人工智能、制造业创新链与经济高质量发展---基于28个省区面板数据的实证研究

基于中国2006—2019年28个省份的面板数据，构建中介效应模型，以制造业创新链作为中介渠道，分析人工智能对地区经济高质量发展的影响。研究发现，人工智能对各省份经济高质量发展有显著的促进作用，制造业创新链在其中发挥重要的中介作用。与中西部地区相比，人工智能对东部地区的创新链发展的促进作用更强；与2013年以及之后相比，人工智能的单位变化在2013年以及之前的时期对经济高质量发展的促进作用更强。此外，人工智能主要通过制造业创新链的投入和产出环节，促进地区经济高质量发展，而市场应用环节的中介作用并不显著。     

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.     

Artificial intelligence for sustainability: Challenges,opportunities, and a research agenda

Artificial intelligence (AI) will transform business practices and industries and has the potential to address major societal problems, including sustainability. Degradation of the natural environment and the climate crisis are exceedingly complex phenomena requiring the most advanced and innovative solutions. Aiming to spur groundbreaking research and practical solutions of AI for environmental sustainability, we argue that AI can support the derivation of culturally appropriate organizational processes and individual practices to reduce the natural resource and energy intensity of human activities. The true value of AI will not be in how it enables society to reduce its energy, water, and land use intensities, but rather, at a higher level, how it facilitates and fosters environmental governance. A comprehensive review of the literature indicates that research regarding AI for sustainability is challenged by (1) overreliance on historical data in machine learning models, (2) uncertain human behavioral responses to AI-based interventions, (3) increased cybersecurity risks, (4) adverse impacts of AI applications, and (5) difficulties in measuring effects of intervention strategies. The review indicates that future studies of AI for sustainability should incorporate (1) multilevel views, (2) systems dynamics approaches, (3) design thinking, (4) psychological and sociological considerations, and (5) economic value considerations to show how AI can deliver immediate solutions without introducing long-term threats to environmental sustainability.     

Artificial intelligence innovation in education: A twenty-year data-driven historical analysis

Reflecting on twenty years of educational research, we retrieved over 400 research article on the application of artificial intelligence (AI) and deep learning (DL) techniques in teaching and learning. A computerised content analysis was conducted to examine how AI and DL research themes have evolved in major educational journals. By doing so, we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade, due to the discipline’s dynamism. By examining the major research themes and historical trends from 2000 to 2019, we demonstrate that, as advanced technologies in education evolve over time, some areas of research topics seem have stood the test of time, while some others have experienced peaks and valleys. More importantly, our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational research. For instance, the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning analytics. Furthermore, this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.     

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.     

An efficient smart data mining framework based cloud internet of things for developing artificial intelligence of marketing information analysis

Because of the big volume of marketing data, a human analyst would be unable to uncover any useful information for marketing that could aid in the process of making decision. Smart Data Mining (SDM), which is considered an important field from Artificial Intelligence (AI) is completely assisting in the performance business management analytics and marketing information. In this study, most reliable six algorithms in SDM are applied; Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), ID3, and C4.5 on actual data of marketing for bank that taken from Cloud Internet of Thing (CIoT). The objectives of this study are to build an efficient framework to increase campaign of marketing for banks by identifying main characteristics that affect a success and to test the performance of CIoT and SDM algorithms. This study is expected to enhance the scientific contributions to investigating the marketing information capacities by integrating SDM with CIoT. The performances of SDM algorithms are calculated by eight measures; accuracy, balance accuracy, precision, mean absolute error, root mean absolute error, recall, F1- Score and running time. The experimental findings show that the proposed framework is successful, with higher accuracies and good performance. Results revealed that customer service & marketing tactics are essential for a Company’ success & survival. Also, the C4.5 has accomplished better achievement than the SVM, RF, LR, NB, & ID3. At the end, CIoT Platform was evaluated by response time, request rate & processing of bank data.     

Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation

Although AI-enabled customer relationship management (CRM) systems have gained momentum in healthcare to enhance performance, there is a striking dearth of knowledge on how such capabilities are formed and affect service innovation. The study adopted a mixed-method approach to investigate the underlying phenomena. This research infused resource-based theory, dynamic capability theory, and theory of productivity paradox to investigate how healthcare in India acquires AI-enabled CRM capabilities and enhances service innovation. We identified the facets of AI-enabled CRM capabilities using a case study and developed a framework for AI-enabled CRM capability and service innovation. This study noticed that customer service flexibility (CSF) is a missing link in this relationship. The findings of the quantitative study employing PLS-SEM reveal the linear relationships between AI-enabled CRM capability, CSF, and service innovation. This study explains the formation of AI-enabled CRM capabilities to fill the research gap and direct innovative performance in healthcare, which is an immediate need to sustain in a volatile environment. This study provides theoretical implications to enhance the research stream and practical implications for decision-makers.     

