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

S2405-8440(24)12229-3
10.1016/j.heliyon.2024.e36198
e36198
Research Article
Does service-oriented manufacturing improve customer stability?
Ou Ling oulin0214@163.com

School of Business, Hunan Institute of Humanities, Science and Technology, China
13 8 2024
30 8 2024
13 8 2024
10 16 e3619828 12 2023
6 8 2024
12 8 2024
© 2024 The Author
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Stabilizing customer relationships plays an important role in improving supply chain resilience. Based on a sample of A-share listed companies in the manufacturing industry from 2009 to 2022, this work investigates the impact of SOM(service-oriented manufacturing) on supply chain resilience from the perspective of customer stability. The results showed that SOM has a positive impact on customer relationship stability, indicating that manufacturing enterprises carrying out SOM can improve customer relationship stability, and this stable relationship is long lasting. Analysis of service-oriented manufacturing structure reveals that service that supports the supplier's product (e.g., after-sale services) is less effective in promoting customer relationship stability than service that supports the customer (e.g., training service). This study theoretically expands the research on the allocation of enterprise resources among supply chains, promotes the deep integration of modern service industry and advanced manufacturing industry, and provides experience for implementation of relevant decision-making and deployment of supply chain security and stability in practice.

Keywords

Service-oriented manufacturing (SOM)
Stable customer relationship
Supply chain resilience
Support the supplier's product service
Support the client's service
==== Body
pmc1 Introduction

At present, China is affected by domestic and international factors such as Sino-US trade friction, geopolitical conflicts, and the dissipation of demographic dividends, resulting in prominent security issues in the industrial chain and supply chain. A stable supply-demand relationship is an important dimension of supply chain resilience [1] and is the key to maintaining the stability of the supply chain [2]. The report of the 20th CPC National Congress stressed that we should accelerate the construction of a modern economic system and focus on improving the resilience and safety of the industrial and supply chains. Therefore, breaking through the supply and demand obstruction, extending and strengthening the supply chain, and enhancing the resilience of the supply chain are important tasks to promote supply-side reform and build a modern industrial system in the current and future long-term periods.

In recent years, the relationship between SOM and customer relationship stability has gradually attracted attention from academia. Existing research has examined the impact of SOM on customer satisfaction [3] and customer relationships [4], but it ignores the role of SOM in customer stability and lacks investigation of the degree and context of influence between the two. Therefore, can SOM improve the stability of customer relationships? If yes, what is the influential mechanism? Is there a functional boundary due to different types and conditions of SOM? Against the backdrop of the prominent issues of supply chain security and stability, research on the relationship between service-oriented manufacturing and customer stability is of great significance, which not only helps to deepen the understanding of the transformation and upgrading of manufacturing but also enriches the research on the factors affecting supply chain resilience, providing a new research perspective for supply chain resilience.

Based on this, this article takes A-share manufacturing listed companies from 2009 to 2022 as the research object, and uses the top five customer relationship data disclosed by listed companies as the basis to study and explore the impact and mechanism of service-oriented manufacturing on customer stability. It was found that service-oriented manufacturing can indeed improve customer relationship stability. Mechanism analysis reveals that service-oriented manufacturing provides resilience in the supply chain by reducing information asymmetry, enhancing product differentiation, and winning customer trust. Through deconstructing the service-oriented manufacturing model, it was found that service-oriented manufacturing that provides customer support is more effective in promoting customer relationship stability than service-oriented manufacturing that provides product related services.

The contributions of the present study to management research and practice are threefold.•First, this study enriches the research on the factors affecting the stability of customer relationships. Previous literature has mainly investigated and studied the factors affecting the stability of customer relationships from the internal characteristics of enterprises and external environmental factors but has not explored the issue of customer relationship stability from the perspective of enterprise production and operation models. This article cuts into the new production mode of “service-oriented manufacturing” and analyzes the reasons and scenarios that affect the stability of customer relationships, enriching the research system of customer relationship stability.

•Second, this study expands the research paradigm of the operational effects of SOM. The few existing studies have investigated the impact of SOM on customer satisfaction through questionnaire surveys. On the one hand, there is a lack of discussion on the mechanisms and specific situations of maintaining customer relationship stability in SOM. On the other hand, although customer satisfaction and customer stability both belong to the category of perception and cognition, they are different. According to the cognition-attitude-emotion theory, customer satisfaction is a prerequisite for customer stability. After customers have obtained a consistently satisfying consumer experience, they may transform into customer stability. This article uses data from listed companies to link the production and operation model of SOM with customer relationship stability and deeply analyzes the mechanism and boundary of the impact of SOM on customer relationship stability. It also decomposes SOM into two modes, i.e., providing product-related services and providing customer support, for more detailed analysis, expanding the research paradigm and system of the economic effects of SOM.

•Third, this study provides practical insights and guidance for enhancing supply chain resilience. This article incorporates the background of the era of enhancing the resilience of industrial and supply chains into the analytical framework, which provides empirical references for implementing strategic decisions and deploying supply chain security and stability.

2 Literature and hypotheses development

2.1 Literature review

2.1.1 Research on customer stability

Customer relationship stability refers to the long-term relationship between an enterprise and its customers and is an important relationship resource for the enterprise [5]. A listed company with a sustainable customer partnership can send a signal to the outside world that its operations and profitability are relatively stable. For customer companies, listed companies with high and stable earnings are important choices for their partners in the supply chain [5]. Stable customer relationships can provide businesses with a stable operating environment, reduce transaction costs, obtain rental income from relationship-based transactions, and enable information sharing, thereby improving corporate performance [[2], [6]]. The market also has positive feedback on customer stability, and investors will also offer higher pricing [7]. Given the important role of customer stability, [8] found that poor internal control in a company can damage the cooperative relationship between its customers, but timely remediation of deficiencies can mitigate this negative impact.

2.1.2 Research on the economic effects of SOM

Service-oriented manufacturing is the combination of products and services, characterized by a shift in business activities from manufacturing-centric to service-centric, ultimately enhancing the competitive advantage of the enterprise [[9], [10]]. A closely related concept is the “servitization of manufacturing".•First, SOM is beneficial for enterprises to extend toward both ends of the value chain [[11], [12]], and the stronger their R&D capabilities are, the higher their production efficiency, and the more value added they can obtain from the value chain [13]. Service-oriented manufacturing also helps improve productivity [14], facilitating targeted product innovation (Golara &Dooley, 2016)[15].

