Problem definition: Motivated by ride-hailing platforms such as Uber, Lyft and Didi, we study the problem of matching riders with self-interested drivers over a spatial network. We focus on the performance impact of two operational platform controls—demand-side admission control and supply-side repositioning control—considering the interplay with two practically important challenges: (i) spatial demand imbalances prevail for extended periods of time; and (ii) self-interested drivers strategically decide whether to join the network, and, if so, whether to reposition when not serving riders. Methodology/results: We develop and analyze the steady-state behavior of a novel game-theoretic fluid model of a two-location, four-route loss network. First, we fully characterize and compare the steady-state system equilibria under three control regimes, from minimal control to centralized control. Second, we provide insights on how and why platform control impacts equilibrium performance, notably with new findings on the role of admission control: the platform may find it optimal to strategically reject demand at the low-demand location even if drivers are in excess supply, to induce repositioning to the high-demand location. We provide necessary and sufficient conditions for this policy. Third, we derive upper bounds on the platform’s and drivers’ benefits caused by increased platform control; these are more significant under moderate capacity and significant cross-location demand imbalance. Managerial implications: Our results contribute important guidelines on the optimal operations of ride-hailing networks. Our model can also inform the design of driver compensation structures that support more centralized network control. Supplemental Material: The e-companion and Supplemental Material are available at https://doi.org/10.1287/msom.2023.1221 .
{"title":"Ride-Hailing Networks with Strategic Drivers: The Impact of Platform Control Capabilities on Performance","authors":"Philipp Afèche, Zhe Liu, Costis Maglaras","doi":"10.1287/msom.2023.1221","DOIUrl":"https://doi.org/10.1287/msom.2023.1221","url":null,"abstract":"Problem definition: Motivated by ride-hailing platforms such as Uber, Lyft and Didi, we study the problem of matching riders with self-interested drivers over a spatial network. We focus on the performance impact of two operational platform controls—demand-side admission control and supply-side repositioning control—considering the interplay with two practically important challenges: (i) spatial demand imbalances prevail for extended periods of time; and (ii) self-interested drivers strategically decide whether to join the network, and, if so, whether to reposition when not serving riders. Methodology/results: We develop and analyze the steady-state behavior of a novel game-theoretic fluid model of a two-location, four-route loss network. First, we fully characterize and compare the steady-state system equilibria under three control regimes, from minimal control to centralized control. Second, we provide insights on how and why platform control impacts equilibrium performance, notably with new findings on the role of admission control: the platform may find it optimal to strategically reject demand at the low-demand location even if drivers are in excess supply, to induce repositioning to the high-demand location. We provide necessary and sufficient conditions for this policy. Third, we derive upper bounds on the platform’s and drivers’ benefits caused by increased platform control; these are more significant under moderate capacity and significant cross-location demand imbalance. Managerial implications: Our results contribute important guidelines on the optimal operations of ride-hailing networks. Our model can also inform the design of driver compensation structures that support more centralized network control. Supplemental Material: The e-companion and Supplemental Material are available at https://doi.org/10.1287/msom.2023.1221 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135686081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Dual sourcing and contingent sourcing are important risk-mitigation strategies to manage supply chain risks, including transportation-related losses of inbound orders. Contingent sourcing as a means of managing transportation risk is made possible by shipment information realized at in-transit inspection points or through shipment monitoring technologies. We examine the impact of contingent sourcing and shipment information in a setting where a buyer can source from two competing suppliers. One supplier (unreliable) has a long transportation lead time and is prone to in-transit yield loss; the other supplier (reliable) has a short, but nonzero, lead time with no yield loss. Methodology/results: We analyze a multistage game-theoretical model in which the two suppliers compete on wholesale prices and then, the buyer determines initial order quantities. Later, the buyer can place an emergency order with the reliable supplier based on shipment information, which reveals (possibly imperfectly) the status of the in-transit order from the unreliable supplier. We show that the buyer will adopt one of four possible sourcing strategies: (1) initially source only from the unreliable supplier but resort to the reliable supplier contingent on the updated shipment information, (2) diversify its initial order across the two suppliers but resort to the reliable supplier if needed, (3) diversify its initial order and not engage in contingent sourcing, or (4) sole source from the reliable supplier. Interestingly, contingent sourcing may or may not benefit the buyer because it may soften the competition between suppliers. Moreover, the buyer’s profit may not be monotonic in the accuracy of shipment information. Managerial implications: The buyer must design its supply base so that the unreliable supplier is particularly cost efficient if the buyer is to benefit from the possibility of contingent sourcing. The buyer may not always benefit from operational improvements that enhance shipment information accuracy because they may soften supplier competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0540 .
