Problem definition: We examine the impact of logistics performance metrics such as delivery time and customer’s requested delivery speed on logistics service ratings and third-party sellers’ sales on an e-commerce platform. Academic/practical relevance: Although e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in e-commerce platforms has mostly analyzed customers’ response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we use the Heckman ordered regression model to explain the variation in customers’ rating of logistics performance and the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer’s likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup [Formula: see text] 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Furthermore, by quantifying the impact of delivery time performance on sales, our stu
{"title":"Logistics Performance, Ratings, and Its Impact on Customer Purchasing Behavior and Sales in E-Commerce Platforms","authors":"Vinayak Deshpande, Pradeep K. Pendem","doi":"10.1287/msom.2021.1045","DOIUrl":"https://doi.org/10.1287/msom.2021.1045","url":null,"abstract":"Problem definition: We examine the impact of logistics performance metrics such as delivery time and customer’s requested delivery speed on logistics service ratings and third-party sellers’ sales on an e-commerce platform. Academic/practical relevance: Although e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in e-commerce platforms has mostly analyzed customers’ response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we use the Heckman ordered regression model to explain the variation in customers’ rating of logistics performance and the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer’s likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup [Formula: see text] 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Furthermore, by quantifying the impact of delivery time performance on sales, our stu","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270519","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: Expansions in product assortment by online retailers often engender operational challenges. In undertaking such expansions, retailers exercise a strategic choice between expanding assortment width or depth. Our understanding of how this choice affects the order fulfillment process is limited. Thus, we examine the impact of these dimensions of assortment strategy on order delivery timeliness. Academic/practical relevance: Order delivery timeliness is a critical measure of operational success in online retail. We contribute to theory and practice by adopting a multidimensional perspective of retailer assortment strategy and studying the relative impact of assortment width and depth on order delivery timeliness. Methodology: Employing a data set comprising more than 200 million orders, we study the effects of assortment strategy on delivery timeliness using an instrumental variable approach. We then utilize a two-stage model to estimate the impact of delivery performance on sales. Further, we employ a matched difference-in-differences and a novel Bayesian structural time-series model to confirm this relationship. Results: We find that assortment width has a greater negative impact on order delivery timeliness compared with assortment depth. A one-standard-deviation increase in assortment width increases average delivery times by 0.55 days. Further, we find this effect to be positively moderated (i.e., worsened) by the average size of orders and to be negatively moderated (i.e., improved) by the logistic service provider’s (LSP) experience. Finally, a one-day increase in delivery times for 10% of the orders results in a 2.7% reduction in sales. Managerial implications: Our findings suggest that online retailers focused on ensuring timely deliveries should be wary of widening product assortments, especially when facing larger average order sizes. We also find that experienced logistic service providers can help mitigate the dilatory effects of assortment width expansions. However, the benefits of experienced LSPs are limited for retailers deepening their assortments. History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1156 .
{"title":"Go Wide or Go Deep? Assortment Strategy and Order Fulfillment in Online Retail","authors":"Sanjith Gopalakrishnan, Moksh Matta, Mona Imanpoor Yourdshahy, Vivek Choudhary","doi":"10.1287/msom.2022.1156","DOIUrl":"https://doi.org/10.1287/msom.2022.1156","url":null,"abstract":"Problem definition: Expansions in product assortment by online retailers often engender operational challenges. In undertaking such expansions, retailers exercise a strategic choice between expanding assortment width or depth. Our understanding of how this choice affects the order fulfillment process is limited. Thus, we examine the impact of these dimensions of assortment strategy on order delivery timeliness. Academic/practical relevance: Order delivery timeliness is a critical measure of operational success in online retail. We contribute to theory and practice by adopting a multidimensional perspective of retailer assortment strategy and studying the relative impact of assortment width and depth on order delivery timeliness. Methodology: Employing a data set comprising more than 200 million orders, we study the effects of assortment strategy on delivery timeliness using an instrumental variable approach. We then utilize a two-stage model to estimate the impact of delivery performance on sales. Further, we employ a matched difference-in-differences and a novel Bayesian structural time-series model to confirm this relationship. Results: We find that assortment width has a greater negative impact on order delivery timeliness compared with assortment depth. A one-standard-deviation increase in assortment width increases average delivery times by 0.55 days. Further, we find this effect to be positively moderated (i.e., worsened) by the average size of orders and to be negatively moderated (i.e., improved) by the logistic service provider’s (LSP) experience. Finally, a one-day increase in delivery times for 10% of the orders results in a 2.7% reduction in sales. Managerial implications: Our findings suggest that online retailers focused on ensuring timely deliveries should be wary of widening product assortments, especially when facing larger average order sizes. We also find that experienced logistic service providers can help mitigate the dilatory effects of assortment width expansions. However, the benefits of experienced LSPs are limited for retailers deepening their assortments. History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1156 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135702916","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 the benefits of operational transparency depend on when the work is done? Academic/practical relevance: This work connects the operations management literature on operational transparency with the psychology literature on the peak-end effect. Methodology: This study examines how customers respond to operational transparency with parcel delivery data from the Cainiao Network, the logistics arm of Alibaba. The sample comprises 4.68 million deliveries. Each delivery has between 4 and 10 track-package activities, which customers can check in real time, and a delivery service score, which customers leave after receiving the package. Instrumental-variable regressions quantify the causal effect of track-package-activity times on delivery scores. Results: The regressions suggest that customers punish early idleness less than late idleness, leaving higher delivery service scores when track-package activities cluster toward the end of the shipping horizon. For example, if a shipment takes 100 hours, then delaying the time of the average action from hour 20 to hour 80 increases the expected delivery score by approximately the same amount as expediting the arrival time from hour 100 to hour 73. Managerial implications: Memory limitations make customers especially sensitive to how service operations end. History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge.
