Marketing Research Seminar Series

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Perfectionist tendencies have been on the rise in recent years. In this paper, we conceptualize and define a specific type of this tendency, called ''product perfectionism,'' and situate it within a broader nomological network that includes trait perfectionism, entitlement, materialism, and maximizing. We construct an eight-item Product Perfectionism Scale, which we use to predict consumption behaviors across the three stages of a typical consumer's journey: acquisition, consumption, and disposal (studies 1–7). We find that consumers higher (vs. lower) on product perfectionism are more susceptible to set-fit effects (study 1), attracted to brands with personalities associated more (vs. less) with perfection (study 2), and willing to pay more for newer (vs. older) products (study 3). We also find that they derive lower enjoyment from less-than-perfect consumption experiences (study 4), are more attracted to product upgrades (study 5), replace both perishable and non-perishable goods faster for smaller flaws (study 6), and are more likely to dispose of and are reluctant to repair broken possessions (study 7). We conclude the paper with a discussion of the theoretical and substantive implications of our findings.

Consumers often choose portion sizes of food for others—including romantic partners, friends, and children. Across eight studies, the authors show that consumers choose larger portion sizes for others, compared to the portion sizes that consumers choose for themselves, the portion sizes that consumers predict others want to eat, and the portion sizes that others want to receive. Moreover, the authors show that one reason consumers choose larger portion sizes for others is because consumers aim to convey warmth via larger portion sizes. The tendency to choose larger portion sizes for others is mitigated when consumers focus on addressing alternative concerns besides conveying warmth, such as when addressing fairness concerns (e.g., wanting to ensure enough food for multiple others) or when addressing health concerns for others (e.g., choosing portion sizes of unhealthy food for dieting adults or for one's young children). Ironically, however, choosing larger portion sizes for others (vs. for self) does not actually convey greater warmth to recipients. Thus, the authors suggest that consumers' tendency to choose larger portion sizes for others may mainly have negative consumer and societal well-being implications, to the extent that receiving larger portion sizes might lead to overeating and/or food waste.

Prior work has revealed negative demand-side implications of engaging consumers for their innovative inputs (e.g., user-generated designs) by luxury brands. Extending this line of work, we examine the supply-side (e.g., idea generation) implications of consumer engagement through the lens of luxury brand usage experience. Across five studies, we demonstrate that consumers who engage in luxury, as compared to non-luxury, brand experience through product usage generate more innovative ideas. This effect stems from feelings of vertical differentiation that arise from using a luxury brand. Explicating the boundary conditions of the effect, we also demonstrate that such a positive effect of luxury brand usage experience on consumer innovativeness does not emerge when 1) individuals are merely exposed to a luxury brand (i.e., engaging with it is a necessary condition), and 2) when the luxury brand usage fails to convey the signals of prestige and exclusivity central to feelings of vertical differentiation. The current research offers theoretical and managerial implications for luxury brand and innovation management.

Risk absorption is ubiquitous in inter-firm relationships. For example, manufacturers may cover small suppliers' production cost risks, and suppliers might take on some buyers' payment default risks. This risk absorption aims to enhance value and foster long-term partnerships. Despite recognizing this intention, there is limited research on the economic consequences for the risk-taker. The authors examine the costs and benefits of risk absorption in indirect car loan markets, where third-party lenders rely on auto dealers to reach consumers. In this scenario, risk absorption happens when third-party lenders approve loans for high-risk consumers, allowing auto dealers to complete transactions, even for consumers who typically would not qualify for loans. Using a comprehensive dataset spanning three years from a third-party lender working with 1,550 dealers, the authors explore (1) the actual costs related to risk absorption, such as loan defaults and overdue payments, (2) how dealers reciprocate and benefit by assessing how lender risk absorption affects dealer referrals in terms of loan amounts (gains) and loan risk (losses), and (3) heterogeneity in how dealers respond to risk absorption. While lenders incur direct costs for offering favorable loans, dealers reciprocate by generating more loan applications in the future. However, the risk associated with referred loans also increases, possibly due to perceived leniency by the lender being exploited. Additionally, we find that as the relationship between a lender and a dealer extends, the reciprocal effects initially strengthen and then decline, while the direct cost and exploitation effect increases monotonically. These findings provide insights into lenders’ effectiveness in absorbing risks and strategies for managing relationships with dealers.

Our study demonstrates the power of using contextual embeddings to evaluate new marketing creatives. To explore the usefulness of contextual embeddings, we exploit an advantageous setting where 34 emails were sent to 1.3 million clients over a 45-day period. To create our contextual embeddings, we use OpenAI to translate email subject lines into Ada embeddings, a class of contextual embeddings. We find that Ada embeddings outperform other commonly used, text classification methods. Further, through a series of leave-one-out exercises, over 85% of our candidate emails have accurate predictions between our contextual embeddings model and the doubly robust scores obtained from market campaigns. Given that OpenAI generated our contextual embeddings, we link our method with ChatGPT to evaluate newly generated email creatives. ChatGPT is given a set of email features that show common support within our observed dataset. We find that while ChatGPT is able to generate novel email promotions quickly, it is unable to accurately predict the success of the new emails. We then conclude by using a data-driven algorithm to conduct a heuristic-driven targeting exercise using both the GPT developed emails and client characteristics. Our study shows the viability of contextual embeddings in academic research and in assessing the development of novel heterogeneous treatments.