
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12654-0
10.1016/j.heliyon.2024.e36623
e36623
Research Article
The implications of smart logistics policy on corporate performance: Evidence from listed companies in China
Liu Yijun liuyijun0330@hotmail.com
a⁎
Kim Seungwoon kimjbnu304@outlook.com
b
Sun Jonghak sun@jbnu.ac.kr
b
a School of Supply Chain Management, Ningbo Polytechnic, Ningbo, Zhejiang, 315800, China
b College of Business and Economics, Jeonbuk National University, Jeollabuk-do, 54896, Republic of Korea
⁎ Corresponding author. liuyijun0330@hotmail.com
21 8 2024
15 9 2024
21 8 2024
10 17 e366239 3 2024
11 8 2024
20 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Since the emergence of smart logistics as a vital paradigm, it has garnered significant interest from independent firms and governments worldwide, including China. This study aims to examine the relationship between Smart Logistics Policy (SLP) and firm performance both theoretically and empirically. Utilizing data from A-share companies listed on the Shanghai and Shenzhen stock exchanges between 2012 and 2017, this study analyzes the relationship between SLP and firm performance using Propensity Score Matching (PSM) and Difference-in-Differences (DID). The results indicate that SLP significantly enhances a firm's financial performance. Additionally, a heterogeneity test on financial performance reveals that the impact of SLP varies based on ownership and industrial sector. Unexpectedly, SLP has a negative impact on corporate social responsibility (CSR) performance. The heterogeneity test on CSR performance shows that the SLP effect on CSR exhibits no significant difference based on ownership. Furthermore, the impact of SLP on CSR is significantly greater for manufacturing firms compared to non-manufacturing firms. Consequently, this study offers theoretical support and empirical evidence regarding the effects of SLP on firm performance.

Keywords

Smart logistics policy
Financial performance
CSR performance
PSM-DID
China
==== Body
pmc1 Introduction

Logistics is a crucial sector that not only enhances a firm's competitiveness but also promotes a country's national economic performance and development [1,2]. Effective logistics drives economic growth by producing and distributing goods at lower costs through reliable mechanisms, resulting in increased customer demands [3,4]. Effective logistics management allows producers to access distant markets [5], reduce firms' inventories [3], attract foreign direct investment [6], enhance business management efficiency [7], achieve sustainable competitiveness [8], and create numerous jobs in the sector.

Recognizing the crucial role of effective logistics in shaping competition within industries and markets, scholars, managers, and policymakers have devoted significant attention to this critical issue [9,10]. Optimizing the flow of goods, finances, and information along the value chain through digital technologies has led to the development of ‘smart logistics’ or ‘logistics 4.0’ [11]. This concept has emerged as a vital paradigm [[12], [13], [14]], referring to a dynamic logistics management process that involves planning, managing, and smartly controlling operations using emerging IT-based technologies [2] that enable participants to swiftly and effectively respond to new environmental changes [15].

With the advent of new logistics modes, many scholars and practitioners have examined the significance and challenges of smart logistics. For instance, some researchers [[16], [17], [18]], focusing on the technological aspects of smart logistics, studied the relationship between smart logistics-related technologies and organizational performance, and the positive relationship between them was supported [19,20]. Conversely, other studies [21,22] examined whether factors such as human capital and governmental policies could play key roles in improving organizational performance within the context of utilizing smart logistics. Four critical elements—Information and Communication Technology (ICT), infrastructure, people, and government policy-making—have been consistently proposed in explaining the success of smart logistics [13,23]. Governmental policies affect not only the success of individual firms but also the development of national economies [22,24]. Specifically, governmental policies may have significant impacts on the expected outcomes of smart logistics implementation [22].

However, McFarlane, Giannikas [25] and Jabeur, Al-Belushi [23] both proposed that government policies are very important for the development of smart logistics, but there has been little research effort to scrutinize the role and effect of governmental policies regarding smart logistics on organizational performance. Although some researchers have attempted to empirically explain the relationship between smart logistics policy and a firm's performance [22], the performance changes caused by a certain issuance of logistics policies have not been studied. And they overlooked multidimensional aspects (e.g., social responsibility measures) in measuring organizational performance. Simply, the financial dimension (e.g., stock price, sales revenue) is used to measure the outcomes of smart logistics policy, making it difficult to capture the comprehensive nature and scope of firm performance associated with smart logistics policy. Additionally, long-term performance studies generally require at least three years of performance data [26,27], otherwise, it is unable to fully measure the long-term performance of companies after the release of the SLP. Therefore, this paper discusses the long-term reaction of the SLP instead of the short-term reaction. Furthermore, existing studies lack theoretical frameworks aimed at explaining the link, thus failing to expand our comprehensive understanding of their correlation. The lack of theoretical frameworks hinders logistics researchers from comparing results across studies. More critically, the concept of Logistics 5.0 is being proposed to guide enterprises from intelligent operations to sustainable ecosystems [2]. We urgently need to know whether the previous smart logistics policies have truly improved corporate performance and whether such policies can provide governance references for the era of logistics 5.0. Therefore, examining the effects of smart logistics policies is of great practical significance. This requires research to fill all the identified significant gaps, which has incentivized this study.

Considering the introduction and identified gaps in the current literature, this study addresses the following key research questions:RQ1 How does the Smart Logistics Policy (SLP) impact Chinese enterprise performance from a theoretical perspective?

RQ2 Does the SLP empirically impact the performance of listed companies?

RQ3 How does the SLP affect different types of companies?

Addressing these research questions, this research contributes to the literature in several ways. First, this research interprets how the Smart Logistics Policy impacts enterprise performance by employing robust theoretical frameworks, including institutional theory, stakeholder theory, and resource cost-effectiveness perspectives, and the study integrates a clear theoretical framework to indicate how theories were linked and what their respective roles and relationships are during policy effect analysis. This study if not the first is among the few studies that explore the theoretical linkage between the SLP and enterprise performance from the perspective of China, thereby enriching the literature on policies influencing corporate performance, especially for the logistics policy. Second, most previous studies on smart logistics have conceptually examined the relationship between policy and corporate performance, lacking empirical validation or the study of long-term corporate performance. This study fills this gap by empirically examining the impact of SLP on long-term corporate performance [26,27], thus contributing to the literature. Third, many previous studies have not considered the multidimensional aspects of firm performance. To address this limitation, this study measures firm performance in two dimensions: financial performance and corporate responsibility performance (non-financial performance), providing a comprehensive examination of the impact of smart logistics policies. The results of our study have significant practical implications for firms, stakeholders, shareholders, and policymakers, particularly for the Chinese government, to better introduce policies in the era of Smart Logistics 5.0 and encourage active enterprise participation.

The remainder of this paper is structured as follows: First, we explore the underlying correlation between SLP and firm performance. Next, we develop the mechanism by which SLP affects firm performance and then empirically examine this correlation. In the final section, we explain our results, describe the limitations of our research, and discuss its implications for theoretical perspectives and business management practices.

2 Policy context and theoretical foundation

2.1 Smart logistics policy context

Currently, traditional logistics systems are less effective in responding to current fast-changing, complex, and global business environments [28]. Consequently, firms are leveraging digital technologies (e.g., AI, IoT, Big Data, etc.) to transform traditional logistics into more sophisticated smart logistics [13,29]. In the digital era, smart logistics has attracted considerable interest from both independent firms and governments worldwide. Ongoing discussions among governments, lawmakers, regulatory organizations, and investors focus on how a more stringent regulatory framework might be imposed on companies’ logistics-related environmental impacts [30].

In 2015, China introduced a series of national-level policies to promote the development of smart logistics. These policies aim to seize technological opportunities, reduce CO2 emissions over the coming decades, and enhance firms’ competitiveness. The most notable and targeted policy for smart logistics development is the “Notice of the Ministry of Commerce on the Implementation Plan of the Smart Logistics Distribution System,” [31] (hereafter SLP)” formulated and issued by the General Office of the Ministry of Commerce (GOMC). The policy announced the first batch of pilot cities: Chongqing, Taiyuan, Nanchang, Wuxi, and Linyi.

The SLP primarily outlines the following requirements for Chinese enterprises in pilot cities as follows: (1) Encourage enterprises to establish intelligent warehousing management systems; (2) Establish interconnected intelligent information-sharing service platforms; (3) Promote the standardization of logistics-related processes; (4) Enhance enterprise's information management and technology application capabilities. Additionally, for logistics industry enterprises, the government promotes their entry into smart logistics parks and the establishment of highly efficient and convenient intelligent terminal delivery networks and distribution systems.

