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.

翻译：组织决策对成功至关重要，然而认知偏差会显著影响风险偏好，导致次优结果。由损失厌恶等偏差驱动的损失风险寻求偏好带来了挑战，并可能导致严重的负面后果，包括财务损失。本研究提出了ABI方法，这是一种新颖的解决方案，旨在通过自动识别和解释决策过程中的风险寻求偏好来支持组织决策者。本研究通过利用行为经济学中的累积前景理论（CPT）来自动化识别和解释风险寻求偏好，做出了新颖的贡献。ABI方法将理论见解转化为可操作的实时指导，使其无需专业人员即可被更广泛的组织和决策者所使用。通过将CPT概念转化为商业语言，该方法促进了广泛采用，并通过深入的行为洞察增强了决策过程。我们的系统性文献综述发现了现有方法中的显著差距，特别是缺乏具有自动识别风险寻求偏好的具体机制的自动化解决方案，以及缺乏用于识别和解释风险偏好的形式化知识表示（如本体）。ABI方法解决了这些差距，为决策研究和实践做出了重要贡献。此外，它能够自动收集带有风险偏好的历史决策数据，为加强战略管理和长期组织绩效提供了宝贵的见解。一项实验提供了初步证据，表明其在帮助决策者识别损失领域决策过程中的风险寻求偏好方面是有效的。