1. Introduction
The complex and turbulent international environment of technology innovation and economic development needs to be driven by digital innovation. According to the survey by Ernst and Young, nearly 90% of companies have made clear plans for strategic digital development [1]. Digital variables are playing an increasingly important role in the process of industrial technology innovation [2,3]. At present, scholars have begun to emphasize the influence of digital empowerment on enhancing technology innovation performance and in realizing the sustainable competitive advantages of enterprises [4].
However, digital empowerment is still an underexplored concept, and the relationship between digital empowerment and technology innovation performance remains unclear. In particular, China’s high-end equipment manufacturing (HEM) enterprises are facing both opportunities and difficulties in technology innovation. HEM is playing an increasingly important role in leading innovative and high-quality development. Meanwhile, it also faces the opportunities and challenges of digital technology innovation and industrial digital transformation. As one of the seven strategic emerging industries in China, HEM is an advanced industrial equipment industry with high levels of technology and high levels of added value. Tang et al. selected seven HEM sub-industries, including: electronic and communication equipment manufacturing, computer and office equipment manufacturing, medical equipment and instrumentation manufacturing, general equipment manufacturing, special equipment manufacturing, transportation equipment manufacturing, electrical machinery and equipment manufacturing [5]. With the emergence of the new methods to generate digital technology, the manufacturing industry is accelerating its comprehensive digital transformation. The digital transformation of the HEM has achieved increasing levels of innovative achievement. For example, since the launch of the major scientific and technological project known as “high–end computer numerical control machine tools and basic manufacturing assembly” in 2009, high-end machine tools for computer numerical control have gradually developed intelligent perceptual monitoring technology and intelligent error compensation technology. Since then, its market share has increased from less than 1% to approximately 7% in 2020, achieving high-precision, complex, intelligent, comprehensive digital transformation. In addition, with the Beijing–Zhangjiakou high-speed railway (HSR) and Beijing–Xiong HSR as example projects, advanced technologies such as self-developed intelligent detection, machine replacement, and automatic hypothesis are used to continuously strengthen the research and applications of intelligent construction. However, the high technology content, high degree of technology system integration and complex production processes of the HEM lead to its high dependence on technology innovation factors, aggravating the difficulty of digital transformation [6]. Therefore, it is necessary to further clarify the mechanism between digital empowerment and technology innovation performance of HEM enterprises.
Existing research has focused on the digital empowerment and technological innovation of the manufacturing industry. First of all, existing studies have regarded digital capability as the main factor affecting digital transformation [7], but there has been no in-depth analysis of the connotation and mechanism of digital empowerment. In addition, there are many other mechanisms for the technology innovation performance of digital empowerment manufacturing, including data empowerment [8], digital technology empowerment [7,8,9], digital platform empowerment [10,11], etc. These previous studies analyzed how digital empowerment promotes technology innovation through resource acquisition, capability generation, and value realization [12]. Therefore, digital empowerment can significantly improve the speed of enterprise innovation and achieve value creation. However, there are few quantitative tests on the relationship between digital empowerment and technology innovation performance. It is necessary to further clarify the connotation and dimension of digital empowerment to reveal the inner driving force of digital empowerment to make up for the limitations of single digital resource perspective analysis.
Most studies have also demonstrated the positive impact of digital empowerment on technology innovation performance. The resource-based view perspective, which revolves around digital technology facilitating innovation performance by improving resource allocation efficiency and optimizing production processes [13]. However, digital technology is not meant to hinder the performance of technology, and there may be the lag effect, or at least more complex effects, than expected [14]. As an advanced but less mature emerging technology, digital technology plays a more significant role in improving production performance and productivity than more mature technologies in the short term [15,16]. Therefore, the relationship between digital empowerment and the technology innovation performance of HEM is analyzed from a nonlinear perspective, which enriches relevant studies on the relationship between digital empowerment and technology.
Finally, due to the complexity and uncertainty of digital innovation environment in HEM, from the perspective of dynamic capability, enterprises first introduce digital technology as a resource into innovation activities and then improve the adaptability to technology embedding to the environment under the integration of resources and knowledge capabilities [17]. Only when the adaptability to technology embedding is higher can the resources be converted into digital capabilities with competitive advantages really be realized and the digital empowerment can be accelerated to enhance the role of innovation. Therefore, considering digital technology with the existing technological systems and the development process of compatibility and adaptability, the adaptability to technology embedding was introduced [18,19] to analyze it by considering digital empowerment and technology innovation performance, extending the application of adaptability to technology embedding in technology innovation.
Therefore, based on the above analysis and research gaps, we will answer the following research questions: (1) What are the connotations and dimensions of digital empowerment? (2) How do different dimensions of digital empowerment affect technology innovation performance? (3) How does adaptability to technology embedding play a role moderating digital empowerment and technology innovation performance?
The rest of this paper is organized as follows: Section 2 briefly reviews existing studies that are relevant to our research and constructs the research model. Section 3 proposes the research hypothesis and constructs the conceptual model. Section 4 describes the research design. Section 5 describes the research results. Section 6 concludes the empirical findings and contributions. Section 7 concludes the management implications, limitations, and directions for future research.
2. Literature Review
The purpose of this research was to explore the relationship between digital empowerment and technology innovation performance enterprises in the HEM. We try to dissect the influencing factors mentioned above from the perspectives of resource-based theory and dynamic capability theory. Thus, the current section is divided into three subsections: (i) a summary of the current state of research on digital empowerment; (ii) a review of technology innovation performance under the background of digital innovation; and (iii) an elaboration on the latest research in digital empowerment and technology innovation performance.
2.1. Digital Empowerment
Empowerment originates from the concept of “give power to empower”. The process of empowerment is not simply delegating power; it can also be a kind of creation of capability. The digital era has given empowerment a new meaning: digital empowerment is a new phenomenon that has come with the popularization and development of digital technology and is the embodiment of enhanced digital ability. The definition of digital empowerment is not yet universally understood. The focus of empowerment has shifted from the enabling actor to the enabling tool, and the value of the digital empowerment process is increasingly being emphasized. Based on the empowerment actor, the dimensions of empowerment include employee empowerment, customer empowerment, individual empowerment, and organizational empowerment. Empowerment tools are generally divided into three key dimensions: resource empowerment, structural empowerment, and psychological empowerment. Therefore, the digital empowerment process should include both the empowerment tool and empowerment actor and should expand the empowerment actor to the actor relationship, which is more in line with the open characteristics of digital innovation. This study believes that digital empowerment is a new phenomenon emerging with the development. Digital empowerment is the process of empowering innovation actors with digital capabilities by relying on digital technologies and other resources; connecting innovation elements and innovation actors; enhancing technology innovation performance; and realizing value creation. On this basis, digital empowerment is divided into resource empowerment [12] and structural empowerment [17,20] according to the empowerment tools and into platform empowerment [20,21,22] and ecological empowerment [23,24,25] according to the actor relationship.
2.1.1. Resource Empowerment
In the era of the digital economy, resource empowerment mainly includes the data empowerment and digital technology empowerment. Resource empowerment enhances digital capability and promotes technological innovation by enhancing digital perception ability, digital resource connection ability, and digital intelligent analysis ability. For example, China Railway Rolling–Stock Corporation TANGSHAN Limited Liability Company (CRRC TANGSHAN Co., Ltd., Tangshan, China) has accumulated rich experience in the intelligence field, including in HSR service; application and maintenance; technology docking, and quality management. A large number of first-hand test data have been systematically mastered in the comprehensive operation tests before the opening of each HSR system, representing a source of technological innovation in HSR equipment.
2.1.2. Structural Empowerment
Through structural empowerment, industrial boundaries, organizational boundaries, departmental boundaries, and even product boundaries are digitally blurred [17,20]. By enhancing the digital supply capability and digital application capability, enterprises redefine the operation mode of the organization and the relationship between enterprises to promote intelligent manufacturing [26].
