Digital Transformation Drives Sustainable Innovation Capability Improvement in Manufacturing Enterprises: Based on FsQCA and NCA Approaches

Abstract

In recent years, digital technologies represented by the Internet, cloud computing, big data, the Internet of Things, and artificial intelligence have developed rapidly and become a strong driving force for economic development. They have effectively driven manufacturing enterprises to innovate their production methods and management models and enhance their innovation capabilities. The improvement of the sustainable innovation capability of manufacturing enterprises is a complex system, which requires various types of collaborative coupling of digital transformation. Therefore, this paper constructs a comprehensive framework of sustainable innovation capability based on Complex System View, using 20 manufacturing enterprises in Dalian, China, as a sample to analyze the driving effects of digital transformation on the improvement of the sustainable innovation capability of manufacturing enterprises by using a mixture of NCA and QCA methods. The findings are: (1) A single type of digital transformation does not constitute a necessary condition for high sustainable innovation capacity, and the digital transformation of service, model and organization play a more universal role in generating high sustainable innovation capability in manufacturing enterprises; and (2) The combination of three paths can make various types of digital transformations couple and interact to achieve the high sustainable innovation capability of manufacturing enterprises in Liaoning Province, including the Pure Product Digital Transformation Driving Path, Model + Organization Digital Transformation Driving Path, and Comprehensive Digital Transformation Driving Path. In this paper, four conditional configurations are found to lead to non-high sustainable innovation capability, which can be summarized as the Process Digitalization Deficiency Type and Organization Digitalization Limitation Type. The findings of this paper have important theoretical and practical implications for making scientific and reasonable digital transformation decisions to improve the sustainable innovation capability of manufacturing enterprises.

1. Introduction

In recent years, China’s manufacturing industry has developed continuously and rapidly in the process of deepening supply-side reform and implementing the comprehensive innovation-driven strategy. However there is still a big gap between China’s manufacturing industry and the world’s advanced level in terms of independent innovation capability, resource utilization efficiency, production efficiency, etc. At present, the key to promote developing quality is to promote innovation of the manufacturing enterprises. Improving the sustainable innovation capacity is essential for the survival of manufacturing enterprises and is also necessary for fostering new growth drivers and achieving economic transformation.

With the rapid development of the digital economy, the digital transformation of enterprises has become the focus of attention from all sectors. Digital transformation has become an important strategy for traditional enterprises, which can ensure the enterprises remain competitive in the changing environment [1]. Both the “Digital China Development Report” and “Manufacturing in China 2025” have clearly showed the important developing direction of China’s manufacturing industry in the future. The digital transformation in manufacturing enterprises can not only effectively solve the problems of high cost and low informationization degree, but also promote the innovation of the production mode and business model of enterprises, so as to enhance consumers’ consumption experience [2], improve sustainable innovation capacity, and move towards the high end of technology and the value chain. On one hand, due to the popularization of digital technological means, enterprises can accelerate the innovation of products and processes through the analysis of large amounts of data [3], which can better enhance the interaction between innovation subjects and customers in the process of marketization. On the other hand, the new generation of digital technology can effectively enhance the ability of collecting, mining, and analyzing various network data, and alleviate the information asymmetry in the market, improve financing efficiency through market-based means [4], and create an environment conducive to innovation and, thus, enhance the innovation capability.

However, at present, only a small part of the small and medium-sized manufacturing enterprises in China have realized digital transformation, and a large number of manufacturing enterprises do not know how to transform [5,6]. Most manufacturing enterprises do not know how to improve their sustainable innovation capability, hence they also do not know how to allocate resources and capabilities in the process of digital transformation, and they cannot choose the appropriate digital transformation path according to their resources and capabilities [7]. The purpose of this paper is to explore the mechanism of how digital transformation promotes the sustainable innovation capability of the manufacturing industry and opens the “black box” to identify the coupling relationships of various types of digital transformations and to explore the driving effects on enterprises’ sustainable innovation. The research is needed to help the manufacturing enterprises avoid failed digital transformation, which can also provide theoretical and policy support for the government to make industrial policies and innovation policies.

The research on digital transformation has roughly experienced three stages. In the early stage, digital technologies have unlimited potential in exploring enterprise governance and innovation. In the growth stage, the issues of product, service, and organizational digital transformation are discussed. In the rapid developing stage, the research explores the model and process of digital transformation and their impacts. This paper summarizes them into four main fields: types, antecedents, consequences, and dynamic evolution. The types of digital transformation include the digital transformation of products, services, processes, models, and organizations [8]. The antecedents are mainly related to internal factors, such as digital technologies [9]; digital transformation strategies [10]; resources or capabilities [11]; and external factors, such as user needs [2]. The results are mainly expressed in terms of new products, new services, new processes and performance [12], and stakeholder satisfaction at the ecosystem level [13].

We further found that digital transformation has important impacts on firms’ innovation capabilities. Scholars have also conducted extensive research and found that digital transformation has a remarkable influence on green innovation [14], the input of digital resources has a huge positive influence on service innovation, and the application of big data technologies has a positive influence on information service innovation [15]. Abdalla S used a hierarchical, multiple regression approach to investigate Japanese manufacturing firms, and the results showed that collaborative innovation plays a catalytic role in digital transformation and supply chain adaptability [16]. The application of the Internet by enterprises has an extremely important role in driving technological innovation [17,18,19]. The research by using public listed companies showed that there is a positive correlation between the level of corporate digitization and innovation performance [20]. However, some studies also found that digital transformation does not necessarily always affect innovation positively. For example, Feifei Yu conducted a questionnaire survey on companies, and the results showed that there is a U-shape relationship between the level of digitalization and innovation performance [21].

In summary, the current academic research on digital transformation and the innovation capability of companies has the following shortcomings. Firstly, the previous literature does not directly link digital technology with the innovation capability of enterprises. There are only a few studies on their internal impact mechanisms, and the internal connection between the two has not been clarified. Secondly, the existing studies mainly concentrate on the impacts of digital technology on enterprise innovation and pay too much attention to the technical aspects of digital transformation, ignoring the changes in services, business processes, models, and even enterprise organization due to digital transformation. Thirdly, because of the complexity of digital transformation in enterprises and the many disciplines and fields involved [22], the current research presents a high degree of fragmentation and ambiguity, especially on the concepts, types, antecedents, results, implementation processes, and dynamic evolution, which do not form a unified viewpoint and break the synergistic relationship between various types of digital transformation.

Based on the Complex System view, this paper uses a combination of fsQCA and NCA to explore the complex interaction between different types of digital transformation and sustainable innovation capability in manufacturing enterprises, and it is dedicated to answering the following questions: Can the different types of digital transformation of manufacturing enterprises work together to invigorate the “digital vitality”? Can the different types of digital transformation of manufacturing enterprises work together to stimulate the sustainable innovation capability? And to what extent are the various types of digital transformation necessary to generate highly sustainable innovation capability? What are the possible mechanisms of how the different types of digital transformation effectively drive sustainable innovation?

This paper makes the following theoretical contributions. First, the Complex System view holds that innovation is a combination of established knowledge and technology. Based on the Complex System view, this paper develops a comprehensive framework to analyze the driving mechanism of digital transformation on sustainable innovation capability in manufacturing enterprises. Furthermore, it considers five different types of digital transformation, offering another hypothetical viewpoint to empirical studies to analyze the connection between digital transformation and sustainable innovation capability, as well as expanding the research context (digitalization context) and research scope (innovation capability enhancement). Secondly, considering the interdependence of the five different types of digital transformation, we systematically analyze the complex mechanisms of how different types of digital transformation couple and synergistically drive the improvement of technological innovation capability in manufacturing enterprises based on the view of configuration. Additionally, the “multiple potential concurrent causalities” between different types of digital transformation and sustainable innovation capability are identified, and we explore the intrinsic influencing mechanism of digital transformation on the sustainable innovation capability in manufacturing enterprises to reveal multiple paths for improving sustainable innovation capability, which enriches the theory of sustainable innovation capability and digital transformation.