•Second, SOM has an impact on international trade. Service-oriented manufacturing has increased the probability and total export volume of manufacturing enterprises 4[16]; [17]. Some scholars have found that there is a U-shaped relationship between service-oriented investment in the manufacturing industry and the domestic value-added of enterprises' exports, and it has a U-shaped impact on the domestic value-added of processing trade and mixed trade enterprises' exports. The impact on the domestic value added of general trade enterprises' exports is significantly positive [18].

The above research on customer stability and SOM provides theoretical support and inspiration for this article's exploration of SOM and CRM relationship stability. However, there are at least three shortcomings: first, existing research has explored the influencing factors of customer stability from both internal and external aspects of the enterprise, but both assume that there has been no significant change in the production mode of the enterprise; Second, research on the economic effects of service-oriented manufacturing is limited to its internal impact on enterprises, lacking exploration of the economic effects of service-oriented manufacturing from the external perspective of the “supplier customer” supply chain; Third, the impact of service-oriented manufacturing on customer satisfaction only remains in the exploration of influencing factors, without measuring the impact effect, and without considering the characteristics of service-oriented manufacturing models, and lacking analysis of different scenarios of impact. Therefore, the theoretical analysis framework for the impact of SOM on CRM needs to be further improved. These shortcomings leave room for further exploration in this article.

2.2 Research hypotheses

The establishment of stable customer relationships involves multidimensional business processes such as customer satisfaction and supplier product uniqueness, with characteristics of heterogeneity, value, scarcity, and difficulty in imitation. It is an important resource for supplier enterprises to have competitive advantages and has become an important feature of supply chain enterprises. Service-oriented manufacturing may enhance customer relationship stability by reducing information asymmetry, increasing product differentiation, and winning customer trust. Thus, Hypothesis 1 is proposed.H1 Service-oriented manufacturing can improve the customer stability relationship.

2.2.1 Reducing information asymmetry

According to signal transmission theory, SOM has an information effect and can reduce information asymmetry in enterprises.•Firstly, SOM adheres to the company's business philosophy of providing thoughtful services and putting reputation first. Frequent interaction and communication with customers can help customers form a perception of the supplier's business status, performance, credit quality, and product quality and thus decide whether to engage in long-term strategic cooperation with the enterprise [19].

•Secondly, unlike traditional financial information that focuses on the “quantity” of information disclosure, SOM has a broader connotation. Introducing customers into product service systems and providing targeted services throughout the entire process from research and development design to operation can enhance the transmission of implicit knowledge between service-oriented manufacturing enterprises and customers. Invisible knowledge integrates multidimensional and multilevel comprehensive nonfinancial information such as future development planning and corporate governance and pays more attention to the “quality” of information disclosure. Once again, service-oriented manufacturing companies often have good performance [20], which can help companies establish a high-quality image in the capital market and release positive signals.

•Finally, based on organizational legitimacy considerations, to gain sustained recognition from customers, enterprises will strive to improve the performance and information disclosure quality of SOM, meet customer expectations, effectively reduce the cost and risk of customer access to information, and reduce information asymmetry between supply chain enterprises.

Thus, Hypothesis 2 is proposed.H2 Service-oriented manufacturing can improve the customer stability relationship by reducing information asymmetry.

2.2.2 Increase product differentiation

The differentiation of products in SOM can form a unique market position for enterprises.•Firstly, service-oriented manufacturing enterprises have strong initiative and a first-mover advantage by opening up blue ocean markets through innovation [14].

•Secondly, the additional attributes of products provided by service-oriented manufacturing enterprises (including after-sales and product use) can attract more customers to increase product market share [21] and increase industry entry barriers.

•Thirdly, the addition of service elements has changed the role of users, making them no longer just recipients of products, but co-creators of value.

Thus, Hypothesis3 is proposed.H3 Service-oriented manufacturing can improve the customer stability relationship by increasing product differentiation.

2.2.3 Win customer trust

According to signal transmission theory, SOM can win customer trust in mutual cooperation.•Firstly, Service-oriented manufacturing can provide customers with long-term contracts for technical support. This type of contract changes the product content and service methods that enterprises deliver to customers, increases customer embeddedness, meets customer expectations for business context needs, improves product performance, and thus increases the level of trust between both parties [22].

•Secondly, SOM helps enterprises and customers solve problems together. enterprises collaborate with customers to jointly solve problems, and further form close cooperative relationships.

•Thirdly, mutual trust is the cornerstone of building a harmonious relationship between both parties in economic transactions, which helps them share information, enhance the possibility of further cooperation, and form a “self-strengthening” effect and a “locking” effect between each other [23].

•Finally, enterprises collaborate with each other in business to form a manufacturing network. Node enterprises on the network continuously transform internal resources into “core competitive advantages” in business management, which help manufacturing enterprises establish stable customer relationships, and enhance customer loyalty [24].

Thus, Hypothesis 4 is proposed.H4 Service-oriented manufacturing mainly improves customer relationship stability by winning customer trust.

3 Data and empirical methodology

3.1 Sample and data

Only listed companies disclose the information of their top five clients and suppliers, also A-share markets are one of the largest domestic stock markets in China, with a huge market size, including numerous listed companies. So, our work takes A-share manufacturing enterprises from 2009 to 2022 as samples, and the specific handling steps are as follows.(1) 2009 is selected as the starting year of the study, and the main reason for choosing 2009 as the starting year for the study is that there have been more corporate disclosures of information on the top five clients since 2009;

(2) Delete companies that have not disclosed the specific names of their top five clients;

(3) Delete samples with “PT” and “ST”1;

(4) Match data from SOM and other variables;

(5) Delete samples with changes in industry categories during the sample period;

(6) Delete samples with customer stability of 0;

(7) Delete samples with severely missing control variables. After handling and preprocessing, this paper finally obtains data from 927 valid sample enterprises with a total of 2809 valid samples.

The data source of this article mainly includes two parts: (1) The data on the service-oriented manufacturing level of listed companies in the manufacturing industry come from the Wind database. According to the “National Economic Industry Classification” (GB/T4754-2017) standard, the main composition of manufacturing enterprises in the Wind database is divided into service revenue and nonservice revenue and then manually organized. (2) The data of the top five customers and other financial data are sourced from the China Stock Market & Accounting Research Database (CSMAR). Due to the need for two consecutive years of data to calculate customer stability and four consecutive years of customer stability data for robustness, the actual sample interval used in this article is from 2007 to 2022. Match data from different sources by securities code, year, province, city, and industry. This article adopts the secondary industry classification standard of the China Securities Regulatory Commission in 2012.

