# The Impact of R&D Investment and Policy Support on Intelligent Manufacturing Level: Based on the fsQCA Method

Authors: Zeyu Qin; Qi Lu; Zhenjiu Yao
Journal: Mathematical Problems in Engineering
 (2023)

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## Abstract

Intelligent manufacturing is the main direction of manufacturing transformation and upgrading in developing countries. However, the issue of how to systematically improve the level of intelligent manufacturing in enterprises has not been effectively solved. The analyses in this study are based on data collected from a sample of 45 manufacturing companies in China from 2016 to 2018, and fuzzy set qualitative comparative analysis (fsQCA) was used to explore the impact of R&D, policy support, and enterprise characteristics on the level of intelligent manufacturing. The results find that three modes and four configurations for enterprises achieve high-level intelligent manufacturing. Specifically, firstly, enterprises with larger scale and better benefits can achieve high-level intelligent manufacturing with less R&D investment. Secondly, large enterprises with a high leverage ratio and high R&D capital investment can sacrifice short-term profitability and complete intelligent transformation through lower human capital investment. With the help of policy support, we maintain a high human capital investment and accelerate the completion of the intelligent upgrade. Thirdly, small companies can use higher R&D human capital investment to achieve the goal of intelligence. These findings provide valuable insights for enterprises to effectively achieve intelligent transformation.


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## Body

## 1. Introduction

As the result of the deep integration of advanced manufacturing technology and information technology, intelligent manufacturing has become the development trend of manufacturing. For example, in China, the “Made in China 2025” plan promulgated by the central government marks that smart manufacturing has become an important direction for the transformation and upgrading of its manufacturing industry. Encouraged by the central government, the development of smart manufacturing has become the trend of development of China’s manufacturing industry, because it is not only an objective requirement for manufacturing transformation and upgrading but also considered to promote supply-side structural reforms and adapt to the “new normal” of China’s economic development. Most major developed countries in the world have already promoted the intelligent development of manufacturing. Especially, in new economies such as China, external government intervention and internal factors of enterprises work together to influence the level of smart manufacturing in enterprises. Therefore, studying the internal and external factors affecting the development of smart manufacturing is of great significance to the development of manufacturing in emerging economies.Since Deng Xiaoping came to power, he has gradually abandoned the mandatory planned economy of the Mao era and implemented comprehensive “economic reform and open up” in China Mainland. After more than 40 years, constraints on resources, land, and labor in the developed eastern coastal areas of China have begun to appear, and the supply of industrial factors has changed tremendously. But for a long time, China’s economic development has come at the cost of excessive consumption of resources and environmental pollution, which has led to a number of negative impacts. The Chinese government and industrial departments urgently need to develop intelligent manufacturing to achieve the intensification of production factors. At present, China Mainland has formed three intelligent manufacturing growth poles, namely, “Bohai Rim,” “Yangtze River Delta,” and “Pearl River Delta.” In particular, the economic foundation of the Yangtze River Delta region is relatively strong; cities in the region have a solid manufacturing base, a strong innovation atmosphere, strong industrial competitiveness, and an industrial foundation and needs for the development of intelligent manufacturing. Therefore, this paper hopes to explore configurations of the three dimensions of the enterprise’s intelligent manufacturing level and R&D investment, policy support, and the enterprise characteristics. Then, we discuss effective ways to improve the intelligence level of manufacturing enterprises and put forward corresponding policy recommendations.The contributions of this article are mainly in the following two aspects: First, previous studies mostly used the qualitative description or regression analysis to test the linear relationship between influencing factors and the development of enterprise intelligent manufacturing but did not consider the complexity of the real environment from the perspective of configuration analysis. This study incorporates the antecedent elements of R&D investment, policy support, and enterprise characteristics into the research, rejecting the assumptions of variable independence and causal symmetry in regression analysis, considering the actual causal asymmetry and the matching effect between antecedent factors, and providing a new way of thinking for future research. Secondly, this study uses fsQCA to clarify the configuration that enables enterprises to achieve high-level intelligent manufacturing and summarizes three modes and four paths, which expands the extension of intelligent manufacturing theory research. In addition, from the perspective of causal asymmetry, the reasons for the low-level intelligent manufacturing of enterprises are discussed. Empirical analysis shows that there is no stable configuration that can explain this phenomenon, which enriches the theoretical research of intelligent manufacturing.


## 2. Literature Review

### 2.1. Theoretical Background

Wright and Bourne first defined intelligent manufacturing in their bookManufacturing Intelligence. They believed that intelligent manufacturing is to model the skills and knowledge of technicians through manufacturing software systems, integrated knowledge engineering, and robot vision, so that intelligent industrial robots can automate production. In addition, intelligent manufacturing is a broad concept of manufacturing. The purpose is to make full use of advanced information and manufacturing technologies to optimize production and product transactions. Intelligent manufacturing is a new manufacturing model based on intelligent technology, which greatly improves the design, production, management, and integration of the whole life cycle of a typical product [1]. Through adaptive decision-making models and data analytics and using various smart sensors, intelligent devices, and advanced materials, it improves production efficiency, product quality, and service level [2], so as to facilitate the entire product life cycle [3].We believe that intelligent manufacturing refers to the realization of real-time optimized management of five system levels, namely, control, collaboration, enterprise, workshop, and equipment based on a new generation of information technology, with product manufacturing processes and life cycles and has self-awareness, self-decision-making, and self-optimizing new advanced manufacturing system. Intelligent manufacturing starts with digitization and gradually upgrades to the stage of networking and intelligence. It can endow the products with intelligent perception, intelligent analysis, and intelligent decision-making capabilities [4]. Compared with the digital and networked stages, intelligent manufacturing fully uses computer automatic control and applies industrial Internet, industrial robots, and big data, which greatly shortens the product development time and reflects the characteristics of the deep integration of physical objects and virtual networks.In recent years, the widespread use of the Internet of things (IoT), cloud computing, big data, and digitalization has led to changes in the way businesses operate. Industry 4.0 has promoted the transformation and upgrading of manufacturing industry, and smart manufacturing has gradually become a hot research topic [5]. Piccarozzi et al. [6] analyzed and classified the main contributions published on the topic of intelligent manufacturing and Industry 4.0 in management literature, which support entrepreneurs in better understanding the implications and fields of application of the Fourth Industrial Revolution and the interplay among them. Dalenogare et al. [7], based on the data from a large-scale survey of 27 industrial sectors representing 2225 companies of the Brazilian industry, analyzed the expected contribution of the development of intelligent manufacturing to the industrial performance of emerging market countries. The research of some Chinese scholars summarized the factors that affect the development of intelligent manufacturing, such as enterprise innovation flexibility and policy support. However, the relationship between the development level of enterprise intelligent manufacturing and related influencing factors is a multicondition concurrent nonlinear relationship. Most previous studies have used qualitative description induction or regression methods to analyze, so the synergy effect between variables is often ignored.


### 2.2. Realistic Background

In the past few decades, many emerging economies represented by China have achieved considerable development in their manufacturing industries, including their overall scale and technological level. However, in general, compared with western developed economies, the manufacturing industries of emerging economies are located in the lower reaches of the industrial chain, and their core technology own rate is low, and they are mainly human-intensive industries. In addition, the rapid decline of the demographic dividend and the continuous increase of factor costs have also weakened the traditional comparative advantages of the manufacturing industries of emerging economies [8].Since Deng Xiaoping came to power, he has gradually abandoned the mandatory planned economy of the Mao era and implemented comprehensive “economic reform and open up” in China Mainland. After more than 40 years, constraints on resources, land, and labor in the developed eastern coastal areas of China have begun to appear, and the supply of industrial factors has changed tremendously [9]. The Chinese government and industrial departments urgently need to develop intelligent manufacturing to achieve intensification of production factors. In order to enhance the competitiveness of the domestic manufacturing industry, the Chinese government has carried out a lot of political interventions to realize the transformation from a large manufacturing country to a strong manufacturing country. Through these political interventions, China has basically achieved national informatization, thus creating the basic conditions for intelligent manufacturing. The “Made in China 2025” program issued by the State Council of China in 2015 marked that smart manufacturing has become a new goal and direction for the development of China’s manufacturing industry.Compared with developed industrial countries, China and other emerging market countries have a lower manufacturing technology level. Many key software and hardware of intelligent manufacturing cannot realize domestic production, and independent innovation capabilities are relatively weak, which restricts the development of intelligent manufacturing. Therefore, it is an interesting question to study how companies realize the transformation of intelligent manufacturing.


