Definition and dimensions of data literacy: which dimensions are essential for business managers?

Sakineh Sajoodi (), Elmira Azizi Norouzabadi () and Mohammad Sabouri ()
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Sakineh Sajoodi: Faculty Member of Economics Department of University of Tabriz
Elmira Azizi Norouzabadi: University of Tabriz
Mohammad Sabouri: University of Tabriz

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 5, No 14, 4222 pages

Abstract: Abstract With the increasing digitization of business processes and the proliferation of big data, organizations must enhance their data literacy to remain competitive. Data literacy, defined as the ability to effectively interpret and utilize data, has become a critical competency for business managers. Despite its importance, limited research has systematically identified the essential dimensions of data literacy in managerial contexts. This study aims to address this gap by defining and validating key dimensions of data literacy for business managers. The research was conducted in three phases. First, a comprehensive literature review identified 25 potential dimensions of data literacy. In the second phase, the Delphi method was employed to refine these dimensions through expert evaluation, resulting in 11 validated dimensions. Finally, a survey was distributed among small and medium-sized enterprise (SME) managers to assess the reliability and validity of the proposed framework. Through confirmatory factor analysis, 10 core dimensions were identified as essential for assessing managerial data literacy. The findings contribute to the literature by providing an empirically validated framework tailored to business managers, distinguishing this study from prior research that primarily focused on general or technical data literacy. These insights offer practical implications for organizations aiming to enhance data-driven decision-making and managerial competency in an increasingly data-intensive business environment.

Keywords: Data literacy; Business managers; Data culture; Data understanding; Data interpretation; Decision making based on data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02172-0

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