Making Business of a Revolutionary New Technology The Eckert-Mauchly Company, 1945-1951

The paper analyzes John Presper Eckert and John William Mauchly＇s endeavours to design, sell, and build the revolutionary new technology of the first-large and commercial computers. It discusses how Eckert and Mauchly＇s conceptualization of the computer grew out of their Electronic Numerical Integrator and Calculator （ENIAC） and Electronic Discrete Variable Automatic Computer （EDVAC） projects at University of Pennsylvania. They incorporated their own business to gain profit from production and attain the freedom needed to develop their revolutionary new computer technology through a series of small and separate computer projects with private and government customers. The paper approaches innovation as a chaotic process and uses uncertainty to conceptualize the basic relations between actors and organizations.

竞争效应、共谋效应与跨国公司选址--基于垂直相关市场的研究

本文分析了垂直相关市场中,政府之间对FDI的政策竞争及其福利效应。利用非对称两国模型,假设两国各拥有一家上游企业以及一体化企业。结论表明,跨国公司的选址取决于竞争效应、共谋效应以及政府补贴政策的共同作用。其中,共谋效应是指,垂直相关市场中,当下游企业向其竞争对手购买某种重要的中间产品时,由于企业在最终产品市场的行为会影响来自中间产品市场的利润,因此企业之间会有动机进行共谋,降低最终产品市场的竞争,提高市场均衡价格,从而两企业都可以获得更高的利润。竞争效应是指,跨国公司在不存在其他竞争者的市场环境中生产运营获得的收益,即企业作为当地市场中唯一的生产者,它将从竞争较低的市场环境中获益。在此基础上,我们分析了FDI竞争的福利效应及政策含义。具体来说,当一国相对优势较强,即两国非对称性较显著时,FDI竞争将会提高社会福利,此时税收竞争是更有利的;而当一国相对优势较弱,即两国非对称性不显著时,FDI竞争将会使得社会福利降低,此时税收协调是更有利的。

国际一体化对宏观经济政策有效性的影响

本文讨论了国内宏观经济政策如何受到国际一体化的影响。宏观经济政策有效性取决于以下几个因素:政策工具的数目、达到经济目标的效果,及运用这些工具时受到的约束。一般认为,国际一体化降低政策有效性,而本文指出,国际一体化在很多情况下可能增强决策者运用宏观经济政策的能力。国际一体化创造了许多新的政策工具。有关货币政策、财政政策和产业结构政策的标准分析显示,国际一体化究竟是提高还是降低了政策工具的效力,取决于国际一体化的具体来源和一国的货币制度。

Health analytics in business research:a literature review

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.

供应链联盟学习的双元模式:交易价值视角

从交易价值视角对供应链联盟学习进行理论探讨,主要贡献在于提出了一个兼顾供应链联盟企业内部学习和供应链联盟企业外部学习的双元模式,因而为供应链联盟研究提供了一个全新的视角。具体上讲,本文提出一个新颖的观点,即供应链联盟企业间学习依靠信任,强调企业间的专业互补,主要是一种路径突破性学习;与此相对,企业内部学习主要依靠正式控制,强调企业的专业特长,主要是一种路径依赖性学习的结论。

Natural language processing(NLP)in management research:A literature review

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.

Blockchain-enabled decentralized identity management:The case of self-sovereign identity in public transportation

Identity management has been ripe for disruption over the past few years due to recurring incidents of data breaches that have led to personal information leaks and identity theft.The rise of blockchain technology has paved the way for the development of self-sovereign identity(SSI)—a new class of user-controlled resilient identity management systems that are enabled by distributed ledger technology.This paper examines how SSI management can be used in a public transportation sector that spans different operators in multiple countries.Specifically,the paper explores how a blockchain-based decentralized identity management system can draw on the SSI framework to provide high-level security and transparency for all involved parties in public transportation ecosystems.Accordingly,building on analyses of the existing public transportation ticketing solutions,we elicited requirements of a comparable system based on the SSI principles.Next,we developed a low-fidelity prototype to showcase how passengers can utilize standardized travel credentials that are valid across different transportation networks in Europe.The proposed system eliminates the need for multiple travel cards(i.e.,one for each transportation provider)and empowers individuals to have better control over the use of their identities while they utilize interoperable ticketing systems across Europe.Overall,building on the public transportation case,we offer a proof-of-concept that shows how individuals can better manage their identity credentials via the SSI framework.

Traversing knowledge networks:an algorithmic historiography of extant literature on the Internet of Things(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.

