Problem definition: We study settings where agents sequentially search among different options under competition. Motivated by labor markets and the allocation of kidneys from deceased donors, we focus on the effect of (i) the mechanism to collect decisions, that is, whether all agents make their decisions simultaneously or sequentially, and (ii) competition, that is, the number of agents who are searching from a shared pool of options. Methodology/results: We introduce a model of sequential search under competition, in which agents are exogenously prioritized and must decide when to stop their search to maximize the chosen option’s value. We characterize the optimal policy, which defines a sequence of thresholds that dictates when each agent should accept an option based on their priority relative to others still searching and the number of remaining options. Our analysis reveals that neither the mechanism for collecting agents’ decisions nor the number of lower-priority agents influences the optimal policy. To test these predictions, we designed and conducted a laboratory experiment replicating our theoretical model. The results indicate significant deviations from the optimal policy. Moreover, we find that the mechanism significantly affects agents’ decisions due to primarily two drivers: (i) saliency of competition and (ii) frustration. Finally, we identify an “illusion of competition” effect, whereby agents use significantly lower thresholds when the number of agents with lower priority increases. Managerial implications: Our results show that a higher perception of competition and using a simultaneous mechanism (i.e., batch offering) significantly decrease the thresholds that agents use to guide their search, making them stop their search earlier. Thus, clearinghouses that suffer from inefficient discard of options should increase the saliency of competition and use batch offerings to reduce agents’ selectivity and mitigate waste.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0621 .
问题定义：我们研究的是在竞争条件下，代理人在不同选择中顺序搜索的情况。受劳动力市场和已故捐赠者肾脏分配的启发，我们重点研究了以下两个因素的影响：(i) 收集决策的机制，即所有代理人是同时还是按顺序做出决策；(ii) 竞争，即从共享选项库中搜索的代理人数量。方法/结果：我们引入了一个竞争条件下的顺序搜索模型，在这个模型中，代理人的优先级是外生的，他们必须决定何时停止搜索，以最大化所选方案的价值。我们描述了最优策略的特征，它定义了一系列阈值，根据每个代理人相对于其他仍在搜索的代理人的优先级以及剩余选项的数量，决定每个代理人何时应该接受一个选项。我们的分析表明，收集代理决策的机制和低优先级代理的数量都不会影响最优策略。为了验证这些预测，我们设计并进行了一个实验室实验，复制了我们的理论模型。结果表明，实验结果明显偏离了最优政策。此外，我们还发现，该机制对代理人的决策产生重大影响的主要原因有两个：(i) 竞争的突出性和 (ii) 挫折感。最后，我们发现了一种 "竞争假象 "效应，即当优先级较低的代理人数量增加时，代理人使用的阈值会明显降低。管理意义：我们的研究结果表明，较高的竞争感知和使用同步机制（即批量发售）会显著降低代理人用于指导其搜索的阈值，使他们更早停止搜索。因此，存在低效放弃选项问题的信息交流中心应该提高竞争的显著性，并使用分批提供的方式来降低代理人的选择性，减少浪费：在线附录见 https://doi.org/10.1287/msom.2022.0621 。
{"title":"Competition in Optimal Stopping: Behavioral Insights","authors":"Ignacio Rios, Pramit Ghosh","doi":"10.1287/msom.2022.0621","DOIUrl":"https://doi.org/10.1287/msom.2022.0621","url":null,"abstract":"Problem definition: We study settings where agents sequentially search among different options under competition. Motivated by labor markets and the allocation of kidneys from deceased donors, we focus on the effect of (i) the mechanism to collect decisions, that is, whether all agents make their decisions simultaneously or sequentially, and (ii) competition, that is, the number of agents who are searching from a shared pool of options. Methodology/results: We introduce a model of sequential search under competition, in which agents are exogenously prioritized and must decide when to stop their search to maximize the chosen option’s value. We characterize the optimal policy, which defines a sequence of thresholds that dictates when each agent should accept an option based on their priority relative to others still searching and the number of remaining options. Our analysis reveals that neither the mechanism for collecting agents’ decisions nor the number of lower-priority agents influences the optimal policy. To test these predictions, we designed and conducted a laboratory experiment replicating our theoretical model. The results indicate significant deviations from the optimal policy. Moreover, we find that the mechanism significantly affects agents’ decisions due to primarily two drivers: (i) saliency of competition and (ii) frustration. Finally, we identify an “illusion of competition” effect, whereby agents use significantly lower thresholds when the number of agents with lower priority increases. Managerial implications: Our results show that a higher perception of competition and using a simultaneous mechanism (i.e., batch offering) significantly decrease the thresholds that agents use to guide their search, making them stop their search earlier. Thus, clearinghouses that suffer from inefficient discard of options should increase the saliency of competition and use batch offerings to reduce agents’ selectivity and mitigate waste.