We develop an analytical model intended as the first stage in the development of expert systems to improve auditor knowledge in, and assist in the decision process of, Going Concern Opinions (“GCOs”). Our approach is consistent with a design science approach to developing information systems, resulting in an initial artifact, the mathematical model, which can, through iterative design science and behavioral research, inform a technology-based expert system. Based on Bayesian networks, our model provides insights about auditors’ revision, or inflation, of the probability to issue a GCO based on the interrelationship that forms with the incremental existence of one, two, or three publicly observable financial statement risk factors – net operating loss, negative cash flows from operations, and negative working capital. We calculate the revised probabilities using empirical data of GCOs from 2004 to 2015. Results reveal that the incremental relationship (one, two, or three factors present) effectively models expert auditors’ decisions to issue a GCO, and suggests the existence of these measurable inflation factors that represent situational and auditor-specific factors. We also find that Non-Big Four auditors inflate these factors differently than Big Four auditors to arrive at a decision to issue a GCO.

我们开发了一个分析模型，旨在作为专家系统开发的第一阶段，以提高审计师对持续经营意见（“GCO”）的了解并协助其决策过程。我们的方法与开发信息系统的设计科学方法一致，产生了一个初始工件，即数学模型，它可以通过迭代设计科学和行为研究，为基于技术的专家系统提供信息。基于贝叶斯网络，我们的模型基于与一个、两个或三个公开可观察的财务报表风险因素的增量存在形成的相互关系，提供了关于审计师对发布 GCO 的可能性的修正或膨胀的见解——净运营亏损、负运营现金流和负营运资本。我们使用 2004 年至 2015 年 GCO 的经验数据计算修订后的概率。结果表明，增量关系（存在一个、两个或三个因素）有效地模拟了专家审计师发布 GCO 的决定，并表明存在这些可衡量的通货膨胀代表情境和审计员特定因素的因素。我们还发现，非四大审计师夸大这些因素的方式与四大审计师不同，以做出签发 GCO 的决定。