Considering that different policies have varying effects on firm performance [32], it is thus, imperative that we understand how SLP affects corporate performance. Consequently, this paper interprets the theoretical mechanism of how policy affects enterprise performance from the perspectives of institutional theory, stakeholder theory, and resource cost-effectiveness.

2.2 Theoretical framework

2.2.1 Institutional theory

The institutional environment has been considered to have a significant impact on an organization's decision-making and development [33]. According to institutional theory, firms' behaviors and decisions are influenced not only by their resources and organizational goals but also by imitation and convergence resulting from pressures related to “legitimacy” [34]. Enhancing social legitimacy will strengthen an organization's ability to acquire resources and gain support from the external environment. Therefore, for an organization to survive and thrive in society, it must seek and obtain social legitimacy by adhering to rules and social norms [35]. Maurer [36] argues that organizations are compelled to seek recognition and legitimacy due to external sources, such as policy and institutional environments. Thus, as a form of institutional pressure, the implementation of a smart logistics policy compels firms to seek legitimacy [36], leading to corresponding adjustments in their decision-making, behavior, and structure [37].

Specifically, the impact of smart logistics policies on enterprises stems from the pursuit of three types of legitimacy behaviors. First, coercive legitimacy, which primarily comes from government regulatory policies, laws, regulations, and standards, set by powerful organizations [38]. The implementation of SLP logistics policies puts enterprises in a process of pursuing specific “coercive legitimacy.” Second, mimetic legitimacy, arises when leading enterprises in pilot cities actively respond to the promotion of smart logistics policies by implementing technological innovations and leadership. As members of the supply chain, enterprises are influenced by the behaviors and technology adoption of other industry members and by mimetic competition pressures from upstream suppliers and downstream distributors, compelling them to obtain “mimetic legitimacy.” Third, “normative legitimacy” arises from professionalization and socialization processes, including social norms and values, professional standards, management traditions, and education and training. For example, typical industry norms compel organizations to follow certain codes of conduct and standards, beyond just utilitarian perspectives. Smart logistics policies, as a national strategy, aim to accelerate the formation of relevant industry standards and norms to improve overall manufacturing efficiency. Enterprises have corresponding responsibilities and obligations to implement and respond to these policies, thereby gaining public recognition and maintaining their image, a process known as obtaining “normative legitimacy."

2.2.2 Stakeholder theory

Stakeholder theory can “point the direction” for corporate social responsibility and serves as the theoretical foundation for the study of corporate social responsibility [39,40]. Enterprises need to consider the conflicting interests of their numerous stakeholders, including managers, employees, shareholders, suppliers, and distributors, comprehensively and balance them during their business operations [41]. As one of the main stakeholders in smart logistics policies [42], the government formulates these policies to influence companies’ managerial processes and guide them to adopt the SLP through direct regulation. The primary goal of the government is to mitigate the environmental impact of logistics activities by encouraging companies to adopt intelligent logistics technology and implement smart management processes. Improper logistics management can escalate waste, energy consumption, and greenhouse gas emissions, leading to excessive pollution [3,43]. It is widely agreed that pollution and waste from manufacturing enterprises threaten life on Earth [44]. Consequently, the environmental performance of the manufacturing industry has garnered significant attention.

The response behavior of enterprises to policies is constrained by their interests and influenced by government policies and supervision. However, due to businesses seeking to maximize value [45], most companies are unwilling to bear excessive environmental costs or pay for pollution caused by traditional technologies. Hence, the conflict between corporate interests and public interests revolves around balancing environmental responsibility with adverse environmental consequences [46]. The game between the government and enterprises revolves around balancing environmental regulation or technology encouragement with corporate economic benefits [47]. In other words, the improvement in a company's environmental responsibility performance results from a dynamic interaction among the company, the government, and the public. Additionally, whether this dynamic will lead companies to assume more social responsibility is influenced by the constraints on resources and costs faced by the companies.

2.2.3 The perspective of resource cost-effectiveness

From a business perspective, the primary focus of enterprise management is economic objectives, guiding organizations to make decisions and allocate resources based on cost-effectiveness [48]. When enterprises pursue intelligent managerial practices and social responsibility, they often face a situation where these practices compete for limited internal resources [49]. Hull and Rothenberg [50], pointed out that trying to improve social performance may drain corporate resources that could be used in core business areas, thus inhibiting corporate innovation. Similarly, increasing investment in technological innovation limits the internal resources available for socially responsible behaviors [49,51]. Therefore, enterprises face the dilemma of balancing resource allocation between innovation and social responsibility, given a highly competitive market and internal resource constraints. From a resource cost-effectiveness perspective, if the perceived benefits (measured or unmeasured) outweigh the associated costs, the enterprise will be willing to comply with the SLP and implement related smart logistics practices. The effectiveness of the SLP in driving companies to adopt smart logistics practices and assume more social responsibility depends on the perceived benefits.

2.3 The SLP and corporate performance

Government policy is a significant source of uncertainty for enterprises and a crucial factor affecting enterprise performance [52]. Previous research on policy and corporate performance is extensive, offering various perspectives on the impact of policies [37,53,54]. However, there has been limited research on the role and impact of governmental policies regarding smart logistics on organizational performance.

Firstly, the SLP enhances enterprise performance by optimizing resource allocation. On the one hand, the SLP encourages firms to adopt emerging technologies that simplify supply chain management and increase the reliability of logistics activities, ultimately enhancing resource utilization efficiency [55]. Secondly, smart logistics solutions facilitate accurate information recording between the supply and demand sides of the supply chain [56], addressing information asymmetry issues and enabling precise resource allocation between suppliers and producers. These enhancements in resource allocation can lower firms’ cost structures and subsequently enhance financial performance [57].

Secondly, the SLP can enhance enterprises' financial performance by improving their level of digitalization. The integration of smart management services, digital platforms, and other digital technologies has transformed traditional business operations and organizational structures [58]. A corporation consists of multiple business units, each with its own responsibilities. Smart logistics adoption within firms can enhance coordination and seamless connections among departments [59], reduce internal and external management and coordination costs [60], and enable effective coordination of human, financial, and information resources. This enhances the efficiency of enterprise organization and management, thereby improving financial performance. Externally, the emerging technologies in smart logistics solutions facilitate precise interactions between enterprises and customers [61], enable quick customer feedback, and offer solutions tailored to customers’ diverse and personalized needs, thus improving customer service [62] and ultimately increasing profitability.

2.4 The SLP and corporate social responsibility performance

Due to the limitations of self-regulation, enterprises cannot prioritize social goals over profit maximization. Therefore, policies are considered a critical factor affecting CSR [63]. Based on institutional theory, implementing a smart logistics policy imposes “coercive legitimacy” on firms [53,64]. Additionally, by cultivating smart logistics demonstration enterprises, the government enhances the impact of “imitation legitimacy” on firms [34]. However, enterprises face the dilemma of allocating resources to either technological innovation or social responsibility practices when following the SLP [50]. In the pursuit of “profit maximization,” companies tend to allocate more of their limited resources to management practices that enhance financial performance [49], resulting in poorer social responsibility performance.

On the other hand, although the SLP provides enterprises with a future direction for development and enhances their competitiveness through innovations in smart technology, it may compel enterprises to focus more on technological innovation in the short term. However, the achievements of intelligent logistics practices may require a relatively long evaluation period. Therefore, improvements in enterprises’ social performance brought by technological innovation may not appear in the short term, potentially reducing socially responsible behavior.

Regulation often has asymmetric effects on competing firms [65]. Hence, companies with superior capabilities to adapt to regulatory requirements are more likely to gain a competitive advantage over their rivals. Consequently, policy implementation has varying impacts on the competitiveness of firms of different sizes, industries, or ownership structures [66]. Thus, this study will further explore the differential impact of smart logistics policies on the performance of firms with different ownership structures and industries.

2.5 Theoretical mechanism

Based on the aforementioned theories, we have outlined the theoretical mechanism by which the implementation of smart logistics policies affects corporate financial performance and social responsibility performance, as shown in Fig. 1. Then we will examine the policy effect empirically.Fig. 1 Theoretical mechanism.

