管理评论

管理评论 ›› 2024, Vol. 36 ›› Issue (7): 82-95.

• 经济与金融管理 • 上一篇    

数智技术赋能可持续制造和循环经济的效应研究

解季非1, 马露露2, 杨勇1, 张晓飞1   

  1. 1. 东北大学秦皇岛分校管理学院, 秦皇岛 066004;
    2. 河北科技师范学院数学与信息科技学院, 秦皇岛 066004
  • 收稿日期:2022-10-13 发布日期:2024-08-03
  • 作者简介:解季非,东北大学秦皇岛分校管理学院讲师,博士;马露露(通讯作者),河北科技师范学院数学与信息科技学院助理研究员,硕士;杨勇,东北大学秦皇岛分校管理学院副教授,博士;张晓飞,东北大学秦皇岛分校管理学院副教授,博士。
  • 基金资助:
    辽宁省社会科学规划基金项目(L21BGL019);河北省自然科学基金项目(G2021501006);国家自然科学基金面上项目(71972033;72372021);中央高校基本科研业务费项目(N2423041)。

Research on the Enabling Effect of Digital Intelligence Technology on Sustainable Manufacturing and Circular Economy

Xie Jifei1, Ma Lulu2, Yang Yong1, Zhang Xiaofei1   

  1. 1. School of Management, Northeastern University at Qinhuangdao, Qinhuangdao 066004;
    2. School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004
  • Received:2022-10-13 Published:2024-08-03

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摘要： 以大数据分析和人工智能(BDA-AI)为核心的数智技术具有全面赋能可持续制造(SM)和循环经济(CE)的独特潜力，其协同发展是当前全球生产系统发展的重要趋势。然而，目前BDA-AI研究仍集中于技术层面，实证研究匮乏，且极少关注其对于SM和CE的赋能效应。基于制度论、资源观、组织文化和动态能力观等理论视角，构建BDA-AI赋能SM和CE的跨领域研究模型；收集中国装备制造业中231家企业的一手数据，采用PLS-SEM方法进行实证检验。结果显示，外部制度压力(强制压力、规范压力和模仿压力)通过影响企业内部资源(有形资源和劳动力技能)配置，推动BDA-AI采用；BDA-AI对于SM和CE具有显著的赋能效应；大数据文化、环境动态性和组织灵活性在模型路径中发挥显著的调节作用。

关键词: 大数据分析, 人工智能, 可持续制造, 循环经济, 赋能效应

Abstract: Digital intelligence technology with big data analytics and artificial intelligence (BDA-AI) as its core has the unique potential of comprehensively enabling sustainable manufacturing (SM) and circular economy (CE). The synergetic development of BDA-AI is an important trend in the current development of global production systems. However, the existing researches on BDA-AI are still focused on the technical level, lacking empirical researches and little attention is paid to its enabling effect on SM and CE. Based on institutional theory, resource-based view, organizational culture and dynamic capability view, a cross domain research model of BDA-AI enabling SM and CE is developed; the primary data of 231 enterprises in China’s equipment manufacturing industry are collected, and the PLS-SEM method is used for empirical test. The results show that external institutional pressures (coercive pressures, normative pressures and mimetic pressures) promote the adoption of BDA-AI by influencing the allocation of internal organizational resources (tangible resources and workforce skills); BDA-AI has a significant enabling effect on SM and CE; big data culture, environmental dynamism and organizational flexibility play significant moderating effects in the model paths.

Key words: big data analytics, artificial intelligence, sustainable manufacturing, circular economy, enabling effect