This dissertation studies the short-term and long-term impacts of return policies and feedback text on firm performance. Archival data, text analytics, and econometric analysis are used to further develop signaling theory, transaction cost economics, and procedural justice theory in operations, logistics, and supply chain management.

The first essay is motivated by the ambiguity of prior research on the relationship between return policies and demand in the online setting. The return policy components that impact landed prices are identified and the relationships between terms of sale and demand are studied. After controlling for price, a lenient return policy is found to signal the unobservable quality of the seller’s product and demonstrate their capability to properly handle sales, shipping, and returns. A lenient

return policy also helps mitigate customers’ risk associated with a mismatch between the product and their expectations and is shown to be positively associated with landed price and demand.

The second essay demonstrates that the impact of a customer’s satisfaction or dissatisfaction with a seller or their product extends to other customers when their satisfaction or dissatisfaction becomes public knowledge, impacting sellers’ future demand. The impact of negative, trust revoking feedback is shown to differ from the impact of non-trust revoking, negative feedback, such as nonspecific complaints and complaints about price. In other words, the text associated with numerical feedback ratings determines the strength of the negative rating’s impact. Moreover, it is shown that negative feedback can be altered and even counteracted with a satisfactory service recovery, while the variance of complaint types in sellers’ feedback histories is negatively associated with demand.

Overall, this dissertation demonstrates the benefits of two signals of quality: a lenient return policy and positive feedback history. Methodological contributions include the use of two original datasets and the combination of text analytics and regression analysis to inform managerial decisions. Managerial implications suggest that firms should take the leniency of their return policies and the strength of their online reputations into consideration when pricing and estimating demand.