Fig. 1

3 Research design

3.1 Data specification

Based on the industry classification guidelines of the China Securities Regulatory Commission announced in 2012, we selected Chinese-listed companies on the A-share market as our preliminary sample. Companies were filtered according to the following criteria: (1) excluding enterprises suffering from continuous losses (known as ST and *ST enterprises) according to standard treatment methods; (2) excluding firms that had conducted an initial public offering within the past year; (3) excluding samples with significant amounts of missing data during the research period; and (4) including only samples with data for the six-year research period. The sample period for this study spans from 2012 to 2017. The firm data were obtained from the CSMAR and WIND databases in China. Additionally, city characteristic data were obtained from the China Urban Statistical Yearbook and China Environmental Yearbook.

3.2 Variables and setting

3.2.1 Dependent variables

We selected Tobin's Q as the proxy variable for corporate financial performance, referencing prior research [67,68]. Additionally, this study adopted the quantified environmental, social, and governance (ESG) score provided by Shanghai Securities Index Information Service Co., Ltd. as a proxy variable for CSR performance [69,70]. The ESG index system of Shanghai Securities Index Information Service Co., Ltd. is based on the mainstream ESG evaluation framework used in foreign countries. The ESG index system includes 26 key indicators, uses the industry-weighted average method for ESG evaluation, is updated quarterly, and includes all listed companies. The ESG rating is divided into nine grades: C, CC, CCC, B, BB, BBB, A, AA, and AAA from low to high. The explanatory variable (ESG) is constructed according to the above rating by assigning an A value from 1 to 9.

3.2.2 Leading independent variable

The primary independent variable in this study is the SLP effect. This study uses a dummy variable to represent the policy. Enterprises located in the five cities where the SLP was implemented were assigned a value of 1, constituting the treatment group. Enterprises in non-pilot cities were assigned a value of 0, constituting the control group.

3.2.3 Control variables

This study considered control variables related to the internal characteristics and external factors of enterprises. Referring to prior research [71], the internal characteristics considered included firm size, firm age, capital density, ownership, and the asset-liability ratio. Capital density and the asset-liability ratio were used to measure the enterprises’ capital capacity and financial status, respectively. External factors primarily involved whether the enterprise received government subsidies. All variables are listed in Table 1.Table 1 Descriptions of variables.

Table 1Variables	Name	Name and Measurement of variables	
Tobin's Q Ratio	Tobin's Q	Tobin's Q Ratio =(Market value of tradable shares + the number of nontradable shares × net assets per share + the book value of liabilities)/total assets;	
ESG score	ESG	The ESG ratings are divided into nine grades, which are as follows from low to high: C, CC, CCC, B, BB, BBB, A, AA, and AAA. The explanatory variable (ESG) is constructed according to the above rating scale by assigning a value from 1 to 9 to each level; that is, when the rating is C, ESG = 1. When the rating is CC, ESG = 2; when it is CCC, ESG = 3; and so on;	
Policy effect	did	did = policy*time;	
Firm size	size	The log of the total assets of the enterprise;	
Capital density	cap	The ratio of the total fixed assets of the enterprise to its number of employees;	
Firm age	age	age = reporting year of the sample - year the firm was established;	
subsidy	sub	A dummy variable that indicates whether the enterprise is in line with a government subsidy based on the database information of Chinese industrial enterprises. If an enterprise is in line with a government subsidy in the same year, the value of sub is 1; otherwise, it is 0;	
ownership	state	Whether the enterprise is a state-owned enterprise (state), defined according to the proportion of state-owned investment in the paid-up capital of the enterprise. If the ratio exceeds 50 %, the enterprise is defined as a state-owned enterprise, and the value is 1; otherwise, it is 0;	
leverage	lev	Leverage = Total liabilities divided by total assets at the end of the year.	

3.3 Model setting

The Difference-in-Differences (DID) method is widely recognized as the most effective approach for studying quasi-natural experiments and evaluating the impacts of external shocks, such as political turmoil or policy implementation [72]. The fundamental principle of the DID model is that the difference between the group affected by the policy (the treated group) and the group not affected by the policy (the control group) can be compared to assess the policy's impact. This paper takes the smart logistics policy of 2015 as a quasi-natural experiment, which conforms to the preconditions for policy analysis using the difference-in-differences (DID) method. Because our study sample consists of listed companies, those in the pilot areas did not anticipate that the “SLP” policy would be implemented in these areas when selecting their locations. Furthermore, it is uncommon for listed companies to relocate because of the SLP policy. Consequently, the SLP policy satisfies the exogeneity requirement of the difference-in-differences model. As such, based on the results of Hausman's test, we used a random-effects model. The model equation for the analysis is as follows:(1) Fmit=β0+β1didit+β2sizeit+β3capit+β4ageit+β5subit+β6stateit+β7levit+φt+δt+εit

where m = 1, 2 in equation (1), the dependent variables F1it,andF2it, refer to Tobin's Q and the ESG score, respectively; i represents the focal enterprise,t represents the time, and didit represents policyi*timei, it is the interaction term of policy i and timei, which is used to examine time-changes in the two corporations); policyi represents a corporate dummy variable that is equal to 1 for affected corporations in the treatment group and 0 for firms in non-affected cities in the control group; and timei is a time dummy variable that is assigned a value of 1 for 2015, 2016 and 2017 (after SLP) and 0 for 2012–2014 (before SLP). δi represents industry fixed effects; φt are time-fixed effects; and εit is an error term.

3.4 Propensity score matching (PSM) results

Due to significant differences among listed companies, we use the PSM-DID method to select similar “non-pilot listed companies” as a control group before performing the difference-in-differences analysis, which helps to address the issue of insufficient matching numbers and eliminate sample selection bias. The propensity score matching (PSM) method was utilized to identify samples from the control group that matched the features of the experimental group, thereby reducing errors from non-random selection, and then a logit regression was performed on the grouping dummy variable of the firm's features against the covariates. The covariates used for PSM in this study included firm size, capital density, firm age, subsidies, state ownership, and leverage. Table 2 shows the results of descriptive PSM analysis.Table 2 Descriptive PSM analysis.

Table 2Variables	Obs	Min	Max	Mean	Std. Dev.	
Tobinq	2,660	0.693	3.258	2.395	0.625	
ESG	2832	6.648	1.082	1	9	
did	2,832	0	1	0.1038	0.305	
size	2,832	18.927	29.489	22.302	1.349	
cap	2,832	6.283	16.874	12.642	1.201	
age	2,832	13	40	23.699	4.980	
sub	2,832	0	1	0.979	0.144	
state	2,832	0	1	0.493	0.500	
lev	2,832	0.0164	1.019	0.465	0.223	

Table 3a, Table 3b(a) and Table 3(b) present details about the matching variables before and after PSM for Tobins'q and ESG. The results show that the estimated bias of all variables significantly decreased to 20 % after matching. Additionally, the t-test results for all variables after matching were not significant, indicating no significant differences between the treatment and control groups. The remaining samples are suitable for further DID analysis.Table 3a Balance test of variables before and after PSM (Tobin's Q).

Table 3aVariable	Unmatched	Mean	Control	%bias	%reduct bias	t-test	p > t	
Matched	Treated	t	
size	U	22.388	22.34	3.7		0.75	0.452	
M	22.388	22.308	6.1	−64.4	1.03	0.304	
cap	U	12.838	12.648	16.4		3.36	0.001	
M	12.838	12.771	5.8	64.9	0.98	0.328	
age	U	24.022	23.895	2.7		0.54	0.591	
M	24.022	23.785	4.9	−86.2	0.84	0.403	
sub	U	0.9802	0.9777	1.7		0.36	0.719	
M	0.9802	0.9811	−0.6	63.6	−0.11	0.912	
state	U	0.4883	0.5226	−6.9		−1.44	0.151	
M	0.4883	0.4775	2.2	68.6	0.36	0.720	
lev	U	0.4790	0.4695	4.2		0.90	0.370	
M	0.4790	0.4631	7.1	−68.2	1.20	0.230	

Table 3b Balance test of variables before and after PSM (ESG).