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0621 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Shocks that trigger supply chain disruptions inflict initial losses by damaging firms’ assets. The disruption can then cascade when an affected firm fails to deliver to its buyer, thereby interrupting the buyer’s operations, and continue thus across multiple levels (tiers) in the supply chain. To protect against such disruption cascades, firms can make ex ante investments in risk mitigation. These investments depend heavily on the operational characteristics of network participants and their interconnections. Gathering operational information can be challenging. Our aim is to shed light on the forces that govern information requirements for risk mitigation. Methodology/results: We introduce a game-theoretic model to characterize the equilibrium mitigation by firms in a decentralized arborescent network facing severe disruptions. We find that when the trigger shocks are nonconcurrent events, the equilibrium mitigation by a firm displays a limited vertical dependence on the operational attributes of suppliers that are farther away in tier (network) distance. Specifically, we show that information about a firm’s extended local neighborhood—up to its tier 2 suppliers—suffices to characterize its equilibrium mitigation. Allowing for concurrent shocks to simultaneously strike multiple firms increases the information requirement at partner firms that typically lie within two tiers downstream from the firms experiencing concurrent shocks. Managerial implications: Full supply chain visibility is costly. The literature offers little guidance on how to prioritize efforts to enhance visibility into the attributes of supply chain partners. Rather than a blanket call for greater visibility, our results proffer nuanced managerial prescriptions for the extent to which risk mitigation requires such visibility.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0408 .
{"title":"Information Dependency in Mitigating Disruption Cascades","authors":"Nitin Bakshi, Shyam Mohan","doi":"10.1287/msom.2022.0408","DOIUrl":"https://doi.org/10.1287/msom.2022.0408","url":null,"abstract":"Problem definition: Shocks that trigger supply chain disruptions inflict initial losses by damaging firms’ assets. The disruption can then cascade when an affected firm fails to deliver to its buyer, thereby interrupting the buyer’s operations, and continue thus across multiple levels (tiers) in the supply chain. To protect against such disruption cascades, firms can make ex ante investments in risk mitigation. These investments depend heavily on the operational characteristics of network participants and their interconnections. Gathering operational information can be challenging. Our aim is to shed light on the forces that govern information requirements for risk mitigation. Methodology/results: We introduce a game-theoretic model to characterize the equilibrium mitigation by firms in a decentralized arborescent network facing severe disruptions. We find that when the trigger shocks are nonconcurrent events, the equilibrium mitigation by a firm displays a limited vertical dependence on the operational attributes of suppliers that are farther away in tier (network) distance. Specifically, we show that information about a firm’s extended local neighborhood—up to its tier 2 suppliers—suffices to characterize its equilibrium mitigation. Allowing for concurrent shocks to simultaneously strike multiple firms increases the information requirement at partner firms that typically lie within two tiers downstream from the firms experiencing concurrent shocks. Managerial implications: Full supply chain visibility is costly. The literature offers little guidance on how to prioritize efforts to enhance visibility into the attributes of supply chain partners. Rather than a blanket call for greater visibility, our results proffer nuanced managerial prescriptions for the extent to which risk mitigation requires such visibility.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0408 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expansion planning problem with different generation resources including renewable energy. We demonstrate the value of adopting adaptive two-stage approach against the existing policies under limited flexibility and highlight the efficiency of the proposed heuristics along with practical implications on the studied problem.Funding: This work was supported by the National Science Foundation [Grant 1633196] and the Office of Naval Research [Grant N00014-18-1-2075].Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0157 .
