Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management

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.

An examination of the organizational impact of business intelligence and big data based on management theory

“…Hazen et al (2016) highlight that institutional theory can explain how external pressures affect the decision to adopt BD for a sustainable supply chain. They further argue that a knowledge-based view highlights the importance of data quality for predictive BDA in supply chain management (Hazen, Skipper, Ezell, & Boone, 2016;Herden, 2020). In the same vein, they argue that resource dependence theory can explain the adoption of BD in supply chain management (Hazen, Skipper, Ezell, & Boone, 2016).…”

“…BI and BD related studies include Braganza, Brooks, Nepelski, Ali, and Moro, 2017, Du, Huang, Yeung, & Jian, 2016, and Fiorini, Seles, Jabbour, Mariano, Jabbour, 2018 17. Knowledge-based view states that knowledge and related intangibles are sources to competitive advantages (Gupta, & George, 2016;Herden, 2020). BI and BD related studies include Côrte-Real, Oliveira, & Ruivo, 2017, Erickson & Rothberg, 2017, Fiorini, Seles, Jabbour, Mariano, Jabbour, 2018, Hazen, Skipper, Ezell, & Boone, 2016, Herden, 2020, and Gupta, & George, 2016 18.…”

“…Knowledge-based view explains how BI and BD can be considered a source of competitive advantage, thus enhances a firm's performance (Fiorini, Seles, Jabbour, Mariano, Jabbour, 2018;Herden, 2020). Furthermore, they observed that organizational information processing view considers BDA as important information processing mechanisms for organizations (Fiorini, Seles, Jabbour, Mariano, Jabbour, 2018).…”

On the current state of combining human and artificial intelligence for strategic organizational decision making

“…As stated in the introduction, for the purpose of this article, strategic organizational decision making is defined as group decision making under uncertainty, as groups are the established type of such decisions in organizations (Rousseau 2018). Heterogeneous groups have been found to make better decisions than homogeneous ones, as information diversity, discussion, and experience lead to improved interpretation, thereby decreasing bounded rationality (Beckmann and Haunschild 2002;Charness and Sutter 2012;Kouchaki et al 2015;Rousseau 2018;Herden 2019). However, whether groups help to reduce bias (Kouchaki et al 2015;Rousseau 2018) or can also introduce it into a decision is not agreed (Marquis and Reitz 1969;Charness and Sutter 2012).…”

“…At the same time, the reliability, validity, consistency, and relevance of information sources must be analyzed. While this can be facilitated when more people are involved in the decision-making process, researchers have also found that using technology that is able to process large amounts of data can have a supportive effect (Long 2017;Herden 2019). Several researchers on group decision making thus call for more exploration of the use of group communication and information systems (Charness and Sutter 2012;Kugler et al 2012), including the effect that computer programs can have to help with structuring decisions (Schwenk and Valacich 1994).…”