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Article

Research on the Configuration of Value Chain Transition in Chinese Manufacturing Enterprises

by
Chengjun Liao
,
Ziwei Xiang
*,
Wei Zhou
,
Zhenyu Li
and
Yuhua Li
School of Economics and Management, Shanghai Institute of Technology, Shanghai 200235, China
*
Author to whom correspondence should be addressed.
Systems 2022, 10(5), 164; https://doi.org/10.3390/systems10050164
Submission received: 17 August 2022 / Revised: 14 September 2022 / Accepted: 21 September 2022 / Published: 23 September 2022
(This article belongs to the Section Systems Practice in Social Science)
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Abstract

:
Under the wave of global industrial structure upgrading, Chinese manufacturing is developing towards a higher value chain. In this paper, 52 cases are selected and fuzzy-set qualitative comparative analysis (fsQCA) is used to analyze the configurations of Chinese manufacturing value chain transition. It was found that there are three configurations to realize value chain transition in Chinese manufacturing enterprises, and that any single condition in technology, environment, organization and individual dimensions cannot contribute to value chain transition, and configuration matching is needed to effectively promote value chain transition. Compared with previous studies, the entry point of this paper is multiple firm-level data, and it further expands the scope of application of the technology-organization-environment-individual (TOEI) framework, which is innovative to a certain extent. In addition, the findings of this paper help to construct analytical ideas for the value chain transition of Chinese manufacturing enterprises, further explore different paths for the upgrading of the value chain, propose strategies for manufacturing development according to local conditions, and promote the high-quality development of the manufacturing industry.

1. Introduction

Value refers to the positive meaning and usefulness that an object exhibits for the subject. It can be considered as the total amount that fairly and appropriately reflects the equivalent value of goods, services, or money. The vision of value has a long history but the definition of value chains was first introduced in recent decades [1], and is based on a process view of the organization. The value chain views the manufacturing or service organization as a system composed of subsystems, each of which has input, transformation, and output processes [2]. These input, transformation and output processes involve capital, labor, materials, equipment, buildings, land and administration, and also determine costs and affect profits. At the firm level, the value-adding processes of these business units structure the value chain [3]. In the context of an increasingly specialized global division of labor, a company’s strategic adjustments will depend more on changes in the external environment. A company’s value chains all exist within a broader value system consisting of suppliers and manufacturers. The value chains of merchants, distributors and consumers are connected to form unity which divide the value chain into internal and external parts [4]. The internal value chain is a series of value-creating activity behaviors of the firm, consisting of the basic and auxiliary activities of that firm [5]. The external value chain is a value-added system consisting of industrial value chain, supplier value chain, enterprise value chain and seller value chain together [6]. In defining internal and external value chains, Porter not only explains the value flow of individual firms, but he also reveals the value chain of the whole industry from a systems theory perspective [1]. As a result, Porter’s value chain concept has become an important foundation for the global value chain concept.
The value chain transition of manufacturing enterprises as defined in this paper refers to the process of manufacturing enterprises embedded in the global value chain at the low end and located at the bottom of the smile curve leaping or migrating to the middle and high end of the global value chain through the empowerment value-added path [7]. Value chain leapfrogging is one of the important ways for Chinese manufacturing companies to achieve high-quality development [8].
There have been many studies on manufacturing value chain, such as how to achieve high performance in the value chain [9], the relationship between technology and manufacturing value chain upgrading [10,11], and industrial upgrading through manufacturing value chain movement [12,13,14,15]. The existing studies provide an important basis for the research in this paper and remind us that the upgrading of manufacturing value chain is influenced by many factors. Scholars have studied the influencing factors of value chain transition in manufacturing enterprises from several aspects and have achieved fruitful results. For example, it can be found that the factors influencing value chain transition are technological complexity [11], organizational structure [16], willingness of investment [17], industrial policy [18], influence of decision-makers [19], and innovation capability [20]. These factors can be categorized into four aspects: technology, organization, environment and individual. This provides the basis for this paper to study the value chain transition in Chinese manufacturing enterprises.
In summary, the study of value chain transition has been conducted from various aspects and explored the influencing factors from two levels of factors, internal and external to the enterprise. But it has focused more on the linear relationship of a single factor in the value chain transition of manufacturing enterprises and ignored the joint effect of multiple factors on multiple levels [21]. Based on the previous studies, this paper expands the research from two aspects. Firstly, based on the TOEI framework, a research model is constructed to more comprehensively consider the influencing factors affecting the value chain transition of manufacturing enterprises. Secondly, from the perspective of configuration, the fsQCA method is used to explore how multiple factors jointly drive the value chain of manufacturing enterprises to achieve upgrading, and several configurations are proposed to achieve the value chain transition of manufacturing enterprises.
The fsQCA method is used in this paper to analyze the configuration path of the value chain transition in Chinese manufacturing enterprises. The traditional methods focus on the linear relationships of variables to outcomes, usually ignoring the non-linear relationships of multiple variables to outcomes [22]. The value chain change is influenced by multiple factors, each of which plays an important role. Traditional studies cannot analyze the joint role of multiple factors and thus choose the fsQCA approach. The fsQCA method could address issues such as causal asymmetry and various scenarios of causal complexity, which adopt a holistic perspective and configurational thinking. It views the research object as a configuration of various combinations of condition variables and discovers the aggregated relationships between configurations of condition variables through ensemble analysis [23] in order to investigate the combined effects of eight components across four dimensions–technology, organization, environment, and individual–and how their interactions impact value chain transformation from a systematic and all-encompassing standpoint.
There are several major contributions of this paper.
  • The research model of influencing value chain transition is established from eight factors internal and external to manufacturing enterprises, and the effects of multiple factors on value chain transition in manufacturing companies is considered.
  • This paper finds three configurations for achieving value chain transition in manufacturing enterprises. After using the fsQCA method to analyze the sufficiency and necessity of the influencing factors, three configurations for promoting value chain transition were obtained.
  • Several management recommendations are proposed to provide guidance for manufacturing enterprises. This paper presents management suggestions based on the three configurations in order to provide guidance to manufacturing companies for achieving value chain transition.
The organization of the rest of the paper is as follows. Firstly, the design of this study is presented in Section 2, including the TOEI framework, research model, variable design, FSQCA method, case selection and data collection, as well as data calibration. Then, Section 3 focuses on data analysis, with necessity analysis and sufficiency analysis. Finally, Section 4 draws the conclusions of the study and proposes some implications and limitations.