### 2.3. Influencing Factors of Intelligent Manufacturing

The theory of studying the path of enterprise transformation and upgrading generally believes that the transformation of an enterprise is not triggered by a single variable change but is often affected by multiple factors taking into account the complexity of the environment. In particular, the following three widely accepted views. First, R&D investment will directly affect the transformation and upgrading of the physical industry. In addition, R&D investment can be divided into R&D capital investment and R&D personnel investment [10]. Secondly, policy support will, to a large extent, promote the transformation of the industry. This phenomenon is particularly evident in centralized countries such as China. The empirical study of green innovation by Zhang et al. [11] on Chinese listed companies shows that state-owned enterprises that receive more policy support have stronger innovation drivers. The government often guides the production direction of real enterprises by issuing support policies, tax reductions or exemptions, or direct subsidies, thereby promoting industrial reforms. Thirdly, the heterogeneity of enterprises is also an important factor in the transformation and upgrading. These characteristics of enterprises are often contained in financial indicators such as enterprise size, capital structure, profitability, and market power [12]. The different characteristics of the enterprise itself determine whether the enterprise has the conditions for transformation and upgrading or enables it to become a key target of government support.R&D investment refers to the innovative elements that an enterprise invests in the development and improvement of products or services. It is an indispensable and important factor for an enterprise to iteratively upgrade its production line and realize informatization, automation, and intelligent, flexible, and lean production. It represents the sum of R&D capital and human capital invested by enterprises to realize innovation. Li [3] discussion of Germany’s “Industry 4.0” and “Made in China 2025” affirmed this view and compared the relationship between China’s R&D capital investment and the growth rate of the manufacturing industry, proving that R&D capital investment is the upgrading and development of manufacturing necessary factors. Soto−Acosta et al. [13] believe that human capital is very important to promote exploratory practice and then promote the transformation of production enterprises. In the process of promoting smart manufacturing in enterprises, both R&D capital and human capital play an important role. They are both inputs made by enterprises in R&D activities and represent their ability to achieve breakthrough innovation. This is supported by the study of Wang et al. [14], which found that both human capital and knowledge capital of the firms contribute to intelligent performance. Therefore, when considering the R&D investment of a company, the two must be included together, so as to objectively reflect the actual degree of R&D investment of the company.Policy support refers to the fact that the central or local government adopts policy support in response to the actual situation of regional economic development to promote the priority development of pillar industries and leading industries. Especially in China, since the availability of policy support is an important indicator of the closeness of the relationship between enterprises and government departments, enterprises that have received government support can often obtain a large amount of funds, which can better alleviate financing constraints. The direct incentive effect of policy support on enterprises is far greater than the substitution and crowding-out effects, thus increasing the total R&D investment of enterprises. Among the various forms of policy support, direct subsidies and tax reductions are often more effective in supporting enterprises, and data is easily available. Therefore, most previous studies tended to use the amount of government subsidies to describe the degree of policy support.The different characteristics of enterprises are also an important factor in whether enterprises can implement intelligent upgrades. The characteristics of enterprises such as size and financial represent their sustainable operation capability, and they will undoubtedly have an important impact on its smart manufacturing level. Generally, larger and better financially positioned enterprises will invest more resources into the transformation and upgrading of smart manufacturing. However, some smaller enterprises may also achieve better smart manufacturing performance with the advantage of flexible organization. The empirical analysis of German companies by Muller et al. [12] proved that large companies with strong operational capabilities can better respond to “Industry 4.0” reforms because these companies usually adopt long-term forward-looking strategies. Previous studies generally used total assets or total number of employees, return on assets, and leverage to measure the three important characteristics of enterprise size, profitability, and capital structure. Together, they determine the characteristics of individual manufacturing companies.In summary, in the research framework of this article, there are three first-level conditions including R&D investment, external policy support, and enterprise characteristics, as well as six second-level conditions included in it. They may constitute different conditional configurations that affect the enterprise’s intelligent manufacturing level internally and externally, instead of following the assumptions of variable independence and causal symmetry in traditional regression analysis. Multicondition linkage matching may strengthen each other through conditional adaptation or may cancel each other through substitution effects. Therefore, we hope to use a method that goes beyond traditional qualitative analysis and quantitative research to explore the development path of enterprise intelligent manufacturing from a configuration perspective. The analysis framework of this study is shown in Figure1.Figure 1
Analysis framework.


## 2.1. Theoretical Background

Wright and Bourne first defined intelligent manufacturing in their bookManufacturing Intelligence. They believed that intelligent manufacturing is to model the skills and knowledge of technicians through manufacturing software systems, integrated knowledge engineering, and robot vision, so that intelligent industrial robots can automate production. In addition, intelligent manufacturing is a broad concept of manufacturing. The purpose is to make full use of advanced information and manufacturing technologies to optimize production and product transactions. Intelligent manufacturing is a new manufacturing model based on intelligent technology, which greatly improves the design, production, management, and integration of the whole life cycle of a typical product [1]. Through adaptive decision-making models and data analytics and using various smart sensors, intelligent devices, and advanced materials, it improves production efficiency, product quality, and service level [2], so as to facilitate the entire product life cycle [3].We believe that intelligent manufacturing refers to the realization of real-time optimized management of five system levels, namely, control, collaboration, enterprise, workshop, and equipment based on a new generation of information technology, with product manufacturing processes and life cycles and has self-awareness, self-decision-making, and self-optimizing new advanced manufacturing system. Intelligent manufacturing starts with digitization and gradually upgrades to the stage of networking and intelligence. It can endow the products with intelligent perception, intelligent analysis, and intelligent decision-making capabilities [4]. Compared with the digital and networked stages, intelligent manufacturing fully uses computer automatic control and applies industrial Internet, industrial robots, and big data, which greatly shortens the product development time and reflects the characteristics of the deep integration of physical objects and virtual networks.In recent years, the widespread use of the Internet of things (IoT), cloud computing, big data, and digitalization has led to changes in the way businesses operate. Industry 4.0 has promoted the transformation and upgrading of manufacturing industry, and smart manufacturing has gradually become a hot research topic [5]. Piccarozzi et al. [6] analyzed and classified the main contributions published on the topic of intelligent manufacturing and Industry 4.0 in management literature, which support entrepreneurs in better understanding the implications and fields of application of the Fourth Industrial Revolution and the interplay among them. Dalenogare et al. [7], based on the data from a large-scale survey of 27 industrial sectors representing 2225 companies of the Brazilian industry, analyzed the expected contribution of the development of intelligent manufacturing to the industrial performance of emerging market countries. The research of some Chinese scholars summarized the factors that affect the development of intelligent manufacturing, such as enterprise innovation flexibility and policy support. However, the relationship between the development level of enterprise intelligent manufacturing and related influencing factors is a multicondition concurrent nonlinear relationship. Most previous studies have used qualitative description induction or regression methods to analyze, so the synergy effect between variables is often ignored.


## 2.2. Realistic Background

In the past few decades, many emerging economies represented by China have achieved considerable development in their manufacturing industries, including their overall scale and technological level. However, in general, compared with western developed economies, the manufacturing industries of emerging economies are located in the lower reaches of the industrial chain, and their core technology own rate is low, and they are mainly human-intensive industries. In addition, the rapid decline of the demographic dividend and the continuous increase of factor costs have also weakened the traditional comparative advantages of the manufacturing industries of emerging economies [8].Since Deng Xiaoping came to power, he has gradually abandoned the mandatory planned economy of the Mao era and implemented comprehensive “economic reform and open up” in China Mainland. After more than 40 years, constraints on resources, land, and labor in the developed eastern coastal areas of China have begun to appear, and the supply of industrial factors has changed tremendously [9]. The Chinese government and industrial departments urgently need to develop intelligent manufacturing to achieve intensification of production factors. In order to enhance the competitiveness of the domestic manufacturing industry, the Chinese government has carried out a lot of political interventions to realize the transformation from a large manufacturing country to a strong manufacturing country. Through these political interventions, China has basically achieved national informatization, thus creating the basic conditions for intelligent manufacturing. The “Made in China 2025” program issued by the State Council of China in 2015 marked that smart manufacturing has become a new goal and direction for the development of China’s manufacturing industry.Compared with developed industrial countries, China and other emerging market countries have a lower manufacturing technology level. Many key software and hardware of intelligent manufacturing cannot realize domestic production, and independent innovation capabilities are relatively weak, which restricts the development of intelligent manufacturing. Therefore, it is an interesting question to study how companies realize the transformation of intelligent manufacturing.


