Frontiers in Operations: When Does Schedule Consistency Improve Shift Worker Productivity?

Problem definition: New product introductions frequently face retail imitation risks. When start-ups use crowdfunding to finance new products, they expose themselves to an even more severe imitation risk specific to crowdfunding platforms. To mitigate this risk, start-ups can employ a progressive launch strategy by initially offering a preliminary version with fewer features or functionalities. Alternatively, they may opt for traditional bank financing, which only carries retail imitation risk. This paper examines how start-ups can effectively compete with crowdfunding copycats through the aforementioned product launch and financing strategies. Methodology/results: We develop a game-theoretical model to analyze the strategic interactions between a start-up and a copycat. Our findings reveal that start-ups may strategically withhold product quality provision to deter or weaken crowdfunding copycats. The optimal quality provision in crowdfunding exhibits nonmonotonic relationships with both crowdfunding imitation efficiency and crowdfunding market share. We also evaluate the social impact of different financing strategies and reveal that the socially optimal funding choice depends on the implementation of the progressive launch strategy. Notably, contrary to conventional wisdom, increasing crowdfunding copycat efficiency does not consistently improve consumer surplus or social welfare. Managerial implications: Although crowdfunding offers numerous advantages, start-ups considering this approach must recognize and address the potential threats posed by copycat imitation. Our research indicates that start-ups seeking external financing can effectively use progressive launch strategies to counter crowdfunding copycats—an approach that generally requires less time and resources than traditional intellectual property protection methods. Funding: G. Xiao acknowledges support from the National Natural Science Foundation of China [Grant 72422016] and the Research Grants Council of Hong Kong [GRF Grant PolyU 15500923]. X. Guo acknowledges support from the Research Grants Council of Hong Kong [GRF Grant PolyU 15507318] and the National Natural Science Foundation of China (72293564/72293560). Z. Chen acknowledges support from the National Natural Science Foundation of China [72542003]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0912 .

Problem definition: In this paper, we compare laissez-faire and mandatory authorization policy regimes for third-party remanufacturing. Under a laissez-faire policy, an independent remanufacturer (IR) chooses whether to get the original equipment manufacturer (OEM) authorization. Under a mandatory authorization policy, the IR is required to get OEM authorization and to pay the OEM a fee for every item remanufactured. Motivated by China’s regulatory journey that first mandated authorized remanufacturing and then moved to a laissez-faire policy, our goal is to understand which policy is better from the perspectives of different stakeholders. Methodology/results: We use a game-theoretic approach and consider a supply chain consisting of a supplier, an OEM, and an IR under the two policy regimes. Conventional wisdom suggests that the IR would be better off under the laissez-faire policy, but the OEM and the supplier would be better off under the mandatory authorization policy. However, we show that this conventional wisdom may not hold. For products with a low remanufacturing cost, all firms benefit from the mandatory authorization policy, whereas for products with a moderately high remanufacturing cost, all firms are better off under the laissez-faire policy. Further, mandatory authorization may outperform the laissez-faire policy in both economic and environmental dimensions. Managerial implications: Our findings reveal that seemingly advantageous policy regimes may backfire for firms. Therefore, before supporting such policies, the firms need to assess the strategic reactions of other firms and the potential impacts on their profits. Furthermore, a mandatory authorization policy can be beneficial in fostering the development of the remanufacturing sector for products with low remanufacturing costs. Nevertheless, it may also lead to an increase in the total environmental impact. Funding: The work of M. Jin was supported by the National Natural Science Foundation of China [Grants 72071020 and 72471038]. The work of Y. Zhou was supported by the National Natural Science Foundation of China [Grants 71971033 and 72371040] and the Fundamental Research Funds for the Central Universities [Grant 2024CDJSKPT14]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0128 .

