Pub Date : 2024-02-15DOI: 10.1177/10946705241232169
B. Rostami-Tabar, Rob J. Hyndman
Accurate forecasts of ambulance demand are crucial inputs when planning and deploying staff and fleet. Such demand forecasts are required at national, regional, and sub-regional levels and must take account of the nature of incidents and their priorities. These forecasts are often generated independently by different teams within the organization. As a result, forecasts at different levels may be inconsistent, resulting in conflicting decisions and a lack of coherent coordination in the service. To address this issue, we exploit the hierarchical and grouped structure of the demand time series and apply forecast reconciliation methods to generate both point and probabilistic forecasts that are coherent and use all the available data at all levels of disaggregation. The methods are applied to daily incident data from an ambulance service in Great Britain, from October 2015 to July 2019, disaggregated by nature of incident, priority, managing health board, and control area. We use an ensemble of forecasting models and show that the resulting forecasts are better than any individual forecasting model. We validate the forecasting approach using time series cross-validation.
{"title":"Hierarchical Time Series Forecasting in Emergency Medical Services","authors":"B. Rostami-Tabar, Rob J. Hyndman","doi":"10.1177/10946705241232169","DOIUrl":"https://doi.org/10.1177/10946705241232169","url":null,"abstract":"Accurate forecasts of ambulance demand are crucial inputs when planning and deploying staff and fleet. Such demand forecasts are required at national, regional, and sub-regional levels and must take account of the nature of incidents and their priorities. These forecasts are often generated independently by different teams within the organization. As a result, forecasts at different levels may be inconsistent, resulting in conflicting decisions and a lack of coherent coordination in the service. To address this issue, we exploit the hierarchical and grouped structure of the demand time series and apply forecast reconciliation methods to generate both point and probabilistic forecasts that are coherent and use all the available data at all levels of disaggregation. The methods are applied to daily incident data from an ambulance service in Great Britain, from October 2015 to July 2019, disaggregated by nature of incident, priority, managing health board, and control area. We use an ensemble of forecasting models and show that the resulting forecasts are better than any individual forecasting model. We validate the forecasting approach using time series cross-validation.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"227 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-15DOI: 10.1177/10946705241232169
B. Rostami-Tabar, Rob J. Hyndman
Accurate forecasts of ambulance demand are crucial inputs when planning and deploying staff and fleet. Such demand forecasts are required at national, regional, and sub-regional levels and must take account of the nature of incidents and their priorities. These forecasts are often generated independently by different teams within the organization. As a result, forecasts at different levels may be inconsistent, resulting in conflicting decisions and a lack of coherent coordination in the service. To address this issue, we exploit the hierarchical and grouped structure of the demand time series and apply forecast reconciliation methods to generate both point and probabilistic forecasts that are coherent and use all the available data at all levels of disaggregation. The methods are applied to daily incident data from an ambulance service in Great Britain, from October 2015 to July 2019, disaggregated by nature of incident, priority, managing health board, and control area. We use an ensemble of forecasting models and show that the resulting forecasts are better than any individual forecasting model. We validate the forecasting approach using time series cross-validation.
{"title":"Hierarchical Time Series Forecasting in Emergency Medical Services","authors":"B. Rostami-Tabar, Rob J. Hyndman","doi":"10.1177/10946705241232169","DOIUrl":"https://doi.org/10.1177/10946705241232169","url":null,"abstract":"Accurate forecasts of ambulance demand are crucial inputs when planning and deploying staff and fleet. Such demand forecasts are required at national, regional, and sub-regional levels and must take account of the nature of incidents and their priorities. These forecasts are often generated independently by different teams within the organization. As a result, forecasts at different levels may be inconsistent, resulting in conflicting decisions and a lack of coherent coordination in the service. To address this issue, we exploit the hierarchical and grouped structure of the demand time series and apply forecast reconciliation methods to generate both point and probabilistic forecasts that are coherent and use all the available data at all levels of disaggregation. The methods are applied to daily incident data from an ambulance service in Great Britain, from October 2015 to July 2019, disaggregated by nature of incident, priority, managing health board, and control area. We use an ensemble of forecasting models and show that the resulting forecasts are better than any individual forecasting model. We validate the forecasting approach using time series cross-validation.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"1 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139774584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10946705241230840
Pankaj C. Patel, G. Sahi
Artificial intelligence (AI)-driven automation is of growing interest in the service sector. Using practice theory in service innovation and recombinant uncertainty frameworks, we ask whether AI patent approval for service firms is received positively by the stock market and whether patent radicalness strengthens or exacerbates the stock market reaction. We draw on 650 service industry firms from the years 1976 to 2019 with 133,813 non-AI patents and AI patents, including 7,543 (AI machine learning), 33,804 (AI hardware), and 53,419 (AI planning/control). The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction. In addition, stock market reaction is insignificant to AI-related hardware patents and increasing radicalness does not influence this relationship. The findings are robust to excluding large firms representing a significant portion of the AI patents. With increasing radicalness, the stock market reaction to machine learning patents is more positive for low temporal depth and exacerbates with higher patent pedigree. The findings have implications for AI patenting among firms in the service sector.
