In credence goods markets such as health care or repair services, consumers rely on experts with superior information to adequately diagnose and treat them. Experts, however, are constrained in their diagnostic abilities, which hurts market efficiency and consumer welfare. Technological breakthroughs that substitute or complement expert judgments have the potential to alleviate consumer mistreatment. This article studies how competitive experts adopt novel diagnostic technologies when skills are heterogeneously distributed and obfuscated to consumers. We differentiate between novel technologies that increase expert abilities, and algorithmic decision aids that complement expert judgments, but do not affect an expert's personal diagnostic precision. We show that high-ability experts may be incentivized to forego the decision aid in order to escape a pooling equilibrium by differentiating themselves from low-ability experts. Results from an online experiment support our hypothesis, showing that high-ability experts are significantly less likely than low-ability experts to invest into an algorithmic decision aid. Furthermore, we document pervasive under-investments, and no effect on expert honesty.

翻译：在医疗保健或维修服务等信任品市场中，消费者依赖拥有信息优势的专家进行充分诊断和治疗。然而，专家的诊断能力存在局限，这损害了市场效率和消费者福利。能够替代或补充专家判断的技术突破有望缓解消费者权益受损问题。本文研究了在技能异质分布且消费者难以辨别技能差异的背景下，竞争性专家如何采纳新型诊断技术。我们区分了两类技术：一类是提升专家能力的新型技术，另一类是补充专家判断但不影响个人诊断精度的算法决策辅助。研究表明，高能力专家可能因规避混同均衡而放弃使用决策辅助工具，以此与低能力专家形成差异化竞争。在线实验结果支持了我们的假设：高能力专家投资算法决策辅助的可能性显著低于低能力专家。此外，我们观察到普遍存在投资不足现象，且该工具对专家诚实度无显著影响。