1. 合作还是不合作？双边市场平台与数据代理的合作

原标题：To Partner or Not to Partner? The Partnership Between Platforms and Data Brokers in Two-Sided Markets

期刊：Information Systems Research

作者：Yugang Yu；Yong Tan；Lihong Cheng；Xin Zhang

出版时间：2024-11-04

DOI：10.1287/isre.2022.0470

摘要：

数据已经成为重要的竞争资产，尤其是对于在线广告平台而言，导致了一个新的数据代理行业的出现。平台可以与数据代理合作获取外部数据以增强其目标定位能力，但这种做法引发了越来越多的消费者隐私担忧。本研究发展了一个博弈论模型，以检验在双边市场中竞争平台和数据经纪人之间的合作关系背后的经济机制。有趣的是，我们的分析显示，数据代理引发的消费者隐私担忧增加，可能会鼓励平台与数据代理合作，而不是阻止他们。驱动力是，由于广告商对消费者的负面跨端网络效应，隐私担忧的增加可以作为一个战略杠杆，软化广告商端的价格竞争。然而，当两个平台都与数据代理合作时，即使数据代理能够大幅提升瞄准能力，均衡中也可能出现囚徒困境。我们发现，当平台采用纯广告赞助模式而不向消费者收费时，平台数据代理伙伴关系损害了消费者剩余，但当平台采用广告赞助和基于订阅的收入混合模式时，可能会使消费者剩余受益。此外，我们的分析表明，平台可能没有采取高水平隐私保护的动机，反而可能采取不对称隐私保护策略，一个平台采用高隐私保护，另一个平台选择低保护。一系列模型扩展证实了我们关键发现的稳健性。最后，我们讨论了管理和政策方面的影响。

Abstract: 

Data have become an important competitive asset, especially for online advertising platforms, leading to the emergence of a new data broker industry. Platforms can partner with data brokers to acquire external data to enhance their targeting capabilities, but this practice has stoked growing consumer privacy concerns. This study develops a game-theoretic model to examine the economic mechanism underlying the partnership between competing platforms and a data broker in a two-sided market. Interestingly, our analysis shows that increasing consumer privacy concerns caused by the data broker may encourage platforms to partner with the data broker rather than discourage them. The driving force is that, because of negative cross-side network effects from advertisers to consumers, increased privacy concerns can serve as a strategic lever to soften price competition on the advertiser side. However, when both platforms partner with the data broker, a prisoner’s dilemma may arise in equilibrium even when the data broker can greatly improve the targeting capabilities. We find that the platform-data broker partnership hurts consumer surplus when platforms adopt a pure ad-sponsored model without charging consumers, but it may benefit consumer surplus when platforms adopt a mixed model with ad-sponsored and subscription-based revenue. Moreover, our analysis indicates that platforms may not have incentives to adopt high levels of privacy protection and may instead adopt asymmetric privacy protection strategies, with one platform adopting high privacy protection and the other opting for low protection. A series of model extensions confirm the robustness of our key findings. We conclude with a discussion of managerial and policy implications.

2. 观念稀缺与最优研发政策

原标题：Scarcity of Ideas and Optimal R&D Policy

期刊：Management Science

作者：Suren Basov, Nisvan Erkal, Deborah Minehart, Suzanne Scotchmer

出版时间：2024-07-12

DOI：10.1287/mnsc.2023.03362

摘要：

我们考虑一种区分理念和创新的创新模式。尽管创新是对激励的回应，但创意是一种稀缺资源，对创新速度提供了外生约束。我们研究了理想的稀缺性是如何塑造最优报酬结构的。模型中创新的替代思想在随机时间到达随机接收者手中。通过放弃对当前想法的投资，社会保留了一种选择，即为同一市场细分市场投资更好的想法，但会延迟。因为接连的想法可能会发生在不同的人身上，所以私人最优和社会最优之间存在冲突。社会规划者不会观察想法的到来，并随着时间的推移了解想法的到来率。我们使用一般连续密度来表示社会规划者的信念，并说明信念的演化可以通过累积风险函数来跟踪。社交规划师设定的奖励有双重目的：了解想法的到达率，并在较低延迟和较低成本的想法之间进行权衡。我们发现，随着时间的推移，最优回报会增加，因为社会规划者开始认为想法更稀缺。

Abstract: 

We consider a model of innovation that distinguishes between ideas and innovations. Although innovation responds to incentives, ideas are a scarce resource that provides an exogenous constraint on the rate of innovation. We investigate how the optimal reward structure is shaped by the scarcity of ideas. Substitute ideas for innovation in the model arrive to random recipients at random times. By forgoing investment in a current idea, society preserves an option to invest in a better idea for the same market niche but with delay. Because successive ideas may occur to different people, there is a conflict between private and social optimality. The social planner does not observe the arrival of ideas and learns over time about the arrival rate of ideas. We represent the social planner’s beliefs using a general continuous density and illustrate that the evolution of beliefs can be tracked by the cumulative hazard function. The reward set by the social planner serves a dual purpose: learning about the arrival rate of ideas and trading off lower delays against lower-cost ideas. We show that the optimal reward increases as time passes because the social planner comes to view ideas as more scarce.