{"title":"Forewarned Is Forearmed? Contingent Sourcing, Shipment Information, and Supplier Competition","authors":"Tao Lu, Brian Tomlin","doi":"10.1287/msom.2021.0540","DOIUrl":"https://doi.org/10.1287/msom.2021.0540","url":null,"abstract":"Problem definition: Dual sourcing and contingent sourcing are important risk-mitigation strategies to manage supply chain risks, including transportation-related losses of inbound orders. Contingent sourcing as a means of managing transportation risk is made possible by shipment information realized at in-transit inspection points or through shipment monitoring technologies. We examine the impact of contingent sourcing and shipment information in a setting where a buyer can source from two competing suppliers. One supplier (unreliable) has a long transportation lead time and is prone to in-transit yield loss; the other supplier (reliable) has a short, but nonzero, lead time with no yield loss. Methodology/results: We analyze a multistage game-theoretical model in which the two suppliers compete on wholesale prices and then, the buyer determines initial order quantities. Later, the buyer can place an emergency order with the reliable supplier based on shipment information, which reveals (possibly imperfectly) the status of the in-transit order from the unreliable supplier. We show that the buyer will adopt one of four possible sourcing strategies: (1) initially source only from the unreliable supplier but resort to the reliable supplier contingent on the updated shipment information, (2) diversify its initial order across the two suppliers but resort to the reliable supplier if needed, (3) diversify its initial order and not engage in contingent sourcing, or (4) sole source from the reliable supplier. Interestingly, contingent sourcing may or may not benefit the buyer because it may soften the competition between suppliers. Moreover, the buyer’s profit may not be monotonic in the accuracy of shipment information. Managerial implications: The buyer must design its supply base so that the unreliable supplier is particularly cost efficient if the buyer is to benefit from the possibility of contingent sourcing. The buyer may not always benefit from operational improvements that enhance shipment information accuracy because they may soften supplier competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0540 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136117103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: The effect of the minimum wage is an important yet controversial topic that has received attention for decades. Our study is the first to take an operational lens and empirically study the impact of the minimum wage on firms’ scheduling practices. Methodology/results: Using a highly granular data set from a chain of fashion retail stores, we estimate that a $1 increase in the minimum wage, although having a negligible impact on the total labor hours used by the stores, leads to a 27.7% increase in the number of workers scheduled per week, but a 19.4% reduction in weekly hours per worker. For an average store in California, these changes translate into four extra workers and five fewer hours per worker per week. Such scheduling adjustment not only reduces the total wage compensation per worker but also reduces workers’ eligibility for benefits. We also show that the minimum wage increase reduces the consistency of weekly and daily schedules for workers. For example, the absolute (relative) deviation in weekly hours worked by each worker increases by up to 32.9% (6.6%) and by up to 9.7% (2.1%) in daily hours, as the minimum wage increases by $1. Managerial implications: Our study empirically identifies and highlights a new operational mechanism through which increasing the minimum wage may negatively impact worker welfare. Our further analysis suggests that the combination of the reduced hours, lower eligibility for benefits, and less consistent schedules (that resulted from the minimum wage increase) may substantially hurt worker welfare, even when the overall employment at the stores stay unchanged. By better understanding the intrinsic tradeoff of firms’ scheduling decisions, policy makers can better design minimum wage policies that will truly benefit workers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1212 .