Table 3bVariable	Unmatched	Mean	Control	%bias	%reduct bias	t-test	p > t	
Matched	Treated	t	
size	U	22.334	22.294	3.1		0.64	0.525	
M	22.334	22.228	8.2	−168.2	1.44	0.150	
cap	U	12.859	12.593	23		4.83	0.000	
M	12.859	12.76	8.5	62.8	1.56	0.119	
age	U	23.749	23.713	0.7		0.16	0.876	
M	23.749	23.501	5.1	−593.7	0.90	0.368	
sub	U	0.9799	0.9784	1		0.23	0.822	
M	0.9799	0.98131	−1	5.7	−0.18	0.860	
state	U	0.46734	0.50242	−7		−1.53	0.127	
M	0.46734	0.45407	2.7	62.2	0.46	0.646	
lev	U	0.46559	0.46553	0		0.01	0.995	
M	0.46559	0.44952	7.2	24524.7	1.25	0.210	

With the initiation of the smart logistics policy in 2015 in five pilot cities, we selected listed companies in these cities as sample enterprises for the treatment group and used companies in non-pilot cities as the control group. Given that the number of non-pilot cities greatly exceeded that of the pilot cities, we further matched the non-pilot cities using the characteristics of the pilot cities. Prior research indicates that freight volume, logistics employment, and total social retail are crucial in determining the need for a smart logistics distribution system in a city [73].

Prior research indicates that freight volume, logistics employment, and total social retail are crucial in determining the need for a smart logistics distribution system in a city [73]. Based on the specific features of the pilot cities, we selected two additional indicators to screen control group cities: the number of industrial enterprises larger than a designated size and the percentage of tertiary industries in the GRP (gross regional product) at the city level. For the treatment group, we obtained the minimum and maximum values for each indicator of the five pilot cities from the Statistical Yearbook of Chinese Cities. Cities with values for these five indicators falling between the minimum and maximum values were selected for the control group. After screening, 16 cities were selected for the control group, and their spatial distribution is shown in Fig. 2.Fig. 2 Spatial distribution of treatment and control group cities.

Fig. 2

4 Empirical results

4.1 Parallel test and dynamic test

Initially, the study adhered to the parallel trend test method to validate the foundational assumption of DID estimation, ensuring conformity with the parallel trend assumption. Subsequently, employing Lane and Jacobson's event study approach [74], empirical examinations were conducted to analyze the dynamic impacts of the SLP, culminating in the formulation of the ensuing model:(2) Fm=β0+∑i=12β1×prei+γ×current+∑j=12δj×postj+ε

The estimated coefficient of the dynamic effect of Tobin's Q was not significant from 2015 to 2017, indicating that the implementation of the 2015 pilot policy was not long-lasting. Although the implementation of the policy was not long-lasting, the coefficient changed from negative to positive, and the T values corresponding to this variable increased significantly.

As shown in Fig. 3(a) and (b), we found that βi was not significant from 2012 to 2014, indicating that there was no significant difference between the treatment group and the control group before the implementation of the pilot policy and satisfying the parallel trend hypothesis. However, the estimated coefficient βi changed in 2016 and became significant two years after the SLP implementation (2017), indicating that the policy's effect on enterprise ESG lagged by two years and that the policy's effects gradually increased.Fig. 3a Dynamic test for Tobin's Q.

Fig. 3a

Fig. 3b Dynamic test for ESG.

Fig. 3b

4.2 Results regarding Tobin's Q and ESG

After eliminating common trend variations and selecting a control group through Propensity Score Matching (PSM), this study conducts both DID and PSM-DID analyses to examine the differences between firms in pilot areas and non-pilot areas before and after policy implementation. The regression results regarding Tobin's Q are presented in Table 4.Table 4 Tobin's Q regression results.

Table 4Variables	Tobinq	Tobinq	Tobinq	Tobinq	Tobinq	
DID	PSM-DID	PSM-DID	PSM-DID	SM-DID	
Model 1	Model 1	Model 1	Model 1	Model 1	
did	0.0091	0.2341***	0.0098	0.2317***	0.0087	
(0.74)	(13.25)	(0.79)	(13.15)	(0.70)	
size	0.0085	0.1722***	0.0116*	0.1802***	0.0114*	
(1.28)	(20.78)	(1.76)	(20.74)	(1.66)	
cap	0.0124**	0.0501***	0.0176***	0.0485***	0.0162***	
(2.38)	(6.41)	(3.33)	(5.80)	(2.90)	
age	0.0680***	0.0655***	0.0703***	0.0649***	0.0692***	
(18.18)	(16.77)	(18.55)	(16.49)	(18.22)	
sub	0.0159	−0.0959***	0.0127	−0.0948***	0.0133	
(0.83)	(-3.19)	(0.65)	(-3.16)	(0.69)	
state	0.1090***	0.1091***	0.1100***	0.1242***	0.1117***	
(5.11)	(3.73)	(4.96)	(4.21)	(5.03)	
lev	0.3282***	0.1371***	0.3201***	0.1482***	0.3295***	
(10.79)	(2.94)	(10.17)	(3.14)	(10.36)	
Constant	−0.2132	−3.7057***	−0.2830	−4.0097***	−0.3505*	
(-1.09)	(-17.52)	(-1.62)	(-16.59)	(-1.81)	
φt	YES	NO	YES	NO	YES	
δi	YES	NO	NO	YES	YES	
Obs	2,788	2,660	2,660	2,660	2,660	
Number of codes	470	455	455	455	455	
Note: ***Significant at p < 0.001.

The results show that the SLP dummy variable became insignificant when we included time-fixed effects in Columns (1), (3), and (5) (βTobinq= 0.0091, 0.0098, and 0.0087). Hence, we propose that collinearity may exist between the temporal effects and the policy's impact on corporate financial performance. Given the recent implementation of the policy and the relatively short time frame examined in this study, listed companies' financial and market strategies may not have had sufficient time to adapt to the policy. Indeed, the time is inadequate for the policy's effects to be fully manifested, potentially leading to deviations in the results due to the inclusion of time-fixed effects.

Consequently, this study utilized the results from column (2) for empirical analysis. The results indicate that the SLP significantly impacted Tobin's Q at the 1 % significance level (βTobinq= 0.2341***), demonstrating a substantial effect on the financial performance of listed enterprises. Compared to the control group, a 23.41 % increase was observed. Additionally, firm size, capital density, age, state, and leverage all significantly promote Tobin's Q (βsize= 0.1722***, βcap= 0.0501***, βage= 0.0655***, βstate= 0.1091***), whereas subsidies negatively affect QTobin's Q (βsub= −0.0959***), The policy has a greater impact on state-owned enterprises compared to non-state-owned enterprises, prompting further exploration into the heterogeneity results.

Applying the same method, we obtained regression results concerning ESG, as shown in Table 5. In Columns (1) and (5), the SLP dummy variable was notably negative (βESG= −0.2051***, −0.2038***) in relation to ESG performance. Using the results from Column (5) as an example, the coefficient suggests that the SLP implementation negatively impacted the ESG performance of listed companies, with the social responsibility performance of the experimental group suppressed by 20.38 % compared to the control group.Table 5 ESG regression results.

Table 5Variable	ESG	ESG	ESG	ESG	ESG	
(DID)	(PSM-DID)	(PSM-DID)	(PSM-DID)	(PSM-DID)	
Model 1	Model 1	Model1	Model 1	Model 1	
did	−0.2051***	−0.3239***	−0.2038***	−0.3185***	−0.2038***	
(-3.81)	(-6.52)	(-3.78)	(-6.40)	(-3.78)	
size	0.2153***	0.1737***	0.2352***	0.1581***	0.2192***	
(8.68)	(7.99)	(9.79)	(6.94)	(8.72)	
cap	−0.0345	−0.0687***	−0.0608***	−0.0522**	−0.0435*	
(-1.61)	(-3.42)	(-3.02)	(-2.34)	(-1.94)	
age	0.0065	0.0119	0.0112	0.0067	0.0063	
(0.86)	(1.55)	(1.45)	(0.87)	(0.82)	
sub	−0.2278***	−0.1855**	−0.2320***	−0.1764**	−0.2215**	
(-2.58)	(-2.12)	(-2.62)	(-2.01)	(-2.49)	
state	0.2759***	0.3298***	0.2975***	0.3200***	0.2953***	
(4.10)	(4.84)	(4.37)	(4.66)	(4.31)	
lev	−0.5325***	−0.3303***	−0.4384***	−0.4339***	−0.5364***	
(-4.30)	(-2.68)	(-3.54)	(-3.42)	(-4.21)	
Constant	2.4723***	3.5615***	2.2770***	4.0310***	2.6957***	
(4.01)	(6.88)	(4.08)	(6.65)	(4.16)	
φt	YES	NO	YES	NO	YES	
δi	YES	NO	NO	YES	YES	
Number of codes	2934	2832	2832	2832	2832	
Note: ***Significant at p < 0.001.

