Yiting Deng, Christopher S. Tang, Wei Wang, Onesun Steve Yoo
Because of the informational advantage of online marketplaces (i.e., platforms), it is a common belief that a platform’s market entry will be detrimental to third-party sellers who sell similar products on the platform. To examine the validity of this belief, we conduct an exploratory analysis using the sales data for a single product category provided by JD.com for the month of March 2018. Our analysis reveals an unexpected result. Upon the platform’s entry, third-party sellers who sell similar products can afford to charge a higher price, obtain a higher demand, and earn a higher profit. To provide a plausible explanation for this unexpected exploratory result, we develop a duopoly model that incorporates the changing competitive dynamic before and after the platform’s entry. Specifically, before entry, the platform earns a commission (based on the seller’s revenue), whereas the seller sets its retail price as a monopoly. After entry, the platform earns a profit generated by its direct sales in addition to the commission from the seller. In addition, the seller and the platform operate in a duopoly and engage in a sequential game. By examining the equilibrium outcomes associated with this sequential game, we identify conditions under which the platform’s entry can create a win-win situation for both parties. Specifically, these conditions hold when the platform’s market potential is moderate and when the platform’s entry creates a sufficiently high spillover effect on the seller, providing a plausible explanation for our empirical finding that the seller can benefit from a platform’s entry. Funding: W. Wang acknowledges the financial support from the National Natural Science Foundation of China [Grant 72173019], the Beijing Social Science Foundation [Grant 20JJA004], the Chinese Ministry of Education Research Funds on Humanities and Social Sciences [Grant 21YJA790056], and the Excellent Young Scholar Funds of UIBE [Grant 19YQ17].
{"title":"Can Third-Party Sellers Benefit from a Platform’s Entry to the Market?","authors":"Yiting Deng, Christopher S. Tang, Wei Wang, Onesun Steve Yoo","doi":"10.1287/serv.2023.0324","DOIUrl":"https://doi.org/10.1287/serv.2023.0324","url":null,"abstract":"Because of the informational advantage of online marketplaces (i.e., platforms), it is a common belief that a platform’s market entry will be detrimental to third-party sellers who sell similar products on the platform. To examine the validity of this belief, we conduct an exploratory analysis using the sales data for a single product category provided by JD.com for the month of March 2018. Our analysis reveals an unexpected result. Upon the platform’s entry, third-party sellers who sell similar products can afford to charge a higher price, obtain a higher demand, and earn a higher profit. To provide a plausible explanation for this unexpected exploratory result, we develop a duopoly model that incorporates the changing competitive dynamic before and after the platform’s entry. Specifically, before entry, the platform earns a commission (based on the seller’s revenue), whereas the seller sets its retail price as a monopoly. After entry, the platform earns a profit generated by its direct sales in addition to the commission from the seller. In addition, the seller and the platform operate in a duopoly and engage in a sequential game. By examining the equilibrium outcomes associated with this sequential game, we identify conditions under which the platform’s entry can create a win-win situation for both parties. Specifically, these conditions hold when the platform’s market potential is moderate and when the platform’s entry creates a sufficiently high spillover effect on the seller, providing a plausible explanation for our empirical finding that the seller can benefit from a platform’s entry. Funding: W. Wang acknowledges the financial support from the National Natural Science Foundation of China [Grant 72173019], the Beijing Social Science Foundation [Grant 20JJA004], the Chinese Ministry of Education Research Funds on Humanities and Social Sciences [Grant 21YJA790056], and the Excellent Young Scholar Funds of UIBE [Grant 19YQ17].","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136091195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Healthcare providers often implement quality improvement (QI) practices to improve clinical quality, which may be measured as the extent to which healthcare workers (HCWs) comply with standardized procedures designed for ensuring patient safety. Unfortunately, the effectiveness of QI practices to improve clinical quality varies from facility to facility because of healthcare delivery complexity. In this paper, we consider the contextual complexity and the provider complexity arising from healthcare delivery processes, and we propose an exploratory study based on discrete choice experiments to examine their roles in the relationship between QI practices and clinical quality. By collecting and analyzing data from 320 HCWs at a university hospital in Taiwan, we found that healthcare delivery complexity significantly moderated the effectiveness of QI practices and how they improved clinical quality. We found that the contextual complexity influenced the level of effectiveness of the QI practices, whereas the provider complexity influenced whether a QI practice may be effective or not. We also studied the effect of implementing multiple QI practices simultaneously to counter the provider complexity. We found that implementing more QI practices does not necessarily lead to better outcomes, but implementing the right ones would. Our findings provide healthcare facilities with ex ante insights for designing QI practices to improve clinical quality. Funding: W. Chen was supported by the National Natural Science Foundation of China [Grant 71902017], and C.-L. Tseng was supported by the University of New South Wales UNOVA Knowledge Hub. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0323 .
