1. 限时优惠和消费者搜索

原标题：Limited Time Offer and Consumer Search

期刊：Management Science

作者：Zheng Gong, Jin Huang

出版时间：2024-12-30

DOI：10.1287/mnsc.2022.00445

摘要：

本文研究了一种被广泛观察但理论上尚未充分探索的销售策略：限时报价。通过聚焦价格导向搜索市场，我们为企业限时要约的应用及其对社会福利的影响提供了一个全新的视角。我们构建了一个搜索模型，公司通过广告动态定价来影响消费者的搜索订单。在各种模型设置中，我们证明，限时优惠是企业实现搜索突出或实施搜索歧视的最有效定价工具，其对社会福利的影响因市场环境而异。特别是，尽管现有文献表明，在价格不可观测的市场中，限时要约是反竞争的，但我们的研究结果表明，在价格可观测的市场中，限时要约可以通过促进社会最优搜索顺序，在某些情况下提高社会效率。

Abstract: 

This paper examines a widely observed yet theoretically underexplored sales tactic: the limited time offer. By focusing on price-directed search markets, we offer a novel perspective on the application of limited time offers by firms and their implications for social welfare. We build a search model where firms advertise dynamic pricing to influence a consumer’s search order. Across various model settings, we demonstrate that limited time offers serve as the most effective pricing tool for firms to achieve search prominence or implement search discrimination, with their impact on social welfare varying depending on the market context. In particular, although existing literature suggests that limited time offers are anticompetitive in markets where prices are unobservable presearch, our findings show that, in markets with observable prices, limited time offers can enhance social efficiency in certain cases by facilitating a socially optimal search order.

2. 信息风险定价吗？来自外层空间的新证据

原标题：Is Information Risk Priced? New Evidence from Outer Space

期刊：Management Science

作者：Xianfeng Hao, Shujing Wang, Yudong Wang, Liangyu Wu

出版时间：2024-12-30

DOI：10.1287/mnsc.2023.00713

摘要：

美国零售公司停车场的卫星图像提供了有关公司未来收益的信息，而对这些图像的有限访问造成了老练和不老练投资者之间的信息不对称。我们构建了一个基于云的信息风险（CIR）测度来捕捉这一卫星信息风险，并考察其资产定价表现。我们发现，CIR在横截面上正向预测零售公司未来的股票收益率，这种可预测性不能用天气原因来解释。我们提供的证据表明，CIR确实捕捉到了投资者之间的信息不对称。空头卖出的盈利能力在晴天更为明显。CIR的收益可预测性在预告期更为显著。多云天气，卫星图像中的停车场交通数据更难获取，使用停车场数据估算的零售店销售额更嘈杂。我们进一步表明，高CIR与低流动性相关，流动性购买的减少大于流动性销售的减少。我们的实证分析表明，信息不对称被定价，CIR通过流动性渠道影响股权溢价，这与不完全竞争下嘈杂理性预期均衡模型的理论预测一致。

Abstract: 

Satellite images of the parking lots of U.S. retail firms provide information about the firms’ future earnings, and the limited access to these images produces information asymmetry between sophisticated and unsophisticated investors. We construct a cloud-based information risk (CIR) measure to capture this satellite information risk and investigate its asset pricing performance. We find that CIR positively predicts future stock returns of retail firms in the cross-section and that the predictability cannot be explained by weather reasons. We provide evidence that CIR indeed captures the information asymmetry among investors. The profitability of short selling is more pronounced on clear days. The return predictability of CIR is more pronounced in preannouncement periods. During cloudy days, parking lot traffic data from satellite images are harder to obtain, and the retail store sales estimated using parking lot data are noisier. We further show that high CIR is associated with low liquidity and that the decrease in liquidity purchases is greater than the decrease in liquidity sales. Our empirical analyses suggest that information asymmetry is priced and that CIR affects equity premiums through the liquidity channel, which are consistent with the theoretical predictions from a noisy rational expectations equilibrium model under imperfect competition.