2. Literature Review

2.1. Digital Transformation

2.1.1. The Concepts of Digital Transformation

Regarding digitalization, the existing research has broadly experienced three stages, namely Informatization, Digitalization, and Digital Transformation [22]. The first stage of Informatization refers to traditional information and communication technology, while digital technology is the process of converting analog information into digital information, including the application of the new generation of information technology, such as big data, artificial intelligence, the Internet of Things, and cloud computing in recent years [23]. The second stage of Digitalization refers to changing existing business processes through digital technology, including communication, distribution, and business relationship management [24]. The third stage is Digital Transformation, which includes business model transformation. Digital transformation affects the way that a company operates, organizes, and runs.

Currently, the concept of digital transformation is defined mainly at the social [25], middle [26], and enterprise levels [27]. According to Warner and Wager [11], digital transformation is the utilization of arising digital technologies, such as mobile Internet, artificial intelligence, cloud computing, blockchain, and the Internet of Things to expand business opportunities, enhance customer experience, streamline operational processes, and pioneer innovative business models. Digital transformation can change the business process and organizational structure of enterprises; form new ways of value creation; enhance the connection between enterprises and the outside world; reshape the objectives, strategies, and corporate culture of enterprises; and open up new markets [26]. As can be seen in Table 1, most scholars believe that digital transformation refers to the digital transformation of enterprises (subjects) through digital technology, mainly including the digital transformation of products, services, processes, models, and organizations, and its goal is to carry out a comprehensive and collaborative transformation in products, services, processes, models, and organizations (types) through the application of digital technology, which will improve the level of digitization, automation, and intelligence of enterprises, with the result of enhancing their competitive advantages and achieving green ecological, industrial, and social effects (results).

2.1.2. The Effects of Digital Transformation

The results of digital transformation are reflected in new products, services, processes, and performance at the enterprise level, reflected in stakeholder satisfaction at the ecosystem environment level and digital dividends, digital entrepreneurship, and digital society at the social level. Most of the research focuses on theoretical methods and empirical research. Chi Maomao [33] conducted an empirical study on the impact of digital transformation on R&D and product performance and tested the results based on survey data from 207 small and medium-sized medical device manufacturing companies in Hubei province. Ferreira [34] investigated 938 companies and studied the reasons why enterprises choose digital transformation and the impacts of digitalization on innovation and performance. Kohtam Ki et al. took 131 manufacturing enterprises as an example to study the influencing mechanism of the U-shaped interaction effects of digitalization and servitization on the performance of companies [35]. Mergel et al. focus on the impacts of digital transformation, including new services, products, processes, and skills in the short-term, and long-term outcomes, including service upgrades, process upgrades, relationship improvements, and digital environment [12]. All of these will affect value creation, organizational change, and digital society. Some other scholars took the Spanish automobile industry as the sample and applied the fsQCA to do an empirical study to analyze the impact of digital transformation on enterprises’ performance and the satisfaction of all participants [13]. Based on the statistical data of 29 countries in 2016, Galdo-Martin et al. discussed the impact of digital transformation on digital dividend, value creation, entrepreneurial activities, and the development of digital society through empirical analysis [36].

2.2. Sustainable Innovation Capability

With the increasingly fierce international competition, innovation capability has become a necessary condition for a country to realize sustainable development, and improving innovation capacity is the key for China to accelerate the new engine of development and the construction of an innovative country. Since the 1990s, the academic circle has explored the source of sustainable innovation in Silicon Valley from the perspective of ecology. As the research continues to deepen, scholars’ research on sustainable innovation capability has become more abundant. The connotation of sustainable innovation capability can be understood from two perspectives: process view and factor view. Xu Qingrui divided the process of technological innovation into six stages, including identifying chances, forming ideas, solving problems, getting solutions, developing, applying, and spreading [37]. In addition to technological innovation ability, the factor view also includes non-technological innovation ability, especially talent and knowledge. D.A. Leonard Barton pointed out that the technology innovation capability of enterprises mainly refers to the technological system capability, management capability, and value orientation of R&D personnel [38]. Sheng Weizhong [39] and others consider innovation capability as the capacity to transform information and thoughts into new products and processes, which is mainly reflected in innovation culture and innovation investment. In addition, innovation capability is closely related to opportunity utilization, especially digital opportunities that often have frontier value, which requires small and medium-sized manufacturing enterprises to acquire high-quality, advanced innovation knowledge by transforming potential needs into opportunities to create new customer value [40]. Ben Arfi [41] argued that through the use of digital opportunities for iterative innovation, the continuous innovation capability would be obtained.

The connotation of sustainable innovation capability explained from the viewpoints of process and factor is not completely separated; instead, the concept evolved from process and factor, and the absorptive capacity and dynamic capability are formed. Teece et al. [42] incorporated the influence of the external environment into the analysis framework, focusing on the continuous growth of enterprise resources and capabilities in a period of time, gradually adapting to the changes of external environment, and, in the end, developed the dynamic capability theory. Based on the enterprise core competence theory and dynamic competence theory, scholars have gradually introduced relevant theories and models, such as life cycle, system dynamics, complex science, and bionics, into the research on the formation and development of enterprise sustainable innovation capability, focusing on the interaction between enterprise internal factors, characteristics, resources, and external environment, as well as the formation of enterprise sustainable core competence.

2.3. Digital Transformation and Sustainable Innovation Capabilities

Due to the importance and difficulties of digital transformation in manufacturing enterprises, how to carry out digital transformation and promote innovation in manufacturing enterprises has become a focal issue [43]. A large number of scholars have conducted research on the types, antecedents, processes, outcomes, and dynamic evolution of digital transformation, striving to guide digital transformation in manufacturing enterprises [44,45]. Through reviewing the literature on digital transformation, Zhu Xiumei [8] pointed out that there was a lack of consensus on the definition, scope, type, and motivation of digital transformation in previous studies, but both theoretical research and practice found that the use of digital opportunities is the key for manufacturing enterprises, especially small and medium-sized manufacturing enterprises, to achieve digital transformation [46]. By making use of digital transformation, manufacturing enterprises can integrate digital technology into business activities to achieve the goal of enhancing sustainable innovation capability [47].

Driven by the wave of sustainable innovation, the application of digital technology has been widely valued by the academic circle. With an improvement of informatization, enterprises can obtain more mature knowledge and accurate customer information to identify market opportunities, thus achieving progressive and radical innovation [47]. The high degree of digital construction not only lays a solid material foundation for the development of enterprises, but also provides a solid foundation for gradual and breakthrough innovation. Existing studies have shown that the positive impact of information infrastructure on the innovation capability of enterprises is obvious [48,49]. Through the management of big data, enterprises can better understand their environment, customer needs, and digital opportunities and generate new ideas in the process of using data [50]. Progressive innovation can make the enterprise’s products achieve continuous optimization, while breakthrough innovation drives product development through technological innovation in order to improve the company’s competitive advantage. The level of digital access and application reflect the interconnection degree of information subjects and the penetration rate of IT technology [51]. Internet technology can not only promote the innovation of individual products and the processes of enterprises, but also promote the large-scale integration of the industry, ultimately affecting the innovation of the whole ecosystem [52].

