Consumer spending inequality is a crucial input for customer management, and the Pareto 80/20 principle of consumer spending is widely accepted as expected regularity. However, research results are inconsistent, and the past focus on brand-level cross-sectional snapshots provides only a partial view of consumer spending inequality. Using 16 years of data (2009–2024) from the U.S. Consumer Expenditure Survey, we provide a first holistic view of category-level consumer expenditure inequality, complementing brand-level evidence and enhancing our understanding of the product-category markets in which brands operate. We find that, in a remarkably consistent pattern over the years, the top 20% of buying households are, on average, responsible for half of the category spending. Consumer packaged goods, which have often been used to study brand-level inequality, do not accurately represent the full spectrum of spending inequality. Consistent with work in economics, we explain why a 10/40 spending ratio may be a complementary measure for managers in this regard and demonstrate that what may appear to be low inequality in a Pareto-based framework still reflects a high level of consumer inequality. We discuss the implications of our results for the broader understanding of consumer inequality.
Consumers often fail to translate pro-environmental attitudes into sustainable behavior, in part because sustainable choices require moral motivation rather than simple preference change. Although prior research shows that awe can promote sustainable consumption, it remains unclear how awe acquires this moral force. We address this question by identifying moral elevation as a key mechanism through which awe motivates sustainable consumption. Specifically, we propose that awe-eliciting stimuli, when experienced as beautiful or sublime, elicit moral elevation, which in turn leads to sustainable choice through self-transcendence. Across four studies (three preregistered, N = 1244), including a behavioral choice task and a marketplace willingness-to-pay context, we provide convergent support for this account. Importantly, both positive and negative awe operate through the same moral elevation–self-transcendence pathway. The findings contribute to sustainability research by clarifying awe's moral motivational role, to moral elevation research by demonstrating that elevation can be elicited by stimuli that are not explicitly moral in content, and to negative awe research by demonstrating its capacity to elicit moral elevation and sustainable behavior. We offer managerial implications for designing sustainability interventions.
Donors seek to make donations that will generate sustainable, long-term changes. However, little is known about how charities can communicate their ability to effect lasting change. Across several charitable donation contexts, this work demonstrates that charities can use progression images to heighten perceptions of solution longevity and yield greater prosocial support than traditional before-and-after images. The authors also establish two moderators for this effect, showing that the presence of copy that outlines how the charity creates lasting change mitigates the effects of progression images and that charities not known as competent are well positioned to capitalize on the benefit of progression images.
Marketing strategy research increasingly relies on merged multi-source datasets spanning firms, brands, and products. Yet such entity matching remains a largely undocumented research design choice, despite its potential to alter samples, estimates, and replicability. This research note develops a cascading entity-matching workflow for marketing strategy research. We outline three progressively intensive matching approaches: deterministic joins using shared identifiers and crosswalks, probabilistic name-based matching with transparent similarity rules, and screening assisted by machine learning (ML) or large language models (LLMs). Finally, we translate this workflow into a practical audit framework specifying what researchers should record, report, and archive. The goal is to establish transparent and auditable matching standards that improve precision, replicability, and cumulative knowledge development in merged-dataset research.
Conversational content, that is, content units organized around spoken interaction between two or more speakers, has become a prominent and economically significant form of communication across contemporary media environments, with podcasts alone reaching over 100 million monthly listeners and generating over $2 billion annual advertising revenue in the U.S. Despite the rapid growth of conversational media and formats such as podcasts and social media livestreams, little is known about what drives audience engagement with this type of content. Drawing on conversation analysis, this research demonstrates that discourse concentration, defined as the extent to which speaker contributions are concentrated among a few dominant voices versus distributed more evenly across many, interacts with content valence to shape audience engagement. We find that for positively-valenced content, a more concentrated conversational structure leads to higher engagement, whereas for negatively-valenced content, a more fragmented structure elicits higher engagement. These effects are mediated by empathy toward the speakers. We test this framework across two controlled experiments using transcript-based stimuli (Study 1) and AI-generated voice stimuli (Study 2), respectively, and the econometric analysis of 401 real podcast episodes comprising more than 42,000 speaker turns and approximately 209 h of audio (Study 3). Together, these studies provide causal evidence for the interaction, progressively extending it from text-based to audio stimuli, and demonstrate its real-world relevance. These findings show that the same conversational structure can either enhance or reduce engagement, offering actionable insights for podcast producers, media platforms, and advertisers.