2.1.3. Platform Empowerment
By playing into the characteristics of digital platforms such as dynamic plasticity [27], a higher processing capacity and low cost [22], multi-functional technologies can be connected through a series of business function layouts and technology architecture designs [21]. Digital platform empowerment aims to improve the infrastructure of digital platforms and to give full play to the role of digital platforms [28], enhancing the development ability of digital platforms. At the same time, platform empowerment can build the bridge to the integration of digital resources and digital production, in order to promote technological innovation. In the process of HSR development, CRRC TANGSHAN Co., Ltd. has built a new generation of HSR, simulations, tests, manufacturing integration platforms, which are used to integrate technological resources and to improve technology innovation performance.
2.1.4. Ecological Empowerment
Ecological empowerment focuses on the symbiotic relationships between actors [23]. By connecting digital innovation elements and actors, it changes the interdependence between individual actors and transforms the traditional value chain into a value network. Ecological empowerment creates value in a non-linear, cross-level dynamic process, facilitating the transformation of the digital innovation ecosystem [24,25] and achieving the goal of ecological empowerment value creation. CRRC TANGSHAN Co., Ltd. has developed strategic cooperation with Tangshan Municipal government for digital intelligence and dual carbon strategies.
2.2. Technology Innovation Performance
Innovation and technology are inseparable. Innovation includes both technology improvement and the upgrading of different technology trajectories [29]. Therefore, technology innovation is the key factor to improve enterprise innovation performance and to achieve a sustainable competitive advantage [30].
Recently, studies have begun to explore the impact of digital technology innovation on technology innovation performance of enterprise [31,32]. Digital technology innovation has become an important driving force to enhance the competitive advantages of enterprises and to promote organizational change [33]. As an important innovation factor in the digital transformation of HEM, digital technology plays the irreplaceable role of improving organizational innovation potential and enhancing digital innovation performance [11,20,34]. For example, in addition to Tsou and Chen exploring the mediating role of digital transformation strategies and organizational innovation, and demonstrating the industrial linkage effect of digital innovation, they also demonstrated the industrial linkage effect of digital innovation [35]. Pereira et al. analyzed the influence of knowledge sharing and the knowledge innovation environment [36]. However, the mechanism of digital empowerment on technology innovation performance has not been fully explored.
2.3. Digital Empowerment and Technology Innovation Performance
As in the above studies, digital empowerment is still an underexplored concept, and the relationship between digital empowerment and innovation performance remains unclear. Most of the existing research has been conducted on the influence of digital resources and digital capabilities on enterprises’ innovation performance and has been based on resource-based theory and dynamic capability theory.
According to the resource-based theory, enterprises can only achieve sustainable innovation with scarce, unreplaceable, and irreplaceable resources [37]. Digital technology is an important operational resource for enterprises to obtain a sustainable competitive advantage [38]. From the perspective of digital resources, technology innovation performance can be enhanced by reducing the cost of innovation and by increasing the efficiency of technology development, especially the widespread use of digital technology. The characteristics of digital technology, such as connectivity, sharing and openness, are helpful to weaken information asymmetries, and to break down the innovation barriers of enterprises [39,40]. Based on the theory of digital technology affordance, Liu et al. argue that digital innovation provides incremental contributions to enterprise value creation through the speed of innovation and operational efficiency [41], accelerating the responsiveness of firm demand [42].
Dynamic capability refers to the ability to integrate, coordinate and reconstruct internal and external capabilities as well as the environment’s adaptive ability [43]. From the perspective of dynamic capability, Annarelli et al. regard digital capability as a kind of high-level capability that drives enterprises to enhance innovation competitiveness through their capability of integrating digital technology and the capability of digital platforms [44]. As a result of the rapid development of digital technology, digital capabilities have created more opportunities for new innovative activities [45]. In addition, relevant studies have also focused on the ability to select digital technology [9], digital intelligent analysis ability [26,46], digital prediction ability [9,26], intelligent responses to production processes or external customer demands, and digital management ability [47], all of which make it possible to co-create value [46]. These studies provide a reference for digital empowerment for enterprise innovation performance. However, the power of digital empowerment is not only limited to only digital resources and digital capabilities. Most importantly, the role of digital empowerment and innovation performance must also be explored by improving digital capabilities, further enhancing the digital innovation process and the relationship between innovation agents.
In order to clarify the relationship between digital empowerment and technology innovation performance, it is necessary to clarify the connotation, dimension and measurement indicators of variables and to use empirical data to test. Therefore, this study follows the idea of empirical testing. Specifically, the following research was conducted in accordance with the process of theoretical derivation, hypothesis proposal and hypothesis verification. First, based on the literature review, the relationship between variables was further analyzed, and the research hypothesis was proposed. Secondly, structural equation modeling (SEM) was used. Because SEM can analyze the relationship between independent variables and dependent variables, it can analyze and verify theory [48]. Additionally, this research process has been fully applied in many fields such as economic management and sociology. Finally, the statistical analysis software programs SPSS and Amos were used to collect, process, and analyze the data. Theoretical and practical discussions are conducted based on the data analysis results.
3. Research Hypothesis
Based on the above literature review, this part further combines existing research and practice phenomena to analyze the relationship between multidimensional digital empowerment and technology innovation performance. It is mainly developed from two aspects: the direct-action mechanism of multi-dimensional digital empowerment and the moderate effect of adaptability to technology embedding:
Multi-dimensional digital empowerment can be divided into resource empowerment and structural empowerment according to the empowerment tool, platform empowerment, and ecological empowerment according to the empowerment actor relationship. Under the influence of the empowerment tools, there is a certain lag in the influence of digital empowerment on technology innovation performance [14]. Therefore, it is speculated that there is a U-shaped relationship between resource empowerment, structural empowerment, and technology innovation performance. However, under the role of the empowerment actor relationship, digital empowerment can accelerate technological innovation from the role of the innovation actor relationship via a certain innovation foundation and accumulation. Therefore, it is speculated that there is a positive relationship between platform empowerment [10,11], ecological empowerment [25,49], and technology innovation performance.
According to Chi et al.’s proposal, adaptability to technology embedding is the integration of new technologies into the inherent technology development chain and their adaptation to the technology system’s environment to achieve lasting innovation [19]. Technology compatibility capability [11] and technology integration capability [50] are important antecedent variables affecting innovation performance and may characterize the level of adaptability to technology embedding. Embedding digital technology into the organization is beneficial to reduce costs and increase efficiency, and in this way, it can improve the response and adaptability to the external environment. Therefore, the moderating effect of adaptability to technology embedding between digital empowerment and technology innovation performance is further analyzed.
3.1. Multidimensional Digital Empowerment and Technology Innovation Performance
To analyze the relationship between multidimensional digital empowerment and technology innovation performance, this study utilizes resource-based theory and dynamic capability theory.
The resource-based view assumes that enterprises have inimitable and irreplaceable heterogeneous resources. Only when converted into unique capabilities can they form the source of lasting competitive advantage. Digital technology, as an important external operational resource for digital innovation in HEM, is the important source of technology innovation for enterprises. In the digital era, enterprises have abundant resources such as data and digital technology, and, most importantly, digital empowerment enhances the acquisition and integration of resources, improves resource utilization, enhances the potential for technology innovation development, and improves technology innovation performance [51]. Resource empowerment focuses on the ability of a large amount of data mining, data analysis and key digital technology equipment and strengthens product design and performance optimization. For example, smart factories optimize production processes [2,52] through the large-scale use of cloud computing, Internet of Things, and other digital technologies. At the same time, big data management is also used to realize information sharing among stakeholders and to significantly improve productivity and innovation benefits [53,54]. Furthermore, improving resource utilization through digital empowerment can stimulate technology innovation potential and improve the technology innovation performance of enterprises. In addition, digital technology, as an important resource for the digital transformation of enterprises, can be leveraged to take advantage of the data integration and analysis of digital technology, which can reduce resource mismatch and improve operational efficiency [55]. At the same time, digital technology is applied to production and manufacturing processes to achieve an overlay of product effectiveness [56]. Therefore, to a certain degree, resource empowerment enhances technology innovation performance. However, the digital transformation of the manufacturing industry is a gradual process [57]. In the early stages of manufacturing digitization, the integration of digital technology into existing production structures was not always seamless. In the initial stage of digital investment in particular, the digital innovation output effect of enterprises may not be fully achieved due to variations in digital absorption capacities [58]. that is, resource empowerment has a weak effect on innovation performance in the early stages of enterprise development.