## 2.3. Influencing Factors of Intelligent Manufacturing

The theory of studying the path of enterprise transformation and upgrading generally believes that the transformation of an enterprise is not triggered by a single variable change but is often affected by multiple factors taking into account the complexity of the environment. In particular, the following three widely accepted views. First, R&D investment will directly affect the transformation and upgrading of the physical industry. In addition, R&D investment can be divided into R&D capital investment and R&D personnel investment [10]. Secondly, policy support will, to a large extent, promote the transformation of the industry. This phenomenon is particularly evident in centralized countries such as China. The empirical study of green innovation by Zhang et al. [11] on Chinese listed companies shows that state-owned enterprises that receive more policy support have stronger innovation drivers. The government often guides the production direction of real enterprises by issuing support policies, tax reductions or exemptions, or direct subsidies, thereby promoting industrial reforms. Thirdly, the heterogeneity of enterprises is also an important factor in the transformation and upgrading. These characteristics of enterprises are often contained in financial indicators such as enterprise size, capital structure, profitability, and market power [12]. The different characteristics of the enterprise itself determine whether the enterprise has the conditions for transformation and upgrading or enables it to become a key target of government support.R&D investment refers to the innovative elements that an enterprise invests in the development and improvement of products or services. It is an indispensable and important factor for an enterprise to iteratively upgrade its production line and realize informatization, automation, and intelligent, flexible, and lean production. It represents the sum of R&D capital and human capital invested by enterprises to realize innovation. Li [3] discussion of Germany’s “Industry 4.0” and “Made in China 2025” affirmed this view and compared the relationship between China’s R&D capital investment and the growth rate of the manufacturing industry, proving that R&D capital investment is the upgrading and development of manufacturing necessary factors. Soto−Acosta et al. [13] believe that human capital is very important to promote exploratory practice and then promote the transformation of production enterprises. In the process of promoting smart manufacturing in enterprises, both R&D capital and human capital play an important role. They are both inputs made by enterprises in R&D activities and represent their ability to achieve breakthrough innovation. This is supported by the study of Wang et al. [14], which found that both human capital and knowledge capital of the firms contribute to intelligent performance. Therefore, when considering the R&D investment of a company, the two must be included together, so as to objectively reflect the actual degree of R&D investment of the company.Policy support refers to the fact that the central or local government adopts policy support in response to the actual situation of regional economic development to promote the priority development of pillar industries and leading industries. Especially in China, since the availability of policy support is an important indicator of the closeness of the relationship between enterprises and government departments, enterprises that have received government support can often obtain a large amount of funds, which can better alleviate financing constraints. The direct incentive effect of policy support on enterprises is far greater than the substitution and crowding-out effects, thus increasing the total R&D investment of enterprises. Among the various forms of policy support, direct subsidies and tax reductions are often more effective in supporting enterprises, and data is easily available. Therefore, most previous studies tended to use the amount of government subsidies to describe the degree of policy support.The different characteristics of enterprises are also an important factor in whether enterprises can implement intelligent upgrades. The characteristics of enterprises such as size and financial represent their sustainable operation capability, and they will undoubtedly have an important impact on its smart manufacturing level. Generally, larger and better financially positioned enterprises will invest more resources into the transformation and upgrading of smart manufacturing. However, some smaller enterprises may also achieve better smart manufacturing performance with the advantage of flexible organization. The empirical analysis of German companies by Muller et al. [12] proved that large companies with strong operational capabilities can better respond to “Industry 4.0” reforms because these companies usually adopt long-term forward-looking strategies. Previous studies generally used total assets or total number of employees, return on assets, and leverage to measure the three important characteristics of enterprise size, profitability, and capital structure. Together, they determine the characteristics of individual manufacturing companies.In summary, in the research framework of this article, there are three first-level conditions including R&D investment, external policy support, and enterprise characteristics, as well as six second-level conditions included in it. They may constitute different conditional configurations that affect the enterprise’s intelligent manufacturing level internally and externally, instead of following the assumptions of variable independence and causal symmetry in traditional regression analysis. Multicondition linkage matching may strengthen each other through conditional adaptation or may cancel each other through substitution effects. Therefore, we hope to use a method that goes beyond traditional qualitative analysis and quantitative research to explore the development path of enterprise intelligent manufacturing from a configuration perspective. The analysis framework of this study is shown in Figure1.Figure 1
Analysis framework.


## 3. Methodology

### 3.1. Methods

This study uses Qualitative Comparative Analysis (QCA) to explore the path of factors affecting the development of enterprise intelligent manufacturing. Based on set theory and Boolean algebra, as well as the perspective of the integrity of conditional combinations, QCA explores the influence of configuration on result variables [15]. It analyzes conditional complexity and causal asymmetry, breaking through the constraints of traditional empirical methods of single factor analysis. QCA combines the advantages of case-oriented analysis methods and variable-oriented analysis methods, focusing on the influence of the combined effects of conditional combinations and is more suitable for case study analysis of small and medium sample sizes. It also conforms to the focus of this research, the impact of multiple conditions on the level of intelligent manufacturing of enterprises.QCA includes qualitative comparison method based on clear set (csQCA), qualitative comparison method based on multivalue set (mvQCA), and qualitative comparison method based on fuzzy set (fsQCA). Among them, csQCA’s assignment of samples is limited to “0” and “1” to indicate complete nonsubscription and complete subordination to the set. Obviously, this dichotomy does not apply to the continuous variables of this study. Both mvQCA and fsQCA are extensions of the clear set analysis method. Both mvQCA and fsQCA are extensions of csQCA. By calibrating variables, fsQCA solves the problem of partial subordination of variables. Compared with other analysis methods, it has more advantages and can better explore the influence of factors on the level of enterprise intelligent manufacturing [16].


### 3.2. Measurement

#### 3.2.1. Outcome Variable

Intelligent Manufacturing Level. In this study, we extract keywords related to the concept of “intelligent manufacturing” from the company’s annual reports and use the intuitionistic fuzzy analytic hierarchy process to establish an evaluation index system to measure the degree of development of intelligent manufacturing of the sample companies. According to the division requirements of the fsQCA six-value fuzzy set, the intelligent manufacturing level of the sample enterprises is divided into six levels from low to high. The set membership degrees required by the fsQCA six-value fuzzy set are 0, 0.1, 0.4, 0.6, 0.9, and 1 from low to high, respectively. The development level of intelligent manufacturing and the degree of collective membership corresponding to each level are shown in Table 1.Table 1
Definition rules of intelligent manufacturing level.
The degree of development of intelligent manufacturingScoringSet membershipThere is no layout for smart manufacturing at all, and smart manufacturing is not included in corporate strategic planning00Only smart manufacturing is mentioned at the level of corporate strategic environment, and the actual level of participation is low10.1Intelligent manufacturing has been incorporated into the development plan, and intelligent construction or transformation has begun20.4The intelligent manufacturing construction or intelligent upgrade of part of the production line has been completed, and the digitalization has basically been realized30.6The construction of intelligent manufacturing or intelligent transformation is almost complete40.9Intelligent manufacturing projects have been completed and put into production and reached a high level, and the production process has basically realized intelligentization51Equations should be provided in a text format, rather than as an image. Microsoft Word’s equation tool is acceptable. Equations should be numbered consecutively, in round brackets, on the right-hand side of the page.


#### 3.2.2. Antecedents

Since the number of configurations in QCA will increase greatly with the increase in the number of factors, resulting in a lack of corresponding sample cases for the configuration, resulting in the configuration lacks corresponding samples, the study of the medium sample generally selects 3–8 factors. Therefore, this study selected three aspects of R&D investment, policy support, and corporate characteristics and a total of six factors.


### 3.3. Data Sources and Descriptive Statistics

#### 3.3.1. Samples and Data Sources

The China Electronics Standardization Institute (CESI) formulated two national standards, namely, Intelligent Manufacturing Capability Maturity Model and Intelligent Manufacturing Capability Maturity Evaluation Method and established the “Intelligent Manufacturing Evaluation and Evaluation Public Service Platform.” As of April 2021, a total of more than 7,000 Chinese companies have completed online self-assessments. Among them, companies registered in Wuxi, Jiangsu Province account for nearly 4,000, which is the highest in the country. Therefore, this research comprehensively considers QCA’s requirements for sample size and the status quo of enterprise intelligent manufacturing development. Based on the 2012 industry classification of the China Securities Regulatory Commission, we selected 45 listed manufacturing companies registered in Wuxi, Jiangsu Province and removed the sample with missing annual reports. The data of the antecedent variables come from China’s CSMAR database and Wind database and are cross-checked. Some missing data are compiled and supplemented by the author by consulting the annual report. Taking into account the lagging effect of some variables and avoiding the interference of possible abnormal data, the study lags the five front dependent variables of leverage (Lev), profitability (Roa), R&D intensity (Exp), human capital (Hc), and government subsidies (Sub) by two years and uses the average value of 2016–2018 as the sample data. This study uses the average number of employees in the company from 2016 to 2018 to measure the size of the company (Size). The definition and measurement of variables are shown in Table2.Table 2
Definition and measurement of variables.
Feature categoryNameInterpretationOutcome factorsIntelligent manufacturing level (Int)Intuitionistic fuzzy analytic hierarchy process scoringAntecedentsEnterprise characteristic factorsEnterprise size (Size)The number of employees at the end of the company’s period, based on the 2016–2018 averageLeverage (Lev)Total liabilities/total assets, based on the 2016–2018 averageProfitability (Roa)Net profit/total assets, based on the 2016–2018 averageR&D investment factorsR&D intensity (Exp)R&D investment/operating income, based on the 2016–2018 averageHuman capital (Hc)Total number of R&D personnel/employees, based on the 2016–2018 averagePolicy support factorsGovernment subsidy (Sub)2016–2018 average total government subsidies, logarithmSince only 3 cases in the sample belong to state-owned enterprises, this study does not focus on the impact of the nature of enterprise property rights on the development of intelligent manufacturing. After the previous steps and removing samples with missing data, a research sample of 45 companies was finally formed.


