1. 全球多面手管理者与企业创新：创新特定机构的角色

原标题：Generalist Managers and Firm Innovation Worldwide: The Role of Innovation-Specific Institutions

期刊：Journal of Accounting and Economics

作者：Sterling Huang, Albert Tsang, Yue Rio Wu, Kun Tracy Wang

出版时间：2024-11-19

DOI：10.1016/j.jacceco.2024.101755

摘要：

我们研究了2001年至2019年间，多面手CEO如何影响25个国家的创新成果。我们收集了一个新的、扩展的多面手CEO数据集，发现多面手CEO对创新产生积极影响，尤其是在创新资源丰富的国家。这一发现与多面手首席执行官利用其广泛的知识和跨行业经验，跨机构环境整合资源，从而促进创新活动的概念一致。然而，在专利制度更严格的国家，对专业知识和资源的需求增加，限制了多面手CEO可以贡献的价值，导致创新活动减少。我们的研究强调了制度环境如何塑造CEO人力资本在推动创新方面的效能，从而为创新政策的设计提供了见解，这些政策可以最大限度地发挥不同制度背景下的领导潜力。

 

Abstract: 

We examine how generalist CEOs influence innovation outcomes across 25 countries from 2001 to 2019. We assemble a novel, extended dataset of generalist CEOs and find that generalist CEOs positively affect innovation, particularly in countries with abundant innovation resources. This finding aligns with the notion that generalist CEOs leverage their broad knowledge and cross-industry experience to integrate resources across institutional environments, thereby fostering innovation activities. However, in countries with stricter patent systems, the increased need for specialized knowledge and resources limits the value that generalist CEOs can contribute, leading to decreased innovation activities. Our research highlights how institutional environments shape the efficacy of CEO human capital in driving innovation, thus offering insights for the design of innovation policies that maximize leadership potential across different institutional contexts.

 

2. GDPR的光明面：福利改善隐私管理

 

原标题：The Bright Side of the GDPR: Welfare-Improving Privacy Management

期刊：Management Science

作者：Shiva Shekhar, Noriaki Matsushima, Chongwoo Choe

出版时间：2024-11-21

DOI：10.1287/mnsc.2024.06653

 

摘要：

我们在一个模型中研究了《通用数据保护条例》（GDPR）的选择加入要求，该模型涉及一家提供数字服务的公司，以及对该公司服务的估值以及与该公司共享个人数据时产生的隐私成本具有异质性的消费者。我们发现，GDPR通过允许隐私成本高的消费者在不共享数据的情况下购买服务，从而提振了对该服务的需求。需求增加导致价格上涨但共享数据数量减少。如果公司的收入主要基于使用而非数据，那么GDPR后公司的利润和消费者剩余都会增加，这意味着GDPR可以改善福利。但如果公司的收入主要来自数据货币化，那么GDPR可以减少公司的利润和消费者剩余。

 

Abstract: 

We study the General Data Protection Regulation (GDPR)’s opt-in requirement in a model with a firm that provides a digital service and consumers who are heterogeneous in their valuations of the firm’s service and the privacy costs incurred when sharing personal data with the firm. We show that the GDPR boosts demand for the service by allowing consumers with high privacy costs to buy the service without sharing data. The increased demand leads to a higher price but a smaller quantity of shared data. If the firm’s revenue is largely usage based rather than data based, then both the firm’s profit and consumer surplus increase after the GDPR, implying that the GDPR can be welfare improving. But if the firm’s revenue is largely from data monetization, then the GDPR can reduce the firm’s profit and consumer surplus.

 

3. 基于点击的MNL模型：产品组合优化中点击数据建模框架

 

原标题：The Click-Based MNL Model: A Framework for Modeling Click Data in Assortment Optimization

 

期刊：Management Science

作者：Dennis J. Zhang, Danny Segev, Ali Aouad, Jacob Feldman

出版时间：2024-11-25

DOI：10.1287/mnsc.2021.00281

 