Image Analytics:A consolidation of visual feature extraction methods

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.

Bibliometrics and Network Analysis of Cryptocurrency Research

Cryptocurrency, as an emerging social phenomenon, has attracted great research attentions and starts to form a new research area in the past decade. A thorough review of the existing cryptocurrency literatures helps us to understand the status-quo and the promising research future of this emerging field. This paper provides a systematic review of the cryptocurrency literatures by employing bibliometrics and network analysis methods. A total of 833 publications from Web of Science Core Collection and Scopus have been collected in the time period of January 2008 to June 2018. We examine the descriptive features, countries’ contributions and collaboration networks, author collaboration networks and reference co-citation networks to identify actors with significant influence in this field. The results show that cryptocurrency research is experiencing exponential growth, but scientific collaborations between different countries and authors are still insufficient. The intellectual bases of the cryptocurrency domain rest on the early-year literatures that provide fundamental understandings for cryptocurrency, and that bridge different research fields. The analysis indicates that the current research trend focuses on cryptocurrency market analysis incorporating market behaviors and trading characteristics.

Fighting Financial Crime:Who Designs Global Governance and Who Does The Work?

Fighting financial crime is a highly institutionalised global governance task.At a time of crisis for many of the institutions of global governance,tackling money laundering and combatting terrorist financing through global cooperation continues to be a priority for public officials.The global regime,if anything,is intensifying.This essay provides an overview of the regime’s development and addresses questions of design and implementation.It is structured around three sets of questions:(1)What does the regime look like and what is it for?(2)Who does the work?(3)And,in conclusion,what can we say about winners and losers?

CALL FOR PAPERS Theme:Embracing the Internet of Things to Drive Data-Driven Decisions

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.

CALL FOR PAPERS Theme:Embracing the Internet of Things to Drive Data-Driven Decisions

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.

Call for Papers

Journal of Management Analytics Special Issue:Blockchain Analytics Guest 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.

Call for Papers Journal of Management Analytics(JMA)Special issue:artificial intelligence and management analytics

Guest 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).

Effects of age on live streaming viewer engagement:a dual coding perspective

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.

Guest Editorial: Special issue on embracing the Internet of Things to drive data-driven decisions

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.

Transformations of trust in society: A systematic review of how access to big data in energy systems challenges Scandinavian culture

In the era of information technology and big data,the extraction,commodification,and control of personal information is redefining how people relate and interact.However,the challenges that big data collection and analytics can introduce in trust-based societies,like those of Scandinavia,are not yet understood.For instance,in the energy sector,data generated through smart appliances,like smart metering devices,can have collateral implications for the end-users.In this paper,we present a systematic review of scientific articles indexed in Scopus to identify possible relationships between the practices of collecting,processing,analysing,and using people’s data and people’s responses to such practices.We contextualise this by looking at research about Scandinavian societies and link this to the academic literature on big data and trust,big data and smart meters,data ethics and the energy sector,surveillance capitalism,and subsequently performing a reflexive thematic analysis.We broadly situate our understanding of culture in this context on the interactions between cognitive norms,material culture,and energy practices.Our analysis identified a number of articles discussing problems and solutions to do with the practices of surveillance capitalism.We also found that research addresses these challenges in different ways.While some research focuses on technological amendments to address users’privacy protection,only few examine the fundamental ethical questions that discuss how big data practices may change societies and increase their vulnerability.The literature suggests that even in highly trusting societies,like the ones found in Scandinavian countries,trust can be undermined and weakened.

Transformative Innovation Policy:A Case-Study from China

Science,technology and innovation policy has been presented as developing through three"frames":innovation for growth;national systems of innovation;and transformative change(Schot and Steinmueller,2018).In this paper,we focus on the third frame by exploring the development of transformative innovation policy in China.More specifically,we present a case analysis of the Beautiful China Initiative(BCI).Within this context,we analyse participating actors,activities,and modes of innovation.In conclusion,we discuss three general challenges of transformative innovation policy at a national and international level.The first challenge is coordination and interaction between governmental agencies and other actors with different responsibilities and jurisdictions.Second,there is a major set of issues related to"development",which must encompass economic development but also societal and environmental development.Third,BCI fits with a broad understanding of the innovation process,including elements which are not directly commercial in orientation.Whether this broader approach can be maintained as BCI develops from its still-early stage into full development will represent a significant challenge.Finally in this paper,we consider the implications of our discussion for future research and practice,and offer three reflections.In particular,we emphasise the importance of scholars’participation across the social sciences rather than leaving this multi-faceted area only to innovation researchers.