Signaling Effects Under Dynamic Capacity in Online Matching Platforms: Evidence from Online Health Consultation Communities

Abstract

Match formation is challenging in online matching platforms where suppliers are subject to dynamic capacity constraints. We provide a theoretical foundation for understanding how online matching platforms support the transmission and triangulation of multi-source information for consumers to infer provider service quality and dynamic capacity states, and achieve desirable matching outcomes. Situating this study in the context of an online health consultation community (OHCC) and drawing upon signaling theory, we theorize how physicians' owned and earned signals influence physicians' voluntary online consultations with new patients they have not consulted with previously. Importantly, we articulate how these signaling effects are contingent upon physicians' dynamic capacity in OHCC. We collected longitudinal data from a large OHCC in China and used a hidden Markov model (HMM) to characterize the dynamic physician capacity in the OHCC and test the hypotheses. Our findings reveal that service professionals' owned and earned signals interactively work together to balance supply and demand dynamically, and thereby facilitating matchmaking. In OHCCs, where physicians provide voluntary service beyond their primary jobs at hospitals, we find that owned and earned signals increase patient consultations in different patterns contingent upon physicians’ capacity states. In addition, we discover the complementary and substitute relationships between owned signals and earned signals change when physicians are in different capacity states. The findings have significant implications for our understanding of online match formation under dynamic capacity constraints and the design of OHCCs.

Keywords: Online match formation, dynamic capacity states, hidden Markov model, online health consultation communities, signaling theory, complementary and substitute relationships