{"title":"Adaptive Two-Stage Stochastic Programming with an Analysis on Capacity Expansion Planning Problem","authors":"Beste Basciftci, Shabbir Ahmed, Nagi Gebraeel","doi":"10.1287/msom.2023.0157","DOIUrl":"https://doi.org/10.1287/msom.2023.0157","url":null,"abstract":"Problem definition: Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expansion planning problem with different generation resources including renewable energy. We demonstrate the value of adopting adaptive two-stage approach against the existing policies under limited flexibility and highlight the efficiency of the proposed heuristics along with practical implications on the studied problem.Funding: This work was supported by the National Science Foundation [Grant 1633196] and the Office of Naval Research [Grant N00014-18-1-2075].Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0157 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We investigate the optimal salesforce compensation scheme in the context of private information and unobservable actions, considering common operational factors encountered in practice, including inventory costs, contractible versus censored demand information, and controlled versus delegated ordering. Methodology/results: Based on an agency model with general demand and cost functions, we derive optimality conditions for implementable contracts that can achieve the second-best outcome in all scenarios. The contracts are in the forms of a menu with linear compensation for demand or sales, incorporating inventory costs. Moreover, the contracts feature adjustments in compensation corresponding to the ordering level if it is delegated. Managerial implications: Our study reveals that, under reasonably mild conditions, optimal salesforce contracts can still maintain relatively simple forms, even when confronted with common operational factors and generalized demand and cost functions. However, the contracts must be tailored to suit the operational settings. Intriguingly, neither the loss of demand information nor the delegation of inventory decisions would compromise system efficiency at optimum.Funding: H. Song is partially supported by the Key International Cooperation and Exchange Projects of the NSFC [Grant W2411062] and the Foundation for Innovative Research Groups of the NSFC [Grant 71821002].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0400 .
问题定义：考虑到实践中常见的运营因素，包括库存成本、可收缩需求信息与可删减需求信息，以及受控订货与委托订货，我们研究了在私人信息和不可观察行动背景下的最优销售人员补偿方案。方法/结果：基于具有一般需求和成本函数的代理模型，我们推导出了可执行合同的最优条件，这些合同在所有情况下都能实现次优结果。这些合同采用菜单形式，对需求或销售进行线性补偿，并包含库存成本。此外，如果委托订货，合同的特点是根据订货水平调整补偿。管理意义：我们的研究表明，在相当温和的条件下，即使面对共同的运营因素和广义的需求与成本函数，最优的销售队伍合同仍能保持相对简单的形式。然而，合同必须量身定制，以适应运营环境。耐人寻味的是，无论是需求信息的损失还是库存决策的委托，都不会影响系统的最佳效率：H. Song 的研究得到了国家自然科学基金委重点国际合作与交流项目[批准号：W2411062]和国家自然科学基金委创新研究群体基金[批准号：71821002]的部分资助：在线附录见 https://doi.org/10.1287/msom.2022.0400 。
{"title":"Optimal Salesforce Compensation with General Demand and Operational Considerations","authors":"Haotian Song, Guoming Lai, Wenqiang Xiao","doi":"10.1287/msom.2022.0400","DOIUrl":"https://doi.org/10.1287/msom.2022.0400","url":null,"abstract":"Problem definition: We investigate the optimal salesforce compensation scheme in the context of private information and unobservable actions, considering common operational factors encountered in practice, including inventory costs, contractible versus censored demand information, and controlled versus delegated ordering. Methodology/results: Based on an agency model with general demand and cost functions, we derive optimality conditions for implementable contracts that can achieve the second-best outcome in all scenarios. The contracts are in the forms of a menu with linear compensation for demand or sales, incorporating inventory costs. Moreover, the contracts feature adjustments in compensation corresponding to the ordering level if it is delegated. Managerial implications: Our study reveals that, under reasonably mild conditions, optimal salesforce contracts can still maintain relatively simple forms, even when confronted with common operational factors and generalized demand and cost functions. However, the contracts must be tailored to suit the operational settings. Intriguingly, neither the loss of demand information nor the delegation of inventory decisions would compromise system efficiency at optimum.Funding: H. Song is partially supported by the Key International Cooperation and Exchange Projects of the NSFC [Grant W2411062] and the Foundation for Innovative Research Groups of the NSFC [Grant 71821002].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0400 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
John Gunnar Carlsson, Xiaoshan Peng, Ilya O. Ryzhov
Problem definition: A service is offered at certain locations (“facilities”) in a geographical region. Customers can appear anywhere in the region, and each customer chooses a facility based on travel distance as well as expected waiting time. Customer decisions affect waiting times by increasing the load on a facility, and thus, they impact other customers’ decisions. The service provider can also influence service quality by adjusting service rates at each facility. Methodology/results: Using a combination of queueing models and computational geometry, we characterize demand equilibria in such spatial service systems. An equilibrium can be visualized as a partition of the region into service zones that form as a result of customer decisions. Service rates can be set in a way that achieves the best-possible social welfare purely through decentralized customer behavior. Managerial implications: We provide techniques for computing and visualizing demand equilibria as well as calculating optimal service rates. Our analytical and numerical results indicate that in many situations, resource allocation is a far more significant source of inefficiency than decentralized behavior.Funding: J. G. Carlsson was funded by the METRANS Transportation Consortium [Grant NCST-USC-RR-24-12] and the Office of Naval Research [Grant N00014-24-1-2277-P00001].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0434 .
