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以智能化科研（AI for Science）为核心的第五科研范式已经在多个自然科学和高技术领域得到了广泛应用。与人工智能（AI）在自然科学领域的应用强调发现新原理、新机理和新规律不同,高技术领域更强调用AI技术来发明创造新方案、新工具和新产品,以解决特定的领域问题。文章总结了AI在高技术领域的应用——“技术智能”（AI for Technology）的典型特征和科学问题,并以CPU芯片全自动设计为例介绍过往的成功案例。最后,文章指出技术智能的目标不仅是加速创新流程并减少人工投入,同时也希望其具备更强的创造能力,最终超过人类的水平。 相似文献
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The latest boom of artificial intelligence (AI) has left the information management community in strong need of structure-providing, high-level overview works. Such works are supposed to allow both researchers and practitioners to keep track of that steep development across the technology's numerous possible application domains. So it is among other things that AI is said to incorporate enormous potential for reducing the operational costs of car manufacturers all over the globe. Nevertheless, many of them are still struggling with adopting it at large scale just because of a lack of knowledge on if and where to apply it. This study is therefore designed to find out which general use cases exist for AI within the context of car manufacturing and which ones might be the most promising ones to pursue at this early stage. We conducted a Delphi study with 39 experts in 25 different globally scattered organizations over one and a half years. As a result, we were able to identify 20 different high-level use cases for AI along the entire car manufacturing process. Our panelists have completely ranked and assessed those 20 use cases within two different dimensions, i.e., their estimated business value and their realizability. Besides being the first study to provide such an overview at one glance and to give such quantitative insights on that steeply emerging topic, four use cases from that list have never been discussed in connection with car manufacturing within the scientific literature until now and can therefore be considered as completely new in that regard. 相似文献
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Organizations face increasing pressure to implement artificial intelligence (AI) within a variety of business processes and functions. Many perceived benefits surround AI, but a considerable amount of trepidation also exists because of the potential of AI to replace human employees and dehumanize work. Questions regarding the future of work in the age of AI are particularly salient in pre-adoption organizations, before employees have the opportunity to gain direct experience with AI. To cope with this potentially stressful situation, employees engage in cognitive appraisal processes based on their own knowledge and personal use of AI. These pre-adoptive appraisals of AI influence both affective and cognitive attitudes, which in turn trigger behavioral responses that influence an organization’s ability to leverage AI successfully. Our survey of 363 Taiwanese employees shows that perceptions of AI’s operational and cognitive capabilities are positively related to affective and cognitive attitudes toward AI, while concerns regarding AI have a negative relationship with affective attitude only. Interaction effects of employee knowledge and affective attitude are also observed. This work’s main contribution lies in the development of an empirically-tested model of the potential impact of AI on organizations from an employee perspective in the pre-adoption phase. These results have practical implications for how organizations prepare for the arrival of this transformative technology. 相似文献
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Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts. 相似文献
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数字经济背景下,人工智能（AI）技术的应用正在深入地影响着企业管理变革、业务边界的扩展和管理模式的改变。结合互补资产的观点和组织学习理论,本文提出了一个基于AI应用能力和AI管理能力的分析框架,强调人工智能与人类智慧结合的必要性,阐述了两种能力的功能和作用及其协同对企业效率和创新成本的影响。本文提出,企业必须具备管理AI的能力才能有效应对大数据、数字技术、AI的不断革新及技术带来的组织内部结构和外部环境变化以及风险;企业AI应用与管理能力的有效结合,有利于控制AI应用带来的成本和风险,增强企业在人工人力、协调沟通、和数据搜寻方面的效率,同时降低AI应用带来的数字基建、道德情感、数据安全、组织结构变革方面的成本,进而促进企业的组织学习、对内外部数字技术使能资源的获取和管理以及互补资产的形成,对企业创新绩效发挥正向作用。最后,本文为企业的数字化创新战略提供了新的发展思路。 相似文献
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《The Journal of High Technology Management Research》2014,25(2):132-147
