Abstract
PurposeThe 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/approachRelevant 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.FindingsThis 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 implicationsThe authors’ findings offer insights to help practitioners manage innovation processes in the physical world while taking investments in big data analytics into account.Originality/valueThe 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.
Subject
Management of Technology and Innovation
Reference120 articles.
1. The digitalization of the innovation process: challenges and opportunities from a management perspective;European Journal of Innovation Management,2019
2. A knowledge management and sharing business model for dealing with disruption: the case of Aramex;Journal of Business Research,2019
3. Business models and technological innovation;Long Range Planning,2013
4. Customer engagement mechanisms: strategies for value creation and value capture;Academy of Management Proceedings,2018
5. Balusamy, B., Jha, P., Arasi, T. and Velu, M. (2017), “Predictive analysis for digital marketing using big data: big data for predictive analysis”, in Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence, IGI Global, pp. 259-283.
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