Knowledge that Transforms

When engaging in corporate political activism (CPA), firms can use different cues (e.g., language and medium choices) to establish the permanence of their activism communications. Across six studies spanning a wide range of timely sociopolitical issues, the current research provides insights into how consumers respond to the relative permanence of a firm’s CPA communications. We show that more permanent (vs. temporary) CPA communications, as might be shared on a firm’s website, social media posts, or retail displays, signal greater firm commitment to the advocated issue, which in turn generates more positive downstream outcomes. Consistent with a mechanism grounded in firm commitment perceptions, the positive impact of communication permanence is attenuated in the presence of other diagnostic signals of firm commitment. Furthermore, we explore whether individual differences might influence consumer responses to such communication permanence, yielding two interrelated insights. First, firm commitment perceptions stemming from permanent CPA communications have a stronger effect on firm outcomes when consumers’ ideology aligns with the stance the firm takes. Second, holding stance alignment constant, the impact of CPA communication permanence appears more pronounced among liberal consumers, who react more strongly to cues of lower firm commitment (e.g., temporary communications).

The paper explores peer effects in user churn within a digital socially-connected platform and investigates how firms can leverage peer effects and network structure for customer relationship management (CRM). The data come from a massively-multiplayer online game, where gamer social ties are endogenous. We build a two-stage structural model to capture network formation and peer effects in churn. Unobserved gamer heterogeneity inferred from network formation corrects for endogeneity bias in peer effects. The main findings are: (i) Peer effects substantially amplify intervention effectiveness, with an average social multiplier of 2.48. Critically, the full model with unobserved gamer heterogeneity reveals substantial cross-network variation in social multipliers that the restricted model largely masks. (ii) Users vary far more in how they influence peers' retention than in their own likelihood of retention. (iii) User's network value is driven primarily by network centrality measures, unlike user's direct value which correlates more with individual characteristics.

Brand logos are key visual elements that shape consumers’ perceptions and behaviors. This research advances the logo literature by systematically examining an understudied design feature: three-dimensionality. Three-dimensional (3D) logos incorporate visual depth cues such as shading, perspective, and occlusion, which can make them appear more like tangible objects than two-dimensional (2D) logos. Across multiple field, lab, and online studies, the authors demonstrate that, relative to their 2D counterparts, 3D logos can increase brand purchase likelihood. This effect arises because depth cues in 3D logos evoke mental representations of tangible, physical forms, thereby increasing perceived logo realism and, in turn, enhancing brand trust. Importantly, these benefits are not universal but conditional. The positive effects of 3D logos emerge when logos appear in visually simple environments or when offerings are perceived as more concrete (i.e., higher in tangible dominance). Conversely, in visually complex environments or for abstract offerings with limited physical contact, 3D logos provide little or no advantage over 2D designs. By identifying when depth cues improve consumer responses, this research offers a nuanced theoretical account of logo three-dimensionality and actionable guidance for managers considering 3D logo adoption.

The ability to quickly capture and adapt to customer preferences is central for firms seeking to offer personalized products and improve retention. This objective becomes challenging when individual-level data on customer interactions are limited, as is often the case for new customers or short consumption sessions. To this end, we propose meta-temporal processes (MetaTP), a meta-learning framework that enables scalable personalization from a small number of individual observations. MetaTP is trained across a large collection of session-based tasks, allowing it to improve data efficiency and transfer shared structure across customers. To model customer interactions over time, MetaTP integrates a Transformer-based architecture that captures sequential consumption patterns within sessions. This design uncovers dynamic preference heterogeneity and enables accurate predictions. We illustrate MetaTP through an application on customer sequential consumption of digital products, focusing on the lukewarm stage of the customer journey, a transition period characterized by limited individual observations. Empirically, MetaTP outperforms a comprehensive set of benchmark methods in few-shot prediction and reveals meaningful patterns of preference evolution through its interpretable parameters. Managerially, we demonstrate how firms can leverage MetaTP to optimize personalized recommendations with limited individual data, including product sequencing decisions and both open-loop and closed-loop session completion strategies.

This research examines whether functional magnetic resonance imaging (fMRI) data add predictive value beyond traditional market and survey data in forecasting two critical outcomes: (1) store manager adoption and (2) consumer sales of consumer packaged goods. Using data from a large retail chain, this study combines observable market variables, survey-based attitudes from a large representative consumer sample, and fMRI signals from a smaller convenience sample. Applying decision tree and least absolute shrinkage and selection operator (LASSO) regression approaches, the authors find that fMRI data enhance sales forecasts—particularly for more innovative products—while survey measures better predict store manager adoption. The research also quantifies the economic value of these improvements relative to data-acquisition costs, providing a framework for evaluating the return on investment of neuroforecasting tools. These findings clarify when neural measures add the most value over conventional analytics — particularly for innovative products at the consumer sales stage — with implications for product launch strategies and data investment decisions.

