The digital transformation of the manufacturing industry has become a key factor in enhancing enterprise competitiveness and promoting high-quality economic development amid globalization and rapid technological advances, for Chinese manufacturing enterprises, it is also a vital path to achieving high-quality development.
Objective
This study explores the factors influencing the digital transformation of manufacturing industries through multi-dimensional analysis with advanced machine learning techniques, assisting Chinese manufacturing enterprises in achieving high-quality transformation while considering local characteristics.
Methods
An interpretable model integrating XGBoost and SHAP values is proposed based on the TOE model to analyze key factors. Additionally, the causal forest approach is used to explore regional variations in these factors.
Results
It was found that factors such as invention patents, digital-oriented management innovation, equity incentives, and profitability significantly drive digital transformation in manufacturing firms. There are also regional differences in the importance of these factors.
Conclusions
The empirical evidence provides a crucial reference for enterprises and decision-makers to formulate more scientifically grounded digital transformation strategies based on regional characteristics, offering strong support for transformation according to local conditions.
YangBWuW. Research on the comparative and synergistic effect of fiscal, tax and financial policies to promote the digital transformation of enterprises. Fiscal Research2023; 44(8): 58–71.
3.
FanZFXuWYZhangW. Research on digital transformation strategy and subsidy mechanism of manufacturing supply chain based on differential game. Aims Math2023; 8(10): 23850–23870.
4.
SongJChenJ. Impact of entrepreneur social network on enterprise digital construction———the regulatory role of strategic flexibility. Studies in Science of Science2022; 40(1): 103–112.
5.
MiaoZZhaoG. Configurational paths to the green transformation of Chinese manufacturing enterprises: a TOE framework based on the fsQCA and NCA approaches. Sci Rep2023; 13(1): 19181.
6.
TianZChenH. How can manufacturing enterprises build digital capabilities?A case study based on resource orchestration theory. J Manag Case Stud2023; 16(4): 489–509.
7.
LiLSuFZhangW, et al.Digital transformation by SME entrepreneurs: a capability perspective. Inf Syst J2017; 28(6): 1129–1157.
8.
WuJChenTGongYW, et al.Digital transformation of firms: theoretical framework and research prospects. J Manag2021; 18(12): 1871–1880.
9.
ZhangLDaiGXiongY, et al.Evaluation and influencing factors of digital transformation of the manufacturing industry in China: based on qualitative comparative analysis of fuzzy set. Scie Tec Manag Res2022; 42(7): 68–78.
10.
DaiYShenWTanW. Research on the impact of big data development on digital technology innovation of enterprises–A quasi-natural experiment based on the national big data comprehensive pilot zone. Western Forum2023; 33(2): 16–28.
11.
ChengHLiuSLuoL. The basic situation and path choice of transformation and upgrading of Chinese enterprises--analysis based on the data of 4,794 employees’ entry survey of 570 enterprises. Manag World2016; 32(2): 57–70.
12.
TongY. Research on the influencing factors of digital transformation of China’s manufacturing industry. Research on Technology Economy and Management2022; 43(3): 124–128.
13.
LiSLiXWangS, et al.Intergenerational inheritance and digital transformation in family firms: incentives or disincentives?Manag World2023; 39(6): 171–191.
14.
LiuSLinZLengZ. Do tax incentives improve the level of corporate innovation? –A test based on firm life cycle theory. Econ Res2020; 55(6): 105–121.
15.
MaoNSunWYangY, et al.Transport infrastructure construction and digital transformation of enterprises-an empirical study taking China's high-speed railway as an example. Res Qua Tec Econ2022; 39(10): 47–67.
16.
MenL. Digital leadership in the public sector: literature review and research outlook. E-government2020; 17(2): 100–110.
17.
JiaoHYangJYingY. Dynamic capabilities: a systematic literature review and an agenda for the Chinese future research. Manag World2021; 37(5): 191–210.
18.
