Marketing Research Seminar Series

Doctoral Student Research Presentations

Welcome and Keynote Address

Introducing a new currency in an existing market offers a potentially influential, alternative currency option—a prediction that prompts the current research into how consumers evaluate cryptocurrencies and the effects on their spending. Nine studies consistently demonstrate that paying with cryptocurrencies reduces purchase intentions, mediated by the pain of payment. However, if a cryptocurrency is positioned as a payment mechanism, consumers' purchase intentions remain similar to those they express for fiat currencies. This mental accounting explanation is confirmed with field data. The authors also demonstrate that the negative impact can be mitigated if the cryptocurrency is introduced as a virtual currency. Finally, the negative impact of cryptocurrencies is mitigated for higher priced products. These results offer important theoretical insights into how payment mechanisms affect purchases, as well as managerial insights about how cryptocurrency providers and retailers can increase uses of cryptocurrency as a payment mechanism.

Self-disclosures, or the voluntary sharing of information about one&aposs vulnerabilities, are common among persuasion agents such as online influencers, entrepreneurs, and marketers. While prior research highlights the &aposdisclosure-liking effect,&apos where self-disclosures increase trust and positive evaluations, we investigate the conditions under which self-disclosures may backfire. Drawing on persuasion knowledge theory, we show that self-disclosures by persuasion agents can elicit inferences of manipulative intent (IMI), leading to reduced liking, crowdfunding interest, and purchase intentions. Further, drawing on attribution theory, we demonstrate that perceived controllability moderates the effect of self-disclosures on consumer responses. When the disclosed issue is attributed to causes controllable by the persuasion agent (e.g., overspending), IMI is heightened, reducing liking and consumer support conversely, when the issue is attributed to uncontrollable causes (e.g., genetic diseases), self-disclosures elicit sympathy, enhancing liking. These findings introduce IMI as a novel consequence to self-disclosures in the marketplace and controllability as a key moderator. Substantively, this research speaks to the double-edged nature of self-disclosures, providing insights for persuasion agents navigating the fine line between authentic engagement and perceived manipulation.

Online content-sharing platforms such as TikTok and Facebook have become integral to daily life, leveraging complex algorithms to recommend user-generated content (UGC) to other users. While prior research and industry efforts have primarily focused on designing recommender systems to enhance users' content consumption, the impact of recommender systems on content production remains understudied. To address this gap, we conducted a randomized field experiment on one of the world's largest video-sharing platforms. We manipulated the algorithm's recommendation of creators based on their popularity, excluding a subset of highly popular creators' content from being recommended to the treatment group. Our experimental results indicate that recommending content from less popular creators led to a significant 1.34% decrease in video-watching time but a significant 2.71% increase in the number of videos uploaded by treated users. This highlights a critical trade-off in designing recommender systems: popular creator recommendations boost consumption but reduce production. To optimize recommendations, we developed a structural model wherein users' choices between content consumption and production are inversely affected by recommended creators' popularity. Counterfactual analyses based on our structural estimation reveal that the optimal strategy often involves recommending less popular content to enhance production, challenging current industry practices. Thus, a balanced approach in designing recommender systems is essential to simultaneously foster content consumption and production.

Digital advertising, which uses consumer data to target ads to users, now accounts for most of global ad expenditures. Privacy concerns have prompted regulations that restrict the use of personal data. To inform these policy debates, we develop an equilibrium model of advertising and market structure to analyze the impact of privacy regulation on market outcomes. We test the model's predictions using the launch of Apple's App Tracking Transparency feature, which created a natural experiment that limited the use of consumer data. Leveraging data from all U.S. advertisers on Meta combined with offline administrative data, we find that reductions in digital ad effectiveness led to decreases in investments in advertising, increases in market concentration, and increases in product prices. These effects are economically meaningful in magnitude and suggest potential harms to both firms and consumers from privacy regulation.

Firms often need to develop a timing policy for interventions in response to a customer action. Examples include how long to wait before sending an online review request after a purchase, and time until sending a promotional offer to shopping cart abandoners. Causal inference in such settings, especially when the timeline for intervention can be over an extended period (e.g., a few weeks), is not straightforward due to the ''triple hurdle'' problem. That is, customers (1) need to enter the sample by taking an action, (2) comply with the intervention timing intended by the firm, and (3) choose their response to the intervention. We present a quasi-experiment design that can recover intervention treatment effects by timing when randomization into treatment and control groups is possible but random assignment to intervention timing is not. Specifically, we use (1) propensity score matching using a rich pre-treatment customer history to match the distribution of customer types across customer cohorts, (2) random assignment to treatment and control groups generating an exclusion restriction for the customer outcome equation, and (3) estimation of a conditional average treatment effects as a function of intervention timing. We discuss how the proposed design can allow a firm to test the effectiveness of interventions by timing while minimizing campaign duration and exposure to the intervention amongst the customer base. We demonstrate our approach in the context of shopping cart interventions that target customers leaving an item unpurchased in their online cart and find an inverted U-shaped lift pattern for intervention timing using a discount coupon.

