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.

Yet it's possible to nudge people to more willingly pay their taxes on time. The Behavioural Insights Team, also known unofficially as the "Nudge Unit," was founded by the UK government in 2010 to use behavioral science to make public policies and services more effective. One of its more successful experiments is the use of peer pressure to improve tax collection. Ordinarily, HM Revenue and Customs, the department responsible for tax collection, sends a reminder notice to those who haven't paid their taxes on time. Only 33% of those who receive the reminders respond by paying their taxes.

My good friend Vinnie participates in an automobile insurance program that rewards him for good driving behaviors; the better driving behaviors he exhibits, the more money he saves on insurance. You stick a device into the vehicle's diagnostic port (usually under the steering wheel in most vehicles manufactured after 1996), and the automobile insurance company tracks your driving behaviors and offers you automobile insurance discounts based upon the quality of your driving behaviors. The program actually "grades" driving behaviors including acceleration, turning, speed and braking, and once a month sends a report card on the past month's driving performance (see Report Card in Figure 1). And for my friend Vinnie, as a result of sharing his detailed driving data, he saved $1.49 over the past 6 months. Vinnie is saving $2.98 per year by sharing his detailed driving data with his auto insurance company.