Therefore, the following hypothesis is predicted:
There is a U-shaped relationship between resource empowerment and technology innovation performance.
According to dynamic capability theory, in addition to mastering digital resources, two problems must be solved: how to improve the dynamic analysis ability of demand through massive data resources, and how to transform data resources into production capacity to adapt the digital technology system. The essence of reforming the production process of HEM is the digital revolution of the manufacturing industry. Since digital innovation presents a multi-level, modular structured form [49], the impact of structural empowerment can be analyzed from the R&D, design, and production links of multiple operational levels [59]. Through structural empowerment, industrial boundaries, organizational boundaries, departmental boundaries and even product boundaries are digitally blurred [17,20]. Through the links of digital R&D, production, and manufacturing, structural enablement can realize the innovation of digitalization of business processes, the digitalization of production management [26], and remote operation and maintenance services through digital transformation to improve the performance of technological innovation. Structural empowerment focuses more on the combination of digital technology with the inherent technology system and technology facilities of the production line. The R&D design is the process of replacing the simulation data with quantitative test data resources to achieve functional simulation as well as changes in prototype manufacturing technology, relying on efficient computer simulation [26]. By adjusting the production parameters of the line, matching the test and actual data match perfectly, which significantly improves the scale accuracy and productivity of the processed objects [9,46].
However, issues such as transformation costs, scenario applicability, and risk management must also be considered in the early stages of digital transformation. For example, in the field of machinery and equipment, through the establishment of intelligent product control centers, an industrial Internet should be built on the basis of extensive data collection and analysis. However, in the initial stage, deep integration and effective collaboration among various business departments, intelligent product control center, intelligent cargo handling centers, and data processing centers cannot be realized, which has a high transformation investment risk.
Therefore, the following hypothesis is predicted:
There is a U-shaped relationship between structural empowerment and technology innovation performance.
According to the resource-based theory, digital resources such as big data and digital technology can be used as core resources for empowering digital platforms [28,60]. A digital platform is a platform that integrates all of the information, computing, and connection technologies needed by enterprises and organizations [11]. It is a bridge that connects the relationship between innovation actors and realizes the goal of multi-actor interaction and common innovation value [61]. Digital platforms rely on features such as dynamic plasticity [27], higher processing power, and low cost [22] to be able to connect multifunctional technologies and innovation actors [21]. The digital platform can expand the space for platform empowerment, break down barriers to accessing more innovative resources [20,21,22], and can quickly adapt to the changing external environment while reducing the investment risk of digital platforms. The “empowerment effect” of digital platforms means that the all-digital innovation resources endow the actor with digital innovation ability, realizing the empowerment effect of value creation [62,63]. Similarly, digital platform empowerment is an important force in integrating digital resources and connecting digital production. Existing studies have shown that digital platform capability has a positive impact on technology innovation performance. Digital platform empowerment can enhance enterprises’ sustainable competitive advantages [64] and improve technology innovation performance by enhancing the connection between the main actors. Specifically, platform empowerment can help enterprises to optimize the process of innovation activities, reduce the cost of innovation coordination, accurately identify digital innovation opportunities, actively respond to the innovation environment, etc. [11]. For example, in the process of HSR development, CRRC Tangshan Co. LTD. has built an integrated platform for the design, simulation, testing, and manufacturing. Regarding the simulation platform, the co-simulation platform of a HSR was developed based on the system integration of digital technology.
Therefore, the following hypothesis is predicted:
Platform empowerment positively influences technology innovation performance.
Ecological empowerment focuses on actor symbiosis, the interaction between individual actors and the behavior of the whole system across boundaries [25]. According to the resource-based theory, ecological empowerment enables resource reorganization and conceptualizes digital innovation ecosystems as complex networks of heterogeneous elements [23,24,25] by fusing technology, knowledge, and social networks [43] to achieve sustainable innovation development and to promote sustainable and innovative development.
Digital technology, as cores in technology networks adapt to the functional evolution of digital innovation ecosystems over time, which promotes iterative innovation evolution. Beltagui et al. used 3D printing technology as an example to verify that emerging digital technologies enable better connectivity between innovation modules and platforms in the process of information sharing and open innovation, promoting significant improvements in innovation performance [25]. Digital technology promotes the symbiotic coupling of innovation actors and enhances the interaction of multi-dimensional complex network systems [65]. Yoo et al. proposed that digital technology has significantly improved the integration and matching efficiency of digital resources, enhanced the cooperation among innovation actors, and fully realized the value co-creation of digital innovation ecosystems [20]. Knowledge networks connect different types of knowledge and actors, and ecological empowerment breaks through the boundaries of knowledge dissemination, facilitating the wide flow, diffusion and sharing of knowledge resources within the ecosystem and promoting value co-creation. Ecological empowerment provides a fluid environment for sharing innovation knowledge for digital innovation [66]. Romano et al. found that the environment for the sustainable innovation development of enterprises is influenced by knowledge exchange, absorption and sharing among innovation actors [67]. Pereira et al. showed that the digital ecosystem effectively avoided the opportunistic risks of knowledge innovation behavior [36]. Boudreau et al. pointed out that digital technology can significantly improve the complex environment of the digital innovation ecosystem and provide continuous digital innovation knowledge for the sustainable innovation development of enterprises [66]. A digital innovation ecosystem is a complex network that realizes renewal in the process of continuous interaction between innovation actors and innovation elements [68]. Social networks can significantly enhance the direction and strength of connections between interconnected and remote actors [23]. It is able to transform traditional value chains into value networks, creating value in nonlinear and cross-level dynamic processes to reinvent the traditional logic of innovation.
Therefore, the following hypothesis is predicted:
Ecological empowerment positively influences technology innovation performance.
3.2. The Moderating Effect of Adaptability to Technology Embedding
Improving the adaptability to technology embedding enhances the relationship between resource empowerment and innovation performance. Technology compatibility is represented by technology similarity, knowledge similarity, and technology application similarity, etc. [69]. Only with similar digital resources among innovation actors or stakeholders can technology compatibility be guaranteed [70]. Firstly, the integration of open data resources can improve the environmental adaptability to digital technology embedding. The data elements present multimodule and rich media characteristics [71], but this is due to the richness of the covering big data and redundancy of big data are highlighted [72]. In this regard, based on systematic basic scientific knowledge and common technology systems, the integration of digital resources with existing resource systems is improved, and data resources are efficiently integrated. Digital resources, such as enterprises increase IT infrastructure and can give full play to the flexibility and openness of digital resources. By focusing on optimizing the performance of digital products and improving digital technology innovation environment response can be improved, and there is an opportunity to improve perception capabilities [73,74].
Digital technologies also have characteristics such as generativity and openness, and the embedding of digital technologies creates new development opportunities for other technological breakthroughs [75,76]. Additionally, the application of digital technology accelerates the flow and sharing of knowledge [77], especially in the process of accelerating iterative updates of digital products and enhancing the adaptability of enterprises to digital technology [78]. Embedding digital technology increases technological relevance and reconstructs technology systems by integrating system basics and common technology systems [75,76]. Digital technology can complement other production and operation management technologies to reconfigure and integrate various factor resources, including production and organizational methods, triggering production paradigm improvements and industrial linkage effects and promoting structural optimization of production sectors [79]. Therefore, it is believed that with the help of knowledge aggregation and interaction, the higher the adaptability to digital technology embedding, the more beneficial it will be to improve the efficiency of innovation, promote the sustainable transformation and development of the industry [80] and improve the performance of technological innovation.
