensive, and integrated toolbox for DDBMI is lacking.
Approach: The research questions are answered by a systematic literature review (SLR) on DDBMI that builds upon a preceding SLR by Fruhwirth et al. (2020). To advance the state of research and look at DDBMI beyond the perspective of its supporting methods and tools, we use bibliometric analysis (co-citation analysis and bibliographic coupling) to cluster large amounts of data. The resulting clusters are interpreted through qualitative content analysis.
Results: The co-citation analysis (based on cited references) reveals the intellectual structure of the research field of DDBMI and its underlying themes. We identified four clusters related to previous research cited by the sample: (1) Conceptual works on BMs and BMI, (2) servitization, IoT, and Industry 4.0, (3) digital transformation triggered by disruptive technologies, and (4) general literature on platforms. Using bibliographic coupling (based on documents), we form thematic clusters and identify intellectual structures based on recent publications on the emerging subject of DDBMI. This resulted in four clusters that signal current and emerging trends: (1) Technical works on Industry 4.0 technologies, servitization, and “smart” developments, (2) digital manufacturing, ecosystems, the circular economy, and sustainability, (3) platforms, marketplaces, and the sharing economy, and (4) digital transformation, platform BMs, and key capabilities for BMI.
Implications and Value: First, this work visualizes and systematizes through a bibliometric analysis the theoretical approaches on which DDBMI is built (cf. co-citation analysis) and the intellectual structures of current DDBMI research (cf. bibliographic coupling). Second, comparing the results of these two analyses identifies established (e.g., servitization, platformization, digital transformation) and emerging themes (e.g., ecosystems, sharing/circular economy, digital manufacturing). Third, existing research gaps become apparent: Topics related to DDBMI (e.g., circular economy, sustainability) have not been comprehensively addressed. Our research shows that digital technologies and tools contribute to the successful innovation of BMs toward DDBMs, but more research is needed on how digital technologies can support sustainable BMI.