Creating value from Social Big Data: Implications for Smart Tourism Destinations

This paper aims to demonstrate how the huge amount of Social Big Data available from tourists can nurture the value creation process for a Smart Tourism Destination. Applying a multiple-case study analysis, the paper explores a set of regional tourist experiences related to a Southern European region and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Findings present and discuss evidence in terms of improving decision-making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models. Finally, implications are presented for researchers and practitioners interested in the managerial exploitation of Big Data in the context of information-intensive industries and mainly in Tourism.     

大数据下的中国高端女性科技人才管理计量与对策分析

本文以近年中国高端女性科技人才群体的基础数据为依据,对其基本情况与发展状况等进行计量分析,借鉴相关国际女性科技人才管理政策的先进经验,尝试提出了运用大数据技术手段建立女性管理监测体系、制定女性人才培养规划、重构女性人才培养建制三大对策,以此改进完善中国高端女性科技人才管理状况。     

地球大数据支撑联合国可持续发展目标：气候变化与应对

气候变化正在成为未来几十年人类可持续发展面临的主要威胁。如何准确监测极端气候事件和灾害,为应对气候变化和灾害防控提供强有力的数据和科学支持,成为亟待回答的重大科学问题和决策命题。联合国可持续发展目标(SDGs)第13项——"气候行动:采取紧急行动应对气候变化及其影响"(SDG 13),就是要通过各国的实际行动,减缓气候变化威胁,增强人类适应能力。然而,目前SDG 13相关指标都缺少空间数据和信息的支撑。地球大数据具有高度协同性和集成性,有利于减小研究和评估结果的不确定性,同时也能满足气候变化和灾害风险研究对科学数据提出的迫切需求。文章围绕减缓气候相关灾害影响和降低温室气体排放两大主题,通过地球大数据平台,综合多源空间数据,研究获取灾害时空分布、碳收支变化趋势的方法;并依据该方法获取具有空间信息的数据集,以支撑SDG 13实现,为气候减灾和碳减排提供决策支持。     

地球大数据支撑可持续发展目标协同与权衡研究：进展与展望

联合国《改变我们的世界:2030年可持续发展议程》是各国实现经济、社会和环境共同发展的重要指南。当前,该议程的17个可持续发展目标(SDGs)的监测和评价已取得重要进展,但各SDGs间相互作用,特别是SDGs间的协同和权衡关系的认知仍较有限。文章首先从全部目标关系的综合分析、典型多目标关系分析、单目标内子指标间的关系3个方面描述了当前SDGs协同与权衡的研究进展和主要发现;并针对研究中的数据瓶颈问题,剖析了地球大数据支撑多目标协同和权衡的思路及典型案例;在此基础上,对地球大数据促进SDGs协同和权衡研究进行了展望。研究表明,地球大数据在提升SDG指标数据一致性、透明性、时效性和准确性等方面能够发挥重要作用,可以改进前期基于专家知识或统计数据等方法的不足,为提升多目标协同和权衡研究的定量水平提供重要数据支撑。最后,应对SDGs权衡的挑战,提出了完善地球大数据支撑SDGs协同与权衡的方法体系并构建模拟与预警平台、加强不同领域和主体的合作、强化技术创新推动等建议。     

Artificial intelligence in information systems research: A systematic literature review and research agenda

AI has received increased attention from the information systems (IS) research community in recent years. There is, however, a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously. This study addresses this concern, by conducting a systematic literature review of AI research in IS between 2005 and 2020. The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented. In doing so, this study makes important contributions, namely (i) an identification of the current reported business value and contributions of AI, (ii) research and practical implications on the use of AI and (iii) opportunities for future AI research in the form of a research agenda.     

大数据背景下的政府数据治理:共享机制、管理机制研究

对大数据驱动的管理与决策的相关文献进行研究,得出大数据资源的共享机制及其信息孤岛互联技术是当今大数据研究的前沿课题之一。对国内外政府数据共享交换应用进行研究分析,归纳政府数据资源共享交换存在管理理念问题和原有系统造成数据壁垒的问题。基于云平台,结合数据即服务的理论,提出构建政府全量数据资源的管理框架,在保证不对原有系统做任何改动的前提下,做到数据不搬家、数据不复制、数据不改变原来的管理模式,界定各个运营主体对数据的权利、义务,解决数据共享交换面临的管理理念问题和系统壁垒问题。     

Supporting performance management with business process management and business intelligence: A case analysis of integration and orchestration

The case(s) demonstrates the importance of business process management (BPM) and business intelligence systems (BIS) in achieving better firm performance. It has been well documented in the literature that research on the effectively usage and combination of knowledge from BPM and BIS in turbulent service environments is limited. In response, we conduct an exploratory comparative case study of four firms in banking and telecommunication industries that have implemented BPM initiative and BIS solution. Our results firstly highlight that actual results of applying BPM and BIS differ greatly from the results that were originally planned. Secondly, we find that BIS initiatives are usually driven by improving marketing and sales, while BPM initiatives are driven by improving business processes. Thirdly, we identify that there is a lack of strong commitment to using both systems for supporting performance management.     

