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Article

Research on the Impact of Digital Transformation on the Product R&D Performance of Automobile Enterprises from the Perspective of the Innovation Ecosystem

by
Feng Men
1,
Fangqi Dong
1,
Yiying Liu
2,* and
Hongxiong Yang
2
1
China Automotive Technology and Research Center Corporation Limited, Tianjin 300300, China
2
School of Management, Tianjin University of Technology, Tianjin 300384, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(7), 6265; https://0-doi-org.brum.beds.ac.uk/10.3390/su15076265
Submission received: 22 February 2023 / Revised: 22 March 2023 / Accepted: 4 April 2023 / Published: 6 April 2023
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
In order to explore the effect and path of digital transformation on the product R&D performance of automobile enterprises, an empirical study was conducted based on data from 240 questionnaire surveys of automobile enterprises, and the influencing mechanism of digital transformation on the product R&D performance of automobile enterprises is discussed. The results show that digital transformation can positively affect the product R&D performance of automobile enterprises; digital technology innovation capability plays an intermediary role between digital transformation and product R&D performance; innovation ecological resources and the innovation ecological environment can significantly regulate the positive relationship between digital transformation and digital technology innovation capability and also regulate the mediating effect of digital technology innovation capability. In order to effectively promote automobile enterprises to improve product research and development performance through digital transformation, the government should ensure that enterprises have the ability and confidence to carry out transformation through policy support and system reform. Relevant associations from the automobile industry should cooperate with the government and ensure that enterprises can continue to carry out product research and development. Automobile enterprises should rationally use resources and the environment to carry out digital transformation, enhance digital technology innovation ability, and improve product research and development performance.

1. Introduction

After experiencing the agricultural economy and industrial economy, human history has entered a more advanced era of the digital economy. In the 14th five-year plan issued by China, it is emphasized to accelerate the construction of the digital economy and promote the transformation of production and lifestyle through accelerating industrial digital transformation. Transformation refers to a fundamental change in the structure form and operation mode of something, and then the external concept of it also changes. The nature of the subject of transformation and its transformation environment jointly determine the content and direction of the transformation. In the era of relying on data to lead resources to play their role, different scholars have given different definitions of the connotation of digital transformation. From the perspective of resources, digital transformation digitizes resources through the application of digital technology and promotes change by expanding the influence of digitalization [1]. The definition of digital transformation from the perspective of enterprises can be regarded as the reconstruction of the enterprise organization mode through the application of digital technology to create value [2]. Digital transformation is also a gradual evolutionary process. The use of digital technology and the improvement of digital capabilities are themselves evolutionary, and they will create value by stimulating business models, customer experience, and operational processes [3]. Through semantic analysis of the existing definition of digital transformation, Vial [4] determined that the definition should contain four attributes: target entity, transformation scope, technical means, and expected result. Based on this, this paper defines digital transformation as being that the subject of transformation makes full use of digital technology and resources to optimize business, upgrade technology, and change organization, so as to improve work efficiency and enhance its own competitiveness.
With the introduction of various policies on digital transformation, the digital transformation of automobile enterprises presents the typical characteristics of comprehensive upgrading, and digital transformation has also risen to the macro strategic level of enterprises. However, in its practical application, there are some problems, such as unspecific development directions, inconsistent strategic routes, and insignificant performance improvement, which make it difficult for enterprises to accept the huge investment expenditure of digital transformation with unknown return and raises doubts on the strategy of implementing digital transformation to solve the difficulties of enterprises [5]. Due to the complexity of automobile manufacturing, agility and intelligence are the key to the sustainable survival of automobile enterprises in the industry. Meanwhile, the automobile industry also plays an important role in the digital field. Opportunities and challenges brought by digital transformation accelerate the development of the automobile industry [6]. The digitalization of automobile enterprises is mainly concentrated in four stages: research and development, production, management, and marketing. The first and most important stage is product research and development, and product research and development performance is an important part of an enterprise’s overall performance. Therefore, it is of certain practical significance to study whether digital transformation will affect product R&D performance for automobile enterprises to formulate development strategies and firm transformation determination.
In the process of enterprise digital transformation, digital components, digital infrastructure, and digital platforms are the basic elements of digital transformation [2]. The process of transformation will be hindered by insufficient staff capacity [7], insufficient enterprise resources, path dependence, and risk avoidance [8]. Strengthening shared infrastructure, exploring transformation and evolution paths [9], cultivating the dynamic ability of design thinking [10], and digital adaptive governance that ADAPTS to new data while balancing organizational risks and benefits are key measures for digital transformation [11]. Digital organizational form, digital institutional infrastructure [12], and their position in the value chain [13] will affect the digital transformation of enterprises. Meanwhile, when studying the driving role of digital transformation, it is found that at the enterprise level, digital transformation can significantly enhance the level of enterprise value [14], support small- and medium-sized enterprises (SMEs) to transform to the sustainable production paradigm [15], improve enterprise performance and supply chain integration except for SMEs [16], promote the external circulation of large- and medium-sized enterprises [17], and significantly improve the innovation and development efficiency of high-tech industries [18]. At the level of urban development, professional digital technology can assist in the construction of smart cities and solve the problems of environmental pollution, traffic congestion, and resource shortages in the process of sustainable urban development [19]. However, at the level of public administration, the incomplete popularization of information technology will make the digitization of local government always be in an immature state, which does not bring work relief, but aggravates communication conflicts between local government and citizens and the workload of government employees [20]. In addition, an effective way for enterprises to carry out digital transformation is an ecological synergy strategy, one of the core elements of which is innovation ecology, which requires giving up the original closed innovation and building an open innovation ecosystem [21]. Using a case study, scholars found that participants of the platform ecosystem can promote their own digital transformation by adopting the three-stage “dependent upgrading” strategy of mutual integration, symbiosis, and autonomy [22]. The implementation path of agricultural digital transformation is to create an innovative ecological environment, optimize value innovation networks, enhance collaborative innovation ability, and strengthen innovation factor support [23]. Data participants who generate and use big data (including academia, industry, government, society, individuals, etc.) constitute a big data and business analysis system. Participants’ continuous development and iteration of their own big data analysis ability in the analysis system will cause social change and value creation, which is also a key factor for digital transformation and the creation of a sustainable society [24].
To sum up, the existing research mainly discusses the theoretical framework and influencing factors and how to promote enterprises’ digital transformation at the enterprise level. However, when analyzing the driving role of digital transformation, research mainly focuses on manufacturing enterprises, and the management inspirations and suggestions proposed lack industry pertinence. At the same time, it is clearly stated in the 14th five-year plan that the digital economy will become an important means to promote China’s economic development. Digital technology is an important production factor of the digital economy, and digital innovation capability is one of the core capabilities of the digital transformation of automobile products. However, few scholars have analyzed the relationship between digital technology innovation capability and enterprise digital transformation. In addition, the promotion effect of the innovation ecosystem on digital transformation, digital technology innovation ability, and product R&D performance is ignored.
Based on the current situation of the digital transformation of automobile enterprises and the literature overview, this paper will study three aspects: digital transformation, product research, and development performance and digital technology innovation ability. Firstly, a direct effect model is established to deeply explore the mechanism of the relationship between digital transformation and the product R&D performance of automobile enterprises, providing a new theoretical perspective for automobile enterprises to formulate product R&D strategies. Secondly, an intermediary model is established to study the transmission effect of digital technology innovation ability between digital transformation and the product R&D performance of automobile enterprises and to explore the internal action path of enterprise digital transformation on product R&D performance. Finally, a moderated mediation effect model is established. The innovation ecosystem includes the innovation subject within the system, various innovation resources involved in innovation, and the innovation environment in which the innovation subject and resources reside [25,26]. Innovation ecological resources usually affect the innovation strategy of enterprises, and the change of the environment will also cause the change of the strategic choice of enterprises [27]. Therefore, two variables of innovation ecological resources and innovation ecological environment are introduced in this paper to explore their regulating effects on the intermediary model and the direct effect model, which not only enriches the theoretical framework of digital transformation, but also complements the improvement path of enterprise R&D performance. The aim of the study is to promote the digital transformation of automobile enterprises and improve the performance of product research and development to provide targeted suggestions.
The remainder of this study is organized as follows: Section 2 presents the research hypotheses of this paper. Section 3 presents the statistics and tests of the measurement data adopted in the article. Section 4 calculates the correctness of the research hypothesis proposed in this paper. Section 5 summarizes and analyzes the full text.