Increasing market volatility demands that firms rapidly adapt their sales channels to stay competitive. Yet many companies struggle to adjust swiftly and effectively. This study conceptualizes sales system agility (SSA) and tests when it leads to organizational performance using multisource data from 356 firms. The findings show that SSA is associated with firm performance, but only under certain conditions. Drawing on multiple agency theory, we identify critical contingencies. First, sales system design moderates SSA's effects: SSA's link to performance is stronger under higher overall direct channel reliance, while it is weaker under emphasis on direct channels for revenue (vs. awareness and support) and under greater channel overlap. Second, SSA's performance impact is stronger under effective sales system management through centralization and orchestration, consistent with multiple agency conflicts being mitigated. Practically, a one-point increase in SSA corresponds to up to US$52 million higher earnings before interest and taxes in our sample, with roughly US$37 million linked to a favorable sales system design and roughly US$15 million to strategic sales system management. Sales and channel managers should thus align their sales system structures and governance strategies to realize SSA's full potential.
The need for understanding the key factors/dimensions of product evaluation becomes more crucial when analyzing high-dimensional data, especially unstructured text reviews. In this study, we propose Text2ConfFact (short for “Texts to Confirmatory Factors”), a novel empirical dimensionality reduction framework for identifying the confirmatory factors underlying product ratings in these online reviews. Text2ConfFact begins with cost-effective preprocessing to generate a word rating matrix capturing word positivity and negativity strengths. It then identifies groups of correlated words that are significantly associated with product ratings while filtering out noise. Finally, the framework estimates factor scores using these identified word groups. In an empirical study analyzing 2825 video game reviews, Text2ConfFact successfully identified the confirmatory factors of game ratings and outperformed six benchmark methods, including two topic models, BERTopic, principal component analysis, principal component regression, and exploratory factor analysis, in terms of both predictability and interpretability. Specifically, Virtual Playability Issues, Money Value Concerns, Worthwhile Fun, and Fantasy emerged as four key factors. In addition to the factor-level effects, our framework captures word-level effects within each identified factor, enhancing managerial actionability. Our results also indicate that these factors impact free and paid game ratings differently.
Research on two-sided review systems—where customers and suppliers evaluate each other —has primarily focused on post-consumption outcomes, showing how reciprocity shapes the evaluations customers and suppliers provide to one another. But do these systems also influence other forms of behavior, especially those that happen during the consumption experience? Across eight experiments, we demonstrate that when suppliers operate on platforms with two-sided (vs. one-sided) review functionality, customers exhibit more ingratiation behaviors, such as being more courteous, polite, and amenable. This occurs because the anticipation of receiving a supplier review increases customers' self-presentational concerns. However, this effect emerges only when customers have an intention to reuse the platform, and amplifies when they perceive a review could impact this to a greater extent, or provides additional value to them.
Firms increasingly use sponsorship announcements as strategic communication events that potentially influence consumer-based brand strength as well as investor behavior. However, most prior research relies on one-time surveys or experiments, which limits understanding of how sponsorship announcements affect brands over time. To address this gap, we develop a novel empirical approach that models how a sponsor's structural sponsorship characteristics disclosed in the announcement (e.g., sponsorship duration), announcement message design (e.g., writing style), and communication activation (e.g., generated news media coverage) shape brand strength. Using an error correction model and daily brand observations linked to 566 sponsorship announcements over 11 years, we identify the key drivers of brand strength following sponsorship announcements.The results show that brand strength increases by about 10% when firms announce long-lasting sponsorships of events, while simultaneously employing rich message features and generating news media coverage (approximately the 75th percentile of observed cases). Sponsorship announcements involving celebrities further strengthen brand perceptions, whereas team sponsorship announcements can backfire and lead to significant declines in brand strength. Importantly, these gains and losses are economically meaningful and translate into changes in firm value of up to USD 205 million in market capitalization. These findings provide actionable guidance for managers on how to design and communicate sponsorship announcements to maximize both brand-building and financial returns.
