Abstract
The purpose of this paper is to provide a theory-based explanation for the generation of competitive advantage from Analytics and to examine this explanation with evidence from confirmatory case studies. A theoretical argumentation for achieving sustainable competitive advantage from knowledge unfolding in the knowledge-based view forms the foundation for this explanation. Literature about the process of Analytics initiatives, surrounding factors, and conditions, and benefits from Analytics are mapped onto the knowledge-based view to derive propositions. Eight confirmatory case studies of organizations mature in Analytics were collected, focused on Logistics and Supply Chain Management. A theoretical framework explaining the creation of competitive advantage from Analytics is derived and presented with an extensive description and rationale. This highlights various aspects outside of the analytical methods contributing to impactful and successful Analytics initiatives. Thereby, the relevance of a problem focus and iterative solving of the problem, especially with incorporation of user feedback, is justified and compared to other approaches. Regarding expertise, the advantage of cross-functional teams over data scientist centric initiatives is discussed, as well as modes and reasons of incorporating external expertise. Regarding the deployment of Analytics solutions, the importance of consumability, users assuming responsibility of incorporating solutions into their processes, and an innovation promoting culture (as opposed to a data-driven culture) are described and rationalized. Further, this study presents a practical manifestation of the knowledge-based view.
Highlights
The use of Analytics is increasing across industries
This section represents the explanation of generating competitive advantage from Analytics
Interviewees clearly emphasized that users should not recalculate the results provided by Analytics solutions
Summary
The use of Analytics is increasing across industries. It is fueled by trending concepts like big data and data science, innovative technologies such as distributed computing and in-memory databases, as well as the rapid increase of data available for processing. Another study goes as far as to suggest that data-empowered organizations may threaten the market survival of companies not using these approaches (Capgemini 2015). Holsapple et al (2014) investigated a plethora of definitions of Analytics, including the definition by Davenport and Harris (2007) who initiated the broader recognition of Analytics with their famous book. Analytic initiatives are diverse and have to fit with people, processes, and tasks to enable their benefits (Ghasemaghaei et al 2017), demanding investigation of which practices and conditions lead to generation of competitive advantage from Analytics
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