The purpose of this study is to empirically investigate the impact of Business-Information Technology Alignment, or BIA, on organizations and to revisit the BIA antecedents by using data from hotel sector of the service industry.The research model was developed based on the literature and inputs from the hotel industry and IT experts, using the Structural Equation Modeling (SEM) technique in data analysis, and data from phone interviews that were conducted with both business and IT personnel from 3 to 5 star hotels in Thailand.We found that Business-IT Alignment does have a positive relationship with organizational performance. Shared domain knowledge was found to have the highest relationship with Business-IT Alignment while IT management sophistication had the least impact, but in a negative direction, while organizational size was found to be a moderator. Other BIA antecedents were effective communication, IT operational and implementation success, and planning sophistication.This study developed a model that integrates the alignment between the strategic and operational levels which offers a holistic view of BIA, different from previous studies that considered only one or the other level. Secondly, we cross verify the antecedents from the literature and actual practice by interviewing experts in the industry. Finally, we revisited measurements and relationships among the constructs so that the model is up-to-date and applicable to the current business environment. 相似文献
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自20世纪70年代美国国家航空航天局首次提出技术成熟度的概念以来,技术成熟度评价作为一种控制研制风险的有效工具,在美国的重大武器装备采办项目管理中发挥了重要的作用。但目前该工具在我国的应用尚不成熟,没有形成科学、系统的评价方法。针对该现状,提出一个技术成熟度评价的指标体系,用以全面刻画技术在向装备实物转化过程中的状态;同时提出基于层次分析法、信息熵和灰色关联聚类的指标权重确定方法,使得专家的决策信息得以充分挖掘利用。通过一个算例验证该方法在应用中的可行性和有效性,旨在为进一步开展技术成熟度管理工作提供借鉴。 相似文献
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The explosive rise in technologies has revolutionised the way in which business operate, consumers buy, and the pace at which these activities take place. These advancements continue to have profound impact on business processes across the entire organisation. As such, Logistics and Supply Chain Management (LSCM) are also leveraging benefits from digitisation, allowing organisations to increase efficiency and productivity, whilst also providing greater transparency and accuracy in the movement of goods. While the warehouse is a key component within LSCM, warehousing research remains an understudied area within overall supply chain research, accounting for only a fraction of the overall research within this field. However, of the extant warehouse research, attention has largely been placed on warehouse design, performance and technology use, yet overlooking the determinants of Artificial Intelligence (AI) adoption within warehouses. Accordingly, through proposing an extension of the Technology–Organisation–Environment (TOE) framework, this research explores the barriers and opportunities of AI within the warehouse of a major retailer. The findings for this qualitative study reveal AI challenges resulting from a shortage of both skill and mind-set of operational management, while also uncovering the opportunities presented through existing IT infrastructure and pre-existing AI exposure of management. 相似文献
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《Research Policy》2022,51(7):104555
This paper analyses the link between the use of Artificial Intelligence (AI) and innovation performance in firms. Based on firm-level data from the German part of the Community Innovation Survey (CIS) 2018, we examine the role of different AI methods and application areas in innovation. The results show that 5.8% of firms in Germany were actively using AI in their business operations or products and services in 2019. We find that the use of AI is associated with annual sales with world-first product innovations in these firms of about €16 billion (i.e. 18% of total annual sales of world-first innovations). In addition, AI technologies have been used in process innovation that contributed to about 6% of total annual cost savings of the German business sector. Firms that apply AI broadly (using different methods for different applications areas) and that have already several years of experience in using AI obtain significantly higher innovation results. These positive findings on the role of AI for innovation have to be interpreted with caution as they refer to a specific country (Germany) in a situation where AI started to diffuse rapidly. 相似文献
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The ethical use of artificial intelligence in human resource management: a decision-making framework
Ethics and Information Technology - Artificial intelligence (AI) is increasingly inputting into various human resource management (HRM) functions, such as sourcing job applicants and selecting... 相似文献
