学术论文

Problem definition: Internal theft poses a significant challenge in retail firms’ operations. Owing to a lack of effective monitoring tools, a firm cannot observe every action in daily operations of its employees, providing opportunity for wrongdoing, such as capacity and cash stealing. As a result, a common practice is to increase the price of goods to offset the loss in revenue due to the increasing threat of theft. However, we show that such practices are not optimal. Methodology/results: We model the internal theft problem in retailing as a principal-agent model, where the principal (firm) contracts an agent (retail manager) for capacity planning and daily sales. The agent is subject to moral hazard and may steal the capacity (procurement budget or company asset) before demand realization (ex ante stealing) or steal the sales revenue after demand realization (ex post stealing). We solve for the optimal capacity, price, and agent’s commission decisions to maximize the principal’s utility. We find that capacity and price decisions are not monotone in terms of the severity of moral hazards. In particular, the principal should first decrease and then increase (increase and then decrease) the price (the capacity) when ex post stealing becomes more prevalent. We also provide an optimal commission scheme to the agent, which is simple and can be easily implemented. Finally, we investigate the sensitivities of price and capacity decisions to demand uncertainties in the presence of moral hazard. Managerial implications: Simply increasing retail prices and shifting the margin to consumers to combat loss in revenue caused by internal theft can amplify the agency problem in some scenarios because it leads to a significant loss in demand and insufficient commission to the agent. Retail firms should instead focus on jointly optimizing capacity and price and providing their employees with appropriate commissions.

Problem definition: How should retailers leverage aggregate (category) sales information for individual product demand prediction? Motivated by inventory risk pooling, we develop a new prediction framework that integrates category-product sales information to exploit the benefit of pooling. Methodology/results: We propose to combine data from different aggregation levels in a transfer learning framework. Our approach treats the top-level sales information as a regularization for fitting the bottom-level prediction model. We characterize the error performance of our model in linear cases and demonstrate the benefit of pooling. Moreover, our approach exploits a natural connection to regularized gradient boosting trees that enable a scalable implementation for large-scale applications. Based on an internal study with JD.com on more than 6,000 weekly observations between 2020 and 2021, we evaluate the out-of-sample forecasting performance of our approach against state-of-the-art benchmarks. The result shows that our approach delivers superior forecasting performance consistently with more than 9% improvement over the benchmark method of JD.com. We further validate its generalizability on a Walmart retail data set and through alternative pooling and prediction methods. Managerial implications: Using aggregate sales information directly may not help with product demand prediction. Our result highlights the value of transfer learning to demand prediction in retail with both theoretical and empirical support. Based on a conservative estimate of JD.com, the improved forecasts can reduce the operating cost by 0.01–0.29 renminbi (RMB) per sold unit on the retail platform, which implies significant cost savings for the low-margin e-retail business.

Problem definition: Emergency department (ED) delay announcement systems are implemented in many countries. We answer three important questions pertaining to the operations and effectiveness of such systems by studying the public hospital network and ED waiting time (WT) announcement system in Hong Kong’s “universal” public healthcare system: (1) How many patients are aware of (and sensitive to) the ED WT announcements? (2) How sensitive are these patients to the announced WT? (3) How can the Hong Kong government improve the WT announcement system? Methodology/results: We study over 1.3 million patient visits to the 17 tier 1 public EDs. We structurally estimate the fraction of patients sensitive to the announced WT and their sensitivity to the announcements as well as patient characteristics that lead to higher sensitivity. In the patient’s ED choice decision, we estimate the trade-off between the travel distance to an ED and the expected WT at the ED. We find that 3.1% of the patients are sensitive to the announced WT, and they are willing to travel an additional 4.8 km to save one hour of waiting. Urgent patients are less likely to be sensitive to the delay announcement than less urgent patients, but those that are sensitive are more WT averse than their less urgent counterparts. Counterfactual analysis shows that the average actual WT and number of patients who leave without being seen can be reduced by 4.6% and 8.5%, respectively, by increasing the fraction of sensitive patients to 15.0% and, simultaneously, reducing the announced WT assessment window to one hour from the current level of three hours. Further improvement can be achieved by providing predicted WT information based on the current level of ED crowding or less extreme past performance—median WT rather than the currently used 95th percentile. Managerial implications: The Hong Kong government should utilize the two levers of the announcement system: the sensitive fraction of patients and information recency. Increasing the sensitive fraction can benefit the system when it is below a certain threshold level. However, administrators should exercise caution when the sensitive fraction becomes large and consider implementing additional measures to mitigate the negative effects of information delay. The sensitive group of patients can unfairly be punished for their proactiveness. Shortening the announced WT assessment window and providing predicted WT are possible alternatives that not only improve overall performance but also exhibit strong robustness to increases in the sensitive population.

Using administrative data on the Chinese National College Entrance Examination, we study how left-digit bias affects college applications. We find strong discontinuities in students’ admission outcomes at ten-point thresholds. Students with scores just below multiples of 10 make more conservative college application choices that place them into less selective colleges and majors. In contrast, students who score at or just above multiples of 10 aim at and achieve higher but are at greater risk of overshooting. The discontinuity reveals that despite the educational and labor market consequences, students’ self-evaluation based on exam scores is subject to information-processing heuristics.

