Seminar Series
The Morrison Center frequently hosts presentations by peer faculty so they may discuss their working papers in our collegial but rigorous environment.
Date Event

More coming soon!

Date Event
April 12, 2024

Gender Disparity in Online Reputation: Evidence from an Online Freelance Platform by Anja Lambrecht

Online ratings aim to reduce information asymmetry between buyers and sellers and help buyers make more informed purchase decisions. However, a problem arises if ratings are biased and do not reflect true buyer satisfaction. We investigate online ratings for services provided by male and female freelancers using a unique data set from an online freelance platform that elicits both public and private ratings from buyers after completion of a job. Public ratings are displayed on the website while private ratings are not shared with either the freelancers or other buyers. Using private ratings as a proxy for buyer satisfaction, we find that, conditional on the same private rating, public ratings are higher for male than for female freelancers. We propose this is a result of buyers' gender stereotypes about confrontational behavior of freelancers following a critical review. Buyers are more concerned about a threat of confrontation with male than female freelancers and, thus, give male freelancers a higher public rating. We present three supporting pieces of evidence. We show that gender disparity in public ratings is most pronounced for buyers from countries with greater gender inequality, for buyers from US states with a lower percentage of women in the labor force, and in service categories where female freelancers are under-represented. These results matter for freelancers and platforms because systematic differences in evaluation by gender can lead to unfair outcomes for platform participants.

March 8, 2024

Where Does Advertising Content Lead You? We Created a Bookstore to Find Out by Ilya Morozov

We study how advertising content influences consumers' decisions. To this end, we create a simulated online bookstore that imitates a real online shopping experience. We then conduct a pre-registered and incentive-compatible experiment in which we randomly expose store visitors to display ads, randomizing both advertising exposures and content. We find that ad content plays a major role in shaping advertising effects. Ads that reveal a book's low price consistently increase demand for the advertised book relative to ads that do not reveal price. By contrast, ads that reveal the book's genre induce some consumers to search and buy the book but lead others to reject it without search. We show these polarized responses can increase or decrease the total number of searches and purchases of the advertised book depending on the share of consumers who favor the revealed genre. Our findings suggest that advertisers should carefully choose which product attributes to reveal in their ad copies. We also show that revealing product attributes in ads does not change the total amount of search. Taken together, our results suggest the primary role of ad content is to affect consumers' beliefs about the advertised book rather than to alter the perceived benefits from search."

February 2, 2024

How do Consumers React to Ads that Meddle in Out-Party Primaries? by Mohamed Hussein

In 2022, Democrats spent $53 million on ads helping far-right candidates win Republican primaries. Paying for ads that support far-right candidates, the reasoning went, could help Democrats win in the general elections because it is easier to beat extreme than moderate candidates. In the current research, we ask: how do consumers react to the use of “Meddle Ads”? Across eight studies (N = 4,237) using a variety of empirical approaches—including incentive-compatible donations studies, conjoint analysis, and analysis of online comments using natural language processing—we find that consumers are averse to the use of Meddle Ads. Consumers spoke more negatively about, donated smaller amounts to, and were less likely to choose candidates who used Meddle Ads. Aversion to Meddle Ads is driven by their perceived riskiness. This riskiness stems from two types of risks: outcome-related risk (losing elections) and system-related risk (losing trust in democracy). We find consistent evidence that system-related risks drive Meddle Ads aversion, and substantial but less consistent evidence that outcome-related risks do, too. These findings contribute to research on political marketing, provide practical guidance for marketers around Meddle Ads, and identify a novel type of risk (system-related risks) with significant implications for consumer behavior research.

January 19, 2024

Consumer Wealth and Price Expectations by Rodrigo Dias

"Prices have reached record-high levels, and inflation is one of the primary concerns for consumers worldwide. Interestingly, changes in prices are in part a self-fulfilling prophecy: if consumers expect prices to rise, prices will rise. Moreover, consumers’ future price expectations affect policymaking, firms’ decisions, and consumer choice. Across 16 studies (N = 294,950), including a nine-wave longitudinal study, a multinational study in eight countries, a multidecade study with 250,000+ consumers, proprietary data from the Federal Reserve, and multiple experiments, we show that consumers who feel more financially constrained expect future prices to be higher, compared to consumers who feel less financially constrained. This effect occurs because financially constrained consumers rely more on their perceptions of current prices (instead of other factors) when forecasting prices. Accordingly, this effect attenuates when current prices seem less relevant to the forecast and amplifies when current prices are perceived as expensive. Critically, we show that higher future price expectations predict greater spending, lower savings, and more debt among financially constrained consumers. Overall, the current work underscores the role of future price expectations as a key driver of consumer behavior, demonstrates how these expectations are formed, and offers insights for consumers, marketers, and policymakers."