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Artificial Intelligence (AI) is viewed as having great potential for the public sector to improve the management of internal activities and the delivery of public services. However, realizing its potential depends on the proper implementation of the technology, which is characterized by unique factors, that afford or constrain its use. What these factors are and how they affect AI implementation is still poorly understood, and scholars call for studies to add empirical evidence to the existing knowledge. This study relies on a case study methodology and, by adopting an abductive approach, applies a double theoretical perspective: the Technology-Organization-Environment (TOE) framework and the Technology Affordances and Constraints Theory (TACT). Drawing on these combined lenses, we develop a conceptual framework that extends previous studies by showing how AI implementation is the result of a combination of contextual factors that are deeply interrelated and, specifically, how AI-related factors bring new affordances and constraints to the application domain. 相似文献
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本文选取天和防务、成都振芯科技等9家典型军民融合型企业为对象进行探索性案例研究,采用扎根理论通过开放式编码、主轴编码和选择性编码构建了军民融合型企业商业模式创新机理的四层次模型。研究发现：外部动因迫使企业进行商业模式创新,企业战略、企业文化、企业家精神作为战略层指引企业商业模式创新,资源基础、组织结构、创新机制为辅助支撑企业商业模式创新,价值创造、价值主张以及价值获取三维度实现商业模式创新。研究丰富和拓展了军民融合型企业商业模式创新研究,也为同类型企业推动商业模式创新提供了参考与借鉴。 相似文献
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基于Delphi的技术预见中,咨询专家自评估熟悉度对技术预见的结果具有重要影响。基于“中国工程科技2035技术预见”咨询专家评估数据,采用复杂网络和统计分析等方法,评估基于Delphi的技术预见中不同熟悉度咨询专家的影响,优化不同熟悉度咨询专家比例权重。通过复杂网络和显著性检验方法,分析技术预见中各领域不同熟悉度咨询专家分布特征、网络关系以及评分差异。研究发现,“中国工程科技2035技术预见”咨询专家的选择基本合理;自评估“很熟悉”的咨询专家在技术评估中相对乐观,自评估“较熟悉”的咨询专家在技术评估中相对保守;在不考虑专家人数影响的情况下,自评估“熟悉”的咨询专家的意见相对更为准确。最后通过统计检验和优化算法,优化技术预见中咨询专家人数、不同熟悉度咨询专家比例和权重等参数,为后续的技术预见活动打下了基础。 相似文献
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The introduction of machine learning (ML), as the engine of many artificial intelligence (AI)-enabled systems in organizations, comes with the claim that ML models provide automated decisions or help domain experts improve their decision-making. Such a claim gives rise to the need to keep domain experts in the loop. Hence, data scientists, as those who develop ML models and infuse them with human intelligence during ML development, interact with various ML stakeholders and reflect their views within ML models. This interaction comes with (often conflicting) demands from various ML stakeholders and potential tensions. Building on the theories of effective use and wise reasoning, this mixed method study proposes a model to better understand how data scientists can use wisdom for managing these tensions when they develop ML models. In Study 1, through interviewing 41 analytics and ML experts, we investigate the dimensions of wise reasoning in the context of ML development. In Study 2, we test the overall model using a sample of 249 data scientists. Our results confirm that to develop effective ML models, data scientists need to not only use ML systems effectively, but also practice wise reasoning in their interactions with domain experts. We discuss the implications of these findings for research and practice. 相似文献
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《The Journal of High Technology Management Research》2020,31(2):100393
In modern times, the digital transformation of organizations and communities is a necessity to achieve a competitive advantage. Therefore, the ability to map the potential of scattered and diverse digital knowledge sources automatically and quickly is urgent for identifying critical knowledge so that organizations can effectively carry out knowledge management. This research is a finalization of the initial smart knowledge mapping model to fit out the limited scope of validation studied previously; this was done by conducting a mix-method approach methodology to examine validation using interviews, forum group discussions (FGD) and questionnaires. The results of our questionnaire validation were analyzed using the Fuzzy Delphi Method to determine the level of agreement between the experts. This study proposed conceptual model smart knowledge mapping, which consists of eleven components: leadership, IT infrastructure, environment, objective, stakeholder, business process, KM process, valuable digital activity, knowledge resource, advanced computing and KMap content. This study also proposed a process and activity combination quadrant to determine the availability of knowledge resources for implementation of this model. 相似文献
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Ethics and Information Technology - This paper approaches the interaction of a health professional with an AI system for diagnostic purposes as a hybrid decision making process and conceptualizes... 相似文献
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As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development. 相似文献