Augmented reality (AR) has become a prominent retail technology because it enables consumers to preview products in immersive, interactive ways. Although prior research has emphasized AR's positive effects on consumer responses, less is known about its negative post-purchase consequences. This research introduces the virtual idealization paradox, defined as the tendency of AR previews to create an overly favorable evaluative standard against which the received physical product is later judged. Drawing on expectation disconfirmation theory, we propose that AR previews elevate pre-purchase expectations by making the product appear personally situated, visually accessible, and diagnostically informative before purchase. When the received product does not meet these expectations, negative disconfirmation intensifies regret and increases product returns. Across four studies, including one laboratory experiment and three online experiments, the results support this account. Study 1 shows that, after product failure, AR increases return behavior relative to image-based previews. Study 2 demonstrates that this effect is serially mediated by expectation and regret. Study 3 identifies social proof as a boundary condition that attenuates the expectation–regret pathway. Study 4 shows that high visual realism weakens this pathway by calibrating AR-based expectations more accurately to the physical product. By linking AR-enabled preview to post-purchase corrective behavior, this research extends the literature on the dark side of AR and offers implications for designing immersive preview technologies that reduce virtual idealization.
Platform rewards for online reviews, now deployed by major e-commerce platforms including Amazon, Walmart, Taobao, and JD, may generate outcomes that run counter to their intended objectives. Using comprehensive review- and merchant-level data from a crowdsourced review platform, we find that platform rewards generate systematically different effects across merchants: while all merchants experience changes in review patterns, those with limited prior visibility are disproportionately harmed. Specifically, platform rewards decrease overall review quantity while increasing review positivity. Crucially, platform rewards disproportionately reduce review quantity for less popular merchants compared to established ones, a differential effect that increases review inequality by 16.85% (measured by the Gini coefficient) and translates into heterogeneous sales effects across merchants. While platform rewards decrease sales overall, less popular merchants experience significantly larger sales declines than their established counterparts. Mechanism analyses indicate that platform rewards crowd out intrinsic motivation among less experienced reviewers and in reviews for less popular merchants, leading to shorter, less informative, and more imitative reviews, although experienced reviewers respond differently. Our findings reveal that platform rewards, contrary to their democratizing intent, actually exacerbate disparities between established and less popular merchants within the platform ecosystem. These results highlight the complex challenges of designing effective review reward programs and their unintended consequences for platform fairness and merchant diversity.
Generative artificial intelligence (Gen AI) is transforming how consumers create and share content online. This study examines how the availability of Gen AI influences the volume and characteristics of consumer reviews. We exploit the sudden, four-week ban of ChatGPT in Italy as a quasi-experiment and implement a difference-in-differences design comparing TripAdvisor reviews of 1930 hotels in Italy to 2979 hotels in Germany, France, Spain, and Greece, where no ban was imposed. Our results show that ChatGPT availability leads to higher review volume and length, particularly for reviews written on desktop devices. The findings are consistent with reduced effort from Gen AI usage as a potential underlying mechanism. In addition, ChatGPT availability leads to more subjective review content and more extreme sentiment in review text. These findings contribute to the emerging literature on the consumer impact of Gen AI and provide insights into how AI-assisted content generation influences online word-of-mouth behavior.
Hotline wait-times, complicated returns, odd charges on a bill, cumbersome applications: negative service experiences are common. And consumer reactions carry major implications for firms. Drawing on the premise that language reflects unspoken cognition, this research adopts a mixed-methods paradigm to investigate whether a subtle linguistic feature in complaints (i.e., the use of passive voice): (1) reveals consumers’ perception of responsibility for a bad service experience, and (2) predicts escalation. To this end, study 1 applies natural language processing to more than 160,000 real complaints filed with the U.S. Consumer Financial Protection Bureau. We find that consumers who write with greater passive voice are significantly more likely to dispute offers of resolution, even after extensive controls. Studies 2A–3B use correlational and experimental methods to demonstrate that passive constructions increase as perceived fault (for a poor service experience) shifts from self to service provider. Study 4 extends this finding by showing that, when consumers attribute responsibility for an incident to the firm, they in turn: (i) use more passive voice to complain and (ii) report stronger escalation intentions—an effect that persists after controlling for individuals’ baseline writing-style. Conceptually, this research uncovers linguistic structure, an understudied aspect of language, as a diagnostic window into consumers’ state of mind. Managerially, we suggest an automated, cost-effective approach to identify early which dissatisfied customers are likely to escalate. Such insight allows in turn for more targeted service-recovery interventions.
Choice research has traditionally aimed to identify general laws that hold across consumers and contexts. This article proposes a shift toward an alternative paradigm: the age of post-generalization. Under this paradigm, structured heterogeneity is the primary object of inquiry. Consumers differ not only in preferences, but in search processes, belief formation, and the mechanisms that generate choice. Firms respond through hyper-personalization, adapting the marketing mix at the individual level. At the same time, choice increasingly unfolds within environments shaped by platforms, inequality, and virtual worlds, where consumers face individualized sets of options. Together, these developments reorient choice research from explaining average behavior to mapping and explaining systematic variation. Progress lies in developing theories, measurements, and models that are designed for structured heterogeneity rather than built on the assumption of generalization.