3.2 Variable definition

3.2.1 Measures of explanatory variable: custom stability

Following [25] and [2] research, the stability of customer relationships was assessed using Cust_stable and Cust_sales. Specifically, the number of stable customers (Cust_stable) is the number of consistent clients in top five clients over the most recent two year divided by 5. The value ranges from 0 to 1, and the larger the value is, the more stable the customer relationship with multiple companies. For example, the top five customers of Company A this year are B, C, D, E, and F. The top five customers of the previous year were B, C, G, H, and I. Customers B and C have been partners of Company A for two consecutive years, so the customer stability of Company A this year is 2/5 (i.e., 0.4). Cust_sales refer to the ratio of sales from repeat customers to the total sales of the top five customers in the current year; the larger the value is, the greater the proportion of sales brought by stable customers, indicating higher customer stability.

3.2.2 Measures of explanatory variables: service-oriented manufacturing

This article measures SOM in three ways by collecting service business revenue from manufacturing enterprises and referring to the data processing method of [26]. The first type is the virtual variable of SOM (Ifser). If the company has revenue from service business in the current year, the value is 1; otherwise, the value is 0. Manufacturing enterprise service business not only includes product related services that support the installation and use of core products, but the main keywords include installation, maintenance, professional services, etc. It also includes a series of user support services aimed at providing comprehensive solutions, with the main keywords being: training, development, finance, finance leasing, consulting, insurance, etc. The second method calculates the proportion of service business income to main business income and measures the degree of service-oriented manufacturing (Ser) by the ratio. The third method sums the types of service businesses in the main business income and uses quantity as the proxy variable for the breadth of service-oriented manufacturing (Ser_kind). In the benchmark regression, the virtual variable of SOM (Ifser) was used, and in the robustness test, the variables of SOM degree (Ser) and SOM breadth (Ser_kind) were used. The higher the values of service-oriented manufacturing degree (Ser) and breadth (Ser_kind), the higher the degree of service-oriented transformation of the enterprise.

3.2.3 Measures of control variables

Following the research of [8], this work selects enterprise growth (Growth), enterprise age (Age), enterprise size (Size), customer concentration (CR), equity concentration (Top1), ESG rating (ESG), accounts receivable turnover (AR), and operating gross profit (OM) as control variables; at the same time, it controls the fixed effects of the year and industry. The specific definitions and measurement methods of all variables are shown in Table 1.Table 1 Variable definition table.

Table 1Variable Name	Variable Symbols	Meaning of variables and metrics	
Stable customer quantity	Cust_stable	Number of duplicate customers of the company's top five customers compared to the previous year/5	
Stable customer sales	Cust_sales	Repeated customer sales of the company's top five customers/total sales of the top five customers	
Service-oriented manufacturing	Ifser	Virtual variable, if the company has service revenue, the value is 1, otherwise the value is 0	
Corporate competitive position	MPK	Lerner Index: (Operating Revenue - Operating Costs - Selling Expenses - Administrative Expenses)/Operating Revenue	
Sale Growth	Growth	Growth rate of operating income in the past three years	
Enterprise age	Age	Years of listing of enterprises	
Enterprise size	Size	Logarithm of total corporate assets	
Capital Structure	CR	Current assets/current liabilities	
Ownership concentration	Top1	Shareholding ratio of the top ten shareholders of the enterprise	
ESG rating	ESG	According to the ESG rating of Huazheng, assign a value of 1–9 from low to high	
Accounts receivable turnover rate	AR	Average accounts receivable/total assets at the end of the period	
Gross operating profit	OM	(Operating revenue-operating costs)/Operating revenue	

3.3 Testing the model

To examine the impact of SOM on customer stability, the model is set to(1) Custi,t=α+βSeri,t+λ∑Controlsi,t+Industryi+Yeart+εi,t

where i and t represent the enterprise and year, respectively. Custi, t represents the dependent variable customer stability, which is the number of stable customers (Cust_stable) and stable customer sales (Cust_sales), respectively; Seri, t represents the service-oriented manufacturing of enterprise i in year t; Controlsi, t represents a series of control variables; α is a constant term, β and λ are regression coefficients; Industryi and Yeart represent industry and year dummy variables, respectively, while εijt represents a random disturbance term. The main observations are the coefficients of β and their significance. If β is significantly greater than 0, it indicates that SOM can form stable customer relationships. In addition, in all regression models in this article, robust standard error estimation methods are used to adjust for heteroscedasticity to obtain more accurate statistical estimates.

4 Empirical results and analysis

4.1 Descriptive statistics

Table 2 presents the descriptive statistical results of the main variables. The average value of Cust_stable is 0.4968, indicating that on average, there are more than 2 duplicate customers in the top five customers of the sample company each year. This proportion is close to the 0.462 of [27], indicating that the data are reliable. At the same time, the maximum and minimum values are 0.2 and 1, respectively, indicating that some companies in the sample have completely duplicate customers in the top five customers of the year, that is, the top five customers have been identical for two consecutive years. The mean of Cust_sales is 0.2353, the standard deviation is 0.2173, the minimum value is 0.0110, and the maximum value is 0.9196, indicating that on average, stable customer sales account for 1/4. The mean of Ifser is 0.2990, and out of 2809 sample companies with annual observations, 840 have implemented service-oriented manufacturing. The average value of Ser is 0.0460, indicating that the overall level of service-oriented manufacturing is not high. The average value of Ser_kind is 0.4108, with a maximum value of 6, indicating that there are as many as 6 types of service businesses in the source of income. In the control variable, the mean growth is 0.2242; the mean age value is 13.8316; the average of Top1 is 0.3033, indicating that the equity concentration of listed companies in China's manufacturing industry is relatively high; the average ESG value is 3.8945, indicating that the ESG rating is at a moderate level; the average AR is 0.4650, while the average OM is 0.7375, indicating that the gross profit level of listed companies in China's manufacturing industry is relatively high.Table 2 Results of descriptive statistics of variables.

