With the proliferation of third-party sellers, counterfeiting has become a serious source of friction in online marketplaces. Different from traditional counterfeiters who target consumers who consciously seek for cheap knockoffs, sellers of deceptive counterfeit products target the whole population of online shoppers and make their products indistinguishable from genuine products. I develop two identification approaches to identify deceptive counterfeit products in online marketplace. The first applies natural language processing techniques to Amazon product reviews to generate a listing-level counterfeit probability, which in turn is used to classify ASIN (Amazon Standard Identification Number) listings as likely counterfeit or likely authentic. The second leverages an AI-based anti-counterfeit project launched by Amazon as an exogenous shock to directly measure the likelihood of a listing being counterfeit. I focus on two product categories, one a taste-based experience good (men’s fragrances) and the other being a utilitarian product (wireless cell phone chargers). I embed the estimated counterfeit probability into a BLP-type choice model to model consumers’ decision-making process and investigate how counterfeiting intensity affects user demand and platform revenues. I confirm that consumer disutility is increasing in the counterfeit probability, more so for high-end or popular products. I further find a substitution effect between likely counterfeit and likely authentic products: a 10% decrease in the price of a likely counterfeit product is associated with an average 0.0011% decrease in the market share of a likely authentic product. I leverage the structural parameter estimates to run a number of counterfactual experiments. These experiments suggest that protecting authentic sellers by simply banning all likely counterfeit listings would drastically reduce platform revenues. Instead, the deployment of counterfeit detection algorithms, and reporting the results to users, would align the interests of authentic sellers with the welfare of the platform. Overall, my analysis provides a robust empirical examination for identifying deceptive online counterfeiting and understanding its impact on the various stakeholders of an online retail platform.

Recent advance in artificial intelligence and machine learning, especially transformer-powered foundation models such as the Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), have revolutionized how people understand and interact with information. As human filter wild world information and communicate in a set of rules and symbols that we call languages, large language models (LLMs) first emerge as our hope for intellectual agents' equivalent. Versatile as they seem, such as autocompleting code and decoding ancient languages from archaeological stones, whether LLMs can perform what we define as intelligent tasks -- such as math, planning, and reasoning -- is still debatable. This study explores the application of fine-tuning LLMs to predict user preferences, with specific focuses on the Movie industry. We call it the FiLM project (Film-interest Language Model). FiLM aims to predict the market preference for a film and identify high-propensity user segments based on early-stage movie synopses, offering a novel approach compared to traditional recommendation systems...

Mobile technology redefines business models and reshapes consumer behaviors, creating a huge and emerging economy of mobile apps. For more efficient monetization and successful development, the app distribution platforms and app developers should consider consumer choices and usage habit into their ranking strategies and app designs. In this dissertation, I conducted three studies to explore consumer choices on apps, platform ranking strategies to display apps, and consumer usage habit formation in app consumption. On consumers’ app installation choices, I examine consumer click and conversion responses to app characteristics at an app distribution platform. For platforms’ ranking strategies, I propose a theory-based hybrid personalized ranking method to display apps in order to maximize revenues. To understand post-installation app usage, I explore consumer habits on app consumption which vary with consumer demographics and app features. I acquire rich data from different sources and adopt theoretical and empirical models, in combination with machine learning, to high granularity data. My analysis finds robust evidence of the impact of app features on consumer consumption and platform profitability. I discuss implications for developer to design, for platforms to display and for users to consume apps.

Sharing economy platforms bring individual buyers and sellers together to promote transactions between the two parties. Since most of the platforms include decentralized network of individual sellers who provide their own products or services, these lack standardized or established quality which may lead to quality uncertainty. Although sharing economy platforms rely on user-generated reviews and seller-provided information to provide trust between buyers and sellers, these would not completely reduce the quality uncertainty. In my dissertation, I examine the impact of platform-managed quality certification and simultaneous review system to find out how these mechanisms address quality uncertainty in the context of Airbnb. Leveraging a quasi-experimental design in combination with a machine learning algorithm, I find that the quality certification launched by Airbnb has differential impacts on consumers, property owners, and the platform. Also, I show how the reciprocity under the bilateral review system affects volume, valence, and semantic diversity of reviews. My findings have significant implications for researchers and practitioners who deal with quality management and review system designs especially within an online platform area.

Technology is closing the distance between users on the one hand and between users and businesses on the other. Social technologies help create social proximity and promote the sharing of information, while location-enabled services enable spatial proximity and allow businesses to leverage the precise dynamic location of users in their marketing strategies. This dissertation examines the impact of social and location-based technologies on consumer choice in the context of the music industry and mobile analytics. In the music context, I examines the interactions between social proximity and consumer choice in the context of an online music community. In the area of mobile analytics, my research studies how location-based services (i.e., local search and geo-fence marketing) impact consumer choice and transform business strategies. My research design applies a variety of empirical methods to highly granular data. My analysis finds robust evidence of the impact of social and spatial proximity on consumer choice. I discuss implications for design and marketing strategies for online communities, mobile local search engine and geo-fence advertising, such as the contexts studied in this dissertation.

With the proliferation of digital technologies, multi-sided platforms become a prevailing choice of asset-light business model in facilitating business transactions. As of 2023, Uber’s market value stands at $90 billion while Airbnb’s market valuation is $100 billion. Neither of them owns the vehicles or the properties. Coordinating users, launching products, and designing rules are challenging for platform managers, especially when they do not fully control either supply or demand side of the marketplace. This dissertation examines three unique interactions of platform users: bypassing the platform, pooling riders in a vehicle, and teaming up donors within a group. Chapter 2 studies disintermediation and its mitigation policies on a home-sharing marketplace. Chapter 3 investigates the impacts of extending product line by introducing carpooling services on a ride-sharing platform. Chapter 4 examines the effects of subteaming on incentivizing donations on an online charity platform. My dissertation applies a variety of empirical methods to highly granular data, and offer implications for platform stakeholders in feature designs, product strategy and platform governance.

This dissertation studies the evolution of consumer taste on digital platforms and its implication on consumers' searching behaviors, firms' product feature design and pricing strategies. Chapter 1 analyzes the clickstream point-of-sale data from a hotel searching website and empirically finds that consumers have different hidden states in the searching process and their price and quality sensitivity changes over the purchase funnel. Chapter 2 assumes that consumers tend to prefer the feature design of their previous consumption and theoretically discovers that this consumer taste shift enhances firms' competition, opposite to the strategic impact of most first-mover advantages like switching costs. When the service suppliers are also consumers for the digital platforms in sharing economy, Chapter 3 analytically searches for the optimal pricing and compensation design from the perceptive of the platforms. We offer significant contribution to the literature in Marketing and Information Systems and provide useful managerial implications to product searching platforms, digital product feature design and the debate of identifying workers as independent contractors or employees in sharing economy.