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From intuitive to data-driven decision-making in digital transformation : a framework of prevalent managerial archetypes

  • The use of analytics in corporate decision-making processes demands a paradigm shift within companies, and particularly among their top executives. Corporate leaders represent a major lever for this change. Therefore, a deeper understanding of their managerial capabilities, characteristics and contribution in this context is required. Aiming to provide actionable guidance on how to manage the shift to data-driven decision making, this study helps to develop a more profound understanding of this emerging managerial role by examining managerial success factors following a semi-structured interview approach. With insights from interviews with 32 top executives from Germany across different industries, this paper research proposes four managerial archetypes that are relevant to mastering the digital transformation towards analytics-based decision-making processes. Furthermore, it sheds light on the characteristics, capabilities, and contributions of the four archetypes—Analytical Thinker, Coach, Guide, and Strategist. Although the archetypes have differentiated attributes and qualities, all four seem of importance in manifesting analytics in organizations. Our findings provide guidelines to assess the top management's abilities to manage digital transformation projects. Furthermore, the results serve as basis for future empirical research on the human aspect of analytical capabilities regarding leadership.

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Document Type:Article
Author:Philipp KorherrORCiD, Dominik K. KanbachORCiD, Sascha KrausORCiD, Patrick MikalefORCiD
Chairs and Professorships:Chair of Strategic Entrepreneurship
Parent Title (English):Digital Business
Date of Publication (online):2022/10/05
Article Number:100045
Date of first Publication:2022/10/05
Tag:Analytics; Decision-making; Digital Transformation; Management research; Top management
Content Focus:Academic Audience
Peer Reviewed:Yes
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International