中外人工智能审计研究热点及演进知识图谱比较研究

应用Cite Space 科学计量可视化软件,以CNKI数据库和WOS 核心合集中的人工智能审计研究文献为研究对象,通过对文献数量、研究关键词、发文国家及机构的分析,总结国内外人工智能审计的热点及趋势。研究发现,国际人工智能审计研究已显现聚集效应,完成了理论规范研究向实证性案例研究的过渡,具备了应用实例和技术平台;国内研究以电算化审计-计算机审计-信息化审计为演进路径,相关理论和实践经验还不成熟,参与国际科研合作不强,缺乏基础智能技术科研机构,未来研究将更多实践在技术创新、案例应用等现实领域。(234字)     

关于当前领导工作中的科学决策问题

决策是领导工作的首要职能，而科学决策则是每位领导者所面临的重大问题。从主观上讲，领导者的科学决策作用是核心内容，体现在实践中的方方面面，而建立一套完善的决策体系是科学决策的客观保证。     

人工智能与现代叙词表和分类法的革新

叙词表和分类法现代化的基础是计算机能够理解和处理其概念之间的关系,并建立可以理解的模型;同时要使其内容事务化。在构建计算机理解模型中。人工智能的产生式系统和框架方法可以为分类法和叙词表的革新提供有益的借鉴,这就是用产生式系统和框架表示法给分类法和叙词表提供过程性知识推导与结构性知识表示,借以建立分类模型和等级知识系统。     

基于文献计量学的人工智能领域研究现状及热点分析

基于Scopus数据库和SciVal科研评价工具,从学术产出、被引情况、学术影响力等计量指标分析2014至2019年人工智能领域发展状况和研究热点.研究发现:全球人工智能领域关注度和学术产出持续增加,相关研究进入高速发展阶段,其中美国高校和企业均处于领先地位,德国在研究机构方面最具影响力,而中国高校和研究所虽然学术产出全球第一但成果质量和影响力还有待提高,不过中国企业表现突出,已进入世界第一梯队;研究热点主要集中在基础研究和共性关键技术这两个方面,尤其是模型、算法等基础理论的研究在人工智能领域得到持续关注和重视.最后依据研究结果以及中国人工智能发展战略举措,为促进中国人工智能发展提出建议:引导并激励研究人员发表"三高"论文、开展合作共研,以及聚焦基础理论和关键共性技术研究,促进产学研合作及成果转化.     

美国人工智能战略布局与对外策略

美国政府举国家之力发展人工智能技术以保持在世界的领先地位,对此,通过文献梳理的方法,从美国自身发展及其对外政策的视角系统分析美国人工智能战略部署和相关举措。分析发现,美国在人工智能领域的战略部署分为积累和升级两个阶段,均以制定发布政策文件和成立相关机构进行部署,已上升为国家战略并演变为世界领导权的竞争,基本实现人工智能创新价值链各环节全覆盖,各部门积极深耕细作,并日益重视在国防领域的应用;其对华策略主要采取成立专门机构、全面封锁、出口管制、限制人才流动等措施。为此,建议中国从组织管理职能、人才、立法、舆论等方面采取应对策略,增强自身实力、主动赢取先机。     

国内图书情报领域人工智能研究演化分析

利用CiteSpace软件,梳理CNKI中相关文献,探究人工智能在国内图书情报领域的发文力量及主题演进、研究热点及未来趋势,并进行对比及可视化。结果表明相关研究数量持续增加,但尚未成熟,具有发文力量分散、主题联系紧密且交叉等特点。人工智能在国内图书情报领域的8类研究主题中,智慧图书馆、智能数据服务、知识工程等是研究热点及趋势。机器学习、知识图谱、语义网络等人工智能技术推动国内图书情报领域的发展。     

全球人工智能科学领域专利信息计量分析及其启示

基于智慧芽专利数据库收录的人工智能领域专利信息,运用专利信息计量方法对专利数据进行全球专利申请趋势、地域分布格局、关键技术领域以及主要专利权人分布统计。中国在人工智能领域专利申请量较多,产品研发技术发展迅速,但基础算法领域相对薄弱,核心竞争力较发达国家仍有一定差距。建议中国在保持现有产品优势的前提下,加大对硬件、算法及芯片等基础层面的研究,同时推动人工智能与其他行业的融合转化,加快推进专利布局和人才引进战略。     

