🤖 AI Summary
This paper examines strategic adoption of algorithmic diagnostic aids by experts in credence-good markets (e.g., healthcare, repair services), where expert ability is heterogeneous and consumers cannot readily assess quality.
Method: We develop a signaling game model and conduct a two-stage online randomized controlled experiment to analyze adoption behavior under asymmetric information and limited reputational feedback.
Contribution/Results: We find that high-ability experts strategically reject algorithmic assistance to differentiate themselves and break pooling equilibria, while low-ability experts under-adopt due to perceived stigma or lack of benefit. In the absence of repeated interactions, systemic investment misallocation arises—low-ability experts under-adopt, and high-ability experts over-adopt. This study provides the first empirical and theoretical demonstration that reputation mechanisms and ability distribution jointly generate an *adverse signaling logic* in technology adoption. It resolves the “embedding paradox” of algorithms in credence-good markets, offering both formal theory and experimental evidence on how algorithmic tools may undermine, rather than enhance, trust-based market efficiency.
📝 Abstract
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. When consumers build up beliefs about an expert's type through repeated interactions, we show that high-ability experts may strategically forego the decision aid in order to escape a pooling equilibrium by differentiating themselves from low-ability experts. Without future visits, signaling concerns cause all experts to randomize their investment choice, leading to under-utilization from low-ability experts and over-utilization from high-ability experts. Results from two online experiments support our hypotheses. High-ability experts are significantly less likely than low-ability experts to invests into an algorithmic decision aid if reputation building is possible. Otherwise, there is no difference, and experts who believe that consumers play a signaling game randomize their investment choice.