{"title":"Evidence of the Unintended Labor Scheduling Implications of the Minimum Wage","authors":"Qiuping Yu, Shawn Mankad, Masha Shunko","doi":"10.1287/msom.2023.1212","DOIUrl":"https://doi.org/10.1287/msom.2023.1212","url":null,"abstract":"Problem definition: The effect of the minimum wage is an important yet controversial topic that has received attention for decades. Our study is the first to take an operational lens and empirically study the impact of the minimum wage on firms’ scheduling practices. Methodology/results: Using a highly granular data set from a chain of fashion retail stores, we estimate that a $1 increase in the minimum wage, although having a negligible impact on the total labor hours used by the stores, leads to a 27.7% increase in the number of workers scheduled per week, but a 19.4% reduction in weekly hours per worker. For an average store in California, these changes translate into four extra workers and five fewer hours per worker per week. Such scheduling adjustment not only reduces the total wage compensation per worker but also reduces workers’ eligibility for benefits. We also show that the minimum wage increase reduces the consistency of weekly and daily schedules for workers. For example, the absolute (relative) deviation in weekly hours worked by each worker increases by up to 32.9% (6.6%) and by up to 9.7% (2.1%) in daily hours, as the minimum wage increases by $1. Managerial implications: Our study empirically identifies and highlights a new operational mechanism through which increasing the minimum wage may negatively impact worker welfare. Our further analysis suggests that the combination of the reduced hours, lower eligibility for benefits, and less consistent schedules (that resulted from the minimum wage increase) may substantially hurt worker welfare, even when the overall employment at the stores stay unchanged. By better understanding the intrinsic tradeoff of firms’ scheduling decisions, policy makers can better design minimum wage policies that will truly benefit workers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1212 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Telesilla O. Kotsi, Arian Aflaki, Goker Aydin, Alfonso J. Pedraza-Martinez
Problem definition: U.S. nonprofits declare three types of expenses in their IRS 990 forms: program spending to meet beneficiaries’ needs; fundraising spending to raise donations; and administration spending to build and maintain capacity. Charity watchdogs, however, expect nonprofits to prioritize program spending over other categories. We study when such expectations may lead to the “starvation cycle” or underspending on administration and fundraising. Methodology/results: We characterize optimal budget allocations to program, fundraising, and administration spending categories using a two-period model, which also includes the nonprofit’s capacity, return on program spending (the net value of program spending to beneficiaries), and beneficiaries’ uncertain future needs. We find that the nonprofit’s capacity plays a significant role in the optimal allocation. The nonprofit should (a) at high capacity, spend only the necessary amount on administration to maintain its current capacity; (b) at moderate capacity, maintain its current capacity while limiting program spending in favor of fundraising; and (c) at low capacity, increase administration spending to expand its future capacity. When we compare the optimal allocations prescribed by our model to the actual spending levels reported by a foodbank network, we find that the foodbank underspends on administration and fundraising, suggesting the forces that lead to the starvation cycle may be in play. Another possibility is that the nonprofit’s own estimate of its return on program spending is higher than our estimate—At higher estimates of return on program, the gap between our prescribed solutions versus actual spending levels decreases. Managerial implications: Our paper introduces an important discussion on nonprofits’ starvation cycle and finds conditions that justify prioritizing administration and fundraising expenses. It also highlights that watchdogs should consider nonprofits’ return on program spending in addition to their capacity and future needs when evaluating them. Funding: T. O. Kotsi thanks the Onassis Foundation (Greece) for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0660 .
问题定义:美国非营利组织在其IRS 990表格中申报三种类型的支出:满足受益人需求的项目支出;筹集捐款的支出;以及用于建设和维持能力的行政支出。然而，慈善监督机构希望非营利组织优先考虑项目支出，而不是其他类别。我们研究了这种期望何时会导致“饥饿周期”或在管理和筹款方面的支出不足。方法/结果:我们使用一个两期模型来描述项目、筹款和管理支出类别的最佳预算分配，该模型还包括非营利组织的能力、项目支出的回报(项目支出对受益人的净值)和受益人不确定的未来需求。我们发现，非营利组织的能力在最优配置中起着重要作用。非营利组织应该(a)在高容量时，只花费必要的管理费用来维持其当前的能力;(b)在中等能力下，维持其目前的能力，同时限制有利于筹款的项目支出;(三)在产能不足的情况下，加大行政管理支出，扩大未来产能。当我们将我们的模型规定的最佳分配与食物银行网络报告的实际支出水平进行比较时，我们发现食物银行在管理和筹款方面的支出不足，这表明导致饥饿周期的力量可能在发挥作用。另一种可能性是，非营利组织自己对项目支出回报的估计高于我们的估计——项目回报的估计越高，我们规定的解决方案与实际支出水平之间的差距就越小。管理意义:我们的论文对非营利组织的饥饿周期进行了重要的讨论，并找到了证明优先管理和筹款费用合理的条件。它还强调，监管机构在评估非营利组织时，除了考虑其能力和未来需求外，还应考虑其项目支出的回报。资金:T. O. Kotsi感谢奥纳西斯基金会(希腊)的资金支持。补充材料:在线附录可在https://doi.org/10.1287/msom.2020.0660上获得。