2. Theoretical Basis and Research Hypotheses

2.1. Digital Transformation and Product R&D Performance

The purpose of the digital transformation of enterprises is to make full use of new information technologies to enhance the survival and development of enterprises and to promote the growth of performance and optimization of value systems, and the digital technology capabilities in the transformation process can contribute to business model innovation and performance improvement [28]. Driven by digital technology, enterprises achieve breakthrough innovation reflected in the transformation of the product R&D process, which is conducive to improving product R&D performance [29]. Product R&D performance, in turn, belongs to the organizational performance among the three major components of enterprise performance; therefore, the study of the impact of digital transformation of automotive enterprises on enterprise performance can be specified on product R&D performance. The process of product development and design in automotive companies includes the preparation and planning stage, concept development stage, product development stage, product certification stage, and pilot production stage. Combined with digital technology, Siemens has introduced a digital vehicle performance development program, which optimizes and finalizes the overall architecture of the product by modeling and analyzing the systems in the vehicle in the preliminary concept design phase, determines the manufacturing plan of each subsystem and component in the design phase of product development based on CAD modeling with the aid of a multidisciplinary optimization platform, and adopts a digital prototype in the final product certification phase. In the final product certification stage, they use digital prototypes to reduce the failure rate of physical prototype testing, obtain more innovative and better-performing products, and improve product development performance.
Based on this, the following research hypothesis is proposed:
Hypothesis (H1).
Digital transformation is positively affecting the product R&D performance of automotive companies.