2. Study Design and Methods

2.1. TOEI Framework

The value chain transition of Chinese manufacturing enterprises is influenced by multiple factors. We integrate three dominant theories from existing studies: technology-organization-environment (TOE) theory [24], diffusion of innovation (DOI) theory [25], institutional theory [26], and developing the TOEI theoretical framework. According to the TOE framework, the success of an enterprise is determined by the environment, organization, and technology. The DOI theory believes that organizational performance is predicted by technological qualities, individual characteristics, and organizational factors. While according to institutional theory, organizations are part of institutional networks, and as a result, institutional factors such as governmental regulations and professional associations can affect the effectiveness of implementations. Some scholars have already integrated these three theories and conducted research [27,28]. Thus, we combine these three theories and construct a technology-organization-environment-individual (TOEI) framework to explore the influencing aspects of Chinese enterprise’s value chain transition. In the TOEI framework, we believe that the technology context, organizational context, environmental context, and individual factors collectively determine value chain transition.

2.2. Research Model

The value chain transition of manufacturing enterprises is a complex activity, and previous studies have shown that it is influenced by many factors. These factors can be categorized into four areas: technology, organization, environment, and individuals, and multiple factors can simultaneously facilitate or inhibit manufacturing enterprises from achieving their value chain transition goals. Chinese manufacturing enterprises are in a period of transformation to gain more competitive advantage, and the upgrading of the value chain is crucial to promote the sustainable development of China’s economy [29]. Based on the above analysis, combined with the practical status of the development of manufacturing enterprises and expanding the eight factors under the TOEI framework, we proposed a configuration research model of value chain transformation and conducted an empirical study analysis of it, so as to explore the law of value chain transformation of Chinese manufacturing enterprises. The research model is presented in Figure 1.