People who persuade can use language that differs in average and variation of certainty. While higher average certainty has been shown to enhance persuasion, excessive certainty may harm it, and the impact of certainty variation has been overlooked. This research uses multiple methods to investigate. We analyze the transcripts of 7,811 sales calls, finding that higher average certainty generally boosts purchase likelihood, though too much certainty can backfire. Notably, at high levels of average certainty, greater variation in certainty reduces its positive effect. Pre-registered scenario-based experiments confirm key findings, showing that perceptions of persuader confidence and credibility mediate these effects, with uncertainty about controllable aspects being particularly harmful to evaluations and purchase intention. This study offers key insights for persuasion and source certainty.

Large Language Models (LLMs) have revolutionized natural language processing but can exhibit biases and may generate toxic content. While alignment techniques like Reinforcement Learning from Human Feedback (RLHF) reduce these issues, their impact on creativity, defined as syntactic and semantic diversity, remains unexplored. We investigate the unintended consequences of RLHF on the creativity of LLMs through three experiments focusing on the Llama-2 series. Our findings reveal that aligned models exhibit lower entropy in token predictions, form distinct clusters in the embedding space, and gravitate towards ''attractor states'', indicating limited output diversity. Our findings have significant implications for marketers who rely on LLMs for creative tasks such as copywriting, ad creation, and customer persona generation. The trade-off between consistency and creativity in aligned models should be carefully considered when selecting the appropriate model for a given application. We also discuss the importance of prompt engineering in harnessing the creative potential of base models. - The emergence of large language models (LLMs) has opened up exciting possibilities for simulating human behavior and cognitive processes, with potential applications in various domains, including marketing research and consumer behavior analysis. However, the validity of utilizing LLMs as stand-ins for human subjects remains uncertain due to glaring divergences that suggest fundamentally different underlying processes at play and the sensitivity of LLM responses to prompt variations. This paper presents a novel approach based on Shapley values from cooperative game theory to interpret LLM behavior and quantify the relative contribution of each prompt component to the model’s output. Through two applications—a discrete choice experiment and an investigation of cognitive biases—we demonstrate how the Shapley value method can uncover what we term ''token noise'' effects, a phenomenon where LLM decisions are disproportionately influenced by tokens providing minimal informative content. This phenomenon raises concerns about the robustness and generalizability of insights obtained from LLMs in the context of human behavior simulation. Our model-agnostic approach extends its utility to proprietary LLMs, providing a valuable tool for marketers and researchers to strategically optimize prompts and mitigate apparent cognitive biases. Our findings underscore the need for a more nuanced understanding of the factors driving LLM responses before relying on them as substitutes for human subjects in research settings. We emphasize the importance of researchers reporting results conditioned on specific prompt templates and exercising caution when drawing parallels between human behavior and LLMs.

Estimating consumer preferences for new products in the absence of historical data is an important but challenging problem in marketing, especially in product categories where brand is a key driver of choice. In these settings, measurable product attributes do not explain choice patterns well, which makes questions like predicting sales and identifying target markets for a new product intractable. To address this ''new product introduction problem,'' I develop a scalable framework that enriches structural demand models with large language models (LLMs) to predict how consumers would value new brands. After estimating brand preferences from choice data using a structural model, I use an LLM to generate predictions of these brand utilities from text descriptions of the brand and consumer. My main result is that LLMs attain unprecedented performance at predicting preferences for brands excluded from the training sample. Conventional models based on text embeddings return predictions that are essentially uncorrelated with the actual utilities. In comparison, my LLM-based model attains a 30% lower mean squared error and a correlation of 0.52 i.e. for the first time, informative predictions can be made for consumer preferences of new brands. I also show how to combine causal estimates of the price effect obtained via instrumental variables methods with these LLM predictions to enable pricing-related counterfactuals. Combining the powerful generalization abilities of LLMs with principled economic modeling, my framework enables counterfactual predictions that flexibly accommodate consumer heterogeneity and take into account economic effects like substitution by consumers and price adjustments by firms. Consequently, the framework is useful for downstream decisions like optimizing the positioning and pricing of a new product and identifying promising target markets. More broadly, these results illustrate how new kinds of questions can be answered by using the capabilities of modern LLMs to systematically combine the richness of qualitative data with the precision of quantitative data.