{"title":"Operational Transparency: Showing When Work Gets Done","authors":"Robert L. Bray","doi":"10.1287/msom.2020.0899","DOIUrl":"https://doi.org/10.1287/msom.2020.0899","url":null,"abstract":"Problem definition: Do the benefits of operational transparency depend on when the work is done? Academic/practical relevance: This work connects the operations management literature on operational transparency with the psychology literature on the peak-end effect. Methodology: This study examines how customers respond to operational transparency with parcel delivery data from the Cainiao Network, the logistics arm of Alibaba. The sample comprises 4.68 million deliveries. Each delivery has between 4 and 10 track-package activities, which customers can check in real time, and a delivery service score, which customers leave after receiving the package. Instrumental-variable regressions quantify the causal effect of track-package-activity times on delivery scores. Results: The regressions suggest that customers punish early idleness less than late idleness, leaving higher delivery service scores when track-package activities cluster toward the end of the shipping horizon. For example, if a shipment takes 100 hours, then delaying the time of the average action from hour 20 to hour 80 increases the expected delivery score by approximately the same amount as expediting the arrival time from hour 100 to hour 73. Managerial implications: Memory limitations make customers especially sensitive to how service operations end. History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge.","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270522","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}
The journal is pleased to publish the abstracts of the six finalists of the 2022 Manufacturing and Service Operations Management Society’s student paper competition. The 2022 prize committee was chaired by Florin Ciocan (INSEAD), Ersin Korpeoglu (University College London), and Nikos Trichakis (Massachusetts Institute of Technology). The judges were Adam Elmachtoub, Adem Orsdemir, Agni Orfanoudaki, Alp Akcay, Alper Nakkas, Amrita Kundu, Amy Pan, Andrew Wu, Antoine DESIR, Anyan Qi, Arian Aflaki, Ashish Kabra, Auyon Siddiq, Bilal Gokpinar, Bob Batt, Bora Keskin, Can Zhang, Dan Iancu, Dan Iancu, Daniel Freund, Daniel Lin, Daniela Saban, David Drake, Dawson Kaaua, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Emre Nadar, Fabian Sting, Fanyin Zheng, Fei Gao, Georgina Hall, Gizem Korpeoglu, Gonzalo Romero, Guoming Lai, Hessam Bavafa, Hummy Song, Ioannis (Yannis) Bellos, Ioannis Stamatopoulos, Iris Wang, Itir Karaesmen, Jiankun Sun, Jiankun Sun, Jiaru Bai, Jiayi Joey Yu, Jing Wu, Joel Wooten, John Silberholz, Jonathan Helm, Jose Guajardo, Karen Zheng, Ken Moon, Kenan Arifoglu, Kimon Drakopoulos, Kostas Bimpikis, Lennart Baardman, Lina Song, Luyi Gui, Luyi Yang, Miao Bai, Mika Sumida, Ming Hu, Mumin Kurtulus, Nazli Sonmez, Negin Golrezaei, Nektarios Oraiopoulos, Nil Karacaoglu, Nitin Bakshi, Nitish Jain, Nur Sunar, Olga Perdikaki, Ovunc Yilmaz, Ozan Candogan, Panos Markou, Pengyi Shi, Philip Zhang, Philipp Cornelius, Qi (George) Chen, Qiuping Yu, Ruslan Momot, Ruth Beer, S. Alex Yang, Saed Alizamir, Safak Yucel, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Scott Rodilitz, Sebastien Martin, Sheng Liu, Shouqiang Wang, Simone Marinesi, Sina Khorasani, So Yeon CHUN, Somya Singhvi, Soo-Haeng Cho, Soroush Saghafian, Sriram Dasu, Stefanus Jasin, Stephen Leider, Tian Chan, Tim Kraft, Tom Tan, Vasiliki Kostami, Velibor Misic, Vishal Agrawal, Xiaojia Guo, Xiaoshan Peng, Xiaoshuai Fan, Xiaoyang Long, Yangfang (Helen) Zhou, Yasemin Limon, Yehua Wei, Ying-Ju Chen, Yonatan Gur, Yuqian Xu, Zhaohui (Zoey) Jiang, Zhaowei She, and Zumbul Atan.