{"title":"Demand Equilibria in Spatial Service Systems","authors":"John Gunnar Carlsson, Xiaoshan Peng, Ilya O. Ryzhov","doi":"10.1287/msom.2023.0434","DOIUrl":"https://doi.org/10.1287/msom.2023.0434","url":null,"abstract":"Problem definition: A service is offered at certain locations (“facilities”) in a geographical region. Customers can appear anywhere in the region, and each customer chooses a facility based on travel distance as well as expected waiting time. Customer decisions affect waiting times by increasing the load on a facility, and thus, they impact other customers’ decisions. The service provider can also influence service quality by adjusting service rates at each facility. Methodology/results: Using a combination of queueing models and computational geometry, we characterize demand equilibria in such spatial service systems. An equilibrium can be visualized as a partition of the region into service zones that form as a result of customer decisions. Service rates can be set in a way that achieves the best-possible social welfare purely through decentralized customer behavior. Managerial implications: We provide techniques for computing and visualizing demand equilibria as well as calculating optimal service rates. Our analytical and numerical results indicate that in many situations, resource allocation is a far more significant source of inefficiency than decentralized behavior.Funding: J. G. Carlsson was funded by the METRANS Transportation Consortium [Grant NCST-USC-RR-24-12] and the Office of Naval Research [Grant N00014-24-1-2277-P00001].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0434 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhaowei She, Turgay Ayer, Bilal Gokpinar, Danny R. Hughes
Problem definition: This study identifies a resource misallocation problem in Medicare Advantage (MA), the United States’ largest healthcare capitation program, which may result in discrepancies between patients’ health status and the healthcare resources allocated to them. Methodology/results: Utilizing a large commercial insurance database with claims from more than 2 million MA enrollees, this research investigates the allocation of MA capitation payments. By exploiting an exogenous policy shock on MA capitation payments through a difference-in-difference design, we find empirical evidence of an illegal practice known as “cross-subsidization.” This practice involves MA health plans strategically reallocating portions of the capitation payments intended for one group of patients to spend on another group of patients. Additionally, we show that this cross-subsidization practice is associated with the risk selection problem in MA, where low-risk patients are more likely to enroll in MA compared with high-risk patients. Managerial implications: This research unveils a previously undocumented healthcare resource misallocation problem, that is, strategic cross-subsidization. This practice is explicitly prohibited by law in the United States due to its heightened effect on the undesired risk selection within capitation programs, where health plans cherry-pick profitable enrollees through strategic benefit designs. Our study has direct practical implications as it underscores the need for greater transparency in MA claims data to enable the Centers for Medicare & Medicaid Services to more effectively administer the MA program.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0637 .