{"title":"AI Patent Approvals in Service Firms, Patent Radicalness, and Stock Market Reaction","authors":"Pankaj C. Patel, G. Sahi","doi":"10.1177/10946705241230840","DOIUrl":"https://doi.org/10.1177/10946705241230840","url":null,"abstract":"Artificial intelligence (AI)-driven automation is of growing interest in the service sector. Using practice theory in service innovation and recombinant uncertainty frameworks, we ask whether AI patent approval for service firms is received positively by the stock market and whether patent radicalness strengthens or exacerbates the stock market reaction. We draw on 650 service industry firms from the years 1976 to 2019 with 133,813 non-AI patents and AI patents, including 7,543 (AI machine learning), 33,804 (AI hardware), and 53,419 (AI planning/control). The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction. In addition, stock market reaction is insignificant to AI-related hardware patents and increasing radicalness does not influence this relationship. The findings are robust to excluding large firms representing a significant portion of the AI patents. With increasing radicalness, the stock market reaction to machine learning patents is more positive for low temporal depth and exacerbates with higher patent pedigree. The findings have implications for AI patenting among firms in the service sector.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139793486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/10946705241230840
Pankaj C. Patel, G. Sahi
Artificial intelligence (AI)-driven automation is of growing interest in the service sector. Using practice theory in service innovation and recombinant uncertainty frameworks, we ask whether AI patent approval for service firms is received positively by the stock market and whether patent radicalness strengthens or exacerbates the stock market reaction. We draw on 650 service industry firms from the years 1976 to 2019 with 133,813 non-AI patents and AI patents, including 7,543 (AI machine learning), 33,804 (AI hardware), and 53,419 (AI planning/control). The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction. In addition, stock market reaction is insignificant to AI-related hardware patents and increasing radicalness does not influence this relationship. The findings are robust to excluding large firms representing a significant portion of the AI patents. With increasing radicalness, the stock market reaction to machine learning patents is more positive for low temporal depth and exacerbates with higher patent pedigree. The findings have implications for AI patenting among firms in the service sector.
{"title":"AI Patent Approvals in Service Firms, Patent Radicalness, and Stock Market Reaction","authors":"Pankaj C. Patel, G. Sahi","doi":"10.1177/10946705241230840","DOIUrl":"https://doi.org/10.1177/10946705241230840","url":null,"abstract":"Artificial intelligence (AI)-driven automation is of growing interest in the service sector. Using practice theory in service innovation and recombinant uncertainty frameworks, we ask whether AI patent approval for service firms is received positively by the stock market and whether patent radicalness strengthens or exacerbates the stock market reaction. We draw on 650 service industry firms from the years 1976 to 2019 with 133,813 non-AI patents and AI patents, including 7,543 (AI machine learning), 33,804 (AI hardware), and 53,419 (AI planning/control). The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction. In addition, stock market reaction is insignificant to AI-related hardware patents and increasing radicalness does not influence this relationship. The findings are robust to excluding large firms representing a significant portion of the AI patents. With increasing radicalness, the stock market reaction to machine learning patents is more positive for low temporal depth and exacerbates with higher patent pedigree. The findings have implications for AI patenting among firms in the service sector.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"51 5-6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139853443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1177/10946705241230851
A. Luo, Anna S. Mattila, Lisa E. Bolton
Consumers’ multisensory preferences bring new ideas to service and experience design—yet do consumers always react favorably to sensory complexity? This research examines variation by time of day in how consumers respond to complex sensory experiences (e.g., purchase behavior, choice, and liking). Specifically, we theorize that arousal levels increase over the course of the day, which increases the perceived fit of complex sensory experiences, leading to more favorable reactions—a pattern that is more prominent among evening than morning chronotypes. A set of five studies provides support for this theorizing and provides important implications for service providers regarding how to vary their sensory offerings and promotions over the course of the day.