该杂志很高兴地发表了2022年制造和服务运营管理协会学生论文竞赛的六位决赛选手的摘要。2022年诺贝尔奖委员会由Florin Ciocan(欧洲工商管理学院)、Ersin Korpeoglu(伦敦大学学院)和Nikos Trichakis(麻省理工学院)担任主席。评委是Adam Elmachtoub, Adem Orsdemir, Agni Orfanoudaki, Alp akday, Alper Nakkas, Amrita Kundu, Amy Pan, Andrew Wu, Antoine DESIR, Anyan Qi, Arian Aflaki, Ashish Kabra, Auyon Siddiq, Bilal Gokpinar, Bob Batt, Bora Keskin, Zhang, Dan Iancu, Dan Iancu, Daniel Freund, Daniel Lin, Daniela Saban, David Drake, Dawson Kaaua, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Emre Nadar, Fabian Sting, Fanyin Zheng, Fei Gao, Georgina Hall, Gizem Korpeoglu, Gonzalo Romero, Alper Nakkas, Amrita Kundu, Amy Pan, Andrew WuHessam Bavafa, Hummy Song, Ioannis (Yannis) Bellos, Ioannis Stamatopoulos, Ioannis (Yannis) Bellos, Ioannis Stamatopoulos, Itir Karaesmen，孙建坤，孙建坤，白家茹，俞家怡，吴静，Joel Wooten, John Silberholz, Jonathan Helm, Jose Guajardo, Karen Zheng, Ken Moon, Kenan Arifoglu, Kimon Drakopoulos, Kostas Bimpikis, Lennart Baardman, Lina Song, Luyi Gui, Luyi Yang, Miao Bai, Mika Sumida, Ming Hu, Mumin Kurtulus, Nazli Sonmez, Nektarios Oraiopoulos, Nil Karacaoglu, Nitin Bakshi, Nitish Jain，Nur Sunar, Olga Perdikaki, Ovunc Yilmaz, Ozan Candogan, Panos Markou, Shi Pengyi, Philip Zhang, Philipp Cornelius, Qi (George) Chen, Qi (George) Yang, Saed Alizamir, Safak Yucel, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Scott Rodilitz, Sebastien Martin, Liu Sheng, Wang shouhaeng, Simone Marinesi, Sina Khorasani, So Yeon CHUN, Somya Singhvi, Soo-Haeng Cho, Soroush Saghafian, Sriram Dasu, Stefanus Jasin, Stephen Leider, Tian Chan, Tim Kraft，Tom Tan, Vasiliki Kostami, Velibor Misic, Vishal Agrawal，郭晓佳，彭晓山，范晓帅，龙晓阳，周阳芳，Yasemin Limon，韦叶华，陈英菊，Gur Yonatan，徐玉倩，蒋朝晖，舍朝伟，Zumbul Atan。
{"title":"MSOM Society Student Paper Competition: Abstracts of 2022 Winners","authors":"","doi":"10.1287/msom.2023.1220","DOIUrl":"https://doi.org/10.1287/msom.2023.1220","url":null,"abstract":"The journal is pleased to publish the abstracts of the six finalists of the 2022 Manufacturing and Service Operations Management Society’s student paper competition. The 2022 prize committee was chaired by Florin Ciocan (INSEAD), Ersin Korpeoglu (University College London), and Nikos Trichakis (Massachusetts Institute of Technology). The judges were Adam Elmachtoub, Adem Orsdemir, Agni Orfanoudaki, Alp Akcay, Alper Nakkas, Amrita Kundu, Amy Pan, Andrew Wu, Antoine DESIR, Anyan Qi, Arian Aflaki, Ashish Kabra, Auyon Siddiq, Bilal Gokpinar, Bob Batt, Bora Keskin, Can Zhang, Dan Iancu, Dan Iancu, Daniel Freund, Daniel Lin, Daniela Saban, David Drake, Dawson Kaaua, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Emre Nadar, Fabian Sting, Fanyin Zheng, Fei Gao, Georgina Hall, Gizem Korpeoglu, Gonzalo Romero, Guoming Lai, Hessam Bavafa, Hummy Song, Ioannis (Yannis) Bellos, Ioannis Stamatopoulos, Iris Wang, Itir Karaesmen, Jiankun Sun, Jiankun Sun, Jiaru Bai, Jiayi Joey Yu, Jing Wu, Joel Wooten, John Silberholz, Jonathan Helm, Jose Guajardo, Karen Zheng, Ken Moon, Kenan Arifoglu, Kimon Drakopoulos, Kostas Bimpikis, Lennart Baardman, Lina Song, Luyi Gui, Luyi Yang, Miao Bai, Mika Sumida, Ming