2.3. Variables Design

In this paper, we select eight factors of TOEI to analyze the value chain transition of Chinese manufacturing enterprises. The variables are referenced from previous studies and combined with the reality of value chain development in Chinese manufacturing enterprises. The eight factors from TOEI are then finally identified after interviews with managers and employees of manufacturing enterprises using the PAPI method.
The technology context mainly includes independent innovation [30,31] and technology import [11,18]. The development of manufacturing enterprises has strong technology dependence, and the leap in technology capability cannot be achieved without an accumulation of technology introduction and independent innovation. Independent innovation is the golden key for manufacturing enterprises to cultivate new growth poles in order to pull up the value chain and contribute to the benign development of the manufacturing industry. In addition, the independent innovation of enterprises is not behind closed doors but is based on the imitation of foreign advanced technology production to investment in the introduction, and finally feeds on the independent innovation. Therefore, technology import is an important guarantee for the further development of the advanced manufacturing industry and an important way to promote the climbing of the value chain of the advanced manufacturing industry.
Organizational context mainly includes talent evaluation [32,33] and financing capability [34,35,36]. The existing studies have shown that the talent level of manufacturing enterprises will affect their upgrading path to a certain extent. The dominant characteristics of manufacturing enterprises are the possession of advanced manufacturing technologies as well as high R&D investment, all of which cannot be achieved without the support of highly skilled talents. In addition, the manufacturing industry needs many resources and strong financial capability to achieve value chain upgrading, which especially tests the financing capability of the manufacturing industry. When enterprises have strong financing ability, especially strong internal financing ability, they can support enterprise innovation and thus have a positive effect on value chain climbing.
The environmental context mainly consists of industrial policy [18,37,38] and government subsidy [19,39]. Industrial policy is enacted by the government through economic functions in order to support emerging high-tech industries and accelerate their development to drive industrial upgrading. Studies have shown that industrial policy has a positive effect on the upgrading of the manufacturing value chain. Government subsidies are an important motivation for advanced manufacturing industries to develop new and emerging products, and they are also the main reason for influencing the transformation and upgrading of advanced manufacturing industries. Government subsidies can further reduce the risk of enterprises to conduct R&D activities, avoid market rent-seeking and other behaviors to destroy the enthusiasm of product innovation, and improve the efficiency of enterprise innovation.
The individual context is composed of entrepreneur background [40,41] and relations with officials [42,43]. The value chain transition of manufacturing enterprisers from a micro perspective is influenced by individual-level factors such as the educational background and team perception of corporate executives. In addition, the entrepreneurial background largely guides the corporate climate, vision, and values, directs the future direction of the company, and is a key determinant of innovation and value chain upgrading in manufacturing. Relations with officials refer to the relationship between corporate executives and government officials; the closer the connection with the government, the easier it is for companies to probe the government’s future development efforts and to receive financial support from official institutions, thus obtaining key resources that are conducive to corporate innovation and reducing the risk that comes from manufacturing upgrading.

