This paper considers orders that arrive one-by-one over time to a fulfillment center. Each order requests a product with some degree of customization that needs to be delivered expeditiously to a nearby location using a delivery vehicle. However, each vehicle can batch multiple orders together for delivery within a single trip. The benefits of batching include more efficient capacity utilization, lower total vehicle ownership requirements, and reduced environmental impact. The main drawback of batching is the consequent reduced average quality of service due to associated delivery delays when waiting for additional orders to arrive and executing a delivery route. To address this trade-off, we consider a set of threshold policies for batching and dispatching groups of orders, and characterize the associated long-run average cost per unit time for each policy that explicitly accounts for the customer’s total order lead time, including the time between order dispatch and delivery to the customer which, in turn, depends on route sequencing policies. For the threshold policies, our state variable may not only include the number of outstanding orders, but may also incorporate information on order arrival times and delivery locations. We model the stochastic dynamics of the system and obtain the long-run average cost per unit time, which we compute using a renewal-reward approach. We also consider different delivery sequencing approaches, including first-come, first-served and shortest traveling salesperson. In addition, we evaluate the effectiveness of accounting for all order information in the decision-making process, as opposed to just the number of outstanding orders or the time in the system for each order. Our analysis shows that a generalized class of cost- and quantity-based threshold policies often outperforms existing policies in the literature with the additional benefits of being robust to overestimates of the optimal cost threshold value and achieving strong delay cost performance. History: This paper has been accepted for the Service Science/Stochastic Systems Joint Special Issue. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2022.0042 .
{"title":"Analysis of Real-Time Order Fulfillment Policies: When to Dispatch a Batch?","authors":"Natarajan Gautam, Joseph Geunes","doi":"10.1287/serv.2022.0042","DOIUrl":"https://doi.org/10.1287/serv.2022.0042","url":null,"abstract":"This paper considers orders that arrive one-by-one over time to a fulfillment center. Each order requests a product with some degree of customization that needs to be delivered expeditiously to a nearby location using a delivery vehicle. However, each vehicle can batch multiple orders together for delivery within a single trip. The benefits of batching include more efficient capacity utilization, lower total vehicle ownership requirements, and reduced environmental impact. The main drawback of batching is the consequent reduced average quality of service due to associated delivery delays when waiting for additional orders to arrive and executing a delivery route. To address this trade-off, we consider a set of threshold policies for batching and dispatching groups of orders, and characterize the associated long-run average cost per unit time for each policy that explicitly accounts for the customer’s total order lead time, including the time between order dispatch and delivery to the customer which, in turn, depends on route sequencing policies. For the threshold policies, our state variable may not only include the number of outstanding orders, but may also incorporate information on order arrival times and delivery locations. We model the stochastic dynamics of the system and obtain the long-run average cost per unit time, which we compute using a renewal-reward approach. We also consider different delivery sequencing approaches, including first-come, first-served and shortest traveling salesperson. In addition, we evaluate the effectiveness of accounting for all order information in the decision-making process, as opposed to just the number of outstanding orders or the time in the system for each order. Our analysis shows that a generalized class of cost- and quantity-based threshold policies often outperforms existing policies in the literature with the additional benefits of being robust to overestimates of the optimal cost threshold value and achieving strong delay cost performance. History: This paper has been accepted for the Service Science/Stochastic Systems Joint Special Issue. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2022.0042 .","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"80 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067377","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}
With the popularity of the internet and the rapid growth of user-generated content facilitated by various platforms of social media, online reviews have become an important source of information for customers to obtain product information and affect their purchasing decisions in the service industry. The accelerated development of bed-and-breakfasts (B&Bs) in the context of a rural revitalization strategy implies a shift in people’s demand from standardized to personalized accommodation. From the first establishment to the well-developed industry, the competition among diverse B&Bs is getting fiercer, and it is essential for business operators to understand their customers deeply and timely. Therefore, online reviews posted by customers freely and in real time were adopted to explore key dimensions reflecting customer experience and examine their relationship with customer satisfaction, which is a vital antecedent for customer loyalty, repurchase intention, etc. A total of 8,691 reviews from Ctrip were collected and then used for extracting relative insights and cognition by qualitative and quantitative analysis. Consequently, several implications for future research development and practical application are discussed for service promotion and development of the B&B industry. Funding: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A2A03052622).
