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Digital Transformation Toward Data-Driven Decision-Making: Theorizing Action Strategies in Response to Transformation Challenges

Sune Dueholm Müller et al.

Communications of the Association for Information Systems2025https://doi.org/10.17705/1cais.05637article
AJG 2ABDC A
Weight
0.37

What the paper says

Organizations are increasingly trying to take advantage of opportunities for digital transformation. However, business leaders may be overwhelmed by the technical, organizational, and societal challenges that come with these opportunities. As a result, business leaders face a high level of uncertainty about how to strategize and manage digital transformation. This article investigates and theorizes how business leaders overcome the challenges of digital transformation toward data-driven decision-making. Based on an in-depth, qualitative case study and interviews with the leadership team of Smukfest, one of the largest festivals in Denmark, we show that (1) business leaders face several digital transformation challenges, including fear of surveillance, balancing intuition and objectivity, and knowing how to leverage data-driven decision-making; and that (2) they can manage these challenges through mitigating actions which include developing digital competencies, storytelling to communicate digital transformation potential, and motivating data use. We synthesize these findings and theorize the Executive Action Strategies of Engagement (EASE) framework, which provides a new perspective on digital transformation management grounded in empirical observations. The framework guides practitioners by clarifying the roles of business leaders in digital transformation toward data-driven decision-making.

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https://doi.org/https://doi.org/10.17705/1cais.05637

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@article{sune2025,
  title        = {{Digital Transformation Toward Data-Driven Decision-Making: Theorizing Action Strategies in Response to Transformation Challenges}},
  author       = {Sune Dueholm Müller et al.},
  journal      = {Communications of the Association for Information Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.17705/1cais.05637},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.