The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Fangfang Wang, Qiang Li, Hong Chen
{"title":"The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era","authors":"Fangfang Wang, Qiang Li, Hong Chen","doi":"10.1002/sres.3046","DOIUrl":null,"url":null,"abstract":"This study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model, a regression analysis explores how different AI‐enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A‐share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production‐based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1002/sres.3046","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

This study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model, a regression analysis explores how different AI‐enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A‐share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production‐based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts.
人工智能时代智能制造、创新均衡与成本粘性的相互作用
本研究探讨了人工智能(AI)驱动的智能制造方法对中国制造企业成本粘性的影响。利用 ABJ 模型,通过回归分析,从创新均衡的角度探讨了不同的人工智能智能制造方法对成本粘性的影响。样本包括 2013 年至 2021 年在中国 A 股市场上市的制造企业。研究结果表明,在这些企业中,智能制造的采用与成本粘性之间存在负相关关系。具体而言,与协作式智能制造方法相比,生产型智能制造对降低成本粘性的影响更为显著。此外,智能制造对联合均衡创新和匹配均衡创新都有积极影响。联合均衡创新与成本粘性负相关,而匹配均衡创新与成本粘性没有显著关系。研究结果表明,创新均衡在人工智能驱动的智能制造与成本粘性之间起着中介作用。总之,这项研究揭示了人工智能能力如何使智能制造流程和创新平衡动态有助于缓解制造企业面临的成本粘性问题。研究强调了采用人工智能系统提高制造业运营效率和成本管理灵活性的战略价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信