{"title":"The Impact of Healthcare Delivery Complexity on Practices for Clinical Quality Improvement: A Case of Healthcare Workers’ Hand Hygiene Compliance","authors":"Wenlin Chen, C. Tseng, Cynthia Tseng","doi":"10.1287/serv.2023.0323","DOIUrl":"https://doi.org/10.1287/serv.2023.0323","url":null,"abstract":"Healthcare providers often implement quality improvement (QI) practices to improve clinical quality, which may be measured as the extent to which healthcare workers (HCWs) comply with standardized procedures designed for ensuring patient safety. Unfortunately, the effectiveness of QI practices to improve clinical quality varies from facility to facility because of healthcare delivery complexity. In this paper, we consider the contextual complexity and the provider complexity arising from healthcare delivery processes, and we propose an exploratory study based on discrete choice experiments to examine their roles in the relationship between QI practices and clinical quality. By collecting and analyzing data from 320 HCWs at a university hospital in Taiwan, we found that healthcare delivery complexity significantly moderated the effectiveness of QI practices and how they improved clinical quality. We found that the contextual complexity influenced the level of effectiveness of the QI practices, whereas the provider complexity influenced whether a QI practice may be effective or not. We also studied the effect of implementing multiple QI practices simultaneously to counter the provider complexity. We found that implementing more QI practices does not necessarily lead to better outcomes, but implementing the right ones would. Our findings provide healthcare facilities with ex ante insights for designing QI practices to improve clinical quality. Funding: W. Chen was supported by the National Natural Science Foundation of China [Grant 71902017], and C.-L. Tseng was supported by the University of New South Wales UNOVA Knowledge Hub. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0323 .","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"39 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85708137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lockdown policies, such as stay-at-home orders, are known to be effective in controlling the spread of the novel coronavirus disease 2019. However, concerns over economic burdens of these policies rapidly propelled U.S. states to move toward reopening in the early stage of the pandemic. Decision making in most states has been challenging, especially because of a dearth of quantitative evidence on health gains versus economic burdens of different policies. To assist decision makers, we study the health and economic impacts of various lockdown policies across U.S. states and shed light on policies that are most effective. To this end, we make use of detailed data from 50 U.S. states plus the District of Columbia on various factors, including number of tests, positive and negative results, hospitalizations, ICU beds and ventilators used, residents’ mobility obtained from cellphone data, and deaths. Our analyses allow quantifying the total cost versus the total quality-adjusted life year (QALY) associated with various lockdown policies. We utilize a compartmental model with Markov chain Monte Carlo simulation to estimate the spread of disease. To calibrate our model separately for each U.S. state, we make use of empirical data on the intensity of intervention policies, age, ratio of Black/Hispanic populations, per capita income, residents’ mobility, and number of daily tests and feed them to a longitudinal mixed-effect model. Finally, we utilize a microsimulation model to estimate the total cost and total QALY for each state and perform cost-effectiveness analysis to identify policies that would have worked best. Our results show that, compared with no intervention during March–June 2020, the policies undertaken across the United States saved, on average, about 41,284.51 years’ worth of QALY (per 100K capita), incurring $164.01 million (per 100K capita). Had the states undertaken more strict policies during the same time frame than those they adopted, these values would be 44,909.41 years and $117.28 million, respectively. By quantifying the impact of various lockdown policies separately for each state, our results allow federal and state authorities to avoid following a “one size fits all” strategy and instead enact policies that are better suited for each state. Specifically, by studying the trade-offs between health gains and economic impacts, we identify the particular states that would have benefited from implementing more restrictive policies. Finally, in addition to shedding light on the impact of lockdown policies during our study period (March–June 2020), our results have important implications on curbing future fast-spreading variants of the coronavirus or other related potential epidemics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0321 .