#### 3.3.2. Descriptive Statistics

In this study, Stata 15.1 was used for descriptive statistics and correlation analysis. Table3 reports the results of the statistical analysis, showing that there is no significant correlation between the enterprise’s intelligent manufacturing development level and the six antecedent variables, that is, none of the antecedent variables alone can affect the enterprise’s intelligent manufacturing development. Therefore, it is necessary to use QCA to explore the influence of conditional configuration on the enterprise’s intelligent manufacturing level.Table 3
Descriptive statistics results.
VariablesMeanStandard deviationIntSizeLevRoaExpHcSubInt0.490.331Size2630.104142.460.1201Lev0.370.200.1780.449∗∗∗1Roa0.050.04−0.007−0.041−0.479∗∗∗1Exp3.521.73−0.085−0.211−0.398∗∗∗0.2371Hc0.140.07−0.151−0.056−0.386∗∗∗0.1940.676∗∗∗1Sub15.722.680.1870.322∗∗0.244−0.026−0.021−0.250∗1Note.∗, ∗∗, ∗∗∗indicate significant correlation at the levels of 0.1, 0.05, and 0.01 (bilateral), respectively.


## 3.1. Methods

This study uses Qualitative Comparative Analysis (QCA) to explore the path of factors affecting the development of enterprise intelligent manufacturing. Based on set theory and Boolean algebra, as well as the perspective of the integrity of conditional combinations, QCA explores the influence of configuration on result variables [15]. It analyzes conditional complexity and causal asymmetry, breaking through the constraints of traditional empirical methods of single factor analysis. QCA combines the advantages of case-oriented analysis methods and variable-oriented analysis methods, focusing on the influence of the combined effects of conditional combinations and is more suitable for case study analysis of small and medium sample sizes. It also conforms to the focus of this research, the impact of multiple conditions on the level of intelligent manufacturing of enterprises.QCA includes qualitative comparison method based on clear set (csQCA), qualitative comparison method based on multivalue set (mvQCA), and qualitative comparison method based on fuzzy set (fsQCA). Among them, csQCA’s assignment of samples is limited to “0” and “1” to indicate complete nonsubscription and complete subordination to the set. Obviously, this dichotomy does not apply to the continuous variables of this study. Both mvQCA and fsQCA are extensions of the clear set analysis method. Both mvQCA and fsQCA are extensions of csQCA. By calibrating variables, fsQCA solves the problem of partial subordination of variables. Compared with other analysis methods, it has more advantages and can better explore the influence of factors on the level of enterprise intelligent manufacturing [16].


## 3.2. Measurement

### 3.2.1. Outcome Variable

Intelligent Manufacturing Level. In this study, we extract keywords related to the concept of “intelligent manufacturing” from the company’s annual reports and use the intuitionistic fuzzy analytic hierarchy process to establish an evaluation index system to measure the degree of development of intelligent manufacturing of the sample companies. According to the division requirements of the fsQCA six-value fuzzy set, the intelligent manufacturing level of the sample enterprises is divided into six levels from low to high. The set membership degrees required by the fsQCA six-value fuzzy set are 0, 0.1, 0.4, 0.6, 0.9, and 1 from low to high, respectively. The development level of intelligent manufacturing and the degree of collective membership corresponding to each level are shown in Table 1.Table 1
Definition rules of intelligent manufacturing level.
The degree of development of intelligent manufacturingScoringSet membershipThere is no layout for smart manufacturing at all, and smart manufacturing is not included in corporate strategic planning00Only smart manufacturing is mentioned at the level of corporate strategic environment, and the actual level of participation is low10.1Intelligent manufacturing has been incorporated into the development plan, and intelligent construction or transformation has begun20.4The intelligent manufacturing construction or intelligent upgrade of part of the production line has been completed, and the digitalization has basically been realized30.6The construction of intelligent manufacturing or intelligent transformation is almost complete40.9Intelligent manufacturing projects have been completed and put into production and reached a high level, and the production process has basically realized intelligentization51Equations should be provided in a text format, rather than as an image. Microsoft Word’s equation tool is acceptable. Equations should be numbered consecutively, in round brackets, on the right-hand side of the page.


### 3.2.2. Antecedents

Since the number of configurations in QCA will increase greatly with the increase in the number of factors, resulting in a lack of corresponding sample cases for the configuration, resulting in the configuration lacks corresponding samples, the study of the medium sample generally selects 3–8 factors. Therefore, this study selected three aspects of R&D investment, policy support, and corporate characteristics and a total of six factors.


## 3.2.1. Outcome Variable

Intelligent Manufacturing Level. In this study, we extract keywords related to the concept of “intelligent manufacturing” from the company’s annual reports and use the intuitionistic fuzzy analytic hierarchy process to establish an evaluation index system to measure the degree of development of intelligent manufacturing of the sample companies. According to the division requirements of the fsQCA six-value fuzzy set, the intelligent manufacturing level of the sample enterprises is divided into six levels from low to high. The set membership degrees required by the fsQCA six-value fuzzy set are 0, 0.1, 0.4, 0.6, 0.9, and 1 from low to high, respectively. The development level of intelligent manufacturing and the degree of collective membership corresponding to each level are shown in Table 1.Table 1
Definition rules of intelligent manufacturing level.
The degree of development of intelligent manufacturingScoringSet membershipThere is no layout for smart manufacturing at all, and smart manufacturing is not included in corporate strategic planning00Only smart manufacturing is mentioned at the level of corporate strategic environment, and the actual level of participation is low10.1Intelligent manufacturing has been incorporated into the development plan, and intelligent construction or transformation has begun20.4The intelligent manufacturing construction or intelligent upgrade of part of the production line has been completed, and the digitalization has basically been realized30.6The construction of intelligent manufacturing or intelligent transformation is almost complete40.9Intelligent manufacturing projects have been completed and put into production and reached a high level, and the production process has basically realized intelligentization51Equations should be provided in a text format, rather than as an image. Microsoft Word’s equation tool is acceptable. Equations should be numbered consecutively, in round brackets, on the right-hand side of the page.


## 3.2.2. Antecedents

Since the number of configurations in QCA will increase greatly with the increase in the number of factors, resulting in a lack of corresponding sample cases for the configuration, resulting in the configuration lacks corresponding samples, the study of the medium sample generally selects 3–8 factors. Therefore, this study selected three aspects of R&D investment, policy support, and corporate characteristics and a total of six factors.


## 3.3. Data Sources and Descriptive Statistics

### 3.3.1. Samples and Data Sources

The China Electronics Standardization Institute (CESI) formulated two national standards, namely, Intelligent Manufacturing Capability Maturity Model and Intelligent Manufacturing Capability Maturity Evaluation Method and established the “Intelligent Manufacturing Evaluation and Evaluation Public Service Platform.” As of April 2021, a total of more than 7,000 Chinese companies have completed online self-assessments. Among them, companies registered in Wuxi, Jiangsu Province account for nearly 4,000, which is the highest in the country. Therefore, this research comprehensively considers QCA’s requirements for sample size and the status quo of enterprise intelligent manufacturing development. Based on the 2012 industry classification of the China Securities Regulatory Commission, we selected 45 listed manufacturing companies registered in Wuxi, Jiangsu Province and removed the sample with missing annual reports. The data of the antecedent variables come from China’s CSMAR database and Wind database and are cross-checked. Some missing data are compiled and supplemented by the author by consulting the annual report. Taking into account the lagging effect of some variables and avoiding the interference of possible abnormal data, the study lags the five front dependent variables of leverage (Lev), profitability (Roa), R&D intensity (Exp), human capital (Hc), and government subsidies (Sub) by two years and uses the average value of 2016–2018 as the sample data. This study uses the average number of employees in the company from 2016 to 2018 to measure the size of the company (Size). The definition and measurement of variables are shown in Table2.Table 2
Definition and measurement of variables.
Feature categoryNameInterpretationOutcome factorsIntelligent manufacturing level (Int)Intuitionistic fuzzy analytic hierarchy process scoringAntecedentsEnterprise characteristic factorsEnterprise size (Size)The number of employees at the end of the company’s period, based on the 2016–2018 averageLeverage (Lev)Total liabilities/total assets, based on the 2016–2018 averageProfitability (Roa)Net profit/total assets, based on the 2016–2018 averageR&D investment factorsR&D intensity (Exp)R&D investment/operating income, based on the 2016–2018 averageHuman capital (Hc)Total number of R&D personnel/employees, based on the 2016–2018 averagePolicy support factorsGovernment subsidy (Sub)2016–2018 average total government subsidies, logarithmSince only 3 cases in the sample belong to state-owned enterprises, this study does not focus on the impact of the nature of enterprise property rights on the development of intelligent manufacturing. After the previous steps and removing samples with missing data, a research sample of 45 companies was finally formed.


