【前沿追踪】人工智能顶刊文献分享

人工智能顶刊文献分享

随着数据呈爆发式增长、计算能力大幅提升以及深度学习算法的不断发展和成熟，人工智能迎来了第三次发展浪潮，进入了大模型时代，并在全球范围内引起了一场新的产业革命。鉴于人工智能技术对企业获取、创造和传递价值产生重要影响，众多学者已开始研究人工智能技术与企业创新之间的关联。本次推送四篇文章着重关注人工智能对企业创新的促进作用：Artificial intelligence and innovation management: A review, framework, and research agenda一文中，作者系统探讨了人工智能对企业创新管理的影响，并分析了人工智能取代人类的可能性。同时，详细阐述了企业数字化创新转型过程中的关键影响因素，并进一步对未来研究方向进行展望。Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors一文中，作者基于国际机器人联合会（IFR）提供的工业机器人数据和2008—2017年中国14个制造业的面板数据，通过实证分析探索了人工智能对技术创新的影响。实证分析结果表明，人工智能对技术创新具有积极影响，并且人工智能对技术创新的影响具有部门异质性。Artificial intelligence and industrial innovation: Evidence from German firm-level data一文中，作者基于2018年社区创新调查（CIS）德国部分的企业级数据，研究了不同人工智能方法和应用领域在创新中的作用。研究结果显示，人工智能在创新方面具有积极影响，但其普适性仍需进一步探讨，因为该结论仅基于德国数据得出。How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops一文中，作者基于六家成功应用人工智能的工业企业案例，探讨了企业如何通过发展人工智能能力推动企业商业模式创新。研究结果揭示了人工智能的三组关键能力：数据管道、算法开发和人工智能民主化，并进一步提出了一个协同演进框架。

PART.1

Artificial intelligence and innovation management: A review, framework, and research agenda

人工智能与创新管理:综述、框架和研究议程

Keywords: artificial intelligence; AI; innovation; review; literature review; innovation management; machine learning; information processing

关键词：自动化；就业；经济韧性；未来工作

Abstract:

Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company's entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.

摘要：

人工智能（AI）重塑了公司创新管理的组织方式。随着技术的飞速发展和对人力组织的替代，人工智能确实可能迫使管理层重新思考公司的整个创新流程。为此，我们回顾并探讨了人工智能对未来创新管理的影响。利用卡内基学派和企业行为理论的观点，我们回顾了人工智能技术和基于机器学习的人工智能系统对创新管理的影响。我们概述了一个框架，该框架显示了人工智能可以在多大程度上取代人类，并解释了在向数字化创新组织转型过程中需要考虑的重要因素。最后，我们探讨了未来的研究方向。

参考文献：

Haefner N, Wincent J, Parida V, et al. Artificial intelligence and innovation management: A review, framework, and research agenda[J]. Technological Forecasting and Social Change, 2021, 162: 120392.

PART.2

Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors

人工智能对技术创新的影响:来自中国制造业面板数据的证据

Keywords: artificial intelligence; technological innovation; sector heterogeneity

关键词：人工智能；技术创新；部门异质性

Abstract:

This paper analyzes the impact of artificial intelligence (AI) on technological innovation through logic reasoning and empirical modeling. Based on the industrial robot data provided by the International Federation of Robotics (IFR) and the panel data of China's 14 manufacturing sectors from 2008 to 2017, this paper empirically analyzes the impact of AI on technological innovation. Our analysis shows that the mechanism of how AI affects technological innovation is that the former promotes technological innovation through accelerating knowledge creation and technology spillover, improving learning and absorptive capacities, while increasing R&D and talent investment. Our empirical results indicate that under the condition of controlling intensity of R&D investment, FDI, ownership structure, technical imitation, AI significantly promotes technological innovation. And the impact of AI on technological innovation experiences sector heterogeneity: AI has more significant impact on the technological innovation of low-tech sectors. The higher the level of AI, the greater its impact on technological innovation. Based on our established conclusions, we provide corresponding suggestions and recommendations for managerial decision-making.