2.2. The Mediating Role of Digital Technology Innovation Capabilities

Digital technology innovation refers to the use of a new generation of information technology to drive the integration of enterprise technology and the transformation of the technology system and is a technological innovation process in which enterprises carry out innovation cooperation based on a well-developed innovation strategy as a way to promote the digital transformation of enterprises [30]. Under the general trend of economic globalization, the emergence of digital technology reduces the entry barriers of the global market, enabling new entrants to gain profits in the highly competitive international market [31]. The emergence of new entrants will inevitably reshape the distribution of interests in the global market. At this time, in order to protect or increase their own interests, members of the global market need to find new technological breakthroughs. The digital transformation of enterprises has to be built based on technology, and the digital technology innovation capability determines the level of technology possessed by enterprises, especially the level of digital technology that is highly relevant to the transformation of enterprises, so digital technology innovation capability is closely related to the digital transformation of enterprises. The application of digital technologies promotes cross-border integration of resources, extends the scope of product functionality implementation, and at the same time can drive adaptive innovation of products based on user needs [32]. The speed and quality of innovation positively contribute to the R&D performance of complex products such as large telecommunication systems and aerospace systems [33]. Advanced digital technologies in turn provide companies with a wealth of information about market needs, guiding them to optimize products at all stages from R&D to production by reallocating resources [34,35]. For example, technology developers have added AR, VR, and MR technologies to the traditional CAE simulation digital R&D process, i.e., they have innovated based on the original digital technology, which has greatly improved the efficiency of the newly upgraded CAE simulation and reduced both the initial investment and R&D cost of new products and shortened the product development cycle.
Based on this, the following research hypothesis is proposed:
Hypothesis (H2).
Digital technology innovation capabilities play a mediating role in the relationship between digital transformation and product R&D performance in automotive companies.

2.3. The Moderating Effect of Innovation Ecological Resources

Resource dependency theory emphasizes that an enterprise should become a collection of capabilities and resources that are difficult to imitate, and the resources include tangible products, technological resources, and intangible resources of market and user needs [36]. The survival of an enterprise needs resources, and the transformation of an enterprise requires the corresponding resources to provide support. When an enterprise lacks the knowledge and resources required to deal with issues related to digital transformation, digital transformation will negatively impact the development of an enterprise [37]. However, it is difficult for an enterprise to obtain all the resources required for transformation by itself, and it often needs to obtain resources from its environment to complete the digital transformation of the enterprise. The innovation ecological resources include human resources, financial resources, physical resources, knowledge resources, and technical resources required by enterprises in the process of digital transformation. With rich innovation ecological resources, enterprises also obtain the elements for digital transformation capability improvement, which is also the original driving force for enterprises to improve their digital transformation level. With rich innovation ecological resources, enterprises can fully develop and utilize the resources they need to create the corresponding technologies and improve their technological innovation capabilities [38]. The rich innovation ecological resources enable companies to promote the digital transformation process, while the level of digital technology is also increasing, which allows companies to deeply explore and innovate digital technology and promote the improvement of their digital technology innovation capabilities.
Based on this, the following research hypothesis is proposed:
Hypothesis (H3a).
Innovation ecological resources positively moderate the positive relationship between digital transformation and the digital technology innovation capability of automotive companies.
Key technological resources will enhance the R&D performance of new products of enterprises by influencing their technological or product innovation capabilities [39]. Innovation requires creative professional knowledge and learning processes among people from different functional fields and levels. Therefore, technological innovation depends to a large extent on the flow and acquisition of knowledge [40]. The rich innovation ecological resources provide enterprises with various basic elements such as the technology, knowledge, and labor required in the transformation process, which enable the continuous improvement and development of digital technology innovation capability and enhance its contribution to the product R&D performance of enterprises. Digital transformation allows enterprises to obtain more adequate innovation information while carrying out more high-quality technological innovation activities supported by rich innovation ecological resources and promoting the value growth of enterprises [14]. On the contrary, since firms’ innovation activities do not take place in a closed environment and require extensive resources from the external environment, the strength of technological innovation capability largely reflects how much resource support firms receive in the ecosystem in which they operate [41]. Therefore, when firms do not have access to the resources needed for digital transformation, it is also difficult to enhance digital technological innovation capabilities, weakening the mediating role of digital technological innovation capabilities between digital transformation and product R&D performance.
Based on this, the following research hypothesis is proposed:
Hypothesis (H3b).
Innovation ecological resources positively moderate the mediating role of digital technology innovation capability in the relationship between digital transformation and the product development performance of automotive companies.

2.4. The Moderating Effect of Innovation Ecological Environment

The innovation ecological environment is the social network relationship between enterprises and other innovation subjects through collaboration and learning and the market scale, infrastructure, policy system, and other elemental conditions of the region where the enterprises are located, which is an important support for enterprises to carry out innovation activities [42]. A perfect innovation ecological environment will provide various favorable policies for digital transformation enterprises and promote more industry–university research cooperation, and universities will provide more professional talents for enterprises to promote their transformation process, enhance their digital transformation level, and accelerate digital technology innovation. The larger market size combined with the perfect infrastructure promotes the technological innovation capability of enterprises while providing the original resources for achieving technological innovation [43]. Therefore, a better innovation ecological environment can enhance the positive contribution of digital transformation. On the contrary, when enterprises are in a poor innovation eco-environment, they will encounter obstacles such as the insufficient supply of professionals, low market demand, and insufficient motivation for transformation, which delay the digital transformation process and make it difficult to carry out digital technology innovation activities efficiently, thus weakening the role of digital transformation in promoting digital technology innovation capability.
Based on this, the following research hypothesis is proposed:
Hypothesis (H4a).
The innovation ecological environment positively moderates the positive relationship between digital transformation and the digital technology innovation capability of automotive companies.
The innovation ecological environment can affect the technological innovation as well as the innovation performance of enterprises by influencing their R&D activities [44]. A good innovation ecological environment can provide a transformation platform with perfect economic infrastructure, a high level of resource allocation, and strong policy support for digital transformation enterprises, which can help enterprises quickly integrate rich knowledge, technology, and other resources for digital technology innovation and put new methods and technologies into the R&D process of new products to improve the R&D performance of products, enabling enterprises to enhance their digital transformation by conducting enhance enterprise value. On the contrary, a poor innovation ecological environment makes it difficult for enterprises to integrate the necessary resources for transformation and to carry out efficient product R&D activities, and the enhancement of digital technology innovation capability is limited, weakening its role in promoting digital transformation and product R&D performance.
Based on this, the following research hypothesis is proposed:
Hypothesis (H4b).
The innovation ecological environment positively moderates the mediating role of digital technology innovation capability in the relationship between digital transformation and the product R&D performance of automotive companies.
The hypothetical model proposed in this paper is shown in Figure 1:

3. Research Design

3.1. Data Collection and Sample Statistics

A questionnaire was used to obtain the data required for the empirical testing part of the paper, and the question items of each research variable in the questionnaire were set concerning the mature scales at home and abroad, and the Likert Scale was used to measure the data. “1” means strongly disagree, “3” means neutral, and “5” means strongly agree. The questionnaire was distributed through the Internet to automobile enterprises undergoing digital transformation. The questionnaire requires the enterprise to have been established and officially operated for more than five years and for the participants to have worked in the enterprise for at least three years and to be familiar with the overall situation of the operation of the enterprise’s management personnel to fill in the questionnaire. A total of 335 questionnaires were collected, and 240 valid questionnaires were collected. The effective recovery rate of the questionnaires was 71.6%, and the sample size was more than 5 times the sum of the items of the various variables; thus, the results are highly reliable [45]. According to the word cloud generated from the non-multiple choice part of the valid questionnaire, the core businesses of the surveyed companies cover vehicle production (passenger cars, commercial vehicles, buses, etc.) and component production (engines, transmissions, power batteries, etc.); the utility model relates to a fuel-powered vehicle and a new energy-powered vehicle (a pure electric vehicle, a plug-in hybrid electric vehicle, and a fuel cell vehicle). Descriptive statistics of the sample enterprise characteristics are shown in Table 1.

3.2. Variable Measurement

The article constructs a model of direct effect, mediating effect, moderating effect, and mediating effect with moderation, and the measure of each variable is as follows. In addition, a description of the measurement items of each variable is shown in Table 2.
(1) The independent variable is digital transformation (DT). Drawing on the existing literature [46,47], this paper measures the digital transformation of automotive companies from seven aspects, including the degree of investment in digital software and hardware, the degree of digital technology integration, and the degree of business digitization, etc.
(2) The dependent variable is product R&D performance (PRDP). Drawing on the existing literature [48,49,50], this paper measures the product development performance of automotive companies from six aspects, including the success rate of new product development, whether the R&D expenses exceed the budget, and whether the initial design targets are met, etc.
(3) The mediating variable is digital technology innovation capability (DTIC). Drawing on the existing literature [28], this paper measures the digital technology innovation capability of automotive enterprises from three aspects: the proportion of digital equipment investment to total internal expenditure on R&D, the proportion of digital technology renovation cost to the amount of new fixed asset investment of enterprises, and the number of patents with digital technology output applied.
(4) The moderating variables are innovation ecological resources (IER) and the innovation ecological environment (IEE). The innovation of ecological resources is measured in terms of human input, knowledge input, and financial expenditure. The innovative ecological environment is measured in terms of market, policy system, and infrastructure.
(5) The control variables are enterprise size, years of establishment, percentage of R&D staff, and R&D investment ratio.

3.3. Reliability and Validity Test

Before testing the hypotheses in the text, it was important to test whether the results of the data collected by the questionnaire are consistent, so a reliability test was conducted. In this paper, Cronbach’s alpha coefficient is adopted for testing, and Table 3 shows that the alpha coefficient of each variable exceeds the standard value of 0.8, and the alpha coefficient of the overall variable does not increase when any of the question items in the variable are deleted, indicating that the reliability of the questionnaire scale is good, and the data results have good consistency.
The overall KMO value of the questionnaire was 0.922, which was greater than 0.8, and the p-value obtained from Bartlett’s spherical test was less than 0.001 which passed the significance test; thus, it can be concluded that the questionnaire scale data are suitable for factor analysis. The cumulative variance contribution of the five extracted principal components reached 74.9%, and the cumulative contribution of the variance of each factor exceeded 60%; thus, it can be concluded that the scale has good internal consistency.
In this paper, validity tests of the questionnaire were conducted using confirmatory factor analysis. From Table 4, we can see that the goodness-of-fit indicators of each variable are within the required range, and the overall structural validity of the model is good. As can be seen from Table 4, the standardized factor loadings of the questions corresponding to each variable are greater than 0.7, indicating that the questions corresponding to each variable are highly representative, and in addition, the mean-variance AVE values of each variable are greater than 0.5, and the combined reliability CR values are greater than 0.8, indicating that the questionnaire has good convergent validity.
The results in Table 5 show that the five variables of digital transformation, product R&D performance, digital technology innovation capability, innovation ecological resources, and innovation ecological environment have significant positive correlations, and all of them are smaller than the corresponding square root of AVE, which indicates that there is both correlation and differentiation among the variables, and the questionnaire data have good differentiation validity.

