针对软件即服务(Software as a service,SaaS)这一新兴的业务模式,分析了其特征、成本结构及客户价值,提出一个适用于该模式的价格歧视框架。该框架把价格歧视要素组织成三个层次:导向层包括价值导向及成本导向两个要素,类别层包括基于...针对软件即服务(Software as a service,SaaS)这一新兴的业务模式,分析了其特征、成本结构及客户价值,提出一个适用于该模式的价格歧视框架。该框架把价格歧视要素组织成三个层次:导向层包括价值导向及成本导向两个要素,类别层包括基于付款的、基于产品的、基于使用且考虑用量的、基于使用且不考虑用量的、基于服务的等五个类别,各类别与导向层某要素相关联,并对应到基本要素层的价格歧视基本要素。最后,采集353家SaaS厂商的价格方案数据对框架中价格歧视要素的应用情况进行分析,结果表明:本文框架能满足较全面地刻画SaaS厂商价格要素的实际需要,SaaS厂商关注的应用类型及目标企业分布十分广泛,但总体上分布不均且同质化现象明显,SaaS厂商综合使用多类价格歧视要素且成本要素不容忽视,价格要素选择与应用类型、目标企业类型间关系不明显。展开更多
Research on the Internet of Things(IoT)has been booming for the past 6 years due to technological advances and potential for application.Nonetheless,the rapid growth of IoT articles and the heterogeneous nature of IoT...Research on the Internet of Things(IoT)has been booming for the past 6 years due to technological advances and potential for application.Nonetheless,the rapid growth of IoT articles and the heterogeneous nature of IoT pose challenges to conducting a systematic review of IoT literature.This study seeks to address the abovementioned challenges by reviewing 1065 IoT articles retrieved from the International Statistical Institute Web of Science via a blend of quantitative citation analysis and qualitative content analysis.For the former,we generated a historiography of IoT research,a citation network,in which we tried to identify main paths of codification and diffusion,as well as path-dependent transitions.For the latter,we explicated the progression of knowledge through 30 central IoT articles in chronological order regarding infrastructures,enabling technologies,potential technologies,and research challenges.Findings from this study contribute to both IoT research and management.展开更多
Natural language processing(NLP)is gaining momentum in management research for its ability to automatically analyze and comprehend human language.Yet,despite its extensive application in management research,there is n...Natural language processing(NLP)is gaining momentum in management research for its ability to automatically analyze and comprehend human language.Yet,despite its extensive application in management research,there is neither a comprehensive review of extant literature on such applications,nor is there a detailed walkthrough on how it can be employed as an analytical technique.To this end,we review articles in the UT Dallas List of 24 Leading Business Journals that employ NLP as their focal analytical technique to elucidate how textual data can be harnessed for advancing management theories across multiple disciplines.We describe the available toolkits and procedural steps for employing NLP as an analytical technique as well as its advantages and disadvantages.In so doing,we highlight the managerial and technological challenges associated with the application of NLP in management research in order to guide future inquires.展开更多
Artificial Intelligence(AI),defined as“a system’s ability to correctly interpret external data,to learn from such data and to use those learnings to achieve specific goals and tasks through flexible adaptation”(Kap...Artificial Intelligence(AI),defined as“a system’s ability to correctly interpret external data,to learn from such data and to use those learnings to achieve specific goals and tasks through flexible adaptation”(Kaplan&Haenlein,2019b,p.17),has attracted immense attention among academics,business managers,entrepreneurs,and politicians alike.As alleged by Huang and Rust(2018,p.155),AI,despite“constituting a major source of innovation”,is“threatening human jobs”.展开更多
Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world.Although e...Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world.Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images,the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance.Consequently,this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques.We describe resources and techniques based on three visual feature extraction methods,namely calculation-,recognition-,and simulation-based.Additionally,we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.展开更多
Technological advances have enabled the collection of large quantities of valuable data for health-related use.Particularly,a growing number of scholars are paying attention to the application of health analytics to b...Technological advances have enabled the collection of large quantities of valuable data for health-related use.Particularly,a growing number of scholars are paying attention to the application of health analytics to business topics.A comprehensive analysis of health analytics application in business research is necessary to realize the commercial value of health analytics.In this article,we summarize peer-reviewed articles that have been published in business journals,with an eye toward formulating a roadmap for applying health analytics across multiple business domains.First,we demonstrate how health-related data can be harnessed to inform business research.Second,we endeavor to consolidate the available datasets and analytical techniques commonly employed to explore health analytics in business domains.Finally,we discuss the practical challenges confronting scholars in health analytics,as well as future research opportunities.Insights from our study yields insights that can be leveraged by business scholars interested in health analytics research.展开更多