For a large sample of countries, this article shows that non-banks curtail their syndicated lending by significantly more than banks during financial crises in borrower countries. Differences in the value of lending relationships explain most of the gap. Relationships with non-banks are less valuable in general and thereby do not improve borrowers’ access to credit during crises. Non-banks are also less likely to form lasting relationships with borrowers. These findings imply that the rise of non-banks could increase the importance of transaction-based lenders and exacerbate the repercussions of financial shocks.

Regulators have increasingly mandated firms to promptly disclose material cybersecurity incidents upon discovering these incidents. We find suggestive evidence indicating that some firms manipulate the discovery date (“misreport”) of a cybersecurity incident to postpone the disclosure of the incident, as evidenced by a pronounced spike in insider sales before the reported discovery date. We also find that misreporting is more prevalent among firms with weak internal control systems, when firms face low litigation risk, and when firms have greater pressure to meet a disclosure deadline. Further, firms suspected of misreporting tend to disclose their remedial actions and assert the restoration of business, mitigating negative market reactions upon disclosure of incidents. Collectively, our results suggest that firms might strategically misreport information about a cybersecurity incident to delay disclosure to gain additional time for remedial actions, which helps them prevent exposing vulnerabilities to malicious actors and alleviate stakeholder anxiety.

Blockchain technologies have catalyzed the rise of decentralized autonomous organizations (DAOs), which operate in an incentive network fueled by crypto tokens. In essence, these tokens are imbued with either payment rights (i.e., transactional tokens) or ownership rights (i.e., governance tokens). The decentralized organizational paradigm dismantles the traditional management structure and bring new research opportunities to Operations Management (OM). While the performance of DAOs has been largely examined in current OM literature, the effectiveness of their internal incentive mechanisms—specifically the one that uses ownership as rewards to promote user contributions—remains unclear. Focusing on DAO-enabled virtual communities, we seek to examine whether decentralized ownership provides stronger incentives for user behaviors, such as creation and curation, in comparison to traditional monetary rewards through the lens of psychological ownership theory. We obtained data from Steemit that captures the reward, creation, curation and transaction behaviors of 98,000 users from May 2017 to April 2019. By leveraging the “power-up” action as a shock that increases user ownership shares, we established a quasi-experimental setting. Employing the PSM-DID model, we found that the use of governance tokens is associated with enhanced creation and curation efforts but declined creation novelty, compared to the use of transactional tokens. Our additional analyses further reveal that the incentive effects of governance tokens diminish over time. However, upon the recurrence of the intended choice, these effects become reinforced. Notably, we find that governance token ownership is more strongly associated with curation efforts for users with weaker social ties. Conversely, for users with high reputation scores, their content creation behaviors are less strongly associated with governance token ownership. This study contributes to the burgeoning discourse on blockchain and cryptocurrency from an operational perspective, providing valuable insights for the design of incentive mechanisms in DAOs and advancing our understanding of operational efficiencies and stakeholder engagement in decentralized structures within Operations Management.

In the burgeoning marketplaces of digital assets, non-fungible tokens (NFTs) revolutionize digital asset ownership and intellectual property (IP) protection, but high minting costs create barriers to marketplace entry and growth. This study examines the impact of “lazy minting,” a new NFT production method introduced by major NFT marketplaces to lower minting costs by deferring blockchain certification until the first sale. In response to the call for further research on emerging technologies in operations management, we explore how this policy affects the net sales performance of existing sellers in the NFT marketplaces. Based on transaction cost economics (TCE) and the literature about different IP protection methods, we distinguish between lazy- and regular-minted NFTs by their differential transaction costs and utilize the staggered difference-in-differences (DID) method to conduct our analysis. We find that lazy minting adoption significantly boosts the net sales performance of existing sellers. This is attributed to their cost-adaptive IP protection behavior. Specifically, they achieve this by minting more NFTs with a larger proportion of style-consistent NFTs through lazy minting, while strategically employing regular minting for style-breaking NFTs, which is contingent upon their reputation. Our study has important theoretical and practical implications for operations management under the emerging technological revolution.

Brands are increasingly finding themselves on the receiving end of negative labels from a variety of sources. While sometimes warranted, many of these negative labels feel like unwarranted or uncivil insults. Brands generally respond to such undeserved degradation by ignoring the insult, denying the insult, or perhaps apologizing to the insulter. This research explores another potential strategy: reappropriating the insult. We reveal that reappropriation—an intentional act of verbatim self-labeling with an externally imposed negative label—can garner unexpected benefits for brands, including greater advertisement click-through rate, interest, and more positive attitudes. The advantage of reappropriation is driven by perceptions of the brand's confidence and humor and is specific to situations in which the reappropriated insult is perceived to be unjustified and ultimately benign in nature. This work contributes to our understanding of how brands can recover from negative events and how reappropriation operates uniquely in an unexplored marketplace context. We also provide a novel recovery tactic for brand managers facing certain types of hostility.