To enhance the online shopping experience, companies are increasingly adopting extended realities (XRs) in the form of 360-degree rotation (3D), augmented reality (AR), and virtual reality (VR) for product presentations. However, research has produced inconsistent results regarding the impact of XRs on consumer responses, leaving practitioners uncertain about the effectiveness of these technologies while facing substantial investment costs. Addressing this issue, the authors meta-analyze 298 effect sizes from 111 experimental studies comparing XRs with traditional product presentations (i.e., images, videos). The findings show that XRs moderately improve consumer responses (i.e., attitudinal and behavioral; d = 0.410) with high underlying heterogeneity. The effect is amplified when XRs include overlay elements (e.g., text that overlays virtual content) and for experiential shoppers, while it is weaker for VR on PCs (vs. AR, 3D, and VR on head-mounted displays) and for familiar brands. The effects are robust across different comparison conditions (i.e., images vs. videos), and outcomes (i.e., attitudinal vs. behavioral). The authors also test the relative importance of the two main theoretical accounts used in prior research to explain the effect of XRs—namely, mental simulation and information assessment. The results highlight that companies benefit from a careful use of XRs, for example, by incorporating overlay elements in XR designs or by using XRs for new brands. The article also provides a comprehensive overview of future research areas in the field of XRs
Misinformation poses a growing threat to firms, distorting consumer beliefs and damaging brand evaluations. A common corrective strategy involves attaching fact-checking labels to false claims, yet concerns persist that such corrections may backfire by strengthening familiarity with the misinformation. Across five studies (N = 4337), this article systematically compares the competing effects of repetition and correction on belief in corporate misinformation and brand evaluations. Repetition reliably increases belief in misinformation (illusory truth effect), while correction typically offsets this effect and even reverses it with strong, unambiguous labels. This research finds no evidence of a familiarity backfire effect: in none of the studies, repetition increases belief in the misinformation more than correction reduces it. While brand evaluations are less affected by repetition, they do decline following exposure to misinformation and are only partially restored by corrections. The article further examines how brand familiarity and the timing of assessment shape these effects. Corrections are effective both immediately and after a delay, and benefit unfamiliar brands more than familiar ones. Finally, corrections issued at first exposure, reaching new audiences, also reduce belief in misinformation without backfiring during future exposures. These findings inform managerial decisions on misinformation response and contribute to understanding how misinformation familiarity and correction compete in shaping consumer judgments.
The Metaverse is often portrayed as a novel context for digital consumption, but this paper argues that its deeper value lies in its potential as a behavioral observatory. By loosening four traditional consumer constraints of identity, time, space, and financial access, the Metaverse enables researchers to examine consumer behavior under conditions that are difficult or impossible to replicate in the physical world. We define the Metaverse through the lens of constraint relaxation, distinguishing it from other digital environments such as social media, gaming, and augmented or virtual reality. We then examine how these relaxed constraints reshape marketing research across consumer data analysis, experimental design, and privacy governance. For instance, the fluidity of identity in the Metaverse, where users can adopt multiple avatars, provides a natural context for exploring identity construction and expression. Nonlinear temporality and borderless spatiality challenge sequential experimental designs and traditional geo-segmentation approaches. Relaxed financial constraints alter how consumers perceive value and engage in aspirational consumption. In conclusion, we synthesize current knowledge and forward-thinking analysis to propose a research agenda. Rather than treating the Metaverse as an endpoint for digital innovation, we position it as a methodological and conceptual tool for rethinking marketing theory and generating insights relevant to both virtual and physical contexts.
Digital platforms increasingly influence most online markets and ecosystems, creating substantial value for their customers, owners, and other partners. Yet, the challenges associated with platform operations, governance, and regulation continue to evolve. This paper aims to help researchers understand the extensive platform literature to facilitate effective academic contributions. First, we lay out emerging research topics and open questions related to platforms, both from an internal (platform design) and external (platform regulation) perspective. Then, we compare techniques for acquiring and using platform data, both with and without the collaboration of the platforms themselves, to facilitate empirical platform research. Our insights highlight the importance of multidisciplinary and multi-method approaches in studying digital platform policies, value chains and regulation.