{"title":"B&B Customer Experience and Satisfaction: Evidence from Online Customer Reviews","authors":"Mengke Jia, Hak-Seon Kim, Shuting Tao","doi":"10.1287/serv.2022.0080","DOIUrl":"https://doi.org/10.1287/serv.2022.0080","url":null,"abstract":"With the popularity of the internet and the rapid growth of user-generated content facilitated by various platforms of social media, online reviews have become an important source of information for customers to obtain product information and affect their purchasing decisions in the service industry. The accelerated development of bed-and-breakfasts (B&Bs) in the context of a rural revitalization strategy implies a shift in people’s demand from standardized to personalized accommodation. From the first establishment to the well-developed industry, the competition among diverse B&Bs is getting fiercer, and it is essential for business operators to understand their customers deeply and timely. Therefore, online reviews posted by customers freely and in real time were adopted to explore key dimensions reflecting customer experience and examine their relationship with customer satisfaction, which is a vital antecedent for customer loyalty, repurchase intention, etc. A total of 8,691 reviews from Ctrip were collected and then used for extracting relative insights and cognition by qualitative and quantitative analysis. Consequently, several implications for future research development and practical application are discussed for service promotion and development of the B&B industry. Funding: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A2A03052622).","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098177","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}
Decentralized Finance (DeFi) marks a transformative shift in financial services, harnessing the power of distributed ledger and smart contract technologies. Despite only five years of development, DeFi has rapidly matured, pioneering state-of-the-art financial products, a stratified ecosystem, and a myriad of innovative vertical and horizontal protocol integrations. Its distinctive characteristics establish DeFi as a crucible for innovation, carrying potential to spur advancements across various service sectors. However, the research community has yet to fully explore this burgeoning field. In this manuscript, we navigate the complexities of this emerging service system, analyzing 362 notable Ethereum-based DeFi initiatives. Our study reveals a robust and dynamic growth trajectory, the advent of a multi-tiered ecosystem, and a sophisticated network of primary market investors. We conclude by highlighting promising avenues for future exploration in DeFi and service system research.
{"title":"Exploring the New Frontier: Decentralized Financial Services","authors":"Enric Junqué de Fortuny, Yifei Zhang","doi":"10.1287/serv.2021.0048","DOIUrl":"https://doi.org/10.1287/serv.2021.0048","url":null,"abstract":"Decentralized Finance (DeFi) marks a transformative shift in financial services, harnessing the power of distributed ledger and smart contract technologies. Despite only five years of development, DeFi has rapidly matured, pioneering state-of-the-art financial products, a stratified ecosystem, and a myriad of innovative vertical and horizontal protocol integrations. Its distinctive characteristics establish DeFi as a crucible for innovation, carrying potential to spur advancements across various service sectors. However, the research community has yet to fully explore this burgeoning field. In this manuscript, we navigate the complexities of this emerging service system, analyzing 362 notable Ethereum-based DeFi initiatives. Our study reveals a robust and dynamic growth trajectory, the advent of a multi-tiered ecosystem, and a sophisticated network of primary market investors. We conclude by highlighting promising avenues for future exploration in DeFi and service system research.","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"152 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86229979","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}
As the fashion industry increasingly embraces artificial intelligence (AI), we investigate how a fast-fashion retailer should choose between using a manual design strategy or an AI-assisted design strategy to enhance existing products. A manual design is a traditional and basic approach that involves human designers only, whereas an AI-assisted design is a more innovative approach that involves both human designers and AI technologies. In this paper, the overall product enhancement is measured by two key attributes: product quality and product trendiness. Product quality can be measured by the product’s longevity as reflected by the quality of the materials and types of fabric and stitching used, where the product’s improvement level can be determined by the retailer in a continuous range. Consequently, the retailer may choose different levels of product quality under different design strategies. The two design approaches also lead to different natures of product trendiness, which is reflected by features such as styles, new materials, and colors, to name just a few. Specifically, we assume that the traditional manual design can predict well how trendy or popular the new product is. Hence, the trendiness attribute under the manual design is deterministic. However, given the uncertain nature of the AI-assisted design technology and the needed coordination between human designers and the adopted technologies, the trendiness of the new product designed under the AI-assisted approach is assumed uncertain. Two sets of designing costs are considered in product enhancement: the fixed design cost that is irrespective of the production volume and the variable marginal cost. Our analysis of the base model highlights the importance of decomposing different costs in determining the optimal design strategy. Specifically, the manual design is preferred when the fixed cost carries more weight, whereas the AI-assisted design is preferred when the marginal cost is a more important factor. Moreover, a higher level of innovation uncertainty under the AI-assisted design gives this strategy an advantage over the manual design. In our extended models, we demonstrate that (1) these results are robust even if the retailer does not have the flexibility to offer the existing product when the AI-assisted design is unpopular, and (2) the relative position of human designers in the two design approaches has an impact on the effects of these costs. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0315 .