4.3 Heterogeneity analysis

4.3.1 Difference in enterprise ownership

Variations in ownership, such as equity rights, result in divergent corporate strategies and discretionary decisions [10]. As such, this study endeavors to examine the influence of SLP on enterprises’ multidimensional performance across various ownership types. The equation model is formulated as follows:(3) Fmit=β0+β1didit+β2didit*stateit+β3sizeit+β4capit+β5ageit+β6subit+β7levit+δi+φt+εit

Equation (2) introduces a new interaction term “did*state” to capture the differences in policy effects arising from ownership characteristics. The settings for other variables remain the same as in Equation (1). The coefficient β2 reflects the difference in policy effects between SOEs and non-state-owned enterprises. Table 6 presents the results of the SLP's impact on the multidimensional performance of state-owned enterprises (SOEs) and non-state-owned enterprises.Table 6 Heterogeneity analysis.

Table 6VARS	Tobin's Q	ESG	Tobin's Q	ESG	
SOEs	SOEs	Manufacturing	Manufacturing	
Model 3	Model 3	Model 4	Model 4	
did	0.1048***	−0.2599***	−0.0428*	−0.0885*	
(5.49)	(-3.76)	(-1.74)	(-1.01)	
did*state	−0.1964***	0.1059			
(-7.45)	(1.11)			
did*manu			0.0944***	−0.1711*	
		(3.29)	(-1.67)	
size	0.0053	0.2312***	0.0039	0.2191***	
(0.64)	(9.22)	(0.47)	(8.73)	
cap	0.0275***	−0.0394*	0.0256***	−0.0436*	
(4.08)	(-1.76)	(3.76)	(-1.95)	
age	0.0742***	0.0101	0.0705***	0.0063	
(17.46)	(1.32)	(17.38)	(0.83)	
sub	0.0107	−0.2185**	0.0099	−0.2181**	
(0.45)	(-2.45)	(0.41)	(-2.45)	
state			0.1802***	0.2969***	
		(6.81)	(4.34)	
lev	0.4909***	−0.4919***	0.4730***	−0.5408***	
(12.69)	(-3.86)	(12.16)	(-4.25)	
Constant	−0.5564**	2.4545***	−0.5293**	2.6611***	
(-2.42)	(3.78)	(-2.32)	(4.11)	
φt	Yes	Yes	Yes	Yes	
δi	Yes	Yes	Yes	Yes	
Obs	2,832	2,832	2,832	2,832	
Number of code	475	475	475	475	
Note:***Significant at p < 0.001.

The SLP significantly enhances the financial performance of non-state-owned enterprises (β1 = 0.1048***) indicating a 10.48 % increase in financial performance for these enterprises in pilot cities due to the implementation of smart logistics policies. Conversely, the coefficient for SOEs (β2 = -0.1964***) is negative and significant at the 1 % level, indicating that the financial performance improvement for state-owned enterprises is 19.64 % lower compared to non-state-owned enterprises.

Regarding the ESG heterogeneity test results, the coefficient of “did” (β1 = -0.2599***) is significantly negative, indicating that the implementation of smart logistics policies inhibits the improvement of social responsibility performance in non-state-owned enterprises in pilot cities. Additionally, the coefficient of “did*state” (β2 = 0.1059) is insignificant, suggesting that the SLP significantly inhibited the ESR of both SOEs and non-state-owned enterprises without notable differences.

4.3.2 Differences in industry

Based on the 2012 Guidance on the Industry Classification of Listed Companies, all industries in this study were categorized as either manufacturing or non-manufacturing. Furthermore, this study examined whether the impact of the SLP on enterprise performance varied across industries using the following equation:(4) Fmit=β0+β1didit+β2didit*manuit+β3sizeit+β4capit+β5ageit+β6subit+β7levit+δi+φt+εit

The variables in this equation are the same as equation (1). Here, ”manuit “is designated as a dummy variable; when the firm belongs to the manufacturing industry, the value is set as 1, otherwise is 0. The coefficient β2 of the interaction “manu*did” represents the policy impact that differs depending on the industrial sector. The results are displayed in Column (3) and (4) of Table 6. As seen in Table 6, the impact of the SLP on Tobin's Q and ESG performance of the examined enterprises is heterogeneous across different industries.

First, column (3) shows Tobin's Q results for the manufacturing and non-manufacturing sectors. The coefficient of “did” (β1 = -0.0428*) indicates that the policy had a negative impact on the financial performance of non-manufacturing enterprises in pilot cities, resulting in a 4.28 % decrease in financial performance compared to the control group. Additionally, the effect of the policy on financial performance showed significant differences (β2 = 0.0944***)between state-owned and non-state-owned enterprises. Manufacturing enterprises experienced a 9.44 % increase in financial performance compared to non-manufacturing enterprises.

Concerning the CSR performance heterogeneity test, the coefficient of “did” (β1 = -0.0885*) is significantly negative, indicating that the policy suppressed the ESG performance of non-manufacturing enterprises in the control group by 8.85 %. The coefficient of “did*manu “(β2 = -0.1711*)is negative at the 10 % significance level, suggesting that the negative effect of SLP on manufacturing firms’ ESG is approximately 17.11 % stronger than on non-manufacturing firms.

4.4 Robustness test results

In this paper, we conducted robustness tests to account for variations in the selection of control group enterprises, which may have influenced policy impacts. Therefore, the sample selection criteria for the control group were broadened. Freight volume, logistics employment, and total social retail are key factors influencing decisions on establishing a smart logistics distribution system in a particular city [73]. We selected cities whose indicator values fell between the minimum and maximum values of each factor. Based on these criteria, we identified a total of 26 cities, and their spatial distribution is shown in Fig. 4.Fig. 4 Spatial distribution of treatment and control group cities in robust test.

Fig. 4

The robustness test results are presented in Table 7.Table 7 Robustness test results.

Table 7Variables	Tobinq	Tobinq	Tobinq	ESG	ESG	ESG	
DID	PSM-DID	PSM-DID	DID	PSM-DID	PSM-DID	
Model 1	Model 1	Model 1	Model 1	Model 1	Model 1	
did	0.0021	0.2292***	0.0019	−0.2128***	−0.3312***	−0.2144***	
(12.14)	(12.19)	(12.19)	(-4.31)	(-6.88)	(-4.32)	
size	0.0233***	0.2269***	0.0216***	0.2600***	0.1983***	0.2648***	
(4.32)	(33.93)	(3.93)	(14.60)	(12.45)	(14.59)	
cap	0.0206***	0.0504***	0.0211***	−0.0410***	−0.0655***	−0.0463***	
(5.32)	(8.47)	(5.16)	(-2.83)	(-4.57)	(-3.06)	
age	0.0634***	0.0547***	0.0628***	0.0087*	0.0171***	0.0090*	
(24.32)	(19.15)	(23.70)	(1.77)	(3.30)	(1.78)	
sub	0.0116	−0.1215***	0.0118	−0.1408**	−0.0911	−0.1329**	
(0.75)	(-4.86)	(0.74)	(-2.16)	(-1.38)	(-2.00)	
state	0.1504***	0.1737***	0.1563***	0.3022***	0.3503***	0.3070***	
(10.12)	(8.10)	(10.27)	(6.66)	(7.44)	(6.62)	
lev	0.4207***	0.2517***	0.4364***	−0.6109***	−0.4886***	−0.6390***	
(18.16)	(6.89)	(17.99)	(-7.10)	(-5.50)	(-7.13)	
Constant	−0.6681***	−4.7811***	−0.5836***	1.3271***	2.7870***	1.7407***	
(-4.23)	(-29.16)	(-3.88)	(2.97)	(7.40)	(3.73)	
φt	YES	NO	YES	YES	NO	YES	
δi	YES	NO	YES	YES	NO	YES	
Obs	5,588	5,377	5,377	5,657	5,443	5,443	
Number of codes	951	921	921	953	923	923	
Note:***Significant at p < 0.001.

Similar to the results in Table 4, the “did” coefficient in Column (2) for Tobin's Q was significant and positive (βtobinq = 0.2292***); that it becomes insignificant when time effects are fixed and are consistent with prior results. The CSR robustness test results also indicate that the estimated results of CSR performance were robust.