3. Model Framework

3.1. Theoretical Basis

The Complex System View argues that different market agents are interrelated, highly interactive, competitive, cooperative, and adaptable to each other. Many unclear problems appear in the economic system; hence, enterprises choose diversified solutions instead of optimal equilibrium. In the process of evolution, enterprises learn, adjust, and choose appropriate measures in the face of the new environment, thus evolving a variety of ecosystems [53]. First, the Complex System View assumes that there is no optimal equilibrium. Instead, the Complex Systems View assumes the existence of a positive, forward feedback mechanism. Under the effect of positive feedback with increasing returns, small stochastic events can dynamically assume a multiplicative equilibrium. Consequently, the Complex Systems View argues that the economic system has complex characteristics, such as multiplicative equilibrium, path dependence, unpredictability, and asymmetry [54]. Second, the Complex Systems View considers innovation a combination of existing knowledge or technologies [53], and, therefore, it may be possible to develop highly sustainable innovation capabilities through different combinations of various types of digital transformations. Third, the Complex Systems View proposes that inductive reasoning is more effective than deductive reasoning in solving complex problems due to the dynamic, multi-causal, and multi-level interaction characteristics of complex systems [55], and it points out the need for new methodologies, such as Mathematics of Combination.

Only through collaboration can various types of digital transformation fully play the role of enhancing enterprises’ sustainable innovation capability. The core of analyzing a complex problem is to find a cyclic pattern from diverse configurations [55], while traditional linear analysis methods (for example, regression analysis focuses on the “net effect” of a variable.) are not suitable for complex system analysis. Therefore, a methodology that can rely on the “combination” effect among variables is needed [56]. Thus, from the perspective of Complex Systems, various types of digital transformation can co-evolve different path mechanisms for the improvement of sustainable innovation capability through interdependence [3], that is, enterprises’ sustainable innovation capability may be improved through the collaborative transformation of products, services, processes, models, and organizations, forming an equivalent multi-path of high sustainable innovation capability.

3.2. Model Construction

The existing studies rarely consider the collaborative coupling between different types of digital transformation, which not only restricts the implementation of digital transformation, but also greatly restricts the improvement of innovation capability. Due to the complex combination of paths for manufacturing enterprises to improve their sustainable innovation capability through digital transformation, it is difficult to use traditional methods [57]. Therefore, we introduce a framework that argues that sustainable innovation capability does not depend on a single type of digital transformation, but rather on the interaction between multiple types of digital transformation (Figure 1).

The five types of digital transformation of enterprises, including product digital transformation, service digital transformation, process digital transformation, model digital transformation, and organization digital transformation, are determined by referring to the research of Zhu Xiumei and Lin Xiaoyue [8]. These five types all have a driving effect on sustainable innovation capability. In the process of product digital transformation, digital technology can provide users with intelligent products that are compatible with market demand. The application of digital technology in service digital transformation can improve service quality and product applicability, thus opening up new business models. Process digital transformation refers to the application of digital technology to business process optimization, R&D, and design, all while embedding new digital technology, thus improving the enterprise technology innovation ability. Model digital transformation can help enterprises introduce new MIS, optimize their business model, and accurately position products. Digital technology is an important guarantee for the digital transformation of enterprise organizations, and enterprise employees and leaders can transmit and obtain information at any time, thereby forming a new business model for users and stakeholders and finally promoting innovation.

In addition, the synergistic relationship between different types of digital transformation can also provide important support for manufacturing enterprises to enhance sustainable innovation capacity, mainly in the following aspects:

• (1). The collaboration between model digital transformation and process, organization digital transformation

First, model digital transformation and process digital transformation. The model digitalization transformation can clarify the specific needs and tasks of process digitalization transformation, and promote enterprises to accelerate automation, intelligent response, and decision making. At the same time, process digital transformation can also effectively use the enterprise’s operational data, market environment data, business operation information, market information, enterprise information, and other data or information, which helps leaders to proactively improve their current business model and enhance commercialization capability.

Second, model digital transformation and organization digital transformation. Through the accelerated interaction of processes and resources, the enterprises can actively improve the existing business model, enhance the overall transformation practice and efficiency, and promote the sustainable innovation capability, so as to reduce the barriers of talent and knowledge in the industry and introduce new management information systems. At the same time, organization digital transformation stimulates the exploration and development of organizations and individuals in terms of data resources, and promotes the generation of new ideas and models, and finally improves the identification of opportunities in sustainable innovation capability.

Third, organization digital transformation and process digital transformation. The digital transformation of the enterprise provides talent and organizational security for the organization. At the same time, the organizational management structure of the enterprise will also change with the digital transformation in order to achieve cooperation and form new business models that can create value for the enterprise.

• (2). The collaboration between process, organization digital transformation and product, service digital transformation

First, process digital transformation and product, service digital transformation. The process digital transformation supports the digital transformation of products and services, integrates resources on the data platform, supports flexible manufacturing and personalized customization through the construction of a flexible supply chain, and enables users to participate in the whole process of value creation, optimization, and product and service innovation. The wide use of digital technology facilitates the use and analysis of a large amount of data in the process of marketization, improves the interaction between innovation subjects and target customers, and accelerates product and process innovation for sustainable innovation [3]. Therefore, the collaboration of digital transformation of processes and products and services can lead to an effective improvement of the sustainable innovation capability of enterprises.

Second, organization digital transformation and product, service digital transformation. “People” in an organization can provide knowledge needed for products and services. Knowledge needs to be understood, organized, and created in the process of information flow. Organizations that are alert and aware of opportunities tend to have the potential to anticipate new knowledge and to have a fast and flexible organizational structure that can adapt to rapidly changing products and services. Enterprises can identify and develop market opportunities through the collaborative development of the digital transformation of organizations and services to enhance their sustainable innovation capabilities.

Third, product digital transformation and service digital transformation. The product digital transformation drives the service digital transformation, as the value creation of services cannot be separated from products, and the service digital transformation will drive the product digital transformation, thus making the corresponding products more functional. Furthermore, these two types support each other and complement each other synergistically, which is conducive to new product development and enhances the sustainable innovation capability of enterprises [58] (Luo, 2020).

• (3). The collaboration between model digital transformation and product, service digital transformation

The efficient model digital transformation requires clearer and more rapid access to users’ needs, rapid updating of existing products and services, and flexible integration of them as well. The new model digital transformation requires companies to be able to effectively identify and process information resources and discover potential business opportunities, so that they can continuously develop new and unique products and services, thus maximizing the value of the company. The product and service digital transformation, on the other hand, prepares data for model digital transformation and the sustainable innovation of enterprise, analyzes data based on the actual demand of customers, and predicts market demand and potential markets.

In summary, the components of digital transformation in enterprises have been discussed in more depth in the literature, but the influence of enterprises’ digital transformation on sustainable innovation capability still needs to be further explored. At present, the relationship between digital transformation and the sustainable innovation capability of enterprises has not formed a complete system, and most of the literature starts from a single perspective, lacking a comprehensive analysis of its influencing factors. Moreover, with the continuous advancement of digitalization, it is difficult for a single factor to provide a more comprehensive explanation. Based on the characteristics of “multiple causes, one effect” and “all paths to the same destination” of configuration analysis, this paper constructs an integrated research framework in accordance with the “cause-and-effect” model, as shown in Figure 1. Based on the perspective of configuration, this paper explores the multiple digital transformation paths and complex influence mechanisms to promote the improvement of enterprises’ sustainable innovation capability.

4. Data and Methods

4.1. Sample and Data

Dalian is one of the important cities of the old industrial base in northeast China. Since the founding of the People’s Republic of China, it has formed a strong manufacturing base. In particular, Dalian is also one of the country’s important petroleum refining and chemical industry bases. In recent years, Dalian’s petrochemical industry has developed rapidly, which has become an important pillar of the development of Dalian’s manufacturing industry. In 2020, petrochemical enterprises above a designated size in Dalian achieved an added value of CNY 52.62 billion, accounting for one third of the scale industry. Its crude oil processing capacity reaches 52.7 million tons. At present, it has formed a multi-industry system, including petroleum refining, petrochemicals, basic chemical materials, fertilizers, pesticides, tires, and fine chemicals, and has prominent features in the fields of catalysts and special gases. Based on this, this paper takes the manufacturing enterprises of chemical raw materials and chemical products in Dalian, China, as the main research object.