3.4. Common Method Deviation Test

Since some artificial covariates can cause systematic errors in the study results due to the same data source, questionnaire filler, or question measurement environment, etc., the questionnaire measurement results should be tested for common method bias. In this paper, a method factor is added to the original factor to form a new model, and the main fit indicators of the new model are compared with the original model: Δχ2/df = −0.606, ΔCFI = 0.035, ΔTLI = 0.033, and ΔRMSEA = −0.016, and the increase of CFI and TLI is less than 0.1, and the decrease of RMSEA is less than 0.05, indicating that the fit of the model did not become much better after the addition of the common method factor, indicating that there is no serious common method bias in the measurement of the scale [51].

4. Analysis Result

4.1. Direct and Mediated Effects Test

The PROCESS procedure in SPSS software was used to test the direct effect of digital transformation and the mediating effect of digital technology innovation capability, controlling for the size, age, percentage of R&D personnel, and percentage of R&D investment of the companies filling in the questionnaire. The results are shown in Table 6 and Table 7.
(1) The effect of digital transformation on the product development performance of automotive companies is significantly positive (β = 0.591, t = 11.224, p < 0.001). This indicates that the digital transformation work carried out by automobile enterprises can positively promote the improvement of product research and the development performance of enterprises, and hypothesis H1 is verified.
(2) The effect of digital transformation on digital technology innovation capability is significantly positive (β = 0.615, t = 12.052, p < 0.001), and the effect of the added mediating variable digital technology innovation capability on product R&D performance is significantly positive (β = 0.406, t = 6.526, p < 0.001), while the positive effect of digital transformation on product R&D performance is weakened (β = 0.341, t = 5.529, p < 0.001). Therefore, digital technology innovation capability has a significant mediating effect between digital transformation and product R&D performance. The results show that the digital transformation actively carried out by automobile enterprises is conducive to the improvement of enterprises’ digital technology innovation ability, which is an important focus for the implementation of innovation-driven development strategy and firm direction of independent innovation and has great significance for the improvement of industrial competitiveness and enterprise performance. Hypothesis H2 is verified. In addition, the upper and lower limits of the 95% confidence interval of Bootstrap for the direct effect of digital transformation on product R&D performance and the mediating effect of digital technology innovation capability are positive and do not contain 0. This also verifies that the hypothesis of both the direct effect and the mediating effect proposed in the paper holds, and the direct effect (0.348) and the mediating effect (0.254) account for 57.8% and 42.2% of the total effect (0.602), respectively.

4.2. Moderating Effect Test

The results of the moderating effects of innovation ecosystem resources and the innovative ecological environment between digital transformation and the digital technology innovation capability of automotive companies can be seen in Table 8. Meanwhile, the sample means of the moderating variables plus or minus one standard deviation are divided into two groups of high level and low level to test the moderating effect of the moderating variables.
(1) The interaction term between digital transformation and innovation ecosystem resources has a significant positive effect on digital technology innovation capability (β = 0.131, t = 2.288, p < 0.05), indicating that innovation ecosystem resources can positively regulate the positive relationship between digital transformation and the digital technology innovation capability of automotive enterprises. Innovation ecological resources are the basic elements for automobile enterprises to carry out innovation activities and implement the strategy of digital transformation. The availability of innovation resources such as talents, knowledge, and technology are the prerequisite for enterprises to carry out transformation. Abundant innovation ecological resources and digital transformation jointly affect the technological innovation ability of enterprises, assuming that H3a is verified. Figure 2 more clearly shows the regulatory effect of innovation ecological resources; when the level of innovation ecosystem resources owned by automotive companies is higher, the promotion effect of digital transformation on digital technology innovation capability is stronger and vice versa, and hypothesis H3a is further verified.
(2) The interaction term between digital transformation and the innovative ecological environment has a significantly positive effect on digital technology innovation capability (β = 0.120, t = 2.239, p < 0.05), indicating that the innovative eco-environment can positively regulate the positive relationship between digital transformation and the digital technology innovation capability of automotive enterprises. The innovation ecological environment is the basic platform for automobile enterprises to implement innovation strategies and carry out innovation activities. The policy support and scientific research guarantee provided by the innovation ecological environment will affect the level of digital transformation of enterprises to a large extent and promote the improvement of technological level by driving enterprise development, assuming that H4a is verified. Figure 3 more clearly shows the regulatory effect of the innovation ecological environment; when the level of the innovative ecological environment in which automotive companies are located is higher, the promotion effect of digital transformation on digital technology innovation capability is stronger and vice versa, and hypothesis H4a is further verified.