The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions

Artificial Intelligence tools have attracted attention from the literature and business organizations in the last decade, especially by the advances in machine learning techniques. However, despite the great potential of AI technologies for solving problems, there are still issues involved in practical use and lack of knowledge as regards using AI in a strategic way, in order to create business value. In this context, the present study aims to fill this gap by: providing a critical literature review related to the integration of AI to organizational strategy; synthetizing the existing approaches and frameworks, highlighting the potential benefits, challenges and opportunities; presenting a discussion about future research directions. Through a systematic literature review, research articles were analyzed. Besides gaps for future studies, a conceptual framework is presented, discussed according to four sources of value creation: (i) decision support; (ii) customer and employee engagement; (iii) automation; and (iv) new products and services. These findings contribute to both theoretical and managerial perspectives, with extensive opportunities for generating novel theory and new forms of management practices.     

A review and future direction of agile,business intelligence,analytics and data science

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.     

A survey towards an integration of big data analytics to big insights for value-creation

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.     

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.     

Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders; and (iii) enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The number of humanitarian relief actions is growing due to the increased frequency of natural and man-made crises. Various streams of research across different disciplines have focused on ICT and AI solutions for enhancing disaster management processes. However, most of the existing research is fragmented without a common vision towards a converging paradigm. Recognizing this, this paper presents the Disaster City Digital Twin as a unifying paradigm. The four main components of the proposed Digital Twin paradigm include: multi-data sensing for data collection, data integration and analytics, multi-actor game-theoretic decision making, and dynamic network analysis. For each component, the current state of the art related to AI methods and approaches are examined and gaps are identified.     

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.     

Considerations for development and use of AI in response to COVID-19

Artificial intelligence (AI) is playing a key supporting role in the fight against COVID-19 and perhaps will contribute to solutions quicker than we would otherwise achieve in many fields and applications. Since the outbreak of the pandemic, there has been an upsurge in the exploration and use of AI, and other data analytic tools, in a multitude of areas. This paper addresses some of the many considerations for managing the development and deployment of AI applications, including planning; unpredictable, unexpected, or biased results; repurposing; the importance of data; and diversity in AI team membership. We provide implications for research and for practice, according to each of the considerations. Finally we conclude that we need to plan and carefully consider the issues associated with the development and use of AI as we look for quick solutions.     

The promise of digital technologies for sustainable entrepreneurship: A systematic literature review and research agenda

Research on sustainable entrepreneurship increasingly recognizes the transformative potential of digital technologies to mitigate and counteract grand environmental and social challenges through entrepreneurial action. However, this emerging field of research, referred to as digital sustainable entrepreneurship, is currently dispersed and fragmented and lacks the consolidated foundation to progress further. This article further establishes this nascent stream by conducting a systematic literature review offering two main contributions. First, common themes are derived from the literature (i.e., enabling value for society and environment, stakeholder inclusion, venture viability, and entrepreneurial individuals) to unravel the field's current state. Second, previous work is discussed and integrated by applying a business model perspective. Specifically, the article offers a framework that contributes to the role of business models for merging sustainability and digital technologies, reconceptualizes digital technologies as business model actors, and further develops the entrepreneur-business model nexus. Based on this, we present a comprehensive and actionable research agenda and practical implications.     

The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations

The Internet of Things (IoT) might yield many benefits for organizations, but like other technology adoptions may also introduce unforeseen risks and requiring substantial organizational transformations. This paper analyzes IoT adoption by organizations, and identifies IoT benefits and risks. A Big, Open, Linked Data (BOLD) categorization of the expected benefits and risks of IoT is made by conducting a comprehensive literature study. In-depth case studies in the field of asset management were then executed to examine the actual experienced, real world benefits and risks. The duality of technology is used as our theoretical lens to understand the interactions between organization and technology. The results confirm the duality that gaining the benefits of IoT in asset management produces unexpected social changes that lead to structural transformation of the organization. IoT can provide organizations with many benefits, after having dealt with unexpected risks and making the necessary organizational changes. There is a need to introduce changes to the organization, processes and systems, to develop capabilities and ensure that IoT fits the organization’s purposes.     

大数据环境下科技期刊同被引文献数的学术评价功能——基于与引证文献数的对比研究

基于科技创新成果评价与管理的视角,研究科技期刊论文同被引文献数的科学内涵和学术价值。通过频数分析、回归分析与变异系数的差异性分析,对比研究引证文献数和同被引文献数的特征及评价功能。研究发现,引证文献数与同被引文献数既有正相关性,又有显著差异性,同被引文献数的内涵更为丰富。大数据环境下同被引文献数在时间上的反应速度比引证文献数快,且具有客观性、科学性和有效性等优势,具有很强的学术评价功能,涉及到期刊、论文、作者的学术影响力以及学科或选题的热度,可以作为学术评价的重要补充手段。     

质量管理在制造技术与企业绩效中的中介效应检验 ——基于Bootstrap法的结构方程分析

摘要：以重庆市制造企业为样本,运用基于Bootstrap的结构方程方法,对质量管理在先进制造技术与企业绩效间的中介作用进行了实证检验。中介效应的三步检验结果表明：先进制造技术对企业绩效的直接作用并不显著;先进制造技术对质量管理、质量管理对企业绩效均有显著的直接影响,质量管理活动在先进制造技术和企业绩效的关系中起到了完全的中介作用。中介效应的进一步分析显示,先进制造技术通过对质量管理核心活动与基础活动的递进作用,最终经由员工管理和流程管理活动间接改善了企业的绩效水平。