{"title":"Space Booking with Multistage Online VCG Mechanism: A Simulation Study Toward Practical Application","authors":"Bingxin Du, Nariaki Nishino, Koji Kimita","doi":"10.1287/serv.2022.0020","DOIUrl":"https://doi.org/10.1287/serv.2022.0020","url":null,"abstract":"Service Science, Ahead of Print. <br/>","PeriodicalId":46249,"journal":{"name":"Service Science","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517784","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 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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