First, in the summer of 2022, we conducted a one-month online and offline survey on twenty chemical raw materials and chemical products manufacturing enterprises in Dalian (once a week on average), and we interviewed and communicated with the company’s general manager and other department heads to understand the current situation of digital transformation of the company’s products, services, processes, models, and organizations, which lays a practical foundation for this research. Subsequently, through in-depth visits to enterprises and interviews of senior executives, we learned about the current situation of the enterprises, their sustainable innovation capability, and the importance they attach to sustainable innovation development, which provides important practical support for this study. Finally, our questionnaires were distributed and collected in August 2022.

In order to ensure the authenticity and reliability of the obtained research data, as well as considering the convenience of the Internet, a combination of telephone communication, online research, and face-to-face research was used for data collection. The enterprises were asked to fill out the electronic questionnaires through wechat, mail, and other online channels, or through the face-to-face method, and, finally, the online and offline questionnaires were received and sorted out. Furthermore, we find that most of the enterprises in the sample are mainly petrochemical enterprises, of which 89.29% are private enterprises or other nature enterprises. Most of the enterprises are in the interval of 50–100 employees and less than 50 employees, which indicates that most of the researched enterprises are small and medium-sized enterprises and meet the requirements of sample size for the data analyzing method of this paper. The characteristics of the sample enterprises are shown in Table 2.

4.2. Research Method: FsQCA Combined with NCA

QCA conducts sufficient and necessary research on causality on the basis of Set Theory, and it has the characteristics of combining qualitative and quantitative analysis. “Qualitative” is reflected in the case as the unit of analysis. QCA can analyze a large amount of case data and analyze it scientifically, as opposed to the qualitative approach of “rooting” and “case”. “Quantitative” is based on a Boolean algorithm, and the relevant causality index is modified in order to determine the necessary conditions and sufficient configuration, providing an equivalent path for the theoretical and practical “homogeneity” of the problem [59]. There are three types of qualitative comparative analyses: csQCA, fsQCA, and mvQCA. FsQCA can better solve the related degree and affiliation problem than csQCA and mvQCA, and it is more focused on individual cases and can explain causality in a more detailed way [60]. Therefore, we chose fsQCA for this study.

NCA is a novel analysis method of complex causality. Different from QCA, it can not only identify the necessary conditions of the result variables, but also can quantify the effect size and bottleneck level of the necessary conditions [61]. QCA has been widely used in various fields, such as economics, management, public administration, medicine, communication, etc., and has been recognized by many authoritative journals. Under the advice of the chief editor of the Journal of International Business Studies, the management field began to combine NCA and QCA to study the necessary and sufficient complex causal relationships [62].

4.3. Variables

The result variables are mainly measured from three dimensions, including opportunity identification capability, innovation realization capability, and commercialization capability, which involve 15 questions. Based on the different types of digital transformation in enterprises, the conditional variables are measured from five dimensions, including product digital transformation, service digital transformation, process digital transformation, model digital transformation, and organization digital transformation, which involves a total of 16 questions. We designed a questionnaire entitled “A Survey on the Innovation Capability of Dalian Manufacturing Enterprises under Digital Transformation” and asked respondents to assign points according to their attitudes towards the questions. This study used a 5-point Likert scale, where a “1” means complete disagreement with the item and a “5” means complete agreement with the item. Below are the measurements for each variable in this study.

4.3.1. Result Variables

Sustainable innovation capability is a multifaceted concept, and the defined dimensions are divided differently; hence, the measurement approach is different. By referring to Sheng Weizhong and Chen Jin [39], this study divides sustainable innovation capability into three dimensions, including opportunity identification capability, innovation realization capability, and commercialization capability. The measurement framework is shown in Figure 2.

• (1). Opportunity identification capability

This capability refers to the ability to actively explore and expand market development opportunities through various ways and means [63]. It involves studying creativity and predicting whether the innovation of a product will adapt to the market prospect. This capability attaches great importance to creativity. Enterprise development opportunities cannot be effectively explored in all environments. Opportunity identification consists of three aspects, including analysis of market and technical opportunities, selection of innovative products and evaluation of ideas, and mastery and control of products and business plans [64].

• (2). Innovation realization capability

This capability refers to an enterprise’s ability to develop existing business opportunities. Developing business opportunities requires a willingness to invest in innovation, resources, and organizational management, as well as requiring a climate that supports innovation. The measurement focuses on the enterprise’s ability to continuously transform innovation opportunities from ideas to reality and turn them into long-term productivity, thus promoting the growth of the enterprise. This capability includes the enterprise’s strategic decision-making ability, the enterprise’s human and capital investment in the innovation project, and the management and regulation ability of the managers in all aspects of the enterprise’s operation.

• (3). Commercialization capability

Commercialization refers to the fact that the idea has been brought to the market and requires market analysis and monitoring, contacting customers, marketing planning, and marketing in the target market [65]. The commercialization capability is mainly manifested in the following aspects: the compatibility of the market test, customer preference, commercialization plan, innovative products, whether the market positioning of innovative products is accurate, whether convenient marketing channels can be found for well-positioned innovative products, and whether customers’ consumption demands can be satisfied through innovative products.

4.3.2. Condition Variables

• (1). Product digital transformation

The product digital transformation is mainly reflected in the use of digital technology to improve product shape characteristics, optimize product functions, and so on. Companies use data collection technologies to determine the functionality of products and improve their usability. The product digitization can make the product have the function of monitoring, digitization, and intelligence. By using digital technologies, products can be produced more efficiently and accurately, development cycles can be shortened, and product quality can be further improved with the help of predictive tools.

• (2). Service digital transformation

The service digital transformation refers to the transformation of services in commodity accessories, so that enterprises can obtain greater profits and improve the added value of products, including two major categories: digital services for user products and digital services for user behavior [66]. In terms of supporting user products, enterprises use digital technology to carry out embedded services and support users to use digital service products. In terms of supporting user behavior, as the enterprise attaches great importance to customers’ satisfaction with the existing products and services, it constantly updates and upgrades the product functions according to customers’ needs, and provides customers with personalized functions and complete solutions.

• (3). Process digital transformation

The process digital transformation refers to the process of creative generation, research and development, and production, whereby sales are redefined and integrated with the characteristics of digital transformation. Additionally, the digital technology is used to optimize business processes, mainly in the front-end business and back-end operations. In the front-end operation, the complete digital transformation of processes enables continuous interaction between companies and users before and after purchase, creating a new customer experience and making customers important participants in the whole value creation process [51]. In the back-end operation, since the application of digital technology is very important for enterprises, it can realize the internal automation and intelligence in enterprises. Through the use of new digital technology for Internet collaborative operation, the operation cost of enterprises can be greatly reduced and the operation efficiency and operation quality of enterprises can be improved.

• (4). Model digital transformation

The model digital transformation refers to the use of digital technology to create new value, new exchange mechanism, and transaction structure [67], which is mainly divided into efficiency type and novel type model digital transformation [68]. The former refers to the rapid and active improvement of the existing business model through communicating with multiple subjects, such as users, to improve the efficiency of interaction. The latter is to use new technologies, new subjects, new activities, new markets, new structures, and new management mechanisms to predict, evaluate, and utilize new opportunities to create added value, which is generally characterized by creativity and openness.

