Big data analytics in innovation processes: which forms of dynamic capabilities should be developed and how to embrace digitization?

Abstract

Purpose: The purpose of this paper is to analyze, from a dynamic capabilities perspective, the role of big data analytics in supporting firms' innovation processes. Design/methodology/approach: Relevant literature is reviewed and critically assessed. An interpretive methodology is used to analyze empirical data from interviews of big data analytics experts at firms within digitally related sectors. Findings: This study shows how firms leverage big data to gain “richer” and “deeper” data at the inter-sections between the digital and physical worlds. The authors provide evidence for the importance of counterintuitive strategies aimed at developing innovative products, services or solutions with characteristics that may initially diverge, even significantly, from established customer/user needs. Practical implications: The authors’ findings offer insights to help practitioners manage innovation processes in the physical world while taking investments in big data analytics into account. Originality/value: The authors provide insights into the evolution of scholarly research on innovation directed toward opportunities to create a competitive advantage by offering new products, services or solutions diverging, even significantly, from established customer demand.