3. 品类领航的推断及其影响

原标题：Inference and Impact of Category Captaincy

期刊：Management Science

作者：Xinrong Zhu

出版时间：2024-12-30

DOI：10.1287/mnsc.2023.02039

摘要：

本文研究了品类领航，这是一种垂直关系，零售商将整个产品类别的货架摆放和定价决策委托给该品类中的一家领先制造商。这些合同涉及给予零售商内某些品牌的优惠待遇。然而，它们是保密的，实证分析也有限。为了研究这些合同的普遍性和福利影响，我开发了一种新方法，根据货架摆放和定价的优惠待遇推断领航合同的存在。我首先根据从需求模型推断出的品牌×零售商特定的货架摆放质量，将零售商分为不同的领航类型。然后，我进行定价测试，发现领航员根据理论预测和行业轶事消除了自己产品的双重加价，但没有消除竞争对手产品的双重加价。比较静态分析表明，领航关系增加了领航员的市场份额，但由于消除了领航员产品的双重加价，它们也可以使消费者福利提高约 5%。

Abstract: 

This paper studies category captaincy, a vertical relationship where retailers delegate shelf placement and pricing decisions of an entire product category to one of the leading manufacturers within that category. These contracts involve preferential treatment given to certain brands within the retailer. However, they are confidential, and empirical analysis has been limited. To study the prevalence and welfare impact of these contracts, I develop a novel approach to infer the presence of captaincy contracts based on preferential treatment in shelf placement and pricing. I first classify retailers into different captaincy types based on a brand × retailer specific quality of shelf placement inferred from a demand model. I then conduct pricing tests and find that captains, in line with theoretical predictions and industry anecdotes, eliminate double markups from their own products but not from competitor products. Comparative statics show that captaincy relationships increase the market share of the captain, but they can also increase consumer welfare by about 5% due to the elimination of double markups on the captain’s products.

4. 受审查需求下的离线特征定价：一种因果推理方法

原标题：Offline Feature-Based Pricing Under Censored Demand: A Causal Inference Approach

期刊：Manufacturing and Service Operations Management

作者：Jingwen Tang, Zhengling Qi, Ethan Fang, Cong Shi

出版时间：2025-01-06

DOI：10.1287/msom.2024.1061

摘要：

问题定义：我们研究一个基于特征的定价问题，在离线、数据驱动的环境下进行需求审查。在这个问题中，一个企业被赋予了有限的库存量，并面临一个依赖于报价和特征（来自产品、客户或两者）的随机需求。任何超出库存水平的未满足需求都将丢失且不可观察。该公司不知道需求函数，但可以访问一个离线数据集，该数据集由历史特征、库存、价格和可能经过审查的销售数量的四个组成。我们的目标是利用线下数据集，寻找基于特征的最优定价规则，从而实现预期利润最大化。方法学/结果：通过因果推理的视角，我们提出了一种基于生存分析和双稳健估计的数据驱动算法。我们推导了一个有限样本遗憾界来证明所提出的离线学习算法的合理性，并证明其鲁棒性。数值实验证明了我们提出的算法在训练和测试数据上准确估计最优价格的鲁棒性。管理含义：这项工作为实践者提供了一个创新的建模和算法框架，用于基于特征的定价问题，通过因果推理的视角进行需求审查。我们的数值实验强调了在基于特征的定价背景下考虑需求审查的价值。

Abstract: 

Problem definition: We study a feature-based pricing problem with demand censoring in an offline, data-driven setting. In this problem, a firm is endowed with a finite amount of inventory and faces a random demand that is dependent on the offered price and the features (from products, customers, or both). Any unsatisfied demand that exceeds the inventory level is lost and unobservable. The firm does not know the demand function but has access to an offline data set consisting of quadruplets of historical features, inventory, price, and potentially censored sales quantity. Our objective is to use the offline data set to find the optimal feature-based pricing rule so as to maximize the expected profit. Methodology/results: Through the lens of causal inference, we propose a novel data-driven algorithm that is motivated by survival analysis and doubly robust estimation. We derive a finite sample regret bound to justify the proposed offline learning algorithm and prove its robustness. Numerical experiments demonstrate the robust performance of our proposed algorithm in accurately estimating optimal prices on both training and testing data. Managerial implications: The work provides practitioners with an innovative modeling and algorithmic framework for the feature-based pricing problem with demand censoring through the lens of causal inference. Our numerical experiments underscore the value of considering demand censoring in the context of feature-based pricing.