In recent years, with the rapid development of computing technology, developing parallel procedures to solve large-scale ranking and selection (R&S) problems has attracted a lot of research attention. In this paper, we take fixed-budget R&S procedure as an example to investigate potential issues of developing parallel procedures. We argue that to measure the performance of a fixed-budget R&S procedure in solving large-scale problems, it is important to quantify the minimal growth rate of the total sampling budget such that as the number of alternatives increases, the probability of correct selection (PCS) would not decrease to zero. We call such a growth rate of the total sampling budget the rate for maintaining correct selection (RMCS). We show that a tight lower bound for the RMCS of a broad class of existing fixed-budget procedures is in the order of [Formula: see text], where k is the number of alternatives. Then, we propose a new type of fixed-budget procedure, namely the fixed-budget knockout-tournament ([Formula: see text]) procedure. We prove that, in terms of the RMCS, our procedure outperforms existing fixed-budget procedures and achieves the optimal order, that is, the order of k. Moreover, we demonstrate that our procedure can be easily implemented in parallel computing environments with almost no nonparallelizable calculations. Last, a comprehensive numerical study shows that our procedure is indeed suitable for solving large-scale problems in parallel computing environments. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: Y. Zhong was supported by the National Natural Science Foundation of China [Grant 72101047]. L. J. Hong was supported by the National Natural Science Foundation of China [Grants 72091211 and 72161160340]. G. Jiang was supported by the National Natural Science Foundation of China [Grants 72121001 and 72171060]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1221 .

Problem definition: Quick response is a classic operations strategy that allows a retailer to place a rapid replenishment order during the selling season using information learned from early sales. The benefits of quick response are widely studied in the literature under the condition that the manufacturer’s wholesale prices are exogenously given. Motivated by the practice of emerging small and medium-sized enterprise (SME) fashion brands, this paper revisits the value of quick response for a retailer when a manufacturer can strategically set its wholesale prices. Methodology/results: We develop a game-theoretic model consisting of one manufacturer and one retailer. In contrast to the traditional quick response setting, the manufacturer can dynamically adjust wholesale prices for both regular and replenishment orders. First, we investigate whether and when quick response still benefits the retailer. We find that, under low or significantly high demand uncertainties, the firms share a common preferred ordering strategy, and quick response benefits the retailer as well as the supply chain. But, under moderately high demand uncertainty, the retailer’s favored ordering strategy conflicts with the manufacturer’s interest; as a result, the manufacturer would set wholesale prices to counter the retailer’s ordering strategy, which makes quick response detrimental to the retailer. Second, we search for mechanisms that can resolve this conflict and restore the beneficial effect of quick response. We show that letting the manufacturer commit to wholesale prices up front is ineffective in fixing the problem. However, if the retailer can propose a take-it-or-leave-it wholesale price for the replenishment order (possibly with the replenishment quantity) once the regular wholesale price is set, then quick response leads to a win–win outcome for both firms. Managerial implications: The findings caution retailers with weak power (e.g., SMEs) when adopting quick response, especially when facing moderately high demand uncertainties. The retailer, although weak, should be aware of the retailer’s natural ability to propose replenishment terms because, otherwise, the retailer can always forgo quick response; this opens up an opportunity to design more favorable arrangements. Funding: The work of J. Chen was supported by the National Natural Science Foundation of China [Grants 72171202, 72232007]; the work of Q. Hu was supported by the National Natural Science Foundation of China [Grants 72271057, 72091211]; the work of D. Shi was supported by the National Natural Science Foundation of China [Grant 72102205] and Shenzhen Stable Support Program for Higher Education Institutions; the work of F. Zhang was supported by the National Natural Science Foundation of China [Grants 71929201, 72131004]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0561 .

Problem definition: We study a procurement problem, where the supplier holds superior cost information and can learn to improve efficiency over time. Despite its prevalence, the supply chain literature provides limited guidance on how to manage learning suppliers with evolving private information. Methodology/results: We use mechanism design. We show that supplier learning has both efficiency and agency effects, it can induce countervailing incentives, and the agency effect can overwhelm the efficiency effect. As a result, (i) supplier learning can hurt profits, (ii) information asymmetry can improve efficiency, (iii) production distortion can go upward, and (iv) ignoring the agency effect of learning can mislead contract design and inflict severe losses. Managerial implications: Our results suggest that previous studies may have overlooked the downside of learning and overestimated the harm of information asymmetry. Moreover, our results help explain when and why firms should overproduce output and disclose private information voluntarily. By highlighting the strategic role of supplier learning, this study sharpens our understanding of supply chain management. Funding: L. Gao is partly supported by the CoR research grant at University of California, Riverside. W. Zhang is partly supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0285 .