### 3.3.2. Descriptive Statistics

In this study, Stata 15.1 was used for descriptive statistics and correlation analysis. Table3 reports the results of the statistical analysis, showing that there is no significant correlation between the enterprise’s intelligent manufacturing development level and the six antecedent variables, that is, none of the antecedent variables alone can affect the enterprise’s intelligent manufacturing development. Therefore, it is necessary to use QCA to explore the influence of conditional configuration on the enterprise’s intelligent manufacturing level.Table 3
Descriptive statistics results.
VariablesMeanStandard deviationIntSizeLevRoaExpHcSubInt0.490.331Size2630.104142.460.1201Lev0.370.200.1780.449∗∗∗1Roa0.050.04−0.007−0.041−0.479∗∗∗1Exp3.521.73−0.085−0.211−0.398∗∗∗0.2371Hc0.140.07−0.151−0.056−0.386∗∗∗0.1940.676∗∗∗1Sub15.722.680.1870.322∗∗0.244−0.026−0.021−0.250∗1Note.∗, ∗∗, ∗∗∗indicate significant correlation at the levels of 0.1, 0.05, and 0.01 (bilateral), respectively.


## 3.3.1. Samples and Data Sources

The China Electronics Standardization Institute (CESI) formulated two national standards, namely, Intelligent Manufacturing Capability Maturity Model and Intelligent Manufacturing Capability Maturity Evaluation Method and established the “Intelligent Manufacturing Evaluation and Evaluation Public Service Platform.” As of April 2021, a total of more than 7,000 Chinese companies have completed online self-assessments. Among them, companies registered in Wuxi, Jiangsu Province account for nearly 4,000, which is the highest in the country. Therefore, this research comprehensively considers QCA’s requirements for sample size and the status quo of enterprise intelligent manufacturing development. Based on the 2012 industry classification of the China Securities Regulatory Commission, we selected 45 listed manufacturing companies registered in Wuxi, Jiangsu Province and removed the sample with missing annual reports. The data of the antecedent variables come from China’s CSMAR database and Wind database and are cross-checked. Some missing data are compiled and supplemented by the author by consulting the annual report. Taking into account the lagging effect of some variables and avoiding the interference of possible abnormal data, the study lags the five front dependent variables of leverage (Lev), profitability (Roa), R&D intensity (Exp), human capital (Hc), and government subsidies (Sub) by two years and uses the average value of 2016–2018 as the sample data. This study uses the average number of employees in the company from 2016 to 2018 to measure the size of the company (Size). The definition and measurement of variables are shown in Table2.Table 2
Definition and measurement of variables.
Feature categoryNameInterpretationOutcome factorsIntelligent manufacturing level (Int)Intuitionistic fuzzy analytic hierarchy process scoringAntecedentsEnterprise characteristic factorsEnterprise size (Size)The number of employees at the end of the company’s period, based on the 2016–2018 averageLeverage (Lev)Total liabilities/total assets, based on the 2016–2018 averageProfitability (Roa)Net profit/total assets, based on the 2016–2018 averageR&D investment factorsR&D intensity (Exp)R&D investment/operating income, based on the 2016–2018 averageHuman capital (Hc)Total number of R&D personnel/employees, based on the 2016–2018 averagePolicy support factorsGovernment subsidy (Sub)2016–2018 average total government subsidies, logarithmSince only 3 cases in the sample belong to state-owned enterprises, this study does not focus on the impact of the nature of enterprise property rights on the development of intelligent manufacturing. After the previous steps and removing samples with missing data, a research sample of 45 companies was finally formed.


## 3.3.2. Descriptive Statistics

In this study, Stata 15.1 was used for descriptive statistics and correlation analysis. Table3 reports the results of the statistical analysis, showing that there is no significant correlation between the enterprise’s intelligent manufacturing development level and the six antecedent variables, that is, none of the antecedent variables alone can affect the enterprise’s intelligent manufacturing development. Therefore, it is necessary to use QCA to explore the influence of conditional configuration on the enterprise’s intelligent manufacturing level.Table 3
Descriptive statistics results.
VariablesMeanStandard deviationIntSizeLevRoaExpHcSubInt0.490.331Size2630.104142.460.1201Lev0.370.200.1780.449∗∗∗1Roa0.050.04−0.007−0.041−0.479∗∗∗1Exp3.521.73−0.085−0.211−0.398∗∗∗0.2371Hc0.140.07−0.151−0.056−0.386∗∗∗0.1940.676∗∗∗1Sub15.722.680.1870.322∗∗0.244−0.026−0.021−0.250∗1Note.∗, ∗∗, ∗∗∗indicate significant correlation at the levels of 0.1, 0.05, and 0.01 (bilateral), respectively.


## 4. Analysis and Results

### 4.1. Data Calibration

Before performing QCA, the variables must be calibrated first, which is the process of converting variables into sets, setting appropriate external standards, and assigning membership scores to samples. The calibrated index is more explanatory to the result than the uncalibrated index.In this study, the direct calibration method was used to convert sample data into membership scores [17]. When fsQCA is calibrated, three anchor points need to be preset, namely, complete membership, maximum fuzzy value, and complete nonmembership. The membership degree after calibration is between 0 and 1. The value represents the strength of the membership to the set. This study refers to the practice of Fiss [18] and Coduras et al. [19] and anchors the five variables of leverage (Lev), profitability (Roa), R&D intensity (Exp), human capital (Hc), and government subsidies (Sub) set to the upper quartile, average, and lower quartile of the sample. In the measures for the classification of large, medium, small, and microenterprises in statistics (2017) issued by the National Bureau of Statistics of China, the standard for the number of employees in an enterprise is set as the anchor point corresponding to the enterprise size (Size). The anchor points of the variables and the calibration results are shown in Table 4.Table 4
Calibration anchor points of variables.
Feature categoryNameCalibration anchorComplete nonmembershipMaximum fuzzy valueComplete membershipOutcome factorsIntelligent manufacturing level (Int)00.51AntecedentsEnterprise characteristic factorsEnterprise size (Size)203001000Leverage (Lev)0.2250.3670.522Profitability (Roa)0.0320.0490.063R&D investment factorsR&D intensity (Exp)3.0173.5164.320Human capital (Hc)0.1080.1440.189Policy support factorsGovernment subsidy (Sub)15.2915.7216.59


### 4.2. Necessary Condition Inspection

This study first conducted a test of necessary conditions before conducting QCA, so as to verify whether the existence of antecedent variables is a necessary condition for the result to occur. The necessary condition test measures the degree to which the outcome variable belongs to the subset of antecedent variables through the level of consistency. In fsQCA, if there is always a certain antecedent condition in the configuration that causes a certain result, then this condition is the necessary condition to cause the result, and it will definitely be included in the conditional configuration of the result. It is generally believed that if the consistency level is higher than 0.9, the condition can be considered as a necessary condition for the result. Table5 reports the necessary condition test results.Table 5
Test results of necessary conditions.
Antecedent variablesHigh-level intelligent manufacturingLow-level smart manufacturingLevel of consistencyCoverageLevel of consistencyCoverageSize0.95090.54420.90700.5331∼Size0.18420.65860.22460.8245Lev0.60720.64780.44470.4873∼Lev0.51940.47660.67850.6395Roa0.50720.57160.48070.5563∼Roa0.60630.53200.62980.5676Exp0.58740.56720.57020.5655∼Exp0.55000.55470.56360.5838Hc0.49140.53300.53420.5950∼Hc0.62660.56710.58070.5397Sub0.69010.61550.54340.4978∼Sub0.43690.48230.58030.6579According to Table5, the test results of the necessary conditions for high-level intelligent manufacturing show that the consistency level of enterprise size (Size) is higher than the critical value of 0.9. Therefore, enterprise size (Size) is a necessary condition for the results, and the other variables are not necessary conditions, and further configuration analysis is required.