Table 2Variable	N	Mean	SD	Min	Max	
Cust_stable	2809	0.4968	0.2326	0.2000	1.0000	
Cust_sales	2809	0.2353	0.2173	0.0110	0.9196	
Ifser	2809	0.2990	0.4579	0.0000	1.0000	
Growth	2791	0.2242	0.8540	−0.6946	6.9016	
Age	2809	13.8316	6.1226	0.0000	28.0000	
Size	2809	22.2897	1.3791	18.6992	26.2043		
Top1	2809	0.3033	0.1666	0.0000	0.7496		
ESG	2712	3.8945	1.1499	1.0000	6.0000		
AR	2755	0.4650	1.6098	0.0077	13.0695	
OM	2749	0.7375	0.1794	0.2031	1.1099	

4.2 Baseline regression analysis

Table 3 reports the results of the benchmark regression. The first three columns are explained as the number of stable customers (Cust_stable), Column (1) adds industry dummy variables, Column (2) adds year dummy variables, and Column (3) adds a series of control variables on top of Column []. The regression results in Column(3) show that the regression coefficient of service-oriented manufacturing (Ifser) on the number of stable customers (Cust_stable) is 0.0235, which is significant at the 5 % level. The latter three columns are explained as stable customer sales (Cust_sales), while the other variables are the same as in the previous three columns. The regression results in Column (6) show that the regression coefficient of service-oriented manufacturing (Ifser) on stable customer sales (Cust_sales) is 0.0273, which is significant at the 1 % level. Taking Column (6) as the standard, it indicates that for every 1 unit increase in service-based manufacturing (Ifser) and 0.0273 units increase in stable customer sales (Cust_sales) when controlling for other variables to remain unchanged, which has significant economic significance. This means that compared to manufacturing enterprises without service transformation, manufacturing enterprises undergoing service transformation have better customer relationship stability, and H1 was established.Table 3 Baseline regression results.

Table 3Variable	（1）	（2）	（3）	（4）	（5）	（6）	
Ifser	0.0234**	0.0206**	0.0235**	0.0320***	0.0227**	0.0273***	
	(0.0099)	(0.0099)	(0.0105)	(0.0096)	(0.0093)	(0.0096)	
Growth			0.0070			−0.0003	
			(0.0052)			(0.0038)	
Age			−0.0012			−0.0047***	
			(0.0008)			(0.0007)	
Size			0.0019			0.0103***	
			(0.0042)			(0.0039)	
Top1			0.0151			−0.0385	
			(0.0312)			(0.0286)	
ESG			−0.0055			0.0089**	
			(0.0045)			(0.0041)	
AR			0.0035			0.0026	
			(0.0028)			(0.0024)	
OM			0.0044			−0.0340	
			(0.0260)			(0.0234)	
_cons	0.4960***	0.4238***	0.3936***	0.3216***	0.2446***	0.0978	
	(0.0280)	(0.0467)	(0.0998)	(0.0322)	(0.0433)	(0.0909)	
N	2809	2809	2635	2809	2809	2635	
r2	0.0242	0.0372	0.0409	0.0365	0.0965	0.1236	
Industry	Yes	Yes	Yes	Yes	Yes	Yes	
Year	No	Yes	Yes	No	Yes	Yes	
Note: Figures in parentheses are robust standard errors, and *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively.

4.3 Robust tests

4.3.1 Measurement method for replacing independent variables

To ensure that the research results are not affected by the measurement methods of the variables, the explanatory variable of service-oriented manufacturing (Ifser) is replaced with two variables: service-oriented manufacturing degree (Ser) and service-oriented manufacturing breadth (Ser_kind). The regression results are shown in Columns (1) and (2) of Table 4. The coefficient of service-oriented manufacturing variable (ser and ser_kind) is still significantly positive at the 1 % level, indicating the robustness of the benchmark regression results.Table 4 Robust regression results for replacement variables.

Table 4Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	
Cust_sales	Cust_sales	Cust_dum	Cust3_sales	Cust3_stable	Cust4_sales	Cust4_stable	
Ser	0.1015***							
	(0.0355)							
Serkind		0.0156***						
		(0.0056)						
Ifser			0.0530**	0.0232***	0.0212**	0.0147**	0.0147*	
			(0.0223)	(0.0082)	(0.0105)	(0.0060)	(0.0077)	
Growth	-0.0002	-0.0003	0.0121	0.0039	0.0121**	-0.0007	0.0002	
	(0.0039)	(0.0038)	(0.0123)	(0.0033)	(0.0060)	(0.0027)	(0.0042)	
Age	-0.0046***	-0.0047***	-0.0031*	-0.0003	0.0034***	0.0007	0.0034***	
	(0.0007)	(0.0007)	(0.0018)	(0.0006)	(0.0008)	(0.0004)	(0.0006)	
Size	0.0110***	0.0106***	0.0079	0.0077**	0.0044	0.0075***	0.0062**	
	(0.0039)	(0.0039)	(0.0091)	(0.0034)	(0.0043)	(0.0025)	(0.0031)	
Top1	-0.0373	-0.0372	0.0111	-0.0054	0.0200	0.0026	0.0257	
	(0.0287)	(0.0286)	(0.0690)	(0.0239)	(0.0309)	(0.0175)	(0.0229)	
ESG	0.0085**	0.0087**	-0.0118	0.0031	-0.0053	-0.0004	-0.0064*	
	(0.0041)	(0.0041)	(0.0097)	(0.0035)	(0.0047)	(0.0025)	(0.0035)	
AR	0.0028	0.0025	0.0075	0.0027	0.0035	0.0024	0.0023	
	(0.0024)	(0.0024)	(0.0060)	(0.0023)	(0.0031)	(0.0018)	(0.0022)	
OM	-0.0342	-0.0344	-0.0222	-0.0396**	-0.0218	-0.0337**	-0.0261	
	(0.0234)	(0.0234)	(0.0561)	(0.0194)	(0.0260)	(0.0137)	(0.0194)	
_cons	0.0887	0.0934	0.1655	-0.0993	-0.0903	-0.1309**	-0.1788***	
	(0.0910)	(0.0910)	(0.2132)	(0.0720)	(0.0925)	(0.0522)	(0.0645)	
N	2635.0000	2635.0000	2635.0000	2635.0000	2635.0000	2635.0000	2635.0000	
r2	0.1240	0.1234	0.0310	0.1117	0.1120	0.1184	0.1355	
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	

4.3.2 Measurement method for replacing dependent variables

In the robustness test, three substitute dependent variables were used.(1) The dummy variable of stable customers (Cust_dum), where the number of stable customers (Cust_stable) is greater than the annual industry median value of 1, indicating customer stability; otherwise, a value of 0 indicates customer instability;

(2) Measure by the proportion of customers who have repeatedly appeared for three consecutive years in the top five customers (Cust3_stable) and the proportion of customer sales who have repeatedly appeared for three consecutive years (Cust3_sales);

(3) Using Cust4_stable and Cust4_sales, the proportion of customers who have repeatedly appeared for four consecutive years among the top five customers, as well as the proportion of stable customer sales for four consecutive years, are measured. The regression results are shown from Columns (3) to (7) of Table 4. The coefficients of independent variable ifser are significantly positive, consistent with the benchmark regression results; this result also reflects the sustainability of SOM in the formation of stable customer relationships.