Problem definition: We study customer service chat (CSC) systems, in which agents can serve multiple customers simultaneously, with generally distributed service and patience times. The multitasking capability of agents introduces idiosyncratic challenges when making routing and staffing decisions. Methodology/results: To determine the dynamic matching of arriving customers with available agents, we first formulate a routing linear program (LP) based on system primitives. Inspired by the optimal solution of the routing LP, we design a parsimonious dynamic routing policy that is independent of arrival rate and service capacity information. We also use the optimal solution to develop closed-form approximations for crucial performance metrics and show that a similar LP can be utilized to make staffing decisions. Through extensive simulation experiments, we showcase the efficacy of our approximations and staffing decisions. Furthermore, under our proposed policy, the CSC system exhibits a unique stationary fluid model in which the steady-state performance measures align with our approximations. Managerial implications: The extant literature primarily focuses on Markovian systems with exponential distributions. In this paper, customers’ service and patience times are allowed to be generally distributed to agree with practical settings. Our findings indicate that the distributions have a significant impact on routing policies, staffing decisions, and system performance. Funding: Z. Long was supported by the National Natural Science Foundation of China [Grants 72101112, 72132005, and 72271119] and Jiangsu Province, China [Grant BK20210171]. J. Zhang was supported by the Hong Kong Research Grants Council, General Research Fund [Grants 16208120 and 16214121]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0114 .

Problem definition: We study the robust formulation of an inventory model with positive fixed ordering costs, where the unfulfilled demand is either backlogged or lost, the lead time is allowed to be positive, the demand is potentially intertemporally correlated, and the information about the demand distribution is limited. Methodology/results: We propose a robust cycle-based policy that manages inventory by dividing the planning horizon into nonoverlapping inventory cycles, where an order is placed at the beginning of each cycle. Our policy selects the lengths and order quantities for all inventory cycles to minimize the worst-case total cost incurred over the planning horizon. When the uncertain demand belongs to a general polyhedral uncertainty set, the decisions in our policy can be computed by solving linear programs (LPs) for the backlogging model with any lead time and the lost-sales model with zero lead time; however, the number of LPs that need to be solved grows exponentially in the length of the planning horizon. In the special case where the uncertain demand belongs to a box uncertainty set, the decisions in our policy can be computed using a dynamic programming (DP) recursion whose complexity grows polynomially in the length of the planning horizon. We also propose a one-cycle look-ahead heuristic to handle large problem instances with a general polyhedral uncertainty set. This heuristic can be applied for both the backlogging and lost-sales models with any lead time, and it only requires solving LPs whose number grows quadratically in the length of the planning horizon. Results from extensive computational experiments clearly show that both a rolling-cycle implementation of our policy and the one-cycle look-ahead heuristic have very strong empirical performance. Managerial implications: Our robust cycle-based policy and the one-cycle look-ahead heuristic are conceptually simple and can accommodate multiple realistic features in inventory management problems. They provide a very effective approach to robust inventory management, especially in the lost-sales setting. Funding: Y. Chen was supported by a start-up grant from Nanyang Technological University. C. Wang was supported by the National Natural Science Foundation of China [Grant 71802115] and the Tsinghua University Initiative Scientific Research Program. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1168 .

Problem definition: Hydrogen fuel-cell vehicles (HFVs) have been proposed as a promising green transportation alternative. For regions experiencing renewable energy curtailment, promoting HFVs can achieve the dual benefit of reducing curtailment and developing sustainable transportation. However, promoting HFVs faces several major hurdles, including uncertain vehicle adoption, the lack of refueling infrastructure, the spatial mismatch between hydrogen demand and renewable sources for hydrogen production, and the strained power transmission infrastructure. In this paper, we address these challenges and study how to promote HFV adoption by deploying HFV infrastructure and utilizing renewable resources. Methodology/results: We formulate a planning model that jointly determines the location and capacities of hydrogen refueling stations (HRSs) and hydrogen plants as well as electricity transmission and grid upgrade. Despite the complexity of explicitly considering drivers’ HFV adoption behavior, the bilevel optimization model can be reformulated as a tractable mixed-integer second-order cone program. We apply our model calibrated with real data to the case of Sichuan, a province in China with abundant hydro resources and a vast amount of hydropower curtailment. Managerial implications: We obtain the following findings. (i) The optimal deployment of HRSs displays vastly different spatial patterns depending on the HFV adoption target. The capital city, a transportation hub, is excluded from the plan under a low target and only emerges as the center of HFV adoption under a high target. (ii) Promoting the HFV adoption can overall help reduce hydropower curtailment, but the effectiveness depends on factors such as the adoption target and the grid upgrade cost. (iii) Being a versatile energy carrier, hydrogen can be transported to various locations, which allows for strategic placement of HRSs in locations distinct from hydrogen plant sites. This flexibility offers HFVs greater potential cost savings and curtailment reduction compared with other alternative fuel vehicles (e.g., electric vehicles) under current cost estimates. Funding: W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72242106, 72188101, and 72272014] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2019-04769]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0381 .

