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TD MDAL Research Grant - 2021 awards

Grant recipients

Data analytics is relevant across a wide range of areas, applications from faculty and PhD students from all areas of the Graduate Department of Management were considered. 

The lab awarded funds to the successful projects listed below in 2021.

Faculty and PhD student recipients include:




Daniel Goetz, Assistant Professor of Marketing, “Learning from Even Weaker Ties: Peer Effects from Strangers in Online Purchases” (with Wei Lu)


Andrey Golubov, Assistant Professor of Finance, “Are takeovers costly for workers?”


Sheng Liu, Assistant Professor of Operations Management and Statistics, “Smart Urban Transport and Logistics Empowered by Data Analytics”


Matt Osborne, Associate Professor of Marketing, “Interactive Customer Feedback in the Digital Economy: When and How Creators Should Respond to Customers?” (with Minjee Sun)


Shreyas Sekar, Assistant Professor of Operations Management, “Learning to Combat Fraudulent Behaviour in E-Commerce"


Avni Shah, Assistant Professor of Marketing, “Payday Lending and Its Implications: Designing More Optimal Lending and Repayment Strategies” (with Dinara Akchurina and Andre Cire)


Mengze Shi, Ellison Professor of Marketing, “How to Engage the Online Community? A Textual Analysis of Comments” (with Clarice Yulai Zhao)


Claire Tsai, Associate Professor of Marketing, “Paying to Write Good Reviews? Positive Skewness of Distribution of Consumer Reviews on E-Commerce Platforms vs. Review Sites” (with Ying Zeng, and Wei Lu) and “’Good Things Satiate and Bad Things Escalate?’ Consumers Adapt to Positive Experiences and Sensitize to Negative Experiences” (with Kailuo Liu)


Irene Yi, Assistant Professor of Finance, “Which Firms Require More Governance? Evidence from Mutual Funds' Revealed Preferences”


PhD Students


Rolando Campusano, Economic Analysis and Policy, "Delineating Neighborhoods using Location Choices”


Stacey Choy, Accounting, “Inside the Black Box of Private Communications: Evidence from Taxi Ride Patterns between Managers and Analysts in New York City” (with Ole-Kristian Hope)


Mohsen Foroughifar, Marketing, “How Do Algorithmic Price Suggestions Impact Home-Sharing Markets? Evidence from Airbnb"


Zheng Gong, Economic Analysis and Policy, “How does popularity information affect product design?” (with Guangrui Li, York University)


Saman Lagzi, Operations Management, “Model-Free Assortment and Bundle Pricing with Transaction Data”


Marco Salerno, Finance, “Who Should Buy Stable Firms?”


Mingyue Zhang, Accounting, “Determinants and consequences of human capital management disclosure”



Abstracts provided below:


“Learning from Even Weaker Ties: Peer effects from Strangers in Online Purchases.”

Daniel Goetz, Assistant Professor of Marketing at UTM and Wei Lu, PhD Student

Abstract: This project aims to measure the relative strength of peer effects from friends versus peer effects from randomly encountered strangers in the context of consumers’ product adoption decisions. Our goal is to answer whether peer effects in product adoption primarily capture the effect of mere additional exposure to the product, or whether other information about the product is conveyed that depends on the nature of the peers' relationship.


“Are takeovers costly for workers?”

Andrey Golubov, Assistant Professor of Finance

Abstract: Using newly available administrative data on the universe of Canadian firms and individuals from Statistics Canada, this project will investigate whether takeovers are costly to target firm workers in terms of jobs, earnings from employment, and overall incomes after taking into account any substitute sources of pay.


“Smart Urban Transport and Logistics Empowered by Data Analytics.”

Sheng Liu, Assistant Professor of Operations Management and Statistics

Abstract: New technologies and innovative business models are leading to connected, shared, autonomous, and electric solutions for the tomorrow of urban transport and logistics. A tremendous amount of data is generated every day from public transit operators, mobility service platforms, and logistics service providers. This research program develops data-driven solutions to emerging urban transport and logistics challenges, including multi-modal transit planning, last-mile delivery, and urban warehouse fulfillment.


“Interactive Customer Feedback in the Digital Economy: When and How Creators Should Respond to Customers?”

Matt Osborne, Associate Professor of Marketing and Minjee Sun, Assistant Professor of Marketing, University of Iowa

Abstract: Technological developments have enabled content producers and their customers to interact with each other in real time. Moreover, content producers may modify their offerings in response to customer feedback. Using data from an online book platform, we explore how novel writers modify their novels as a result of customer feedback and what kind of modifications enhance the novels’ performances. In this research, we apply natural language processing and text mining to characterize readers’ comments and quantify the changes in writers’ novel contents. Our preliminary analysis suggests that the sentiment of readers’ comments matters, as well as writers’ past publishing experience.