BaiXWangXSongP. Research on the influencing factors of digital transformation of Chinese enterprises - an empirical analysis based on TOE framework. Sci Res2024; 19: 1–18.
19.
QiangYLiJ. Research on service trade innovation and digital transformation of service enterprise. Asia-Pacific Economy2023; 40(6): 38–50.
20.
LiuYXueW. Help or resistance: preferential ’tax policies and digital transformation of enterprises. Econ Issues2024; 46(4): 37–43.
21.
ChenQWangY. Strategic diffusion of corporate digital transformation based on executive linkage: realization mechanism and boundary conditions. Financial Research2022; 48(12): 48–62.
22.
WangHYanZGuoG, et al.Digital infrastructure policy and enterprise digital transformation: ‘Enabling’ or ‘Negative’? Quantitative and Technical. Econ Res2023; 40(5): 5–23.
23.
JinJLiSHuangL. Research on the influencing factors of digital transformation of traditional manufacturing enterprises. Inn Tec2020; 20(6): 22–34.
24.
ChenJHanM. Research on antecedent grouping state of digital transformation of manufacturing enterprises--an analysis based on TOE theoretical framework. Technol Manag2023; 25(2): 89–98.
25.
ZhaoFXuY. Enterprise digital transformation and customer concentration. Journal of Zhejiang Gongshang University2023; 37(5): 128–139.
26.
WenKLiC. Digital transformation, supply chain concentration and supplier innovation. Statistics and Information Forum2024; 39(3): 107–128.
27.
JiangWGuiBZhangH, et al.Identification and empirical analysis of driving factors for digital transformation in military industrial enterprises. Industrial Technology and Economics2023; 42(1): 71–78.
28.
CalebHTYimCKBYinE, et al.R&D activities and innovation performance of MNE subsidiaries: the moderating effects of government support and entry mode. Technol Forecast Soc Change2021; 166: 120603.
29.
LuRChoSLuD. Enterprise digital transformation, managerial time orientation and core technology capability: an inverted U-shaped relationship. Science and Technology Progress and Countermeasures2024; 41(7): 11–20.
30.
WangZDengYXieW. Research on the influencing factors of digital innovation in manufacturing enterprises: based on fsQCA method. Sci Tec Manag Res2023; 43(17): 20–30.
31.
WatsonH. Preparing for the cognitive generation of decision support. MIS Q Exec2017; 16(3): 153–169.
32.
LinHHuYChenJ. What determines regional digital development? --An analysis of linkage effects based on the Technology-organization-Environment (TOE) framework. Science and Technology Management Research2022; 42(14): 24–32.
LiTLiB. The impact of employee compensation incentives on risk taking in commercial banks. J Beijing Inst Technol (Soc Sci Ed)2020; 22(1): 70–81.
35.
ZhengMYouBWuP. Whether commercial credit financing can promote the high-quality development of mining enterprises--Based on the moderating effect of enterprise scale. Gold Science and Technology2021; 29(6): 874–883.
36.
LiXZhangWZhaoJ. Enterprise digital transformation, human capital upgrading and high-quality employment. J Beijing Technol Bus Univ (Soc Sci)2024; 39(3): 40–52.
37.
ZhangZZhuX. A study on the relationship between innovation capability of Chinese technology enterprises and IPO, profitability, capital structure and firm size. Macroeconomic Research2022; 44(2): 147–154.
38.
MaLNieY. Analysis of the successful grouping of enterprise digital transformation. Finance and Accounting Monthly2023; 44(10): 118–125.
39.
LiBYangRLuJ, et al.Enterprise digital transformation, supply chain spillovers and labour skill bias. Res Qua Tec Econ2024; 41(8): 133–153.
40.
TianSYouB. Can carbon trading pilot policies promote digital transformation of enterprises?J Harb Univers Commerc2024; 40(3): 3–16.
41.
LiaoSWeiJ. Cognitive decision-making mechanisms of managers driving the evolution of digital transformation strategies - a case study based on attention focus. Manag Case Stud Rev2024; 17(3): 343–360.