5. Shoppattainment直播模式

原标题：A Model of Shoppertainment Live Streaming

期刊：Management Science

作者：Zheyin (Jane) Gu, Xuying Zhao, D. J. Wu

出版时间：2024-11-21

DOI：10.1287/mnsc.2023.01724

摘要：

融合娱乐和产品销售的“Shoppattainment”直播正变得越来越受欢迎。在Shoppattainment会话中，拖缆面临带宽限制，因此需要在提供产品信息和娱乐（如唱歌、跳舞和讲故事）之间保持平衡。我们将Shoppattainment live streamer概念化为一种新型在线零售平台，最大限度地提高销售一系列制造商产品的总佣金。我们的研究调查了拖缆如何利用娱乐带宽作为长期策略来影响制造商的定价决策和消费者的出席率，以及购买行为。我们的模型揭示了几个有趣的发现。首先，我们发现更高的娱乐带宽鼓励更多制造商采用面向需求的低价策略来吸引买家，而不是只吸引那些认为找到合适产品的可能性很高的买家的以利润为导向的高价策略。第二，我们观察到，娱乐带宽的增加最初会提升消费者对Shoppattainment环节的流量，但一旦带宽变得过高，这种趋势可能会逆转，表明存在倒U型关系。反之，流媒体每参加消费的预期利润随着娱乐带宽的增加先降后升，形成U型关系。第三，我们的结果表明，面临带宽限制的佣金最大化拖缆不应该只专注于提供产品信息。相反，将大量带宽分配给娱乐是有利的，即使娱乐能力有限，如果容量很大，将所有带宽专用于娱乐可能也是有利的。最后，我们证明了拖缆的质量阈值缓和了娱乐带宽对这些动态的影响。

Abstract: 

“Shoppertainment” live streaming, a blend of entertainment and product selling, is becoming increasingly popular. In shoppertainment sessions, streamers face bandwidth constraints so balance between providing product information and entertainment (such as singing, dancing, and storytelling). We conceptualize a shoppertainment live streamer as a novel type of online retail platform that maximizes total commissions from selling a range of manufacturer products. Our study investigates how streamers can leverage entertainment bandwidth as a long-term strategy to influence manufacturers’ pricing decisions and consumers’ attendance, as well as purchasing behaviors. Our model reveals several intriguing findings. First, we find that higher entertainment bandwidth encourages more manufacturers to adopt a demand-oriented, low-pricing strategy to attract buyers rather than a margin-oriented, high-pricing strategy that appeals only to buyers who perceive a high likelihood of finding a suitable product. Second, we observe that an increase in entertainment bandwidth initially boosts consumer traffic to shoppertainment sessions, but this trend may reverse once the bandwidth becomes excessively high, indicating an inverted U-shaped relationship. Conversely, the streamer’s expected profit per attending consumer first decreases and then increases with higher entertainment bandwidth, forming a U-shaped relationship. Third, our results suggest that a commission-maximizing streamer facing bandwidth constraints should not exclusively focus on providing product information. Instead, it is advantageous to allocate significant bandwidth to entertainment, even with limited entertaining capability, and potentially beneficial to dedicate all bandwidth to entertainment if the capability is substantial. Lastly, we demonstrate that the streamer’s quality threshold moderates the impact of entertainment bandwidth on these dynamics.

6. 一种解决电子商务盒尺寸问题的机器学习方法

原标题：A Machine Learning Approach to Solve the E-commerce Box-Sizing Problem

期刊：Production and Operations Management

作者：Shanthan Kula, Kerem Akartunalı, Debjit Roy

出版时间：2024-09-01

DOI：10.1177/10591478241282249

摘要：

电子商务软件包因空间利用效率低下而臭名昭著。超过四分之一的典型电商包装包含空气和填充材料。空间的低效利用显著降低了运输和配送能力，增加了运营成本。因此，设计一套最优的包装盒尺寸对于提高效率至关重要。我们提出了第一个基于学习的框架来确定最佳包装盒尺寸。特别是，我们提出了一个结合无监督学习、强化学习和树搜索的三阶段优化框架来设计盒子尺寸。将包装优化问题转化为一个称为箱子大小博弈的顺序决策任务。然后设计了一个神经网络代理来进行博弈，学习启发式规则来解决问题。此外，为了提高学习网络的性能，开发了树搜索算子。以基于公司的优化公式和两种备选优化模型为基准，我们发现基于ML的方法能够在规定时间内有效解决大规模问题。我们在一个大型电子商务平台提供的真实数据集上评估了我们的模型。该框架目前被一家大型电商公司在其28个履行中心采用，预计每年可为公司节省710万美元左右。此外，预计每年减少纸张消耗2080公吨，减少温室气体排放1960公吨。本文提出的优化框架可作为大型电商仓库包装箱设计的决策支持工具。

Abstract: 