5 Discussion

The baseline regression results indicate that SLP improved firms' financial performance. Smart logistics solutions were claimed to influence a firm's production, transportation, and distribution, in various forms and efficient ways [23,75,76]. Previous research has shown that smart warehousing and transportation practices can boost a company's financial performance [12]. The construction of an intelligent warehouse distribution system is the core content of the SLP. When enterprises actively implement the SLP by developing information-sharing platforms, they can achieve real-time data sharing, dynamically adjusting stakeholders' plans and schedules [77], mitigating the bullwhip effect and reducing inventory costs [78], ultimately ensuring the efficient operation of sustainable supply chains [4,79], which significantly contributes to corporate financial performance [80]. Standardized logistics practices which were encouraged in the policy can significantly enhance business performance [81]. These points illustrate that our research findings are consistent with previous studies.

In detail, we find that the SLPs' effect on different types of companies varies by firm's ownership and industry. The heterogeneity test results indicate that SLP has a stronger positive effect on the financial performance of non-state-owned enterprises compared to SOEs. One possible explanation is that SOEs in China face continuous political pressure and close monitoring [82,83], superficial or symbolic conformity to the regulations or policies could thus be punished (Meyer and Rowan, 1977). Hence, SOEs shoulder more social responsibilities and political tasks such as increasing technological investment when implementing SLP [84]. These measures reduce their market investment costs [85] and might lead to inferior financial growth because the return cycle for technology and infrastructure investments is long [86,87]. The results showing a higher effect on manufacturing firms' performance than non-manufacturing firms can be attributed to the supportive subsidy policies of the “Smart Manufacturing Program” in 2015. As such, Chinese manufacturing enterprises are undergoing intelligent transformation. The smart logistics policy can enhance the integration of manufacturing and logistics enterprises, thereby improving their financial performance more effectively compared to non-manufacturing enterprises [88,89].

Besides, the empirical results suggest that SLP negatively influenced firms’ CSR performance. Although this finding is surprising, it is not difficult to understand. From resource cost-effectiveness and stakeholder theory perspectives, firms are likely to face increasing pressure to prioritize between technological investment and social responsibility under limited internal resources [[49], [50], [51]]. SLP encourages enterprises to invest more in smart logistics-related technologies and facilities to enhance competitiveness. Therefore, firms may prefer to allocate more resources to technological investments, potentially earning more profits and benefits [49]. However, it is widely accepted that mandatory social responsibility practices via governmental policy negatively impact social benefits in the short term while improving social performance and stakeholder relations in the long term [90]. The SLP mandates firms to adopt smart and green logistics practices to enhance cleaner production and reduce greenhouse gas and waste emissions [91]. Consequently, in the long term, the social responsibility performance of enterprises will improve due to sustainable development. Regarding the heterogeneity test, the results show that the effect of SLP on CSR for manufacturing firms is significantly higher than for non-manufacturing firms. One possible reason is that manufacturing firms in China are undergoing digital transformation; the SLP and “Smart Manufacturing 2025” strategy push them to prioritize investment in smart facilities and technology adoption [92], which leads to smaller efforts put into CSR performance.

6 Conclusion

The primary aim of this study was to establish theoretical foundations for the relationship between smart logistics policy (SLP) and firm performance. This was done using institutional theory, stakeholder theory, and the perspective of resource cost-effectiveness to provide a theoretically compelling foundation for the relationship between SLP and firm performance. This conceptual relationship was empirically tested using the PSM-DID method. Considering that the size and scope of governmental policy effects on a firm's performance may vary based on the individual firm's characteristics [93], the study conducted a heterogeneity test to examine whether the treatment effect varies by firm ownership and industry type. Additionally, two robustness tests were conducted to confirm the empirical results by expanding the sample size.

The findings of the study suggested that the implementation of the SLP promotes firms' financial performance. Smart logistics serves as a source of competitive advantage in today's market environment [94]. It may be relevant in ensuring cost savings leading to efficient production through the adoption of smart logistics technologies and smart logistics practices and eventfully resulting in enterprise efficient development [95]. The heterogeneity test revealed that SLP significantly improves the financial performance of non-state-owned manufacturing firms. However, SLP is not significantly related to the financial performance of state-owned enterprises (SOEs) and non-manufacturing firms. Previous studies have found that not all enterprises exhibit the same sensitivity to the external institutional environment or adopt the same policy response strategies; enterprises exert efforts in ways that are beneficial to themselves in response to the policy environment [96]. In China, SOEs implement and adopt policies mainly based on the influence of “coercive legitimacy” during policy promotion [82,83]. Additionally, SOEs, due to their greater social responsibility, are relatively passive in the policy implementation process and incur higher costs [84], directly affecting their financial performance. Non-state-owned enterprises, when implementing the SLP, are more influenced by “mimetic legitimacy” and “normative legitimacy,” exhibiting greater autonomy and initiative in policy implementation. Leveraging their flexible organizational structure [97], private enterprises can respond faster and execute policies more effectively than state-owned enterprises, gaining a first-mover advantage [98]. This supports non-state-owned enterprises in achieving quicker financial performance returns during policy implementation.

Surprisingly, the SLP was significant but negatively impacted firms' CSR performance. This result suggests that SLP hinders firms' CSR performance. The heterogeneity test showed that the impact of SLP on CSR performance does not differ between SOEs and non-state-owned firms, but the negative effect on manufacturing firms' CSR performance is greater than that on non-manufacturing firms. Manufacturing, as the backbone of China's economy, has caused various environmental pollution problems that have drawn the attention of stakeholders [44], especially local governments., Coupled with the impact of the carbon emission reduction trading pilot policy [99], manufacturing enterprises have borne more “coercive legitimacy” to implement clean and intelligent production under the smart logistics policy [91], forcing them to increase technological investments. The benefits from these investments require a certain time cycle, thereby weakening short-term social responsibility performance.

6.1 Theoretical implication

Compared to existing studies, this study has several theoretical implications. First, our study presents theoretical lenses of institutional theory, stakeholder theory, and the perspective of Resource cost-effectiveness in exploring the nature of the relationship between the SLP and firm performance and digging into the functional mechanism between them. Based on institutional theory, smart logistics policies influence firms' strategic decision-making behaviors through three forms of legitimacy, then firms will choose the ways that are beneficial to themselves to react to the policies. The stakeholder theory and the perspective of resource cost-effectiveness provide a theoretical lens for firms to determine which one is more beneficial to them, to actively or passively respond to the SLP. By doing so, this study expands our understanding of why and how the SLP influences the firm's performance. Second, although some studies have empirically examined the impact of SLP on firm performance, most of them relied on a single measure such as financial scales (stock price, ROE, etc.) in the short term, which is insufficient to fully understand the underlying aspects of SLP on firm performance. Compared to previous research, by quantitatively examining the causal relationship with empirical data via the PSM-DID method, this study provides empirical support for the role of SLP in a firm's performance changes, thereby filling the gap of lacking empirical validity in this research field. Furthermore, by exploring double-dimensional performance aspects (e.g., CSR performance) instead of relying on a single measure of organizational performance, this study helps to capture the full nature of firm performance associated with smart logistics policy.

6.2 Managerial implications

This paper provides empirical implications for the future research and decision-making of the government and companies. Given the findings of this study, the following policy implications are proposed. First, we argue that the Chinese government should increase the number of smart logistics pilot cities, gradually simplify the approval standards process, and further promote the construction of smart logistics systems, preparing for the upcoming Era of Logistics 5.0. Second, the governments of SLP cities should establish a supportive mechanism for enterprises that provides a compensation mechanism for technology adoption and innovation in the early stages of policy implementation. A compensation mechanism in the early stages of policy implementation can enhance the mimetic legitimacy of enterprises implementing the policy, encouraging them to adopt more proactive policy response strategies, thereby enhancing the effectiveness of policy implementation, and also reducing the firm's pressures on CSR expenditures. Finally, we find that it is necessary to establish and implement differentiated supportive and compensation policies for state-owned and non-state-owned enterprises in pilot cities.

This study offers managers guidelines on how smart logistics contribute to effective management and long-term profitability for firms. Firstly, we encourage corporate managers to actively respond to smart logistics policies to gain technological leadership and improve financial performance, especially for non-state-owned and manufacturing enterprises. Secondly, managers of state-owned enterprises should adhere to government directives and utilize their institutional advantages to spearhead smart logistics initiatives. Additionally, they should seek more policy subsidies to mitigate the financial pressures resulting from their higher investments in smart equipment and technology compared to other enterprises.

