大数据资源与新服务开发绩效:人工智能能力调节作用的跨国实证

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1181 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Artificial intelligence capability (AIC)affects whether firms can use big data to improve their new service development (NSD)performance.Yet the specific role and effect of AIC in big data-driven NSD need further theoretical deduction and empirical examination.Based on information processing theory,this study builds a theoretical model of big data resources and NSD performance with AIC as the moderating variable.Using data collected from 477 U.S.and 632 Chinese NSD projects over three years,this study employed regression analysis to examine the theoretical model and make cross-national comparison.The results show that when AIC is lowest,big data decreases,not increases,the positive effects of big data on NSD performance.When AIC is lower,the positive effects of big data on NSD performance become insignificant.When AIC reaches medium or above level,big data has a positive impact on NSD performance,and increasing AIC increases the positive effects of big data on NSD performance.For U.S.projects,the positive moderating effect of AIC is stronger and increasing year by year.In contrast,for Chinese projects,the positive moderating effect of AIC is weaker and not increasing year by year.

Key words： Big data Artificial intelligence capability Information processing New service development Performance

Received: 29 August 2022

PACS:

F272.3

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhang Xiaotang
	Michael Song
	Zhang Haili
	Wang Yufan

Cite this article:

Zhang Xiaotang,Michael Song,Zhang Haili, et al. A Cross-National Empirical Study of Big Data and New Service Development Performance:Moderating Effect of Artificial Intelligence Capability[J]. , 2023, 4(8): 169-179.

URL:

http://www.zgkjlt.org.cn/EN/ OR http://www.zgkjlt.org.cn/EN/Y2023/V4/I8/169

[1]谢康,夏正豪,肖静.大数据成为现实生产要素的企业实现机制:产品创新视角[J].中国工业经济,2020 (5):42-60.
[2]HAO S,ZHANG H,SONG M.Big data,big data analytics capability,and sustainable innovation performance[J].Sustainability,2019,11 (24):7145.
[3]GEORGE G,OSINGA E C,LAVIE D,et al.Big data and data science methods for management research[J].Academy of management journal,2016,59 (5):1493-1507.
[4]池毛毛,王俊晶,王伟军.数字化转型背景下企业创新绩效的影响机制研究:基于NCA与SEM的混合方法[J].科学学研究,2022,40 (2):319-331.
[5]WU L,HITT L,LOU B.Data analytics,innovation,and firm productivity[J].Management science,2020,66 (5):2017-2039.
[6]FERRARIS A,MAZZOLENI A,DEVALLE A,et al.Big data analytics capabilities and knowledge management:impact on firm performance[J].Management decision,2019,57 (8):1923-1936.
[7]DAVENPORT T H,RONANKI R.Artificial intelligence for the real world[J].Harvard business review,2018,96 (1):108-116.
[8]孙璇.美国人工智能发展策略与大国科技竞争格局[J].中国科技论坛,2022 (6):172-178.
[9]王宇凡,张海丽,SONG M.大数据嵌入的新产品开发过程[J].科学学研究,2020,38 (12):2202-2211.
[10]TUSHMAN M,NADLER D.Information processing as an integrating concept in organization design[J].Academy of management review,1978,3 (3):613-624.
[11]ZHANG H,ZHANG X,SONG M.Does knowledge management enhance or impede innovation speed[J].Journal of knowledge management,2020,24 (6):1393-1424.
[12]陈国青,曾大军,卫强,等.大数据环境下的决策范式转变与使能创新[J].管理世界,2020 (2):95-105.
[13]UMANATH N S.The concept of contingency beyond “it depends”:illustrations from is research stream[J].Information and management,2003 (6):551-562.
[14]杨祎,刘嫣然,李垣.替代或互补:人工智能应用管理对创新的影响[J].科研管理,2021,42 (4):46-54.
[15]刘业政,孙见山,陈夏雨,等.大数据的价值发现:4C模型[J].管理世界,2020 (2):129-138.
[16]高山行,刘嘉慧.人工智能对企业管理理论的冲击及应对[J].科学学研究,2018,36 (11):2004-2010.
[17]SONG M,MONTOYA-WEISS M M.The effect of perceived technological uncertainty on Japanese new product development[J].Academy of management journal,2001,44 (1):61-80.
[18]CHURCHILL G A.A paradigm for developing better measures of marketing constructs[J].Journal of marketing research,1979,16 (1):64-73.
[19]张晓棠,张海丽,衡金金.大数据-大数据分析能力匹配与产品创新度——医疗健康服务业的实证研究[J].科学决策,2022 (7):27-43.
[20]SALEHIA H,BURGUEÑO R.Emerging artificial intelligence methods in structural engineering[J].Engineering structures,2018,171:170-189.
[21]GOYACHE F,BAHAMONDE A,ALONSO J,et al.The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry[J].Trends in food science and technology,2001,12 (10):370-381.
[22]KIBRIA M G,NGUYEN K,VILLARDI G P,et al.Big data analytics,machine learning,and artificial intelligence in next-generation wireless networks[J].IEEE access,2018 (6):32328-32338.
[23]DILLMAN D A,SMYTH J D,CHRISTIAN L M.Internet,phone,mail and mixed-mode surveys:the tailored design method[M].New Jersey:John Wiley,2014.
[24]聂洪光,范海荣.基于专利数据的中美人工智能创新能力比较研究[J].中国科技论坛,2020 (5):154-162.
[25]沙德春,荆晶.中美人工智能产业国家顶层政策比较研究[J].科学管理研究,2021,39 (3):154-162.