“Learning to Combat Fraudulent Behaviour in E-Commerce.”

Shreyas Sekar, Assistant Professor of Operations Management and Faculty Affiliate at the Schwartz Reisman Institute for Technology and Society

Abstract: Digital marketplaces such as Amazon, AirBnB, and Upwork play a significant role in influencing our choices by means of recommendation and ranking algorithms. As a result, sellers on these platforms are incentivized to resort to fraudulent behaviour---e.g., fake reviews, click farms, duplicitous listings, bots, etc----to game the algorithm. Existing methods to combat fraud tend to be reactive rather than proactive. This project will centre around developing prescriptive analytics and proactive learning algorithms that converge to socially desirable outcomes despite the presence of manipulative behaviour.


“Payday Lending and Its Implications: Designing More Optimal Lending and Repayment Strategies.”

Avni Shah, Assistant Professor of Marketing with Andre Cire, Associate Professor of Operations Management and Dinara Akchurina, Assistant Professor of Marketing

Abstract: Payday loans are considered an expensive way for consumer to borrow money, and yet the number of Canadians using payday loans has doubled over the last decade. For many borrowers, failing to repay the amount borrowed can result in far more costly expenses as the interest payments can result in repayment amounts that are nearly six times the original borrowed amount. In this research project, we use a rich dataset combining precise borrower-specific characteristics, outstanding loan-specific characteristics (loan amount, loan purpose, interest rate, loan duration, loan type—i.e., cash advance or title loan, repayment schedule) coupled with a detailed list of banking transactions to investigate a number of important research questions: What drives consumers to take out a payday loan in the first place? What trade-offs are made within the pay cycle or what external forces drive this need? When (and for whom) is it financially prudent to use payday loans rather than other sources of short-term credit? What factors increase the likelihood of timely payday repayments? Do reminders via text or via direct phone contact increase the likelihood of paying a loan on time? Using insights from our data and survey responses, we will develop novel predictive, prescriptive, and operational models that can improve consumer decision-making and product design.


“How to Engage the Online Community? A Textual Analysis of Comments.”

Mengze Shi, Ellison Professor of Marketing (with Clarice Yulai Zhao)

Abstract: Digital platforms enable a massive number of independent creators to engage their online communities. The creators facilitate and participate in the discussions about their contents. The proposed project intends to study the nature of engagement activities and the impact on the strength of customer relation, using purchase and comment data from a major online publishing platform in Asia. We plan to use natural language processing methods to analyze the unstructured data, convert them into quantifiable measures, and relate them to behavioral outcomes. We expect the research outcomes to inform the effective engagement strategies in the online communities.


“Paying to Write Good Reviews? Positive Skewness of Distribution of Consumer Reviews on E-Commerce Platforms vs. Review Sites.”

Claire Tsai, Professor of Marketing with Wei Lu, PhD Student, Marketing and Ying Zeng, PhD Student, Marketing

Abstract: This research applies econometrics, machine learning, and text analysis approaches to investigate whether and how consumer review distributions differ across different types of web platforms, and the mechanisms driving these differences. Are consumers more critical on review websites than on e-commerce platforms? If this is the case, what might be driving this difference? While existing research has shown that the distribution of consumer reviews is skewed, understanding why the degree of skewness varies across websites remains largely unknown. Our research fills this gap by using data analytics and big data to study this question.


“Which Firms Require More Governance? Evidence from Mutual Funds' Revealed Preferences.”

Irene Yi, Assistant Professor of Finance

Abstract: This project estimates mutual funds’ preferences for corporate governance structures, by examining how mutual funds voted on companies’ shareholder proposals. The project develops rankings that measure which firms benefit more from adopting governance provisions that increase shareholder rights, from the perspective of mutual funds. In doing so, the project implements a novel machine-learning algorithm: the Metropolis-Hastings Markov chain Monte Carlo algorithm of Vitelli et al. (2018). The project complements the literature that questions the “one-size-fits-all” approach toward governance.


“Delineating Neighborhoods using Location Choices.”

Rolando Campusano, PhD Student, Economic Analysis and Policy

Abstract: Research using neighbourhoods as the unit of analysis has relied on administrative definitions that have been delineated from a process that does not coincide with agents’ decision problems. This produces a spatial misalignment between administrative and "economic" boundaries that bias research findings and the policies designed around them. I propose a novel methodology to delineating neighbourhoods based on a machine learning algorithm that groups locations based on revealed preferences. I apply it to delineate Toronto's industrial and residential neighbourhoods and show that they are not like their administrative counterparts. Neighbourhoods are different across industries or property types, have an elliptical shape and tend to locate around major streets.