Therefore, the following hypothesis is predicted:
Adaptability to technology embedding positively regulates the U-shaped relationship between resource empowerment and technology innovation performance.
Accordingly, structural empowerment can enhance digital technology to create new products, cope with the changing market environment, and promote enterprises to carry out digital innovation activities. Digital technology supports the value process of innovation actors, gradually embedding the innovation process and its intrinsic nature [17,80]. Based on the dynamic capability theory, digital capability can be regarded as the high-order dynamic capability, which is the ability to organize product innovation and cope with the changing market environment [81]. However, not all enterprises have or are quick to embrace new digital technologies. Only when an enterprise’s digital capability is compatible and complementary in the realization of technology and product innovation [82] can the digital R&D and innovation links be optimized and the performance of technological innovation be enhanced [77,83].
In order to improve the adaptability to technology embedding, the HEM improves the compatibility between digital technology and R&D processes according to technology compatibility and interface compatibility. It is important to ensure both the systemic nature of the technology’s functional modules and the seamless connection between modules to enhance the fault tolerance of production equipment [84]. As digital technology is embedded into technology innovation processes, product boundaries, organizational boundaries and industrial boundaries are gradually blurred, which intensifies the dynamic nature of innovation research and development processes. Only on the premise of ensuring the adaptability to technology embedding and the internal R&D system and external environment can we further promote the transformation of enterprise value creation in the digital innovation environment. For example, the China Railway High-Speed (CRH) “Harmony” was developed on different platforms, but at the time, it was difficult to connect the technologies due to inconsistent standards, leading to higher operating and maintenance costs. Subsequently, CRH has improved the technological compatibility through integrated digital technology and digital platform, which enabling the software and hardware systems to coordinate with each other. An example of this is the CR400, which was independently developed by China Railway Rolling Corporation (CRRC), which combines a number of technical features and technology development platforms, expanding the innovation space of high-speed rail technology and promoting the level of independent innovation of CRH technology. CR400 was independently developed by CRRC with independent property rights and combines a number of technology features and creates its own technology development platform, which promotes the independent technology innovation of CRH.
Therefore, the following hypothesis is predicted:
Adaptability to technology embedding positively regulates the U-shaped relationship between structural empowerment and technology innovation performance.
Digital platforms become the major driver of technology innovation because of their compatibility, openness, and flexibility brought by digital infrastructure. Improving the adaptability of technology embedding enhances the relationship between platform empowerment and innovation performance [85,86].
Enhancing the adaptability to technology embedding, which can guide the tacit knowledge communication and sharing with an open and unified source module. The digital platform includes the hardware modules, software modules, and module interaction rules and standards [85,86] that provide protection for the effects of adaptability to technology embedding on platform empowerment and technology innovation performance. According to Bush et al., digital platform architecture compatibility refers to the standardization of the interfaces between different subsystems of a platform [87]. Zhu et al. confirm that enhancing the compatibility of a digital platform architecture with partner systems and achieving open connectivity helps to promote interconnection and knowledge sharing between platform enterprises and their partners [88]. Therefore, through the digital platform empowerment, enterprises gradually break down the boundaries of informatization and reduce coordination and transaction costs [89] to ensure that the resource flow and sharing are adequate for innovation [90].
Enhancing the adaptability to technology embedding can stimulate the technology innovation potential by maximizing the cost-effectiveness and openness of digital platforms [91]. Digital platforms contain the interactivity and dependence between the innovation actor and the technology element, and this relationship develops continuously over time [92]. As the functions of digital platforms continue to expand, newer social and technology elements emerge, increasing the complexity of technology innovation [92,93]. For example, the Haier Cosmoplat platform connects system integration, software suppliers, technology partners, distributors, etc., providing a bridge to create an open and win–win ecological pattern [94]. The flexibility of the digital platform architecture provides HEM with a high level of digital access. It can effectively acquire and use internal and external innovation knowledge and finally provide real-time innovation information transmission for the realization of technology innovation.
Therefore, the following hypothesis is predicted:
Adaptability to technology embedding has a positive moderating effect on the relationship between platform empowerment and technology innovation performance.
Improving the adaptability to technology embedding can enhances the relationship between ecological empowerment and technology innovation performance. The effect of ecological empowerment and technology innovation performance is depends on the protection function of the digital innovation ecosystem, which referring to the technology standardization and technology collaborative innovation in technology [95]. With the support of digital technology, enhancing the adaptability to technology embedding can realize the collaborative innovation in digital ecosystems according to technology intelligent control and technology prediction [22,96]. Finally, it can significantly improve the technology innovation performance and create more sustainable competitive advantages.
The digital innovation ecosystem is dominant and disruptive [97] and plays an important part in achieving circular and sustainable economic development [98]. However, digital innovation is a new phenomenon that involves the emerging technologies, which may result in uncertainty and complexity among innovation actors. Ecological empowerment focuses on the overall state of coordination of technology innovation activities and promotes the synergistic innovation of peripheral and core technologies, with the ultimate goal of creating a virtuous cycle of digital innovation ecosystems [23,24,25]. Thus, to achieve the best-performing of technology integration and to enable new technologies and new actors for smoother integration into original innovation activities, it is necessary to consider both the interdependence and integration of technologies with the system environment. adaptability to technology embedding not only enables participating actors to break through spatial and temporal barriers, but also achieves the cross-level technology innovation resource sharing, providing new paths for value creation. Additionally, ecological empowerment can fully guarantee the sustainable and healthy development of an open and complex ecosystem driven by innovation and development [99].
Therefore, the following hypothesis is predicted:
Adaptability to technology embedding has a positive moderating effect on the relationship between ecological empowerment and technology innovation performance.
In summary, the analysis leads to the conceptual model shown in Figure 1, with the hypothetical directions identified.
4. Research Design
4.1. Sample Survey
In order to ensure the randomness of the research samples and the scientific of results, this study adopts the random sampling method. First, according to the segmented industries of HEM [5], we determined the main research enterprises and questionnaire distribution targets. On this basis, combining with the preliminary survey results, HEM is mainly distributed in the eastern, central and western regions of China. Therefore, the sample data used in this study were selected from 28 manufacturing enterprises in eastern, central and western provinces for the formal survey. Second, focusing on enterprise technology innovation, we choose enterprises’ technological R&D personnel and middle and senior managers as the main survey respondents. Third, in order to obtain as much data as possible, we determined the questionnaire distribution methods, including online professional questionnaire distribution platform, offline interview survey and social software.
The section covers data collection from the questionnaire and covers the perids of questionnaire item design, pre-survey and formal survey. In the first step, the independent variable, digital empowerment, was adapted and refined according to the scales of digital capability, resource empowerment, structural empowerment, platform empowerment, and ecological empowerment. The dependent variable (technology innovation performance) and the moderating variable (adaptability to technology embedding) are referred to as the maturity scale and were adjusted when designing the questionnaire. A scale suitable to rank technology innovation in China’s HEMs was constructed.
Then, in the pre-investigation stage, we sent out 50 questionnaires and had 31 questionnaires returned through email and Wechat. After quality inspection, these questionnaires were retained. Based on the survey respondents’ feedback, we modified some of the questions in the questionnaire. Then, the respondents were expanded, and a questionnaire distribution platform was used to limit the respondents’ credibility. During this stage, 150 questionnaires were screened, and 45 were eliminated due to the answering time, IP, and option results. A total of 200 questionnaires were collected in the above stages, and 136 were recovered. Thus, there was an effective response rate of about 80%. The ratio of the number of questionnaires collected to the number of variables (10) in the pre-investigation stage was about 14, meeting the requirements of exploratory factor pre-test analysis [100].