December 1, 2023

Splurging With AI: How Conversational Devices Increase Product Upgrades by Aner Sela

AI-enabled conversational devices, such as Amazon Alexa and Google Assistant, are gaining popularity, but do they influence consumer choice, and if so, how? We propose that consumers are more likely to purchase an expensive upgrade option when shopping using a conversational device, compared with other mediums such as a computer screen, a chatbot, or a conversation with a human agent. Nine studies using real conversational devices, including secondary data from an online vendor, suggest that this tendency to splurge reflects consumers’ decreased processing of quantitative information, such as price, when auditorily interacting with a conversational device. Alternative explanations, including expression modality, social presence, social norms, and unnaturalness of the interaction are not supported. The findings make important contributions to research on modality effects, information processing, and the emerging field of consumer-AI interaction.

September 22, 2023

Preferences for Firearms and Their Implications for Regulation by Brad Shapiro

This paper estimates consumer demand for rearms with the aim of predicting the likely im- pacts of alternative regulations on purchasing. We conduct a stated-choice-based conjoint and estimate an individual-level demand model for rearms. We validate our estimates using aggre- gate moments from observational data. Counterfactual simulations indicate that price based regulations largely deter purchases from new gun buyers who purchase handguns while an as- sault weapons ban increases demand for handguns, which are associated with the majority of rearm-related violence. We describe the distribution of consumer surplus under dierent counterfactuals and discuss how those distributions could be useful for crafting policy. JEL Classication: L50, L51, L67, L68, H23, I18, K23, M31, M38, C11, C83

Click here to find out more about Brad Shapiro.

May 12, 2023

Yu Ding - A Novel Crowdsourcing Approach for Improving the Information Ecosystem

"Given the explosion of news transmitted to, and shared by, consumers across different media, the veracity of information is of critical importance. However, the scale of existing fact-checking organizations is limited, hence resulting in a scant proportion of news articles being fact-checked. We address the challenge of scaling up fact-checking operations in the domain of science-related articles by proposing and testing a novel crowdsourcing solution. A key challenge with asking lay consumers to rate the veracity of scientific news articles is that they are likely to be biased by their prior beliefs. Using articles that have been rated for veracity by scientists as a starting point, we overcome this bias by proposing the use of crowdsourced similarity ratings rather than veracity ratings. We find that asking lay consumers to rate the similarity between scientist-rated and unrated articles provides an unbiased, effective, and efficient way to scale up veracity ratings of scientific articles. Our proposed method (human similarity-judgments) outperforms algorithm-rated similarity (e.g., by TF-IDF and by Word Embedding) to predict more accurately an article’s scientific veracity. Our alternative is also superior to previous approaches for judging veracity—such as utilizing the semantic markers of false news previously detected by previous research. We further compute a “transitivity index” to identify consumers likely to be more accurate at making similarity judgments and show how veracity predictions can be improved by paying close attention to consumer segments recruited for the similarity-judgment task. We demonstrate that our method can predict the scientific veracity of articles with over 95% accuracy and that both type-I and type-II errors are minimized. Lastly, we find preliminary evidence that the advantage of using similarity judgments comes from the fact that consumers are more likely to distance themselves from the arguments being rated. We also discuss the limitation of this method (e.g., topic level granularity) and further research (e.g., increase trust by involving consumers in fact-checking)."

March 10, 2023

Tesary Lin - Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data

"How much do consumers value their personal data? How do their privacy valuations change under the influence of choice architecture? How does this influence affect the efficiency of data collection, not only by changing the quantity of data collected, but also by altering its representativeness? To answer these questions, we run a large-scale choice experiment to elicit consumers’ valuation for their Facebook data while randomizing two common choice frames: default and price anchor. An opt-out default decreases valuations by 14.4% compared to opt- in, while a $0-$50 price anchor decreases valuation by 51.1% compared to a $50-$100 anchor. Moreover, in some consumer segments, the susceptibility to frame influence is negatively correlated with consumers’ valuation absent frames. Our results suggest that conventional frame optimization practices with a goal to maximize the supply of data can have opposite effects on its representativeness: A bias exacerbating effect emerges when consumers who value their data less are also easier to be influenced by frames. On the other hand, such a volume- maximizing frame may also mitigate the bias by getting a high percentage of consumers into the sample data, thereby improving its coverage."