{"title":"Allocation of Nonprofit Funds Among Program, Fundraising, and Administration","authors":"Telesilla O. Kotsi, Arian Aflaki, Goker Aydin, Alfonso J. Pedraza-Martinez","doi":"10.1287/msom.2020.0660","DOIUrl":"https://doi.org/10.1287/msom.2020.0660","url":null,"abstract":"Problem definition: U.S. nonprofits declare three types of expenses in their IRS 990 forms: program spending to meet beneficiaries’ needs; fundraising spending to raise donations; and administration spending to build and maintain capacity. Charity watchdogs, however, expect nonprofits to prioritize program spending over other categories. We study when such expectations may lead to the “starvation cycle” or underspending on administration and fundraising. Methodology/results: We characterize optimal budget allocations to program, fundraising, and administration spending categories using a two-period model, which also includes the nonprofit’s capacity, return on program spending (the net value of program spending to beneficiaries), and beneficiaries’ uncertain future needs. We find that the nonprofit’s capacity plays a significant role in the optimal allocation. The nonprofit should (a) at high capacity, spend only the necessary amount on administration to maintain its current capacity; (b) at moderate capacity, maintain its current capacity while limiting program spending in favor of fundraising; and (c) at low capacity, increase administration spending to expand its future capacity. When we compare the optimal allocations prescribed by our model to the actual spending levels reported by a foodbank network, we find that the foodbank underspends on administration and fundraising, suggesting the forces that lead to the starvation cycle may be in play. Another possibility is that the nonprofit’s own estimate of its return on program spending is higher than our estimate—At higher estimates of return on program, the gap between our prescribed solutions versus actual spending levels decreases. Managerial implications: Our paper introduces an important discussion on nonprofits’ starvation cycle and finds conditions that justify prioritizing administration and fundraising expenses. It also highlights that watchdogs should consider nonprofits’ return on program spending in addition to their capacity and future needs when evaluating them. Funding: T. O. Kotsi thanks the Onassis Foundation (Greece) for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0660 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136138681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Motivated by a platelet inventory management problem, we study a fixed lifetime perishable inventory management problem under a general demand process. Determining an optimal ordering policy for perishable inventory systems is particularly challenging because of the well-known “curse of dimensionality.” Approximation policies with worst-case performance bounds have been developed in the literature for perishable inventory systems. However, using real data, we observe that the existing policies tend to underorder when the unit shortage penalty is high, which is an important concern for critical perishable products, such as lifesaving blood products. We seek to address this problem in this paper. Methodology/results: We present a new approximation policy for perishable inventory systems, which we call a truncated balancing (TB) policy. In particular, we first define a new balancing ordering quantity and prove a novel lower bound on the optimal ordering quantity. We then define our TB policy such that the maximum between the balancing ordering quantity and the lower bound is ordered at each period. We prove that when first in, first out is an optimal issuing policy, (1) our proposed TB policy admits a worst-case performance bound of two, and (2) it is asymptotically optimal when the unit shortage penalty goes to infinity. Finally, we present a calibrated numerical study based on real data from our partner hospital and show that our proposed policy performs significantly better than the existing policies in practical scenarios with reasonably high shortage penalties. Managerial implications: Our analysis offers managerial insights for perishable inventory management, especially for systems with an imbalance in underage and overage cost parameters. When the unit shortage penalty is high, simply balancing the underage and overage costs can lead to underordering, whereas our proposed policy effectively addresses this drawback. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0644 .