2.4. FSQCA Method

It is challenging to describe the interdependence between independent variables when using traditional statistical approaches (such as regression analysis), which focus primarily on the net effect of independent factors on dependent variables. It is thought that the outcome of fsQCA, a case-study-oriented method, is dependent on the interaction of linked elements [44,45,46,47]. The configuration is the result of each piece combined. Based on this, we select the independent innovation, technology import, talents evaluation, financing capability, industrial policy, government subsidy, entrepreneur background, and officer relations to study the interaction mechanism between variables.
The main reasons for using the fsQCA method are: (1) The traditional linear relationship is typically used to explain symmetry-related issues, but there are so many non-linear correlations in life that it is challenging to analyze this complicated situation using the traditional linear relationship. The fsQCA approach is more theoretically coherent and realistic since it takes into account the set of conditional factors rather than the individual variables as having a significant impact on the outcomes [48,49]. (2) The fsQCA technique primarily considers “concurrent causality”, the idea that different influencing factors might combine to produce the same outcome. Contrary to conventional statistical approaches, which examine the impact of individual elements on the outcomes, this approach yields the same conclusion when two different paths are taken [50,51]. (3) The PLS structural equation method is a quantitative method, that investigates the impact of a single factor on the outcomes, so it is unable to reveal configuration for multiple factors. The analysis of complex antecedent circumstances, asymmetric causation, cross-level variables, and other specific problems is made easier by the qualitative and quantitative advantages of fsQCA. It can also minimize the complexity of sample objects, allowing for a more thorough case interpretation [52].
Although the fsQCA method has some advantages in studying the value chain transition in manufacturing enterprises, it also has certain limitations. It pays attention to the configuration of multiple factors but ignores the prominent influence of individual factors. Additionally, the results found by the fsQCA method are the effects of the factors that have been found, while the factors not included in the study are ignored.
These are the calculation formulas of C o n s i s t e n c y and C o v e r a g e :
C o n s i s t e n c y X i ≤ Y i = ∑ min X i , Y i ∑ min X i  
C o v e r a g e X i ≤ Y i = ∑ min X i , Y i ∑ min Y i  
In the equation, the composition of case i in group set X is represented by Xi, and the composition of case i in result Y is represented by Yi. C o n s i s t e n c y is a metric for determining whether the conditional setups are what determine the outcome. C o v e r a g e reveals the extent to which the interaction of X and Y affected the understanding of result Y [53,54].

2.5. Case Selection and Data Collection

The process of selecting case companies begins with determining the basic principles of sample selection. On the premise of considering the richness and accessibility of the sample quantity, we ensure that the case companies meet the requirements of implementing the value chain transition, so as to satisfy the needs of theoretical construction. Secondly, 190 manufacturing enterprises covering a large, medium and small size were selected from the “Announcement of Advanced Manufacturing Cluster Finalists” and the “Top 500 Chinese Manufacturing Enterprises” released by the Ministry of Industry and Information Technology of China to understand the basic situation of enterprises in all aspects, establish a database of basic information of companies, and mark the manufacturing companies implementing value chain transition strategies. Finally, we ensured that the case companies come from different enterprise attributes, different industry technology degrees and different industry concentrations, and the data not only met the principles of representativeness, richness and accessibility, but also conformed to the relevant research of fsQCA model regulation. This paper combines the actual situation of the study and finally selects 52 case companies under the premise of satisfying the operational specification of the fsQCA method. The 52 Chinese manufacturing companies selected are mainly distributed across nine industries, among which the manufacturing enterprises of computer, communication and other electronic equipment manufacturing, special equipment manufacturing and special equipment manufacturing are ranked in the top three. In addition, the 52 Chinese manufacturing enterprises selected above are in 14 provinces and cities, with the majority of enterprises in Guangdong Province, Jiangsu Province, and Shanghai.
The data for this paper were obtained from the database of CSMAR (https://www.gtarsc.com/, accessed on 10 January 2022). The data are conducted with a partial least squares analysis. The internal consistency of the measurement results is referred to as reliability. Reliability testing was measured using Cronbach’s α (CA) and composite reliability (CR). Construct validity, which is determined through convergent and discriminant validity, is a way to express validity [55]. To determine if various observed variables may be used to assess the same hidden variable, convergent validity is used. The criterion is the factor loading of the construct and the average variance extracted (AVE) of the latent variable. Whether or not there are discernible differences between latent variables is referred to as discriminant validity [56]. The discriminant validity of a variable is good if the square root of the average variance extracted from the variable is greater than the correlation coefficient between the variable and the other variables. In addition, we tested the data with Smart PLS 3.0 software, and the results were good.

2.6. Data Calibration

In the fsQCA method, each condition and outcome are considered as a set, and each case has its affiliation in these sets. Assigning membership scores is calibrated, according to the three-valued fuzzy set approach proposed by Ragin [23]; a total of nine variables in five dimensions, including technical dimension, organizational dimension, environmental dimension, individual dimension, and outcome variables, are used as thresholds for full affiliation, intersection, and full non-affiliation by applying the direct calibration method with 5%, 50%, and 95% scores of the continuous variables, respectively, which in turn are distributed fuzzy affiliation values between 0 and 1 [57]. The calibration of all variables is shown in Table 1.