### 4.3. Sufficiency Analysis of Conditional Configuration

This study uses the truth table algorithm in fsQCA3.0 software to analyze the configuration adequacy. From the perspective of set theory, the purpose of configuration analysis is to explore whether the set represented by the conditional configuration can constitute a subset of the result set. Adequacy analysis needs to set a consistency threshold and a frequency threshold, so as to screen out the configuration of antecedent variables that can fully explain the results. In previous studies, the consistency threshold is usually set to 0.8, and the frequency threshold is set to 1 [20]. On this basis, the consistency threshold adopted in this study is 0.8, and the frequency threshold is 1.According to whether the result contains a logical remainder, fsQCA will output three different solutions, namely, complex solution, intermediate solution, and parsimonious solution. Researchers generally only need to pay attention to intermediate solution and parsimonious solution. The conditions that appear in both the reduced solution and the intermediate solution are the core conditions, and the conditions that only appear in the intermediate solution are the marginal conditions. This research refers to the results of Fiss and Ragin [21], using ● to indicate the existence of antecedents, ⊗ to indicate the absence of antecedents, and blank to indicate a fuzzy state. A larger circle indicates that the antecedent variable is a core condition, and a smaller circle indicates that the variable is an edge condition. Table 6 reports the results of the adequacy analysis of the conditional configuration.Table 6
Adequacy analysis of conditional configuration for high-level intelligent manufacturing.
Antecedent conditionsAntecedent configuration12a2b3Size●●●⊗Lev●●⊗Roa●⊗⊗Exp⊗●●⊗Hc⊗⊗•●Sub•⊗Level of consistency0.84060.85260.89730.9123Original coverage0.23510.20590.14960.0703Unique coverage0.17610.13600.09910.0423Overall consistency0.8595Overall coverage0.5428It can be seen from Table6 that four conditional configurations have been found, and other combinations are rejected in this study. The consistency level of each configuration and the overall solution is higher than the threshold (0.8), indicating that the previous four configurations meet the requirements. We can see that the overall coverage is 0.5428, which explains 54.28% of the enterprise’s intelligent manufacturing development level, and the explanation effect is very satisfactory.The four configurations in Table6 represent the combination of sufficient conditions for manufacturing enterprises to achieve intelligent development. From the perspective of each condition configuration, configuration 1 (Size·Roa∼Exp∼Hc) has the highest coverage, but the consistency level is relatively low. Among them, the larger enterprise size and higher profitability played an important role, but the absence of R&D investment also played an important role. In configuration 2a (Size·Lev∼Roa·Exp∼Hc), enterprise scale and leverage ratio and R&D intensity also play a core role, while the absence of profitability and human capital play a supporting role. In configuration 2b (Size·Lev·Exp·Hc·Sub), enterprise size, leverage ratio, and R&D intensity are all taken as core conditions, while human capital and government subsidies are taken as marginal conditions. In configuration 3 (∼Size∼Lev∼Roa∼Exp·Hc∼Sub), except for the absence of enterprise scale and human capital as the core condition, the absence of other conditions are the marginal conditions of this configuration.Finally, we try to analyze the conditional configuration that causes low-level intelligent manufacturing of enterprises through the perspective of causal asymmetry. However, the empirical results show that there is no conditional configuration that can reasonably explain the low-level intelligent manufacturing of enterprises. This means that companies can follow the previous four paths to explore how to achieve high-level intelligent manufacturing. However, it is not yet possible to draw clear and robust conclusions about the reasons for the low-level intelligent manufacturing.


### 4.4. Analysis of the Development Path

This research has accurately identified four paths for manufacturing companies to achieve high-level intelligent manufacturing through fsQCA, which shows that there are multiple concurrencies of conditional combinations for intelligent manufacturing. On the basis of the previous analysis, the study summarizes the results theoretically. According to the configuration of different conditions, three types of intelligent manufacturing modes are summarized, which are also in line with the development trend of QCA research [22].


#### 4.4.1. High-Performance & Large Size

The first type of intelligent manufacturing development model proposed in this research is “high-performance & large size.” This type of enterprise has a low proportion of R&D investment, but it has a higher level of intelligent manufacturing. High profitability and large enterprise scale are the necessary antecedents for this model. Typical representative enterprises are Heilan Home (600398.SH) and Hongdou Industrial (600400.SH). Heilan Home and Hongdou Industrial are both large-scale textile and garment enterprises in China. Among them, Heilan House is equipped with advanced logistics warehousing equipment and SAP information system, which realizes the intelligentization of warehousing and logistics. In addition, an informatization transformation project has been fully initiated to realize a high degree of automation of the warehousing system. Hongdou Industrial promotes the “smart Hongdou” strategy, builds automated integrated systems, transforms smart factories, and forms an intelligent manufacturing system.This development model does not mean that R&D investment is not carried out, but because of the large scale and good efficiency of the enterprise under this model, R&D investment accounts for a small proportion of total operating income. At the same time, this development model may also be related to the industry. According to the samples, this model may be more suitable for leading enterprises in the apparel and home textile industry but less applicable to industries such as equipment manufacturing. Therefore, this study believes that enterprises with large scale and good economic efficiency can complete the intelligent transformation of enterprises through a lower proportion of R&D investment.


#### 4.4.2. High-Leverage & High-Investment & Large Size

The second type of intelligent manufacturing development model proposed in this research is the “ High-leverage & high-investment & large size.” This model contains two submodels. The first type of submodel requires companies to invest a lot of R&D capital, and at the same time, have a low return on assets, and the proportion of R&D personnel is not high. The second type of submodel is characterized by high-level R&D element input including capital input and personnel input under the support of the government. Typical representative companies of the first type of submode are Hodgen Technology (300279.SZ) and Miracle Automation Engineering (002009.SZ). Hodgen Technology vigorously develops the intelligent Internet of things business and establishes an intelligent manufacturing business operation platform and has become an important supplier of intelligent equipment. Miracle Automation Engineering developed the intelligent equipment business and achieved a steady growth in the revenue of intelligent equipment. It was rated as “Informatization and Industrialization Integration Transformation and Upgrading Demonstration Enterprise” by the Jiangsu Provincial Government of China. The characteristic of this type of enterprise is that it invests a large amount of R&D capital to upgrade the production line intelligently. However, due to the long investment return period, the short-term profitability of the enterprise is affected to a certain extent. In the long run, the intelligent upgrade of enterprises will reduce costs and increase efficiency, thereby increasing the inherent value of the enterprise. Therefore, this study believes that although large companies maintain high leverage and high R&D capital investment and although profitability may decrease, they can achieve intelligent transformation of enterprises through lower R&D human capital investment.The second type of submodel is represented by Lead Intelligent Equipment (300450.SZ) and Shuangliang Eco-Energy (600481.SH). Different from the first type of submodel, enterprises under this model will receive more policy support and thus have more sufficient R&D investment capacity and higher profitability. Based on this, this study believes that under the condition that large enterprises maintain high leverage and high R&D capital investment, through the mediating effect of policy support, high R&D human capital investment can be maintained at the same time, thereby completing the intelligent upgrade of the enterprise.


#### 4.4.3. High Human Capital & Small Size

The third intelligent manufacturing development model proposed in this research is “High human capital & small size.” Different from the first two types of intelligent manufacturing development models, companies in this model rely on the input of a large number of R&D personnel, and in the absence of all the other elements, they can develop another way to promote the intelligent process of enterprise production. A typical representative company is Zhongsheng Gaoke (002778.SZ). Its average number of employees in the past three years is only 159.7, which is the least among the 45 sample companies. At present, it has realized the automation of the whole production process, and the MES system has reached the international advanced level, which can meet the requirements of production management and personalized manufacturing mode. ABB robots, DDU, and other equipment have also been widely used in the production process, and the whole process of automatic mass production integrated control has been constructed, as well as an internationally leading intelligent workshop. Therefore, this study believes that small companies can use high R&D human capital investment to achieve intelligent production in the absence of other conditions.


## 4.1. Data Calibration

Before performing QCA, the variables must be calibrated first, which is the process of converting variables into sets, setting appropriate external standards, and assigning membership scores to samples. The calibrated index is more explanatory to the result than the uncalibrated index.In this study, the direct calibration method was used to convert sample data into membership scores [17]. When fsQCA is calibrated, three anchor points need to be preset, namely, complete membership, maximum fuzzy value, and complete nonmembership. The membership degree after calibration is between 0 and 1. The value represents the strength of the membership to the set. This study refers to the practice of Fiss [18] and Coduras et al. [19] and anchors the five variables of leverage (Lev), profitability (Roa), R&D intensity (Exp), human capital (Hc), and government subsidies (Sub) set to the upper quartile, average, and lower quartile of the sample. In the measures for the classification of large, medium, small, and microenterprises in statistics (2017) issued by the National Bureau of Statistics of China, the standard for the number of employees in an enterprise is set as the anchor point corresponding to the enterprise size (Size). The anchor points of the variables and the calibration results are shown in Table 4.Table 4
Calibration anchor points of variables.
Feature categoryNameCalibration anchorComplete nonmembershipMaximum fuzzy valueComplete membershipOutcome factorsIntelligent manufacturing level (Int)00.51AntecedentsEnterprise characteristic factorsEnterprise size (Size)203001000Leverage (Lev)0.2250.3670.522Profitability (Roa)0.0320.0490.063R&D investment factorsR&D intensity (Exp)3.0173.5164.320Human capital (Hc)0.1080.1440.189Policy support factorsGovernment subsidy (Sub)15.2915.7216.59


## 4.2. Necessary Condition Inspection

This study first conducted a test of necessary conditions before conducting QCA, so as to verify whether the existence of antecedent variables is a necessary condition for the result to occur. The necessary condition test measures the degree to which the outcome variable belongs to the subset of antecedent variables through the level of consistency. In fsQCA, if there is always a certain antecedent condition in the configuration that causes a certain result, then this condition is the necessary condition to cause the result, and it will definitely be included in the conditional configuration of the result. It is generally believed that if the consistency level is higher than 0.9, the condition can be considered as a necessary condition for the result. Table5 reports the necessary condition test results.Table 5
Test results of necessary conditions.
Antecedent variablesHigh-level intelligent manufacturingLow-level smart manufacturingLevel of consistencyCoverageLevel of consistencyCoverageSize0.95090.54420.90700.5331∼Size0.18420.65860.22460.8245Lev0.60720.64780.44470.4873∼Lev0.51940.47660.67850.6395Roa0.50720.57160.48070.5563∼Roa0.60630.53200.62980.5676Exp0.58740.56720.57020.5655∼Exp0.55000.55470.56360.5838Hc0.49140.53300.53420.5950∼Hc0.62660.56710.58070.5397Sub0.69010.61550.54340.4978∼Sub0.43690.48230.58030.6579According to Table5, the test results of the necessary conditions for high-level intelligent manufacturing show that the consistency level of enterprise size (Size) is higher than the critical value of 0.9. Therefore, enterprise size (Size) is a necessary condition for the results, and the other variables are not necessary conditions, and further configuration analysis is required.