{"title":"Health and Economic Impacts of Lockdown Policies in the Early Stage of COVID-19 in the United States","authors":"Alireza Boloori, S. Saghafian","doi":"10.1287/serv.2023.0321","DOIUrl":"https://doi.org/10.1287/serv.2023.0321","url":null,"abstract":"Lockdown policies, such as stay-at-home orders, are known to be effective in controlling the spread of the novel coronavirus disease 2019. However, concerns over economic burdens of these policies rapidly propelled U.S. states to move toward reopening in the early stage of the pandemic. Decision making in most states has been challenging, especially because of a dearth of quantitative evidence on health gains versus economic burdens of different policies. To assist decision makers, we study the health and economic impacts of various lockdown policies across U.S. states and shed light on policies that are most effective. To this end, we make use of detailed data from 50 U.S. states plus the District of Columbia on various factors, including number of tests, positive and negative results, hospitalizations, ICU beds and ventilators used, residents’ mobility obtained from cellphone data, and deaths. Our analyses allow quantifying the total cost versus the total quality-adjusted life year (QALY) associated with various lockdown policies. We utilize a compartmental model with Markov chain Monte Carlo simulation to estimate the spread of disease. To calibrate our model separately for each U.S. state, we make use of empirical data on the intensity of intervention policies, age, ratio of Black/Hispanic populations, per capita income, residents’ mobility, and number of daily tests and feed them to a longitudinal mixed-effect model. Finally, we utilize a microsimulation model to estimate the total cost and total QALY for each state and perform cost-effectiveness analysis to identify policies that would have worked best. Our results show that, compared with no intervention during March–June 2020, the policies undertaken across the United States saved, on average, about 41,284.51 years’ worth of QALY (per 100K capita), incurring $164.01 million (per 100K capita). Had the states undertaken more strict policies during the same time frame than those they adopted, these values would be 44,909.41 years and $117.28 million, respectively. By quantifying the impact of various lockdown policies separately for each state, our results allow federal and state authorities to avoid following a “one size fits all” strategy and instead enact policies that are better suited for each state. Specifically, by studying the trade-offs between health gains and economic impacts, we identify the particular states that would have benefited from implementing more restrictive policies. Finally, in addition to shedding light on the impact of lockdown policies during our study period (March–June 2020), our results have important implications on curbing future fast-spreading variants of the coronavirus or other related potential epidemics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0321 .","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"82 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76131899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emerging on-demand service platforms (OSPs) have recently embraced teamwork as a strategy for stimulating workers’ productivity and mediating temporal supply and demand imbalances. This research investigates the team contest scheme design problem considering work schedules. Introducing teams on OSPs creates a hierarchical single-leader multi-follower game. The leader (platform) establishes rewards and intrateam revenue-sharing rules for distributing workers’ payoffs. Each follower (team) competes with others by coordinating the schedules of its team members to maximize the total expected utility. The concurrence of interteam competition and intrateam coordination causes dual effects, which are captured by an equilibrium analysis of the followers’ game. To align the platform’s interest with workers’ heterogeneous working-time preferences, we propose a profit-maximizing contest scheme consisting of a winner’s reward and time-varying payments. A novel algorithm that combines Bayesian optimization, duality, and a penalty method solves the optimal scheme in the nonconvex equilibrium-constrained problem. Our results indicate that teamwork is a useful strategy with limitations. Under the proposed scheme, team contest always benefits workers. Intrateam coordination helps teams strategically mitigate the negative externalities caused by overcompetition among workers. For the platform, the optimal scheme can direct teams’ schedules toward more profitable market equilibria when workers have inaccurate perceptions of the market. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by the National Science Foundation [Grant FW-HTF-P 2222806]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/serv.2023.0320 .