3. Data Analysis

3.1. Analysis of Necessity

Before starting the fsQCA analysis, it is necessary to determine the necessity of condition variables in the process of result variable implementation. The necessity analysis measures whether a condition variable is necessary for the outcome variable. Two indicators are commonly used to measure necessity: the consistency is usually taken to determine the level of necessity for the condition variables being the outcome, and the coverage reflects the number of cases that can explain the existence of the necessity for the condition variables [46]. If the consistency of the single factor is greater than 0.9, the condition variable is considered to be necessary for the outcome variable. The analysis results of necessity are shown in Table 2.
As shown in Table 2, there is no single factor whose necessity is greater than 0.9, which indicates that the single factor cannot be the necessary condition for value chain transition in manufacturing enterprises. This shows that the explanatory power of a single factor for manufacturing value chain transition is weak and should not be discussed as a necessary condition, and configuration matching of multiple factors is needed to influence manufacturing value chain transition. The value chain transition of Chinese manufacturing enterprises is influenced by the linkage of several factors, and the value chain transition of manufacturing enterprises can be promoted when the eight factors of “TOEI” are properly combined.

3.2. Analysis of Sufficiency

The sufficiency analysis lies in revealing whether different configurations consisting of multiple factors are sufficient conditions for the outcome variable. Three types of solutions can be obtained by fsQCA analysis: simple, intermediate, and complex solutions. The results of the sufficiency analysis are mainly intermediate solutions and simple solutions. The conditions that appear in both intermediate and simple solutions are defined as core conditions, and the conditions that appear only in intermediate solutions are defined as peripheral conditions [58]. In this paper, the raw consistency threshold is set to 0.75 and the case frequency threshold is set to 1.
The findings from fsQCA on the configurations for the value chain transition of Chinese manufacturing enterprises are presented in Table 3.
This paper presents the results of the configuration analysis of the value chain transition in manufacturing enterprises by referring to Ragin’s approach [23]. A condition is present when a circle is black (“⬤”), and it is absent when a circle has a cross through it (“⊗”). Small circles (“●”) represent peripheral conditions, while large circles (“⬤”) represent core conditions. The causative condition may be present or missing in situations with blank spaces, which are referred to as “do not care” situations [59,60].
Configuration 1 is the type of technology-organization-individual (TOI) co-dominant. The core conditions of this value chain transition configuration are independent innovation, financing capability, entrepreneur background, and officer relations, while the peripheral conditions of the configuration are technology import, talent evaluation, and government subsidy. It is represented as II∗ti∗te∗FC∗gs∗EB∗OR (upper case denotes core conditions, lower case denotes peripheral conditions). This indicates that the advanced manufacturing industry with a high degree of independent innovation, abundant cash flow from operating activities, a strong entrepreneurial background and political connections, a moderate technology introduction, a good talent level, and reasonable government subsidies can promote the development of value chain transition in manufacturing enterprises. More importantly, the four core conditions appear in the areas of technology, organization, and individuals, indicating that the enterprise’s own development capabilities are more important and that environmental factors could assist in the transition of the manufacturing enterprises value chain. The raw coverage rate of configuration 1 is 0.193, indicating that nearly one-fifth of the antecedent combinations of the advanced manufacturing value chain climbing paths can be classified into this configuration.
Configuration 2 is the type of technology-environment-individual (TEI)co-driven. The core conditions are independent innovation, industrial policy and officer relations. It is represented as II∗~TI∗~TE∗~FC∗IP∗~GS∗~EB∗OR. This indicates that the manufacturing industry with high R&D investment resources, good industrial policies, and strong political ties can lead to value chain transition. In this configuration, there are three core conditions appearing in the areas of technology, environment, and individuals, which indicate that manufacturing enterprises strengthen technological development and enhance entrepreneurial capabilities, while also taking advantage of environmental factors and actively using industrial policies that work well for value chain transition. The raw coverage rate of configuration 2 is 0.211, which means that more than one-fifth of the antecedent combinations of the value chain transition of manufacturing enterprises can be classified into this configuration.
Configuration 3 is the type of organization-environment-individual (OEI) co-dominant. The core conditions are financing capability, industrial policy, and entrepreneur background. Configuration 3 represents as ~II∗ti∗~TE∗FC∗IP∗~GS∗EB∗~OR. It indicates that the manufacturing industry with a high cash flow from operating activities, good industrial policies and a strong entrepreneurial background, as well as moderate technology introduction, can promote the value chain climbing of the manufacturing enterprises in the technology cycle more quickly. In this configuration, there are three core conditions, present in the areas of environment, organization, and individual. It indicates that manufacturing enterprises can improve their organizational management capabilities and develop entrepreneurial capabilities, which are effective for the transition of the value chain. In addition, manufacturing enterprises have to take advantage of environmental factors and proactively use industrial policies, which would facilitate value chain change. The original coverage rate of grouping path 3 is 0.223, indicating that more than one-fifth of the antecedent combinations of value chain transition paths of case manufacturing enterprises can be classified into configuration 3.