6.3 Limitation

Although this study analyzed the impact of the smart logistics pilot policy on listed companies’ financial and nonfinancial performance, the empirical results are not comprehensive due to the exclusion of long-term policy effects and reliance on short-term data. Therefore, tracking the long-term impacts of smart logistics policies on enterprises is suggested for future research.

Data availability statement

Data are available upon request due to privacy restrictions. The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.

CRediT authorship contribution statement

Yijun Liu: Writing – original draft, Validation, Software, Formal analysis, Data curation, Conceptualization. Seungwoon Kim: Writing – review & editing. Jonghak Sun: Writing – review & editing.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Yijun Liu reports financial support was provided by Ningbo Philosophy and Social Sciences. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was funded by the Ningbo Department of Philosophy and Social Sciences Planning，grant number G2024-1-13 , and The 2023 Special Project for Talent Introduction and Cultivation at Ningbo Polytechnic, grant number NZ23RC11
==== Refs
References

1 Rashidi K. Cullinane K. Evaluating the sustainability of national logistics performance using Data Envelopment Analysis Transport Pol. 74 2019 35 46
2 Li J. Logistics 5.0: from intelligent networks to sustainable ecosystems IEEE Transactions on Intelligent Vehicles 8 7 2023 3771 3774
3 Domagała J. Górecka A. Roman M. Sustainable Logistics: How to Address and Overcome the Major Issues and Challenges 2022 CRC Press
4 Nikseresht A. Golmohammadi D. Zandieh M. Sustainable green logistics and remanufacturing: a bibliometric analysis and future research directions Int. J. Logist. Manag. 35 3 2024 755 803
5 Lean H.H. Huang W. Hong J. Logistics and economic development: experience from China Transport Pol. 32 2014 96 104
6 Hong J. Chu Z. Wang Q. Transport infrastructure and regional economic growth: evidence from China Transportation 38 2011 737 752
7 Waiyawuththanapoom P. Does proactive logistics management enhance business management? Polish Journal of Management Studies 24 1 2021 457 471
8 Klymenko O. Lillebrygfjeld Halse L. Sustainability practices during COVID-19: an institutional perspective Int. J. Logist. Manag. 33 4 2022 1315 1335
9 Sandberg E. Abrahamsson M. Logistics capabilities for sustainable competitive advantage Int. J. Logist. Res. Appl. 14 1 2011 61 75
10 Li Z. Zou F. Mo B. Does mandatory CSR disclosure affect enterprise total factor productivity? Economic research-Ekonomska istraživanja 35 1 2022 4902 4921
11 Uckelmann D. A definition approach to smart logistics International Conference on Next Generation Wired/Wireless Networking 2008 Springer
12 Kemendi Á. Michelberger P. Mesjasz-Lech A. Industry 4.0 and 5.0–organizational and competency challenges of enterprises Polish Journal of Management Studies 26 2 2022 209 232
13 Korczak J. Kijewska K. Smart logistics in the development of smart cities Transport. Res. Procedia 39 2019 201 211
14 Van Woensel T. Smart Logistics 2012
15 Ding Y. Smart logistics based on the internet of things technology: an overview Int. J. Logist. Res. Appl. 24 4 2021 323 345
16 Qin R. Web3-based decentralized autonomous organizations and operations: architectures, models, and mechanisms IEEE Transactions on Systems, Man, and Cybernetics: Systems 53 4 2022 2073 2082
17 Li J. Qin R. Wang F.-Y. The future of management: DAO to smart organizations and intelligent operations IEEE Transactions on Systems, Man, and Cybernetics: Systems 53 6 2022 3389 3399
18 Shee H.K. Miah S.J. De Vass T. Impact of smart logistics on smart city sustainable performance: an empirical investigation Int. J. Logist. Manag. 32 3 2021 821 845
19 Bag S. Gupta S. Luo Z. Examining the role of logistics 4.0 enabled dynamic capabilities on firm performance Int. J. Logist. Manag. 31 3 2020 607 628
20 Chen D.Q. Preston D.S. Swink M. How the use of big data analytics affects value creation in supply chain management J. Manag. Inf. Syst. 32 4 2015 4 39
21 Pincay Nieves J. Insights into smart cities and smart logistics Smart Urban Logistics: Improving Delivery Services by Computational Intelligence 2022 Springer 17 31
22 Liu W. Effect of intelligent logistics policy on shareholder value: evidence from Chinese logistics companies Transport. Res. E Logist. Transport. Rev. 137 2020 101928
23 Jabeur N. Toward leveraging smart logistics collaboration with a multi-agent system based solution Procedia Comput. Sci. 109 2017 672 679
24 Asongu S.A. Government quality determinants of stock market performance in African countries J. Afr. Bus. 13 3 2012 183 199
25 McFarlane D. Giannikas V. Lu W. Intelligent logistics: involving the customer Comput. Ind. 81 2016 105 115
26 Corbett C.J. Montes-Sancho M.J. Kirsch D.A. The financial impact of ISO 9000 certification in the United States: an empirical analysis Manag. Sci. 51 7 2005 1046 1059
27 Hendricks K.B. Singhal V.R. Association between supply chain glitches and operating performance Manag. Sci. 51 5 2005 695 711
28 Khan S.A.R. A state-of-the-art review and meta-analysis on sustainable supply chain management: future research directions J. Clean. Prod. 278 2021 123357
29 Chung S.-H. Applications of smart technologies in logistics and transport: a review Transport. Res. E Logist. Transport. Rev. 153 2021 102455
30 Houlder V. Livsey A. Lex-in-depth: How Carbon Prices Will Transform Industry vol. 3 2021 Financial Times
31 Mofcom. Ministry of Commerce of the People’s Republic of China Implementation plan for establishing smart logistics distribution system 2015 [cited 2023 2023/11/7]; Available from: http://www.mofcom.gov.cn/article/b/fwzl/201507/20150701057024.shtml 2015
32 Chao H. Xingzhi X. Shu L. How did industrial policies affect business performance: does the locus of different policies and paths matter? J. Finance Econ. 43 1 2017 122 133
33 Schout A. Institutions, Institutional Change and Economic Performance 1991 Oxford University Press Oxford, UK
34 DiMaggio P.J. Powell W.W. The iron cage revisited: institutional isomorphism and collective rationality in organizational fields Am. Socio. Rev. 48 2 1983 147 160
35 Jay J. Navigating paradox as a mechanism of change and innovation in hybrid organizations Acad. Manag. J. 56 1 2013 137 159
36 Maurer M. TVA and the Grass Roots: A Study of Politics and Organization 1986 JSTOR
37 Meyer D.S. Staggenborg S. Thinking about strategy Strategies for social change 1 1 2012 3 22
38 Olsen T.D. Rethinking collective action: the co-evolution of the state and institutional entrepreneurs in emerging economies Organ. Stud. 38 1 2017 31 52
39 Carroll A.B. The pyramid of corporate social responsibility: toward the moral management of organizational stakeholders Bus. Horiz. 34 4 1991 39 48
40 Clarkson M.E. A stakeholder framework for analyzing and evaluating corporate social performance Acad. Manag. Rev. 20 1 1995 92 117
41 Kujala J. Stakeholder engagement: past, present, and future Bus. Soc. 61 5 2022 1136 1196
42 Lidasan H.S. City logistics: policy measures aimed at improving urban environment through organization and efficiency in urban logistics systems in Asia Transport Commun. Bull. Asia Pac 80 2011 84 99
43 Demir E. A selected review on the negative externalities of the freight transportation: modeling and pricing Transport. Res. E Logist. Transport. Rev. 77 2015 95 114
44 Zailani S. Sustainable supply chain management (SSCM) in Malaysia: a survey Int. J. Prod. Econ. 140 1 2012 330 340
45 Malik M. Value-enhancing capabilities of CSR: a brief review of contemporary literature J. Bus. Ethics 127 2015 419 438
46 Wan-hong L. Na L. Fang L. Three parties' evolutionary game of stakeholders in green technology innovation and their simulation Oper. Res. Manag. Sci. 30 9 2021 216
47 Hoen K. Effect of carbon emission regulations on transport mode selection under stochastic demand Flex. Serv. Manuf. J. 26 2014 170 195
48 Lasrado F. Pereira V. Achieving Sustainable Business Excellence: the Role of Human Capital 2018 Springer
