BUSINESS PROJECT SUMMARIES
SPRING 2026
Brian Grant
Accounting
Villanova School of Business
My research focuses on understanding how U.S. multinational companies (“MNCs”) operate abroad and use international tax strategies. Specifically, I have collected a large set of novel financial statements filed by the Irish subsidiaries of U.S. MNCs. These financial statements provide a unique window into both operations and tax practices of U.S. MNCs operating in Ireland. On the operational side, they disclose the number of Irish employees and describe the key activities these employees perform. On the tax side, they report detailed information on profits recognized in Ireland, the corporate taxes paid there, and the intricacies of their tax strategies.
Ireland plays an important role in international business because it has historically attracted U.S. MNCs through its combination of low tax rates and access to the European market. Yet little research has used these subsidiary-level disclosures to study how companies structure their activities and avoid taxation. By systematically extracting and organizing the information contained in these financial statements, we can build a new dataset that allows us to test important questions about MNC decision-making.
The ultimate goal of the project is to transform thousands of unstructured financial statements into a reliable, analyzable dataset. This work will provide a valuable empirical foundation for understanding how U.S. companies behave in a globalized economy and how they respond to changes in global tax laws. The data we assemble will also support multiple research projects that examine the trade-offs companies face between reducing their tax burdens and maintaining substantial operations in Ireland. This research will contribute to broader academic and policy discussions about corporate tax reform, tax avoidance (i.e., “profit shifting”), and the economic impact of multinational activity.
The student will help develop data extraction tools, involving coding and AI. Specifically, the student will i) implement Python code to process financial statements, ii) use AI-based Optical Character Recognition (“OCR”) tools (e.g., Google Document AI, AWS Textract) for scanned documents, and iii) apply large-language models (e.g., ChatGPT’s API) to parse and structure extracted text into usable datasets. I will provide guidance and training, so no prior experience is required.
The goal of this task is to extract data (i.e., the number of Irish employees, the company’s Irish activities, and financial information) from thousands of financial statements associated with the ~500 largest U.S. MNCs operating in Ireland from 2015 to 2024.
The student will learn how to interpret corporate financial disclosures, including operational and tax information. The student will also develop data analysis skills, including programming and building datasets from raw documents. Additionally, because there are issues included in Irish financial statements that I have not yet discovered, the student and I could identify new patterns together. This allows the student to develop research skills (including how to develop research questions relevant to tax policy) while contributing their own ideas to shape the direction of future projects.
Siyu Wang
Economics
Villanova School of Business
This project investigates the role of partisan motivated reasoning in how individuals process political information, update their beliefs, and form policy preferences. Motivated reasoning refers to the tendency to interpret evidence in ways that confirm existing attitudes, protect identity, or reinforce desired conclusions.
The project uses a series of online experiments in which participants are presented with factual information about salient issues such as economic performance, health care, and climate policy. The design randomizes whether information is framed as favorable or unfavorable to each political party, allowing us to measure whether individuals update beliefs asymmetrically depending on partisan alignment. This provides direct evidence on the mechanisms and magnitude of partisan motivated reasoning in belief formation.
Beyond belief updating, the project explores how motivated reasoning shapes preferences for policies such as redistribution, trade, and climate action. Biased interpretations of factual information may spill over into attitudes toward taxation and welfare, support for tariffs and trade restrictions, or willingness to address environmental and other challenges. By linking information processing with behavior, the study highlights how motivated reasoning contributes not just to polarized beliefs but also to persistent policy divides.
By combining theoretical modeling with high-quality experimental data, this project provides an integrated account of the cognitive and social mechanisms underlying partisan motivated reasoning and its broader implications for political economy. The findings will advance academic debates on belief formation and policy preferences, while informing the design of interventions aimed at fostering more informed and less polarized decision-making.
The first-year Match research assistant will contribute to the initial development of an iOS application designed to support experience sampling research. In the first phase, the student will focus on creating a simple prototype app capable of sending randomized notifications to users throughout the day and recording basic responses (e.g., How was your day today on a scale from terrible to very good?). In addition to meeting regularly with Dr. Tavakoli, the student will then work alongside a PhD researcher to expand the app’s functionality, including features such as data storage, user interface refinement, and potential integration with backend services. Depending on the student’s interests, there may also be opportunities to contribute to usability testing and human-centered design aspects of the app.
