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Comparative Analysis and Research Progress on Quantitative Evaluation of Digital Transformation |
Lu Yang1,2,Wang Chaoxian2 |
(1.Department of Investment Economics, University of Chinese Academy of Social Sciences;2.Institute for Policy and Economic Research,China Academy of Information and Communications Technology, Beijing 100191, China) |
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Abstract Nowadays, the digital transformation of the economy and society is the general trend. Digital transformation has become the focus of strategic deployment and academic research in various countries. Scientific and accurate evaluation of digital transformation level is the premise and foundation for measuring its economic performance and promoting high-quality development of digital economy. This paper reviews, summarizes and analyzes the literature context and new progress of evaluation methods of digital transformation. Firstly, according to the vertical and horizontal dimensions, this paper puts forward two evaluation paradigms based on enterprise maturity and industry level, analyzes the mathematical methods, economic values and application directions of each paradigm, and then comments on the characteristics of the main evaluation methods. Finally, the trend of data as a factor of production into the evaluation system is prospected, which will lay a foundation for future research.
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Received: 15 January 2021
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[1] GORDON R J. The rise and fall of American growth[M]. Princeton University Press, 2016. [2] 周济. 智能制造:“中国制造2025”的主攻方向[J]. 中国机械工程,2015,26(17):2273-2284. [3] 赵剑波. 推动新一代信息技术与实体经济融合发展:基于智能制造视角[J]. 科学学与科学技术管理,2020,41(3):3-16. [4] 王灏晨,温珂. 新冠肺炎疫情的危中之机:加速我国数字化转型[J]. 科学学研究,2020,38(3):393-395.[5] MANYIKA J,RAMASWAMY S,KHANNA S,et al.Digital America: a tale of the haves and have-mores[R]. McKinsey Global Institute, 2015.[6] CALVINO F, CRISCUOLO C, MARCOLIN L, et al. A taxonomy of digital intensive sectors[R]. OECD Science, Technology and Industry Working Papers, 2018.[7] GAL P, NICOLETTI G, RENAULT T, et al. Digitalisation and productivity: in search of the holy grail-firm-level empirical evidence from eu countries[R]. OECD Science, Technology and Industry Working Papers, 2019. [8] 王莉娜. 数字化对企业转型升级的影响:基于世界银行中国企业调查数据的实证分析[J]. 企业经济,2020,39(5):69-77. [9] BARTEL A,ICHNIOWSKI C,SHAW K. How does information technology affect productivity? plant-level comparisons of product innovation,process improvement,and worker skills[J]. The Quarterly Journal of Economics,2007,122(4):1721-1758. [10] SYVERSON C.What determines productivity[J]. Journal of Economic Literature,2011,49(2):326-365. [11] 蔡跃洲,付一夫. 全要素生产率增长中的技术效应与结构效应:基于中国宏观和产业数据的测算及分解[J]. 经济研究,2017,52(1):72-88.[12] VAN ARK B. The productivity paradox of the new digital economy[J]. International Productivity Monitor, 2016 (31): 3-18. [13] PARENTE S L,PRESCOTT E C. Barriers to technology adoption and development[J]. Journal of Political Economy,1994,102(2):298-321.[14] OLIVEIRA T, MARTINS M F. Literature review of information technology adoption models at firm level[J]. Electronic Journal of Information Systems Evaluation, 2011, 14(1): 110. [15] 李海舰,李燕. 企业组织形态演进研究:从工业经济时代到智能经济时代[J]. 经济管理,2019,41(10):22-36. [16] 孟凡生,赵刚. 传统制造向智能制造发展影响因素研究[J]. 科技进步与对策,2018,35(1):66-72.[17] BAJGAR M, CALLIGARIS S, CALVINO F, et al. Bits and bolts: the digital transformation and manufacturing[J]. OECD Science, Technology and Industry Working Papers, 2019. [18] 谢康,肖静华,周先波,等. 中国工业化与信息化融合质量:理论与实证[J]. 经济研究,2012,47(1):4-16,30. [19] 马眸眸. 区域信息化与工业化融合的影响因素实证研究[J]. 工业技术经济,2018,37(1):145-152. [20] 臧冀原,王柏村,孟柳,等. 智能制造的三个基本范式:从数字化制造、“互联网+”制造到新一代智能制造[J]. 中国工程科学,2018,20(4):13-18. [21] 周剑,陈杰. 制造业企业两化融合评估指标体系构建[J]. 计算机集成制造系统,2013,19(9):2251-2263.[22] 中国信息通信研究院. 中国数字经济发展白皮书(2020年)[R]. 北京: 中国信息通信研究院, 2020. [23] 国务院发展研究中心. 传统产业数字化转型的模式和路径[R]. 北京: 国务院发展研究中心, 2018. [24] 陈杰,周剑,付宇涵. 我国工业企业两化融合评价体系及实证研究[J]. 制造业自动化,2016,38(6):143-146,156. [25] 李君,成雨,窦克勤,等. 互联网时代制造业转型升级的新模式现状与制约因素[J]. 中国科技论坛,2019,35(4):68-77. [26] 师丽娟,马冬妍,高欣东. 企业数字化转型路径分析与现状评估:以某区工业企业数字化转型为例[J]. 制造业自动化,2020,42(7):157-161.[27] ACATHCH. Industrie 4.0 maturity index:managing the digital transformation of companies [R].德国国家科学与工程院, 2017. [28] 王晰巍,安超,初毅. 信息化与工业化融合的评价指标及评价方法研究[J]. 图书情报工作,2011,55(6):96-99. [29] 章穗,张梅,迟国泰. 基于熵权法的科学技术评价模型及其实证研究[J]. 管理学报,2010,7(1):34-42. [30] 续继,唐琦. 数字经济与国民经济核算文献评述[J]. 经济学动态,2019,60(10):117-131. [31] HOLLY S,JORGENSON D W,GOLLOP F M,et al. Productivity and US economic growth[J]. The Economic Journal,1990,100(399):274. [32] JORGENSON D W.Productivity and postwar US economic growth[J]. Journal of Economic Perspectives,1988,2(4):23-41. [33] JORGENSON D W,STIROH K J. Information technology and growth[J]. American Economic Review,1999,89(2):109-115. [34] 黄勇峰,任若恩,刘晓生. 中国制造业资本存量永续盘存法估计[J]. 经济学(季刊),2002,1(1):377-396. [35] 孙琳琳,任若恩. 资本投入测量综述[J]. 经济学(季刊),2005,4(3):823-842. [36] BRYNJOLFSSON E,MCELHERAN K. The rapid adoption of data-driven decision-making[J]. American Economic Review,2016,106(5):133-139. |
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