February 10, 2023

Nils WernerfeltEstimating the Value of Offsite Data to Advertisers on Meta

"We study the extent to which advertisers benefit from data that are shared across applications. These types of data are viewed as highly valuable for digital advertisers today. Meanwhile, product changes and privacy regulation threaten the ability of advertisers to use such data. We focus on one of the most common ways advertisers use offsite data and run a large-scale study with hundreds of thousands of advertisers on Meta. Within campaigns, we experimentally estimate both the effectiveness of advertising under business as usual, which uses offsite data, as well as how that would change under a loss of offsite data. Using recently developed deconvolution techniques, we flexibly estimate the underlying distribution of treatment effects across our sample. We find a median cost per incremental customer using business as usual targeting techniques of $43.88 that under the median loss in effectiveness would rise to $60.19, a 37% increase. Similarly, analyzing purchasing behavior six months after our experiment, ads delivered with offsite data generate substantially more long-term customers per dollar, with a comparable delta in costs. Further, there is evidence that small scale advertisers and those in CPG, Retail, and E-commerce are especially affected. Taken together, our results suggest a substantial benefit of offsite data across a wide range of advertisers, an important input into policy in this space."

Click Here to View Paper

February 3, 2023

Ryan Hamilton - In Lieu of Gifts: Understanding and Overcoming Givers’ Reduced Generosity Toward Charitable Gift Requests

"Charitable organizations are increasing their reliance on fundraisers created by individuals on behalf of a charity. Many such fundraisers feature requests for donations in lieu of gifts for celebratory occasions such as weddings and baby showers. Despite the donation being requested as a gift, givers tend to be markedly less generous when giving a charitable donation gift relative to giving typical gifts, including gifts of cash. Across six studies, including a study utilizing secondary data from a gift-registry organization, this research examines why givers are less generous when presented with requests for a charitable gift compared to other gift requests. We find that givers’ reduced generosity is driven by their desire to give a gift that offers a practical benefit to the recipient, rather than by consideration of the recipients’ desires. This motivation toward practicality is especially pronounced for giving associated with rites-of-passage occasions, such as weddings. Understanding this motivation is important, because such occasions are often utilized to request charitable donations in lieu of gifts. We also identify practical methods to reduce the generosity gap by increasing the efficacy of charitable gift requests."

January 20, 2023

Kristin Donnelly - Once and Again: Repeated Viewing Affects Judgments of Spontaneity and Preparation

"Reality is fleeting, and any moment can only be experienced once. Re-watching a video, on the other hand, allows people to repeatedly observe the exact same moment. We propose that people apply their understanding of repetition in the real world to the replay context, failing to fully distinguish behavior that they merely observe again (through video replay) from that behavior being performed again in the exact same way. Eleven experiments (N = 10,602) support this idea across a broad assortment of stimuli that includes auditions, dances, commercials, public speeches, and potential evidence in a murder trial. We demonstrate that re-watching makes a videotaped behavior appear more rehearsed and less spontaneous, as if the actor in the replayed video were simply precisely repeating the same set of actions. We rule out alternate explanations including repetition increasing accuracy of judgments, mere exposure leading to a positivity bias, and experimenter demand effects. These findings build on an influential literature showing that incidental video features like perspective or slow motion can meaningfully change how people judge the action of the video. Video re-watching may inadvertently shape judgments in contexts ranging from mundane to consequential. To understand how a video is going to influence its viewer, one will need to consider not only its content, but also how often it is viewed."

January 13, 2023

Ada Aka - Inferring Consideration Sets: A Computational Model of Naturalistic Memory-Based Decision Making

"Memory plays a crucial role in everyday decision making and especially in consumer decision making. We propose a new computational framework to study how people retrieve and choose between hundreds of common choice items stored in memory during such decisions. Our approach combines established theories of consideration set formation and memory search, with techniques from natural language processing (which use text data to derive representations and associations for choice items) and recommender systems (which provide algorithms for capturing individual-specific preferences and retrieval tendencies for such items). We show that our framework successfully describes the items that are retrieved from memory even when memory processes are not directly observed. It also captures the effects of situational variables and individual differences on memory. Thus, it provides data-driven insights into the core cognitive mechanisms at play in memory-based decision making. We demonstrate the power of this approach in three sets of studies, each with several different types of naturalistic decision prompts. In doing so, we show how established theories in marketing and psychology can be combined with new computational techniques to explain complex everyday decisions."