4.3.3 Lagging explanatory variable

Because enterprise service-oriented transformation is a process, the impact on customer stability may have a lag effect. To solve this problem, this article explains that the explanatory variables lag for 1 and 2 periods and then use Equation (1) for regression. The regression results are shown in Columns (1) and (2) of Table 5. The coefficients of service-oriented manufacturing (L.Ifser) lagged for 1 period and service-oriented manufacturing (L2.Ifser) lagged for 2 periods are significantly positive at the 5 % level, consistent with the benchmark regression results; this result also reflects the sustainability of SOM in the formation of stable customer relationships.Table 5 Other robust regression results.

Table 5
Variable	(1)	(2)	(3)	(4)	(5)	
Explanatory variable lag 1 period	Explanatory variable lag 2 period	Delete CSI 300	Delete related party customers	Repeated sampling	
L.Ifser	0.0307**					
	(0.0128)					
L2.Ifser		0.0383**				
		(0.0157)				
Ifser			0.0217**	0.0271***	0.0273***	
			(0.0100)	(0.0095)	(0.0095)	
Growth	0.0005	0.0066	−0.0013	−0.0024	−0.0003	
	(0.0049)	(0.0055)	(0.0039)	(0.0035)	(0.0038)	
Age	−0.0056***	−0.0076***	−0.0044***	−0.0044***	−0.0047***	
	(0.0010)	(0.0012)	(0.0007)	(0.0007)	(0.0007)	
Size	0.0091*	0.0136**	0.0079*	0.0116***	0.0103***	
	(0.0051)	(0.0057)	(0.0040)	(0.0038)	(0.0038)	
Top1	−0.0058	−0.0556	−0.0342	−0.0248	−0.0385	
	(0.0383)	(0.0458)	(0.0296)	(0.0279)	(0.0285)	
AR	0.0032	0.0038	0.0023	0.0031	0.0026	
	(0.0031)	(0.0040)	(0.0024)	(0.0023)	(0.0023)	
ESG	0.0091*	0.0074	0.0095**	0.0057	0.0089**	
	(0.0051)	(0.0063)	(0.0042)	(0.0040)	(0.0041)	
OM	−0.0720**	−0.1091***	−0.0326	−0.0418*	−0.0340	
	(0.0303)	(0.0359)	(0.0242)	(0.0226)	(0.0230)	
_cons	0.1986*	0.1953	0.1330	0.0812	0.0978	
	(0.1165)	(0.1353)	(0.0945)	(0.0890)	(0.0912)	
N	1589	1068	2508	2635	2635	
r2	0.1309	0.1676	0.1164	0.1205	0.1236	
Industry	Yes	Yes	Yes	Yes	Yes	
Year	Yes	Yes	Yes	Yes	Yes	
Note: Figures in parentheses are robust standard errors, and *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively.

4.3.4 Excluding competitive hypothesis

There may be an alternative hypothesis regarding the impact of SOM on improving customer relationship stability. The first alternative hypothesis is that enterprises engaged in SOM are high-quality enterprises with good profitability, which may be because high-quality enterprises have stable customer relationships rather than because SOM leads to stable customer relationships. The second alternative hypothesis is that there may be related parties in the company's customers, which leads to a false increase in customer stability and is not related to service-oriented manufacturing. To exclude the first alternative hypothesis, this article excluded companies that entered the Shanghai and Shenzhen 300 Index, that is, companies with strong profitability and good performance, to exclude the impact of high-quality companies on the results. This article deleted 127 samples, and the regression results are shown in Column (3)of Table 5. The coefficient of service-oriented manufacturing (Ifser) is still significantly positive at the 5 % level, consistent with the benchmark regression results. Further excluding the second alternative hypothesis, after deleting related party customers, the stability of customer relationships was recalculated. This only reported the results of stable customer sales (Cust_sales) as the dependent variable, as shown in Column (4)of Table 5. The coefficient of service-oriented manufacturing (Ifser) is still significantly positive at the 1 % level, consistent with the benchmark regression results.

4.3.5 Repeated random sampling

This work uses the bootstrap method to perform repeated random sampling to alleviate the problem of sample selection bias. The sample size is set to 1000 times, and the repeated random sampling is 500 times. The regression results are shown in Column (5) of Table 5, indicating that there is no substantial difference between the results of repeated random sampling and the benchmark regression results, further proving the robustness of the conclusion.

4.4 Endogeneity issues

The basic regression model mentioned earlier has set industry and year dummy variables and conducted repeated random sampling tests, which can to some extent alleviate the endogeneity problems caused by missing variables and sample selection, but there may still be other endogeneity problems.

4.4.1 Reverse causality

Because the stability of enterprise customer relationships may have a reverse effect on SOM, the more stable customer relationships are, the more they can drive the transformation of manufacturing enterprise services. Therefore, this article uses instrumental variables for two-stage regression to control for endogeneity issues in the model. This article uses two different methods to construct instrumental variables.

The first method uses the mean of SOM divided by year, region, and industry (Ser_iv1) as the instrumental variable for two-stage regression; this is because the SOM of similar enterprises has a certain trend stability, and the infrastructure for service-oriented transformation in the same city is consistent. However, the stability of customer relationships mainly depends on the individual characteristics and capabilities of the enterprise and is not necessarily related to the level of SOM in the city where the enterprise is located. The regression results of the second stage are shown in Column(1) of Table 6. According to the test results, the estimated coefficient of service-oriented manufacturing (Ifser) is significantly positive at the 5 % level, indicating that the research conclusion of this article remains unchanged after controlling for possible endogeneity issues. To further identify the effectiveness of instrumental variables, first, the Anderson statistic is 670.837 with a p value of 0.000, rejecting the original hypothesis that there is a problem of insufficient recognition. Second, according to the weak instrumental variable test, the Cragg Donald statistic is 882.877, which is greater than the critical value of 16.38 under 10 % bias; this can exclude the problem of weak instrumental variables and reject the hypothesis of weak instrumental variables. Once again, the Sargan Hansen test for over recognition cannot reject the original hypothesis of “no over recognition” at the 1 % level, indicating that the selection of instrumental variables is reasonable.Table 6 Regression results of endogeneity treatment.