{"title":"Product Design Enhancement for Fashion Retailing","authors":"Yiwei Wang, Vidyanand Choudhary, Shuya Yin","doi":"10.1287/serv.2023.0315","DOIUrl":"https://doi.org/10.1287/serv.2023.0315","url":null,"abstract":"As the fashion industry increasingly embraces artificial intelligence (AI), we investigate how a fast-fashion retailer should choose between using a manual design strategy or an AI-assisted design strategy to enhance existing products. A manual design is a traditional and basic approach that involves human designers only, whereas an AI-assisted design is a more innovative approach that involves both human designers and AI technologies. In this paper, the overall product enhancement is measured by two key attributes: product quality and product trendiness. Product quality can be measured by the product’s longevity as reflected by the quality of the materials and types of fabric and stitching used, where the product’s improvement level can be determined by the retailer in a continuous range. Consequently, the retailer may choose different levels of product quality under different design strategies. The two design approaches also lead to different natures of product trendiness, which is reflected by features such as styles, new materials, and colors, to name just a few. Specifically, we assume that the traditional manual design can predict well how trendy or popular the new product is. Hence, the trendiness attribute under the manual design is deterministic. However, given the uncertain nature of the AI-assisted design technology and the needed coordination between human designers and the adopted technologies, the trendiness of the new product designed under the AI-assisted approach is assumed uncertain. Two sets of designing costs are considered in product enhancement: the fixed design cost that is irrespective of the production volume and the variable marginal cost. Our analysis of the base model highlights the importance of decomposing different costs in determining the optimal design strategy. Specifically, the manual design is preferred when the fixed cost carries more weight, whereas the AI-assisted design is preferred when the marginal cost is a more important factor. Moreover, a higher level of innovation uncertainty under the AI-assisted design gives this strategy an advantage over the manual design. In our extended models, we demonstrate that (1) these results are robust even if the retailer does not have the flexibility to offer the existing product when the AI-assisted design is unpopular, and (2) the relative position of human designers in the two design approaches has an impact on the effects of these costs. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0315 .","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"55 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":"135895728","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}
Ningyuan Chen, Ragıp Gürlek, Donald K. K. Lee, Haipeng Shen
Service Science, Ahead of Print.
服务科学，领先于印刷。
{"title":"Can Customer Arrival Rates Be Modelled by Sine Waves?","authors":"Ningyuan Chen, Ragıp Gürlek, Donald K. K. Lee, Haipeng Shen","doi":"10.1287/serv.2022.0045","DOIUrl":"https://doi.org/10.1287/serv.2022.0045","url":null,"abstract":"Service Science, Ahead of Print. <br/>","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"17 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542597","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 paper aims to investigate the influence of customer–organization identification from a customer–employee relationship perspective. Specifically, this paper examines the mediating effects of the customer–employee identification and customer–employee trust between the relationship between customer–organization identification and customer citizenship behavior. Based on social identity theory, this paper builds a research framework that is empirically tested using a sample of 465 patients or their families from one of the largest high-level hospitals in China. Structural equation modeling and a bootstrapping method were adopted to test the model and the mediation effects. Results of data analysis reveal that customer–organization identification has a direct positive influence on customer citizenship behavior, and customer–employee identification and customer–employee trust have positive mediation effects between customer–organization identification and customer citizenship behavior. Funding: This study was supported by the National Natural Science Foundation of China [Grant 71772186 to L. Xie and Grant 71802052 to X. Guan].
本文旨在从顾客-员工关系的角度研究顾客-组织认同的影响。具体而言，本文考察了顾客-员工认同和顾客-员工信任在顾客-组织认同与顾客公民行为关系中的中介作用。基于社会认同理论，本文构建了一个研究框架，并以中国最大的一家高水平医院的465名患者或家属为样本进行了实证检验。采用结构方程模型和自举方法对模型和中介效应进行检验。数据分析结果表明，顾客-组织认同对顾客公民行为具有直接的正向影响，顾客-员工认同和顾客-员工信任在顾客-组织认同与顾客公民行为之间具有正向中介作用。基金资助:本研究由国家自然科学基金资助[基金资助:71772186 to L. Xie;基金资助:71802052 to X. Guan]。
{"title":"Exploring Customer Citizenship Behavior Through Customer–Organization Identification","authors":"Lishan Xie, Wenxuan Zhang, Xinhua Guan, T. Huan","doi":"10.1287/serv.2021.0051","DOIUrl":"https://doi.org/10.1287/serv.2021.0051","url":null,"abstract":"This paper aims to investigate the influence of customer–organization identification from a customer–employee relationship perspective. Specifically, this paper examines the mediating effects of the customer–employee identification and customer–employee trust between the relationship between customer–organization identification and customer citizenship behavior. Based on social identity theory, this paper builds a research framework that is empirically tested using a sample of 465 patients or their families from one of the largest high-level hospitals in China. Structural equation modeling and a bootstrapping method were adopted to test the model and the mediation effects. Results of data analysis reveal that customer–organization identification has a direct positive influence on customer citizenship behavior, and customer–employee identification and customer–employee trust have positive mediation effects between customer–organization identification and customer citizenship behavior. Funding: This study was supported by the National Natural Science Foundation of China [Grant 71772186 to L. Xie and Grant 71802052 to X. Guan].","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"101 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75707491","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}
Revenue management (RM) is the application of analytical methodologies and tools that predict consumer behavior and optimize product availability and prices to maximize a firm’s revenue or profit. In the last decade, data has been playing an increasingly crucial role in business decision making. As firms rely more on collected or acquired data to make business decisions, it brings opportunities and challenges to the RM research community. In this review paper, we systematically categorize the related literature by how a study is “driven” by data and focus on studies that explore the interplay between two or three of the elements: data, model, and decisions, in which the data element must be present. Specifically, we cover five data-driven RM research areas, including inference (data to model), predict then optimize (data to model to decisions), online learning (data to model to decisions to new data in a loop), end-to-end decision making (data directly to decisions), and experimental design (decisions to data to model). Finally, we point out future research directions. Funding: The research of N. Chen is partly supported by Natural Sciences and Engineering Research Council of Canada Discovery [Grant RGPIN-2020-04038]. The research of M. Hu is in part supported by Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2021-04295].