Finally, the formal survey was be conducted from October 2021 to March 2022 and was mainly collected in the following ways: first, valid questionnaires were obtained through a professional questionnaire distribution platform, with responses mainly written by enterprise managers and technology R&D personnel. The second method was to rely on MBA or EMBA students to fill in part of the questionnaire. Third, student and their colleagues and friends working in an HEM were also encouraged to fill in part of the questionnaire. A total of 800 questionnaires were distributed, and 636 questionnaires were returned, for the response rate of 79.5%. Excluding invalid questionnaires, 436 valid questionnaire scores were finally obtained, with an effective rate of 68.55% (see Table 1). This research questionnaire is in the form of a scale containing 34 questions and is measured on a 5-point Likert scale.
This study analyzed and processed the collected data using SPSS software. First, the Pearson correlation coefficient was used for variable correlation analysis. Secondly, a multiple linear regression model was added to verify the research hypothesis through hierarchical regression and the bootstrap test. Finally, this study analyzed the impact of digital empowerment on technology innovation performance through testing the hypotheses. Details are described in Section 5.
4.2. Variable Selection and Measurement
Independent Variable: Digital empowerment mainly reflects the process of value creation in high-end equipment manufacturing through digital transformation and enhanced digital capabilities. Based on existing studies, the level of digital empowerment was measured in four dimensions: resource empowerment (RE) [12], structural empowerment (SE) [17,20], platform empowerment (PE) [20,21,22], and ecological empowerment (EE) [23,24,25]. A total of 24 questions were included.
Dependent Variable: The technology innovation performance (TIP) scale mainly refers to the measurement methods of Yayavaram and Chen [101] and considers technology innovation performance in terms of cost, quality, efficiency, growth, etc. Integrating the existing research scales, the revised Technology Innovation Performance Scale contains “digitally enabled enterprises’ production and management”. The revised technology innovation performance scale consists of four factors: “Cost reduction, more advanced production processes and equipment, faster development of digital technologies, and higher growth rate of patents”.
Moderating Variables: Adaptability to technology embedding (TEA) refers to the research scale of Chi et al. [19] to measure adaptability to technology embedding in terms of three aspects of impact from access, development and application of digital technology resources and contains three question items.
Control Variables: Year of establishment, size, industry, stage of development, ownership, etc., are all likely to affect technology innovation performance. Thus, this paper has identified these five factors as being the most important and they are used as control variables. Industry affiliation is set as a dummy variable from 1 to 10. Firm size is measured as the natural logarithm of the number of employees, and firm establishment is measured as the natural logarithm of the number of years the firm has been in business. The type of ownership is set as a dummy variable, with nonstate manufacturing enterprises coded as 0 and state manufacturing enterprises coded as 1. The industry development stages are divided into four stages: input, growth, maturity and decline, which are set as dummy variables from 1 to 4, respectively.
In summary, the question items are shown in Table 2.
5. Empirical Testing and Analysis
5.1. Reliability and Validity Test
Cronbach’s alpha coefficient values were used to test the reliability of each variable. The Cronbach’s alpha coefficient values for each variable exceeded 0.9, indicating a high level of internal consistency across the variables. Validation factor analysis was conducted using Amos 24.0 on the above question items, and both AVE and combined reliability were used to determine their construct validity. The square root of AVE for each variable was greater than the correlation coefficient of the remaining variables, indicating high discriminant validity. The fit indices for each of the 6-factor models hypothesis by the study were χ2/df ≤ 3, TLI ≥ 0.9, CFI ≥ 0.9, GFI ≥ 0.9, AGFI ≥ 0.9 and RMSE ≤ 0.05, which indicate an acceptable fit for the model (see Table 3).
5.2. Homogeneous Variance Test
The problem of homogeneous variance was tested using the Hausman test, where the question items of all the variables were combined into a single one-way model for principal component rotated factor analysis or principal components, and the first factor with an eigenvalue greater than 1 explained 29.8% (<30%) of the total variance data, with no serious common method bias.
5.3. Descriptive Statistical Analysis
As shown in Table 4, there were significant correlations between the independent and dependent variables and between the moderating and dependent variables in this study, and the correlation coefficients were below 0.50, which provided the necessary basis to further develop the argument.
5.4. Analysis of the Results
Cascade regression was used to test the hypotheses, as shown in Table 5. The squared term of resource empowerment in Model 2 has a significant and positive effect on technology innovation performance (β = 0.423, p < 0.05), indicating a U-shaped relationship between resource empowerment and technology innovation performance; therefore, H1a is supported. Similarly, the squared term of Model 3 for structural empowerment is significantly and positively related to technology innovation performance (β = 0.793, p < 0.01), indicating a U-shaped relationship between structural empowerment and technology innovation performance; therefore, H1b is supported. Furthermore, in Models 4 and 5, platform empowerment has a significant positive effect on technology innovation (β = 0.361, p < 0.01), and ecological empowerment has a significant positive effect on technology innovation (β = 0.424, p < 0.001). Therefore, both H1c and H1d were supported.
Models 6–9 incorporate interaction terms for independent and moderating variables. The results show that the interaction terms of resource empowerment (β = 0.128, p < 0.01), platform empowerment (β = 0.204, p < 0.001), ecological empowerment (β = 0.110, p < 0.05) and adaptability to technology embedding all have a significant positive effect on innovation performance. Adaptability to technology embedding positively moderates the relationship between resource empowerment, platform empowerment, ecological empowerment and innovation performance but does not moderate the U-shaped relationship between resource empowerment and innovation performance. Thus, Hypothesis H2a was not supported, while H2c and H2d were supported. The interaction term between the squared term of structural empowerment and adaptability to technology embedding also has a significant positive relationship with innovation performance (β = 0.142, p < 0.05), and adaptability to technology embedding can strengthen the U-shaped relationship between structural empowerment and innovation performance; thus, H2b is supported.
As shown in Figure 2, to further analyze the moderating effect, the relationship between structural empowerment and technology innovation performance under the condition of adaptability to technology embedding is depicted with the help of the simple slope test principle. Compared with structural empowerment, structural empowerment is more embodied in design, R&D, manufacturing, control, and requires greater compatibility and adaptability between technologies and between technology and systems, while the impact of resource empowerment on technology innovation in the HEM industry is more involved in the input link of data, digital technology, and other resources. The high-end equipment manufacturing industry’s access to digital resources and analysis ability is a continuously improving process that will weaken the impact of adaptability to technology embedding.
5.5. The Robustness Test
To further test the impact of digital empowerment on technology innovation performance, the robustness of the structural equation model is tested by constancy. Approximately 50% (218), 70% (305) and 85% (370) of the total sample was randomly selected for validation with the structural equation model. The results show that the findings obtained from the different sample sizes are also consistent with the results of the full sample analysis. It is confirmed that the structural equation model in this paper is valid and that the findings are robust (see Table 6, Table 7 and Table 8).
6. Discussion
6.1. Empirical Findings
This paper discussed the connotations and dimensions of digital empowerment, and constructed a model to determine the multidimensional digital empowerment and technology innovation performance using the adaptability to technology embedding as the moderating variable. Furthermore, this research demonstrates the relationship between different dimensions of digital empowerment and technology innovation performance, and explored the moderating effect of adaptability to technology embedding.
6.1.1. Clarify the Connotation and Dimension of Digital Empowerment
The connotations and dimensions of digital empowerment are clarified. Digital empowerment is a new phenomenon that as appeared with the development of digital technology. Digital empowerment is the process of empowering innovation actors with digital capabilities by relying on digital technologies and other resources, connecting innovation elements and innovation actors, enhancing technology innovation performance and realizing value creation. Furthermore, in order to reveal the driving force of digital empowerment, this digital empowerment was divided into resource empowerment and structural empowerment according to the empowerment tool and into platform empowerment and ecological empowerment according to the empowerment actor relationship.