3.3. Robustness Test

A robustness test was conducted to make the results credible. The method used for the robustness test is to adjust the consistency index and observe whether the results of the solution would change significantly. If the results of the solution do not change significantly, the results can be considered convincing. In this paper, the consistency was adjusted to 0.7 and the data were again analyzed using fsQCA. By comparing the results, it can be found that the configurations do not change visibly. This indicates that the research results possess good robustness.

4. Conclusions and Recommendations

4.1. Summary of Findings

In this paper, we summarized the key factors of value chain transition in Chinese manufacturing enterprises, constructed a research model by combining the TOEI framework, and then analyzed the data of case companies using fsQCA. It was found that the single conditions in the technology, environment, and organization dimensions cannot constitute the necessary conditions for value chain transition alone, and the antecedent conditions need to be matched by the configuration in order to promote the value chain transition of manufacturing enterprises. There are three main configurations for manufacturing value chain transition, which are TOI co-dominant (configuration 1), TEI co-driven (configuration 2), and OEI co-dominant (configuration 3).
The configuration of the TOI co-dominant suggests that the transition of manufacturing value chains is driven by a combination of six conditional variables: innovation capability, technology introduction capability, talent level, financing capability, government subsidies, entrepreneurial background, and political ties. It is also worth noting that both independent innovation and financing capability are the core condition variables.
The configurations of TEI co-driven and OEI co-dominant suggest that the environment and individual are core conditions, and that industrial policy is an extremely important core condition for the manufacturing value chain transition, which is shown in both configurations. Therefore, enhancing industrial policy is a very important initiative for Chinese manufacturing enterprises to leap up the value chain.

4.2. Managerial Implications

In the value chain transition configuration of Chinese manufacturing enterprises, all of them have their own common core conditions. Industrial policy appears in all of them. For those manufacturing enterprises with a high degree of independent innovation capability in the technology inner cycle to achieve the leap in the value chain, the primary guarantee is whether they are in the policy dividend period or future development direction. For example, to today’s advocate of carbon peak and carbon neutral, from the manufacturing enterprises, the next few decades will be the time for rapid development of new energy industry. Manufacturing enterprises should quickly take the opportunity to seize the market, and in this way constantly feed their own R & D investment, always maintaining the industry’s leading technology and mastering the industry discourse so as to control the entire industry’s value chain. For those advanced manufacturing enterprises without a high degree of independent research and development capabilities outside the technology cycle to achieve the leap in the value chain, the first objective is to ensure that their own technology is not eliminated by the market. For example, if a manufacturing enterprise cannot adapt its own technology to the changing market demand, it must introduce new technology from outside even if it wants to realize the leap in the value chain quickly. On this basis, it is important to ensure the survival of enterprises and have the ability to imitate and learn from advanced technology, so as to surpass the value chain climb again.
From the perspective of influencing factors, enterprise managers should pay attention to the characteristics of each precondition and formulate corresponding long-term cultivation strategies for the factors in conjunction with their own conditions. Firstly, managers of manufacturing industry should pay great attention to the positive role of independent innovation and financing ability in the rise of the value chain of advanced manufacturing industry. Secondly, managers of enterprises should realize that they themselves, the level of enterprise talent, government subsidies and relations with officials are also important influencing factors to achieve the rise of the value chain. Finally, managers of enterprises should understand that industrial policies and technology introduction are the keys to promoting the rise of the value chain of advanced manufacturing industry. Therefore, enterprise managers need to combine the characteristics of each precondition and the enterprise’s own characteristics to develop a corresponding factor cultivation program to make up for the shortcomings in the preconditions of the enterprise, while alleviating the dependence on a single advantage and driving the high-quality transformation and upgrading of the enterprise.

