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Beyond data : people. Lessons from a data-driven decision making adoption process

Roes, J.M. (2022) Beyond data : people. Lessons from a data-driven decision making adoption process.

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Abstract:This research aims to describe the process of data-driven decision making (DDDM) adoption within the insurance industry. To understand this process, we analyze the adoption of datadriven decision making as an organizational learning process. This organizational learning process can be described as the creation of improvements (i.e., single loop learning) or innovations (i.e., double loop learning) and the creation of norms, rules, and conditions by which these knowledge creation processes may be done best (i.e., deutero loop learning). To describe the organizational learning challenges for this adoption process of a DDDM tool, an in-depth, qualitative case study is conducted. The main method is participant observation, including informal (semi-structured) interviews and conversations with organizational members to follow up and verify our observations. We identify the influence of each organizational learning process for the adoption of the DDDM tool. For each of this learning processes we describe double loop, triple loop, and institutional deutero loop learning processes that must be realized for an effective DDDM adoption. If transparency about the DDDM tool recommendations is not realized during the internalization process, triple loop learning is not possible. In the discussion, we identify the theoretical and practical implications, and we generate further research directions based on the limitations of this research.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Programme:Business Administration MSc (60644)
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