Hu, Mumin Kurtulus, Nazli Sonmez, Negin Golrezaei, Nektarios Oraiopoulos, Nil Karacaoglu, Nitin Bakshi, Nitish Jain, Nur Sunar, Olga Perdikaki, Ovunc Yilmaz, Ozan Candogan, Panos Markou, Pengyi Shi, Philip Zhang, Philipp Cornelius, Qi (George) Chen, Qiuping Yu, Ruslan Momot, Ruth Beer, S. Alex Yang, Saed Alizamir, Safak Yucel, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Scott Rodilitz, Sebastien Martin, Sheng Liu, Shouqiang Wang, Simone Marinesi, Sina Khorasani, So Yeon CHUN, Somya Singhvi, Soo-Haeng Cho, Soroush Saghafian, Sriram Dasu, Stefanus Jasin, Stephen Leider, Tian Chan, Tim Kraft, Tom Tan, Vasiliki Kostami, Velibor Misic, Vishal Agrawal, Xiaojia Guo, Xiaoshan Peng, Xiaoshuai Fan, Xiaoyang Long, Yangfang (Helen) Zhou, Yasemin Limon, Yehua Wei, Ying-Ju Chen, Yonatan Gur, Yuqian Xu, Zhaohui (Zoey) Jiang, Zhaowei She, and Zumbul Atan.","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135050716","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: Participant retention is one of the significant issues faced by clinical studies. This paper analyzes the economic impact of combining two mechanisms (monetary payments to participants and effort exerted during a clinical study) observed in practice to improve retention. Methodology/results: Given an incentive scheme, under full information and information asymmetry regarding participants’ characteristics, we model the problem of identifying optimal payment and effort to improve retention for a clinical study using a nonlinear integer program. We propose polynomial-time algorithms to solve the problem under full information for a participant-specific linear payment scheme and two commonly observed incentive schemes: Fixed Payment (FP) and Logistics Reimbursement (RE). We also provide exact methods to solve the problem under information asymmetry for the FP and RE schemes. We conduct a comprehensive computational study to gain insights into the relative performance of these schemes. Under full information, the participant-specific scheme can reduce the retention cost by about 46%, on average, compared with that under the RE and FP schemes. Information asymmetry causes the RE scheme to be more favorable than the FP scheme in a wider variety of clinical studies. Further, the value of acquiring participants’ characteristics information is significant under the FP scheme compared with that under the RE scheme. Managerial implications: The determination of monetary payments is ad hoc in practice. Further, an economic analysis of the two mechanisms for improving retention in clinical studies is absent. Given the participants and the clinical study characteristics under full information and information asymmetry, our analysis enables a decision maker to identify an optimum incentive scheme, monetary payment, and effort level for improving retention. Further, our analysis allows a clinical study decision maker to assess budget requirements to improve retention and adapt the incentive payments to Institutional Review Board guidelines, if any. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1184 .