## 4.3. Sufficiency Analysis of Conditional Configuration

This study uses the truth table algorithm in fsQCA3.0 software to analyze the configuration adequacy. From the perspective of set theory, the purpose of configuration analysis is to explore whether the set represented by the conditional configuration can constitute a subset of the result set. Adequacy analysis needs to set a consistency threshold and a frequency threshold, so as to screen out the configuration of antecedent variables that can fully explain the results. In previous studies, the consistency threshold is usually set to 0.8, and the frequency threshold is set to 1 [20]. On this basis, the consistency threshold adopted in this study is 0.8, and the frequency threshold is 1.According to whether the result contains a logical remainder, fsQCA will output three different solutions, namely, complex solution, intermediate solution, and parsimonious solution. Researchers generally only need to pay attention to intermediate solution and parsimonious solution. The conditions that appear in both the reduced solution and the intermediate solution are the core conditions, and the conditions that only appear in the intermediate solution are the marginal conditions. This research refers to the results of Fiss and Ragin [21], using ● to indicate the existence of antecedents, ⊗ to indicate the absence of antecedents, and blank to indicate a fuzzy state. A larger circle indicates that the antecedent variable is a core condition, and a smaller circle indicates that the variable is an edge condition. Table 6 reports the results of the adequacy analysis of the conditional configuration.Table 6
Adequacy analysis of conditional configuration for high-level intelligent manufacturing.
Antecedent conditionsAntecedent configuration12a2b3Size●●●⊗Lev●●⊗Roa●⊗⊗Exp⊗●●⊗Hc⊗⊗•●Sub•⊗Level of consistency0.84060.85260.89730.9123Original coverage0.23510.20590.14960.0703Unique coverage0.17610.13600.09910.0423Overall consistency0.8595Overall coverage0.5428It can be seen from Table6 that four conditional configurations have been found, and other combinations are rejected in this study. The consistency level of each configuration and the overall solution is higher than the threshold (0.8), indicating that the previous four configurations meet the requirements. We can see that the overall coverage is 0.5428, which explains 54.28% of the enterprise’s intelligent manufacturing development level, and the explanation effect is very satisfactory.The four configurations in Table6 represent the combination of sufficient conditions for manufacturing enterprises to achieve intelligent development. From the perspective of each condition configuration, configuration 1 (Size·Roa∼Exp∼Hc) has the highest coverage, but the consistency level is relatively low. Among them, the larger enterprise size and higher profitability played an important role, but the absence of R&D investment also played an important role. In configuration 2a (Size·Lev∼Roa·Exp∼Hc), enterprise scale and leverage ratio and R&D intensity also play a core role, while the absence of profitability and human capital play a supporting role. In configuration 2b (Size·Lev·Exp·Hc·Sub), enterprise size, leverage ratio, and R&D intensity are all taken as core conditions, while human capital and government subsidies are taken as marginal conditions. In configuration 3 (∼Size∼Lev∼Roa∼Exp·Hc∼Sub), except for the absence of enterprise scale and human capital as the core condition, the absence of other conditions are the marginal conditions of this configuration.Finally, we try to analyze the conditional configuration that causes low-level intelligent manufacturing of enterprises through the perspective of causal asymmetry. However, the empirical results show that there is no conditional configuration that can reasonably explain the low-level intelligent manufacturing of enterprises. This means that companies can follow the previous four paths to explore how to achieve high-level intelligent manufacturing. However, it is not yet possible to draw clear and robust conclusions about the reasons for the low-level intelligent manufacturing.


## 4.4. Analysis of the Development Path

This research has accurately identified four paths for manufacturing companies to achieve high-level intelligent manufacturing through fsQCA, which shows that there are multiple concurrencies of conditional combinations for intelligent manufacturing. On the basis of the previous analysis, the study summarizes the results theoretically. According to the configuration of different conditions, three types of intelligent manufacturing modes are summarized, which are also in line with the development trend of QCA research [22].


### 4.4.1. High-Performance & Large Size

The first type of intelligent manufacturing development model proposed in this research is “high-performance & large size.” This type of enterprise has a low proportion of R&D investment, but it has a higher level of intelligent manufacturing. High profitability and large enterprise scale are the necessary antecedents for this model. Typical representative enterprises are Heilan Home (600398.SH) and Hongdou Industrial (600400.SH). Heilan Home and Hongdou Industrial are both large-scale textile and garment enterprises in China. Among them, Heilan House is equipped with advanced logistics warehousing equipment and SAP information system, which realizes the intelligentization of warehousing and logistics. In addition, an informatization transformation project has been fully initiated to realize a high degree of automation of the warehousing system. Hongdou Industrial promotes the “smart Hongdou” strategy, builds automated integrated systems, transforms smart factories, and forms an intelligent manufacturing system.This development model does not mean that R&D investment is not carried out, but because of the large scale and good efficiency of the enterprise under this model, R&D investment accounts for a small proportion of total operating income. At the same time, this development model may also be related to the industry. According to the samples, this model may be more suitable for leading enterprises in the apparel and home textile industry but less applicable to industries such as equipment manufacturing. Therefore, this study believes that enterprises with large scale and good economic efficiency can complete the intelligent transformation of enterprises through a lower proportion of R&D investment.


### 4.4.2. High-Leverage & High-Investment & Large Size

The second type of intelligent manufacturing development model proposed in this research is the “ High-leverage & high-investment & large size.” This model contains two submodels. The first type of submodel requires companies to invest a lot of R&D capital, and at the same time, have a low return on assets, and the proportion of R&D personnel is not high. The second type of submodel is characterized by high-level R&D element input including capital input and personnel input under the support of the government. Typical representative companies of the first type of submode are Hodgen Technology (300279.SZ) and Miracle Automation Engineering (002009.SZ). Hodgen Technology vigorously develops the intelligent Internet of things business and establishes an intelligent manufacturing business operation platform and has become an important supplier of intelligent equipment. Miracle Automation Engineering developed the intelligent equipment business and achieved a steady growth in the revenue of intelligent equipment. It was rated as “Informatization and Industrialization Integration Transformation and Upgrading Demonstration Enterprise” by the Jiangsu Provincial Government of China. The characteristic of this type of enterprise is that it invests a large amount of R&D capital to upgrade the production line intelligently. However, due to the long investment return period, the short-term profitability of the enterprise is affected to a certain extent. In the long run, the intelligent upgrade of enterprises will reduce costs and increase efficiency, thereby increasing the inherent value of the enterprise. Therefore, this study believes that although large companies maintain high leverage and high R&D capital investment and although profitability may decrease, they can achieve intelligent transformation of enterprises through lower R&D human capital investment.The second type of submodel is represented by Lead Intelligent Equipment (300450.SZ) and Shuangliang Eco-Energy (600481.SH). Different from the first type of submodel, enterprises under this model will receive more policy support and thus have more sufficient R&D investment capacity and higher profitability. Based on this, this study believes that under the condition that large enterprises maintain high leverage and high R&D capital investment, through the mediating effect of policy support, high R&D human capital investment can be maintained at the same time, thereby completing the intelligent upgrade of the enterprise.


### 4.4.3. High Human Capital & Small Size

The third intelligent manufacturing development model proposed in this research is “High human capital & small size.” Different from the first two types of intelligent manufacturing development models, companies in this model rely on the input of a large number of R&D personnel, and in the absence of all the other elements, they can develop another way to promote the intelligent process of enterprise production. A typical representative company is Zhongsheng Gaoke (002778.SZ). Its average number of employees in the past three years is only 159.7, which is the least among the 45 sample companies. At present, it has realized the automation of the whole production process, and the MES system has reached the international advanced level, which can meet the requirements of production management and personalized manufacturing mode. ABB robots, DDU, and other equipment have also been widely used in the production process, and the whole process of automatic mass production integrated control has been constructed, as well as an internationally leading intelligent workshop. Therefore, this study believes that small companies can use high R&D human capital investment to achieve intelligent production in the absence of other conditions.