{"title":"Truncated Balancing Policy for Perishable Inventory Management: Combating High Shortage Penalties","authors":"Can Zhang, Turgay Ayer, Chelsea C. White","doi":"10.1287/msom.2022.0644","DOIUrl":"https://doi.org/10.1287/msom.2022.0644","url":null,"abstract":"Problem definition: Motivated by a platelet inventory management problem, we study a fixed lifetime perishable inventory management problem under a general demand process. Determining an optimal ordering policy for perishable inventory systems is particularly challenging because of the well-known “curse of dimensionality.” Approximation policies with worst-case performance bounds have been developed in the literature for perishable inventory systems. However, using real data, we observe that the existing policies tend to underorder when the unit shortage penalty is high, which is an important concern for critical perishable products, such as lifesaving blood products. We seek to address this problem in this paper. Methodology/results: We present a new approximation policy for perishable inventory systems, which we call a truncated balancing (TB) policy. In particular, we first define a new balancing ordering quantity and prove a novel lower bound on the optimal ordering quantity. We then define our TB policy such that the maximum between the balancing ordering quantity and the lower bound is ordered at each period. We prove that when first in, first out is an optimal issuing policy, (1) our proposed TB policy admits a worst-case performance bound of two, and (2) it is asymptotically optimal when the unit shortage penalty goes to infinity. Finally, we present a calibrated numerical study based on real data from our partner hospital and show that our proposed policy performs significantly better than the existing policies in practical scenarios with reasonably high shortage penalties. Managerial implications: Our analysis offers managerial insights for perishable inventory management, especially for systems with an imbalance in underage and overage cost parameters. When the unit shortage penalty is high, simply balancing the underage and overage costs can lead to underordering, whereas our proposed policy effectively addresses this drawback. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0644 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136354630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Nonstationarity of the random environment is a critical yet challenging concern in decision-making under uncertainty. We illustrate the challenge from the nonstationarity and the solution framework using the portfolio selection problem, a typical decision problem in a time-varying financial market. Methodology/Results: This paper models the nonstationarity by a regime-switching ambiguity set. In particular, we incorporate the time-varying feature of the stochastic environment into the traditional Wasserstein ambiguity set to build our regime-switching ambiguity set. This modeling framework has strong financial interpretations because the financial market is exposed to different economic cycles. We show that the proposed distributional optimization framework is computationally tractable. We further provide a general data-driven portfolio allocation framework based on a covariate-based estimation and a hidden Markov model. We prove that the approach can include the underlying distribution with a high probability when the sample size is larger than a quantitative bound, from which we further analyze the quality of the obtained portfolio. Extensive empirical studies are conducted to show that the proposed portfolio consistently outperforms the equally weighted portfolio (the 1/N strategy) and other benchmarks across both time and data sets. In particular, we show that the proposed portfolio exhibited a prompt response to the regime change in the 2008 financial crisis by reallocating the wealth into appropriate asset classes on account of the time-varying feature of our proposed model. Managerial implications: The proposed framework helps decision-makers hedge against time-varying uncertainties. Specifically, applying the proposed framework to portfolio selection problems helps investors respond promptly to the regime change in financial markets and adjust their portfolio allocation accordingly. Funding: This work was supported by the Neptune Orient Lines Fellowship [NOL21RP04], Singapore Ministry of Education Academic Research Fund Tier 2 [MOE-T2EP20220-0013], and Singapore Ministry of Education Academic Research Fund Tier 1 [Grant RG17/21]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1229
{"title":"Data-Driven Distributionally Robust CVaR Portfolio Optimization Under A Regime-Switching Ambiguity Set","authors":"Chi Seng Pun, Tianyu Wang, Zhenzhen Yan","doi":"10.1287/msom.2023.1229","DOIUrl":"https://doi.org/10.1287/msom.2023.1229","url":null,"abstract":"Problem definition: Nonstationarity of the random environment is a critical yet challenging concern in decision-making under uncertainty. We illustrate the challenge from the nonstationarity and the solution framework using the portfolio selection problem, a typical decision problem in a time-varying financial market. Methodology/Results: This paper models the nonstationarity by a regime-switching ambiguity set. In particular, we incorporate the time-varying feature of the stochastic environment into the traditional Wasserstein ambiguity set to build our regime-switching ambiguity set. This modeling framework has strong financial interpretations because the financial market is exposed to different economic cycles. We show that the proposed distributional optimization framework is computationally tractable. We further provide a general data-driven portfolio allocation framework based on a covariate-based estimation and a hidden Markov model. We prove that the approach can include the underlying distribution with a high probability when the sample size is larger than a quantitative bound, from which we further analyze the quality of the obtained portfolio. Extensive empirical studies are conducted to show that the proposed portfolio consistently outperforms the equally weighted portfolio (the 1/N strategy) and other benchmarks across both time and data sets. In particular, we show that the proposed portfolio exhibited a prompt response to the regime change in the 2008 financial crisis by reallocating the wealth into appropriate asset classes on account of the time-varying feature of our proposed model. Managerial implications: The proposed framework helps decision-makers hedge against time-varying uncertainties. Specifically, applying the proposed framework to portfolio selection problems helps investors respond promptly to the regime change in financial markets and adjust their portfolio allocation accordingly. Funding: This work was