• (5). Organization digital transformation

The organization digital transformation refers to the transformation in organizational structure, culture, leadership, roles, and skills of employees, etc. In terms of organizational structure, through restructuring the organizational form of enterprises, a new business model based on data asset-based operation is established to provide services for users and stakeholders. At the corporate culture level, the creativity of the enterprise is improved through the digital management of internal employees. In terms of leadership, leaders need to be aware and capable of new, dynamic, and continuous learning, as well as de-administration, flattening, and decentralization for R&D positions. In terms of employees’ roles and skills, electronic technology is used to replace simple and repetitive human labor; thus, the enterprises need to improve the employees’ digital quality, guide them to share their knowledge and creativity, and enhance their ability to handle complex business problems.

5. Results and Discussion

5.1. Necessary Condition Analysis by NCA

NCA constructs the Ceiling Line in the x-y scatter plot to separate the observable and non-observable areas and judges the necessity (insufficient) of the condition variables by observing whether there is an empty area above the Ceiling Line. NCA mainly uses two main techniques of Ceiling Analysis, according to variable category. Ceiling Envelopment (CE) will be adopted if the variables are less than five levels, and Ceiling Regression (CR) will be adopted for multiple variables (≥5 levels). As there are five condition variables in this paper, CR was used.

The NCA ceiling line calculation method differs from the conventional linear regression in that the criterion for a better fit of conventional linear regression is to cross as many scatter points in the coordinate graph as possible, whereas the ceiling line analysis is based on the criterion of distinguishing the blank area from the observed area. In addition, the necessary conditions obtained by NCA analysis may not have a significant linear relationship with the result variables. In this paper, both CE and CR methods were used to build the x-y scatter plot, and the parameters were sorted out. The final results of the NCA necessary conditions are shown in Table 3.

NCA requires that the necessary conditions must meet two criteria: (1) The effect size should not be lower than the threshold value (d = 0.1); (2) Monte Carlo simulations of permutation tests show that the simulations have significant effect sizes. In summary, product, service, process, model, and organization digital transformations are all necessary (insufficient) conditions to achieve sustainable innovation capability, and the effect size is significant.

Table 4 is the bottleneck level analysis of the necessary conditions, which indicates the minimum level (%) that the condition variable x needs to reach in order for result variable y to reach a certain level (%). As shown in the table, if the sustainable innovation capacity is reached at 40% or above, each condition variable needs to reach a different level of necessity (insufficient). For example, in the total observation range, to achieve 40% of sustainable innovation capability, the product digital transformation should reach at least 8.7%, the service digital transformation should reach at least 24.7%, the process digital transformation should reach at least 17.1%, and the model digital transformation and organization digital transformation should reach 25.5% and 31.0%, respectively. In order to meet the 20% level of sustainable innovation capability, only the service, model, and organization digital transformation are necessary conditions, and other conditions are unnecessary, indicating that the service, model, and organization digital transformation are the basic prerequisites for the improvement of sustainable innovation capability.

5.2. Necessary Condition Analysis by QCA

According to QCA related functions, this paper analyzes the necessary conditions of high sustainable innovation capability and non-high sustainable innovation capability, and the results are shown in Table 5. Consistency and coverage are related to validity and explanatory power, respectively. Consistency means the proportion of cases that exhibit a specific outcome in the set of cases with corresponding conditions, and coverage represents the extent of cases with corresponding conditions and specific results covered. If the consistency threshold is set at 0.90, then the condition variable whose consistency is higher than 0.9 is regarded as a necessary condition. As shown in Table 5, the necessary conditions leading to high sustainable innovation capability are product digital transformation, model digital transformation, and organization digital transformation (consistency > 0.90).

QCA and NCA have different judgment criteria for necessary conditions. QCA uses the diagonal of scatter plot as the reference line, while NCA shifts or rotates the upper limit line to become a reference line with intercept to analyze the necessary conditions for the result variable at different specified levels [61]. Therefore, the result of the QCA necessary condition analysis is a subset of NCA, and usually QCA analysis has fewer necessary conditions than NCA (DUL et al., 2020) [61]. This also confirms our results that product, service, process, model, and organization digital transformation are all necessary conditions for achieving sustainable innovation capability by NCA, while the QCA method examines that the necessary conditions are product digital transformation, model digital transformation, and organization digital transformation, which shows that the result of the QCA necessary conditions analysis is a subset of the NCA.

5.3. Conditional Configuration Analysis

In this paper, fsQCA 3.0 software is used to analyze the conditional configuration leading to high sustainable innovation capacity. Three types of solutions can be obtained: complex solution (without logical residue), intermediate solution (including simple logical residue), and reduced solution (including simple and complex logical residue). Finally, intermediate solutions are reported in order to preserve the necessary conditions to prevent them from being simplified by reduced solutions. Based on this, the condition that both the reduced solution and the intermediate solution occur simultaneously is the core condition, and if the condition only has intermediate solution, then it is the auxiliary condition [62]. In this paper, the original consistency threshold is set as 0.8 to ensure the interpretation strength of the configuration, the PRI (subset relation consistency) threshold is set at 0.75 to eliminate the interference of “simultaneous subset relation”, and the case frequency threshold is set as 1. The configuration is shown in Table 6.

We identified three digital transformation paths that can lead to high sustainable innovation capability, including Path 1, Path 2, and Path 3. The four configuration paths leading to non-high sustainable innovation capability are Path 4, Path 5a, Path 5b, and Path 6. There is no corresponding relationship between the non-high and high sustainable innovation capability. The total consistency of high sustainable innovation capability is 0.89 and the total coverage is 0.892, indicating that the three configuration paths could explain about 89.2% of the cases. We further identified 4 configuration paths that may lead to non-high sustainable innovation capability, with the total consistency of 0.92 and the coverage of 0.87.

5.3.1. Paths to High-Level Sustainable Innovation Capability

(1) Pure product digital transformation driving path. As shown in Table 7, configuration Path 1 is DTP*~DTS*~DTPR, with coverage of 0.037. At this time, enterprises take product digital transformation as the core condition, service and process digital transformation as the auxiliary condition, and model and organization digital transformation as optional. Most manufacturing enterprises that conform to the configuration path 1 (consistency: 0.833667) attach great importance to product digital transformation, and when they facing fierce competition and insufficient market demand, manufacturing enterprises should increase their support for product digital transformation and promote their sustainable innovation capability.

Figure 3 shows the explanation cases corresponding to Path 1, including BT.CX Technology Co., Ltd., LS.FY Technology Co., Ltd., ZZ Chemical Co., Ltd. and XH Fine Chemical Co., Ltd., which have realized a high level of sustainable innovation capability mainly relying on the product digital transformation. As one of the typical cases, BT.CX Technology Co., Ltd. is a manufacturing enterprise engaged in chemical raw materials. In recent years, it is mainly committed to providing intelligent products through digital technology. With the help of automated and intelligent defect detection and prediction tools, this company has shortened the development cycle of new products, and the quality of the existed products has been significantly improved. Meanwhile, it still follows the original service and process model.

Model + organization digital transformation driving path. As shown in Table 7, configuration Path 2 is ~DTPR*DTM*DTO, and the coverage is 0.056. At this time, both model and organization digital transformation are simultaneously the core conditions, and the improvement of sustainable innovation capability of manufacturing enterprises can be promoted without process digital transformation. Manufacturing enterprises that conform to configuration path 2 (consistency: 0.916327) have a certain innovation consciousness of model digital transformation and organization digital transformation. In the face of market demand, enterprises actively carry out organization and model digital transformation, and increase the innovation skills that they did not have before. Even if the awareness and ability to process digital transformation are low, the improvement of the sustainable innovation ability cannot be affected.