Table 6
Variable	(1)	(2)	(3)	(4)	
Ser_iv1	Ser_iv2	PSM	Heckman	
Ifser	0.0430**	0.0609***	0.0312***	0.0284***	
	(0.0183)	(0.0232)	(0.0103)	(0.0097)	
Growth	−0.0003	−0.0006	0.0071	−0.0036	
	(0.0051)	(0.0053)	(0.0044)	(0.0043)	
Age	−0.0048***	−0.0052***	−0.0049***	−0.0128***	
	(0.0008)	(0.0008)	(0.0009)	(0.0046)	
Size	0.0099***	0.0091**	0.0086*	−0.0165	
	(0.0038)	(0.0040)	(0.0046)	(0.0155)	
Top1	−0.0396	−0.0231	−0.0511	−0.0942**	
	(0.0286)	(0.0305)	(0.0338)	(0.0391)	
AR	0.0024	0.0016	0.0057*	−0.0070	
	(0.0025)	(0.0027)	(0.0033)	(0.0057)	
ESG	0.0091**	0.0080*	0.0089*	0.0243***	
	(0.0040)	(0.0043)	(0.0049)	(0.0091)	
OM	−0.0324	−0.0306	−0.0408	0.0623	
	(0.0235)	(0.0249)	(0.0280)	(0.0550)	
IMR				−0.4420*	
				(0.2381)	
_cons	0.1035	0.1398	0.1635	1.3365*	
	(0.0913)	(0.0984)	(0.1139)	(0.6896)	
N	2635	2354	1873	2618	
r2	0.1226	0.1062	0.1381	0.1251	
Industry	Yes	Yes	Yes	Yes	
Year	Yes	Yes	Yes	Yes	
Note: Figures in parentheses are robust standard errors, and *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively.

The second method is to construct a weighted average service-oriented manufacturing level as a tool variable (Ser-iv2) for endogeneity testing as follows:(2) Seri=∑W≠iNSerw×SIwiN−1

In formula (2), the subscript i represents this company, and the subscript w represents others. Seri is the weighted average level of SOM for enterprise i, Serw is the weighted average SOM level for enterprise w, and SIwi is the similarity weight between enterprise i and enterprise w, determined based on the asset size between enterprises.SIwi=1−{AssetwAssetw+Asseti}2−{AssetiAssetw+Asseti}2

It is calculated that the larger the weight, the higher the similarity of the enterprise. The reason why it can be used as a tool variable for service-oriented manufacturing is because the level of service-oriented manufacturing of a company is influenced by the development status of other companies' service-oriented manufacturing, but the level of service-oriented manufacturing of other companies does not have an impact on the stability of their customer relationships. The regression results of the second stage are shown in Column (2) of Table 6, and the impact of SOM on customer relationship stability is still significantly positive, consistent with the benchmark regression results. To further identify the effectiveness of instrumental variables, first, the Anderson statistic is 429.736 with a p value of 0.00, rejecting the source hypothesis that there is a problem of insufficient recognition. Second, according to the weak instrumental variable test, the Cragg Donald statistic is 514.540, which is greater than the critical value of 16.38 under 10 % bias. This can exclude the problem of weak instrumental variables and reject the hypothesis of weak instrumental variables. Once again, the Sargan Hansen test for over recognition cannot reject the original hypothesis of “no over recognition” at the 1 % level, indicating that the selection of instrumental variables is reasonable.

4.4.2 Selection bias

To overcome the endogeneity caused by systematic differences between samples that have already implemented SOM and those that have not, this article uses the PSM method for testing. Based on whether the company implements SOM, the samples are divided into two categories: SOM enterprises and nonservice-oriented manufacturing enterprises. Referring to [28], variables representing the main characteristics of the enterprise, including growth, enterprise size, equity concentration, and intangible asset ratio, are selected as covariates. The 1:3 nearest neighbor matching method is used for matching. After matching, the two types of samples (processing group and control group) meet the common support assumption. There is no significant difference in the mean values of the two sets of covariates, which satisfies the hypothesis of equilibrium, indicating that using the PSM method for sample matching is effective. Furthermore, benchmark regression tests were conducted on the matched samples, and the results are shown in Column 3 of Table 6, indicating that the service-oriented manufacturing (Ifser) coefficient is still significantly positive at the 1 % level, indicating that the benchmark regression results are robust.

4.4.3 Selection problem

Because the sample selected in this article is a manufacturing enterprise that has undergone service transformation, only enterprises with detailed disclosure of their main business income in their financial statements are included in the research object. Whether listed companies disclose detailed information on their main business income is an independent decision, which may lead to sample selection bias caused by information disclosure. This work uses the Heckman two-stage method to address this issue. In the first stage of the Heckman test, “whether to disclose detailed information on main business income” was used as the dependent variable, and probit model regression was used. Then, the IMR variable obtained from the first-stage regression was put into Equation (1) for the second-stage regression. After self-selection bias adjustment, the regression results are shown in Column (4) of Table 6. The coefficient of service-oriented manufacturing (Ifser) is still significantly positive at the 1 % level, indicating that the benchmark regression results remain robust.

5 Impact mechanism testing

Based on theoretical analysis, SOM may promote customer relationship stability by reducing information asymmetry, increasing product differentiation, and winning customer trust. To verify the existence of these three mechanisms, this article presents Equation (3) to test, where Medi,t represents mechanism variables, including information asymmetry, product differentiation, and trust. The meanings of the other variables are the same as in Equation (1).(3) Medi,t=α+βseri,t+λ∑Controlsi,t+industryi+yeart+εi,t

5.1 Reducing information asymmetry

According to theoretical analysis, SOM has an information effect that can reduce the degree of information asymmetry between enterprises, enhance customer trust, maintain long-term strategic cooperation, and improve the stability of customer relationships. To verify the establishment of this mechanism, referring to [29], this work uses the index reflecting stock price synchronicity (Syn) to measure the degree of information asymmetry. The larger Syn is, the less transparent the information. Using Equation (3) for regression testing, as shown in Column (4) of Table 7, the coefficient of the explanatory variable service-oriented manufacturing (Ifser) is negative and significantly not zero at the 5 % significance level, indicating that service-oriented manufacturing can significantly reduce information asymmetry and thus improve customer stability. Based on the above, service-oriented manufacturing can reduce the degree of information asymmetry, thereby improving the stability of customer relationships.Table 7 Mechanism verification.