Though the emerging live streaming industry has attracted growing attention,the dominant yanzhi category where streamers mostly interact with the audience through amateur talent shows and casual chats has not been wid...Though the emerging live streaming industry has attracted growing attention,the dominant yanzhi category where streamers mostly interact with the audience through amateur talent shows and casual chats has not been widely investigated.To decode the mechanism behind the popularity of yanzhi streamers,this study draws on Dual Coding Theory(DCT)to posit that age estimated from a streamer’s face and voice can influence the level of viewer engagement.To validate our hypothesized relationships,274 one-minute video records ahead of a viewer commenting or/and gifting were collected and analyzed via deep learning algorithms.Analytical results attest to the negative effects of both facial and vocal age on viewer engagement,while their interaction has a positive impact on viewer engagement.展开更多
vip Editors(in alphabetical order)Michael Haenlein,ESCP Europe Business School,Paris,France Andreas Kaplan,ESCP Europe Business School,Berlin,Germany Chee-Wee Tan,Copenhagen Business School(CBS),Copenhagen,Denmark P...vip Editors(in alphabetical order)Michael Haenlein,ESCP Europe Business School,Paris,France Andreas Kaplan,ESCP Europe Business School,Berlin,Germany Chee-Wee Tan,Copenhagen Business School(CBS),Copenhagen,Denmark Pengzhu Zhang,Shanghai Jiao Tong University,China Artificial Intelligence(AI),defined as“a system’s ability to correctly interpret external data,to learn from such data and to use those learnings to achieve specific goals and tasks through flexible adaptation”(Kaplan&Haenlein,2019).展开更多
Journal of Management Analytics Special Issue:Blockchain Analytics vip Editors Bin Gu Arizona State University,USA Ling Li Old Dominion University,USA Chee-Wee Tan Copenhagen Business School,Denmark Eric Zheng Unive...Journal of Management Analytics Special Issue:Blockchain Analytics vip Editors Bin Gu Arizona State University,USA Ling Li Old Dominion University,USA Chee-Wee Tan Copenhagen Business School,Denmark Eric Zheng University of Texas at Dallas,USA Blockchain is transforming industries by enabling innovative business practices.Its revolutionary power has permeated areas such as remittance,payment,banking,financing,trading,manufacturing,supply chain management,legal service,education,healthcare,government,and so on.Blockchain enables trustless transactions,smart contracts,decentralized economics,and disintermediated society.It has fundamentally altered how business value is being discovered,created,transferred,distributed,and realized in economics.This poses new challenges for both business practitioners and academic researchers.In particular,there is a gazing gap between the need for analytics expertise and the existing skills to comprehend the transformative power of Blockchain.This special issue hence calls for innovative and advanced research on Blockchain analytics.展开更多
The Internet of Things(IoT)is a fast emerging phenomenon that is characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange.It has attracted enormous atten...The Internet of Things(IoT)is a fast emerging phenomenon that is characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange.It has attracted enormous attention from both academics and practitioners alike.McKinsey&Company(2015)estimated that IoT could potentially generate annual economic returns in the range of USD$3.9-11.1 trillion by 2025.By blurring the boundaries among people,process,data and devices.展开更多
The Internet of Things(IoT),an emerging phenomenon characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange,has received enormous attention from both aca...The Internet of Things(IoT),an emerging phenomenon characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange,has received enormous attention from both academics and practitioners.It is estimated that IoT could potentially generate annual economic benefits in the range of USD$3.9 trillion to$11.1 trillion by 2025.By blurring the boundaries between people,process,data and devices,IoT has rendered networked connections more relevant and valuable than ever before.Specifically,massive data sets generated by distributed sensors and devices point to a growing urgency in developing data analytical tools and techniques to augment the decision-making process in order to realize business innovations in the likes of location-based services and customized production.展开更多