supported by the Neptune Orient Lines Fellowship [NOL21RP04], Singapore Ministry of Education Academic Research Fund Tier 2 [MOE-T2EP20220-0013], and Singapore Ministry of Education Academic Research Fund Tier 1 [Grant RG17/21]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1229","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136116683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Education technology innovations, such as massive open online course (MOOC) platforms, could potentially enable a more inclusive learning environment by delivering education to traditionally disadvantaged learners, like women. However, inclusivity does not necessarily translate into equal treatment on the platform. We investigate whether female and male learners benefit equally from forum discussions—typically the only form of interaction available—in online learning platforms. Methodology/results: Utilizing a large-scale, interaction-level data set on 174 courses on Coursera, we uncover an economically sizable and statistically significant disparity between male and female learners in receiving responses to their posts in MOOC discussion forums. On average, female learners’ questions are 3.11 percentage points less likely to receive responses from teaching staff than male learners’ questions, which equals 15.2% of the female group average. We also find significant gender disparity in staff response quality and sentiments. We investigate possible mechanisms behind the gender disparity using new techniques, including textual analysis tools. We show that the disparity is not because of content differences in male and female learners’ posts, nor is it attributable to their linguistic styles or the reputation of the posters. Instead, our results are most consistent with a male-driven gender homophily mechanism; although female staff members are gender neutral in their interactions with learners, male staff members systemically prefer responding to posts from male learners. We additionally show that receiving staff response leads to significant improvement in course passing rates, particularly for female learners. Therefore, the unequal access to information through course forums unfavorably hinders female learners’ performance. Managerial implications: Our results provide operational and organizational suggestions to platforms and content providers, including degendering user identifiers, implementing a content-focused post recommendation system, incorporating a gender-neutral user reputation system, promoting the recruiting of female teaching staff, and providing staff training that highlights the importance of gender-neutral interactions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0426 .
{"title":"Mind the Gap: Gender Disparity in Online Learning Platform Interactions","authors":"Zhihan (Helen) Wang, Jun Li, Di (Andrew) Wu","doi":"10.1287/msom.2022.0426","DOIUrl":"https://doi.org/10.1287/msom.2022.0426","url":null,"abstract":"Problem definition: Education technology innovations, such as massive open online course (MOOC) platforms, could potentially enable a more inclusive learning environment by delivering education to traditionally disadvantaged learners, like women. However, inclusivity does not necessarily translate into equal treatment on the platform. We investigate whether female and male learners benefit equally from forum discussions—typically the only form of interaction available—in online learning platforms. Methodology/results: Utilizing a large-scale, interaction-level data set on 174 courses on Coursera, we uncover an economically sizable and statistically significant disparity between male and female learners in receiving responses to their posts in MOOC discussion forums. On average, female learners’ questions are 3.11 percentage points less likely to receive responses from teaching staff than male learners’ questions, which equals 15.2% of the female group average. We also find significant gender disparity in staff response quality and sentiments. We investigate possible mechanisms behind the gender disparity using new techniques, including textual analysis tools. We show that the disparity is not because of content differences in male and female learners’ posts, nor is it attributable to their linguistic styles or the reputation of the posters. Instead, our results are most consistent with a male-driven gender homophily mechanism; although female staff members are gender neutral in their interactions with learners, male staff members systemically prefer responding to posts from male learners. We additionally show that receiving staff response leads to significant improvement in course passing rates, particularly for female learners. Therefore, the unequal access to information through course forums unfavorably hinders female learners’ performance. Managerial implications: Our results provide operational and organizational suggestions to platforms and content providers, including degendering user identifiers, implementing a content-focused post recommendation system, incorporating a gender-neutral user reputation system, promoting the recruiting of female teaching staff, and providing staff training that highlights the importance of gender-neutral interactions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0426 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135891099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Do physicians exhibit predictable behavioral bias when making admission control decisions under patient diagnosis uncertainty with stochastic arrivals and lengths of stay? How can we structure feedback to help improve their decision making? Methodology/results: We use a behavioral model to theorize how a diagnosis anchoring and insufficient adjustment heuristic may lead to an over-rationing bias, and we hypothesize when this bias is greatest. We then propose that feedback for rejected patients—above and beyond feedback for admitted patients—is critical for mitigating this bias. This is because feedback for only admitted patients may suffer from a type of path dependency that prevents decision makers from receiving the most helpful disconfirming feedback. We provide evidence supporting these hypotheses using preregistered experiments in which medical students, Amazon Mechanical Turk workers, or Prolific workers manage admissions for simulated hospital units. Managerial implications: Our results (1) illuminate an important anchoring bias in admission control under diagnosis uncertainty, (2) identify rejected-patient feedback as a critical component for mitigating this bias, and (3) provide insight into the circumstances under which these phenomena are likely to be most significant. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [Grant 2022R1F1A1076045]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0194 .