The inefficient usage of space significantly reduces the transportation and distribution capacity increasing the operational costs. Therefore, designing an optimal set of packaging box sizes is crucial for improving efficiency. We present the first learning-based framework to determine the optimal packaging box sizes. In particular, we propose a three-stage optimization framework that combines unsupervised learning, reinforcement learning, and tree search to design box sizes. The package optimization problem is formulated into a sequential decision-making task called the box-sizing game. A neural network agent is then designed to play the game and learn heuristic rules to solve the problem. In addition, a tree-search operator is developed to improve the performance of the learned networks. When benchmarked with company-based optimization formulation and two alternate optimization models, we find that our ML-based approach can effectively solve large-scale problems within a stipulated time. We evaluated our model on real-world datasets supplied by a large e-commerce platform. The framework is currently adopted by a large e-commerce company across its 28 fulfillment centers, which is estimated to save the company about 7.1 million USD annually. In addition, it is estimated that paper consumption will be reduced by 2,080 metric tons and greenhouse gas emissions by 1,960 metric tons annually. The presented optimization framework serves as a decision support tool for designing packaging boxes at large e-commerce warehouses.

7. 社交技术能否推动电商直播购物？广播公司认知与情感社会行动号召的实证研究

原标题：Can Social Technologies Drive Purchases in E-Commerce Live Streaming? An Empirical Study of Broadcasters’ Cognitive and Affective Social Call-to-Actions

期刊：Production and Operations Management

作者：Ying Zhang, Khim-Yong Goh, Xixian Peng, Yutong Guo

出版时间：2024-08-09

DOI：10.1177/10591478241276131

摘要：

流媒体直播作为一种新兴的社交技术，为电商卖家和消费者提供了同步互动的销售环境。尽管电子商务直播（ELS）中的运营管理（OM）不断增长，但之前的文献在很大程度上忽略了ELS广播公司的定向社交运营，这对公司的业务运营和营销策略至关重要。为了缓解这一差距，我们研究了ELS特定指令性社会操作社会行动呼吁（SCTAs）对ELS中消费者购买结果的影响。为了阐明SCTA购买效应的驱动机制，我们借鉴认知和情感营销诉求的概念，将SCTA分为两类：认知型和情感型SCTA。我们使用文本挖掘技术从ELS语音到文本数据测量广播公司的SCTA，并通过工具变量识别方法采用计量经济学模型估计，量化ELS广播公司的SCTA与消费者购买之间的关系。我们的研究结果发现，情感性SCTA对ELS中的产品级采购订单有显著的正向购买效应，而认知性SCTA对ELS中的产品级采购订单影响不显著。具体而言，每分钟情感SCTA相关词增加一个单位，与产品采购订单增加0.43%有关，即平均每分钟需求增长1.10%。此外，我们发现认知和情感SCTA的效果取决于产品和广播类型。我们的会话级中介效应分析验证了驱动广播公司SCTA购买效应的潜在机制。具体而言，我们发现认知SCTA阻碍社交活动，进而阻碍购买，而情感SCTA可以促进社交活动，导致会话流量增加，最终导致ELS的产品销售。我们的研究为ELS平台和广播公司提供了新的实证发现和重要的实践启示。

Abstract: 

emerging social technology, live streaming has facilitated a synchronous and interactive selling setting for e-commerce sellers and consumers. Despite growing operations management (OM) in e-commerce live streaming (ELS), the prior literature has largely neglected the directive social operations of ELS broadcasters, which are crucial to a company's business operations and marketing strategies. To mitigate this gap, we investigated the effects of social call-to-actions (SCTAs), an ELS-specific directive social operation, on consumer purchase outcomes in ELS. To explicate the mechanisms driving the purchase effect of SCTAs, we draw upon the notions of cognitive and affective marketing appeals to categorize SCTAs into two types: cognitive and affective SCTAs. We measure broadcasters’ SCTAs from ELS speech-to-text data using text-mining techniques and adopt econometric model estimations via an instrumental variables identification approach to quantify the relationships between ELS broadcasters’ SCTAs and consumer purchases. Our results uncover a significant positive purchase effect of affective SCTAs but an insignificant effect of cognitive SCTAs on product-level purchase orders in ELS. Specifically, a one-unit increase in affective SCTA-related words per minute is related to increased purchase orders of a product by 0.43%, i.e., averaging a 1.10% growth in demand per minute. Additionally, we find that the effects of cognitive and affective SCTAs are contingent on product and broadcaster types. Our session-level mediation effect analysis verifies the underlying mechanisms that drive the purchase effects of broadcasters’ SCTAs. Specifically, we find that cognitive SCTAs impede social engagements and thereby purchases, whereas affective SCTAs can boost social engagements, leading to increased session traffic and ultimately product sales in ELS. Our study provides novel empirical findings and important practical implications for ELS platforms and broadcasters.