Consumers rely heavily on product reviews in their purchasing and consumption decisions, and how reviews influence product sales is well-documented. This research asks whether reviews might have an afterlife beyond purchase—that is, whether they can affect the product experience itself. Across five studies involving real consumer-generated reviews and product interaction, this research demonstrates that consumers who read reviews before consumption have different experiences with a product than consumers who experience the same product without reading reviews. Specifically, consumers who are exposed to negative reviews before they consume a product experience the reviewed product more negatively than consumers who have not read any reviews. Reading positive reviews, however, does not significantly affect the consumption experience. This asymmetrical assimilative effect arises because negative reviews increase the salience of negative attributes of the reviewed product during consumption, making those attributes more influential during product experience. The effect is robust across various contexts, although forewarning consumers and prompting them to form independent opinions can reduce it. These findings open the door for investigation into the broader influence of reviews on consumption, beyond the point of purchase, and have important practical implications for marketers seeking to navigate this influence.

Firms use privacy-sensitive data to make targeting decisions, which can inadvertently reveal the underlying information driving those decisions—a risk the authors term targeting privacy risk . The authors use differential privacy to quantify and control this risk. Although firms increasingly adopt differential privacy, policymakers offer limited guidance on its implementation in a targeting framework. The crucial question, therefore, becomes: where should firms implement differential privacy? The authors find that profitability critically depends on where differential privacy is implemented within a typical targeting workflow. This insight stems from two novel targeting strategies: one implements differential privacy during model training, the other at the targeting-decision stage. Using a large-scale field experiment involving 747,975 customers and extensive simulations, the authors show that both strategies remain profitable under strong privacy protection. Notably, the decision-stage strategy yields, on average, fivefold higher profits than alternative implementations. To generalize this finding, the authors derive the expected profit for a given privacy risk level and a privacy elasticity of targeting profits. Collectively, the results offer guidelines for firms and policymakers to ensure privacy protection and profitability.

Online content platforms monetize user engagement through advertising and share ad revenue with content creators to incentivize content provision. A central design decision for these platforms is the choice of ad intensity policy, which governs how advertising load is determined and shapes creator incentives, content quality, consumer consumption behaviors, and platform profitability. We analyze a model with one platform and two competing content creators to study three ad intensity policies: differentiated advertising (DA), uniform advertising (UA), and creator-set advertising (CA). With symmetric creators and quality-independent marginal ad revenue, UA intensifies quality-based competition among creators and leads to higher revenue-sharing rate, content quality, advertising intensity, and platform profit than DA. Creators benefit more from UA than DA when creator substitutability is low, but may prefer DA when substitutability is high. Compared to DA, CA weakens incentives for content investment, resulting in lower content quality, lower ad intensity, and reduced platform profit; its effects on creators and consumers depend on the degree of creator substitutability. Extensions show that relaxing the benchmark assumptions—such as allowing for creator asymmetry or quality-dependent marginal ad revenue—can overturn UA’s advantage and make DA more profitable, highlighting that no single ad intensity policy is universally optimal.

User-generated photos play an increasingly important role in online reviews, yet little is known about the process by which they are initially created. This paper develops a theory of image creation arguing that reviewers invest more creative effort in photographing products/services they feel positively (vs. negatively) about. This greater effort results in higher-quality photos that observers find to be more helpful, both because they are easier to visually process and because they engender greater trust in the reviewer. Results across six studies—including controlled experiments and an observational laboratory study (N = 4,218) as well as field analyses of Amazon and Yelp reviews (N = 669,937)—lend support to these predictions. By identifying valence as a driver of creative effort in user-generated photography, the findings advance knowledge on how visual content is produced in online marketplaces and how such content is evaluated by consumers.

Overproduction and overconsumption represent key issues in the fight against resource waste and environmental degradation. While the scientific debate has mainly focused on how to increase product durability by improving tangible aspects (e.g., technical characteristics, materials used, production processes), the present research focuses on a consumer-based view of durability, looking at how long consumers plan to use a product for, a construct labeled length of product usage (LPU). The core thesis is that LPU might depend not only on tangible, but also on intangible product characteristics, such as creativity, defined in terms of high novelty and adequate appropriateness. In advancing this creativity-based LPU account, the authors argue that higher product creativity leads to higher LPU by strengthening consumers’ emotional attachment to products. Seven pre-registered studies, employing different products, populations, and creativity manipulations, empirically support the proposed framework. Results also show that the effect of creativity on LPU, via emotional attachment, is especially pronounced for non-owners, vs. owners, and, for mass-market, vs. luxury, products. By emphasizing that creativity can be key to fostering sustainable consumption, this research advances the literature on the antecedents of LPU and product durability, offers implications for companies, and avenues for future research.