Knowledge that Transforms

Colorism, or discrimination based on skin tone, has been widely documented as privileging lighter and disadvantaging darker skin tones in personal and professional contexts. In contrast, the current research uncovers a novel and positive “dark is hardworking” association in India. This association is theorized based on sociological accounts linking dark skin to outdoor labor, personal accounts of dark skin–toned individuals overcoming discrimination to achieve success, and widespread media cues featuring dark skin–toned achievers in arduous career pursuits. Darker skin tone thus cues perceived competence of service providers, driven by perceptions of being more hardworking. Evidence from six studies, including five experiments and a behavioral field study, demonstrates that although lighter skin continues to be advantageous in attractiveness-favoring professions (such as beauty consulting), darker skin offers a distinct advantage to service providers in competence-favoring fields (such as financial and technical services). Furthermore, highlighting competence can increase the preference for darker skin–toned professionals, even in attractiveness-favoring domains. This research contributes to the literature on stigmatized groups, specifically colorism and its effects on stereotyped evaluation of service providers. The findings provide strategic insights for service providers and have implications for social policy.

The rise of content-creation technologies such as generative artificial intelligence (GenAI) has reshaped the way people produce and consume content. These technologies democratize content creation, enabling inexperienced individuals to generate content that rivals the quality produced by professionals. This article adopts a game-theoretic model to study how the democratization of content creation affects welfare. While the content-democratization technology can assist low-quality creators in improving their content quality, it can precipitate a flood of such content in the market and crowd out high-quality content production, leading to welfare losses. An incremental technology development with a small reduction in the quality gap can hurt individual consumers and all creators, leading to a lose-lose market outcome. In contrast, a development with a high reduction in the quality gap benefits individual consumers and all creators, leading to a win-win outcome. Furthermore, when platforms can more effectively screen content and promote high-quality content, welfare rises, but the content-democratization technology is more likely to reduce welfare. The findings help explain the distinct impacts of content democratization in different industries.

This article studies how receiving negative peer feedback, in the form of downvotes, affects user-generated content creation on Reddit. The authors focus on the following outcomes: (1) propensity to post (incidence), (2) where users post, and (3) the strength of opinion (intensity), measured by the extremity of users’ texts. The latter two outcomes are important given ongoing concerns about how social media platforms may contribute to echo chamber formation and polarization. The authors find that negative feedback, relative to no feedback, increases users’ subsequent posting activity, and they do not find evidence that receiving negative feedback drives users away, alleviating concerns about echo chamber formation. In addition, negative peer feedback moderates extreme sentiments—when initial views are extreme, users temper the intensity of their subsequent posts. These effects of negative feedback are consistent with users attempting to maintain their reputation.

Platform endorsement is becoming increasingly popular and plays a critical role in online health care platforms by aiding patient choices and shaping doctors’ decisions. Using a unique dataset from a major Chinese online doctor consultation platform, this study applies a generalized synthetic control method to examine the impact of endorsement on patient demand, doctor service pricing, quantity, and quality. The findings show that endorsement significantly increases the price and quantity of paid services, while reducing free services among endorsed doctors. Despite handling higher service volumes, endorsed doctors maintain or improve quality. The endorsement program generates greater revenue for endorsed doctors and the platform, but it raises concerns about equity in health care access due to reduced free service provision for underprivileged patients. The analysis further reveals that the impact of endorsement varies between initial endorsement and reendorsement as well as between high-prosocial and low-prosocial doctors. These findings underscore the nuanced role of information disclosure in shaping doctor behavior and patient access, offering important implications for platform design.

From salespeople and entrepreneurs to politicians and influencers, marketplace actors often communicate with their hands. Yet despite how integral these movements are to communication, little work in marketing has examined their effects. Might moving one's hands while speaking increase persuasion? And if so, what types of movements are more impactful, and why? A multimethod investigation, including automated video analysis of thousands of presentations, application of a large multimodal model on almost 200,000 video segments, and preregistered controlled experiments, demonstrates that hand movement can boost impact. Further, the results demonstrate that certain hand gestures (i.e., illustrators) are particularly impactful. By making content easier to understand, illustrators make speakers seem more competent, which increases persuasion. Taken together, these findings shed light on hand movement's impact, deepen understanding around nonverbal communication, and highlight how automated video analysis and multimodal models can provide insights into consumer behavior.