November 18, 2022

Greg Lewis  "Online Search and Product Rankings: A Double Index Approach"

"We develop a flexible yet tractable model of consumer search and choice, and apply it to the problem of product rankings optimization by online retail platforms. In the model, products are characterized by an observable search index, which governs what consumers search; and a utility index, which governs which of the searched options is purchased. We show that this framework generalizes several commonly used search models. We then consider how platforms should assign products to search ranks. To optimize consumer surplus, platforms should promote “diamonds in the rough,” products whose utility index exceeds their search index. By contrast, to maximize static profits, the platform should favor high-margin products, creating a tension between the two objectives. We develop computationally tractable algorithms for estimating consumer preferences and optimizing rankings, and we provide approximate optimality guarantees in the latter case. When we apply our approach to data from Expedia, our suggested ranking achieves both higher consumer surplus and higher revenues than is achieved by the Expedia ranking, and also dominates ranking the products in order of utility."

October 21, 2022

Hortense Fong, "A Theory-Based Explainable Deep Learning Architecture for Music Emotion"

Music is used to evoke emotion throughout the customer journey. This paper develops a theory-based, explainable deep learning convolutional neural network (CNN) classifier—MusicEmoCNN—to predict the time-varying emotional response to music. To develop a theory-based CNN, we first transform the raw music data into a format—mel spectrogram—that accounts for human auditory response. Next, we design and construct novel CNN filters for higher-order music features that are based on the physics of sound waves and associated with perceptual features of music, like consonance and dissonance, which are known to impact emotion. The key advantage of our theory-based filters is that we can connect how the predicted emotional response (valence and arousal) is related to human-interpretable features of music. Our model outperforms traditional machine learning models and performs comparably to state-of-the-art black-box CNN models. Finally, we illustrate an application involving digital advertising. Motivated by YouTube’s mid-roll advertising, we first conduct a lab experiment in which we exogenously place ads at different times within content videos and find that ads placed in emotionally similar contexts are more memorable in terms of higher brand recall rates. For a given ad, we then use the model's predictions to identify emotionally similar contexts in content videos.

June 22, 2022

Stephanie Chen, "Getting Credit and Minimizing Blame: How Social Affiliations Influence Perceptions of Charitable Giving (and Not Giving)"

While prior research on charitable giving has focused on exploring how much credit consumers and organizations get for charitable giving, less attention has been focused on the negative reputational consequences that may accompany the decision not to give. Based on accounts that suggest that social categories and affiliations mark interpersonal obligations, I explore whether perceptions of these obligations influence how the act of donating or not donating is viewed. An asymmetry occurs in which consumers do not get more credit for donating to people within their social groups (vs. outside of their social groups) but are blamed more for not donating to those within their social groups (vs. outside of their social group) because they are seen as breaking an obligation. The asymmetry is attenuated when the charitable cause is focused on providing benefits rather than removing harm, consistent with the view that there is a greater obligation to avoid harm than to provide benefits. The results highlight the importance of understanding perceptions of consumers both when they donate and when they do not, and suggest that feelings of obligation may be a barrier to efficient giving.

May 20, 2022

Tong Guo, "Adoption of Bio-Engineered Food"

We study the early-stage adoption of impossible meat, a novel food technology that synthesizes meat substitutes by closely simulating the texture, flavor, and appearance of real meat. Unlike traditional veggie meat that targets vegans and vegetarians, impossible meat attempts to attract meat lovers with minimal taste differentiation. How would this apparently artificial food product gain its success while the market demands less bio-engineered and more organic food? We document the early-stage adoption pattern of impossible meat by overcoming the common challenges in understanding adoption of bio-engineered food: 1) the new technology usually does not reach consumers before first being adopted by intermediaries, for whom the data is often hard to obtain; 2) the documentation of the marketing antecedent of adoption decisions is incomplete and endogenous, making it hard to causally identify the driving factors behind the technology adoption. Focusing on the key players in their US market debut between 2015-2019, we construct a novel location-specific adoption metric that accurately measures the decisions of local intermediaries. To explain the adoption pattern, we analyze a comprehensive set of media corpus using Natural Language Processing. We find that local news focusing on health and financing of the new technology positively increases the regional adoption of impossible meat, whereas discussion on sustainability and consumer WOM has little impact.

April 8, 2022

Sam Maglio, "How Long Do People Use Their Stuff?"