Figure 4 shows the explanation cases corresponding to the configuration Path 2, including CF Technology Co., Ltd., BT.CX Technology Co., Ltd., and LS Trading Co., Ltd. For example, as a professional international chemical products trading company, LS Trading Co., Ltd. has a relatively good foundation in the construction of digital transformation of model and organization. The company has a professional team and technology service center, and has carried out various forms of cooperation with the R&D centers of well-known chemical enterprises and famous chemical institutes at home and abroad. This company formed a new business form to serve users and stakeholders, based on data asset operation, and promote the training of digital quality of employees to realize intelligent manufacturing.

(3) Comprehensive digital transformation driving path. As shown in Table 7, configuration Path 3 is DTP*DTM*DTS*DTPR, with a coverage of 0.44, which is significantly higher than the other two paths, indicating that this path has universality. At this time, the product and model digital transformation are the core conditions, and service and process digital transformation must be achieved to improve the sustainable innovation ability of manufacturing enterprises. Manufacturing enterprises that meet configuration Path 3 (consistency: 0.946903) have higher awareness and ability of product and model digital transformation, supplemented by service and process digital transformation. They make changes according to product and model characteristics, and increase innovation efforts, with the result of high sustainable innovation capability.

Figure 5 shows the explanation cases corresponding to the configuration Path 3, including WF Pharmaceutical Co., Ltd., KH New Technology Engineering Co., Ltd., BL Technology Co., Ltd., FSD Special Chemical Co., Ltd., YH Metal Products Co., Ltd., XYG Material Technology Co., Ltd., LY Chemical Co., Ltd., XD New Material Technology Co., Ltd., XD Carbon Co., Ltd., and BS Fine Chemical Technology Co., Ltd. As a typical case, WF Pharmaceutical Co., Ltd. is a large private enterprise engaged in the R&D, production, and sales of high-end apis and intermediates. With a relatively high-quality product, the company uses digital technology for business process optimization, continuously improves the quality of existing products and customer services, and enhances its sustainable innovation capability.

Manufacturing enterprises that adopt the first configuration path emphasize the importance of product digital transformation to enhance sustainable innovation capability. They believe that product digital transformation is a key factor in determining the ability to innovate sustainably. For small and medium-sized enterprises with insufficient funds, they can temporarily give up the investment in the digital transformation of services and processes and focus on the digital transformation of products. This is consistent with Danet and Nakamura’s research that new digital products are a major innovation to promote the overall digital transformation of industry and have positive significance for improving the sustainable innovation capability in the manufacturing industry. In the field of manufacturing, the use of artificial intelligence, machine learning, and big data analysis technologies can be utilized to improve product quality through automated defect monitoring and prediction tools [69]. This is also in line with the configuration Path 1, in which high product digital transformation can optimize product functions, improve product quality, and, thus, improve the sustainable innovation capability of enterprises.

Manufacturing enterprises that adopt the second configuration path emphasize the importance of model digital transformation and organization digital transformation in enhancing the sustainable innovation capability of the enterprises. They believe that organization digital transformation can encourage organizations and individuals to explore and develop data resources, promote the generation of new concepts and models, and then improve the ability to identify opportunities in sustainable innovation capability. This is consistent with the research of LI F [67] and HININGS B [70]. They believe that in order to realize the new management model, it is necessary to reduce the barriers of talent and knowledge among various industries, build a flexible and efficient organizational structure, give full play to the creativity of leaders and employees, and improve the overall transformation ability and efficiency of the organization.

5.3.2. Paths to Non-High-Level Sustainable Innovation Capability

The first type is the Process Digitalization Deficiency Type, including Paths 4, 5a, and 5b. Path 4 (~DTS*~DTPR*~DTM*~DTO) shows that in the absence of service, process, model, and organization digital transformation, product digital transformation does not play a significant role in creating a high level of innovation capability. Path 5a (~DTS*~DTPR*DTM*DTO) shows that even if model digital transformation and organization digital transformation are carried out, in the absence of service and process digital transformation, product digital transformation does not play a significant role and still cannot achieve a high level of innovation capability. Path 5b (~DTP*~DTPR*DTM*DTO) shows that even with model digital transformation and organization digital transformation, in the absence of product and process digital transformation, the service digital transformation does not play a significant role and still cannot achieve a high level of sustainable innovation capability. The commonality of these three configuration paths is that the enterprises lack process digital transformation, hence they are summarized as “Process Digitalization Deficiency Type.

Configuration Path 6 (DTP*DTS*DTPR*DTM*~DTO) can be summarized as Organization Digitalization Limitation Type. When a manufacturing enterprise does not carry out organization digital transformation, although it has carried out certain practices of digital transformation in product, service, process, and model, the enterprise still cannot improve its sustainable innovation capability through these kinds of digital transformation, t this path is an inefficient digital transformation path.

5.4. Robustness Test

This paper adopts the method of Set Theory to adjust the consistency threshold for the robustness test. The consistency threshold was adjusted from 0.80 to 0.85, PRI value was increased from 0.70 to 0.75, and the occurrence frequency threshold was set to 1. The results obtained by using fsQCA 3.0 software showed that the configuration did not change substantially. Therefore, the results can be considered to have passed the robustness test and the empirical research results are reliable.

6. Conclusions, Implications, and Prospects

6.1. Conclusions

Taking 20 manufacturing enterprises as examples, this paper uses fsQCA and NCA methods to explore the configuration paths for manufacturing enterprises to use digital transformation to drive the improvement of sustainable innovation capability. The conclusions are:

(1) This paper finds that a single type of digital transformation is not a necessary condition to generate high sustainable innovation capability, and the digital transformation of service, model, and organization is a key bottleneck to improve the sustainable innovation capability of manufacturing enterprises.

(2) This paper finds that the combination of three different types of digital transformation can effectively drive manufacturing enterprises to improve their sustainable innovation capability, including pure product digital transformation driving path, model + organization digital transformation driving path, and comprehensive digital transformation driving path.

For the first type of enterprises, a higher level of product digital transformation is the leading factor to produce high sustainable innovation capability, which can make up for the disadvantages of manufacturing enterprises, such as low service level and incomplete digital process. This kind of manufacturing enterprise makes full use of digital technology to develop new products, use intelligent tools to improve the production efficiency and accuracy of products, and greatly improve the product quality and enterprise innovation level.

For the second type of enterprises, the model and organization digital transformation simultaneously serve as the core conditions to promote the improvement of sustainable innovation capability. At this time, manufacturing enterprises can postpone the process of digital transformation. Based on the digital transformation of the industrial Internet platform, such manufacturing enterprises carry out business model reform through digital technology. At the same time, at the organizational level, with the help of fine management and digital transformation, they can achieve “people-centered” management and improve management efficiency.

For the third type of comprehensive digital transformation, enterprises take the digital transformation of product and model as the core conditions, supplemented by the digital transformation of service and process, which can also promote the sustainable innovation capability of manufacturing enterprises. This kind of manufacturing enterprise chooses to focus on comprehensive and coordinated development and uses digital technology to transform core products and business models. At the same time, in terms of digital transformation of service and process, they build flexible production systems and make personalized customizations according to customer needs, so that customers can participate in the optimization and innovation of products and services in the production process.

There are four configuration paths of non-high sustainable innovation capability, which can be summarized as Process Digitalization Deficiency Type and Organization Digitalization Limitation Type. That is, the absence of high process digitalization transformation or high organization digitalization transformation, regardless of other conditions, may lead to the result of non-high sustainable innovation capability in manufacturing enterprises.

6.2. Implications

6.2.1. Theoretical Implications

From the perspective of Complex System, different types of digital transformation are interdependent, interrelated, and highly interactive, which can evolve multiple ecosystems and achieve diversified equilibrium. The selection of various digital transformation strategies may lead to different paths to improve the sustainable innovation capability of different enterprises [53]. Therefore, based on the perspective of configuration, this paper systematically analyzes how different types of digital transformation can be coupled to achieve an effective combination of high sustainable innovation capability. The findings of this paper will have theoretical significance and implications for the current study of digital transformation and sustainable innovation capability.