4.3. Limitations and Further Research

There are two shortcomings in this paper: First, considering the ease of data collection, only relevant listed manufacturing companies were selected for this study, and a considerable number of top “invisible champion” small and medium-sized manufacturing enterprises were ignored, so this study has some limitations. Secondly, the setting of fsQCA anchor points is not strictly argued, but only uses the general rule of upper and lower quartiles, which is subjective and will inevitably produce some data errors. This is one of the characteristics of the QCA method, which goes beyond qualitative and quantitative research; likewise, it is one of its drawbacks. In future research, QCA and other methods can be used together to cross-validate the value chain transition of Chinese manufacturing enterprises.

Author Contributions

Conceptualization and design, Y.L., W.Z. and C.L.; data acquisition and analysis, C.L., W.Z.; data visualization, Z.L., Z.X. and C.L.; writing—original draft preparation, W.Z. and C.L.; writing—review and editing, C.L., Z.X. and W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Social Science Foundation of China; the grant number is 19BJY099.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy of study participants.

Acknowledgments

The researchers would like to express their gratitude to the anonymous reviewers for their efforts to improve the quality of this paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Systems 10 00164 g001 550
Figure 1. Research model.
Figure 1. Research model.
Systems 10 00164 g001
Table
Table 1. Calibration anchor points of the variables.
Table 1. Calibration anchor points of the variables.
VariablesMeaning of
Variables		Full
Affiliation Point		Intersection PointCompletely
Unaffiliated Points
VCTValue chain transition0.3450.1220.004
IIIndependent innovation0.108 0.053 0.031
TITechnology import50,379.915 790.935 26.386
TETalent evaluation0.5010.160 0.084
FCFinancing capability2,707,892.839 67,008.401 8525.784
IPIndustrial policy11.1 5.5 2
GSGovernment subsidy20,441.028 0.0670
EBEntrepreneur background210
RORelations with officials10.55 10
Table
Table 2. The results of necessity analysis.
Table 2. The results of necessity analysis.
VariablesConsistencyCoverage
II0.68270.6697
TI0.54320.6745
TE0.61540.6577
FC0.57530.6823
IP0.64890.5880
GS0.39870.6474
EB0.64410.6092
RO0.58240.5743
Table
Table 3. FSQCA analysis results.
Table 3. FSQCA analysis results.
VariablesConfigurations
123
II⬤⬤⊗
TI●⊗●
TE●⊗⊗
FC⬤⊗⬤
IP ⬤⬤
GS●⊗⊗
EB⬤⊗⬤
OR⬤⬤⊗
Consistency0.8540.8350.951
Raw coverage0.1930.2110.223
Unique coverage0.0750.1040.095
Solution coverage0.605
Solution consistency0.844
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Liao, C.; Xiang, Z.; Zhou, W.; Li, Z.; Li, Y. Research on the Configuration of Value Chain Transition in Chinese Manufacturing Enterprises. Systems 2022, 10, 164. https://doi.org/10.3390/systems10050164

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Liao C, Xiang Z, Zhou W, Li Z, Li Y. Research on the Configuration of Value Chain Transition in Chinese Manufacturing Enterprises. Systems. 2022; 10(5):164. https://doi.org/10.3390/systems10050164

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Liao, Chengjun, Ziwei Xiang, Wei Zhou, Zhenyu Li, and Yuhua Li. 2022. "Research on the Configuration of Value Chain Transition in Chinese Manufacturing Enterprises" Systems 10, no. 5: 164. https://doi.org/10.3390/systems10050164

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