摘要：

我们介绍了基于点击的MNL选择模型，这是一个在电子商务环境下捕获客户购买决策的框架。具体来说，我们通过假设客户在继续比较随机效用之前只考虑他们点击的项目，来扩充经典的多项式Logit选择模型。在此背景下，我们研究由此产生的品类优化问题，其目标是选择可供购买的产品子集，以实现预期收益最大化。我们的主要算法贡献来自于对这个问题的多项式时间近似方案（PTAS）的形式，表明可以在任何精度范围内有效地逼近最优预期收益。为了建立这个结果，我们开发了几个技术思路，包括枚举方案和随机不等式，它们可能具有更广泛的兴趣。使用阿里巴巴在线市场的数据，我们将基于点击的MNL和潜在类MNL模型与历史销售额进行拟合，并在在线平台向每个用户推荐个性化的六款产品展示的设置中点击数据。我们提出了一种基于点击的MNL模型的估计方法，该模型利用点击流数据和机器学习分类算法。我们的数值结果表明，clickstream数据对于预测选择是有价值的，基于click的MNL模型在某些情况下可以优于基于logit的标准模型。

 

Abstract: 

We introduce the click-based MNL choice model, a framework for capturing customer purchasing decisions in e-commerce settings. Specifically, we augment the classical Multinomial Logit choice model by assuming that customers only consider the items they have clicked on before they proceed to compare their random utilities. In this context, we study the resulting assortment optimization problem, where the objective is to select a subset of products, made available for purchase, to maximize the expected revenue. Our main algorithmic contribution comes in the form of a polynomial-time approximation scheme (PTAS) for this problem, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. To establish this result, we develop several technical ideas, including enumeration schemes and stochastic inequalities, which may be of broader interest. Using data from Alibaba’s online marketplace, we fit click-based MNL and latent class MNL models to historical sales and click data in a setting where the online platform recommends a personalized six-product display to each user. We propose an estimation methodology for the click-based MNL model that leverages clickstream data and machine learning classification algorithms. Our numerical results suggest that clickstream data are valuable for predicting choices and that the click-based MNL model can outperform standard logit-based models in certain settings.

 

4. 根据收入管理数据估算未观察到的未购买需求

 

原标题：Estimating Demand with Unobserved No-Purchases on Revenue-Managed Data

 

期刊：Manufacturing and Service Operations Management

作者：Kalyan Talluri, Anran Li, Müge Tekin

出版时间：2024-11-27

DOI：10.1287/msom.2021.0291

 

摘要：

问题定义：本文研究了当“无购买者”（考虑了产品但没有购买的顾客）不可观测时，消费者到达率和选择模型参数的联合估计。即使使用最简单的离散选择模型，如多项式logit（MNL），估计这种不受约束的需求也变得具有挑战性，因为我们不知道选择外部选项（即，未购买）的比例。有人提出了使用市场份额来确定与外部期权相关的参数的方法。不过，市场份额数据在很多情况下很难获得，在一些行业，比如时尚零售，由于项目难以比较，意义不大。在本文中，我们指出了在实践中可能出现的另一个困难：许多公司根据部分观察到的需求，在销售期内监控销售并优化价格和产品组合，这是其收入管理（RM）流程的一部分。这可能会导致收入管理诱导的内生性，因为用于估算的数据是优化的结果（反过来基于之前的数据），以设置控制。正如我们所展示的，在随机生成的产品组合上效果良好的方法可能在优化的产品组合数据上效果不佳。方法学/结果：在本文中，当公司无法观察到没有购买、没有市场份额信息，且数据已被收入管理时，我们提出了一种稳健的方法。我们开发了一种基于修正矩条件的两步广义矩量法（GMM）程序，重要的是，它不需要工具变量（IVs），这在实践中具有显著优势。管理含义：在Monte Carlo模拟中，当控制随机生成时，我们的方法的性能与现有方法匹配，并且无论RM诱导的内生性是否存在，在所有条件下都是稳健的。在时装行业的一个大型现实世界数据集上，根据缺货和降价定价以及未知的管理控制，我们的方法与现有方法相比提供了稳健的估计，而不需要任何市场份额的输入，这在类别和季节/收集级别上尤其难以确定。

 

Abstract: 

