  Organizational decision-making is crucial for success, yet cognitive biases
can significantly affect risk preferences, leading to suboptimal outcomes. Risk
seeking preferences for losses, driven by biases such as loss aversion, pose
challenges and can result in severe negative consequences, including financial
losses. This research introduces the ABI approach, a novel solution designed to
support organizational decision-makers by automatically identifying and
explaining risk seeking preferences during decision-making. This research makes
a novel contribution by automating the identification and explanation of risk
seeking preferences using Cumulative Prospect theory (CPT) from Behavioral
Economics. The ABI approach transforms theoretical insights into actionable,
real-time guidance, making them accessible to a broader range of organizations
and decision-makers without requiring specialized personnel. By contextualizing
CPT concepts into business language, the approach facilitates widespread
adoption and enhances decision-making processes with deep behavioral insights.
Our systematic literature review identified significant gaps in existing
methods, especially the lack of automated solutions with a concrete mechanism
for automatically identifying risk seeking preferences, and the absence of
formal knowledge representation, such as ontologies, for identifying and
explaining the risk preferences. The ABI Approach addresses these gaps,
offering a significant contribution to decision-making research and practice.
Furthermore, it enables automatic collection of historical decision data with
risk preferences, providing valuable insights for enhancing strategic
management and long-term organizational performance. An experiment provided
preliminary evidence on its effectiveness in helping decision-makers recognize
their risk seeking preferences during decision-making in the loss domain.