The Internet of Things(IoT),an emerging phenomenon characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange,has received enormous attention from both aca...The Internet of Things(IoT),an emerging phenomenon characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange,has received enormous attention from both academics and practitioners.It is estimated that IoT could potentially generate annual economic benefits in the range of USD$3.9 trillion to USD$11.1 trillion by 2025.By blurring the boundaries between people,process,data and devices,IoT has rendered networked connections more relevant and valuable than ever before.Specifically,massive datasets generated by distributed sensors and devices point to a growing urgency in developing data analytical tools and techniques to augment the decision-making process in order to realize business innovations in the likes of location-based services and customized production.展开更多
文摘针对软件即服务(Software as a service,SaaS)这一新兴的业务模式,分析了其特征、成本结构及客户价值,提出一个适用于该模式的价格歧视框架。该框架把价格歧视要素组织成三个层次:导向层包括价值导向及成本导向两个要素,类别层包括基于付款的、基于产品的、基于使用且考虑用量的、基于使用且不考虑用量的、基于服务的等五个类别,各类别与导向层某要素相关联,并对应到基本要素层的价格歧视基本要素。最后,采集353家SaaS厂商的价格方案数据对框架中价格歧视要素的应用情况进行分析,结果表明:本文框架能满足较全面地刻画SaaS厂商价格要素的实际需要,SaaS厂商关注的应用类型及目标企业分布十分广泛,但总体上分布不均且同质化现象明显,SaaS厂商综合使用多类价格歧视要素且成本要素不容忽视,价格要素选择与应用类型、目标企业类型间关系不明显。
文摘Research on the Internet of Things(IoT)has been booming for the past 6 years due to technological advances and potential for application.Nonetheless,the rapid growth of IoT articles and the heterogeneous nature of IoT pose challenges to conducting a systematic review of IoT literature.This study seeks to address the abovementioned challenges by reviewing 1065 IoT articles retrieved from the International Statistical Institute Web of Science via a blend of quantitative citation analysis and qualitative content analysis.For the former,we generated a historiography of IoT research,a citation network,in which we tried to identify main paths of codification and diffusion,as well as path-dependent transitions.For the latter,we explicated the progression of knowledge through 30 central IoT articles in chronological order regarding infrastructures,enabling technologies,potential technologies,and research challenges.Findings from this study contribute to both IoT research and management.
基金This work was supported by National Natural Science Foundation of China:[Grant Number 71571177,71622009,71801204,71921001,71971202].
文摘Natural language processing(NLP)is gaining momentum in management research for its ability to automatically analyze and comprehend human language.Yet,despite its extensive application in management research,there is neither a comprehensive review of extant literature on such applications,nor is there a detailed walkthrough on how it can be employed as an analytical technique.To this end,we review articles in the UT Dallas List of 24 Leading Business Journals that employ NLP as their focal analytical technique to elucidate how textual data can be harnessed for advancing management theories across multiple disciplines.We describe the available toolkits and procedural steps for employing NLP as an analytical technique as well as its advantages and disadvantages.In so doing,we highlight the managerial and technological challenges associated with the application of NLP in management research in order to guide future inquires.
文摘Artificial Intelligence(AI),defined as“a system’s ability to correctly interpret external data,to learn from such data and to use those learnings to achieve specific goals and tasks through flexible adaptation”(Kaplan&Haenlein,2019b,p.17),has attracted immense attention among academics,business managers,entrepreneurs,and politicians alike.As alleged by Huang and Rust(2018,p.155),AI,despite“constituting a major source of innovation”,is“threatening human jobs”.
文摘Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world.Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images,the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance.Consequently,this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques.We describe resources and techniques based on three visual feature extraction methods,namely calculation-,recognition-,and simulation-based.Additionally,we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.
基金supported by the National Natural Science Foundation of China(Grant No.91646205)National Social Science Fund of China(Grant No.21ZDA105)+2 种基金Humanity and Social Science Youth Foundation of Ministry of Education of China(Grant No.22YJC860034)Philosophy and Social Science Planning Project of China Tianjin(Grant No.TJXC21-007)Liberal Arts Development Foundation of Nankai University(Grant No.ZB21BZ0333).