First, based on the necessity causality and the combination of QCA and NCA, this paper finds that a single type of digital transformation is not a necessary condition for high sustainable innovation capability, indicating that a single factor does not constitute the bottleneck of high sustainable innovation capability. Previous literature indicated that product digital transformation [68], service digital transformation [65], process digital transformation [50], model digital transformation [67], and organization digital transformation [66] have a positive impact on sustainable innovation capability. However, this study finds that a single type of digital transformation has a limited role in promoting the sustainable innovation capability of manufacturing enterprises. Though configuration Path 1 and Path 3 indicate that product digital transformation plays a key role in high sustainable innovation, configuration Path 2 indicates that in the absence of product digital transformation, model and organization digital transformation can promote high sustainable innovation capability.

Second, based on Configuration Theory, this paper systematically integrates different types of digital transformation and contributes to the analysis of the relationship between various types of digital transformation and the sustainable innovation capability of manufacturing enterprises. The configuration analysis of digital transformation provides a new idea for the research of digital transformation and sustainable innovation capability, and the conclusions provide rich and detailed evidence and implications for digital transformation and sustainable innovation in the context of the digital economy. Digital transformation is a complex and multi-dimensional phenomenon, not only a simple “quantitative change”, but also a comprehensive and multi-dimensional “qualitative change”. Therefore, the sustainable innovation capability is driven by digital transformation in multiple ways rather than a single optimal equilibrium [52], and the interaction between various types of digital transformation makes it easier to achieve high sustainable innovation capability. According to the Complex System View, there may be a variety of equivalent and satisfactory equilibrium states for the influence of various factors on the result variables [71]. Due to differences in enterprise size, resource endowment, and development stage of manufacturing enterprises, enterprises can promote the sustainable innovation capability through different combinations of product digital transformation, service digital transformation, process digital transformation, model digital transformation, and organization digital transformation, thereby forming a multi-path for sustainable innovation capability enhancement. Based on the Complex System View, this paper holds that the improvement of the sustainable innovation capability of manufacturing enterprises does not depend on a single type of digital transformation, but on the coupling and collaboration between various types of digital transformation, and builds a comprehensive model framework to discover the relationship between the digital transformation and sustainable innovation capability, as well as how to combine different types to lead to high/non-high sustainable innovation capability. These findings are of positive significance for research on the enhancement of sustainable innovation capability driven by digital transformation from the perspective of configuration.

Third, this paper adopts the method of combining NCA and QCA. On the one hand, QCA is suitable for analyzing sufficient conditions in complex causality. On the other hand, NCA can analyze the causal relationship of necessary conditions in a more detailed way, which is suitable for analyzing the relationship between various types of digital transformation and sustainable innovation capability. Combining NCA and QCA to explore the complex causal relationship between digital transformation and the sustainable innovation capability of manufacturing enterprises is helpful to develop the research on the necessary and sufficient relationship between digital transformation and sustainable innovation capability.

6.2.2. Practical Implications

This study has good practical value, which can help enterprises better solve the decision-making problems of whether to make digital transformations and how to make digital transformation to drive the improvement of sustainable innovation capability. Specifically, this paper identifies three paths of digital transformation to drive enterprises’ sustainable innovation capability. These paths can guide the manufacturing enterprises to foster sustainable innovation capability through digital transformation. For example, based on Path 2, some companies can achieve the goal of high levels of sustainable innovation by combining model and organization digital transformation. Alternatively, some manufacturing enterprises choose Path 3 to achieve a high level of sustainable innovation capability by simultaneously developing model and product digital transformation, combined with the use of digital technologies in service and process. In addition, “product-oriented enterprises” can be guided to choose the appropriate product digital transformation strategy by considering product characteristics to realize the goal of sustainable innovation.

• (1). Focus on product digital transformation

Under the background of accelerating product updating and iteration, manufacturing enterprises can gradually improve their sustainable innovation capability by innovating independently of product digital transformation. The application of digital technology allows enterprises to quickly catch the changes of the market and make corresponding responses in order to quickly iterate and optimize products.

Therefore, in the context of digital transformation, manufacturing enterprises should adjust their development strategies in a timely manner to achieve continuous innovation through product digital transformation. Secondly, enterprises should actively use digital technology to develop and create digital products and improve product quality. Based on artificial intelligence, machine learning, and big data analysis, enterprises can process massive data and obtain knowledge and information to create new products, improve existing products, and improve product quality. Thirdly, enterprises can develop digital products and complementary products with the help of digital platforms to support and promote the digital transformation of enterprises, which will lead to multiple organizational and industrial changes.

• (2). Focus on model digital transformation

Model digital transformation plays a guiding role in enhancing the sustainable innovation capability of manufacturing enterprises by using digital technology. The application of digital technology is conducive to developing new business models, improving customer experience, and changing the original rules and mechanisms of enterprises. The implementation of model digital transformation in manufacturing enterprises can change the way of communication and interaction between enterprises, users, and suppliers, which will increase the communication between enterprises, users, and suppliers, improve the flexibility, and also help enterprises to predict and make use of undeveloped value-creating opportunities in order to improve enterprises’ sustainable innovation capability.

Manufacturing enterprises should pay much attention to the economic and social environment when they use digital technology to change their business and management models. Through model digital transformation, the enterprises can establish cost control, resource sharing, and precise marketing processes, which can accelerate the pace of automation, intelligent response, and decision making. In addition, the manufacturing industry can also use the platform to build a new business model and turn the business model into a series of specific actions through digital technology in order to deliver the strategy of digital transformation and promote collaboration between departments.

• (3). Focus on the comprehensive synergy between all types of digital transformation

The digital transformation of enterprises should not only focus on the promotion of a single type, but also involve the comprehensive synergy between multiple types. The synergistic coupling of various types of digital transformation is the optimal path to improve the sustainable innovation capability of manufacturing enterprises. The results of configuration analysis show that the enterprises should focus on the coordinated development of model, product, and organization digital transformation. For example, the enterprises can integrate product and organization digital transformation in the process of implementing the model digital transformation. If an enterprise is committed to carrying out efficiency model digital transformation, it must clearly and quickly obtain the needs of users and optimize the existing product structure. If an enterprise wants to carry out a novel type of model digitalization, it is necessary to discover and identify the potential demand of users from a wider range of information sources and develop new and unique products and organizational forms.

6.3. Limitations and Prospects

This paper explores the influencing mechanism of digital transformation on the sustainable innovation capability of manufacturing enterprises. However, there are still several shortcomings:

(1) With the change of the life cycle of manufacturing enterprises, the focus of digital transformation will change and the priority of transformation may be different. Future research can collect longitudinal data and adopt dynamic methods to explore the complex impacts of different types of digital transformation on sustainable innovation capability in the process of dynamic changes.

(2) This paper adopts the method of questionnaire survey to study the configuration paths of five types of digital transformation, but the measurement scale has not been unified. Therefore, in the future, the measurement scales of product, service, process, model, and organization digital transformation should be developed in order to further deepen the empirical analysis of complex collaborative paths and collaborative relationships and draw more universal conclusions.

Based on quantitative analysis, this paper makes a qualitative analysis of the case, which is conducive to revealing the mechanism. However, the qualitative research by QCA is hardly as deep and rich as the case study. Therefore, researchers can conduct further in-depth case studies on different driving modes of sustainable innovation capability in manufacturing enterprises in the future to reveal the process of digital transformation promoting sustainable innovation capability.

Author Contributions

Conceptualization, X.F.; Methodology, X.L.; Formal analysis, X.F. and Y.W.; Investigation, X.L.; Resources, X.F.; Project administration, Y.W. and X.L.; Funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

“Not applicable” for studies not involving hu-mans or animals.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Mechanism model of digital transformation-driven sustainable innovation capability improvement in manufacturing enterprises.