3. 客户搜索下的最优动态机制

原标题：Optimal Dynamic Mechanism Under Customer Search

期刊：Operations Research

作者：Zhenyu Hu, Yangge Xiao

出版时间：2024-06-11

DOI：10.1287/opre.2022.0136

摘要：

本文研究了当顾客在有限的时间范围内寻找外部替代品时，卖方的收益最大化机制。客户在搜索中的效用被建模为一个通用函数，称为过去搜索结果的回忆函数。在不观察客户对产品的估值或搜索结果的任何实现的情况下，卖方可以在搜索过程开始前提出并承诺与客户签订合同。在一般召回函数下，我们证明了卖方的最优策略是提供由存款和履约价格组成的美式期权菜单。在客户只能回忆最近几次外部替代品的情况下，我们进一步确定，在最优机制下，估值较低的客户在没有与卖家接触的情况下搜索外部替代品，而估值较高的客户立即行使期权，有效地将期权转化为爆炸式报价。中等估值的客户仅在搜索期结束时行使该选项，如果有的话。而更长的搜索期限或更小的搜索成本都会增加客户从搜索中获得的效用，但它们对卖家的收入有不同的影响。更多的搜索机会导致卖家收入呈指数下降，在极限情况下，最优机制收敛为张贴价格机制。相比之下，随着搜索成本的增加，卖家的收入可能会先减少后增加。在搜索成本超过外部替代品平均值的极端情况下，客户的顺序搜索问题归结为战略性地定时购买卖方产品。在这种情况下，我们的最优机制简化为以垄断价格发出单一爆炸性报价。

Abstract:

This paper investigates the seller’s revenue-maximizing mechanism in the face of a customer who searches for outside alternatives over a finite horizon. The customer’s utility from searches is modeled as a general function—referred to as the recall function—of the past search outcomes. Without observing the customer’s valuation of the product or any realization of search outcomes, the seller can propose and commit to a contract with the customer before the search process begins. Under a general recall function, we show that the optimal strategy for the seller is to offer a menu of American options consisting of deposits and strike prices. In the case in which the customer can only recall a few recent outside alternatives, we further establish that, under the optimal mechanism, customers with low valuation search for outside alternatives without engaging with the seller, whereas high-valuation customers exercise the option immediately, effectively turning the option into an exploding offer. Customers with intermediate valuation only exercise the option, if ever, at the end of the search horizon. Whereas a longer search horizon or smaller search cost both increase the customer’s utility from searches, they have different impacts on the seller’s revenue. More search opportunities lead to an exponential decrease in the seller’s revenue, and in the limit, the optimal mechanism converges to a posted price mechanism. In contrast, as the search cost increases, the seller’s revenue may initially decrease and then increase. In the extreme case in which the search cost exceeds the average value of outside alternatives, the customer’s sequential search problem reduces to strategically timing purchases of the seller’s product. Our optimal mechanism, in this case, reduces to making a single exploding offer with a monopoly price.

4. 平衡配置和动态效率与冗余研发配置：组织接近和集中的作用

原标题：Balancing allocative and dynamic efficiency with redundant R&D allocation: The role of organizational proximity and centralization

期刊：Strategic Management Journa

作者：Kannan Srikanth, AnNkumar, Ronak Mogra, Vivek Ton

出版时间：2024-09-02

DOI：10.1002/smj.3662

摘要：

研究综述：基于资源的观点学者们主要研究了多单元企业竞争优势的两种资源配置方式：资源共享和资源重新配置。这些方法强调配置效率，即对资源进行优化配置，使其当前价值最大化。在技术密集型行业，企业成功还需要实现动态效率，以增加其未来价值创造。我们提出资源冗余配置，即非标度自由资源向同一目标并行部署，虽然配置效率低下，但通过刺激单元间竞争来提高动态效率。企业的结构特征缓和了这些影响。对大型制药企业的分析显示，冗余研发增加了企业特定价值高的创新，但同时增加了项目终止以减少浪费。组织接近度增加了前者的效应，降低了后者。企业的研发集中放大了单位邻近效应。管理总结：在技术密集型行业，多单元企业往往采用研发资源的冗余配置，即在不同单元平行部署科学家和设备，以实现相同的业务目标。尽管这种做法很常见，但对于这种做法如何影响企业的研发成果，以及组织特征如何影响这种关系，几乎没有管理指导。对大型制药企业的分析显示，跨部门冗余配置研发资源会增加浪费，但也会刺激竞争单位创造具有高企业特定价值的创新。组织上接近的单位不太可能终止其冗余项目，同时创造更多高价值创新。公司研发集中化放大了单位邻近效应。