{"title":"Strategic Cross-Subsidization in Healthcare Capitation Programs: Evidence from Medicare Advantage","authors":"Zhaowei She, Turgay Ayer, Bilal Gokpinar, Danny R. Hughes","doi":"10.1287/msom.2023.0637","DOIUrl":"https://doi.org/10.1287/msom.2023.0637","url":null,"abstract":"Problem definition: This study identifies a resource misallocation problem in Medicare Advantage (MA), the United States’ largest healthcare capitation program, which may result in discrepancies between patients’ health status and the healthcare resources allocated to them. Methodology/results: Utilizing a large commercial insurance database with claims from more than 2 million MA enrollees, this research investigates the allocation of MA capitation payments. By exploiting an exogenous policy shock on MA capitation payments through a difference-in-difference design, we find empirical evidence of an illegal practice known as “cross-subsidization.” This practice involves MA health plans strategically reallocating portions of the capitation payments intended for one group of patients to spend on another group of patients. Additionally, we show that this cross-subsidization practice is associated with the risk selection problem in MA, where low-risk patients are more likely to enroll in MA compared with high-risk patients. Managerial implications: This research unveils a previously undocumented healthcare resource misallocation problem, that is, strategic cross-subsidization. This practice is explicitly prohibited by law in the United States due to its heightened effect on the undesired risk selection within capitation programs, where health plans cherry-pick profitable enrollees through strategic benefit designs. Our study has direct practical implications as it underscores the need for greater transparency in MA claims data to enable the Centers for Medicare & Medicaid Services to more effectively administer the MA program.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0637 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Facing emergent business challenges, entrepreneurs often seek guidance from experienced advisors. When multiple alternatives could potentially solve the entrepreneur’s problem, advisors can lead the entrepreneur’s exploration by choosing which alternative(s) to suggest and in what sequence. Methodology/results: We develop a dynamic game-theoretic model that captures the sequential interaction between an advisor and an entrepreneur. The advisor chooses how to recommend alternative solutions, and the entrepreneur chooses which solution to try. The trial’s success depends on the viability of a solution and the entrepreneur’s execution capability. When a trial of a recommended solution fails, the belief about the viability of the solution is updated. Managerial implications: Our analysis reveals that the advisor should strategically recommend alternatives based on the entrepreneur’s execution capability, trial costs, and correlation between alternatives (among other factors). When the trial of the first alternative fails, the advisor should readily offer a new alternative if the entrepreneur’s capability is either very high or very low. Otherwise, the advisor should encourage the entrepreneur to try the same solution multiple times. In order to motivate and sustain the entrepreneur’s exploration over time and across solutions, the advisor may find it optimal to recommend inferior solutions before superior ones (e.g., when trial costs are different or the entrepreneur can improve her capability with experience) or recommend multiple solutions simultaneously (e.g., when there is correlation between alternatives).Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0361 .
{"title":"Advising Entrepreneurs: Optimal Recommendation of Alternatives","authors":"Zeya Wang, Morvarid Rahmani, Karthik Ramachandran","doi":"10.1287/msom.2022.0361","DOIUrl":"https://doi.org/10.1287/msom.2022.0361","url":null,"abstract":"Problem definition: Facing emergent business challenges, entrepreneurs often seek guidance from experienced advisors. When multiple alternatives could potentially solve the entrepreneur’s problem, advisors can lead the entrepreneur’s exploration by choosing which alternative(s) to suggest and in what sequence. Methodology/results: We develop a dynamic game-theoretic model that captures the sequential interaction between an advisor and an entrepreneur. The advisor chooses how to recommend alternative solutions, and the entrepreneur chooses which solution to try. The trial’s success depends on the viability of a solution and the entrepreneur’s execution capability. When a trial of a recommended solution fails, the belief about the viability of the solution is updated. Managerial implications: Our analysis reveals that the advisor should strategically recommend alternatives based on the entrepreneur’s execution capability, trial costs, and correlation between alternatives (among other factors). When the trial of the first alternative fails, the advisor should readily offer a new alternative if the entrepreneur’s capability is either very high or very low. Otherwise, the advisor should encourage the entrepreneur to try the same solution multiple times. In order to motivate and sustain the entrepreneur’s exploration over time and across solutions, the advisor may find it optimal to recommend inferior solutions before superior ones (e.g., when trial costs are different or the entrepreneur can improve her capability with experience) or recommend multiple solutions simultaneously (e.g., when there is correlation between alternatives).Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0361 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lisanne van Rijn, Harwin de Vries, Luk N. Van Wassenhove
Problem definition: United Nations Sustainable Development Goal (SDG) 3.8 states that health coverage should be universal by 2030. This is challenging in rural and poor areas. To address this challenge, mobile outreach teams of healthcare workers visit a fixed set of remote sites to provide healthcare services. Because of dynamics in demand and supply, once-rational site-to-team assignment decisions can become far from optimal over time. This paper considers the problem of reassigning sites to teams to maximize effectiveness. Solving this problem through a central planner does not fit the context: outreach teams commonly have a high degree of decision-making autonomy. We study a decentralized approach where subsets of teams collaborate in a series of team meetings to reassign sites. The key question for decision makers is whether and when such an approach is effective. Methodology/results: We propose a mixed-integer programming model for centralized site reassignment. We extend this model to represent the decentralized approach and develop a set of simple decision rules for this approach. We use empirical data from six country outreach programs of the nongovernmental organization MSI Reproductive Choices. Our results suggest that, when properly designed, decentralized decision making performs close to centralized decision making, and even outperforms it in the case of inaccurate information at the central level. The finding remains valid when demand and supply fluctuate, and is insensitive to the chosen objective. Managerial implications: Humanitarian organizations currently deploy mobile outreach teams to provide a wide variety of health services. We present several useful insights for decision makers in humanitarian organizations when making design choices, taking account of context. In particular, we show that decentralized site reassignment provides a feasible and effective alternative to centralized approaches in many contexts.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0437 .
