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【文献速递】Marketing Science,Volume 45, Issue 5, September–October 2026
1.Leveraging Generative Artificial Intelligence to Create Visual Content in Digital Advertising
利用生成式人工智能创作数字广告视觉内容
2.Consumer Impatience, Technological Innovation, and Market Structure
消费者不耐心、技术创新与市场结构
3.Privacy-Preserving Data Fusion
隐私保护型数据融合
4.Unraveling Multifaceted User Preferences on Digital Platforms: A Bayesian Deep-Learning Approach
揭示数字平台上的多维用户偏好：一种贝叶斯深度学习方法
5.The Ripple Effect of Platform Quality Controls in a Submarket: Evidence from a Secondhand Marketplace
子市场中平台质量控制的涟漪效应：来自二手交易平台的证据
6.Talking Without Speaking: Paid Trolls on Social Media and Court Decision
不言而“语”：社交媒体付费水军与法院裁决
7.Optimizing Reserve Prices in Display Advertising Auctions
展示广告拍卖中的保留价优化
8.An Empirical Analysis of Optimal Nonlinear Pricing in Business-to-Business Markets
企业间市场最优非线性定价的实证分析
9.Does Access to Human Coaches Lead to More Weight Loss Than With AI Coaches Alone?
获得真人教练支持是否比单独使用AI教练带来更大的减重幅度？
摘要
1.Leveraging Generative Artificial Intelligence to Create Visual Content in Digital Advertising
利用生成式人工智能创作数字广告视觉内容
作者：Remi Daviet和Yohei Nishimura
Abstract: Generative artificial intelligence (AI) for image synthesis has the potential to transform the digital advertising industry. However, a wide range of uncertainties persists regarding its integration into traditional advertising processes, including finding effective implementations, training methodologies, and achievable performance gains. Specifically, two core challenges limit its practical adoption: a search problem of finding high-performing visuals in a vast creative space, and an alignment problem of ensuring brand and campaign compatibility. This paper proposes a novel end-to-end framework that combines a generative AI with two predictive Bayesian neural networks to identify high-performance and brand-acceptable visuals. We develop a cost-effective Bayesian active learning approach solving simultaneously the dual objectives of performance and alignment. We test the framework in a live advertising campaign for an outdoor activities company. Our system generated a portfolio of visuals achieving a higher mean click-through rate and more consistency (lower variance) than creatives from both a professional human designer and a competing AI model optimizing purely for aesthetics. This research provides a validated methodology that bridges the gap between the theoretical potential of generative AI and its practical application, offering a cost-effective solution to the critical search and alignment problems in creative design.
摘要：用于图像合成的生成式人工智能（AI）有望变革数字广告行业。然而，关于其融入传统广告流程仍存在广泛的不确定性，包括如何找到有效的实施方式、训练方法以及可实现的性能提升。具体而言，两个核心挑战限制了它的实际应用：一个是在广阔的创意空间中寻找高性能视觉素材的搜索问题，另一个是确保品牌和广告活动兼容性的对齐问题。本文提出了一种新颖的端到端框架，将生成式人工智能与两个预测性贝叶斯神经网络相结合，以识别高性能且符合品牌要求的视觉素材。我们开发了一种具有成本效益的贝叶斯主动学习方法，同时解决性能和对齐这两个目标。我们在一家户外活动公司的实际广告活动中对该框架进行了测试。我们的系统生成的视觉素材组合，与专业设计师以及另一款仅以美学为优化目标的竞争AI模型所制作的创意素材相比，实现了更高的平均点击率和更高的一致性（更低的方差）。本研究提供了一种经过验证的方法，弥合了生成式人工智能的理论潜力与实际应用之间的差距，为创意设计中的关键搜索和对齐问题提供了一种具有成本效益的解决方案。
2.Consumer Impatience, Technological Innovation, and Market Structure
消费者不耐心、技术创新与市场结构
作者：Chaewon Seol, Federico Rossi, Sara Valentini和Elisa Montaguti
Abstract: Consumer impatience, that is, customers’ desire to receive their online purchases as quickly as possible, is a key factor in online shopping decisions. Despite that, there is little empirical research on how consumer impatience is shaping markets and how markets will change if delivery time is drastically reduced. In this research, we show that consumer impatience significantly affects market structure. Using online purchase data on pizza delivery, we find that it reduces substitution, softens price competition, and allows low-quality pizzerias to survive in the market. The introduction of a technology that reduces delivery time initially decreases market concentration by eroding the market power of pizzerias that benefit from proximity, especially those in the downtown area, while allowing suburban pizzerias to become more competitive. However, when delivery time is reduced substantially (e.g., by more than 75%), this effect reverses: the market becomes increasingly concentrated among high-quality pizzerias, driving low-quality establishments out of the market. We also find that the platform can increase its profits by almost 20% by offering a premium delivery service targeted at impatient consumers.