文摘Technological advances have enabled the collection of large quantities of valuable data for health-related use.Particularly,a growing number of scholars are paying attention to the application of health analytics to business topics.A comprehensive analysis of health analytics application in business research is necessary to realize the commercial value of health analytics.In this article,we summarize peer-reviewed articles that have been published in business journals,with an eye toward formulating a roadmap for applying health analytics across multiple business domains.First,we demonstrate how health-related data can be harnessed to inform business research.Second,we endeavor to consolidate the available datasets and analytical techniques commonly employed to explore health analytics in business domains.Finally,we discuss the practical challenges confronting scholars in health analytics,as well as future research opportunities.Insights from our study yields insights that can be leveraged by business scholars interested in health analytics research.
文摘Though the emerging live streaming industry has attracted growing attention,the dominant yanzhi category where streamers mostly interact with the audience through amateur talent shows and casual chats has not been widely investigated.To decode the mechanism behind the popularity of yanzhi streamers,this study draws on Dual Coding Theory(DCT)to posit that age estimated from a streamer’s face and voice can influence the level of viewer engagement.To validate our hypothesized relationships,274 one-minute video records ahead of a viewer commenting or/and gifting were collected and analyzed via deep learning algorithms.Analytical results attest to the negative effects of both facial and vocal age on viewer engagement,while their interaction has a positive impact on viewer engagement.
文摘vip Editors(in alphabetical order)Michael Haenlein,ESCP Europe Business School,Paris,France Andreas Kaplan,ESCP Europe Business School,Berlin,Germany Chee-Wee Tan,Copenhagen Business School(CBS),Copenhagen,Denmark Pengzhu Zhang,Shanghai Jiao Tong University,China Artificial Intelligence(AI),defined as“a system’s ability to correctly interpret external data,to learn from such data and to use those learnings to achieve specific goals and tasks through flexible adaptation”(Kaplan&Haenlein,2019).
文摘Journal of Management Analytics Special Issue:Blockchain Analytics vip Editors Bin Gu Arizona State University,USA Ling Li Old Dominion University,USA Chee-Wee Tan Copenhagen Business School,Denmark Eric Zheng University of Texas at Dallas,USA Blockchain is transforming industries by enabling innovative business practices.Its revolutionary power has permeated areas such as remittance,payment,banking,financing,trading,manufacturing,supply chain management,legal service,education,healthcare,government,and so on.Blockchain enables trustless transactions,smart contracts,decentralized economics,and disintermediated society.It has fundamentally altered how business value is being discovered,created,transferred,distributed,and realized in economics.This poses new challenges for both business practitioners and academic researchers.In particular,there is a gazing gap between the need for analytics expertise and the existing skills to comprehend the transformative power of Blockchain.This special issue hence calls for innovative and advanced research on Blockchain analytics.
基金This work was supported by the National Natural Science Foundation of China[grant numbers 71531010,71325003].
文摘The Internet of Things(IoT)is a fast emerging phenomenon that is characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange.It has attracted enormous attention from both academics and practitioners alike.McKinsey&Company(2015)estimated that IoT could potentially generate annual economic returns in the range of USD$3.9-11.1 trillion by 2025.By blurring the boundaries among people,process,data and devices.
文摘The Internet of Things(IoT),an emerging phenomenon characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange,has received enormous attention from both academics and practitioners.It is estimated that IoT could potentially generate annual economic benefits in the range of USD$3.9 trillion to$11.1 trillion by 2025.By blurring the boundaries between people,process,data and devices,IoT has rendered networked connections more relevant and valuable than ever before.Specifically,massive data sets generated by distributed sensors and devices point to a growing urgency in developing data analytical tools and techniques to augment the decision-making process in order to realize business innovations in the likes of location-based services and customized production.
文摘The Internet of Things(IoT),an emerging phenomenon characterized by an expanding network of interconnected sensors and actuators to facilitate data collection and exchange,has received enormous attention from both academics and practitioners.It is estimated that IoT could potentially generate annual economic benefits in the range of USD$3.9 trillion to USD$11.1 trillion by 2025.By blurring the boundaries between people,process,data and devices,IoT has rendered networked connections more relevant and valuable than ever before.Specifically,massive datasets generated by distributed sensors and devices point to a growing urgency in developing data analytical tools and techniques to augment the decision-making process in order to realize business innovations in the likes of location-based services and customized production.