{"title":"Site Reassignment for Mobile Outreach Teams: Investigating the Effectiveness of Decentralized Decision Making","authors":"Lisanne van Rijn, Harwin de Vries, Luk N. Van Wassenhove","doi":"10.1287/msom.2021.0437","DOIUrl":"https://doi.org/10.1287/msom.2021.0437","url":null,"abstract":"Problem definition: United Nations Sustainable Development Goal (SDG) 3.8 states that health coverage should be universal by 2030. This is challenging in rural and poor areas. To address this challenge, mobile outreach teams of healthcare workers visit a fixed set of remote sites to provide healthcare services. Because of dynamics in demand and supply, once-rational site-to-team assignment decisions can become far from optimal over time. This paper considers the problem of reassigning sites to teams to maximize effectiveness. Solving this problem through a central planner does not fit the context: outreach teams commonly have a high degree of decision-making autonomy. We study a decentralized approach where subsets of teams collaborate in a series of team meetings to reassign sites. The key question for decision makers is whether and when such an approach is effective. Methodology/results: We propose a mixed-integer programming model for centralized site reassignment. We extend this model to represent the decentralized approach and develop a set of simple decision rules for this approach. We use empirical data from six country outreach programs of the nongovernmental organization MSI Reproductive Choices. Our results suggest that, when properly designed, decentralized decision making performs close to centralized decision making, and even outperforms it in the case of inaccurate information at the central level. The finding remains valid when demand and supply fluctuate, and is insensitive to the chosen objective. Managerial implications: Humanitarian organizations currently deploy mobile outreach teams to provide a wide variety of health services. We present several useful insights for decision makers in humanitarian organizations when making design choices, taking account of context. In particular, we show that decentralized site reassignment provides a feasible and effective alternative to centralized approaches in many contexts.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0437 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Contrary to traditional queueing theory, recent field studies in B2C services indicate that pooled queues may be less efficient than dedicated queues. Methodology/results: We use two online experiments in the healthcare delivery context to replicate this finding and assess the interplay of servers’ customer ownership and queue length awareness as potential underlying mechanisms. We operationalize customer ownership as the extent to which servers feel ownership toward their customers and queue length awareness as the extent to which servers are able to accurately quantify their number of customers. We find that, following a change in queue configuration, dedicated queues outperform pooled queues with respect to processing speed without sacrificing quality. The reduction in speed is partially mediated by the servers’ queue length awareness and partially suppressed by their ownership of customers in queue. The former is because servers turn out to be less likely to underestimate their load, which makes them work faster. The latter is because ownership of customers in queue may distract servers from the customer in service. When the queue configuration changes from a dedicated to a pooled one, the shorter processing times and higher levels of queue length awareness persist across the change, unlike the higher ownership of customers in the queue. Managerial implications: In discretionary service settings, switching to a dedicated queue is often beneficial in terms of operational performance, partly because the increased queue length awareness motivates servers to work faster; however, the increased degree of customer ownership of those in queue may distract them and result in a slowdown.Funding: This work was supported by the Wharton Behavioral Lab, the Claude Marion Endowed Faculty Scholar Award, the Wharton-INSEAD Alliance, and the Wharton Dean’s Research Fund.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0202 .
