Heterogeneous Rank Effects in Online Marketplace

This paper studies the rank effect heterogeneity in the online marketplace and suggests a practical implication for marketing managers to set the optimal digital marketing strategies. Because of the increasing economy of online marketplaces, the position or rank effect is a crucial issue in the marketing literature. The latest literature has focused on the effects of sponsored search results on search engine advertising, though it is known that organic results are more critical than search ads. This research is novel to focus on the effect of organic results in the online marketplace. For analysis on the unit of product level, this paper constructs the rank index through weighted average by keyword search volumes. In the model, the rank effect was specified by the interaction of product-level and category-level averaged variables with the rank index, with the covariates of product-level time-variant variables and two-way fixed effects. Some products were selected randomly to escape the curse of dimensionality. The estimation result suggests that product sales increased in rank and the number of Q&A and reviews. Meanwhile, categories with high price dispersion experienced a lower rank effect, and categories with information asymmetry experienced a lower rank effect. The overall characteristics of the category, such as average price, product attributes, and competition intensity, do not have a significant rank effect. In conclusion, I suggest that marketing managers implement search engine optimization in online marketplaces if their products are in the category with a higher rank effect. This paper finally took a snapshot of the online marketplace by exploiting a vast dataset and extending the marketing literature to the new area. Future research considering hierarchical modeling and endogeneity can investigate more robust and rigorous causality.Chapter 1. Introduction 2 Chapter 2. Literature Review 6 Chapter 3. Data 12 Chapter 4. Model 20 Chapter 5. Results 21 Chapter 6. Discussion 26 Bibliography 30 Abstract in Korean 34Maste

Optimising Trade-offs Among Stakeholders in Ad Auctions

We examine trade-offs among stakeholders in ad auctions. Our metrics are the revenue for the utility of the auctioneer, the number of clicks for the utility of the users and the welfare for the utility of the advertisers. We show how to optimize linear combinations of the stakeholder utilities, showing that these can be tackled through a GSP auction with a per-click reserve price. We then examine constrained optimization of stakeholder utilities. We use simulations and analysis of real-world sponsored search auction data to demonstrate the feasible trade-offs, examining the effect of changing the allowed number of ads on the utilities of the stakeholders. We investigate both short term effects, when the players do not have the time to modify their behavior, and long term equilibrium conditions. Finally, we examine a combinatorially richer constrained optimization problem, where there are several possible allowed configurations (templates) of ad formats. This model captures richer ad formats, which allow using the available screen real estate in various ways. We show that two natural generalizations of the GSP auction rules to this domain are poorly behaved, resulting in not having a symmetric Nash equilibrium or having one with poor welfare. We also provide positive results for restricted cases.Comment: 18 pages, 10 figures, ACM Conference on Economics and Computation 201

Consumer online search with partially revealed information

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Pareto-improve both revenue and consumer welfare for our OTA.This paper was accepted by Juanjuan Zhang, marketing

Macroscopic properties of buyer–seller networks in online marketplaces

Online marketplaces are the main engines of legal and illegal e-commerce, yet their empirical properties are poorly understood due to the absence of large-scale data. We analyze two comprehensive datasets containing 245M transactions (16B USD) that took place on online marketplaces between 2010 and 2021, covering 28 dark web marketplaces, i.e. unregulated markets whose main currency is Bitcoin, and 144 product markets of one popular regulated e-commerce platform. We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology. Specifically, we find remarkable regularities in the distributions of transaction amounts, number of transactions, interevent times, and time between first and last transactions. We show that buyer behavior is affected by the memory of past interactions and use this insight to propose a model of network formation reproducing our main empirical observations. Our findings have implications for understanding market power on online marketplaces as well as intermarketplace competition, and provide empirical foundation for theoretical economic models of online marketplaces

Social influence and position effects

A wide range of personal choices rely on the opinions or ratings of other individuals. This information has recently become a convenient way of simplifying the decision process. For instance, in online purchases of products and services, the possible choices or alternatives are often characterized by their position in a certain presentation order (or list) and their popularity, derived from an aggregate signal of the behavior of others. We have performed a laboratory experiment to quantify and compare popularity (or social influence) and position effects in a stylized setting of homogeneous preferences, with a small number of alternatives but considerable time constraints. Our design allows for the distinction between two phases in the decision process: (1) how agents search (i.e., not only which alternatives are analyzed but also in which order) and (2) how they ultimately choose. We find that in this process there are significant popularity and position effects. Position effects are stronger than social influence effects for predicting the searching behavior, however, social influence determines to a larger extent the actual choice. The reason is that social influence generates a double effect; it directly affects the final choice (independently on what alternative has been searched more thoroughly) and indirectly alters choice through the searching behavior which, in turn, is also affected by popularity. A novelty of our approach is that we account for personal traits and provide an individual analysis of sensitivity to both social influence and position effects. Surprisingly, we find that overconfident individuals are more influenceable, whereas other personal characteristics (e.g., gender and risk aversion) do not play a significant role in this context

