Student Research Initiative (SRI)
Pathway for students to gain first-hand research experience working with faculty on interesting and novel challenges industry and society face.
Attend Information Session | September 11
The Student Research Initiative (pronounced SiRI) at Saunders allows eligible undergraduate and graduate students to participate in research projects that seek to solve interesting business trends and challenges, allowing them to develop basic research and data-analytics skills, methods or tools. SRI Student Scholars closely collaborate with faculty members in solving real business problems using research mindset, processes, methods, and analytical tools and techniques.
Students may be able to use summer research experience to replace one co-op; students are paid a stipend over the summer and hourly during the fall and spring semesters.
To apply, students must complete the application form. Below is a listing of SRI research projects and student requirements.
Charles S. Brown, Jr. and Renee A. Brown International Project Fund
Students working on a project with an international component may be eligible for additional support. Please check with faculty sponsors to see if the Charles S. Brown, Jr. and Renee A. Brown International Project Fund supports their project.
Important Dates for Student Research Initiative
Kick-off Event
September 11, 2024 | 2:30 p.m.-3:30 p.m. | Free pizza
View event details
Application Deadline
TBD
Faculty Research Selections
TBD
Research Work Begins
TBD
Research Projects
*International Component - Projects with an international component are eligible for a one time $5,000 budget. Funding can be used to support international travel for the student or faculty member, if accompanied by the student. (This opportunity is made possible by a gift from Saunders MBA alum, Charlie Brown.)
State of the art website quality / customer experience metrics
Professor Neil Hair
Work with an award-winning marketing professor and renowned expert in the field of online customer experience for two semesters looking at contemporary best practices on how consumers assess website quality and their experiences. Much has been written on the components of online customer experience and website quality. This study will marry both concepts and involve a step-by-step guide to conducting systematic literature review methods to reveal the key indicators of these two domains. The study will initially focus on already published, secondary data, though opportunities for primary research involving consumers exist at an evolved stage of this research program. The student will receive training on how to conduct robust literature surveys, guidance on how to build out thematic areas and their component parts, and have an opportunity to work on a publishable piece in either an academic or a practitioner journal.
Skills required
Good secondary research skills, systematic approaches to database research (training offered on this), a curious nature, willingness to work hard, a sense of humor, and the ability to work independently.
Duration of the project
Two semesters.
Consumer Experiences with Finger Lakes Wineries
Professor Matthew Vollmer
How do we know that consumers had a great experience with a brand? And what made it so good? To gain a competitive advantage, how can a business elevate the consumer experience to make the brand stand out and truly become exceptional. These type of consumer experience are what business should strive for; the memories that are created while interacting with a brand create stronger bonds with consumers and lead to lifelong customers. This research will examine consumers’ experiences with wineries within the Finger Lakes region to better understand and identify the moments where consumers felt like their experience with the product while visiting a tasting room went beyond ordinary and became extraordinary.
Skills required
Basic understanding of Marketing, knowledge of Consumer Behavior (MKTG350) would be ideal.
Duration of the project
One semester;Two semesters.
Strategies for Utilizing Artificial Intelligence Solutions for the Climate Crisis
Professor Kenan Guler
In recent years, there has been a growing focus on both artificial intelligence and societal challenges, especially climate change. Scholars and practitioners are increasingly exploring innovative approaches to address grand societal challenges. This paper centers on the intersection of artificial intelligence and climate change, aiming to investigate how AI can contribute to resolving the ongoing climate crisis. The study will utilize an analysis of over 18,000 academic articles and 30,000 news articles related to AI and climate change, bridging the fields of strategic management, innovation, and AI literature to provide valuable insights into orchestrating innovative solutions for the existing climate crisis. The intended outlet for this paper is submission to an upcoming special issue at the Strategic Management Journal. I am enthusiastic about collaborating with an interested student over the next two semesters.
Skills required
The student's tasks will involve assisting in the literature review, analyzing data, and performing specific tasks using Python, R, and Excel.
Duration of the project
Two semesters.
Investigating Residential Real Estate Market Conditions via the Deep Learning Large Language Models (LLMs)
Professor Soon Hyeok Choi
The US residential real estate market comprises both structured and unstructured texts. In particular, real estate agents use the multiple listing service (MLS) to describe key features of a property as part of a prospectus. The textual information contained in MLS over time can reveal important market conditions (e.g., cold market, bidding war). The project intends to use the Bidirectional Encoder Representations from Transform- ers (BERT), a deep learning AI transformer model, to deploy its natural language processing capacity in the context of the agent descriptions on the MLS. In the final outcome, we want to unambiguously measure the impact of the market conditions on the transaction price, listing price, and ex-ante estimates.
Skills required
- Familiarity with Excel, R or Python is a plus
- Data collection and processing
- Attention to detail
- Ability to meet in-person or Zoom on a weekly basis
Duration of the project
Two semesters.
Markets for technology along the Supply Chain
Professor Shubhobrata Palit
The aim of this project is to understand the dynamics of markets for technology along the supply chain. Markets for technology consist of transactions of technology between firms. Firms develop new technologies for developing new products and services. However, there is limited understanding on why they sell new technologies. In this project, we aim to understand when firms sell their competitive advantage, which in this case is their Intellectual Property Rights to (new) technologies, to their supply chain partners.
Skills required
- Data extraction, cleaning and manipulation using Python/R/Stata and SQL, etc.
- Basic knowledge of data analytics.
Duration of the Project
One semester;Two semesters.
Impact of Stringent Regulation on Ratings Market: Evidence from Death of a Rating Agency
Professor Sriniwas Mahapatro
Do strict regulatory sanctions, such as banning a rating agency and reducing competition in the rating market, improve rating quality? Or does the suspension of an agency lead to unintended consequences of downward biased ratings? Exploiting a rare instance of a regulator sanctioned forced exit of a credit rating agency (CRA) in India, I examine the impact on rating standards of other agencies. Using a difference-in-differences design, I find that the ban on rating services of a CRA leads to a one-notch rating downgrade in one out of five impacted firms. The results are robust to the use of the Ordered logistical regression model. Further, the deflation of ratings is associated with a 30% decline in type I errors (missed defaults) but is also accompanied by an unintended 154% increase in type II errors (false warnings). My findings are consistent with the “pessimistic behavior” hypothesis, where incumbent raters issue downwardly biased ratings to mitigate higher regulatory costs. Further, the decline in ratings leads to real consequences of an increase in borrowing costs for firms that solicit ratings. I further examine the pessimistic behavior hypothesis using sentiment analysis. I test whether the sentiment of the text contained in the credit rating reports of the impacted firms carry a more negative tone after the ban on the rating agency. My findings highlight the unintended consequences of regulator-led forced rating agency exits.
Skills required
Python coding, Data cleaning and data handling skills, text analytics using python, Basic knowledge of accounting and finance.
Duration of the Project
One semester;Two semesters.
Probabilistic programming of sports data
Professor Victor Perotti
Contemporary sports analytic approaches use machine learning or AI to make predictions about sporting events and outcomes. These approaches are generally “frequentist” relying on traditional assumptions of statistics. This project aims to explore a probabilistic programming approach, which defines boundaries using bayesian assumptions and then includes simulations to describe likely outcomes.
Skills required
Python coding, Data cleaning and data handling skills, predictive modeling.
Duration of the Project
One semester;Two semesters.