Problem definition: We consider a platform’s problem of how to staff its operations given the possibilities of hiring employees and setting up a contractor marketplace. We aim to understand the operational difference between these two work arrangement models. Methodology/results: We consider a model where demand is not only stochastic but also evolving over time, which we capture via a state of the world that determines the demand distribution. In the case of employees, the platform controls the number of employee hours it uses for serving demand, whereas in the case of contractors, it sets the wage paid to them per utilized hour. We show that although the employee problem is equivalent to a standard newsvendor, the contractor one corresponds to an unusual version of the newsvendor model where utilization is the control variable. Managerial implications: This distinction makes the contractor model more flexible, allowing us to prove that it performs significantly better, especially if the order of magnitude of demand is unknown. Meanwhile, hybrid solutions that combine both employees and contractors have complex optimal solutions and offer relatively limited benefits relative to a contractor marketplace. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0029 .

Problem definition: Motivated by the significance of side information in numerous operations management problems, this paper studies conditional stochastic optimization to enable more informed decisions. The side information constitutes observable exogenous covariates that alter the conditional probability distribution of the random problem parameters. Decision makers who adapt their decisions according to the observed side information solve a stochastic optimization problem where the objective function is specified by the conditional expectation of the random cost. If the joint probability distribution is unknown, then the conditional expectation can be approximated in a data-driven manner using kernel regression. Although this approximation scheme has found successful applications in diverse decision problems under uncertainty, it is largely unknown whether the scheme can provide any reasonable out-of-sample performance guarantees, and how such statistical guarantees can guide the decision-making process. Methodology/results: We employ the Nadaraya–Watson kernel regression for data-driven approximation of the conditional expectation and leverage moderate deviations theory to establish its performance guarantees. Our analysis and resultant statistical bounds motivate the use of a conditional standard deviation regularization scheme to enhance out-of-sample performances. As the designed regularization scheme leads to a nonconvex optimization problem, we further adopt ideas from distributionally robust optimization to obtain tractable formulations. We examine our proposed models on portfolio optimization, inventory management, and wind energy commitment problems. The numerical results demonstrate the effectiveness of our proposed regularization scheme. Managerial implications: Our paper illustrates the importance of side information in real-world decision-making problems. Incorporating side information through a regularized Nadaraya–Watson scheme offers managers a robust framework to enhance decision making under uncertainty. The theoretical guarantees provide guidance on the number of samples required to obtain high-quality solutions and how to optimally adjust the regularization parameter. For problem instances with high-dimensional covariates, we further present a simple dimensionality reduction procedure that helps improve the sample complexity of the scheme. All our proposed formulations are concise and straightforward for the operations manager to implement using any popular programming language interfaced with standard off-the-shelf solvers. Funding: Y. Wang was supported by the National Natural Science Foundation of China [Grant 72501204] and the Fundamental Research Funds for the Central Universities. G. A. Hanasusanto was funded by the National Science Foundation [Grants CCF-2343869 and ECCS-2404413]. C. P. Ho was supported by the Research Grants Council [General Research Fund 11508623] and the CityUHK Start-Up Grant [9610481]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0997 .

We find that the degree of extrapolative weighting in investors’ beliefs (DOX) has strong predictive power for a broad set of overreaction-related anomalies in the stock market. The average return spread of these anomalies is about [Formula: see text] per month following high DOX periods and [Formula: see text] per month following low DOX periods. In sharp contrast, DOX has opposite, but weaker, predictive power for underreaction-related anomalies. In addition, the predictive power of DOX is robust after controlling for a broad set of economic forces. Moreover, most of the DOX effect on long-short anomaly returns derives from the short legs of these overreaction-related anomalies, suggesting that time variation in DOX leads to more time variation in overpricing than in underpricing, probably because of short-sale impediments. This paper was accepted by Lukas Schmid, finance. Funding: Z. Su received financial support from Lingnan University [Faculty Research Grants DB25A5 and 103664]. Y. Wang received financial support from the National Natural Science Foundation of China [Grant 72503264]. J. Yu received financial support from the National Natural Science Foundation of China [Grants 72141304 and 72342020]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06850 .