{"title":"An Analysis of Incentive Schemes for Participant Retention in Clinical Studies","authors":"Xueze Song, Mili Mehrotra, Tharanga Rajapakshe","doi":"10.1287/msom.2022.1184","DOIUrl":"https://doi.org/10.1287/msom.2022.1184","url":null,"abstract":"Problem definition: Participant retention is one of the significant issues faced by clinical studies. This paper analyzes the economic impact of combining two mechanisms (monetary payments to participants and effort exerted during a clinical study) observed in practice to improve retention. Methodology/results: Given an incentive scheme, under full information and information asymmetry regarding participants’ characteristics, we model the problem of identifying optimal payment and effort to improve retention for a clinical study using a nonlinear integer program. We propose polynomial-time algorithms to solve the problem under full information for a participant-specific linear payment scheme and two commonly observed incentive schemes: Fixed Payment (FP) and Logistics Reimbursement (RE). We also provide exact methods to solve the problem under information asymmetry for the FP and RE schemes. We conduct a comprehensive computational study to gain insights into the relative performance of these schemes. Under full information, the participant-specific scheme can reduce the retention cost by about 46%, on average, compared with that under the RE and FP schemes. Information asymmetry causes the RE scheme to be more favorable than the FP scheme in a wider variety of clinical studies. Further, the value of acquiring participants’ characteristics information is significant under the FP scheme compared with that under the RE scheme. Managerial implications: The determination of monetary payments is ad hoc in practice. Further, an economic analysis of the two mechanisms for improving retention in clinical studies is absent. Given the participants and the clinical study characteristics under full information and information asymmetry, our analysis enables a decision maker to identify an optimum incentive scheme, monetary payment, and effort level for improving retention. Further, our analysis allows a clinical study decision maker to assess budget requirements to improve retention and adapt the incentive payments to Institutional Review Board guidelines, if any. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1184 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135563152","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: Retailers have become increasingly interested in personalizing their products and services such as promotions. For this, we need new personalized demand models. Unfortunately, social data are not available to many retailers because of cost and privacy issues. Thus, we focus on the problem of detecting customer relationships from transactional data and using them to target promotions to the right customers. Academic/practical relevance: From an academic point of view, this paper solves the novel problem of jointly detecting customer trends and using them for optimal promotion targeting. Notably, we estimate the causal customer-to-customer trend effect solely from transactional data and target promotions for multiple items and time periods. In practice, we provide a new tool for Oracle Retail clients that personalizes promotions. Methodology: We develop a novel customer trend demand model distinguishing between a base purchase probability, capturing factors such as price and seasonality, and a customer trend probability, capturing customer-to-customer trend effects. The estimation procedure is based on regularized bounded variables least squares and instrumental variable methods. The resulting customer trend estimates feed into the dynamic promotion targeting optimization problem, formulated as a nonlinear mixed-integer optimization model. Though it is nondeterministic polynomial-time hard, we propose a greedy algorithm. Results: We prove that our customer-to-customer trend estimates are statistically consistent and that the greedy optimization algorithm is provably good. Having access to Oracle Retail fashion client data, we show that our demand model reduces the weighted-mean absolute percentage error by 11% on average. Also, we provide evidence of the causality of our estimates. Finally, we demonstrate that the optimal policy increases profits by 3%–11%. Managerial implications: The demand model with customer trend and the optimization model for targeted promotions form a decision-support tool for promotion planning. Next to general planning, it also helps to find important customers and target them to generate additional sales. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the U.S. National Science Foundation [Grant CMMI-156334]. Funding from the Oracle Corporation through an ERO grant is also gratefully acknowledged. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0893 .
{"title":"Detecting Customer Trends for Optimal Promotion Targeting","authors":"Lennart Baardman, Setareh Borjian Boroujeni, Tamar Cohen-Hillel, Kiran Panchamgam, Georgia Perakis","doi":"10.1287/msom.2020.0893","DOIUrl":"https://doi.org/10.1287/msom.2020.0893","url":null,"abstract":"Problem definition: Retailers have become increasingly interested in personalizing their products and services such as promotions. For this, we need new personalized demand models. Unfortunately, social data are not available to many retailers because of cost and privacy issues. Thus, we focus on the problem of detecting customer relationships from transactional data and using them to target promotions to the right customers. Academic/practical relevance: From an academic point of view, this paper solves the novel problem of jointly detecting customer trends and using them for optimal promotion targeting. Notably, we estimate the causal customer-to-customer trend effect solely from transactional data and target promotions for multiple items and time periods. In practice, we provide a new tool for Oracle Retail clients that personalizes promotions. Methodology: We develop a novel customer trend demand model distinguishing between a base purchase probability, capturing factors such as price and seasonality, and a customer trend probability, capturing customer-to-customer trend effects. The estimation procedure is based on regularized bounded variables least squares and instrumental variable methods. The resulting customer trend estimates feed into the dynamic promotion targeting optimization problem, formulated as a nonlinear mixed-integer optimization model. Though it is nondeterministic polynomial-time hard, we propose a greedy algorithm. Results: We prove that our customer-to-customer trend estimates are statistically consistent and that the greedy optimization algorithm is provably good. Having access to Oracle Retail fashion client data, we show that our demand model reduces the weighted-mean absolute percentage error by 11% on average. Also, we provide evidence of the causality of our estimates. Finally, we demonstrate that the optimal policy increases profits by 3%–11%. Managerial implications: The demand model with customer trend and the optimization model for targeted promotions form a decision-support tool for promotion planning. Next to general planning, it also helps to find important customers and target them to generate additional sales. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the U.S. National Science Foundation [Grant CMMI-156334]. Funding from the Oracle Corporation through an ERO grant is also gratefully acknowledged. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0893 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135544216","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: Online retailers disclose product availability to influence customer decisions as a form of pressure selling designed to compel customers to rush into a purchase. Can the revelation of this information drive sales and profitability? We study the effect of disclosing product availability on market outcomes—product sales and returns—and identify the contexts where this effect is most powerful. Academic/practical relevance: Increasing sell-out is key for online retailers to remain profitable in the presence of thin margins and complex operations. We provide insights into how their information-disclosure policy—something they can tailor at virtually no cost—can contribute to this important objective. Methodology: We collaborate with an online retailer to procure a year of transaction data on 190,696 products that span 1,290 brands and 472,980 customers. To causally identify our results, we use a generalized difference-in-differences design with matching that exploits one policy of the firm: it discloses product availability only for the last five units. Results: The disclosure of low product availability increases hourly sales—they grow by 13.6%—but these products are more likely to be returned—product return rates increase by 17.0%. Because returns are costly, we also study net sales—product hourly sales minus hourly returns—which increase by 12.5% after the retailer reveals low availability. Managerial implications: The positive effects on sales and profitability amplify over wide assortments and when low-availability signals are abundantly visible and disclosed for deeply discounted products whose sales season is about to end. In addition, we propose a data-driven policy that exploits these results by using machine learning to prescribe the timing of disclosure of scarcity signals in order to boost sales without spiking returns. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition.