“Private Communication between Managers and Financial Analysts: Evidence from Taxi Ride Patterns in New York City.”

Stacey Choy, PhD Student, Accounting (with Prof. Ole-Kristian Hope)

Abstract: This study constructs a novel measure that aims to capture face-to-face private communications between firm managers and sell-side analysts by mapping detailed, large-volume taxi trip records from New York City to the GPS coordinates of companies and brokerages. Consistent with earnings releases prompting needs for private communications, we observe that daily taxi ride volumes between companies and brokerages increase significantly around earnings announcement dates (EAD) and reach their peak on EAD. After controlling for an extensive set of fixed effects (firm-quarter, analyst, and year) and other potential confounding factors, we find that taxi rides undertaken around EAD are negatively associated with analysts’ earnings forecast errors in periods after EAD. Analysts having more taxi trips around EAD also issue more profitable recommendations after EAD. Our results suggest that analysts may obtain a private source of information orthogonal to their pre-existing information from these in-person meetings, which may help them better understand the implications of current earnings signals for future earnings.


“How Do Algorithmic Price Suggestions Impact Home-Sharing Markets? Evidence from Airbnb.”

Mohsen Foroughifar, PhD Student, Marketing

Abstract: Starting from December 2015, Airbnb used machine learning to recommend "smart prices" to its hosts with the hope that they use these recommendations in their pricing decisions. Smart Pricing algorithm uses past data to recommend product-specific prices to Airbnb hosts. Although there might be some benefits with using smart pricing algorithm - being free, easy to turn on and off, and allowing hosts to set price boundaries - many hosts do not use it. In this work, we study the impact of these algorithmic price suggestions on home-sharing markets. We examine how the introduction of Smart Pricing has impacted Airbnb's hosts and what the subsequent welfare implications of this technology are on the home-sharing markets.


“How does popularity information affect product design?”

Zheng Gong, PhD Student, Economic Analysis and Policy (with Guangrui Li, Assistant Professor of Operations Management and Information Systems, York University)

Abstract: This project aims to examine the impact of popularity information revealing on firms’ product design strategy. We make use of a policy change – that articles’ popularity is revealed to subscribers – on Wechat Official Account platform, which is the biggest blog platform in China. We are interested in applying natural language processing technics to understand the following aspects of product design: first, the quality and amount of advertisement in the contents; second, the topic choice of the contents; and the tendency to use click-baits in the titles.


“Model-Free Assortment and Bundle Pricing with Transaction Data.”

Saman Lagzi, PhD Student, Operations Management

Abstract: Online retailing has seen steady growth in the past decade. While the granularity of the data gathered from past customers far exceeds that of the offline setting, the firms are also able to roll out products rapidly. Thus, there may be little purchase information for a firm to take timely actions on the pricing of its products. This research project intends to address the challenge of pricing an assortment of products, or bundles of products, when the firm runs the risk of demand model misspecification, due to data sparsity. We intend to take an approach that does not impose a model on the distribution of the customers' valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. We also intend to design approximation strategies that are of low computational complexity and interpretable, hence helping firms make timely pricing decisions that lead to robust performance.


“Who Should Buy Stable Firms?”

Marco Salerno, PhD Student, Finance

Abstract: The current low interest rate environment poses challenges for liability-driven investors (i.e., pension funds): while being a good hedge towards liabilities, long-term bonds do not provide enough income to repay investors’ liabilities. This project shows both theoretically and empirically that liability-driven investors should tilt their portfolios towards “stable equities”, which are defined as firms that operate in stable sectors of the economy. A new methodology has been developed to classify stable equities using textual analysis and detailed data on consumption by type of product.


“Determinants and consequences of human capital management disclosure.”

Mingyue Zhang, PhD Student, Accounting

Abstract: Despite increasing attention to firms’ human capital management (HCM), scant large-scale empirical evidence exists regarding the determinants and consequences of HCM disclosures. I develop HCM disclosure transparency measures, using a recent machine learning technique (the word embedding model), and hypothesize that heightened awareness of social-oriented considerations and operational-oriented competitiveness drive HCM disclosure incentives. I find that firms disclose more about their social-oriented HCM information but less about operational-oriented HCM information when product market competition is high. Exploiting reductions in import tariff rates as exogenous variation in product market competition, I document a causal link between market competition and HCM disclosures. Further evidence shows that social-oriented disclosures improve social performance ratings and attract sustainable investors. However, I only find that more operational-oriented HCM disclosures are associated with higher subsequent shareholder value. Taken together, my findings suggest that social-oriented HCM disclosures satisfy investors’ information demand and operational-oriented HCM information reflects value-enhancing HCM practices.