智慧供应链建设如何提升企业绩效？

doi:10.16381/j.cnki.issn1003-207x.2023.0482

Abstract

Abstract:

Under the background of China’s increased focus on the stability and security of its industrial and supply chains， as well as the growing challenges faced by Chinese enterprises in a dynamic market environment， there is a pressing need to systematically evaluate the impact of smart supply chain construction on supply chain resilience optimization and enterprise performance improvement. Employing the panel data of Shanghai and Shenzhen A-share listed companies over 2013-2020， the implementation of supply chain innovation and application policies is approached as a quasi-natural experiment in this study. To estimate the impact of constructing a smart supply chain on enterprise performance， a robust DDD model is utilized.The empirical analysis results demonstrate that the construction of a smart supply chain has significant influences on enterprise performance. Firstly， it significantly enhances enterprise performance， thereby indicating that the intelligent transformation and upgrading of the supply chain can assist enterprises in achieving sustainable performance improvement. Secondly， supply chain resilience optimization serves as a key mechanism through which a smart supply chain enhances enterprise performance. This mechanism facilitates the development of dynamic capabilities enabling professional division of labor， control of transaction costs， internal and external financing， and innovation synergy. Thirdly， the policy effect exhibits significant heterogeneity. The construction of smart supply chains has a greater impact on the performance improvement of growing period enterprises， non-state-owned enterprises， labor-intensive enterprises， and technology-intensive enterprises. Moreover， the policy effect is more favorable in situations where there is a lower degree of market segmentation for goods and labor， and when the enterprises are located in the eastern region. The findings of this study have important policy implications for expediting the intelligent transformation and upgrading of supply chains， ensuring the continuous enhancement of supply chain resilience， and accelerating the high-quality development of enterprises.

Key words: smart supply chain, supply chain resilience, enterprise performance, difference-in-differences-in-differences （DDD）

CLC Number:

F425

Daqian Shi, Xueqin Li, Dandan Li. How Can Intelligent Supply Chain Improve Enterprise Performance: Study on Supply Chain Optimization Resilience Perspective[J]. Chinese Journal of Management Science, 2025, 33(4): 325-334.

Figures/Tables 6

变量	(1)	(2)	(3)	(4)	(5)
$T i m e t × C i t y j × F i r m i$	0.447^**	0.400^**	0.684^***	0.648^***	0.597^**
$T i m e t × C i t y j × F i r m i$	(0.195)	(0.163)	(0.089)	(0.084)	(0.236)
$T i m e t × C i t y j$	0.086	0.075	0.100^*	0.090^*	0.115^**
$T i m e t × C i t y j$	(0.063)	(0.057)	(0.055)	(0.049)	(0.057)
$T i m e t × F i r m i$	-0.803^***	-0.870^***	-1.013^***	-1.034^***	-0.145
$T i m e t × F i r m i$	(0.159)	(0.104)	(0.047)	(0.046)	(0.394)
$C i t y j × F i r m i$	0.073^*	0.180^***	-0.170	-0.061	0.536^*
$C i t y j × F i r m i$	(0.037)	(0.032)	(0.170)	(0.200)	(0.281)
$C i t y j$	-0.091^**	-0.086^**	0.411^***	0.350^***	-0.143
$C i t y j$	(0.034)	(0.034)	(0.110)	(0.117)	(0.112)
$F i r m i$	0.604^***	0.216^***	—	—	—
$F i r m i$	(0.024)	(0.026)	—	—	—
控制变量	否	是	否	是	是
企业固定效应	否	否	是	是	是
行业固定效应	是	是	否	否	否
年份固定效应	是	是	是	是	是
观测值	12957	12957	12957	12957	12957
R²	0.158	0.259	0.625	0.631	0.042

变量	(1)
$T i m e t × C i t y j × Y e a r 2014$	0.157
$T i m e t × C i t y j × Y e a r 2014$	(0.266)
$T i m e t × C i t y j × Y e a r 2015$	0.328
$T i m e t × C i t y j × Y e a r 2015$	(0.301)
$T i m e t × C i t y j × Y e a r 2016$	0.164
$T i m e t × C i t y j × Y e a r 2016$	(0.290)
$T i m e t × C i t y j × Y e a r 2017$	0.541
$T i m e t × C i t y j × Y e a r 2017$	(0.339)
$T i m e t × C i t y j × Y e a r 2018$	0.254
$T i m e t × C i t y j × Y e a r 2018$	(0.176)
$T i m e t × C i t y j × Y e a r 2019$	0.604
$T i m e t × C i t y j × Y e a r 2019$	(0.427)
$T i m e t × C i t y j × Y e a r 2020$	1.153^**
$T i m e t × C i t y j × Y e a r 2020$	(0.554)
观测值	12957
R²	0.632

变量	$V e r$	$A s s$	$I n t$	$W W i$	$I n n$	$I n q$
$T i m e t × C i t y j × F i r m i$	-0.043^***	0.193^***	0.046^***	-0.023^**	0.572^***	0.220^***
$T i m e t × C i t y j × F i r m i$	(0.008)	(0.041)	(0.008)	(0.009)	(0.092)	(0.051)
观测值	12957	12957	12957	12957	12957	12957
R²	0.748	0.927	0.468	0.749	0.459	0.441

变量	(1) 成长期	(2) 成熟和衰退期	(3) 非国有企业	(4) 国有企业	(5) 劳动密集型	(6) 资本密集型	(7) 技术密集型
$T i m e t × C i t y j × F i r m i$	2.426^***	-0.045	1.332^***	0.030	0.201^*	-0.101^*	1.057^***
$T i m e t × C i t y j × F i r m i$	(0.061)	(0.057)	(0.114)	(0.110)	(0.096)	(0.049)	(0.069)
观测值	6265	6328	8373	4555	3811	3653	5273
R²	0.633	0.682	0.604	0.705	0.599	0.676	0.633
	(8)商品市场分割程度低	(9)商品市场分割程度高	(10)劳动力市场分割程度低	(11)劳动力市场分割程度高	(12)东部地区	(13)中西部地区	—
$T i m e t × C i t y j × F i r m i$	0.832^***	0.632^***	0.965^***	-0.076	1.355^***	0.157	—
$T i m e t × C i t y j × F i r m i$	(0.130)	(0.100)	(0.074)	(0.091)	(0.251)	(0.118)	—
观测值	9877	3064	7588	5349	9068	3878	—
R²	0.624	0.698	0.640	0.644	0.630	0.654	—

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How Can Intelligent Supply Chain Improve Enterprise Performance: Study on Supply Chain Optimization Resilience Perspective

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