Table 7	(1)	(2)	(3)	(4)	
Variable	SYN3	Differ	Spa	Tc	
Ifser	−0.0978**	0.0178**	−0.0219**	0.0139***	
	(0.0402)	(0.0079)	(0.0089)	(0.0050)	
Growth	0.0179	−0.0008	−0.0058	0.0113***	
	(0.0227)	(0.0033)	(0.0069)	(0.0032)	
Age	0.0098***	−0.0031***	−0.0003	−0.0031***	
	(0.0034)	(0.0005)	(0.0007)	(0.0004)	
Size	0.0212	−0.0080***	0.0069*	−0.0057***	
	(0.0158)	(0.0027)	(0.0040)	(0.0021)	
Top1	0.2557**	0.0300	0.0272	−0.0573***	
	(0.1192)	(0.0246)	(0.0296)	(0.0144)	
ESG	−0.0042	0.0086***	−0.0183***	−0.0009	
	(0.0172)	(0.0028)	(0.0039)	(0.0021)	
AR	−0.0052	0.0003	0.0011	−0.0161***	
	(0.0125)	(0.0028)	(0.0029)	(0.0012)	
OM	0.1211	0.0290*	0.1739***	0.1293***	
	(0.1068)	(0.0167)	(0.0234)	(0.0119)	
_cons	−0.3388	0.3698***	0.2030**	0.2273***	
	(0.3542)	(0.0594)	(0.0986)	(0.0508)	
N	2362	2635	2375.0000	2635	
r2	0.2933	0.0700	0.0961	0.1848	
Industry	Yes	Yes	Yes	Yes	
Year	Yes	Yes	Yes	Yes	
Note: Figures in parentheses are robust standard errors, and *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively.

5.2 Increase product differentiation

According to the previous theoretical analysis, SOM can transform product competitive advantages into a unique market position for the enterprise, thereby attracting customers to cooperate with the enterprise. To verify the establishment of this mechanism, referring to the calculation of differentiation strategy by [30], this work uses the average operating gross profit margin and period expenses to measure the degree of differentiation (Difference), as customers tend to have a good impression of enterprises providing unique products and services, forming a certain brand dependence, and thus being insensitive to price. This can enable enterprises to achieve higher operating gross profit margins, with a larger indicator value and a greater degree of differentiation of the product. Using Equation (3) for regression testing, the regression results are shown in Column (2) of Table 7. The coefficient (Ifser) of SOM is significantly positive, indicating that SOM can significantly improve the degree of product differentiation. Based on the above, service-oriented manufacturing can increase product differentiation, thereby improving customer relationship stability.

5.3 Win customer trust

Trust belongs to the category of informal institutions, but sometimes when formal contracts are too complex, trust as an informal institution can play a more important role. The higher the level of trust in a certain trading network is, the easier it is for members within the network to establish stable relationships, and these relationships are persistent and not easily replaced. To verify the establishment of this mechanism, two types of indicators are used to represent the trust of the enterprise in its customers. One type is dedicated asset investment. Following the approach of [31], (fixed asset net value + construction in progress + intangible assets + long-term deferred expenses)/the total assets of the enterprise (Spa) is used to measure the level of dedicated asset investment; another type of indicator is commercial credit, following the approach of [32], which is characterized by （accounts receivable + notes receivable + prepayments）/total assets (Tc).

Using Equation (3) for regression testing, the regression results are shown in Table 7. The explanatory variables in Columns (3) and (4)are both service-oriented manufacturing (Ifser), while the dependent variable in Column (3) is the level of proprietary asset investment represented by the proportion of noncurrent assets to total assets (Spa). The coefficient of the explanatory variable in Column [](4) is also significantly negative, the dependent variable in Column (4)is commercial credit (Tc), and the coefficient of the explanatory variable service-oriented manufacturing (Ifser) is significantly positive. The results of the above two columns indicate that SOM gains customer trust not by increasing the company's dedicated asset investment but by providing more commercial credit, sharing benefits, and establishing stable and friendly cooperative relationships with customers emotionally. Previous studies have shown that Chinese enterprises have a severe dependence on large customers, leading to severe specialized investments. Once customers are lost, it will cause a relatively short supply chain relationship [33]. This study suggests that SOM maintains customers without the need for excessive dedicated investment, mainly maintaining the emotional stability of customer relationships, which is a new discovery of this article. Based on the above, service-oriented manufacturing can win customer trust, thereby improving the stability of customer relationships.

All the above certificates prove that SOM can reduce the degree of information asymmetry between enterprises, enhance customer trust, and maintain long-term strategic cooperation, thereby improving the stability of customer relationships, so H2 is verified.

6 Deconstruction of the service-oriented manufacturing mode

Service-oriented manufacturing refers to the transformation of manufacturing enterprises from product-oriented logic to service-oriented logic to enhance their competitive advantage by providing services or solutions. In 2020, 15 departments, including the Ministry of Industry and Information Technology, jointly issued the “Guiding Opinions on Further Promoting the Development of Service oriented Manufacturing”, which further clarified nine categories of service oriented manufacturing models, including industrial design services, customized services, supply chain management, shared manufacturing, inspection, monitoring and certification services, full life cycle management, general integration and contracting, energy conservation and environmental protection services, and productive financial services. The production processes of SOM in different modes are different. Based on policy documents and previous research, this article divides SOM into two categories: providing product-related services and providing client support services. Product-related services refer to services that support the installation and use of a company's main products, with the aim of ensuring that the main products operate well. Typical services include product installation, product monitoring, repair and maintenance, and productive financial services. Client support services refer to services that support customer behavior or operations. Typical services include providing process optimization, industrial design services, customized services, and supply chain management [34]. Supporting auxiliary product-related services for the main business, such as installation and after-sales, with less customization for users and lower imitation barriers, and customer support services, such as research and development design and engineering construction services, have a high degree of specialization and customization, and the differentiated competitive advantages created are not easily imitated and surpassed by competitors. In the long run, they will form the core competitiveness of the enterprise and better reflect the proactive customer-centric service logic, establishing and stabilizing customer relationships [21]. Next, this article further examines whether the two modes of SOM have a differentiated impact on customer relationship stability.