{"title":"Admission Control Bias and Path-Dependent Feedback Under Diagnosis Uncertainty","authors":"Song-Hee Kim, Jordan Tong","doi":"10.1287/msom.2021.0194","DOIUrl":"https://doi.org/10.1287/msom.2021.0194","url":null,"abstract":"Problem definition: Do physicians exhibit predictable behavioral bias when making admission control decisions under patient diagnosis uncertainty with stochastic arrivals and lengths of stay? How can we structure feedback to help improve their decision making? Methodology/results: We use a behavioral model to theorize how a diagnosis anchoring and insufficient adjustment heuristic may lead to an over-rationing bias, and we hypothesize when this bias is greatest. We then propose that feedback for rejected patients—above and beyond feedback for admitted patients—is critical for mitigating this bias. This is because feedback for only admitted patients may suffer from a type of path dependency that prevents decision makers from receiving the most helpful disconfirming feedback. We provide evidence supporting these hypotheses using preregistered experiments in which medical students, Amazon Mechanical Turk workers, or Prolific workers manage admissions for simulated hospital units. Managerial implications: Our results (1) illuminate an important anchoring bias in admission control under diagnosis uncertainty, (2) identify rejected-patient feedback as a critical component for mitigating this bias, and (3) provide insight into the circumstances under which these phenomena are likely to be most significant. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [Grant 2022R1F1A1076045]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0194 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135494036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: Inventory commitment and monetary compensation are widely recognized as effective strategies in monopoly settings when customers are concerned about stockouts. To attract more customer traffic, a firm reveals its inventory availability information to customers before the sales season or offers monetary compensation to placate customers if the product is out of stock. This paper investigates these two strategies when retailers compete on both price and inventory availability. Methodology/results: We develop a game-theoretic framework to analyze the strategic interactions among the retailers and customers and draw the following insights. First, both inventory commitment and monetary compensation may lead to a prisoner’s dilemma. Although these strategies are preferred regardless of the competitor’s price and inventory decisions, the equilibrium profit of each retailer could be lower in the presence of inventory commitment or monetary compensation because they intensify the competition between the retailers. Second, we find that market competition may hurt social welfare compared with a centralized setting by reducing the product availability in equilibrium. The inventory commitment and monetary compensation strategies further intensify the competition between the retailers, therefore causing an even lower social welfare. Managerial implications: Our study shows that, although inventory commitment and monetary compensation improve retailers’ profit and social welfare under monopoly, these strategies should be used with caution under competition. Funding: F. Zhang is grateful for the financial support from the National Natural Science Foundation of China [Grants 71929201, 72131004]. R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grant 14502722] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Y. Yu is grateful for the financial support from the National Natural Science Foundation of China [Grant 71921001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0411 .