{"title":"Simple Morning and Complex Night: Time of Day and Complex Sensory Experiences","authors":"A. Luo, Anna S. Mattila, Lisa E. Bolton","doi":"10.1177/10946705241230851","DOIUrl":"https://doi.org/10.1177/10946705241230851","url":null,"abstract":"Consumers’ multisensory preferences bring new ideas to service and experience design—yet do consumers always react favorably to sensory complexity? This research examines variation by time of day in how consumers respond to complex sensory experiences (e.g., purchase behavior, choice, and liking). Specifically, we theorize that arousal levels increase over the course of the day, which increases the perceived fit of complex sensory experiences, leading to more favorable reactions—a pattern that is more prominent among evening than morning chronotypes. A set of five studies provides support for this theorizing and provides important implications for service providers regarding how to vary their sensory offerings and promotions over the course of the day.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1177/10946705241230851
A. Luo, Anna S. Mattila, Lisa E. Bolton
Consumers’ multisensory preferences bring new ideas to service and experience design—yet do consumers always react favorably to sensory complexity? This research examines variation by time of day in how consumers respond to complex sensory experiences (e.g., purchase behavior, choice, and liking). Specifically, we theorize that arousal levels increase over the course of the day, which increases the perceived fit of complex sensory experiences, leading to more favorable reactions—a pattern that is more prominent among evening than morning chronotypes. A set of five studies provides support for this theorizing and provides important implications for service providers regarding how to vary their sensory offerings and promotions over the course of the day.
{"title":"Simple Morning and Complex Night: Time of Day and Complex Sensory Experiences","authors":"A. Luo, Anna S. Mattila, Lisa E. Bolton","doi":"10.1177/10946705241230851","DOIUrl":"https://doi.org/10.1177/10946705241230851","url":null,"abstract":"Consumers’ multisensory preferences bring new ideas to service and experience design—yet do consumers always react favorably to sensory complexity? This research examines variation by time of day in how consumers respond to complex sensory experiences (e.g., purchase behavior, choice, and liking). Specifically, we theorize that arousal levels increase over the course of the day, which increases the perceived fit of complex sensory experiences, leading to more favorable reactions—a pattern that is more prominent among evening than morning chronotypes. A set of five studies provides support for this theorizing and provides important implications for service providers regarding how to vary their sensory offerings and promotions over the course of the day.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"52 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-24DOI: 10.1177/10946705241229423
S. Sönnichsen, Ad de Jong, Jesper Clement, R. Maull, Chris Voss
The rising awareness of climate challenges and resource constraints has strengthened interest in the circular economy (CE), characterized as an economic system aimed to minimize the depletion of the world’s natural resources through processes of value retention and value regeneration. Because CE research originated in the engineering field, studies to date have mostly focused on technical and management-related topics. However, due to increasing demands from customers, investors, governmental institutions, and regulatory bodies, companies are increasingly considering how to effectively implement the CE. Despite its increasing importance, the CE is yet an uncharted area of transformative service research (TSR), and little is known about how the CE can support change for greater well-being among individuals and collectives. To fill this research gap, we integrate notions of the CE with TSR and research on value co-creation. The purpose of this paper is to expand research on CE and services by taking a TSR perspective to delineate how value retention and regeneration processes for different levels and spheres in services can effect change for greater individual and collective well-being.