{"title":"The Dual Effects of Team Contest Design on On-Demand Service Work Schedules","authors":"Tingting Dong, Xiaotong Sun, Qi Luo, Jian Wang, Yafeng Yin","doi":"10.1287/serv.2023.0320","DOIUrl":"https://doi.org/10.1287/serv.2023.0320","url":null,"abstract":"Emerging on-demand service platforms (OSPs) have recently embraced teamwork as a strategy for stimulating workers’ productivity and mediating temporal supply and demand imbalances. This research investigates the team contest scheme design problem considering work schedules. Introducing teams on OSPs creates a hierarchical single-leader multi-follower game. The leader (platform) establishes rewards and intrateam revenue-sharing rules for distributing workers’ payoffs. Each follower (team) competes with others by coordinating the schedules of its team members to maximize the total expected utility. The concurrence of interteam competition and intrateam coordination causes dual effects, which are captured by an equilibrium analysis of the followers’ game. To align the platform’s interest with workers’ heterogeneous working-time preferences, we propose a profit-maximizing contest scheme consisting of a winner’s reward and time-varying payments. A novel algorithm that combines Bayesian optimization, duality, and a penalty method solves the optimal scheme in the nonconvex equilibrium-constrained problem. Our results indicate that teamwork is a useful strategy with limitations. Under the proposed scheme, team contest always benefits workers. Intrateam coordination helps teams strategically mitigate the negative externalities caused by overcompetition among workers. For the platform, the optimal scheme can direct teams’ schedules toward more profitable market equilibria when workers have inaccurate perceptions of the market. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by the National Science Foundation [Grant FW-HTF-P 2222806]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/serv.2023.0320 .","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"103 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80835294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonas Schwamberger, Moritz Fleischmann, Arne Strauss
The outbreak of the COVID-19 pandemic led demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) to outstrip delivery capacity by a wide margin. In the United Kingdom, the booking systems of some e-retailers could not handle the flood of incoming requests, forcing the retailers to proactively reach out to certain priority customer segments with the aim of serving as many high-priority customers as possible. To determine when to contact each customer segment in this extraordinary demand environment, we investigate the new demand management concept of proactively contacting customers. We first develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. We then extend this approach to the AHD setting. To cope with increased problem complexity, we propose a three-step procedure to solve this problem. First, we subdivide the delivery area into smaller subareas; second, we select the most promising subareas; and third, we determine which customers to contact within the chosen subareas. To gain managerial insights and to show the practical benefits of our approaches, we apply both approaches to realistic data from the London area. Our results show that proactive customer contacting allows for a tailored allocation of scarce capacity in e-groceries. We conclude with a discussion on how these contacting techniques can be valuable in postcrisis times. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services.
{"title":"Feeding the Nation—Dynamic Customer Contacting for E-Fulfillment in Times of Crisis","authors":"Jonas Schwamberger, Moritz Fleischmann, Arne Strauss","doi":"10.1287/serv.2022.0304","DOIUrl":"https://doi.org/10.1287/serv.2022.0304","url":null,"abstract":"The outbreak of the COVID-19 pandemic led demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) to outstrip delivery capacity by a wide margin. In the United Kingdom, the booking systems of some e-retailers could not handle the flood of incoming requests, forcing the retailers to proactively reach out to certain priority customer segments with the aim of serving as many high-priority customers as possible. To determine when to contact each customer segment in this extraordinary demand environment, we investigate the new demand management concept of proactively contacting customers. We first develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. We then extend this approach to the AHD setting. To cope with increased problem complexity, we propose a three-step procedure to solve this problem. First, we subdivide the delivery area into smaller subareas; second, we select the most promising subareas; and third, we determine which customers to contact within the chosen subareas. To gain managerial insights and to show the practical benefits of our approaches, we apply both approaches to realistic data from the London area. Our results show that proactive customer contacting allows for a tailored allocation of scarce capacity in e-groceries. We conclude with a discussion on how these contacting techniques can be valuable in postcrisis times. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services.","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"28 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":"135907509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niels A. H. Agatz, Soo-Haeng Cho, Hai Wang, S. Benjaafar
{"title":"Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1","authors":"Niels A. H. Agatz, Soo-Haeng Cho, Hai Wang, S. Benjaafar","doi":"10.1287/serv.2023.0317","DOIUrl":"https://doi.org/10.1287/serv.2023.0317","url":null,"abstract":"","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"90 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80477641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online reviews have become increasingly important to both consumers and businesses and, as a result, have attracted considerable research attention. However, all reviews are not created equal as consumers may differ in their propensities to leave reviews, often as a function of their satisfaction. To ensure a more representative customer voice, companies often utilize different strategies to moderate the biases in online reviews. The strategies deployed by many hospitality firms differ dramatically in both how reviews are collected and where they are posted. This study investigates four review-collection strategies of major hospitality companies and analyzes how each strategy affects review ratings and length. We find that the effort required to post a review impacts review characteristics. We show that reviews collected through self-motivation methods tend to be lower rated and longer, whereas reviews solicited from companies through poststay emails tend to exhibit different characteristics.
