Knowledge that Transforms

Returning products has become standard practice for consumers and a significant “pain point” for retailers. The authors contend that returns can be harnessed to increase profits. Doing so requires retailers to manage the interweaving dynamics of product returns and purchases. The strategy is to comanage a virtuous cycle, whereby current returns increase future purchases, and a vicious cycle, whereby current returns increase future returns. Marketing executes the strategy by generating purchases, and the returns that follow impose a direct cost due to the vicious cycle. However, the virtuous cycle of returns can offset the vicious cycle, enabling the retailer to “free ride” on returns by optimizing its marketing. The approach follows the decision support system paradigm by combining a conceptual model, a statistical model, data, and optimization. A core construct is a stock variable tracking consumers’ memory of return experiences, which drives both the virtuous and vicious cycles. The authors optimize marketing spend while accounting for return stock. The best results occur when dynamics are incorporated into both the statistical and optimization models. The results suggest that managers should avoid strict return policies aimed at eliminating returns. Instead, they should design policies that optimally balance the long-term benefits and costs of returns.

Channel restructuring is a firm's redesign and optimization of its channel system to serve markets more effectively. Restructuring demands significant investments of time, capital, and managerial attention, as firms must reconfigure processes, renegotiate agreements with channel partners, and manage the operational disruptions that inevitably accompany structural change. As even the most rigorously planned restructuring efforts can falter if partners do not buy in, it is paramount that firms secure the compliance of partners they seek to retain in the revised channel system. Prior research lacks a comprehensive and integrated conceptualization of channel restructuring, the available restructuring options, and the implications of restructuring for the firm's downstream channel partners. The authors offer a channel restructuring typology comprising six restructuring types and delineate the functional role, status, and financial implications of each type for the firm's partners. They provide theoretically grounded, testable propositions regarding the impact of restructuring on partner compliance. This article offers scholars guidance for empirical investigation. It also highlights factors that practitioners should consider when designing and implementing channel restructuring to both achieve operational efficiencies and sustain partner relationships.

Knowledge is a key resource in marketing, and an assumption across literatures is that a firm must prevent valuable knowledge from leaking to competitors. This research develops a theoretical framework that calls for rethinking this view by offering a fresh perspective on when and how a firm should protect marketing knowledge to minimize the likelihood of harm to the firm. This framework integrates research across disciplines and interviews with executives to provide a more accurate analysis of the payoffs of knowledge protection. This is accomplished, first, by introducing a set of unintended hidden costs of the most commonly used knowledge-protection strategy—leakage prevention—that harm a firm's competitive advantage. Firm and industry moderators that influence the magnitude and consequences of these hidden costs are outlined. Second, the framework suggests that the benefits of knowledge protection may be more limited than previously recognized. It does so by delineating the knowledge “leakage-to-harm” process and identifying competitor and industry moderators that can impede the process. Third, the framework offers a typology of alternative protection strategies firms can use to preserve their knowledge-based competitive advantage. Across these contributions, the framework calls for substantial rethinking of marketing knowledge protection with important implications for marketing practice and future research.

Novice art pricing is an understudied domain. Novice artists operate as microenterprises, making crucial price-setting decisions. Research shows that newcomers often risk overpricing or underpricing their work, and existing online tools offer basic, cost-based pricing advice. Using a three-study framework, the authors examine novice art pricing on Etsy, where artwork listings include structured data, images, and textual descriptions. They first analyze how these inputs relate to final selling prices using a hedonic regression on structured data, followed by a multimodal fusion deep learning model that integrates structured, visual, and textual features. The results show that features related to artist authenticity (e.g., certificates), customer service (e.g., shipping, returns, personalization), and art style (e.g., genre) are important price predictors. Thus, novice art sales on online platforms exhibit some features typical of mature art markets (e.g., authenticity and reputation) but emphasize customer-focused services. Finally, using a Cox proportional hazards model, the authors show that, while higher artist reputation is associated with faster sales, discounting correlates with longer time on market. These associations suggest the importance of price setting. From these insights, the authors develop a price recommender application that predicts both selling prices and time to sell, offering practical guidance for newcomer artists and online platforms.

The pervasive use of smartphones has raised concerns about their addictive and maladaptive nature. This article introduces an intervention based on rational addiction theory to cost-effectively nudge consumers to reduce smartphone usage, promoting sustainable digital consumption. The authors examine whether preannouncing future targets to reduce smartphone usage influences current consumption and behavioral change. They develop a mathematical model incorporating habit formation, satiation, and projection bias and test its predictions in three preregistered randomized controlled trials using objectively measured smartphone usage. When future incentives and targets are preannounced, consumers reduce usage preemptively compared with their baseline, consistent with rational addiction. This occurs only when participants are given fixed daily reduction targets, not when incentivized proportionally for reductions over time, and seems to reflect forward-looking habit formation, as other explanations (e.g., goal priming, capability testing) are unlikely to drive results. Interestingly, preemptive reductions are stronger among heavy users and those with stronger beliefs in meeting their targets. The authors also find that preemptive reductions help consumers meet their targets during the incentivized period and might support posttreatment behavioral sustenance. The model fitting results reveal considerable heterogeneity and offer insights into how digital detox experiences can be structured to promote sustainable behavior change.