42.
WangXLiY. Chief information officers, business leaders and corporate digital innovation. Sci Tec Prog Count2022; 39(13): 83–93.
43.
YangZChenJShangH. What kind of experience drives digitalization: executive academic experience and corporate digital transformation. Econ Issues2022; 44(10): 1–11.
44.
ChenZLiCXingW. Impact of controlling shareholders’ equity pledge on digital transformation. J Nanjing Universit Finan Econ2024; 42(2): 45–55.
45.
WangYWangA. Open innovation, institutional environment and enterprise digital transformation-an empirical test based on listed companies in China’s manufacturing industry. Sci Techn Manag2024; 46: 1–24.
46.
KoreaGGuoX. Can government service informatisation drive enterprise digital transformation?Industrial Economics Review, 2024.
47.
ZhaoGLiJ. Does enterprise digital transformation empower new quality productivity development of enterprises? --Micro evidence based on Chinese listed firms. Industrial Economics Review2024; 12(4): 23–34.
48.
LiR. Research on the impact of management incentives on enterprise digital transformation. Technical Economics and Management Research2023; 44(5): 47–52.
49.
TaoCDingY. How the digital economy policy affects the innovation of manufacturing enterprises - based on the perspective of appropriateness supply. Contemp Finance Econ2022; 43(3): 16–27.
50.
LvFZhuYCatherineR, et al.A study on the impact of external environment on the adoption of digital technology in small and medium-sized enterprises. Sci Res2021; 39(12): 2232–2240.
51.
ChengQDingH. Research on the impact of tax incentives on digital transformation of resource-based enterprises. J Manag2022; 19(8): 1125–1133.
52.
FanZTaoYGongY. Can government subsidies promote digital transformation of manufacturing enterprises? –Analysis of digital transformation behaviour of manufacturing enterprises based on evolutionary game. Technical Economy2022; 41(11): 128–139.
53.
FangXGuoHLiuC, et al.How can digital infrastructure help enterprises' digital transformation? –Evidence from Enterprise Performance Presentations. Industrial Economics Review2023; 14(5): 61–81.
54.
WangTZhouHYuF. The impact of geographic location on digital transformation of listed firms-Moderating effects based on the combination of CEO and chairman positions. Econ Geogr2024; 44(5): 33–42.
55.
DenisDJSibilkovV. Financial constraints, investment, and the value of cash holdings. Rev Financ Stud2010; 23(1): 247–269.
56.
CaiYCaoYChenG, et al.Multiple concurrent causality of R&D investment in high-tech enterprises - a study based on fuzzy set qualitative comparative analysis. China Science and Technology Forum2022; 38(2): 86–94.
57.
ZhaiWZhangX. Impact of supply chain management on digital input of manufacturing enterprises. China Circulation Economy2021; 35(10): 82–92.
58.
TongYLiuKZhaoZ, et al.The impact of enterprise digital transformation on customer concentration. J Beijing Inst Technol (Soc Sci Ed)2024; 26(1): 177–194.
59.
AtheySTibshiraniJWagerS. Generalised random forests. Ann Stat2019; 47(2): 1148–1178.
60.
YiMYaoYHuM. An interpretable model of public value consensus in governmental new media integrating XGBoost and SHAP–The example of ‘Today's Headlines’ ten municipal governmental numbers. Library and Intelligence Work2022; 66(16): 36–47.
61.
WenTBaiY. An interpretable model of fake news group interaction quality incorporating RF-GA-XGBoost and SHAP. Data Analys Knowled Dis2024; 66: 1–18.
62.
JiangJLiM. An interpretable model of microblog users' extreme emotions under negative corporate events integrating LGBM and SHAP. Data Analysis and Knowledge Discovery2023; 8(7): 103–117.
63.
HuZXiongYDengL, et al.Re-assessment of the effect of regional coordinated development policy–an analysis of heterogeneity treatment effect from causal forest algorithm. J Econ2022; 9(2): 201–235.