## 4.4.1. High-Performance & Large Size

The first type of intelligent manufacturing development model proposed in this research is “high-performance & large size.” This type of enterprise has a low proportion of R&D investment, but it has a higher level of intelligent manufacturing. High profitability and large enterprise scale are the necessary antecedents for this model. Typical representative enterprises are Heilan Home (600398.SH) and Hongdou Industrial (600400.SH). Heilan Home and Hongdou Industrial are both large-scale textile and garment enterprises in China. Among them, Heilan House is equipped with advanced logistics warehousing equipment and SAP information system, which realizes the intelligentization of warehousing and logistics. In addition, an informatization transformation project has been fully initiated to realize a high degree of automation of the warehousing system. Hongdou Industrial promotes the “smart Hongdou” strategy, builds automated integrated systems, transforms smart factories, and forms an intelligent manufacturing system.This development model does not mean that R&D investment is not carried out, but because of the large scale and good efficiency of the enterprise under this model, R&D investment accounts for a small proportion of total operating income. At the same time, this development model may also be related to the industry. According to the samples, this model may be more suitable for leading enterprises in the apparel and home textile industry but less applicable to industries such as equipment manufacturing. Therefore, this study believes that enterprises with large scale and good economic efficiency can complete the intelligent transformation of enterprises through a lower proportion of R&D investment.


## 4.4.2. High-Leverage & High-Investment & Large Size

The second type of intelligent manufacturing development model proposed in this research is the “ High-leverage & high-investment & large size.” This model contains two submodels. The first type of submodel requires companies to invest a lot of R&D capital, and at the same time, have a low return on assets, and the proportion of R&D personnel is not high. The second type of submodel is characterized by high-level R&D element input including capital input and personnel input under the support of the government. Typical representative companies of the first type of submode are Hodgen Technology (300279.SZ) and Miracle Automation Engineering (002009.SZ). Hodgen Technology vigorously develops the intelligent Internet of things business and establishes an intelligent manufacturing business operation platform and has become an important supplier of intelligent equipment. Miracle Automation Engineering developed the intelligent equipment business and achieved a steady growth in the revenue of intelligent equipment. It was rated as “Informatization and Industrialization Integration Transformation and Upgrading Demonstration Enterprise” by the Jiangsu Provincial Government of China. The characteristic of this type of enterprise is that it invests a large amount of R&D capital to upgrade the production line intelligently. However, due to the long investment return period, the short-term profitability of the enterprise is affected to a certain extent. In the long run, the intelligent upgrade of enterprises will reduce costs and increase efficiency, thereby increasing the inherent value of the enterprise. Therefore, this study believes that although large companies maintain high leverage and high R&D capital investment and although profitability may decrease, they can achieve intelligent transformation of enterprises through lower R&D human capital investment.The second type of submodel is represented by Lead Intelligent Equipment (300450.SZ) and Shuangliang Eco-Energy (600481.SH). Different from the first type of submodel, enterprises under this model will receive more policy support and thus have more sufficient R&D investment capacity and higher profitability. Based on this, this study believes that under the condition that large enterprises maintain high leverage and high R&D capital investment, through the mediating effect of policy support, high R&D human capital investment can be maintained at the same time, thereby completing the intelligent upgrade of the enterprise.


## 4.4.3. High Human Capital & Small Size

The third intelligent manufacturing development model proposed in this research is “High human capital & small size.” Different from the first two types of intelligent manufacturing development models, companies in this model rely on the input of a large number of R&D personnel, and in the absence of all the other elements, they can develop another way to promote the intelligent process of enterprise production. A typical representative company is Zhongsheng Gaoke (002778.SZ). Its average number of employees in the past three years is only 159.7, which is the least among the 45 sample companies. At present, it has realized the automation of the whole production process, and the MES system has reached the international advanced level, which can meet the requirements of production management and personalized manufacturing mode. ABB robots, DDU, and other equipment have also been widely used in the production process, and the whole process of automatic mass production integrated control has been constructed, as well as an internationally leading intelligent workshop. Therefore, this study believes that small companies can use high R&D human capital investment to achieve intelligent production in the absence of other conditions.


## 5. Conclusion and Discussion

### 5.1. Conclusion

This research starts from the configuration perspective of R&D investment, policy support, and enterprise characteristics linkage matching and innovatively applies the qualitative comparative analysis based on fuzzy sets to explore the complex causal mechanism that affects the high level of intelligent manufacturing of enterprises. In addition, on the basis of the analysis results, it summarizes the different paths for enterprises to achieve high-level intelligent manufacturing. This study found that enterprise size is a necessary condition that affects the degree of high-level intelligent manufacturing, and the driving mechanism of high-level intelligent manufacturing is divided into four paths. Specifically, first of all, some companies with larger scale and better economic efficiency can achieve a higher level of intelligent manufacturing through a relatively low proportion of R&D investment. Second, large enterprises with high leverage and high R&D capital investment can sacrifice short-term profitability and complete intelligent transformation through lower R&D human capital investment or with the help of policy support, maintain high R&D human capital investment, thereby accelerating the completion of the enterprise intelligence upgrade. Third, small companies can use high research and development human capital investment, in the absence of other conditions and find another way to achieve the goal of intelligence. In addition, there is no clear conclusion for the reason why enterprises produce low-level intelligent manufacturing.


### 5.2. Theoretical Implication

The theoretical implication of this research mainly has the following two aspects: First of all, the research results of this article prove that there are many ways to enable enterprises to achieve high-level intelligent manufacturing, among which the size of the enterprise is an important prerequisite. Managers should explore suitable paths to achieve high-level intelligent manufacturing based on the company’s own conditions. Specifically, when companies invest large amounts of R&D capital to achieve high-level smart manufacturing through debt, they will reduce profitability in the short term but will reduce costs and increase efficiency in the long run and increase long-term profitability. However, a company with a large enough scale and strong enough profitability can achieve intelligent upgrades without having a significant impact on the company’s benefits. For smaller companies, intelligent upgrades can be achieved through high R&D human capital investment. The previous findings suggest that companies should expand their scale, improve profitability, and provide sufficient funding and personnel support for innovation. It is also necessary to increase the investment of R&D personnel and increase the proportion of R&D personnel to support the intelligent transformation of enterprises. The government should also support regional leading enterprises and provide policy support, financial appropriations, and tax refunds for enterprises capable of intelligent upgrades, so as to reduce the operating pressure on enterprises. It is also possible to use talent introduction policies to encourage manufacturing companies to invest more R&D funds and personnel for intelligent transformation.


## 5.1. Conclusion

This research starts from the configuration perspective of R&D investment, policy support, and enterprise characteristics linkage matching and innovatively applies the qualitative comparative analysis based on fuzzy sets to explore the complex causal mechanism that affects the high level of intelligent manufacturing of enterprises. In addition, on the basis of the analysis results, it summarizes the different paths for enterprises to achieve high-level intelligent manufacturing. This study found that enterprise size is a necessary condition that affects the degree of high-level intelligent manufacturing, and the driving mechanism of high-level intelligent manufacturing is divided into four paths. Specifically, first of all, some companies with larger scale and better economic efficiency can achieve a higher level of intelligent manufacturing through a relatively low proportion of R&D investment. Second, large enterprises with high leverage and high R&D capital investment can sacrifice short-term profitability and complete intelligent transformation through lower R&D human capital investment or with the help of policy support, maintain high R&D human capital investment, thereby accelerating the completion of the enterprise intelligence upgrade. Third, small companies can use high research and development human capital investment, in the absence of other conditions and find another way to achieve the goal of intelligence. In addition, there is no clear conclusion for the reason why enterprises produce low-level intelligent manufacturing.


## 5.2. Theoretical Implication

The theoretical implication of this research mainly has the following two aspects: First of all, the research results of this article prove that there are many ways to enable enterprises to achieve high-level intelligent manufacturing, among which the size of the enterprise is an important prerequisite. Managers should explore suitable paths to achieve high-level intelligent manufacturing based on the company’s own conditions. Specifically, when companies invest large amounts of R&D capital to achieve high-level smart manufacturing through debt, they will reduce profitability in the short term but will reduce costs and increase efficiency in the long run and increase long-term profitability. However, a company with a large enough scale and strong enough profitability can achieve intelligent upgrades without having a significant impact on the company’s benefits. For smaller companies, intelligent upgrades can be achieved through high R&D human capital investment. The previous findings suggest that companies should expand their scale, improve profitability, and provide sufficient funding and personnel support for innovation. It is also necessary to increase the investment of R&D personnel and increase the proportion of R&D personnel to support the intelligent transformation of enterprises. The government should also support regional leading enterprises and provide policy support, financial appropriations, and tax refunds for enterprises capable of intelligent upgrades, so as to reduce the operating pressure on enterprises. It is also possible to use talent introduction policies to encourage manufacturing companies to invest more R&D funds and personnel for intelligent transformation.



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*Source: 4265155-2023-02-21.xml*