Decomposing the variable of service-oriented manufacturing degree (Ser) into service-oriented manufacturing related to providing customers (Ser_c) and service-oriented manufacturing related to providing products (Ser_p), using Equation (1) group regression test, the results are shown in Table 8. Comparing the results in Columns (1) and (2), it can be seen that the coefficient (Ser_c) of SOM related to providing customer support in Column (1)[] is significantly positive, and the coefficient (Ser_p) of product-related SOM in Column (2)is positive but not significant. Furthermore, the breadth of SOM (Ser_kind) is decomposed into customer-related SOM (Ser_kind_c) and product-related SOM (Ser_kind_p). The regression results are shown in Columns (3) and (4), and the coefficient (Ser_kind_c) in Column (3)is significantly positive. The coefficient (Ser_kind_p) in Column (4) is positive but not significant, indicating that compared to service-based manufacturing that provides product-related services, service-based manufacturing that provides customer support can have a significant customer stability-promoting effect.Table 8 The impact of different service-oriented manufacturing types on customer stability.

Table 8
Variable	(1)	(2)	(3)	(4)	
Client support services	Product related services	Client support services	Product related services	
Ser_c	0.1642**				
	(0.0678)				
Ser_p		0.0591			
		(0.0368)			
Ser_kind_c			0.0332***		
			(0.0096)		
Ser_kind_p				0.0052	
				(0.0080)	
Growth	−0.0004	−0.0001	−0.0006	−0.0001	
	(0.0039)	(0.0039)	(0.0038)	(0.0039)	
Age	−0.0046***	−0.0045***	−0.0049***	−0.0045***	
	(0.0007)	(0.0007)	(0.0007)	(0.0007)	
Size	0.0109***	0.0111***	0.0103***	0.0110***	
	(0.0039)	(0.0039)	(0.0039)	(0.0039)	
Top1	−0.0346	−0.0377	−0.0347	−0.0371	
	(0.0286)	(0.0287)	(0.0286)	(0.0286)	
ESG	0.0088**	0.0084**	0.0091**	0.0085**	
	(0.0042)	(0.0041)	(0.0042)	(0.0041)	
AR	0.0028	0.0028	0.0026	0.0028	
	(0.0024)	(0.0024)	(0.0024)	(0.0024)	
OM	−0.0331	−0.0368	−0.0298	−0.0370	
	(0.0235)	(0.0235)	(0.0234)	(0.0235)	
_cons	0.0857	0.0883	0.0867	0.0899	
	(0.0905)	(0.0913)	(0.0906)	(0.0913)	
N	2635	2635	2635	2635	
r2	0.1231	0.1217	0.1254	0.1209	
Industry	Yes	Yes	Yes	Yes	
Year	Yes	Yes	Yes	Yes	
Note: Figures in parentheses are robust standard errors, and *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively.

7 Conclusion

7.1 Summary of the key findings

The security and stability of the supply chain are important guarantees for enhancing China's comprehensive international competitiveness and building a dual cycle of internal and external factors. How to enhance the resilience of the supply chain has become a hot topic of concern in both theoretical and practical fields today. This article explores the impact, transmission mechanism, and context of SOM on SCR from the perspective of stable customer relationships using manufacturing companies listed on the A-share market from 2009 to 2022 as samples. Two conclusions were found: first, SOM has a promoting effect on the stability of customer relationships, and this effect still holds after robustness tests such as replacing explanatory and dependent variables, excluding competitive hypotheses, and changing research methods. Second, through structural analysis of SOM, it was found that providing customer-related services can better reflect the differentiated characteristics of products and the core competitiveness of enterprises. Therefore, providing product-related service-oriented manufacturing can enhance customer stability more than providing customer-related service-oriented manufacturing.

7.2 Managerial implication

Through the research in this article, the following insights can be drawn.(1) We should further unleash the potential of SOM in improving the resilience of the supply chain and regard SOM as an important lever for maintaining the security and stability of the supply chain. The government and relevant departments should follow the development trend of SOM and encourage and support manufacturing enterprises to carry out SOM. Targeted policies are introduced to encourage and support manufacturing enterprises in areas with low marketization and underdeveloped digital inclusive finance to participate in SOM, maximizing the role of SOM in enhancing CRM.

(2) Manufacturing enterprises should actively layout service-oriented manufacturing, especially providing support for customer-related service-oriented manufacturing, focusing on the opportunities and resources brought by service-oriented manufacturing, and enhancing the innovation ability and product differentiation level of the enterprise to meet the diverse, refined, dynamic and personalized needs of customers, create value for customers, stabilize customers, and bring good profit effects to the enterprise.

(3) In addition, when considering the stability of customer relationships in SOM, enterprises should understand the important role of trust between suppliers and customers. Although there are currently many transactions conducted online, our research shows that SOM plays an important role in buyer supplier relationships through trust, as its positive impact on customer relationship stability is amplified when conducting SOM business in the distance. Therefore, establishing a trust relationship between buyers and sellers is one of the important factors for stable customer relationships.

7.3 Limitations and suggestions for further research

This article uses data from listed manufacturing companies to examine customer stability, which cannot represent all manufacturing enterprises. Moreover, the stability of the top five customers only represents the stability of the top five customers that can have a significant impact on the enterprise, and does not represent the stability of all customers of the company. Future research can expand the examination of the operational effects of service-oriented manufacturing. The current research only focuses on service-oriented manufacturing from the perspective of customer stability. Future research can follow the main line of examining operational effects, expand research levels, and systematically study the connection between service-oriented manufacturing and capital operation management practices such as risk management and investment management.

Data availability statement

The data will be made available on request.

CRediT authorship contribution statement

Ling Ou: Writing – review & editing, Writing – original draft, Methodology, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Ou ling reports writing assistance was provided by Hunan University of Science and Technology. Ou ling reports a relationship with Hunan University of Humanities Science and Technology that includes: employment. 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

The authors thank the reviewers for their careful, unbiased, and constructive suggestions that led to this revised manuscript. This work was supported by the Project of Hunan Provincial Social Science Achievement Evaluation Committee (Grant Nos: XSP2023JJC023 ); the Natural Science Foundation of Hunan (Grant Nos: 2023JJ50503 ); Hunan Provincial Education Planning Project (Grant Nos: ND247283 ); Annual Research Topic of the Loudi City Philosophy and Social Sciences Achievements Evaluation Committee (Grant Nos: 202434 B ).

1 “PT”（Particular Transfer）refers to firms with negative net profit within the three most recent years, and ”ST”(Special Treatment) refers to firms with negative net profit within the two most recent years.
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