摘要：

基于国际机器人联合会（IFR）提供的工业机器人数据和中国14个制造业部门2008年至2017年的面板数据，本文经验性地分析了AI对技术创新的影响。我们的分析表明，AI通过加速知识创造和技术溢出、提高学习和吸收能力以及增加研发和人才投资来促进技术创新。我们的实证结果表明，在控制研发投资强度、外商直接投资、所有权结构、技术模仿等因素条件下，AI显著促进了技术创新。而且，AI对技术创新的影响存在行业异质性：在低科技领域，AI对技术创新有更显著的影响。AI水平越高，其对技术创新的影响越大。根据我们得出的结论，为管理决策提供相应建议和意见。

参考文献：

Liu J, Chang H, Forrest J, et al. Influence of artificial intelligence on technological innovation: Evidence from the panel data of china’s manufacturing sectors[J]. Technological Forecasting and Social Change, 2020, 158: 120142.

PART.3

Artificial intelligence and industrial innovation: Evidence from German firm-level data

人工智能与产业创新:来自德国企业层面数据的证据

Keywords: artificial intelligence; innovation; CIS data; Germany

关键词：人工智能；创新；社区创新调查数据；德国

Abstract:

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.

摘要：

本文分析了人工智能（AI）的使用与企业创新绩效之间的联系。基于2018年社区创新调查（CIS）德国部分的企业级数据，我们研究了不同人工智能方法和应用领域在创新中的作用。结果显示，2019年德国有5.8%的企业在其业务运营或产品和服务中积极使用人工智能。我们发现，在这些企业中，人工智能的使用与世界首创产品创新的年销售额相关，约为160亿欧元（即占世界首创创新年销售额总额的18%）。此外，人工智能技术还被用于流程创新，为德国企业部门节省了约6%的年度总成本。广泛应用人工智能（在不同的应用领域使用不同的方法）并且在使用人工智能方面已有多年经验的企业，其创新成果要高得多。这些关于人工智能在创新中的作用的积极发现必须谨慎解读，因为它们是在人工智能开始迅速普及的情况下，针对一个特定国家（德国）得出的。

参考文献：

Rammer C, Fernández G P, Czarnitzki D. Artificial intelligence and industrial innovation: Evidence from German firm-level data[J]. Research Policy, 2022,51(7): 104555

PART.4

How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops

人工智能功能如何实现商业模式创新:通过共同进化过程和反馈循环扩展人工智能

Keywords: artificial intelligence; digital servitization; digital transformation; digitalization; business model innovation; platform

关键词：人工智能；数字服务化；数字化转型；数字化；商业模式创新；平台

Abstract:

Artificial intelligence (AI) is predicted to radically transform the ways manufacturing firms create, deliver, and capture value. However, many manufacturers struggle to successfully assimilate AI capabilities into their business models and operations at scale. In this paper, we explore how manufacturing firms can develop AI capabilities and innovate their business models to scale AI in digital servitization. We present empirical insights from a case study of six leading manufacturers engaged in AI. The findings reveal three sets of critical AI capabilities: data pipeline, algorithm development, and AI democratization. To scale these capabilities, firms need to innovate their business models by focusing on agile customer co-creation, data-driven delivery operations, and scalable ecosystem integration. We combine these insights into a co-evolutionary framework for scaling AI through business model innovation underscoring the mechanisms and feedback loops. We offer insights into how manufacturers can scale AI, with important implications for management.

摘要：

人工智能（AI）被预测将彻底改变制造企业创造、交付和捕获价值的方式。然而，许多制造商在整合AI能力到商业模式和运营过程中遇到困难。本文探讨了制造企业如何发展AI能力并通过数字服务化创新其商业模式以实现规模化的AI应用。我们通过对六家领先制造商参与AI的案例研究提供了实证洞见。研究结果揭示了三组关键的AI能力：数据管道、算法开发和人工智能民主化。为了扩大这些能力，企业需要通过注重敏捷客户共创、数据驱动的交付运营和可扩展生态系统整合来创新其商业模式。我们将这些洞见结合成一个共同演进框架，以强调通过商业模式创新来推动AI规模化应用的机制和反馈循环。我们提供了有关制造商如何扩大规模应用人工智能的洞见，并对管理层产生重要影响。

参考文献：

Sjodina D, Parida V, Palmie M, et al. How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops[J]. Journal of Business Research, 2021, 134: 574-587.

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图文：徐翠丰、罗均梅

编辑：李发珍

审核：宁靓、姜忠辉