The rise of social media has transformed consumer experience in service innovation, particularly in video-based influencer marketing. This study investigates the unintended consequences of chapter progress bars—a widely used interface feature intended to facilitate video navigation—on consumer engagement. Although designed to enhance user experience, progress bars may inadvertently heighten consumer impatience and, in turn, reduce purchase intention. Across five studies, including large-scale behavioral analysis of more than 181,500 real-time user comments and a series of controlled experiments, results show that chapter progress bars increase consumer impatience and lower purchase intention. Furthermore, impatience mediates this relationship, and the effect is moderated by efficiency goals; consumers with stronger efficiency orientations are more sensitive to segmentation cues and exhibit greater impatience and lower engagement, whereas those with lower efficiency orientations are largely unaffected. This research extends the literatures on influencer marketing, consumer impatience, and digital service design by demonstrating how interface features shape consumer psychology and purchase intention. The findings offer practical insights for creators and platform designers, emphasizing the need to balance navigation efficiency with sustained consumer purchase intention in digital content strategies. Funding: Z. Zhang was supported by the National Natural Science Foundation of China [Grant 71972043]. X. Fan was supported by the National Natural Science Foundation of China [Grant 72572040]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2024.0172 .

We study endogenous merger decisions among three competing firms with asymmetric capacity: one large firm has ample capacity, and two small firms have limited capacity. Each firm can either operate independently or merge with another firm. Because of antitrust laws, all three firms merging is not permitted. When two firms merge, the merged entity and the remaining independent firm use the resulting capacity to capture their own “loyal buyers” and compete in price for price-sensitive “disloyal switchers.” We first consider a base model in which the size of loyal buyers for different firms is symmetric. We find that, in equilibrium, either there is no merger or the large firm will merge with a small firm as a “mixed merger.” We explain how this equilibrium structure is driven by the mergers’ impacts on market competition and firms’ pricing aggressiveness. Specifically, the mixed merger that can most effectively reduce price competition is the equilibrium under relatively competitive market conditions (i.e., the size of loyal buyers is small and capacity is large but limited). We also extend to the case with asymmetric size of loyal segments and show that two small firms merging (“small merger” structure) can be the equilibrium structure when the large firm has a much larger loyal segment than the small firms and the small firms’ capacity is sufficiently large. The impacts of mergers on social welfare and consumer surplus provide practical implications from the antitrust perspective. The antitrust agencies can approve a mixed merger to improve social welfare under certain conditions, but if the agencies pay more attention to consumer surplus, then mixed mergers should not be permitted. Small mergers, though sometimes not the equilibrium structures, can improve social welfare and consumer surplus. In such cases, the government can appropriately promote mergers that are beneficial for society and/or consumers by adopting some restrictive measures or offering incentives. This paper was accepted by Jeannette Song, operations management. Funding: The work of D. Wang was supported by the National Natural Science Foundation of China [Grant 72401275]; the work of X. Wu was supported by the National Natural Science Foundation of China [Grants 72025102, 72293560/72293565]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.01943 .

Prevalence and Associated Factors of Vision Impairment Combined With Distance and Near Vision: A Cross Sectional Study in Coastal Province of Southern China Among Urban and Rural Adults

Problem definition: The main goal of many nonprofit or nongovernmental organizations is to increase the number of customers who receive service (i.e., service coverage) and social welfare. However, the limited number of employees, volunteers, and service locations results in long service wait. In addition, getting customers living in remote areas to receive services by traveling long distances is difficult. We propose an innovative distance-based service priority policy that would reduce the service waiting time for customers who must travel farther for the service by giving them higher service priority, thereby providing them with a new incentive to seek service. Methodology/results: Using a game-theoretic queueing model, we show that the proposed policy can significantly attract more customers to a service. The increase can be up to 50% compared with the ordinary first-come-first-served service discipline. The policy can also achieve higher social welfare, however, that may come at the cost of reduced customer welfare. We therefore propose a possible remedy for a social planner to coordinate welfare under such circumstance. It ensures all stakeholders, including the service provider, customers, and society, can benefit from the policy at the same time. Finally, we compare our distance-based service priority policy with two existing strategies from the literature—namely, the price discrimination strategy and the probabilistic priority strategy. Managerial implications: Our proposed policy can play a pivotal role in a nonprofit service provider’s mission to increase service coverage and social welfare, especially when customers’ travel costs to obtain service are significant. Furthermore, our policy may create fewer implementation and fairness concerns compared with related strategies. Funding: Z. Wang acknowledges financial support from the National Natural Science Foundation of China [Grants 72001118 and 72132007], and L. Fang acknowledges financial support from the National Natural Science Foundation of China [Grant 72271129]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1157 .