{"title":"Frontiers in Service Science: Data-Driven Revenue Management: The Interplay of Data, Model, and Decisions","authors":"Ningyuan Chen, Ming Hu","doi":"10.1287/serv.2023.0322","DOIUrl":"https://doi.org/10.1287/serv.2023.0322","url":null,"abstract":"Revenue management (RM) is the application of analytical methodologies and tools that predict consumer behavior and optimize product availability and prices to maximize a firm’s revenue or profit. In the last decade, data has been playing an increasingly crucial role in business decision making. As firms rely more on collected or acquired data to make business decisions, it brings opportunities and challenges to the RM research community. In this review paper, we systematically categorize the related literature by how a study is “driven” by data and focus on studies that explore the interplay between two or three of the elements: data, model, and decisions, in which the data element must be present. Specifically, we cover five data-driven RM research areas, including inference (data to model), predict then optimize (data to model to decisions), online learning (data to model to decisions to new data in a loop), end-to-end decision making (data directly to decisions), and experimental design (decisions to data to model). Finally, we point out future research directions. Funding: The research of N. Chen is partly supported by Natural Sciences and Engineering Research Council of Canada Discovery [Grant RGPIN-2020-04038]. The research of M. Hu is in part supported by Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2021-04295].","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135006081","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}
How should digital service firms design and bundle their offering to capture a large market while seeking differentiation from competition? To answer this question, we consider the most generic model of competition, namely, two symmetric firms competing on price with regard to two (independent or complementary) components with an arbitrary distribution of valuations, without restrictions on their product offering. We show that three outcomes emerge in equilibrium, namely, a full-mixed bundling monopoly, a full-mixed bundling competitive duopoly leading to a price war, and a pure or partial-mixed bundling differentiated duopoly yielding positive profits for both firms. The latter equilibrium is the most plausible because it is the only one that is both trembling-hand perfect and not payoff dominated. We demonstrate the benefits of bundling under competition, thereby explaining the online platforms’ motivation for and success in expanding their offering horizontally. Yet not all products may be offered in equilibrium to avoid direct or indirect competition; hence, competition may lead to a narrower range of products available than a monopoly. Is bundling anticompetitive? It is a double-edged sword. On the one hand, it softens price competition by allowing firms to differentiate their offering. On the other hand, it stimulates competition by creating some product overlap.
{"title":"Competitive Bundling and Offer Design in a Symmetric Bertrand Duopoly","authors":"G. Roels, Araz Khodabakhshian, U. Karmarkar","doi":"10.1287/serv.2023.0325","DOIUrl":"https://doi.org/10.1287/serv.2023.0325","url":null,"abstract":"How should digital service firms design and bundle their offering to capture a large market while seeking differentiation from competition? To answer this question, we consider the most generic model of competition, namely, two symmetric firms competing on price with regard to two (independent or complementary) components with an arbitrary distribution of valuations, without restrictions on their product offering. We show that three outcomes emerge in equilibrium, namely, a full-mixed bundling monopoly, a full-mixed bundling competitive duopoly leading to a price war, and a pure or partial-mixed bundling differentiated duopoly yielding positive profits for both firms. The latter equilibrium is the most plausible because it is the only one that is both trembling-hand perfect and not payoff dominated. We demonstrate the benefits of bundling under competition, thereby explaining the online platforms’ motivation for and success in expanding their offering horizontally. Yet not all products may be offered in equilibrium to avoid direct or indirect competition; hence, competition may lead to a narrower range of products available than a monopoly. Is bundling anticompetitive? It is a double-edged sword. On the one hand, it softens price competition by allowing firms to differentiate their offering. On the other hand, it stimulates competition by creating some product overlap.","PeriodicalId":46249,"journal":{"name":"Service Science","volume":"85 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85985406","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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