Problem definition: This paper studies the joint estimation of the consumer arrival rate and choice model parameters when “no-purchasers” (customers who considered the product but did not purchase) are not observable. Estimating this unconstrained demand even with the simplest discrete-choice model such as the multinomial logit (MNL) becomes challenging as we do not know the fraction that have chosen the outside option (i.e., not purchased). Methods have been proposed to use market share to pin down the parameter associated with the outside option. However, market share data are difficult to obtain in many situations, and in some industries, such as fashion retail, have little meaning as the items are difficult to compare. In this paper, we point out an additional difficulty that can arise in practice: Many firms monitor sales and optimize their prices and assortments within the sale period as part of their revenue management (RM) process, based on partially observed demand. This can potentially cause a revenue management induced endogeneity as the data used for estimation is the result of optimization (in turn based on prior data) to set controls. As we demonstrate, methods that work well on randomly generated assortments may do badly on optimized assortment data. Methodology/results: In this paper, we propose a robust method when the firm cannot observe no-purchases and has no market share information, and the data have been revenue-managed. We develop a two-step generalized method-of-moments (GMM) procedure that is based on a modified moment condition, and importantly, does not require instrumental variables (IVs), a significant advantage in practice. Managerial implications: In Monte Carlo simulations, the performance of our method matches existing methods when the controls are generated randomly, and is robust under all conditions, whether RM-induced endogeneity is present or not. On a large real-world data set from the fashion industry, subject to stock-outs and markdown pricing along with unknown management controls, our method provides robust estimates compared with existing methods without requiring any input on market shares, which is especially difficult to pin down at a category and season/collection level.

 

5. 区块链新闻供应商：新鲜度透明度和智能合约的价值

 

原标题：The Blockchain Newsvendor: Value of Freshness Transparency and Smart Contracts

 

期刊：Management Science

作者：N. Bora Keskin, Chenghuai Li, Jing-Sheng Song

出版时间：2024-11-14

DOI：10.1287/mnsc.2021.02949

 

摘要：

受鲜活农产品行业区块链应用的推动，我们考虑了一个零售商面临随机、依赖新鲜度的消费者需求的新闻供应商问题。零售商可以采用区块链技术，获得更透明的供应新鲜度信息。我们通过推导零售商预期利润增长和区块链采用带来的食物垃圾减少的封闭式表达式，量化区块链启用的新鲜度透明度的价值。利用公开可获得的数据，我们提供了一个数字示例，说明对于沃尔玛在美国的草莓业务（约占沃尔玛鲜活农产品销售额的4%），区块链可以通过运营改善每年增加6000万美元的年利润，同时消除2300万磅的食品浪费。尽管对零售商来说价值巨大，但区块链采用可能会降低零售商供应商的预期利润。我们设计了一系列基于区块链新鲜度共识的阈值型智能合约，并研究此类合约何时为零售商和供应商提供双赢主张。此外，当零售商提供基于新鲜度的价格折扣时，我们发现新鲜度供应减少导致食物浪费减少。相比之下，当供应商根据新鲜度调整批发价格时，鲜活供应减少会导致更多的食品浪费。我们还将我们的发现概括为（i）双重采购、（ii）将数据送入区块链的物联网传感器中的噪声测量，以及（iii）零售商的筛选过程。

 

Abstract: 

Motivated by blockchain applications in the fresh produce industry, we consider a newsvendor problem in which a retailer faces stochastic and freshness-dependent consumer demand. The retailer can adopt blockchain technology to have more transparent information on the freshness of supply. We quantify the value of blockchain-enabled freshness transparency by deriving closed-form expressions for the retailer’s expected profit growth and food waste reduction brought by blockchain adoption. Using publicly available data, we provide a numerical example illustrating that for Walmart’s strawberry business in the United States (which is about only 4% of Walmart’s fresh produce sales), blockchain can increase annual profit by $⁢60

 million while eliminating 23 million pounds of food waste annually through operational improvements. Despite this substantial value for the retailer, blockchain adoption can decrease the expected profit of the retailer’s supplier. We design a family of threshold-type smart contracts contingent on a blockchain-based freshness consensus and examine when such contracts offer a win-win proposition to the retailer and the supplier. Moreover, when the retailer offers freshness-based price discounts, we find that less fresh supply leads to less food waste. In contrast, when the supplier adjusts the wholesale price based on freshness, less fresh supply causes more food waste. We also generalize our findings to the cases of (i) dual sourcing, (ii) noisy measurements in the Internet of Things sensors feeding data into blockchain, and (iii) the retailer’s culling processes.