How does the balance between text and pictorial content in marketer-generated social media posts affect user engagement? The authors address this question by using computer vision and natural language processing tools to extract the visual and textual features of 34,610 organic brand posts from Facebook and Instagram. Using a confounding-and-cluster-robust causal forests model, they test how the balance of text and picture affects social media engagement across content and visual contexts. Results show that posts with greater emphasis on overlay text over pictorial content tend to have fewer likes and comments. However, the performance of text-oriented posts improves if text is more centered, informative, emotionally positive, and congruent with the pictorial content and if the picture contains fewer prominent objects or less information such as social cues. They quantify how incremental changes in such content composition affect social media engagement. These findings set forth evidence-based principles for optimizing text and picture balance in marketer-generated content and provide actionable guidelines on whether, where, when, and how to present text on an image. This research highlights the potential for transforming content and media creation from an imprecise art form into an empirical science nested within a data-driven visual optimization framework.

Consumers often take on debt at different points in time. As a result, debts may be months, if not years, apart in age. In this research, the authors ask whether and how debt age affects installment debt prepayment decisions. While the ideal strategy for prepaying older versus newer debt depends on specific account parameters (e.g., interest rate, monthly payment), in many circumstances it is financially advantageous to prioritize paying down newer debt. This is because debt amortization (i.e., repayment) schedules often result in reduced interest payments when newer installment debts are paid first. However, across eight studies, including a secondary dataset of consumer loans, the authors find that consumers prefer to prepay older debt first—even when it is financially disadvantageous to do so. The authors refer to this as a FIFO (first-in, first-out) preference and demonstrate that consumers prioritize older versus newer debt prepayment because they feel they have invested greater effort (i.e., mental or physical work and energy) to repay it. Accordingly, reducing the effort required to repay an older debt (e.g., through automated payments) or shifting consumers’ focus from invested effort to remaining effort attenuates the FIFO preference. These findings offer implications for theory, managerial practice, and consumer welfare.

When analyzing a sequence of customer interactions, it is important for firms to understand how these interactions align with key objectives, such as generating qualified customer leads, driving conversion events, or reducing churn. The authors introduce a transformer-based framework that models customer interactions in a sequence similar to how a sentence is modeled as a sequence of words by large language models. They propose a heterogeneous-mixture multihead self-attention mechanism that captures individual heterogeneity in touchpoint effects. The model identifies self-attention patterns that reflect both population-level trends and the unique relationships between touchpoints within each customer journey. By assigning varying weights to each attention head, the model accounts for the distinctive aspects of the journey of each user. This results in more accurate predictions, enabling precise targeting and outperforming existing approaches such as hidden Markov models, point process models, and long short-term memory (LSTM) models. This empirical application in a multichannel marketing context demonstrates how managers can leverage the model's features to identify high-potential customers for targeting. Extensive simulations further establish the model's superiority over competing approaches. Beyond multichannel marketing, the transformer-based model also has broad applicability in customer journeys across other domains.

Marketers can display their products horizontally or vertically in both online and offline settings. This display orientation has been shown to influence consumers’ judgments about individual products. The present research extends the literature by investigating the moderating impact of display orientation on the attraction effect, one of the most well-established context effects in choice. A total of 11 studies, including 7 preregistered experiments, document a novel finding that the attraction effect is stronger when choice alternatives are displayed horizontally rather than vertically. This moderating influence is replicated in both consequential choices and hypothetical scenarios and shown to generalize over diverse product categories. The authors explain this influence by proposing that a horizontal (vs. vertical) display increases the ease of comparing choice alternatives, leading consumers to notice the asymmetric dominance relationship among them more easily. Consistent with this mechanism, the authors find that the moderating influence of display orientation attenuates when individuals are guided to recognize the asymmetric dominance relationship or when their ability to compare vertically displayed products is momentarily enhanced. The present research thus demonstrates a significant effect of spatial orientation on the comparison and evaluation of alternatives. Theoretical and managerial implications of findings are discussed.