摘要：消费者不耐心，即顾客希望尽快收到在线购买的商品，是在线购物决策中的一个关键因素。尽管如此，关于消费者不耐心如何塑造市场以及如果配送时间大幅缩短市场将如何变化的实证研究却很少。在本研究中，我们表明消费者不耐心显著影响市场结构。利用披萨外卖的在线购买数据，我们发现消费者不耐心降低了市场中的替代程度，缓和了价格竞争，并使得低质量披萨店能够在市场中生存。引入一种缩短配送时间的技术，最初会通过削弱那些受益于邻近优势的披萨店（尤其是市中心地区的披萨店）的市场势力来降低市场集中度，同时使郊区披萨店变得更具竞争力。然而，当配送时间大幅缩短（例如缩短超过75%）时，这种效应发生逆转：市场日益集中于高质量披萨店，将低质量店铺挤出市场。我们还发现，平台通过提供针对不耐心消费者的高级配送服务，可以将其利润提高近20%。
3.Privacy-Preserving Data Fusion
隐私保护型数据融合
作者：Longxiu Tian, Dana Turjeman和Samuel Levy
Abstract: Data fusion combines multiple datasets to improve inference, but can inadvertently reveal user identities, even when data appear anonymous. We introduce a Privacy Preserving Data Fusion (PPDF) methodology, that does not require overlapping users across sources and corrects for missingness in certain conditions. Additionally, to aid managerial interpretability, we develop a posterior reidentification metric to quantify individual-level reidentification risks under data fusion and formally derive the analytical bound to the heightened privacy risks posed by data fusion. We showcase PPDF’s abilities by fusing an anonymous customer satisfaction survey and a CRM database of a large U.S. telecom provider. In a predictive churn-prevention campaign, PPDF achieves a 1.46% campaign lift without revealing any user identities, compared to 1.66% lift in a model without privacy guarantees, in which nearly 7% of users are reidentified. A heterogeneity analysis further reveals these individuals as outlier customers who exhibit persistently higher risk of reidentification, underscoring the nuanced trade-off between individual-level privacy guarantees and overall inference quality. More broadly, PPDF enables organizations to harness the full power of data fusion without violating user privacy—offering a practical solution for firms, governments, and researchers seeking to extract value from sensitive data while ensuring individual confidentiality.
摘要：数据融合通过合并多个数据集来改进推断，但即便数据看似匿名，也可能在无意中泄露用户身份。我们提出了一种隐私保护数据融合（PPDF）方法，该方法不要求各数据源之间存在重叠用户，并且在一定条件下能够校正数据缺失问题。此外，为了便于管理者进行解释，我们开发了一种后验再识别指标，用于量化数据融合下个体层面的再识别风险，并正式推导出数据融合所带来的更高隐私风险的解析界限。我们通过融合一家美国大型电信提供商的匿名客户满意度调查与其客户关系管理（CRM）数据库，展示了PPDF的能力。在一次预测性流失预防活动中，PPDF在未泄露任何用户身份的情况下实现了1.46%的活动提升幅度，而没有隐私保障的模型则实现了1.66%的提升，但其中近7%的用户被再识别。进一步的异质性分析显示，这些被再识别的个体属于离群客户，他们持续表现出更高的再识别风险，这凸显了个体层面隐私保障与整体推断质量之间微妙的权衡。更广泛而言，PPDF使组织能够在不侵犯用户隐私的前提下充分发挥数据融合的潜力，为希望从敏感数据中提取价值并确保个体信息保密性的企业、政府和研究人员提供了一种切实可行的解决方案。
4.Unraveling Multifaceted User Preferences on Digital Platforms: A Bayesian Deep-Learning Approach
揭示数字平台上的多维用户偏好：一种贝叶斯深度学习方法
作者：Mingzhang Yin, Ziwei Cong和Jia Liu
Abstract: Given the increasing importance of user engagement on digital platforms, this paper proposes a Bayesian deep-learning model called the Multi-Dynamic Neural Poisson System (MDNPS), which can capture the multifaceted nature of user sparse but high-dimensional activities on such platforms. MDNPS yields semantically interpretable factors underlying high-dimensional items, quantifies user preferences at different granularity levels, and adeptly captures their temporal and cross-activity dependencies. This model is scalable to large empirical data and can be inferred efficiently with a stochastic variational Bayes algorithm. We apply MDNPS to the largest knowledge-sharing platform in China, focusing on the dynamics in user content consumption and contribution behaviors. We show that MDNPS significantly outperforms benchmark factor models in factor quality and out-of-sample data fitting. Our platform-level and individual-level estimates show rich and interpretable insights about consumer preference dynamics and their relationships with user popularity. These insights offer valuable managerial implications for platforms to understand user segments, personalize content offerings, and enhance user engagement.
