Abstract: E-commerce platforms generally provide various functions that can be adopted as signals for online sellers to convey implicit information to customers to promote sales. In this article, based on signaling theory and the stereotype content model, we categorize e-commerce signals into two types: signals of competence and signals of warmth. Signals of competence refer to the platform functions or mechanisms that can be leveraged by online sellers to indirectly convey information about their capabilities, such as promised delivery times and free return days. Signals of warmth refer to the platform functions or mechanisms that can be leveraged by online sellers to indirectly convey information about their kindness and care, such as the availability of online customer service agents. We explore the impacts of the two different types of signals on product sales for sellers with different credit rating levels. The empirical analysis is conducted on China’s largest e-commerce platform, Taobao.com. The results show that online sellers with higher credit ratings should focus more on signals of warmth, while those with median and lower credit ratings should concentrate more on signals of competence. This study provides a theoretical framework that explains the effects of signaling on e-commerce platforms and may facilitate further exploration on signaling mechanisms. Our findings also provide implications for online sellers in terms of how to better utilize various signals as well as for e-commerce platforms on designing more effective supporting functions.

Keywords: signaling theory, stereotype content model, signal of competence, signal of warmth, online sellers