Based on the empowerment tool, resource empowerment provides more possibilities for enterprises to identify data sources, to judge digital technology development opportunities, and to tap digital technology innovation capabilities accurately. Based on the informatization, automation, and intellectualization of R&D as well as manufacturing processes, structural empowerment can significantly enhance the capability of optimizing resource allocation, fault warning and diagnosis, and predictive remote maintenance services. For example, 3D printers, which are widely used in HEM enterprises, transformed the data in the manufacturing process into important production factors that were able to produce any customized products on the basis of self-calculation judgment and analysis decisions at any time. Therefore, resource empowerment and structural empowerment provide tool support for technological innovation in HEM enterprises.
Based on the perspective of empowerment actor relationship, the digital platform and digital ecosystem change the interactive relationship between technology and innovation actor elements. Platform empowerment is the process of bringing together digital resources, integrating production data resources, and expanding scene innovation application, and the goal is to achieve cross-domain value creation. Ecological empowerment creates value in nonlinear and cross-layer dynamic interactions based on changing the interdependence and interactions among actors. For example, compared with the general manufacturing industry, HEM enterprises need higher technology integration and innovation capability and need to protece the function of the platform ecology. The digital platform and digital ecosystem could break the limitations of time and space to promote the interaction of innovation elements. Finally, the empowerment actor relationship can provide a steady flow of innovation resources flowing for the digital transformation.
6.1.2. Verify the Influences of Different Dimension Digital Empowerment
From the perspective of empowerment tools, resource empowerment and structural empowerment have a U-shaped influence on technology innovation performance; and from the perspective of empowerment actor relationships, platform empowerment and ecological empowerment positively influence technology innovation performance.
From the perspective of empowerment tools, resource empowerment refers to the application of digital resources such as big data and digital technology. By optimizing product efficiency, the digitalization and intelligentization of production can be realized, technology innovation performance can be enhanced, and the sustainable competitive can be fostered. However, in the early stages of enterprise innovation investment, there are limitations imposed by the innovation environment and cost. Therefore, the impact of empowerment tools can only be stimulated by continuous digital resources investment and changes in the relationship between innovation actors. In general, there is a U-shaped relationship between resource empowerment, structural empowerment and technological innovation performance.
From the perspective of the empowerment actor relationship, platform empowerment is an important factor for enhancing the connection relationship between actors. Digital platform development is greatly improved through optimizing the digital platform capability of connectivity and reliability. Ecological empowerment can enhance data integration capabilities and the collaborative innovation capabilities of digital technologies through breaking the boundaries of time and space, which is conducive to realizing value co-creation. Based on the theoretical hypotheses and empirical analysis, platform empowerment and ecological empowerment are the driving force that further expand the interaction between innovation elements under a certain foundation of digital transformation or mature digital innovation systems. With the goal of fostering enterprises’ lasting competitive advantages within enterprises, it can improve the actors relationships through the digital platform and digital ecosystem as quickly as possible. Therefore, platform empowerment and ecological empowerment can provide a steady stream of innovation power to enhance the connectivity capability of digital platforms and the collaborative innovation capability of digital ecosystem, which is helpful to promote technology innovation directly.
6.1.3. Reveal the Moderating Effect of Adaptability to Technology Embedding
The results also show that the greater the adaptability to technology embedding, the greater the influence between digital empowerment and technology innovation performance.
From the perspective of dynamic capabilities, transforming digital resources into digital innovation capabilities with more competitive advantages can further accelerate knowledge integration capabilities, cultivate dynamic capabilities, and enhance environmental adaptability. Specially, from the perspective of adaptability to technology embedding, this study explained the reasons why it is difficult for technology systems to make breakthroughs and experience innovation in digital transformation in HEM enterprises, and verified the influence of adaptability to technology embedding on different digital empowerment levels. The technology innovation R&D activities of HEM enterprises are relatively complex, involving the multi-component and multi-module innovation, among which the function loss, contradiction or mismatch of any aspect would lead to product innovation. Only by integrating digital technology and improving the adaptability to technology embedding to technology systems can enterprises maximize the integration of innovation elements and meet the needs of technology innovation [102]. For example, the innovation process of intelligent HSR technology is not the superposition of intelligent technology and the HSR business, but is instead the deep integration of advanced intelligent technology with various HSR equipment, specialties, and different stages of the HSR life cycle [11]. By integrating common technology systems, embedding digital technology increases the relevance of technology, which not only ensures the integrity and how systematic the technology of functional modules are, but also ensures the seamless connection between the modules. Therefore, by improving the compatibility, integration and environmental adaptability to digital technology and the original technology system, the fault-tolerant capability of production equipment was improved [84], which can achieve the goal of digital empowerment technology innovation quickly.
6.2. Research Contributions
Extending the connotation of empowerment to the field of digital transformation in HEM enterprises. Digital empowerment is a new phenomenon emerging in the field of digital technology, and is the embodiment of enhancing digital capability. Based on a resource-based view and dynamic capability theory, digital capability, as a high-level dynamic capability, can promote technology innovation and adapt to external environmental changes [81]. Scholars have gradually begun to pay attention to the important role of digital innovation resources and digital capability in the digital transformation of the manufacturing industry. Referring to the dimensional division of empowerment, digital empowerment gradually weakens the effect of psychological empowerment and places more emphasis on the role of the empowerment tool and the empowerment actor relationship. Therefore, this study divided digital empowerment into resource empowerment [12] and structural empowerment [17,20] according to the empowerment tools and divided digital empowerment into platform empowerment [20,21,22] and ecological empowerment [23,24,25] according to the actor relationship. The influence of data resources [8] and digital platforms [10,11] on technology innovation has experience a breakthrough.
The nonlinear effect of digital empowerment on technology innovation performance was explained in terms of both empowerment tools and empowerment actor relationships. It is the theoretical and practical value that the nonlinear effect of the relationship between digital empowerment and technology innovation performance in HEM enterprises that has enriched the relevant research on technology innovation performance. There is a general consensus that digital transformation in the manufacturing industry contributes significantly to both financial and nonfinancial performance [13]. However, a series of practical surveys show that it is still difficult for digital technology, as an emerging technology and as a simple resource input, to significantly improve the technology innovation performance in a short period of time [15,16]. For example, relying on external empowerment tools alone may only be for the allocation and optimization of innovation resources. In contrast, by empowering actor relationships, it is possible to go deeper into the internal level of connectivity, moving from effective to efficient connections. As a result, there will also be differences in the role of empowerment tools and empowerment actor relationships in influencing innovation performance. In particular, because of the high technology, high capital input and product added value, China’s HEM enterprises want to obtain sustainable competitive advantages by relying on digital enabling tools that may have little innovation effect [14]. Additionally digital platform empowerment and ecological empowerment can enhance enterprises’ digital information generation, rapid responses to information, and decision-making ability [103]. Driven by digital platforms with obvious competitive advantages, they can provide more long-lasting and steady competitive advantages for enterprises’ technology innovation in enterprises.
Adaptability is the inevitable path of technology innovation and change and adaptability to technology embedding has been introduced, elucidating the moderating role of adaptability to technology embedding between digital empowerment and innovation performance by exploring the role of adaptability to technology embedding in technological innovation. Adaptability to technology embedding can moderate the relationship between digital empowerment and technology innovation performance. That is, in order to cultivate sustainable competitive advantages and to enhance technology innovation performance, enterprises have to consider the connectivity and adaptability to digital technology with digital resources, digital production, digital platforms and digital ecosystem [19]. Additionally, with HEM enterprises facing a long research and development cycle, a complex industrial chain, and a fixed production process, the adaptability between emerging technologies and other technological systems, infrastructure, manufacturing systems and organizational structure should be fully considered in the initial stages of introducing emerging digital technologies. Only when new technology is integrated into the R&D system and production process can enterprises cultivate sustainable competitive advantages under the integration innovation of the two [49]. It also further confirms the finding of Sedera et al. that the higher the compatibility between the original technology production system and new technology, the more effectively organizational innovation flexibility can be realizef and productivity can be improved [11].
