Three Essays on Irregular Entries to the End-Customer Market

I study irregular entries to the end-customer market and the impact of such entries on suppliers, buyers, and customers. I am particularly interested in the irregularities of supplier encroachment and counterfeiting problems. This dissertation addresses these issues and proposes solutions in the form of three essays. In the first essay, I study a supply chain, consisting of a supplier and a buyer where the supplier can encroach on the end-customer market and keeps private information on its own production capacity. The supplier can decide on its capacity allocation and the buyer can order strategically, hoarding the supply capacity, to remove the competition. I find that the supplier is worse off, and the buyer is better off, when the supplier keeps its capacity information private. Further, I demonstrate that the supplier may no longer encroach on the end-customer market when it has more capacity. The second and third essays are inspired by the counterfeiting problem on online e-commerce platforms. In the second essay, I develop an algorithm that analyzes customers’ reviews on an online platform and provides an authenticity score for the products. I trained context-specific word embedding based on a large corpus of Amazon customer reviews to show that my unsupervised methodology provides good predictive power. Next, I study the effect of customers’ reviews on an e-commerce platform’s anti-counterfeiting strategy against third-party sellers. The platform can provide a tool for customers that analyzes just the product reviews or a more advanced tool that analyzes both the product and seller reviews to help customers determine if products are fake or genuine. On the seller’s side, it can choose to reveal its fake products by charging a lower separating price based on its profit under these two options. I demonstrate that even when the tools are free, the platform does not provide the advanced tool if the seller sells products with a low authenticity score (fake products), and it provides the basic tool if and only if the demand of the genuine product is sufficiently high. Together, these papers provide solutions on how to maximize profits by making informed decisions in the face of market irregularities for the supplier, the buyer, and the customer

The Digital Dilemma: Counterfeit Culture And Brand Protection Reform In The E-Commerce Era

In recent decades, the Internet’s growth has revolutionized the modern shopping experience. With the rise of e-commerce platforms, consumers can now instantly access thousands of products. Unfortunately, the ease of online shopping has also supported the development of counterfeit culture and fueled a coinciding increase in trademark infringement. Furthermore, given the expected expansion of e-commerce, brand identity conveys substantial value in online marketplaces. This backdrop, coupled with a surge in trademark litigation since Tiffany v. eBay, demonstrates the importance of trademark reform. The current framework for assessing trademark infringement in e-commerce settings disproportionately burdens small businesses, and this Comment proposes a solution that aims to balance the interests of rightsholders, online marketplaces, and consumers. Moreover, additional safeguards like artificial intelligence and blockchain technology provide an extra layer of protection for businesses. Through better legislation and improved regulations, Congress can ensure that online marketplaces adapt to challenges posed by the digital age and advance the public good

Dynamics of Dark Web Financial Marketplaces: An Exploratory Study of Underground Fraud and Scam Business

The number of Dark Web financial marketplaces where Dark Web users and sellers actively trade illegal goods and services anonymously has been growing exponentially in recent years. The Dark Web has expanded illegal activities via selling various illicit products, from hacked credit cards to stolen crypto accounts. This study aims to delineate the characteristics of the Dark Web financial market and its scams. Data were derived from leading Dark Web financial websites, including Hidden Wiki, Onion List, and Dark Web Wiki, using Dark Web search engines. The study combines statistical analysis with thematic analysis of Dark Web content. Offering promotions and customer services with the payment methods of cryptocurrencies were prevalent, similar to the Surface Web\u27s e-commerce market. The findings suggest that the Dark Web financial market is likely to harbor scams targeting Dark Web buyers. Dark Web sellers construct a website to sell scam products and recommend purchasing Escrow services to ensure safe transactions as an additional scam. The results from this study provided empirical support for the components of the routine activity theory of the Dark Web financial market to substantiate a more comprehensive view of patterns of fraud/ scams. Enhancing law enforcement capabilities of investigating financial marketplaces and promoting public awareness and consumer safety programs are discussed as effective preventive measures

Progetto FATA: From Awareness To Action. Rafforzare la conoscenza e la cooperazione pubblico-privata contro le nuove forme della contraffazione online

Questo studio rappresenta il primo tentativo in Italia di analizzare in modo sistematico gli schemi e i modi operandi della contraffazione online

Local Image Patterns for Counterfeit Coin Detection and Automatic Coin Grading

Abstract Local Image Patterns for Counterfeit Coin Detection and Automatic Coin Grading Coins are an essential part of our life, and we still use them for everyday transactions. We have always faced the issue of the counterfeiting of the coins, but it has become worse with time due to the innovation in the technology of counterfeiting, making it more difficult for detection. Through this thesis, we propose a counterfeit coin detection method that is robust and applicable to all types of coins, whether they have letters on them or just images or both of these characteristics. We use two different types of feature extraction methods. The first one is SIFT (Scale Invariant Feature transform) features, and the second one is RFR (Rotation and Flipping invariant Regional Binary Patterns) features to make our system complete in all aspects and very generic at the same time. The feature extraction methods used here are scale, rotation, illumination, and flipping invariant. We concatenate both our feature sets and use them to train our classifiers. Our feature sets highly complement each other in a way that SIFT provides us with most discriminative features that are scale and rotation invariant but do not consider the spatial value when we cluster them, and here our second set of features comes into play as it considers the spatial structure of each coin image. We train SVM classifiers with two different sets of features from each image. The method has an accuracy of 99.61% with both high and low-resolution images. We also took pictures of the coins at 90˚ and 45˚ angles using the mobile phone camera, to check the robustness of our proposed method, and we achieved promising results even with these low-resolution pictures. Also, we work on the problem of Coin Grading, which is another issue in the field of numismatic studies. Our algorithm proposed above is customized according to the coin grading problem and calculates the coin wear and assigns a grade to it. We can use this grade to remove low-quality coins from the system, which are otherwise sold to coin collectors online for a considerable price. Coin grading is currently done by coin experts manually and is a time consuming and expensive process. We use digital images and apply computer vision and machine learning algorithms to calculate the wear on the coin and then assign it a grade based on its quality level. Our method calculates the amount of wear on coins and assign them a label and achieve an accuracy of 98.5%

The Fault in the Stars: Understanding the Underground Market of Amazon Reviews

In recent times, the Internet has been plagued by a tremendous amount of misinformation. Online markets such as Amazon are also not free from misinformation. In this work, we study the misinformation propagated to consumers through the form of Amazon reviews. There exists a vast underground market where reviews by real Amazon users are purchased and sold. While such a practice violates Amazon's terms of service, we observe that there exists a complex network consisting of thousands of sellers and agents, who provide a rebate to consumers for leaving positive reviews to over $5000$ products. Based on interviews with members involved in the reviews market, we understand the working of this market, and the tactics used to avoid detection by Amazon. We also present a set of recommendations of features that Amazon and similar online markets can take into consideration to detect such reviews.Comment: This is a work in progress