Research in marketing, psychology, and behavioral economics has long examined what people consume, when people consume, and why people consume. But what about how long people consume? For things that aren’t swallowed, snuffed, or savored in an instant, there exists a clear duration of time over which people acquire a continued stream of utility. As the owners of it, they get to decide how long that consumption period lasts. This research asks what goes into that decision. A series of longitudinal studies and supplemental experiments—across a variety of duration measures and products—offers three answers. First, while incentives like nudges and norms increase choosing, they subsequently reduce consuming. Second, certain personality traits make people more prone to keep or discard their belongings. Third, people can consider planned ownership for new acquisitions, which colors how they treat that stuff now and later.

April 1, 2022

Joachim Vosgerau, "Big Data - Big Biases"

Big data provide incredibly accurate descriptive statistics, and — as input to sophisticated AI methods — allow for accurate predictions of complex phenomena, causing big data’s “aura of truth, objectivity, and accuracy” (Boyd & Crawford 2012). The aim of this research project is to demonstrate that this “aura of truth” is dangerous. Because larger sample sizes (e.g., big data) vastly improve descriptive statistics, decision-makers erroneously believe that they also improve statistical and causal inferences, rendering decision-makers more susceptible to data-inferential biases. Specifically, we demonstrate that the larger is the size of a data sample, the more likely are decision-makers to interpret correlations as indicative of causation. 

Contrary to the popular belief that “With enough data, the numbers speak for themselves,” our research highlights that data do not speak for themselves, but human decision-makers interpret data patterns, and because they are human, they make mistakes in their inferential judgments.