 

6. 常规变量规模收益率生产前沿与效率测度

 

原标题：Regular Variable Returns to Scale Production Frontier and Efficiency Measurement

 

期刊：Operations Research

作者：Xinju He, Sung Ko Li, Shu Kam Lee, Chun Kei Tsang

出版时间：2024-11-12

DOI：10.1287/opre.2021.0470

 

摘要：

数据包络分析中最常用的实证生产前沿变量规模收益前沿，具有凸技术集，在经济学中表现出特殊的结构，本文称之为正则变量规模收益前沿；生产技术在生产过程开始时表现出规模收益递增，随后规模收益不变，规模收益递减。当凸性假设放松时，建模规则变量规模收益变得困难，目前在多产出生产中还没有令人满意的解决方案。克服这些困难，本文采用文献中的一个建议，将规则变量规模收益纳入多重产出下的自由处置船体前沿。我们建立了一个分析规则变量规模收益率的框架，并推荐了一个实证生产前沿，用于衡量这种模式和多重产出的技术效率。在存在无凸性的规则变量规模收益率的情况下，从这个新前沿计算的技术效率测度值比从自由处置船体前沿计算的值更接近“真实”值，常规变量规模收益率前沿可能会造成误导性影响。

 

Abstract: 

The most frequently used empirical production frontier in data envelopment analysis, the variable returns to scale frontier, has a convex technology set and displays a special structure in economics, called the regular variable returns to scale in this paper; the production technology exhibits increasing returns to scale at the beginning of the production process followed by constant returns to scale and decreasing returns to scale. When the assumption of convexity is relaxed, modeling regular variable returns to scale becomes difficult, and currently, no satisfactory solution is available in multioutput production. Overcoming these difficulties, this paper adopts a suggestion in literature to incorporate regular variable returns to scale into the free disposal hull frontier under multiple outputs. We establish a framework for analyzing regular variable returns to scale and recommend an empirical production frontier for measuring technical efficiency with such pattern and multiple outputs. In the presence of regular variable returns to scale without convexity, the value of the technical efficiency measure computed from this new frontier is closer to the “true” value than that from the free disposal hull frontier, and the conventional variable returns to scale frontier may cause misleading implications.

 

7. 隐私政策变化的自动分析：一种结构化的自我关注句子嵌入方法

 

原标题：Automated Analysis of Changes in Privacy Policies: A Structured Self-Attentive Sentence Embedding Approach

 

期刊：MIS Quarterly

作者：Fangyu Lin, Sagar Samtani, Hongyi Zhu, Laura Brimarte, Hsinchun Chen

出版时间：2024-12-01

 

摘要：

社会对消费者信息隐私的日益关注，导致了全球范围内隐私法规的实施。为了遵守《通用数据保护条例》（GDPR）等隐私法规，许多公司的隐私政策变得越来越冗长和复杂。在本研究中，我们采用计算设计科学范式设计了一个新颖的隐私政策演化分析框架，以帮助识别公司如何根据隐私法规改变和呈现其隐私政策。该框架包括一个自我关注的注释系统（SAAS），该系统自动注释隐私政策中的段落长度片段，以帮助利益相关者确定感兴趣的数据实践，以便进一步调查。我们在一个完善的隐私政策数据集OPP-115上，根据最先进的机器学习（ML）和深度学习（DL）方法，对SAAS进行了严格评估。就F1得分而言，SAAS的表现优于传统的ML和DL车型，差异具有统计学意义。我们通过GDPR对亚马逊隐私政策影响的深入案例研究，展示了该框架的实用性。案例研究结果表明，亚马逊的后GDPR隐私政策可能违反了GDPR的一项基本原则，导致消费者更加努力地寻找有关第一方数据收集的信息。鉴于消费者信息隐私越来越重要，拟议框架对监管机构和公司具有重要影响。我们讨论了SAAS遵循的几个设计原则，这些原则有助于指导未来基于设计科学的电子商务、健康和隐私研究。

 

Abstract: 

The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as the General Data Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a self-attentive annotation system (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art machine learning (ML) and deep learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility with an in-depth case study of GDPR’s impact on Amazon’s privacy policies. The case study results indicate that Amazon’s post-GDPR privacy policy potentially violates a fundamental principle of GDPR by causing consumers to exert more effort to find information about first-party data collection. Given the increasing importance of consumer information privacy, the proposed framework has important implications for regulators and companies. We discuss several design principles followed by the SAAS that can help guide future design science-based e-commerce, health, and privacy research.