{"title":"Disclosing Product Availability in Online Retail","authors":"Eduard Calvo, Ruomeng Cui, Laura Wagner","doi":"10.1287/msom.2020.0882","DOIUrl":"https://doi.org/10.1287/msom.2020.0882","url":null,"abstract":"Problem definition: Online retailers disclose product availability to influence customer decisions as a form of pressure selling designed to compel customers to rush into a purchase. Can the revelation of this information drive sales and profitability? We study the effect of disclosing product availability on market outcomes—product sales and returns—and identify the contexts where this effect is most powerful. Academic/practical relevance: Increasing sell-out is key for online retailers to remain profitable in the presence of thin margins and complex operations. We provide insights into how their information-disclosure policy—something they can tailor at virtually no cost—can contribute to this important objective. Methodology: We collaborate with an online retailer to procure a year of transaction data on 190,696 products that span 1,290 brands and 472,980 customers. To causally identify our results, we use a generalized difference-in-differences design with matching that exploits one policy of the firm: it discloses product availability only for the last five units. Results: The disclosure of low product availability increases hourly sales—they grow by 13.6%—but these products are more likely to be returned—product return rates increase by 17.0%. Because returns are costly, we also study net sales—product hourly sales minus hourly returns—which increase by 12.5% after the retailer reveals low availability. Managerial implications: The positive effects on sales and profitability amplify over wide assortments and when low-availability signals are abundantly visible and disclosed for deeply discounted products whose sales season is about to end. In addition, we propose a data-driven policy that exploits these results by using machine learning to prescribe the timing of disclosure of scarcity signals in order to boost sales without spiking returns. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition.","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479574","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: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .
{"title":"Understanding the Value of Fulfillment Flexibility in an Online Retailing Environment","authors":"Levi DeValve, Yehua Wei, Di Wu, Rong Yuan","doi":"10.1287/msom.2021.0981","DOIUrl":"https://doi.org/10.1287/msom.2021.0981","url":null,"abstract":"Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479576","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}
Eryn Juan He, Sergei Savin, Joel Goh, Chung-Piaw Teo
Problem definition: Online platforms that provide on-demand services are often threatened by the phenomenon of leakage, where customer-provider pairs may decide to transact “off-platform” to avoid paying commissions to the platform. This paper investigates properties of services that make them vulnerable or resistant to leakage. Academic/practical relevance: In practice, much attention has been given to platform leakage, with platforms experimenting with multiple approaches to alleviate leakage and maintain their customer and provider bases. Yet, there is a current dearth of studies in the operations literature that systematically analyze the key factors behind platform leakage. Our work fills this gap and answers practical questions regarding the sustainability of platform. Methodology: We develop two game-theoretical models that capture service providers’ and customers’ decisions whether to conduct transactions on or off the platform. In the first (“perfect information”) model, we assume that customers are equipped with information to select their desired providers on the platform, whereas in the second (“imperfect information”) model, we assume customers are randomly matched with available providers by the platform. Results: For profit maximizing platforms, we show that leakage occurs if and only if the value of the counterparty risk from off-platform transactions exceeds a threshold. Across both models, platforms tend to be more immunized against leakage as provider pool sizes increase, customer valuations for service increase, their waiting costs decrease, or variability in service times are reduced. Finally, by comparing the degree of leakage between both settings, we find that neither model dominates the other across all parameter combinations. Managerial implications: Our results provide guidance to existing platform managers or entrepreneurs who are considering “platforming” their services. Namely, based on a few key features of the operating environment, managers can assess the severity of the threat of platform leakage for their specific business context. Our results also suggest how redesigning the waiting process, reducing service time variability, upskilling providers can reduce the threat of leakage. They also suggest the conditions under which revealing provider quality information to customers can help to curb leakage. Funding: J. Goh’s work was supported by a National University of Singapore Start-Up [Grant R-314-000-110-133] and a 2021 Humanities and Social Sciences Fellowship from the National University of Singapore. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1179 .