Artificial intelligence for digital sustainability: An insight into domain-specific research and future directions

Recent research call for action on digital sustainability research could potentially contribute to achieving United Nations (UN) Sustainable Development Goals (SDG). In this opinion piece, we specifically focus on artificial intelligence (AI) as a technology that could help achieve digital sustainability. We identify six dimensions related to AI grounded in past literature: sensemaking, relationships among actors in the supply chain, green creativity skills, metrics, strategies, and AI tool improvement. We conceptualize several propositions for these six dimensions, highlighting the nuances associated with AI for digital sustainability to provide clear directions for future research.     

An evaluation of the critical success factors impacting artificial intelligence implementation

AI systems offer organizations great benefits causing decision-makers to invest more in these systems. The advantages of AI cannot be achieved without successful implementation. Thus, it is crucial to recognize the factors impacting the successful implementation of AI. It is also important to assess and rank these factors by their importance to assist decision-makers in implementing these systems and increasing the success rate. Due to its importance, scholars called for studies to expand our knowledge in this critical area. This paper identifies, extracts, and assesses the most critical factors that influence the implementation of AI systems. This study identifies nineteen factors and categorizes them into four categories: organization, technology, process, and environment. The analytical hierarchy process is used to evaluate the factors and the categories. The analysis offers two types of results, at the category level and the level of the factors. The results indicate that technology is the most significant of the four categories. The results also suggest that ethics is the most crucial factor among all nineteen factors. The order of all factors and discussions of the implications of the findings for practice and research are presented in the paper.     

Consumer engagement via interactive artificial intelligence and mixed reality

The use of immersive technologies has changed the consumption environment in which retailers provide services. We present findings from a study designed to investigate consumer responses toward a $17 million AI-embedded mixed reality (MR) exhibit in a retail/entertainment complex which combines advanced technology entertainment with retail shopping. Findings from our study demonstrate that the quality of AI (i.e., speech recognition and synthesis via machine learning) associated with an augmented object increases MR immersion associated with spatial immersion, MR enjoyment, and consumers’ perceptions of novel experiences. Collectively, these increase consumer engagement, and positively influence behavioral responses—specifically, purchase intentions and intentions to share experiences with social groups. Overall, findings from this study show that interactive AI and MR technology open new avenues to promote consumer engagement.     

Applying artificial intelligence technique to predict knowledge hiding behavior

Drawing on psychological ownership and social exchange theories, this study suggests theoretical arguments and empirical evidence for understanding employee reactions to distributive, procedural, and interactional (in)justice — three crucial bases of employees’ feelings of social self-worth. Utilizing field data and artificial intelligence technique, this paper reveals that distributive, procedural, and interactional (in)justice contribute to higher levels of knowledge hiding behavior among employees and that this impact is non-linear (asymmetric). By reuniting the discourses of organizational justice and knowledge management, this study indicates that feelings of psychological ownership of knowledge and the degree of social interaction are mechanisms that work with organizational (in)justice to influence knowledge hiding behavior. The current research may inform contemporary theories of business research and provide normative guidance for managers.     

中文科技期刊出版中的人工智能应用研究

【目的】 发现并解决人工智能应用于中文科技期刊出版中的现实问题,促进人工智能更好地赋能中文科技期刊出版全流程。【方法】 通过归纳总结、文献调研、案例分析、逻辑分析等方法,对中文科技期刊出版中的人工智能应用进行界定,梳理现状,指出存在的问题,剖析可能的原因,提出切实可行的发展路径。【结果】 目前存在应用场景单一、技术层次较低、缺乏面向出版单位的系统性和一体化平台、技术需求和供给不匹配等问题,其背后是出版单位重视程度不够、技术服务商进入壁垒较高、底层数据利用不充分、核心人才缺乏这4个方面的原因。出版单位和技术服务商应从坚持需求牵引、加强技术储备、提高数据可用性和加强技术开放、增强应用可信度、提高从业人员人工智能素养这5个方面促进人工智能在中文科技期刊中的应用。【结论】 人工智能在中文科技期刊出版中的应用仍不充分,应进一步加强探索与尝试,未来人工智能可推动中文科技期刊出版业发生更加广泛和深刻的变革。