Abstract: 

Research Summary: Resource-based-view scholars have mainly examined two resource allocation approaches for competitive advantage in multiunit firms: resource sharing and resource redeployment. These approaches emphasize allocative efficiency—the optimal allocation of resources to maximize their current value. In technology-intensive industries, firm success also requires achieving dynamic efficiency to increase its future value-creation. We propose that the redundant allocation of resources—the parallel deployment of non-scale-free resources towards the same objective—although allocatively inefficient, increases dynamic efficiency by stimulating inter-unit competition. Firms' structural features moderate these effects. An analysis of large pharmaceutical firms reveals that redundant R&D increases innovations with high firm-specific value but simultaneously increases project terminations to reduce wastage. Organizational proximity increases the former effect and decreases the latter. Firm's R&D centralization amplifies the effect of unit proximity.

Managerial Summary: In technology-intensive industries, multiunit firms often employ redundant allocation of R&D resources, that is, the parallel deployment of scientists and equipment in different units towards realizing the same business objective. Although common, there is little managerial guidance on how this practice impacts firms' R&D outcomes, and how organizational characteristics influence this relationship. An analysis of large pharmaceutical firms reveals that redundant allocation of R&D resources across units increases wastage but also stimulates competing units to create innovations with high firm-specific value. Organizationally proximate units are less likely to have their redundant projects terminated, while creating more high-value-innovations. Centralization of the firm's R&D amplifies the effect of unit proximity.

5. 从公共数据中学习个性化隐私偏好

原标题：Learning Personalized Privacy Preference from Public Data

期刊：Information Systems Research

作者：Wen Wang, Beibei Li

出版时间：2024-06-13

DOI：10.1287/isre.2023.0318

摘要：

了解消费者的个性化隐私偏好，对于企业和政策制定者建立信任和合规，指导有效决策至关重要。现有方法大多依赖私有信息，如专有用户行为数据和个人层面的人口和社会经济因素，或需要明确的用户输入，这可能具有侵入性和负担，可能导致用户不满。如今，个人在公共领域生成并分享大量关于自己的信息，这可以为他们的行为、态度和偏好提供有价值的多方面视角。因此，这些信息有可能为个人隐私偏好提供有价值的见解。在本研究中，我们提出了一个新的框架，通过利用无处不在的公共数据源社交媒体帖子来预测个性化隐私偏好。我们深深植根于心理学和隐私理论，利用深度学习模型和自然语言处理算法，从社交媒体帖子中学习理论驱动的心理社会特征，如生活方式、风险偏好、个性、隐私相关的经济偏好、语言风格等。有趣的是，我们发现公共数据中的心理社会特征比私人信息提供更大的预测能力。此外，我们还进行了多重可解释性分析，以了解驱动模型性能的因素。最后，我们展示了我们模型的实用价值，并表明我们的框架可以帮助平台和政策制定者预测隐私政策的后果。总体而言，我们的框架为加强消费者隐私控制和信任、优化平台数据管理以及向政策制定者通报更好的数据隐私法规提供了管理启示。

Abstract: 

Learning consumers’ personalized privacy preferences is crucial for firms and policymakers to establish trust and compliance and guide effective policymaking. Existing approaches rely mostly on private information such as proprietary user behavior data and individual-level demographic and socio-economic factors, or require explicit user input, which can be invasive and burdensome, potentially leading to user dissatisfaction. Nowadays, individuals generate and share vast amounts of information about themselves in the public domain, which can provide a valuable multifaceted view of their behaviors, attitudes, and preferences. This information thus has the potential to provide valuable insights into individuals’ privacy preferences. In this study, we propose a novel framework to predict personalized privacy preference by leveraging a ubiquitous source of public data—social media posts. Deeply rooted in psychological and privacy theories, we use deep learning model and natural language processing algorithms to learn theory-driven psychosocial traits such as lifestyle, risk preference, personality, privacy-related economic preferences, linguistic styles, and more from social media posts. Interestingly, we find that psychosocial traits from public data provide greater predictive power than private information. Furthermore, we conduct multiple interpretability analyses to understand what drives the model’s performance. Finally, we demonstrate the practical value of our model and show that our framework can assist platforms and policymakers in forecasting the consequences of privacy policies. Overall, our framework provides managerial implications for enhancing consumer privacy control and trust, optimizing platform data management, and informing policymakers about better data privacy regulations.