{"title":"Queue Configurations and Operational Performance: An Interplay Between Customer Ownership and Queue Length Awareness","authors":"Hummy Song, Mor Armony, Guillaume Roels","doi":"10.1287/msom.2023.0202","DOIUrl":"https://doi.org/10.1287/msom.2023.0202","url":null,"abstract":"Problem definition: Contrary to traditional queueing theory, recent field studies in B2C services indicate that pooled queues may be less efficient than dedicated queues. Methodology/results: We use two online experiments in the healthcare delivery context to replicate this finding and assess the interplay of servers’ customer ownership and queue length awareness as potential underlying mechanisms. We operationalize customer ownership as the extent to which servers feel ownership toward their customers and queue length awareness as the extent to which servers are able to accurately quantify their number of customers. We find that, following a change in queue configuration, dedicated queues outperform pooled queues with respect to processing speed without sacrificing quality. The reduction in speed is partially mediated by the servers’ queue length awareness and partially suppressed by their ownership of customers in queue. The former is because servers turn out to be less likely to underestimate their load, which makes them work faster. The latter is because ownership of customers in queue may distract servers from the customer in service. When the queue configuration changes from a dedicated to a pooled one, the shorter processing times and higher levels of queue length awareness persist across the change, unlike the higher ownership of customers in the queue. Managerial implications: In discretionary service settings, switching to a dedicated queue is often beneficial in terms of operational performance, partly because the increased queue length awareness motivates servers to work faster; however, the increased degree of customer ownership of those in queue may distract them and result in a slowdown.Funding: This work was supported by the Wharton Behavioral Lab, the Claude Marion Endowed Faculty Scholar Award, the Wharton-INSEAD Alliance, and the Wharton Dean’s Research Fund.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0202 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: How can retailers incentivize customers to make healthier food choices? Price, convenience, and taste are known to be among the main drivers behind such choices. Unfortunately, healthier food options are often expensive and not adequately promoted. However, we are observing recent efforts to nudge customers toward healthier food. Methodology/results: In this paper, we conducted a field experiment with a global convenience store chain to better understand how different add-on bundle promotions influence healthy food choices. We considered three types of add-on bundles sequentially: (i) an unhealthy bundle (when customers purchased a coffee, they could add a pastry for $1), (ii) a healthy bundle (offering a healthy snack, such as fruit, vegetable, or protein, as a coffee add-on for $1), and (iii) a choice bundle (the option of either a pastry or a healthy snack as an add-on to coffee for $1). In addition to our field experiment, we conducted an online laboratory study to strengthen the validity of our results. Managerial implications: We found that offering healthy snacks as part of an add-on bundle significantly increased healthy purchases (and decreased unhealthy purchases). Surprisingly, this finding continued to hold for the choice bundle, that is, even when unhealthy snacks were concurrently on promotion. However, we did not observe a long-term stickiness effect, meaning that customers returned to their original (unhealthy) purchase patterns once the healthy or choice bundle was discontinued. Finally, we show that offering an add-on choice bundle is also beneficial for retailers, who can earn higher revenue and profit.Funding: This research was supported by the James McGill Scholar Award Fund, the Scale AI Chair Program, IIVADO (Institut de valorisation des données) Fundamental Research Project Grant, and two Discovery Grants from the Natural Sciences and Engineering Research Council of Canada.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0336 .
{"title":"Incentivizing Healthy Food Choices Using Add-On Bundling: A Field Experiment","authors":"Nymisha Bandi, Maxime C. Cohen, Saibal Ray","doi":"10.1287/msom.2023.0336","DOIUrl":"https://doi.org/10.1287/msom.2023.0336","url":null,"abstract":"Problem definition: How can retailers incentivize customers to make healthier food choices? Price, convenience, and taste are known to be among the main drivers behind such choices. Unfortunately, healthier food options are often expensive and not adequately promoted. However, we are observing recent efforts to nudge customers toward healthier food. Methodology/results: In this paper, we conducted a field experiment with a global convenience store chain to better understand how different add-on bundle promotions influence healthy food choices. We considered three types of add-on bundles sequentially: (i) an unhealthy bundle (when customers purchased a coffee, they could add a pastry for $1), (ii) a healthy bundle (offering a healthy snack, such as fruit, vegetable, or protein, as a coffee add-on for $1), and (iii) a choice bundle (the option of either a pastry or a healthy snack as an add-on to coffee for $1). In addition to our field experiment, we conducted an online laboratory study to strengthen the validity of our results. Managerial implications: We found that offering healthy snacks as part of an add-on bundle significantly increased healthy purchases (and decreased unhealthy purchases). Surprisingly, this finding continued to hold for the choice bundle, that is, even when unhealthy snacks were concurrently on promotion. However, we did not observe a long-term stickiness effect, meaning that customers returned to their original (unhealthy) purchase patterns once the healthy or choice bundle was discontinued. Finally, we show that offering an add-on choice bundle is also beneficial for retailers, who can earn higher revenue and profit.Funding: This research was supported by the James McGill Scholar Award Fund, the Scale AI Chair Program, IIVADO (Institut de valorisation des données) Fundamental Research Project Grant, and two Discovery Grants from the Natural Sciences and Engineering Research Council of Canada.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0336 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142210138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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