7. Conclusions
7.1. Management Implications
In the context of global manufacturing digital transformation [3], China’s HEM is enterprises are increasingly focusing on the potential of digital innovation to achieve sufficient sustainable competitive advantages and technology innovation performance. Next, to improve the technology innovation performance of the HEM enterprises, the following practical insights are proposed:
First, in general, digital empowerment can promote innovation performance. Due to the extensive flow of digital innovation resource elements, it is necessary to fully consider the collaborative innovation relationship between digital technology and other technology systems. Therefore, due to the pressure of digital transformations, the HEM enterprises must rise to the challenge and seize the trend of digital transformation. To enhance technology innovation performance, enterprises should specifically take measures from digital resources, digital R&D design and production process optimization, digital platform construction, and digital ecosystem environmental protection.
Second, the effects of empowerment tools and empowerment actor relationships on technology innovation performance are varied. The impact of digital empowerment tools on technology innovation performance is not significant in the early stage. Therefore, enterprises should weigh the input–output relationship, take short time risks with limited innovation benefits, and fully exploit the digital resources that are currently available. More importantly, the positive role of digital platform empowerment and ecological empowerment should be explored in a timely manner, starting from the relationships between digital innovation actors. In order to help enterprises better adapt to the uncertain, dynamic, and complex technology innovation environment, the environmental support function of the digital innovation ecosystem can be strengthened from the environmental adaptability and flexibility. This is helpful to identify digital transformation pain points and opportunities more accurately, thereby realizing the goal of breaking through key core technologies and making progress in industrial technology. By giving full play to the role of digital platform connectivity and digital ecosystem synergy, it is possible to achieve sustainable improvements in technology innovation performance.
Third, the adaptability to technology embedding will affects the impact of digital empowerment on technology innovation performance. Therefore, in the process of introducing, digesting, and learning new technologies, it is important to consider the compatibility and adaptability of emerging technologies with the technological system and the production structure of the enterprise. In this way, the risks of digital innovation can be minimized. In addition, in the process of R&D and innovation, the integration of digital resources with existing technology development chains or networks should be considered. At the same time, the ability to adapt the technology system is improved by enhancing its own technological absorption capacity. Only on the existing platform can the path of digital innovation change be explored and sustainable competitive advantage be created. For example, in traditional medium-sized mechanical engineering, due to the high stability of its production and R&D processes, it is necessary to maintain the attitude of “seeking progress in stability”. Thus, it should seize the digital innovation opportunities to enhance adaptability to technology embedding and reduce the technology change risk. Additionally, for the new energy equipment industry, which has relatively large policy support, it is necessary to enhance the environmental responsiveness and adaptability with the attitude of “corner overtaking”. This includes doing everything as soon as possible to increase investment in innovation resources and to broaden the innovation domain, which can gain a competitive edge and promote technology progress quickly.
7.2. Limitations and Future Research
First, due to the complexity and uncertainty of the digital transformation environment of the manufacturing industry, the moderate factors need to be further expanded upon. This research focuses on the impact of digital empowerment from the perspective of innovation in micro technology. In the future, this research can be further expanded upon to conduct a more comprehensive analysis of the technology attributes of different categories, industrial differences, and regional differences.
Second, the research sample can be further expanded upon to enrich the applicability of digital empowerment. Digital empowerment itself is the complex process, and different industries may have different stages of digital empowerment and specific practical technology innovation activities. The samples in this research were mainly HEM enterprises because as the industry with the most difficult digital transformation, it has certain limitations. Thus, future studies can verify the digital transformation of other industries, which can enhance the universality of digital empowerment measurement and show more interesting findings regarding digital empowerment and technology innovation performance.
Finally, this research verifies the effect of adaptability to technology embedding between different digital empowerment and technology innovation performance but does not further elaborate on the cross-sectional effect of digital empowerment on different dimensions. Therefore, the cross-impact effects of different empowerment levels could be explored in depth in the future.
Conceptualization, Z.L. and H.L.; methodology, H.L.; software, H.L.; validation, Z.L, H.L. and S.W.; supervision, Z.L.; formal analysis, H.L.; investigation, H.L.; resources, Z.L.; data curation, H.L.; writing—original draft preparation, H.L.; writing—review and editing, H.L. and S.W.; visualization, H.L.; supervision, S.W.; project administration, S.W.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data presented in this study are available on request from the corresponding author.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 2. The moderating effect of adaptability to technology embedding on structural empowerment and innovation performance.
Composition distribution of the sample (N = 436).
| Category | Classification indicators | Number | Frequency (%) |
|---|---|---|---|
| Number of years of business establishment | 5 | 66 | 15.14% |
| 10 | 80 | 18.35% | |
| 15 | 69 | 15.83% | |
| 20 | 82 | 18.81% | |
| 25 | 67 | 15.37% | |
| 30 | 72 | 16.51% | |
| Size of business | <100 people | 62 | 14.29% |
| 101 to 300 people | 145 | 33.16% | |
| 301 to 500 people | 93 | 21.43% | |
| 501~1000 people | 69 | 15.82% | |
| >1000 people | 67 | 15.31% | |
| Industry of the company | Electrical machinery and equipment manufacturing | 16 | 3.57% |
| Electronics and communications equipment manufacturing | 78 | 17.86% | |
| High-end energy and energy conservation and environmental protection industries | 58 | 13.27% | |
| Construction machinery manufacturing | 65 | 14.80% | |
| Rail transit equipment manufacturing | 7 | 1.53% | |
| Marine engineering equipment manufacturing | 4 | 1.02% | |
| Aerospace equipment manufacturing | 4 | 1.02% | |
| Computer and office equipment manufacturing | 102 | 23.47% | |
| New energy vehicle manufacturing | 60 | 13.78% | |
| Medical equipment and instrumentation manufacturing | 42 | 9.69% | |
| Stage of business development | Entry period | 36 | 8.16% |
| Growth period | 240 | 55.10% | |
| Maturity period | 156 | 35.71% | |
| Recession period | 4 | 1.02% | |
| Type of business ownership | State-owned manufacturing companies | 187 | 42.86% |
| Nonstate manufacturing companies | 249 | 57.14% |
Composition distribution of the sample (N = 436).
| Variables | Dimension | Code | Measurement Questions |
|---|---|---|---|
| Resource Empowerment | Digital perception capabilities | RE1 | Companies are able to identify big data sources that meet their technology needs |
| RE2 | Companies can rely on big data to identify new technology development opportunities | ||
| Digital resource connectivity | RE3 | Companies have the ability to connect digital products via wireless communication networks | |
| RE4 | Companies are increasingly using digital technology to connect people, systems, companies, products and services | ||
| Digital intelligence analysis capabilities | RE5 | Companies are able to apply data to research and development and automatically collect storage device parameters | |
| RE6 | Companies are able to apply data to the manufacturing of equipment and monitor the operational status of equipment in real time | ||
| RE7 | Companies improve the efficiency of their business intelligence decisions with digital tools and components | ||
| Structural Empowerment | Digital supply capacity | SE1 | Companies are able to abstract and analyse digital information for precise market positioning |
| SE2 | Companies are able to use digital tools to optimise production processes or resource allocation | ||
| Digital application capabilities | SE3 | Companies are able to trace product quality | |
| SE4 | Companies can implement product fault warning and diagnosis | ||
| SE5 | Companies are able to perform predictive remote maintenance services | ||
| Platform Empowerment | Digital platform development capabilities | PE1 | Companies will develop or build their own digital platforms and infrastructure |
| PE2 | Companies apply digital platforms that support digital products and services | ||
| Digital platform development capabilities | PE3 | Companies have a digital platform infrastructure that adequately meets current production needs in terms of connectivity | |
| PE4 | The enterprise has a digital platform infrastructure that adequately meets current production needs in terms of reliability | ||
| PE5 | Companies have a digital platform infrastructure that adequately meets current production needs in terms of speed | ||
| Ecological |
Data cosharing value | EE1 | Companies are able to integrate external data with internal data to facilitate high-value analysis of the technology development landscape |
| Digital technology resonance value | EE2 | Digital technology drives maximum synergy across process nodes to deliver value breakthroughs in terms of cost | |
| EE3 | Digital technology drives maximum synergy across process nodes to deliver value breakthroughs in terms of cycle time | ||
| EE4 | Digital technology drives maximum synergy across process nodes to deliver value breakthroughs in terms of cost | ||
| Digital production of symbiotic value | EE5 | Companies build internal value networks through digital technology systems | |
| EE6 | The company’s digital technology system enables the integration of the entire value chain with upstream suppliers, downstream customers and partners across the industry | ||
| EE7 | Companies build cross-industry, cross-sector industrial interoperability ecosystems through digital technology systems | ||
| adaptability to technology embedding | TEA1 | Companies are able to integrate access to data, digital technology and other resources into existing resource systems | |
| TEA2 | Companies are able to integrate traditional and digital technologies to develop and apply new R&D processes and operational management models | ||
| TEA3 | Companies are able to mitigate and deal with the impact of digital technology entering the business | ||
| Technology innovation performance | TIP1 | Digitization enables companies to reduce production and management costs | |
| TIP2 | More advanced digital production processes and equipment | ||
| TIP3 | Faster development of digital technology | ||
| TIP4 | Increased growth rate in the number of digital technology-related patents | ||
The reliability of the variable scale measures.