Is Google the next Microsoft? Competition, Welfare and Regulation in Internet Search

Internet search (or perhaps more accurately `web-search') has grown exponentially over the last decade at an even more rapid rate than the Internet itself. Starting from nothing in the 1990s, today search is a multi-billion dollar business. Search engine providers such as Google and Yahoo! have become household names, and the use of a search engine, like use of the Web, is now a part of everyday life. The rapid growth of online search and its growing centrality to the ecology of the Internet raise a variety of questions for economists to answer. Why is the search engine market so concentrated and will it evolve towards monopoly? What are the implications of this concentration for different `participants' (consumers, search engines, advertisers)? Does the fact that search engines act as `information gatekeepers', determining, in effect, what can be found on the web, mean that search deserves particularly close attention from policy-makers? This paper supplies empirical and theoretical material with which to examine many of these questions. In particular, we (a) show that the already large levels of concentration are likely to continue (b) identify the consequences, negative and positive, of this outcome (c) discuss the possible regulatory interventions that policy-makers could utilize to address these

Essays On Dynamic Updating Of Consumer Preferences

Consumers dynamically update their preferences over time based on information learned through product search and consumption experiences, particularly in online media. Using three unique datasets from different domains, we address specific ways in which firms can use rich information about their customers\u27 behaviors to improve (1) the visual display of products on a webpage in online shopping, (2) predictions of new product adoption in online gaming, and (3) the timing of product release in online learning. First, we explore how consumers visually search through product options using eye-tracking data from two experiments conducted on the websites of two online clothing stores, which can inform retailers on how to position products on a virtual webpage. Second, we examine how consumers\u27 variety-seeking preferences change depending on past consumption outcomes within the context of an online multi-player video game, which can be used to improve predictions of new product adoption. Third, we use clickstream data from an online education platform to test theories of goal progress, knowledge accumulation, and boundedly rational forward-looking behavior, which can be used to explain binge consumption patterns and inform content providers on the best way to structure and release content. In each of these three projects, we build a mathematical model of individual decisions, with the parameterization grounded in theories of consumer behavior, and we demonstrate through in-sample prediction that our model is able to capture specific heterogeneous patterns within the data. We then test that our model is able to make out-of-sample predictions related to managerial interventions, and empirically verify our predictions using either lab experiments or new field data following a natural experiment policy change

Being in the right place: A natural field experiment on list position and consumer choice

This paper uses a natural field experiment to better understand the reasons why individuals show a disproportionate tendency to select items placed in the top position of a list. After randomizing the order in which new economics research papers are presented in email alerts and tracking economists' subsequent download activity, we provide evidence of position effects and reject three common explanations regarding item order, choice fatigue and quality signals. Instead, after developing some novel tests based on the user-level nature of our data, we show that three more subtle explanations are consistent with the behavior of different groups of individual

Steering of Consumer Search by Information Intermediaries: The Value of Consumers' Personal Data.

In the dissertation, I discuss how information intermediaries influence markets with consumer search frictions, especially in big tech and Internet industries. I study both theoretically and empirically how the information that platforms as Amazon, Google, Expedia, etc. have about consumers' preferences affects market efficiency, market prices, and welfare. In the first chapter, I empirically study how the information a search intermediary has about consumer preferences impacts the market. Consumers participate in costly search among different sellers’ products, relying on the rankings order provided by the intermediary based on their preferences. Better product targeting affects consumer search and purchases, which, in turn, changes the seller pricing incentives. I considered these aspects by modeling both sides of the market under various ranking algorithms used by the intermediary. On the demand side, I develop a model consumer costly search and purchase joint decision. On the supply side, I model the firms’ pricing game. To estimate the demand and supply models, I utilized a rich dataset provided by Expedia, which includes consumer search and purchase data and information on the hotels and prices they charge. I find that if the intermediary uses data on consumers’ preferences to provide them personalized rankings of products, consumers, on average, experience a 3.6% ($4.9) utility decrease due to increased transaction prices, a 0.8$1.1) utility gain due to a reduction in search spending, and 0.5% ($0.7) utility gain due to finding a better-fitted hotel. The second chapter provides the theoretical model to discuss markets with consumer search frictions and a partially informed intermediary. The intermediary gives consumers individual advice on what products to explore first. The main finding is that with an improvement in the information the intermediary has, the average quality of the product consumers purchase, as well as the total economic welfare and the consumer surplus, might decrease. The mechanism is as follows: if the intermediary gives better advice on average to consumers on what product to explore first, all consumers have lower expectations about the next products and explore them less often. That reduces the quality of products purchased by consumers who got wrong advice and might lower the average quality of purchased products. This effect appears in the case of a low search cost, which makes it particularly important to analyze online search intermediaries, such as Google, Amazon, Expedia, etc.Doctor of Philosoph