摘要：鉴于数字平台上用户参与度的重要性日益增加，本文提出了一种名为多动态神经泊松系统（MDNPS）的贝叶斯深度学习模型，该模型能够捕捉此类平台上用户稀疏但高维活动的多面性特征。MDNPS能够得出高维项目背后具有语义可解释性的因子，在不同粒度层级上量化用户偏好，并有效地捕捉其时间依赖和跨活动依赖。该模型可扩展至大规模实证数据，并可通过随机变分贝叶斯算法进行高效推断。我们将MDNPS应用于中国最大的知识分享平台，重点关注用户内容消费和贡献行为的动态变化。我们表明，MDNPS在因子质量和样本外数据拟合方面显著优于基准因子模型。我们的平台层面和个体层面估计结果揭示了关于消费者偏好动态及其与用户受欢迎度之间关系的丰富且可解释的洞见。这些洞见为平台理解不同用户群体、提供个性化内容以及提升用户参与度提供了有价值的管理启示。
5.The Ripple Effect of Platform Quality Controls in a Submarket: Evidence from a Secondhand Marketplace
子市场中平台质量控制的涟漪效应：来自二手交易平台的证据
作者：Yu Feng, Hui Li和Qiaowei Shen
Abstract: Digital platforms increasingly engage in quality controls by regulating product listings and transactions. Although their immediate effect on the involved sellers is evident, their carryover effect on adopters and their spillover effect on nonadopters remain unclear. We address this gap using a natural experiment on a secondhand platform. The platform introduced a submarket with quality controls in the mobile phone category. The submarket offers quality checks, detailed quality reports, standardized listing information, recommended prices, post-purchase guarantees, and free delivery. We find that the quality-controlled submarket not only immediately increases adopters’ sales but also has a positive carryover effect. After adopting, sellers learn from the platform’s quality standards and improve their subsequent listing behaviors and sales performance, even outside the quality-controlled submarket. Moreover, we uncover a U-shaped effect for nonadopters; when the adoption rate is low, nonadopters are disadvantaged because of intensified competition from adopters; however, as adoption increases, they benefit from enhanced buyer perception of platform quality. The competition effect and the perception improvement effect dominate at different stages of adoption. These findings have important implications for platform strategies and policies concerning anticompetitive effects of platform quality controls.
摘要：数字平台正日益通过规范产品的上架和交易来实施质量控制。尽管这些措施对相关卖家的即时影响显而易见，但它们对采纳者的延续效应以及对未采纳者的溢出效应仍不清楚。我们利用一个二手平台上的自然实验来填补这一空白。该平台在手机品类中引入了一个实施质量控制的子市场。该子市场提供质量检测、详细质量报告、标准化上架信息、推荐价格、购后保障和免费配送。我们发现，实施质量控制的子市场不仅能立即提升采纳者的销售额，还具有正向的延续效应。在采纳之后，卖家会从平台的质量标准中学习，并改善其后续的上架行为和销售表现，即便是在实施质量控制的子市场之外也是如此。此外，我们还发现未采纳者存在一种U形效应：当采纳率较低时，未采纳者因来自采纳者的竞争加剧而处于不利地位；然而，随着采纳率的提高，他们会因买家对平台质量的感知提升而受益。竞争效应和感知改善效应在采纳的不同阶段分别占据主导地位。这些发现对平台有关质量控制反竞争效应的战略和政策具有重要启示。
6.Talking Without Speaking: Paid Trolls on Social Media and Court Decision
不言而“语”：社交媒体付费水军与法院裁决
作者：Yi Liu和Haitao (Tony) Cui
Abstract: With the massive growth of social media and other informational platforms that businesses and individuals may use to share their opinions, a society’s perspectives of certain contentious issues may be influenced significantly. At the same time, it has been noted that various parties have used paid trolls (i.e., fake comments made by bot accounts) to sway public opinions concerning some hotly debated issues or legal proceedings. This article aims to investigate how public opinions and paid trolls may affect the outcome of legal disputes between opposing parties. Our research indicates that litigating parties involved in lawsuits with social media engagement may purchase paid trolls to influence public opinions, and they do so more significantly when the truth does not match the prior expectation of the public. We also discover that social media users’ investigation may be a pitfall for the involved parties. Additionally, the court may be either constrained or aided to identify the truth by having a tendency to render decisions that align with prevailing public opinions, depending on the stake of the lawsuit. Finally, treating public opinions on social media as additional information that aids court decision may backfire.
