文献类型：期刊文献

中文题名：人工智能技术应用下制造业企业就业效应研究——基于101家上市公司的实证检验

英文题名：Research on the Employment Effect of Manufacturing Enterprises under the Application of Artificial Intelligence Technology: Based on the Empirical Test of 101 Listed Companies

作者：李新娥[1];喻子君[1];夏静[1];唐少清[1]

第一作者：李新娥

机构：[1]北京联合大学管理学院,北京100101

第一机构：北京联合大学管理学院

年份：2021

期号：S01

起止页码：277-286

中文期刊名：中国软科学

外文期刊名：China Soft Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；CSSCI:【CSSCI2021_2022】；

基金：国家社科基金重点项目“人工智能对劳动力市场的冲击及劳动者知识技能转换应对研究”(19AGL025)。

语种：中文

中文关键词：人工智能技术应用;制造业;就业效应

外文关键词：artificial intelligence technology application;manufacturing;employment effect

摘要：人工智能技术与生产和管理的融合,提升了制造业企业的生产效率,优化了制造业企业的管理过程。然而机器换人的担忧,以及机器替代人工引发的岗位结构和技能结构的变化,引起了学术界的关注。以制造业企业101家上市公司为样本,分析人工智能技术应用对员工规模、结构、收入、学历水平等的影响。研究发现:人工智能技术应用与制造业企业的员工规模和生产人员结构比例呈显著负相关关系,与技术人员结构比例呈显著正相关关系,与财务人员结构比例、销售人员结构比例、行政人员结构比例的关系不明显,与员工学历水平和员工收入水平显著正相关。
The integration of artificial intelligence technology with production and management has improved the production efficiency of manufacturing enterprises and optimized the management process of manufacturing enterprises.However,the concern of machine replacement,as well as the change of job structure and skill structure caused by machine replacement of human,has attracted the attention of academic circles.Taking 101 listed companies of manufacturing enterprises as samples,this paper analyzes the impact of the application degree of artificial intelligence technology on the scale,structure,income and educational level of the enterprises’employees.It finds thatthe application of artificial intelligence technology has a significant negative correlation with the scale of employees and the proportion of production personnel in manufacturing companies.There is a significant positive correlation with the proportion of technical personnel,and the relationship with the proportion of financial personnel,sales personnel,and administrative personnel is not obvious.It is significantly positively correlated with the educational level of employees and the income level of employees.