{"title":"How Firm Strategies Affect Consumer Biases in Online Reviews","authors":"Frances Xinhua Wang, C. Anderson","doi":"10.1287/serv.2023.0316","DOIUrl":"https://doi.org/10.1287/serv.2023.0316","url":null,"abstract":"Online reviews have become increasingly important to both consumers and businesses and, as a result, have attracted considerable research attention. However, all reviews are not created equal as consumers may differ in their propensities to leave reviews, often as a function of their satisfaction. To ensure a more representative customer voice, companies often utilize different strategies to moderate the biases in online reviews. The strategies deployed by many hospitality firms differ dramatically in both how reviews are collected and where they are posted. This study investigates four review-collection strategies of major hospitality companies and analyzes how each strategy affects review ratings and length. We find that the effort required to post a review impacts review characteristics. We show that reviews collected through self-motivation methods tend to be lower rated and longer, whereas reviews solicited from companies through poststay emails tend to exhibit different characteristics.","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"61 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78326015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Call for Papers: Service Science Special Issue: Navigating the Use of Technology in Service Marketing","authors":"Haitao (Tony) Cui, A. Xu, Ting Zhu","doi":"10.1287/serv.2023.0319","DOIUrl":"https://doi.org/10.1287/serv.2023.0319","url":null,"abstract":"","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"55 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73858356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the nonlinear modeling of customer satisfaction (CS) and loyalty for resource allocation strategies that promote efficient loyalty programs. We first introduce global models to reflect several important nonlinear characteristics, particularly (a) a saturation-attainable limit of effectiveness, (b) nonconstant marginal returns, and (c) asymmetric responses between satisfied and dissatisfied customers. In the proposed models, we put forth the joint use of two types of mixture models to deal with different levels of heterogeneity. Based on the model best supported among alternatives in the literature, we targeted customers using expected incremental loyalty, which is derived from the joint use of an estimated response curve of loyalty to satisfaction and an empirical distribution of CS scores. Then, we evaluate the efficiency of loyalty programs under the assumption of full and limited access to customers and subsequently derive managerial implications. For instance, through a counterfactual simulation of the models for the three industries, we find that improving the perceived quality in the mobile communication industry may have a greater effect on loyalty than in the hotel and convenience store industries because of its higher switching cost. Funding: N. Terui acknowledges funding by JSPS KAKENHI [Grants (A)17H01001 and (A)22H00056]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2022.0313 .
{"title":"Modeling Customer Satisfaction’s Impact on Loyalty: Insights for Customer-Centric Resource Allocation","authors":"Yinxing Li, Aijing Xing, Nobuhiko Terui","doi":"10.1287/serv.2022.0313","DOIUrl":"https://doi.org/10.1287/serv.2022.0313","url":null,"abstract":"This study explores the nonlinear modeling of customer satisfaction (CS) and loyalty for resource allocation strategies that promote efficient loyalty programs. We first introduce global models to reflect several important nonlinear characteristics, particularly (a) a saturation-attainable limit of effectiveness, (b) nonconstant marginal returns, and (c) asymmetric responses between satisfied and dissatisfied customers. In the proposed models, we put forth the joint use of two types of mixture models to deal with different levels of heterogeneity. Based on the model best supported among alternatives in the literature, we targeted customers using expected incremental loyalty, which is derived from the joint use of an estimated response curve of loyalty to satisfaction and an empirical distribution of CS scores. Then, we evaluate the efficiency of loyalty programs under the assumption of full and limited access to customers and subsequently derive managerial implications. For instance, through a counterfactual simulation of the models for the three industries, we find that improving the perceived quality in the mobile communication industry may have a greater effect on loyalty than in the hotel and convenience store industries because of its higher switching cost. Funding: N. Terui acknowledges funding by JSPS KAKENHI [Grants (A)17H01001 and (A)22H00056]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2022.0313 .","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"33 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83669600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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