| 高端装备制造企业数字化成熟度测度及影响因素研究 |
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| 引用本文： | 唐孝文,缪应爽,孙悦,董莉.高端装备制造企业数字化成熟度测度及影响因素研究[J].科研管理,2022,43(9):10-19. |
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| 作者姓名： | 唐孝文 缪应爽 孙悦 董莉 |
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| 作者单位： | 1.北京工业大学经济与管理学院，北京100124；
2.中华女子学院管理学院，北京100101 |
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| 基金项目： | 国家自然科学基金面上项目：“自组织演化视角下的企业战略转型:能力系统衍生与成长过程分析——基于高端装备制造业的实证”（71772009，2018.01—2021.12）；教育部人文社会科学研究项目：“混合所有制改革背景下集团化管理与国企创新研究”（19YJC630129，2019.01—2021.12）。 |
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| 摘 要： | 从“投入-产出”视角分析影响企业数字化成熟度的关键因素，通过模糊粗糙集法和随机前沿法构建指标体系，运用“VHSD-EM”模型赋权指标。基于2015—2020年49家高端装备制造上市公司年报数据，测算企业数字化成熟度，结合K-means聚类划分成熟度层级。在此基础上借助Tobit模型检验影响数字化成熟度的外部因素。研究表明：①实证测度方面，数字化投入产出效率_利润效率、数字化投入产出效率_成本效率、数字化设备投入和数字化软件投入是数字化成熟度的关键测度指标。研究对象数字化成熟度分化明显，在指标维度上具有集聚效应，可以划分出五个成熟度层级（高，较高，一般，较低，低）。②外部影响因素中，政府补贴、金融科技对高端装备制造企业数字化成熟度具有显著正向影响。
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| 关 键 词： | 高端装备制造企业 数字化成熟度 实证测度 影响因素 |
| 收稿时间： | 2022-02-07 |
| 修稿时间： | 2022-07-05 |
Research on digital maturity measurement and influencing factors of advanced equipment manufacturing enterprises |
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| Tang Xiaowen,Miao Yingshuang,Sun Yue,Dong Li.Research on digital maturity measurement and influencing factors of advanced equipment manufacturing enterprises[J].Science Research Management,2022,43(9):10-19. |
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| Authors: | Tang Xiaowen Miao Yingshuang Sun Yue Dong Li |
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| Institution: | 1. College of Economics and Management, Beijing University of Technology, Beijing 100124, China;
2. School of Management, China Women′s University, Beijing 100101, China; |
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| Abstract: | With the vigorous development of digital economy and the deep promotion of industrial digitalization, traditional manufacturing enterprises have accelerated the pace of digital transformation. In this context, advanced equipment manufacturing enterprises urgently need to realize digital transformation in design, production and sales, and build a new model for development. Enterprise digital maturity was defined as the degree of completion in the process of digital transformation. The evaluation and analysis of digital maturity is an important means to promote digital transformation. Therefore, it is necessary to measure the digital maturity of advanced equipment manufacturing enterprises and explore the main restrictive factors. However, there was a lack of research in this field.
Firstly, based on the "input-output" perspective, this paper proposed a conceptual model of digital maturity of advanced equipment manufacturing enterprises, including four dimensions: human resource investment, capital investment, efficiency of operation and innovation efficiency. Then, through literature analysis, fuzzy rough set method and stochastic frontier approach, the digital maturity index system with 4 primary indicators and 12 secondary indicators was finally constructed. The "VHSD-EM" evaluation model was used to weight the indicators. First, based on the weights under "VHSD" and "EM" method, two comprehensive scores were calculated respectively. Second, Spearman rank correlation test was used to explore the consistency of the two measures. The results showed that the evaluation results had strong positive correlation, indicating that the two methods had good consistency. The final weight of each index could be obtained by averaging the weights determined by the two methods.
Secondly, based on the characteristics of advanced equipment manufacturing industry and the desirability of statistical data, 49 listed companies were selected as the research object. Using data from annual reports of these enterprises, this paper finally measured the digital maturity scores of 49 enterprises from 2015 to 2020. Then, K-means method was used to cluster the evaluation objects. It was observed that there were significant differences in the digital maturity of advanced equipment manufacturing enterprises, with aggregation effect in distribution. Combined with the definition of maturity model, the digital maturity of advanced equipment manufacturing enterprises was divided into five levels (the highest, higher, average, lower, the lowest). The above maturity hierarchy further enrich the research on capability maturity model in theory and provide new insights for the development of digital maturity model. In practice, it is helpful for advanced equipment manufacturing enterprises to evaluate their own digital transformation level and potential, and then scientifically and comprehensively plan the transformation path.
Thirdly, based on Tobit model, it was concluded that government subsidies and the development of Fintech had positive impacts on the digital maturity of advanced equipment manufacturing enterprises. Among them, the government′s financial support is an important factor to accelerate the digital transformation. The research enriches the relevant literature on the influencing factors of enterprise digital maturity, further verifies the conclusions of relevant scholars, and provides empirical support for the view that government service resources and means of production are also the key inputs of output in the "input-output" theory.
Finally, based on the above analysis, this paper put forward some suggestions to promote the digital maturity of advanced equipment manufacturing enterprises. First, enterprises should increase digital investment in human resources and capital, and formulate reasonable investment plans according to the characteristics of industry attributes, scale, technical advantages and so on. At the same time, enterprises should make full use of digital technologies such as internet of things, cloud computing and big data to improve operation efficiency and innovation efficiency. Second, local governments should increase financial subsidies, promote the development of Fintech, improve the digital and intelligent level of public services and social governance to create good external conditions for the digital transformation of advanced equipment manufacturing enterprises. |
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| Keywords: | advanced equipment manufacturing enterprise digital maturity empirical measurement influencing factor |
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