摘要：随着社交媒体和其他信息平台的巨大增长，企业及个人可以利用这些平台分享观点，一个社会对某些争议性议题的看法可能会因此受到显著影响。与此同时，人们注意到，各方已使用付费水军（即由机器人账户发布的虚假评论）来左右公众对某些争议激烈的议题或法律诉讼的看法。本文旨在探究公众舆论和付费水军如何影响对立双方之间法律纠纷的结果。研究表明，在存在社交媒体参与的诉讼中，当事方可能会购买付费水军以影响公众舆论；当真相与公众先前的预期不一致时，这种行为会更加明显。我们还发现，社交媒体用户的调查可能成为涉案方的一个陷阱。此外，法院倾向于作出与主流公众舆论相一致的裁决，而这种倾向可能制约或有助于法院查明真相，具体取决于诉讼所涉利益。最后，将社交媒体上的公众舆论视为有助于法院裁决的额外信息，可能会适得其反。
7.Optimizing Reserve Prices in Display Advertising Auctions
展示广告拍卖中的保留价优化
作者：Hana Choi和Carl F. Mela
Abstract: This paper examines how a publisher should set reserve prices for real-time bidding (RTB) auctions when selling display advertising impressions through ad exchanges, a $595 billion and growing market. Conducting a field experiment to induce exogenous variation in reserve prices at a major publisher, we find that setting reserve prices increases the publisher’s revenues by 35% compared with using a zero reserve price. We also find empirical evidence that advertisers face a minimum impression constraint to ensure sufficient advertising reach. Based on this insight, we develop a structural model of advertiser bidding behavior that incorporates impression constraints, allowing us to infer overall demand for advertising as a function of reserve prices. We then use this demand model to solve the publisher’s pricing problem. Accounting for minimum impression constraints in setting reserve prices yields a revenue increase of nine percentage points over a solution that does not incorporate the constraint. In a final field experiment, we validate our model’s predictions by showing that ad revenues tend to be highest near the model-predicted optimal reserve prices.
摘要：本文研究了一家媒体发布商在通过广告交易平台销售展示广告曝光量时，应如何为实时竞价（RTB）拍卖设置保留价。这一市场规模已达5950亿美元且仍在增长。我们在一家大型媒体发布商处开展了一项田野实验，以引入保留价的外生变动，结果发现，与采用零保留价相比，设置保留价使媒体发布商的收入提高了35%。我们还发现实证证据表明，广告商面临最低曝光量约束，以确保获得足够的广告覆盖。基于这一发现，我们构建了一个纳入曝光量约束的广告商竞价行为结构模型，从而能够推断出作为保留价函数的广告总体需求。随后，我们利用这一需求模型求解媒体发布商的定价问题。在设置保留价时考虑最低曝光量约束，相较于未纳入该约束的方案，收入提升幅度高出9个百分点。在最后的一项田野实验中，我们验证了模型的预测，结果表明，广告收入往往在模型预测的最优保留价附近达到最高。
8.An Empirical Analysis of Optimal Nonlinear Pricing in Business-to-Business Markets
企业间市场最优非线性定价的实证分析
作者：Soheil Ghili和Russ Yoon
Abstract: In continuous-choice settings, consumers decide not only on whether to purchase a product but also, on how much to purchase. Thus, firms optimize a full price schedule rather than a single price point. This paper provides a methodology to empirically estimate the optimal schedule under multidimensional consumer heterogeneity with a focus on business-to-business applications. We apply our method to novel data from an educational services firm, which contain purchase-size information not only for deals that materialized but also, for potential deals that eventually failed. We show that these data combined with identifying assumptions help infer how price sensitivity varies with “customer size.” Using our estimated model, we show that the optimal second-degree price discrimination (i.e., optimal nonlinear tariff) improves the firm’s profit upon linear pricing by at least 8.2%. That said, this second-degree price discrimination scheme only recovers 7.1% of the gap between the profitability of linear pricing and that of infeasible first-degree price discrimination. We also conduct several further simulation analyses: (i) empirically quantifying the magnitude by which incentive-compatibility constraints impact the optimal pricing and profits, (ii) comparing the role of demand-side versus cost-side factors in shaping the optimal price schedule, and (iii) studying the implications of fixed fees for the optimal contract and profitability.