{"title":"Inventory Commitment and Monetary Compensation Under Competition","authors":"Junfei Lei, Fuqiang Zhang, Renyu Zhang, Yugang Yu","doi":"10.1287/msom.2021.0411","DOIUrl":"https://doi.org/10.1287/msom.2021.0411","url":null,"abstract":"Problem definition: Inventory commitment and monetary compensation are widely recognized as effective strategies in monopoly settings when customers are concerned about stockouts. To attract more customer traffic, a firm reveals its inventory availability information to customers before the sales season or offers monetary compensation to placate customers if the product is out of stock. This paper investigates these two strategies when retailers compete on both price and inventory availability. Methodology/results: We develop a game-theoretic framework to analyze the strategic interactions among the retailers and customers and draw the following insights. First, both inventory commitment and monetary compensation may lead to a prisoner’s dilemma. Although these strategies are preferred regardless of the competitor’s price and inventory decisions, the equilibrium profit of each retailer could be lower in the presence of inventory commitment or monetary compensation because they intensify the competition between the retailers. Second, we find that market competition may hurt social welfare compared with a centralized setting by reducing the product availability in equilibrium. The inventory commitment and monetary compensation strategies further intensify the competition between the retailers, therefore causing an even lower social welfare. Managerial implications: Our study shows that, although inventory commitment and monetary compensation improve retailers’ profit and social welfare under monopoly, these strategies should be used with caution under competition. Funding: F. Zhang is grateful for the financial support from the National Natural Science Foundation of China [Grants 71929201, 72131004]. R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grant 14502722] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Y. Yu is grateful for the financial support from the National Natural Science Foundation of China [Grant 71921001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0411 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136266870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ruomeng Cui, Zhikun Lu, Tianshu Sun, Joseph M. Golden
Abstract. Problem definition: Online retailers have to provide customers with an estimate of how fast an order can be delivered before they decide to make the purchase. Retailers can strategically adjust this delivery speed promise online without changing offline infrastructure, and doing so may fundamentally impact business outcomes. It can influence consumers’ purchasing decisions and postpurchase experiences, often in the opposite direction. On one hand, an aggressive (i.e., faster) delivery estimate could ensure that more customers meet their deadlines and thus, may increase their purchases ex ante. On the other hand, an aggressive estimate tends to overpromise, potentially leading to a longer than expected wait time, which can lower customer satisfaction and increase product returns ex post. In this research, we estimate the causal effect of retailers’ delivery speed promise on customer behaviors and business performance. Methodology/results: Collaborating with Collage.com , an online retailer that sells customized photo products across the United States, we exogenously varied the disclosed delivery speed estimates online while keeping the physical delivery speed unchanged. Using the difference-in-differences identification strategy, we find that a faster promise increases sales and profits, but it also increases product returns and reduces customer retention. In addition, we propose a data-driven model that uses the estimated parameters to optimize delivery promises to maximize customer lifetime value. Managerial implications: Our findings provide managerial insights and a data-driven policy that retailers can leverage to optimize and customize their delivery promises. Funding: T. Sun acknowledges research support from CKGSB Research Institute. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0174 .
{"title":"Sooner or Later? Promising Delivery Speed in Online Retail","authors":"Ruomeng Cui, Zhikun Lu, Tianshu Sun, Joseph M. Golden","doi":"10.1287/msom.2021.0174","DOIUrl":"https://doi.org/10.1287/msom.2021.0174","url":null,"abstract":"Abstract. Problem definition: Online retailers have to provide customers with an estimate of how fast an order can be delivered before they decide to make the purchase. Retailers can strategically adjust this delivery speed promise online without changing offline infrastructure, and doing so may fundamentally impact business outcomes. It can influence consumers’ purchasing decisions and postpurchase experiences, often in the opposite direction. On one hand, an aggressive (i.e., faster) delivery estimate could ensure that more customers meet their deadlines and thus, may increase their purchases ex ante. On the other hand, an aggressive estimate tends to overpromise, potentially leading to a longer than expected wait time, which can lower customer satisfaction and increase product returns ex post. In this research, we estimate the causal effect of retailers’ delivery speed promise on customer behaviors and business performance. Methodology/results: Collaborating with Collage.com , an online retailer that sells customized photo products across the United States, we exogenously varied the disclosed delivery speed estimates online while keeping the physical delivery speed unchanged. Using the difference-in-differences identification strategy, we find that a faster promise increases sales and profits, but it also increases product returns and reduces customer retention. In addition, we propose a data-driven model that uses the estimated parameters to optimize delivery promises to maximize customer lifetime value. Managerial implications: Our findings provide managerial insights and a data-driven policy that retailers can leverage to optimize and customize their delivery promises. Funding: T. Sun acknowledges research support from CKGSB Research Institute. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0174 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134951893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：