Contextual advertising involves matching features of ads to features of the media context where they appear. The authors propose AdGazer, a new machine learning procedure to support contextual advertising. It comprises a theoretical framework organizing high- and low-level features of ads and contexts, feature engineering models grounded in this framework, an XGBoost model predicting ad and brand attention, and an algorithm optimally assigning ads to contexts. AdGazer includes a multimodal large language model to extract high-level topics predicting the ad–context match. This research uses a unique eye-tracking database containing 3,531 digital display ads and their contexts, and aggregate ad and brand gaze times. The authors compare AdGazer’s predictive performance with that of two feature learning models, VGG16 and ResNet50. AdGazer predicts highly accurately with holdout correlations of .83 for ad gaze and .80 for brand gaze, outperforming both feature learning models and generalizing better to out-of-distribution ads. Context features jointly contributed at least 33% to predicted ad gaze and about 20% to predicted brand gaze, good news for managers practicing or considering contextual advertising. The authors demonstrate that the theory-informed AdGazer effectively matches ads to advertising vehicles and their contexts, optimizing ad gaze more than current practice and alternatives like text-based and native contextual advertising.

The shift from physical music consumption to online music streaming has fundamentally transformed the market for recorded music, giving consumers access to millions of songs on demand. Because streaming platforms pay artists and labels on a per-play basis, sustained listening and effective discovery have become essential for generating meaningful revenue. With traditional marketing losing relevance for recorded music, curated collections in the form of playlists have emerged as a potentially vital tool for labels to lift streaming demand. As a result, labels have started to deploy playlist marketing by either curating their own playlists or targeting influential playlists curated by others. However, the success factors behind the impact of playlists on song demand remain insufficiently understood. An analysis of 200,455 quasi-experiments where songs are listed on playlists and later delisted shows an average listing effect of 8.5% more global streams and a carryover effect of 4% after delisting. Although these effects are highly heterogeneous, they can be systematically explained by playlist design characteristics, including playlist popularity, the song's prior playlist exposure, the fit between the song and playlist, curator identity, and the curation approach. These findings empower labels to deploy more informed playlist marketing to lift streaming revenue.

New forms of shared micromobility services, such as e-scooters, are growing rapidly across cities. However, their impact beyond the retail and restaurant sectors is less understood in the marketing literature. The authors examine how the entry of e-scooters impacts other incumbent shared mobility services (i.e., ride-sharing and bike-sharing) and consumer safety (i.e., crimes) using data on the entry of e-scooters in parts of Chicago in 2019 and a generalized synthetic control approach. The results show that the entry of e-scooters increases the number of short rideshare trips by 15.72% but decreases the number of bikeshare trips by 7.62%. The effects are consistent with a category expansion mechanism for ride-sharing and a category cannibalization mechanism for bike-sharing. The authors also find that the entry of e-scooters increases the number of crimes (e.g., vehicle break-ins) by 17.94%, mostly due to street and vehicle crimes. The effects of e-scooters are heterogeneous by the age and racial composition of a neighborhood. Overall, e-scooters contribute about $8.1 million in ride-sharing revenues, but they also have an unintended negative environmental effect amounting to over 800 metric tons of carbon emissions per year. This research offers an app companion for stakeholders.

Managers often face the challenge of protecting the parent brand’s equity when launching brand extensions into low-fit categories. This article recommends that managers minimize any negative impact of low-fit extensions by using a unique segmentation strategy as well as framing the extension fit in terms of brand personality. Specifically, this research introduces person–thing orientation as a framework in the brand extension context to segment consumers effectively based on their environmental orientation. Person-oriented individuals selectively examine the environment and direct their attention toward people and social relationships. Thing-oriented individuals primarily focus on objects and their functionality. We demonstrate that thing-oriented consumers are less likely to exhibit negative feedback effects. Person-oriented consumers respond positively when low-fit extensions align with the parent brand's personality. Importantly, we demonstrate that person–thing orientation can be leveraged managerially through observable proxies (e.g., college major, media context). These findings offer actionable insights for tailoring brand extension strategies to target the right audience, frame extensions effectively, and minimize parent-brand dilution.

Retailers frequently offer customers the opportunity to customize in addition to buying ready-made products. Although prior research shows that customization can increase product evaluations, purchase intent, and willingness to pay, customers often do not opt into customization. Using a multimethod approach, the authors resolve this discrepancy by demonstrating that the customization journey is dynamic rather than static; an initial customization experience has a lasting impact on the customer journey. Nine years of transaction data from a retailer show that customers who customize at least one product are more likely to customize again with the retailer; they also spend more, visit more often, and buy more items. Next, the authors conduct a series of longitudinal experimental studies. By randomly assigning some participants to customize and others to choose among a matched set of products, they disentangle the effects of the customer's inherent preference for customizing from their experience with customization. The results show that an initial experience with customization significantly increases the subsequent likelihood of customization, leading to more favorable outcomes for both the customer and the retailer. The findings suggest that customizers need not be “born”—they can be “created” via interventions designed to increase experience with customization, shaping the trajectories of customer journeys.