49 Mithani M.A. Innovation and CSR—do they go well together? Long. Range Plan. 50 6 2017 699 711
50 Hull C.E. Rothenberg S. Firm performance: the interactions of corporate social performance with innovation and industry differentiation Strat. Manag. J. 29 7 2008 781 789
51 Yang C. Empirical study on the influential factors of social responsibility of Chinese enterprises Economist 1 2009 69 79
52 Boddewyn J.J. Understanding and advancing the concept ofnonmarket' Bus. Soc. 42 3 2003 297 327
53 Pache A.-C. Santos F. Embedded in hybrid contexts: how individuals in organizations respond to competing institutional logics Institutional Logics in Action, Part B 2013 Emerald Group Publishing Limited 3 35
54 Meyer J.W. Rowan B. Institutionalized organizations: Formal structure as myth and ceremony Am. J. Sociol. 83 2 1977 340 363
55 Gogas M. Papoutsis K. Nathanail E. Optimization of decision-making in port logistics terminals: using analytic hierarchy process for the case of port of Thessaloniki Transport and Telecommunication Journal 15 4 2014 255 268
56 Issaoui Y. Smart logistics: study of the application of blockchain technology Procedia Comput. Sci. 160 2019 266 271
57 Sinkovics R.R. Kuivalainen O. Roath A.S. Value co-creation in an outsourcing arrangement between manufacturers and third party logistics providers: resource commitment, innovation and collaboration J. Bus. Ind. Market. 33 4 2018 563 573
58 Sivamani S. Kwak K. Cho Y. A study on intelligent user-centric logistics service model using ontology J. Appl. Math. 2014 2014
59 Forman C. Zeebroeck N.v. From wires to partners: how the Internet has fostered R&D collaborations within firms Manag. Sci. 58 8 2012 1549 1568
60 Shi D. Li G. Liu J. Informatization impact, transaction cost and TFP of enterprise—natural experiments based on Chinese smart city construction Financ. Trade Econ 3 2020 117 130
61 Pei C. Ni J. Li Y. Approach digital economy from the perspective of political economics Financ. Trade Econ 39 2018 5 22
62 Wang J. Kosaka M. Xing K. Manufacturing Servitization in the Asia-Pacific 2016 Springer
63 Carroll A.B. Shabana K.M. The business case for corporate social responsibility: a review of concepts, research and practice Int. J. Manag. Rev. 12 1 2010 85 105
64 Kerlin J.A. Peng S. Cui T.S. Strategic responses of social enterprises to institutional pressures in China Journal of Asian Public Policy 14 2 2021 200 224
65 Lehne R. Industry and politics: United States in comparative perspective Pol. Stud. J. 22 2 1994 402 409
66 Kreng V.B. Huang M.-Y. Corporate social responsibility: consumer behavior, corporate strategy, and public policy SBP (Soc. Behav. Pers.): Int. J. 39 4 2011 529 541
67 Hoshi T. Kashyap A. Scharfstein D. Corporate structure, liquidity, and investment: evidence from Japanese industrial groups Q. J. Econ. 106 1 1991 33 60
68 Wang C. Ma H. Wang Y. Debt financing and equity structure of Chinese state owned enterprises-from the perspective of macroeconomic policy Chinese Journal of Management Science 2016 5 2016 158 167
69 Yoon B. Lee J.H. Byun R. Does ESG performance enhance firm value? Evidence from Korea Sustainability 10 10 2018 3635
70 Miralles-Quirós M.M. Miralles-Quirós J.L. Valente Gonçalves L.M. The value relevance of environmental, social, and governance performance: the Brazilian case Sustainability 10 3 2018 574
71 Feng Y. Chen S. Failler P. Productivity effect evaluation on market-type environmental regulation: a case study of SO2 emission trading pilot in China Int. J. Environ. Res. Publ. Health 17 21 2020 8027
72 Li G. Environmental non-governmental organizations and urban environmental governance: evidence from China J. Environ. Manag. 206 2018 1296 1307
73 Pan X. The effects of a Smart Logistics policy on carbon emissions in China: a difference-in-differences analysis Transport. Res. E Logist. Transport. Rev. 137 2020 101939
74 Lane V. Jacobson R. Stock market reactions to brand extension announcements: the effects of brand attitude and familiarity J. Market. 59 1 1995 63 77
75 Kauf S. Smart logistics as a basis for the development of the smart city Transport. Res. Procedia 39 2019 143 149
76 Sanchez Rodrigues V. Kumar M. Synergies and misalignments in lean and green practices: a logistics industry perspective Prod. Plann. Control 30 5–6 2019 369 384
77 Tan B.Q. A blockchain-based framework for green logistics in supply chains Sustainability 12 11 2020 4656
78 Huang Y.-S. Hung J.-S. Ho J.-W. A study on information sharing for supply chains with multiple suppliers Comput. Ind. Eng. 104 2017 114 123
79 Yildiz Çankaya S. Sezen B. Effects of green supply chain management practices on sustainability performance J. Manuf. Technol. Manag. 30 1 2019 98 121
80 Kazmi S.W. Ahmed W. Understanding dynamic distribution capabilities to enhance supply chain performance: a dynamic capability view Benchmark Int. J. 29 9 2022 2822 2841
81 Sila I. Investigating changes in TQM's effects on corporate social performance and financial performance over time Total Qual. Manag. Bus. Excel. 31 1–2 2020 210 229
82 Haveman H.A. The dynamics of political embeddedness in China Adm. Sci. Q. 62 1 2017 67 104
83 Lee E. Walker M. Zeng C. Do Chinese government subsidies affect firm value? Account. Org. Soc. 39 3 2014 149 169
84 Huang S. Yu J. The nature, objectives and social responsibility of state-owned enterprises China Industrial Economy 2 2006 68 76
85 Guo Y. Huy Q.N. Xiao Z. How middle managers manage the political environment to achieve market goals: insights from C hina's state‐owned enterprises Strat. Manag. J. 38 3 2017 676 696
86 Jacobs B.W. Singhal V.R. Subramanian R. An empirical investigation of environmental performance and the market value of the firm J. Oper. Manag. 28 5 2010 430 441
87 Li G. Green supply chain management in Chinese firms: innovative measures and the moderating role of quick response technology J. Oper. Manag. 66 7–8 2020 958 988
88 Zhang X. Jiang Y. Feng J. The impact of the opening of the high-speed railway in the Silk Road economic belt on the regional economy based on the double difference method (PSM-DID) J. Comput. Methods Sci. Eng. 22 4 2022 1311 1331
89 Liu Weihua The impact of implementation policy for deep integration and innovative development of manufacturing and logistics industries on enterprise operational performance—an empirical study based on PSM-DID model Journal of Industrial Technological Economic 361 11 2023 97 107
90 Munilla L.S. Miles M.P. The corporate social responsibility continuum as a component of stakeholder theory Bus. Soc. Rev. 110 4 2005 371 387
91 Iqbal M.W. Kang Y. Jeon H.W. Zero waste strategy for green supply chain management with minimization of energy consumption J. Clean. Prod. 245 2020 118827
92 Yin Y. Stecke K.E. Li D. The evolution of production systems from Industry 2.0 through Industry 4.0 Int. J. Prod. Res. 56 1–2 2018 848 861
93 Jian Z. Duan Y. Enterprise heterogeneity, competition and convergence of total factor productivity Manag. World 8 2012 15 29
94 Laari S. Töyli J. Ojala L. The effect of a competitive strategy and green supply chain management on the financial and environmental performance of logistics service providers Bus. Strat. Environ. 27 7 2018 872 883
95 Baah C. Jin Z. Tang L. Organizational and regulatory stakeholder pressures friends or foes to green logistics practices and financial performance: investigating corporate reputation as a missing link J. Clean. Prod. 247 2020 119125
96 Baysinger B.D. Domain maintenance as an objective of business political activity: an expanded typology Acad. Manag. Rev. 9 2 1984 248 258
97 Oliver C. Holzinger I. The effectiveness of strategic political management: a dynamic capabilities framework Acad. Manag. Rev. 33 2 2008 496 520
98 Tan J. Innovation and risk-taking in a transitional economy: a comparative study of Chinese managers and entrepreneurs J. Bus. Ventur. 16 4 2001 359 376
99 Yang L. Li Y. Liu H. Did carbon trade improve green production performance? Evidence from China Energy Econ. 96 2021 105185
