本文精选了运营管理领域国际顶刊《Production and Operations Management》近期发表的论文，提供运营管理研究领域最新的学术动态。

Provision of helpful review videos: Effects of video characteristics on perceived helpfulness

原刊和作者：

Production and Operations Management Volume 32, Number 7

Kyungmin Park （University of Washington）

Stephanie Lee （University of Washington）

Shahryar Doosti （Chapman University）

Yong Tan （University of Washington）

Abstract

With the rapid growth and popularity of YouTube, an increasing number of consumers rely on online product review videos to obtain product-related information. As the provision of online review videos grows and consumers increasingly rely on them for their purchase decisions, understanding factors that contribute to the perceived helpfulness of video reviews becomes critical for video review management. This paper examines how various visual and vocal characteristics of online review videos are associated with the perceived helpfulness of videos. We collect detailed observational data on 13,840 electronic product review videos posted on YouTube and employ video content analysis, speech recognition, and natural language processing techniques to extract the visual and vocal characteristics of review videos. By using econometric models, we find that the increase in visual stimulation, captured by brightness and visual dynamics, increases the perceived helpfulness of reviews. In addition, featuring reviewers’ faces in review videos increases the perceived helpfulness of videos. Consumers also perceive review videos in which reviewers express more positive facial emotions as more helpful. Furthermore, lower voice pitch and faster speech rates are associated with higher perceived helpfulness of reviews. To complement the empirical analysis and further isolate the causal effects of review brightness and pitch, we conduct controlled experiments. Overall, the findings can facilitate the management and operation of online review videos for product reviewers, businesses, review platforms, and consumers. In particular, the findings provide direct and actionable guidance to content generators who aim to create more helpful product reviews.

Link: https://doi.org/10.1111/poms.13969

Dynamic incentives for sustainable contract farming

原刊和作者：

Production and Operations Management Volume 32, Number 7

Wei Zhang （Zhejiang University）

Long Gao （University of California）

Mohammad Zolghadr （University of California）

Dawei Jian （University of Wisconsin-Milwaukee）

Mohsen ElHafsi （University of California）

Abstract

The rise of contract farming has transformed millions of farmers' lives. We study a new class of contract farming problems, where the farmer holds superior information and can invest effort to improve productivity over time. Despite their prevalence, the literature offers little guidance on how to manage such farmers with dynamic incentives. We build a game-theoretic model that captures the dynamic incentives of learning and gaming, with hidden action and information. We characterize the optimal contract: it internalizes both the vertical and intertemporal externalities, with performance pay and deferred payment; the performance pay is to motivate the farmer to invest and improve the relationship-specific productivity; the deferred payment is to ensure that the farmer is willing to share information and behave honestly over time. Even with random yield, the optimal contract can still have a simple implementation of a yield-adjusted revenue-sharing policy. Using real data, we show that the learning effect is significant. We then quantify when and how contract farming can improve smallholder farmers' productivity and income, creating shared value. We find when buyers have a long-term perspective and can internalize the benefit of farmer improvement, they will pay higher prices to ensure farmers' long-term viability. Our results inform the policy debate on contract farming: traditional procompetitive policies (based on spot transactions) can be counterproductive for modern agrifood value chains, hurting both buyers and farmers.

Link: https://doi.org/10.1111/poms.13956

Allocating scarce resources in the presence of private information and heterogeneous favoritism

原刊和作者：

Production and Operations Management Volume 32, Number 7

Xiaoshuai Fan （Southern University of Science and Technology）

Ying-Ju Chen （The Hong Kong University of Science and Technology）

Christopher S. Tang （UCLA Anderson School）

Abstract

Motivated by the challenge of allocating scarce resources from the federal government to different states during the COVID-19 pandemic, this paper studies optimal schemes for allocating scarce resources to agents with private demand information under different favoritism structures. Through an investigation of a mechanism design model that aims to induce agents to report their demands truthfully, we find the following results. First, when the principal purely cares about social welfare and when the principal has sufficient resources to satisfy all agents' demands, we find that the optimal allocation scheme is efficient in the sense that it is identical to the optimal scheme for the “benchmark” case when favoritism differentials and information asymmetry are both absent. Second, when rationing is needed due to resource scarcity, we show that heterogeneity in “event-independent” favoritism across agents will cause the principal to allocate more resources to agents with larger favoritism and less resources to others, resulting in inefficient allocations. Third, when agents possess heterogeneous “event-specific” favoritism due to the existence of outside options, the resulting allocation may boost all agents' expected utilities, including those agents who do not have any outside option. Finally, we show that the “allocation distortion” caused by both information asymmetry and heterogeneous favoritism can be reduced when “positive externality” is present (i.e., allocating resources to one agent can benefit other agents).

Link: https://doi.org/10.1111/poms.13957

A bane or a boon? Profit-margin-guarantee contract in a channel with downstream competition

原刊和作者：

Production and Operations Management Volume 32, Number 7

Hong Zheng （Beijing Institute of Technology）

Lin Tian （Fudan University）

Guo Li （Beijing Institute of Technology）

Abstract

In recent decades, manufacturers have relied on giant retailers or e-tailers to distribute their products. Given this evolution, some retailers have started demanding a profit-margin-guarantee contract (PMG contract), under which the manufacturer must ensure that the retailer's profit margin does not fall below a certain level (PMG rate). Conventional wisdom suggests that a PMG contract creates a life-or-death struggle for the manufacturer and that a retailer with a PMG contract can gain a competitive edge over his competitors. This study investigates the strategic impact of the PMG contract in a competitive environment. We consider a distribution channel consisting of one manufacturer and two competing retailers. In this channel, the manufacturer has signed a PMG contract with one retailer (signed retailer) but not with the other (unsigned retailer). Our analyses show that in response to the PMG contract, the manufacturer can adopt a “cost-independent” pricing strategy (i.e., setting the wholesale price independent of the production cost) to strategically trigger or void the PMG contract. For this reason, interestingly, the manufacturer is not always hurt by, nor does the signed retailer always benefit from, the PMG contract. Depending on the production cost and the downstream competition intensity, the PMG contract may yield a win–win, win–lose, lose–win, or lose–lose outcome for the manufacturer and the signed retailer. Moreover, the unsigned retailer may be able to free ride on the PMG contract, making her even better off under this unfavorable competitive situation. Nevertheless, the PMG contract cannot yield a win–win–win outcome for all firms, whereas a lose–lose–lose outcome may arise under certain conditions. Our results have useful managerial and regulatory implications.

Link: https://doi.org/10.1111/poms.13958

Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

Production and Operations Management Volume 32, Number 7

Ka Chung Ng （The Hong Kong Polytechnic University）

Ping Fan Ke （Singapore Management University）

Mike K. P. So （Hong Kong University of Science and Technology）

Kar Yan Tam （Hong Kong University of Science and Technology）

Abstract

Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to obtain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing deceptive and trustworthy general news constructed from a large collection of labeled news sources based on human judgments and opinions. We then extract discriminating linguistic features commonly found in source domain news using advanced deep learning models. We transfer these features associated with the source domain to augment fake content detection in three target domains: political news, financial news, and online reviews. We show that domain invariant linguistic features learned from a source domain with abundant labeled examples can effectively improve fake content detection in a target domain with very few or highly unbalanced labeled data. We further show that these linguistic features offer the most value when the level of transferability between source and target domains is relatively high. Our study sheds light on the platform operation in managing online content and resources when applying machine learning for fake content detection. We also outline a modular architecture that can be adopted in developing content screening tools in a wide spectrum of fields.

Link: https://doi.org/10.1111/poms.13959