Figure 2. Sustainable innovation capability measurement framework.

Figure 3. Explanation case diagram of conditional configuration 1.

Figure 4. Explanation case diagram of conditional configuration 2.

Figure 5. Explanation case diagram for conditional configuration 3.

Table 1

Digital Transformation Concepts.

Representative Authors	Concept
HESS et al. [28]	Digital transformation refers to the transformation of a company’s business model with digital technology, which will cause structural changes in products and organizations or the automation of business processes.
SINGH & HESS [29]	Digital transformation refers to the use of new digital technologies, such as social media, mobile, analytics, or embedded devices to improve business efficiency, such as enhancing user experience, simplifying operations, or developing new business models.
CHANIAS et al. [30]	Digital transformation refers to the significant economic and technological changes occurring in enterprises at the organizational and industry levels, and this transformation carried out under the support of information systems.
VIAL [9]	Digital transformation refers to the integration of information, computing, communication, and connectivity technologies to significantly change the physical attributes of an enterprise and improve its processes.
BAIYERE et al. [31]	The essence of digital transformation is to change the operational business processes of different enterprises.
SOLUK & KAMMERLANDER [32]	Digital transformation refers to the comprehensive transformation of enterprises’ business strategy, business process, capability, products, and services by using digital technology, and the expansion of the connections between enterprises into the business network.

Table 2

Characteristics of Sample Enterprises.

Characteristics of Samples	Indicators	Sample	Percent (%)
Enterprise Information	Years of establishment
	Less than 3 years old	7	35%
	3–5 years	5	25%
	6–9 years	4	20%
	Over 9 years	4	20%
	Total assets
	Assets less than $500,000	8	40%
	Assets of $500,000–1,000,000	8	40%
	Assets 1 million to 3 million	1	5%
	Assets of more than 3 million	3	15%
Information of Respondents	Years of working
	Less than 3 years of experience	4	20%
	3 to 5 years of experience	13	65%
	6–8 years in the industry	1	5%
	More than 8 years of experience	2	10%
	Status of respondent
	Top management	3	15%
	Middle management	17	85%

Table 3

Results of necessary conditions of NCA.

Conditional Variables	Method	Accuracy	Ceiling Zone	Scope	Effect	p-Value
Product Digital Transformation	CR	90%	0.251	0.81	0.310	0.000
	CE	100%	0.269	0.81	0.332	0.002
Service Digital Transformation	CR	80%	0.297	0.81	0.367	0.000
	CE	100%	0.361	0.81	0.445	0.000
Process Digital Transformation	CR	90%	0.251	0.81	0.310	0.000
	CE	100%	0.264	0.81	0.326	0.000
Model Digital Transformation	CR	90%	0.302	0.81	0.373	0.000
	CE	100%	0.376	0.81	0.464	0.000
Organization Digital Transformation	CR	75%	0.341	0.81	0.422	0.000
	CE	100%	0.419	0.81	0.518	0.000

Table 4

Necessary condition bottleneck level of NCA/%.

Innovation Capability	Product Digital Transformation	Service Digital Transformation	Process Digital Transformation	Model Digital Transformation	Organizational Digital Transformation
0	NN	NN	NN	NN	NN
10	NN	NN	NN	NN	NN
20	NN	3.6	NN	4.5	9.9
30	NN	14.2	6.4	15.0	20.4
40	8.7	24.7	17.1	25.5	31.0
50	22.8	35.2	27.9	36.0	41.6
60	37.0	45.8	38.7	46.5	52.1
70	51.2	56.3	49.4	57.0	62.7
80	65.3	66.9	60.2	67.5	73.5
90	79.5	77.4	70.9	78.0	83.8
100	93.7	87.9	81.7	88.5	94.4

Note: This table uses the method CR; NN indicates not required.

Table 5

Results of one-factor necessity analysis.

Number	Variable Name	Consistency	Coverage
1	Digital Transformation of Products	0.904412	0.842466
2	~Digital Transformation of Products	0.421875	0.551683
3	Digital Transformation of Services	0.875919	0.918112
4	~Digital Transformation of Services	0.484375	0.547817
5	Digital Transformation of Processes	0.881434	0.877402
6	~Digital Transformation of Processes	0.472426	0.566704
7	Digital Transformation of Models	0.922794	0.844407
8	~Digital Transformation of Models	0.387868	0.520345
9	Digital Transformation of Organizations	0.926471	0.913043
10	~Digital Transformation of Organizations	0.444853	0.540179

Table 6

Configuration analysis results.

Conditional Variables	High Level of Sustainable Innovation Capability			Non-High Level of Sustainable Innovation Capability
	1	2	3	4	5a	5b	6
Product Digital Transformation	[Image omitted. Please see PDF.]		[Image omitted. Please see PDF.]			[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]
Service Digital Transformation	[Image omitted. Please see PDF.]		[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]		[Image omitted. Please see PDF.]
Process Digital Transformation	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]
Model Digital Transformation		[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]
Organizational Digital Transformation		[Image omitted. Please see PDF.]		[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]	[Image omitted. Please see PDF.]
Original coverage	0.382	0.413	0.787	0.712	0.447	0.360	0.440
Unique coverage	0.037	0.056	0.440	0.386	0.024	0.00	0.081
Consistency	0.834	0.916	0.947	0.977	0.897	0.894	0.889
Total Consistency	0.894			0.919
Total coverage	0.892			0.868

Note: The presence of the core condition is indicated by [Image omitted. Please see PDF.]; the absence of the core condition is indicated by [Image omitted. Please see PDF.]; the presence of the edge condition is indicated by [Image omitted. Please see PDF.]; the absence of the edge condition is indicated by [Image omitted. Please see PDF.]; blank indicates that the condition is optional.

Table 7

Qualitative comparison of configuration for digital transformation to achieve high sustainable innovation capability.

Name and Configuration	Pure Product Digital Transformation Driving Path (Configuration Path 1)	Model + Organization Digital Transformation Driving Path (Configuration Path 2)	Comprehensive Digital Transformation Driving Path (Configuration Path 3)
Sustainable Innovation Capability Configuration	DTP*~DTS*~DTPR	~DTPR*DTM*DTO	DTP*DTM*DTS*DTPR
Driving Mechanism	Focus on product digital transformation to promote sustainable innovation capability.	Focus on model and organization digital transformation to promote sustainable innovation capability.	Enterprises focus on balanced development, mainly on product and model digital transformation, complemented by service and process digital transformation, and all kinds of digital transformation synergistic pioneering innovation.
Representative companies	BT. CX Technology Co., Ltd., LS.FY Technology Co., Ltd., ZZ Chemical Co., Ltd., XH Fine Chemical (Dalian) Co., Ltd.	SF Technology Co., Ltd., BT.CX Technology Co., Ltd., LS Trading Co., Ltd.	WF Pharmaceutical Co., Ltd., KH New Technology Engineering Co., Ltd., BL Technology Co., Ltd., FSD Special Chemical Co., Ltd., YH Metal Products Co., Ltd., XYG Material Technology Co., Ltd.
Qualitative Evidence	The products of BT.CX Technology Co., Ltd. are supporting for the third generation or more nuclear power, which belongs to the national support project and has a wide market development space in the future.	LS Trading Co., Ltd. has a professional team and technical service center, and carries out a variety of forms of cooperation with domestic and foreign famous chemical enterprises research and development centers and famous chemical institutions.	During the critical period of COVID-19, WF Pharmaceutical Co., Ltd. immediately adjusted its product structure and operation mode, and increased the production line of disinfectants on the basis of the original production of apis. Through the optimization of process and using digital technology, it meets the city’s demand for “anti-epidemic”.

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