4.3. Moderated Mediating Effect Tests

The moderating results of innovation ecosystem resources and the innovative ecological environment on the mediating role can be seen in Table 9. Meanwhile, the sample means of the two moderating variables plus and minus one standard deviation were divided into high-level and low-level groups to test the moderating effect of the moderating variables on the mediating variables at different levels.
(1) The indirect effect of digital transformation on product R&D performance through digital technology innovation capability is 0.192 when the level of innovation eco-resources owned by automotive companies is high, which is higher than the indirect effect of 0.113 at lower levels of innovation eco-resources, and the 95% deviation correction confidence interval of innovation eco-resources at different levels is non-zero and positive. The results in Table 10 show that the moderating effect of the innovation eco-resource on the mediating effect is determined as 0.060 with a non-zero confidence interval. Thus, it can be seen that innovation ecological resources can positively moderate the mediating role of digital technology innovation capability in the relationship between digital transformation and the product development performance of automotive companies. The better the ecological resources of innovation, the stronger the mediating effect of digital technology innovation ability between digital transformation and product R&D performance, and vice versa. The digital technology innovation capability of automobile enterprises needs good resource allocation to develop and expand. Rich resource allocation is the basis of the resource integration and utilization of enterprises. Better innovation ecological resources are conducive to improving the technological innovation capability of enterprises and promoting the output of innovation results, so that the technological innovation of enterprises in digital transformation can bring value and performance improvement to enterprises. Therefore, hypothesis H3b is verified.
(2) The indirect effect of digital transformation on product R&D performance through digital technology innovation capability is 0.207 when the level of the innovative ecological environment is high, which is higher than that of 0.138 at lower levels of the innovative ecological environment, and the 95% deviation correction confidence interval of the innovative ecological environment at different levels is non-zero and positive. The results in Table 10 show that the moderating effect of the innovative eco-environment on the mediating effect is determined as 0.065 with a non-zero confidence interval. Thus, the innovative ecological environment can positively moderate the mediating role of digital technology innovation capability in the relationship between digital transformation and the product development performance of automotive companies. The better the innovation ecological environment, the stronger the mediating role of digital technology innovation ability between digital transformation and product R&D performance and vice versa. The sustainable development of technological innovation capability requires a positive and healthy innovation ecological environment. Efficient resource allocation and large market demand can strengthen the positive role of digital transformation, accelerate the flow and integration of resources in the innovation ecosystem, promote the technological innovation activities of enterprises, and improve the performance of product research and development. Therefore, hypothesis H4b is verified.

5. Conclusions and Discussion

5.1. Research Conclusions

This paper establishes a direct correlation between digital transformation and enterprise product R&D performance. Based on the empirical test of questionnaire data, it concludes that there is a significant positive promoting relationship between the two, which not only supplements the outcome variable of digital transformation but also expands the influence variable of product R&D performance. The application of digital technology has an impact on automobile enterprises based on product research and development. The concept of building an innovation ecosystem provides a new development strategy for enterprises to carry out digital transformation. This paper draws the following conclusions through theoretical analysis and empirical data research:
(1) Digital transformation can improve the product research and development performance of automobile enterprises. With the advancement of enterprise digital transformation, digital technology is gradually introduced into every link of product R&D and design, which makes the advantages of enterprise data management and organizational processes prominent. The application scenarios of digital technology cover different stages of product development, which can reduce the cost of trial and error while saving the cost of research and development.
(2) Digital transformation can promote the improvement of product research and the development performance of automobile enterprises by improving the innovation ability of digital technology. The new digital technology brought by digital transformation enables enterprises to innovate and upgrade on the existing technical level. Through the redevelopment of traditional resources and capabilities, they can provide more efficient and less consuming working methods for each stage of automobile product manufacturing. This echoes the conclusion proposed by Li [16] that enterprises’ digital transformation can gain market competitive advantages through the use of digital technology.
(3) Innovation ecological resources and the innovation ecological environment not only regulate the positive relationship between digital transformation and digital technology innovation capability, but also regulate the mediating role of digital technology innovation capability in this relationship. In the innovation ecosystem established according to ecological theory, besides innovation subjects, innovation ecological resources and the innovation ecological environment are also included, which can provide support in the process of the digital transformation of automobile enterprises. The former provides fuel and the latter clears roadblocks. This also responds to Henry’s [52] proposal to expand the dissemination range and cognition depth of digital technology through extensive innovation policies, so as to combine digital technology with relevant departments and applications, so as to promote the generation of innovation achievements and enhance enterprise value.

5.2. Management Enlightenment

Based on the research results, this paper puts forward the following suggestions:
(1) The government can promote the efficient digital transformation of automobile enterprises by formulating new policies or system reform. First of all, improve the introduction of high-end talents policy for automobile enterprises, establish the innovation and entrepreneurship base for automobile product R&D personnel, and introduce fiscal and tax subsidy policies for automobile enterprises to carry out digital transformation, so as to help enterprises improve their viability and complete digital transformation, so as to enhance their competitiveness and better cope with external industry pressure. Secondly, the legal and regulatory system for the protection of data assets such as automobile patents and intellectual property rights should be constructed and perfected to eliminate the improper competition of digital technology in the automobile industry, so as to reassure enterprises about the innovation and development of digital technology. Finally, a typical enterprise database for the digital transformation of automobile enterprises is established, and excellent digital transformation cases are classified into the enterprise database according to different stages of the whole life cycle of automobile products to provide transformation ideas for other enterprises.
(2) Relevant associations of the automobile industry should ensure that enterprises can continue to carry out efficient product research and development. First of all, while ensuring the steady growth of the export market of automobile products, we should constantly expand the domestic market of automobile products and guarantee the market demand of automobile enterprises. Secondly, rational allocation of resources should be carried out to optimize the market structure, ensure an adequate supply of resources for enterprises with product research and development strength, encourage enterprises with insufficient research and development strength to invest in the automobile aftermarket service, and promote the shift of the focus of the value chain of the automobile industry. While reducing the external competition pressure of strong R&D enterprises, we should expand the business service scope of weak R&D enterprises and ensure the full utilization of resources. Finally, the establishment of the industry–university research base and the introduction of foreign advanced technological means and automotive products, support enterprises and research institutes, universities, and other institutions’ full cooperation for reverse research and development reduce the risk and cost of research and development at the same time to develop new digital technologies and products.
(3) Promoted by the government and industry, enterprises should attach importance to the role of digital transformation in improving product R&D performance. First of all, after obtaining rich innovation ecological resources and a high-level innovation ecological environment by building or joining the innovation ecosystem, automobile enterprises should speed up the establishment of collaborative research and development platforms, so as to smooth the circulation of resources among different levels and departments and break the information barriers within research and development, production, and sales departments to improve research and development efficiency. Secondly, build a cloud database, integrate the data information of planning, research and development, production, after-sales, and other departments, improve the sensitive collaboration between research and development links and other links, promote the data of all departments to feed product research and development, shorten the product research and development cycle, and improve the applicability of products. Finally, strengthen the application of digital technology, improve the enterprises’ digital technology innovation ability, provide better technical support for the product research and development stage, and promote the improvement of product research and development performance.

