Data-driven innovation processes within federated networks

PurposeWithin digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is cre...

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Bibliographic Details
Published in:European journal of innovation management 2022-12, Vol.25 (6), p.498-526
Main Authors: Rizk, Aya, Ståhlbröst, Anna, Elragal, Ahmed
Format: Article
Language:English
Subjects:
analytics
case studies
Data acquisition
Data analysis
data analytics
data science
data-driven innovation
Diffusion
Digital data
Digital technology
Empirical analysis
Information systems
Informationssystem
innovation process
Innovations
Knowledge
Networks
Pattern analysis
Pattern matching
smart cities
Technological change
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Summary:PurposeWithin digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.Design/methodology/approachA multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.FindingsEvidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.Originality/valueThe paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.
ISSN:1460-1060
1758-7115
1758-7115
DOI:10.1108/EJIM-05-2020-0190