March 4, 2022 Wesley Hartmann, “Marketing & Experimentation for Behavioral Change”
February 25, 2022 Serena Hagerty, “Zero-Sum Perceptions Reduce Acceptability of Premium Services”
February 18, 2022 Zhihao Zhang, “Integrating Models of Cognitive Process and Economic Decisions: The Case of Open-Ended Decisions”
January 21, 2022 Andrey Simonov, "Gaming or Gambling? An Empirical Investigation of the Role of Loot Boxes Addiction in Video Games"
May 21, 2021 Alix Barasch, “Fairness and the Psychology of Technological Disruption”
May 14, 2021 Sarah Moshary, “Advertising Effects In Equilibrium”
April 23, 2021 Daniel Ershov, “The Effects of Influencer Advertising Disclosure Regulations: Evidence from Instagram”
February 21, 2020 David Godes, "What Drives Extremity Bias In Online Reviews?"
February 7, 2020 Soheil Ghili, "Network Formation and Bargaining in Vertical Markets: The Case of Narrow Networks in Health Insurance"
January 31, 2020 Abby Sussman, “When Shrouded Prices Signal Transparency: A Preference for Costly Complexity”
January 10, 2020 Joe Goodman, “Rethinking the Experiential Advantage”
November 15, 2019 Ed O’Brien, “Combating Hedonic Adaptation”
November 7, 2019 Ilya Morozov, “Measuring Benefits from New Products in Markets with Information Frictions”
October 31, 2019 Sarah Memmi, “Budgeting Time First Increases Multiple Goal Achievement”
March 15, 2019 Silvia Bellezza, Columbia University: Topic Pending
February 22, 2019 Giovanni Compiani, University of California, Berkeley: "Nonparametric demand estimation in differentiated products markets"
February 8, 2019 David Hardisty, University of British Columbia: "The sign effect in past and future discounting"
February 1, 2019 Ed O'Brien, University of Chicago: Topic Pending
January 11, 2019 Gaston Illanes, Northwestern University: "Competition, Asymmetic Information, and the Annunity Puzzle: Evidence from a Government-run Exchange in Chile"
October 26, 2018 Stephan Seiler, Stanford University: "Estimation of Preference: Heterogeneity in Markets with Costly Search"
March 16, 2018  Keisha Cutright, Duke University: The Price is Right: Perceptions of Control Influence How Consumers Use Price in Judging Product Quality
March 9, 2018 Pinar Yildirim, Wharton School of Business: Matching Pennies on the Campaign Trail: An Empirical Study of Senate Elections and Media Coverage
February 2, 2018 Alix Barasch, New York University Stern School of Business: Signaling and Cooperation
January 19, 2018 Matt Shum, California Institute of Technology: A Closed-Form Estimator for Dynamic Discrete Choice Models: Assessing Taxicab Drivers' Dynamic Labor Supply
January 12, 2018 Javier Donna, Ohio State University: Measuring the Welfare of Intermediation in Vertical Markets
December 1, 2017 Andrew Rhodes, Toulouse School of Economics: Multiproduct Intermediaries and Optimal Product Range
November 6, 2017 Caio Waisman, Stanford University: Selling Mechanisms for Perishable Goods: An Empirical Analysis of a Resale Market for Event Tickets
November 3, 2017 Stephanie Chen, Booth School of Business: "Causal Beliefs in Identity and Consumption"
October 23, 2017 Kathy Lin, Wharton School of Business: Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods
October 20, 2017 Marco Qin, Carlson School of Business: Planes, Trains, and Co-Opetition: Evidence from China
October 9, 2017 Franklin Shaddy, Booth School of Business: Seller Beware: How Bundling Affects Valuation
October 6, 2017 Matt Rocklage, Kellogg School of Management: "The Phenomenal Disjunction: Emotionality for Ourselves vs Others"
September 25, 2017 John McCoy, Massachusetts Institute of Technology: "A Solution to the Single-Question Crowd Wisdom Problem"
May 19, 2017 Avi Goldfarb, Rotman School of Management: "Exit, Tweets, and Loyalty"
May 12, 2017 Peter Rossi, Anderson School of Managementi: "The Value of Flexible Work: Evidence from Uber Drivers"
May 5, 2017 Deborah A. Small, The Wharton School: "Impediments to Effective Altruism: Charity as a Taste-Based Decision"
April 28, 2017 Bryan Bollinger, The Fuqua School of Business: "Peer Effects in Outdoor Water Conservation: Evidence from Consumer Migration"
April 14, 2017 Jordan Etkin, The Fuqua School of Business: "The Downside of Productive Leisure"
February 10, 2017 Garrett Johnson, Kellogg School of Management: "The Impact of Privacy Policy on the Auction Market for Online Display Advertising"
November 11, 2016 Andrey Simonov, The University of Chicago Booth School of Business: "Demand for (Un)Biased News: The Role of Government Control in Online News Markets"
November 4, 2016 Kaitlin Woolley, Johnson Cornell SC Johnson College of Business: "The Experience Matters More Than You Think: People Value Intrinsic Incentives More Inside Than Outside Activity" and "For the Fun of It: Harnessing Immediate Rewards to Increase Persistence in Long-Term Goals"
October 28, 2016 Adam N. Smith, UCL School of Management: "Inference for Product Competition and Separable Demand"
October 17, 2016 Khai Chiong, Naveen Jindal School of Management: "An Empirical Model of the Mobile Advertising Market" 
October 14, 2016 Peter Newberry, The Pennsylvania State University: "Economies of Density in E-Commerce: A Study of Amazon's Fulfillment Center Network"
October 7, 2016 Evan Weingarten, The Wharton School: "The Effect of Salience on Valuation: Addressing the Dual-Casuality Problem in Decision Biases"
September 23, 2016 Ping Dong, Kellogg School of Management: "Witnessing Moral Violations Increase Conformity in Consumption"
April 29, 2016 Leslie John, Harvard Business School: "Hiding Personal Information Reveals the Worst"
February 19, 2016 Derek D. Rucker, Kellogg School of Management: "Power and Communion: When the Powerless Evince More Versus Less Prosocial Behavior." 
February 12, 2016 Samir Mamadehussene, Catolica-Lisbon School of Business and Economics: "Do Low Price Guarantees Hurt Consumers? Theory and Evidence"
February 5, 2016 Tom Gilovich, Cornell University: "We'll Always Have Paris: The Hedonic Payoff from Experiential and Material Investments"
January 29, 2016 Alexander MacKay: "The Structure of Costs and the Duration of Supplier Relationships"
January 22, 2016 Eesha Sharma: "Discretionary Debt: Drivers of Willingness To Borrow for Experiences and Material Goods"
January 20, 2016 Bradley Shapiro: "Advertising in Health Insurance Markets"
January 8, 2016 Ayman Farahat: "Empirical Evaluation of the Cost of Intrusive Ads"
November 13, 2015 "The Power of Rankings: Quantifying the Effects of Rankings on Online Consumer Search and Choice"
November 6, 2015 "Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err"
November 3, 2015 "Competing Fundraising Models in Crowdfunding Markets"
October 30, 2015 "How Do Vertical Contracts Affect Product Assortments?"
October 23, 2015 "Advertising and Demand for Addictive Goods: The Effects of E-Cigarette Advertising"
October 16, 2015 "Product Similarity Network in the Motion Picture Industry"
October 6, 2015 "Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity"
September 28, 2015 "Measuring Substitution and Complementarity among Offers in Menu Based Choice Experiements"