人们对气候挑战和资源限制的认识不断提高，从而加强了对循环经济（CE）的兴趣，循环经济是一种旨在通过价值保留和价值再生过程最大限度地减少世界自然资源损耗的经济体系。由于循环经济研究起源于工程领域，因此迄今为止的研究大多集中在技术和管理相关主题上。然而，由于客户、投资者、政府机构和监管机构的需求不断增加，企业越来越多地考虑如何有效地实施 CE。尽管行政首长协调会的重要性与日俱增，但它仍是变革性服务研究（TSR）的一个未知领域，人们对行政首长协调会如何支持变革以提高个人和集体的福祉知之甚少。为了填补这一研究空白，我们将行政首长协调会的概念与变革性服务研究和价值共创研究结合起来。本文旨在从 TSR 的视角出发，扩展有关 CE 和服务的研究，以阐明服务中不同层次和领域的价值保留和再生过程如何能为个人和集体的更大福祉带来变化。
{"title":"The Circular Economy: A Transformative Service Perspective","authors":"S. Sönnichsen, Ad de Jong, Jesper Clement, R. Maull, Chris Voss","doi":"10.1177/10946705241229423","DOIUrl":"https://doi.org/10.1177/10946705241229423","url":null,"abstract":"The rising awareness of climate challenges and resource constraints has strengthened interest in the circular economy (CE), characterized as an economic system aimed to minimize the depletion of the world’s natural resources through processes of value retention and value regeneration. Because CE research originated in the engineering field, studies to date have mostly focused on technical and management-related topics. However, due to increasing demands from customers, investors, governmental institutions, and regulatory bodies, companies are increasingly considering how to effectively implement the CE. Despite its increasing importance, the CE is yet an uncharted area of transformative service research (TSR), and little is known about how the CE can support change for greater well-being among individuals and collectives. To fill this research gap, we integrate notions of the CE with TSR and research on value co-creation. The purpose of this paper is to expand research on CE and services by taking a TSR perspective to delineate how value retention and regeneration processes for different levels and spheres in services can effect change for greater individual and collective well-being.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"60 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-23DOI: 10.1177/10946705241229419
K. B. Q. Le, Laszlo Sajtos, Werner H. Kunz, Karen V. Fernandez
The use of digital employees (DEs)—chatbots powered by artificial intelligence (AI)—is becoming increasingly common in the service industry. However, it is unclear whether collaborations between the human employee (HE) and DE can influence customer outcomes, and what the mechanisms behind such outcomes are. This research proposes and tests a theoretical model that explains how the communication of HE-DE collaboration in the form of interdependent behavioral cues can influence customer evaluations of the service they received from such a team. Five experimental studies involving a total of 1403 participants demonstrate that making HE-DE collaboration visible to customers during the service encounter can reinforce their perception of HE-DE team cohesiveness and service process fluency, driving satisfaction. The communication of coordination and team goal cues are two strong stimulants that strengthen such impressions. Further, this research also reveals that the HE-DE collaboration (vs. augmentation or substitution) appeals to customers thanks to their perception of a transparent process, which is induced through collaborative cues. This research provides theoretical implications for a transparent collaborative process between HE and DE and practical advice for firms seeking to integrate DE into their organizations’ workflows.
{"title":"The Future of Work: Understanding the Effectiveness of Collaboration Between Human and Digital Employees in Service","authors":"K. B. Q. Le, Laszlo Sajtos, Werner H. Kunz, Karen V. Fernandez","doi":"10.1177/10946705241229419","DOIUrl":"https://doi.org/10.1177/10946705241229419","url":null,"abstract":"The use of digital employees (DEs)—chatbots powered by artificial intelligence (AI)—is becoming increasingly common in the service industry. However, it is unclear whether collaborations between the human employee (HE) and DE can influence customer outcomes, and what the mechanisms behind such outcomes are. This research proposes and tests a theoretical model that explains how the communication of HE-DE collaboration in the form of interdependent behavioral cues can influence customer evaluations of the service they received from such a team. Five experimental studies involving a total of 1403 participants demonstrate that making HE-DE collaboration visible to customers during the service encounter can reinforce their perception of HE-DE team cohesiveness and service process fluency, driving satisfaction. The communication of coordination and team goal cues are two strong stimulants that strengthen such impressions. Further, this research also reveals that the HE-DE collaboration (vs. augmentation or substitution) appeals to customers thanks to their perception of a transparent process, which is induced through collaborative cues. This research provides theoretical implications for a transparent collaborative process between HE and DE and practical advice for firms seeking to integrate DE into their organizations’ workflows.","PeriodicalId":506408,"journal":{"name":"Journal of Service Research","volume":"115 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：