THREE EMPIRICAL STUDIES ON ONLINE REPUTATION AND REGULATION MECHANISMS
COM2 Level 4
Executive Classroom, COM2-04-02
Abstract:
Almost all kinds of trade and commerce activities rely on effective mechanisms to ensure buyers' and sellers' fulfillment of their contractual obligations in the economic exchange. Recent developments in information technology (IT) have given risen to various online reputation and regulation mechanisms which generate reliable digital cues for trust formation and facilitate economic transactions in online marketplaces. Despite the prevalence of online reputation and regulation mechanisms, there are still under-explored areas in existing online reputation mechanisms, lack of research on newly developed reputation mechanisms, as well as scant empirical evidence in the context of newly emerged online marketplaces. Thus, this dissertation conducts three empirical studies in response to the aforementioned gaps.
Study 1 focuses on third-party online review platforms, a widely adopted online reputation mechanism in retailing and service markets that allows consumers to learn about sellers' trustworthiness and products' quality prior to purchases. Despite the numerous prior research investigating the economic values of user-generated content (UGC) on online review platforms in the past decade, the competitive and associative information reflected in different forms of UGC on online review platforms is still underexplored. The objective of study 1, therefore, is to investigate the underexplored informational value of products' competitive or associative relationships conveyed in UGC on product sales in specific competition contexts. Study 1 focuses on two forms of UGC, online reviews and social tags, to infer product quality and product network associations. By using a unique dataset of a leading third-party review platform in China, study 1 empirically shows how the quality information of competitive and associative products revealed through UGC influences product sales.
Study 2 focuses on online product sampling, a new type of promotional strategies in e-commerce that offers free samples to selected credible consumers in return for high quality and authentic online sampling reviews. Since the selection process is monitored by the platform, it restricts firms' strategic manipulation and mitigates the concerns of fake reviews to some extent, which hence helps ensure the trustworthiness of sellers in e-commerce. Despite the increasing popularity of online product sampling, empirical research investigating its sales impacts still lags. Study 2 hence attempts to examine various spillover effects of online product sampling on sales in e-commerce. Adopting the insights from marketing promotion and online word-of-mouth (WOM) literature, we propose that online product sampling promotions lead to in-store and cross-store sales spillovers on an e-commerce platform through two effects, sampling publicity effect and sampling appraisal effect. Using data from the largest e-commerce platform in China, we employ multiple identification strategies and model estimation methods to disentangle and quantify these two effects in driving in-store and cross-store sales spillovers.
Study 3 focuses on the reputation and regulation mechanisms in sharing economy platforms, a type of online peer-to-peer market where anonymous trading parties and sequential execution of trades raise concerns on information asymmetry. To mitigate information asymmetry problems, sharing economy platforms have practically attempted various online reputation and regulation designs. While a growing number of platforms have been equipped with more than one reputation and regulation designs, past studies have seldomly examined the differential effects among different reputation and regulation designs. Using a unique dataset from a leading meal-sharing platform in China, study 3 examines how two "laissez-faire" reputational designs (e.g., consumer screening and service provider popularity) and one "draconian" regulation design (e.g., platform regulation) influence service providers' business performance on sharing economy platforms. By accounting for the distinct characteristics of sharing economy (i.e., high heterogeneity in service quality and self-scheduling capacity), we uncover and explicate the differential effects of these designs in the context of sharing economy.