5.3. Research Limitations and Prospects

As there are few empirical studies on digital transformation and digital technology innovation at present, this paper only makes a preliminary exploration, which can be further discussed in the following aspects in the future:
(1) The focus of this paper is the impact of digital transformation on product R&D performance from the perspective of the innovation ecosystem, rather than how enterprises can efficiently complete digital transformation. Subsequent studies can take digital transformation as the result variable to analyze its influencing factors under the innovation ecosystem.
(2) This paper mainly obtains the research results based on the questionnaire survey data of automobile enterprises. Since the digitalization level and main business of different industries are different, the questionnaire content can be modified according to the characteristics of the industries for empirical research, reflecting the difference in the influence path of digital transformation of different industries.
(3) The data in the empirical test part of this paper come from the questionnaire survey of automobile enterprises. Subsequent studies can use the data published by the National Bureau of Statistics and other data websites to more accurately measure the relationship between variables to enrich the research conclusions.

Author Contributions

Conceptualization, Y.L. and H.Y.; methodology, H.Y.; software, F.M.; validation, Y.L., F.D. and F.M.; formal analysis, Y.L.; investigation, Y.L.; data curation, H.Y.; writing—original draft preparation, Y.L.; writing—review and editing, Y.L., F.D. and H.Y.; visualization, F.M.; supervision, F.D.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Tianjin 2021 Philosophy and Social Science Planning Project “Research on the Influence of Supply Chain Partnership on R&D Innovation of High-end Equipment Manufacturing Enterprises in Tianjin-The Heterogeneous Perspective of Regional Institutional Environment.” Project number TJGL21-011.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 06265 g001 550
Figure 1. Hypothetical model diagram.
Figure 1. Hypothetical model diagram.
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Figure 2. Moderating effect of innovation ecosystem resources.
Figure 2. Moderating effect of innovation ecosystem resources.
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Figure 3. Moderating effect of innovative ecological environment.
Figure 3. Moderating effect of innovative ecological environment.
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Table
Table 1. Descriptive statistics of the enterprise characteristics.
Table 1. Descriptive statistics of the enterprise characteristics.
VariableIndexSample SizeFrequency
Enterprise Size
(ten thousand people)		<57631.67%
5–108635.83%
10–155121.25%
>152711.25%
Years of Establishment<156426.67%
15–309941.25%
30–506426.67%
>50135.42%
Percentage of R&D Staff<3%4117.09%
3–10%12451.67%
10–15%5522.91%
>15%208.33%
R&D Investment Ratio<5%3615%
5–10%10142.08%
10–15%8133.75%
>15%229.17%
Table
Table 2. Questionnaire item description.
Table 2. Questionnaire item description.
Variable DimensionItem Description
Digital TransformationDT1: Enterprises invest more in digital software and hardware.
DT2: Enterprise digital technology integration and application ability is strong.
DT3: Enterprises to promote digital design, manufacturing, and management.
DT4: Enterprises adopt digital technology to transform and upgrade products, production processes and pre-sale and after-sales services.
DT5: Enterprises develop digital products and services.
DT6: Enterprises are willing to promote and publicize digital skills and management knowledge.
DT7: Enterprise internal consensus that the use of digital technology and digital management is conducive to enterprise development.
Product R&D PerformancePDP1: The sales volume of new products of enterprises is higher than that of the same type of products of competitors.
PDP2: The success rate of enterprise new product research and development is higher.
PDP3: The R&D expenditure of new products can be controlled within the project budget.
PDP4: The new products developed by the enterprise can reach the initial design index.
PDP5: The sales of the new products developed by the enterprise can reach the expected target.
PDP6: Industry research and development of new product profits can reach the expected target.
Digital Technology Innovation
Capability		DTIC1: Enterprise digital equipment investment accounts for a high proportion of the total internal expenditure of R&D funds.
DTIC2: The cost of enterprise digital technology transformation accounts for a higher proportion of new investment in fixed assets.
DTIC3: Companies produce more patents after applying digital technology than before.
Innovation Ecological