Through this project, the student will gain hands-on experience with Swift and iOS frameworks (SwiftUI or UIKit), app prototyping, and the fundamentals of backend communication and database management (e.g., Firebase, Core Data). They will also develop problem-solving, project documentation, and collaborative research skills while engaging in the lifecycle of app development. This position is designed to provide a foundation for continued work beyond the semester, with opportunities for deeper involvement in applied research on human-technology interaction, in case the applicant is interested.
Thomas Griffin
Finance and Real Estate
Villanova School of Business
Academics and practitioners rely on standard databases to conduct research on Mergers & Acquisitions, Debt Capital Markets, and Equity Capital Markets. Academics use these databases to answer economic questions that advance our understanding of how capital is allocated in the economy. Practitioners use these data platforms to price deals based on comparable transactions. In both cases, data accuracy is paramount. Yet, standard databases often report conflicting values for the same transaction. The goal of this project is to compare standard databases used by investment bankers and academic researchers, document systematic differences in data quality, and determine whether these differences lead to biased outcomes. This project is ideal for students interested in a career in investment banking, as it will allow them to learn key deal terms, gain experience using financial databases, and develop a broad understanding of capital markets.
Students will access financial data platforms in the Victoria and Justin Gmelich '90 Lab for Financial Markets, verify source documentation via SEC EDGAR, compare data using Microsoft Excel, write reports about whether/how biases in these databases would lead to flawed decisions and present their findings with a poster at VSB Research Day.
Kyoung Yong Kim
Management and Operations
Villanova School of Business
Entrepreneurial firms play a vital role in today’s economy. They contribute approximately 44% of the U.S. gross domestic product (GDP) (Dore, 2019) and are responsible for creating two out of every three new jobs (Jobanputra, 2023). Beyond economic impact, these firms are often a primary source of innovation, frequently driving disruptive and radical breakthroughs. For instance, the ride-sharing business model was pioneered and popularized by small entrepreneurial ventures.
However, to make such meaningful contributions, entrepreneurial firms must first survive in an intensely competitive environment. A critical factor in their survival is the ability to establish legitimacy—defined as “a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions” (Suchman, 1995, p. 574).
Entrepreneurial firms pursue legitimacy through various strategies: obtaining endorsements from credentialing bodies, forming strategic alliances with established organizations, appointing influential individuals to leadership roles, and crafting compelling entrepreneurial narratives (e.g., Plummer, Allison, & Connelly, 2016). Prior research suggests that being perceived as legitimate by external stakeholders significantly increases a firm’s ability to attract the critical resources needed for survival.
Yet, despite these efforts, a substantial proportion of entrepreneurial firms still fail. Statistics show that, on average, four out of five new ventures do not survive beyond their first five years. Why, despite deliberate attempts to gain legitimacy, do so many entrepreneurial firms continue to fail?
This study aims to address this paradox and explore how entrepreneurial firms might improve their chances of survival in a highly competitive landscape.
The Match student research assistant will work closely with me to support various aspects of the research project. Responsibilities will include conducting literature reviews, assisting in the design and development of surveys and questionnaires, performing data entry, and contributing to basic statistical analysis.
Attention to detail and a solid understanding of research methods will be important for developing effective survey tools. The student will also be responsible for collecting, organizing, and maintaining research data to ensure accuracy and accessibility. While the role may involve conducting statistical analyses using appropriate software, prior experience is not required—students will have the opportunity to learn and receive guidance as needed
This role offers valuable hands-on experience with real-world research processes, making it an excellent opportunity for students considering graduate studies, careers in analytical fields, or professional paths where critical thinking, communication, and data literacy are essential.
Students can gain a variety of skills by participating in this project. Among others, they will have the opportunity to strengthen their critical thinking and conceptual abilities. Depending on their statistical background, they may also significantly enhance their statistical skills. These competencies are highly valuable and widely applicable, forming a strong foundation for success in both academic and professional careers.