问题定义:提供按需服务的在线平台经常受到泄漏现象的威胁，客户-供应商对可能会决定进行“场外”交易，以避免向平台支付佣金。本文研究使服务易受泄漏或抵抗泄漏的属性。学术/实践相关性:在实践中，人们对平台泄漏给予了很大的关注，平台尝试了多种方法来减轻泄漏并维护其客户和提供商基础。然而，目前运营文献中缺乏系统分析平台泄漏背后关键因素的研究。我们的工作填补了这一空白，并回答了有关平台可持续性的实际问题。方法论:我们开发了两个博弈论模型，捕捉服务提供商和客户是否在平台上进行交易的决定。在第一个(“完全信息”)模型中，我们假设客户有信息来选择他们想要的平台供应商，而在第二个(“不完全信息”)模型中，我们假设客户是由平台随机匹配到可用的供应商的。结果:对于利润最大化平台，我们表明，当且仅当场外交易的交易对手风险价值超过阈值时，泄漏才会发生。在这两种模式中，随着提供商池规模的增加、客户对服务的估值的增加、等待成本的降低或服务时间的可变性的减少，平台往往更容易免受泄漏的影响。最后，通过比较两种设置之间的泄漏程度，我们发现在所有参数组合中，任何一个模型都不优于另一个模型。管理启示:我们的研究结果为现有的平台管理者或正在考虑将其服务“平台化”的企业家提供了指导。也就是说，基于操作环境的几个关键特征，管理人员可以针对其特定的业务环境评估平台泄漏威胁的严重程度。我们的研究结果还表明，如何重新设计等待过程，减少服务时间的变化，提高供应商的技能，可以减少泄漏的威胁。他们还提出了向客户披露供应商质量信息有助于遏制泄漏的条件。资助:J. Goh的工作得到了新加坡国立大学创业基金(Grant r - 314000 -110-133)和新加坡国立大学2021年人文社会科学奖学金的支持。补充材料:在线附录可在https://doi.org/10.1287/msom.2022.1179上获得。
{"title":"Off-Platform Threats in On-Demand Services","authors":"Eryn Juan He, Sergei Savin, Joel Goh, Chung-Piaw Teo","doi":"10.1287/msom.2022.1179","DOIUrl":"https://doi.org/10.1287/msom.2022.1179","url":null,"abstract":"Problem definition: Online platforms that provide on-demand services are often threatened by the phenomenon of leakage, where customer-provider pairs may decide to transact “off-platform” to avoid paying commissions to the platform. This paper investigates properties of services that make them vulnerable or resistant to leakage. Academic/practical relevance: In practice, much attention has been given to platform leakage, with platforms experimenting with multiple approaches to alleviate leakage and maintain their customer and provider bases. Yet, there is a current dearth of studies in the operations literature that systematically analyze the key factors behind platform leakage. Our work fills this gap and answers practical questions regarding the sustainability of platform. Methodology: We develop two game-theoretical models that capture service providers’ and customers’ decisions whether to conduct transactions on or off the platform. In the first (“perfect information”) model, we assume that customers are equipped with information to select their desired providers on the platform, whereas in the second (“imperfect information”) model, we assume customers are randomly matched with available providers by the platform. Results: For profit maximizing platforms, we show that leakage occurs if and only if the value of the counterparty risk from off-platform transactions exceeds a threshold. Across both models, platforms tend to be more immunized against leakage as provider pool sizes increase, customer valuations for service increase, their waiting costs decrease, or variability in service times are reduced. Finally, by comparing the degree of leakage between both settings, we find that neither model dominates the other across all parameter combinations. Managerial implications: Our results provide guidance to existing platform managers or entrepreneurs who are considering “platforming” their services. Namely, based on a few key features of the operating environment, managers can assess the severity of the threat of platform leakage for their specific business context. Our results also suggest how redesigning the waiting process, reducing service time variability, upskilling providers can reduce the threat of leakage. They also suggest the conditions under which revealing provider quality information to customers can help to curb leakage. Funding: J. Goh’s work was supported by a National University of Singapore Start-Up [Grant R-314-000-110-133] and a 2021 Humanities and Social Sciences Fellowship from the National University of Singapore. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1179 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136173852","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: We consider a two-sided market competition problem where two platforms, such as Uber and Lyft, compete on both supply and demand sides and study the impact of precommitments in a variety of practically motivated instruments on the equilibrium outcomes. Academic/practical relevance: We extend a set of classic oligopoly pricing results to account for two-sided competition under demand uncertainty. Methodology: We investigate multi-stage competition games. Results: We start with a sufficiently low demand uncertainty. First, we show that a precommitment made on the less competitive (demand or supply) side (on price or wage) has a less intense outcome than no commitment (i.e., spot-market price and wage competition). Then we show that, somewhat surprisingly, if the competition intensities of both sides are sufficiently close, the commission precommitment, where the platforms first compete in setting their commission rates and then their prices, is less profitable than no precommitment at all, and vice versa. Furthermore, we show that the capacity precommitment, in which the platforms first commit to a matching capacity and then set price and