Customer reviews and ratings are critical for the success of online platforms in that they help consumers makechoices by reducing uncertainty and motivate supplier (worker) incentives. Existing literature has shown that rating systems face problems primarily due to fake or discriminatory reviews. However, customers also differ in their rating styles: some are generous and others are harsh. In this paper, we introduce the novel idea: even if raters are honest and unbiased, differences in the early rater mix (of generous and harsh raters) for a supplier can lead to biased ratings and unfair outcomes for suppliers. This is because platforms display past ratings to customers whose own ratings and acceptance of suppliers are impacted by it and platform uses the past ratings for its prioritization and recommendations. These lead to the path dependence. Using data from a gig-economy platform, we estimate a structural model to analyze how early ratings affect longterm worker ratings and earnings. Our findings reveal that early ratings significantly impact future ratings leading to persistent advantages for early lucky workers and disadvantages for unlucky ones. Further, the use of these ratings in the platform's prioritization algorithms magnify these effects. We propose a neutral adjusted rating metric that can mitigate these effects. Counterfactuals show that using the metric enhances the accuracy of rating systems for customers, fairness in earnings for workers, and better retention of high quality workers for the platform. The resulting supplier turnover can lead to lower quality supplier mix on platforms.

Audio advertising is a large industry reporting a billing of $14 billion in 2022 and reaching up to 86.8% of the U.S. population. Reflecting the importance of audio advertising, AI startups are offering marketers generative AI tools to efficiently create multiple audio ads. Also, ad targeting platforms like Spotify can deliver audio ads to targeted audiences. This raises a key question: Which of the numerous ads should marketers launch on the ad targeting platforms? Marketers may rely on conventional methods such as A/B testing or Multi-Armed Bandit to answer this question. However, they are slow and require significant resources, particularly when assessing numerous ad executions. Moreover, online audio platforms such as Spotify or iHeartRadio do not support A/B testing or Multi-Armed Bandit. Given this background, the authors propose DeepAudio, an AI system that integrates insights of behavioral literature on ad likeability with AI algorithms to automatically assess the likeability of audio ads. Benchmarking DeepAudio with different approaches, the authors find that integrating behavioral features into AI systems significantly increases system performance, robustness, and generalizability. By quickly assessing the likeability of multiple audio ads, DeepAudio enables marketers to select the most promising ad executions and fully harness the power of Generative AI. Thus, DeepAudio completes the modern pipeline of generating, selecting, and targeting audio ads.

Targeted advertising, advanced by behavioral tracking and data analytics, is now extensively utilized by firms to present relevant information to consumers, potentially enhancing consumer experience and marketing effectiveness. Despite these advantages, targeted advertising has raised significant privacy concerns among consumers and policymakers due to unintended consequences from the extensive collection and use of personal data. Consequently, comprehending the tradeoff between the enhanced match value and privacy concerns is crucial for effective implementation of targeted advertising. In this research, we develop a structural model to empirically analyze this tradeoff, addressing a gap in the literature. We assume consumers form correlated beliefs about privacy invasion and match value from targeted advertising in a Bayesian fashion, and use these beliefs to decide whether to click an ad and whether to opt out of ad tracking. Consumers update their privacy invasion beliefs by considering how each received ad corresponds to their clicked ads and update their match value beliefs by considering how well each ad engages them, and do so jointly due to potential correlation between these two beliefs. Leveraging the Limit Ad Tracking (LAT) policy change with iOS 10 in September 2016, which allows consumers to opt out of ad tracking, we estimate the proposed model using panel ad impression and consumer response data from 166,144 opt-out and 166,144 opt-in consumers, across two months pre and three months post of the policy change. We find that consumers generally have a negative preference for privacy invasion and a positive preference for match value in their clicking decisions, with notable heterogeneity in these preferences. Consumers with higher uncertainty about privacy invasion are more likely to opt out of tracking. Upon opting-out, highly privacy-sensitive consumers (about 20%) experience net benefits, while the majority faces a loss from reduced match value that outweighs their gain from decreased privacy invasion. Through counterfactual analyses, we propose a probabilistic targeting strategy which balances match value and privacy concern, and demonstrate that such privacy-preserving targeting strategy can benefit consumers, advertisers, and the ad network.

Firms often ask consumers to either spend time to save money (e.g., Lyft’s ''Wait & Save'') or spend money to save time (e.g., Uber’s ''Priority Pickup''). Across six preregistered studies (N = 3,631), including seven reported in the Web Appendix (N = 2,930), we find that asking consumers to spend time to save money is perceived as fairer than asking them to spend money to save time (all else equal), with downstream consequences for word-of-mouth, purchase intentions, willingness-to-pay (WTP), and incentive-compatible choice. This is because spend-time-to-save-money offers reduce concerns about firms' profit-seeking motives, which consumers find aversive and unfair. The effect is thus mediated by inferences about profit-seeking and attenuates when concerns about those motives are less salient (e.g., for non-profits). At the same time, we find that spend-money-to-save-time offers (e.g., expedited shipping) are more common in the marketplace. This research reveals how normatively equivalent trade-offs can nevertheless yield contradictory fairness judgments, with meaningful implications for marketing theory and practice.