6. 职场同伴影响力：来自企业数字平台的证据

原标题：Peer Influence in the Workplace: Evidence from an Enterprise Digital Platform

期刊：MIS Quarterly

作者：Haoyuan Liu, Wen Wen, Andrew B. Whinston, Stephen He

出版时间：2024-11-05

摘要：

我们研究了在企业数字平台上分享同行成功如何影响其他员工的工作努力。我们关注组织从使用数字平台中获得的一个重要优势，即通过整合关于同行的各种元素来监管消息内容的能力。具体而言，我们研究了两种类型的同伴成功消息：突出同伴努力的消息（即专注于努力的成功消息）和突出同伴能力的消息（即专注于能力的成功消息）。我们使用了来自一家信息技术服务公司的一群销售人员的数据，并对在线参与者进行了一项实验，发现专注于能力的成功信息和专注于努力的成功信息都可以激励员工更加努力地工作，尽管我们在回应中观察到了重要的异质性。尤其是，无论员工与同伴之间的社交距离如何，员工对专注于努力的信息的反应仍然强烈。相比之下，当信息称赞社交关系亲密的同龄人的能力时，员工对以能力为中心的信息的反应更强，而称赞社交关系疏远的同龄人的能力时则没有。

Abstract: 

We study how the sharing of peer success on an enterprise digital platform affects worker productivity. Using sales workers in an information technology service company as our research context, we leverage the unexpected resignation of several human resources staff as an exogenous shock to the sharing of peer success and implement a difference-in-differences estimation. The empirical evidence suggests the sharing of peer success motivates workers to exert more effort. However, there exists important heterogeneity based on the specific content of peer success messages. In particular, we explore two types of content: messages that highlight peer’s ability (ie ability-based messages) and messages that highlight peer’s effort (ie effort-based messages). We find that a worker’s response to ability-based messages is stronger if the messages praise the ability of peers who are socially close or have worse historical performance than the focal worker. By contrast, a worker’s response to effort-based messages does not vary by peer characteristics.

7. 消费者为何以及如何进行不同的在线评论

原标题：Why and How Consumers Perform Online Reviewing Differently

期刊：Journal of Consumer Research

作者：Gwarlann de Kerviler, Catherine Demangeot, Pierre-Yann Dolbec

出版时间：2024-07-04

DOI：10.1093/jcr/ucae040

摘要：

回顾产品和服务是一项广泛的消费活动，有数百万人参与其中。消费者为什么以及如何评价彼此不同？此前的研究假设，消费者通常理解什么是回顾。因此，它将回顾的差异归因于心理、动机和社会人口特征、消费体验和专业知识的个体差异。这个核心假设是有问题的，因为它没有考虑到消费者理解审核方式的差异可能会解释他们为什么会以不同的方式进行审核和执行审核。为了解决这一差距，我们分析了一个由综述和作者访谈组成的大型定性数据集。我们的洞见通过将复习的社会文化塑造理论化，补充了之前的工作。我们通过归纳总结出评价取向的概念来回答消费者为什么会有不同的评价——这是一种文化模式，由一组相互关联的特征组成，这些特征塑造了消费者如何评价，并转化为独特的评价者声音——评价者在评价中表达的观点。我们通过开发三种评审方向来回答消费者如何进行不同的评审：公共分享、系统评估和竞争性专家。最后，我们讨论了调查结果的可转移性、制度动力在审查中的作用，以及对在线审查平台和营销人员的建议。

Abstract: 

Reviewing products and services is a widespread consumer activity in which millions engage. Why and how do consumers review differently from one another? Prior work assumes that consumers commonly understand what reviewing is. Consequently, it attributes differences in reviewing to individual variations in psychological, motivational, and sociodemographic characteristics, consumption experiences, and expertise. This central assumption is problematic because it fails to consider that differences in how consumers understand reviewing may explain why they approach and perform reviewing differently. To address this gap, we analyze a large qualitative dataset composed of reviews and interviews with their authors. Our insights complement prior work by theorizing the sociocultural shaping of reviewing. We answer why consumers review differently by inductively theorizing the concept of reviewing orientation—a cultural model comprising a set of interconnected characteristics that shapes how consumers review and translates into a distinct reviewer voice—a reviewer’s standpoint expressed within a review. We answer how consumers review differently by developing three reviewing orientations: communal sharing, systemic evaluation, and competitive punditry. Finally, we discuss the transferability of the findings, the role of institutional dynamics in reviewing, and recommendations for online review platforms and marketers.