Resources		IER1: The region where the enterprise is located has a higher market demand for digital technology.
IER2: Enterprises can easily obtain the knowledge resources needed for digital transformation.
IER3: Enterprises can easily obtain the activity funds required for digital transformation.
Innovation Ecological
Environment		IEE1: The region where the enterprise is located has a higher market demand for digital technology.
IEE2: The company is located in a region with favorable policies for the application of digital technology.
IEE3: The enterprise is located in a region with better digital technology infrastructure.
Table
Table 3. Reliability and validity measurements of the variables.
Table 3. Reliability and validity measurements of the variables.
Questionnaire ItemsFactor LoadingKMOCronbach’s αAVEC.R.
DT1–DT70.683–0.8940.8800.9110.5980.912
PRDP1–PRDP60.705–0.8620.8490.8860.5700.888
DTIC1–DTIC30.851–0.9140.7520.9210.8060.926
IER1–IER30.830–0.9370.7250.9080.7740.911
IEE1–IEE30.784–0.9550.7160.9030.7660.907
Table
Table 4. Overall fitting coefficient.
Table 4. Overall fitting coefficient.
χ2/dfRMSEAIFITLICFI
2.7990.0870.9160.9020.915
Table
Table 5. Variable means, standard deviations, and correlation coefficients.
Table 5. Variable means, standard deviations, and correlation coefficients.
MeanStandard
Deviation		DTPDPDTICIERIE
DT4.3990.7090.773
PRDP4.0130.7230.637 ***0.750
DTIC3.3970.7310.637 ***0.668 ***0.898
IER3.1630.7170.642 ***0.696 ***0.783 ***0.880
IEE3.4560.5880.593 ***0.620 ***0.715 ***0.684 ***0.875
Note: *** p < 0.001; the square root of AVE is indicated by a thickened diagonal line.
Table
Table 6. Direct and mediated effects test.
Table 6. Direct and mediated effects test.
Regression Equation (n = 240)Fitting IndexSignificance of the
Coefficient
Outcome VariablePredictor VariableR2Fβt
PRDP 0.35325.553 ***
Size 0.0470.867
Years 0.0310.542
R&D Staff −0.021−0.330
R&D Investment −0.029−0.484
DT 0.59111.224 ***
DTIC 0.39230.182 ***
Size 0.0731.400
Years 0.0420.756
R&D Staff −0.075−1.223
R&D Investment −0.041−0.700
DT 0.61512.052 ***
PDP 0.45332.176 ***
Size 0.0170.342
Years 0.0140.265
R&D Staff 0.0100.163
R&D Investment −0.013−0.226
DT 0.3415.529 ***
DTIC 0.4066.526 ***
Note: *** p < 0.001.
Table
Table 7. Decomposition of the total effect, direct effect, and mediation effect of digital technology innovation capability.
Table 7. Decomposition of the total effect, direct effect, and mediation effect of digital technology innovation capability.
Effect SizeBoot Standard
Error		Boot LLCIBoot ULCIEffect Ratio
Mediation Effect0.2540.0430.1750.34442.21%
Direct Effect0.3480.0620.2190.46157.80%
Total Effect0.6020.0540.4970.708
Table
Table 8. Test for moderating effects.
Table 8. Test for moderating effects.
Regression Equation (n = 240)Fitting IndexSignificance of the Coefficient
Outcome VariablePredictor VariableR2Fβt
DTIC 0.59147.897 ***
Size 0.0270.625
Years 0.0290.644
R&D Staff 0.0040.087
R&D Investment −0.012−0.250
DT 0.3695.427 ***
IER 0.54510.010 ***
DT×IER 0.1312.288 *
DTIC 0.55340.955 ***
Size 0.0420.937
Years 0.0621.292
R&D Staff −0.032−0.594
R&D Investment −0.019−0.381
DT 0.4177.080 ***
IEE 0.4849.060 ***
DT×IEE 0.1202.239 *
Note: * p < 0.05; *** p < 0.001.
Table
Table 9. Mediated effects with the moderation test.
Table 9. Mediated effects with the moderation test.
Mediating
Variable		Indirect Effects
DTICIEREffect SizeBoot Standard ErrorBoot LLCIBoot ULCI
M − 1SD0.1130.0280.0600.168
Mean0.1530.0330.0900.221
M + 1SD0.1920.0440.1090.283
IEEEffect SizeBoot Standard ErrorBoot LLCIBoot ULCI
M − 1SD0.1380.0300.0850.203
Mean0.1730.0350.1100.247
M + 1SD0.2070.0440.1280.299
Table
Table 10. Reconciliation intermediary index.
Table 10. Reconciliation intermediary index.
Adjustment of Intermediary Index
Adjustment variablesJudgment indexBoot Standard ErrorBoot LLCIBoot ULCI
IER0.0600.0250.0130.112
IEE0.0650.0270.0150.122
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Men, F.; Dong, F.; Liu, Y.; Yang, H. Research on the Impact of Digital Transformation on the Product R&D Performance of Automobile Enterprises from the Perspective of the Innovation Ecosystem. Sustainability 2023, 15, 6265. https://0-doi-org.brum.beds.ac.uk/10.3390/su15076265

AMA Style

Men F, Dong F, Liu Y, Yang H. Research on the Impact of Digital Transformation on the Product R&D Performance of Automobile Enterprises from the Perspective of the Innovation Ecosystem. Sustainability. 2023; 15(7):6265. https://0-doi-org.brum.beds.ac.uk/10.3390/su15076265

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Men, Feng, Fangqi Dong, Yiying Liu, and Hongxiong Yang. 2023. "Research on the Impact of Digital Transformation on the Product R&D Performance of Automobile Enterprises from the Perspective of the Innovation Ecosystem" Sustainability 15, no. 7: 6265. https://0-doi-org.brum.beds.ac.uk/10.3390/su15076265

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