Revisiting the construct of data-driven decision making: antecedents, scope, and boundaries

Constant Berkhout (), Abhi Bhattacharya (), Carlos Bauer () and Ross W. Johnson ()
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Constant Berkhout: University of Hasselt
Abhi Bhattacharya: University of Alabama
Carlos Bauer: University of Alabama
Ross W. Johnson: University of North Texas

SN Business & Economics, 2024, vol. 4, issue 10, 1-23

Abstract: Abstract The benefits of engaging in data-driven decision making (DDDM) are well established in prior literature, which largely touts the value of DDDM. However, a comprehensive understanding of the antecedents of its adoption is lacking. Our understanding of what constitutes the “data” of DDDM is also nebulous. Through a qualitative approach using semi-structured interviews with practitioners and an additional empirical study, we investigate what drives DDDM to understand whether and how theorized drivers of DDDM are employed in practice. Our interviews yield additional drivers of DDDM not identified in past literature and generate insights into the boundaries of when and where the effectiveness of DDDM is greatest, as opposed to the use of intuition. Our results indicate that the absence of factors enabling DDDM decreases the ability to engage in DDDM and enhances a firm’s intuition. However, even when data and related capabilities are present, intuitive decision making is likely to be more successful some scenarios. These are when tasks are ambiguous or predictive, when error costs are low, and opportunity costs of inaction are high, when radical changes are sought, and when affect and subjective judgments are critical.

Keywords: Data-driven decision; Intuition; Decision making; Error cost; Project (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s43546-024-00724-4

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