wage simultaneously subject to the precommitted capacity, leads to the most profitable outcome of all competition modes and extends the celebrated Kreps-Scheinkman equivalency to the two-sided market (without demand uncertainty). Then we extend the comparisons of various competition modes to account for a relatively high demand uncertainty. We show that the comparison between the spot-market price and wage competition and the commission precommitment stays the same as that with a sufficiently low demand uncertainty. In addition, the more flexible competition modes, such as no commitment and commission precommitment, benefit from higher demand uncertainty (with a fixed mean demand) because of their operational flexibility in response to the market changes. Further, a relatively high demand uncertainty may undermine or enhance the value of the wage precommitment, as opposed to no commitment. Finally, we also account for platforms with asymmetric parameters and matching friction and find that our main insights tend to be robust. Managerial implications: Our results caution platforms that a precommitment to the wrong instrument can be worse than no commitment at all. Moreover, the regulation of classifying gig workers as employees, despite many of its benefits to workers, may lead to a less competitive market outcome and, surprisingly, hurt gig workers by paying them lower wages. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2021-04295]. Y. Liu was supported by the Hong Kong Research Grants Council, Direct Allocation Grant [Project ID P0036818]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1173 .
{"title":"Precommitments in Two-Sided Market Competition","authors":"Ming Hu, Yan Liu","doi":"10.1287/msom.2022.1173","DOIUrl":"https://doi.org/10.1287/msom.2022.1173","url":null,"abstract":"Problem definition: We consider a two-sided market competition problem where two platforms, such as Uber and Lyft, compete on both supply and demand sides and study the impact of precommitments in a variety of practically motivated instruments on the equilibrium outcomes. Academic/practical relevance: We extend a set of classic oligopoly pricing results to account for two-sided competition under demand uncertainty. Methodology: We investigate multi-stage competition games. Results: We start with a sufficiently low demand uncertainty. First, we show that a precommitment made on the less competitive (demand or supply) side (on price or wage) has a less intense outcome than no commitment (i.e., spot-market price and wage competition). Then we show that, somewhat surprisingly, if the competition intensities of both sides are sufficiently close, the commission precommitment, where the platforms first compete in setting their commission rates and then their prices, is less profitable than no precommitment at all, and vice versa. Furthermore, we show that the capacity precommitment, in which the platforms first commit to a matching capacity and then set price and wage simultaneously subject to the precommitted capacity, leads to the most profitable outcome of all competition modes and extends the celebrated Kreps-Scheinkman equivalency to the two-sided market (without demand uncertainty). Then we extend the comparisons of various competition modes to account for a relatively high demand uncertainty. We show that the comparison between the spot-market price and wage competition and the commission precommitment stays the same as that with a sufficiently low demand uncertainty. In addition, the more flexible competition modes, such as no commitment and commission precommitment, benefit from higher demand uncertainty (with a fixed mean demand) because of their operational flexibility in response to the market changes. Further, a relatively high demand uncertainty may undermine or enhance the value of the wage precommitment, as opposed to no commitment. Finally, we also account for platforms with asymmetric parameters and matching friction and find that our main insights tend to be robust. Managerial implications: Our results caution platforms that a precommitment to the wrong instrument can be worse than no commitment at all. Moreover, the regulation of classifying gig workers as employees, despite many of its benefits to workers, may lead to a less competitive market outcome and, surprisingly, hurt gig workers by paying them lower wages. Funding: M. Hu was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2021-04295]. Y. Liu was supported by the Hong Kong Research Grants Council, Direct Allocation Grant [Project ID P0036818]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1173 .","PeriodicalId":49901,"journal":{"name":"M&som-Manufacturing & Service Operations Management","volume":"703 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135957656","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}
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