摘要：在连续选择情境下，消费者不仅决定是否购买某种产品，还决定购买多少。因此，企业优化的是完整的价格表，而非单一价格点。本文提供了一种在多维消费者异质性条件下实证估计最优价格表的方法，重点关注企业对企业（B2B）的应用场景。我们将该方法应用于一家教育服务公司的新数据，这些数据不仅包含已成交交易的购买量信息，还包含最终未成交的潜在交易的购买量信息。我们表明，将这些数据与识别假设相结合，有助于推断价格敏感度如何随“客户规模”而变化。利用估计出的模型，我们表明，最优的二级价格歧视（即最优非线性定价）相较于线性定价至少能使企业利润提高8.2%。尽管如此，这种二级价格歧视方案仅弥补了线性定价与不可行的一级价格歧视之间利润差距的7.1%。我们还进行了若干进一步的模拟分析：(i) 实证量化激励相容约束对最优定价和利润的影响程度；(ii) 比较需求侧因素与成本侧因素在塑造最优价格表中的作用；(iii) 研究固定费用对最优合同和盈利能力的影响。
9.Does Access to Human Coaches Lead to More Weight Loss Than With AI Coaches Alone?
获得真人教练支持是否比单独使用AI教练带来更大的减重幅度？
作者：Anuj Kapoor, Sridhar Narayanan和Puneet Manchanda
Abstract: Obesity and excess weight are major global health challenges. Several technological solutions, including mobile apps, have been developed to help people lose weight, many of which provide access to human coaches who help consumers set goals, motivate them, answer questions, and help them in their weight loss journeys. Alternatively, AI coaches could provide similar services efficiently and at scale. In this study, we ask how having access to AI coaches alone compares in terms of achieving the goal of weight loss to also having access to human coaches. Our empirical context is a mobile app with two types of subscription plans, those with AI coaches only and those with additional access to human coaches. We compare adopters of the two types of plans on their weight loss achievements. We address potential self-selection in these plans using a matching-based approach that leverages rich behavioral data. Our empirical analysis of about 65,000 consumers suggests that access to AI plus human coaches leads to higher weight loss than with AI coaches alone. We document heterogeneity in these differences based on age, gender, and starting BMI of the consumers. We also explore potential mechanisms for the human coach’s impact on weight loss.
摘要：肥胖和超重是全球面临的重大健康挑战。包括移动应用在内的多种技术解决方案已被开发出来帮助人们减重，其中许多应用提供真人教练服务，帮助消费者设定目标、激励他们、解答问题、在他们的减重过程中提供支持。作为替代，AI教练能够高效、大规模地提供类似服务。在本研究中，我们提出的问题是：仅使用AI教练与同时使用AI教练和真人教练相比，在实现减重目标方面表现如何？我们的实证情境是一款提供两种订阅计划的移动应用：一种是仅含AI教练的计划，另一种是额外提供真人教练服务的计划。我们比较了两种计划使用者在减重成效上的差异。我们利用丰富的用户行为数据，采用基于匹配的方法来处理计划选择中可能存在的自选择问题。我们对约65,000名消费者的实证分析表明，同时使用AI教练和真人教练比仅使用AI教练能够带来更大的减重幅度。我们发现，这些差异会因消费者的年龄、性别和初始BMI而有所不同。我们还探讨了真人教练影响减重效果的潜在机制。
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