| Variables | Factor loading | Cronbach’s Alpha | AVE | CR |
|---|---|---|---|---|
| RE1 | 0.814 | 0.936 | 0.675 | 0.936 |
| RE2 | 0.838 | |||
| RE3 | 0.826 | |||
| RE4 | 0.813 | |||
| RE5 | 0.798 | |||
| RE6 | 0.833 | |||
| RE7 | 0.827 | |||
| SE1 | 0.883 | 0.949 | 0.789 | 0.949 |
| SE2 | 0.894 | |||
| SE3 | 0.886 | |||
| SE4 | 0.900 | |||
| SE5 | 0.878 | |||
| PE1 | 0.814 | 0.915 | 0.683 | 0.915 |
| PE2 | 0.802 | |||
| PE3 | 0.822 | |||
| PE4 | 0.830 | |||
| PE5 | 0.864 | |||
| EE1 | 0.867 | 0.948 | 0.724 | 0.948 |
| EE2 | 0.868 | |||
| EE3 | 0.866 | |||
| EE4 | 0.871 | |||
| EE5 | 0.831 | |||
| EE6 | 0.830 | |||
| EE7 | 0.822 | |||
| TEA1 | 0.841 | 0.905 | 0.761 | 0.905 |
| TEA2 | 0.900 | |||
| TEA3 | 0.875 | |||
| TIP1 | 0.874 | 0.935 | 0.783 | 0.935 |
| TIP2 | 0.889 | |||
| TIP3 | 0.884 | |||
| TIP4 | 0.893 |
Descriptive statistics and correlation coefficients for variables.
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Year | 1 | ||||||||||
| 2. Stage | –0.091 | 1 | |||||||||
| 3. Industry | –0.084 | 0.002 | 1 | ||||||||
| 4. Size | 0.012 | 0.025 | 0.222 ** | 1 | |||||||
| 5. Ownership | 0.038 | –0.002 | –0.010 | 0.218 ** | 1 | ||||||
| 6. RE | –0.048 | –0.016 | –0.081 | 0.069 | 0.017 | 0.822 | |||||
| 7. SE | 0.011 | 0.015 | 0.012 | 0.046 | –0.016 | 0.356 ** | 0.888 | ||||
| 8. PE | –0.046 | 0.005 | 0.036 | 0.061 | 0.050 | 0.313 ** | 0.312 ** | 0.826 | |||
| 9.EE | –0.042 | 0.027 | –0.020 | –0.029 | 0.023 | 0.233 ** | 0.265 ** | 0.257 ** | 0.851 | ||
| 10. TEA | –0.025 | –0.088 | –0.005 | –0.033 | –0.055 | 0.121 * | 0.090 | 0.067 | 0.062 | 0.872 | |
| TIP | 0.005 | 0.042 | 0.018 | 0.048 | 0.024 | 0.360 ** | 0.457 ** | 0.356 ** | 0.423 ** | 0.095 * | 0.885 |
| Average value | 2.619 | 2.590 | 4.460 | 5.772 | 0.430 | 2.691 | 3.175 | 2.890 | 3.031 | 3.128 | 3.001 |
| Standard deviation | 0.982 | 1.136 | 2.196 | 1.020 | 0.496 | 0.963 | 1.2334 | 1.095 | 0.112 | 1.210 | 1.160 |
Note: The diagonal data (data in bold) are the square root of the AVE value of each variable. ** means significant at the level of 0.01 (p < 0.01), and * means significant at the level of 0.05 (p < 0.05).
Regression analysis (n = 436).
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 |
|---|---|---|---|---|---|---|---|---|---|
| Control variables | |||||||||
| Year | 0.007 | 0.024 | −0.002 | 0.022 | 0.025 | 0.033 | 0.003 | 0.017 | 0.027 |
| Stage | 0.047 | 0.056 | 0.033 | 0.047 | 0.036 | 0.054 | 0.037 | 0.041 | 0.047 |
| Industry | 0.029 | 0.053 | 0.026 | 0.012 | 0.043 | 0.060 | 0.016 | 0.015 | 0.032 |
| Size | 0.058 | 0.029 | 0.023 | 0.020 | 0.062 | 0.019 | 0.031 | 0.032 | 0.067 |
| Ownership | 0.008 | −0.003 | 0.023 | −0.006 | −0.006 | 0.008 | 0.016 | −0.011 | −0.011 |
| Independent variable | |||||||||
| RE | −0.034 | 0.342 *** | |||||||
| RE2 | 0.423 * | ||||||||
| SE | −0.335 | −0.217 | |||||||
| SE2 | 0.793 ** | 0.638 * | |||||||
| PE | 0.361 *** | 0.343 *** | 0.413 *** | ||||||
| EE | 0.424*** | ||||||||
| Adjustment variables | |||||||||
| TEA | 0.087 | −0.038 | 0.096 * | 0.098 * | |||||
| Interaction items | |||||||||
| TEA*RE | 0.128 ** | ||||||||
| TEA*SE | 0.120 ** | ||||||||
| TEA*SE2 | 0.142 * | ||||||||
| TEA*PE | 0.204 *** | ||||||||
| TEA*EE | 0.110 * | ||||||||
| R2 | 0.005 | 0.156 | 0.219 | 0.133 | 0.183 | 0.166 | 0.241 | 0.182 | 0.202 |
| Adjusted R2 | −0.007 | 0.142 | 0.207 | 0.121 | 0.172 | 0.151 | 0.223 | 0.167 | 0.187 |
| F | 0.395 | 11.286 *** | 17.179 *** | 11.388 *** | 16.034 *** | 10.643 *** | 13.500 *** | 11.867 *** | 13.508 *** |
| DW | 1.945 | 2.061 | 1.908 | 2.035 | 1.957 | 2.047 | 2.008 | 2.044 |
Note: *** means significant at the level of 0.001 (p < 0.001), ** means significant at the level of 0.01 (p < 0.01), and * means significant at the level of 0.05 (p < 0.05).
Results of regression analysis of the moderating effect (n = 218).
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 |
|---|---|---|---|---|---|---|---|---|---|
| Control variables | |||||||||
| Year | 0.004 | 0.019 | −0.026 | 0.050 | 0.044 | 0.053 | −0.022 | 0.086 | 0.059 |
| Stage | −0.049 | −0.015 | −0.001 | −0.026 | −0.037 | 0.014 | 0.021 | −0.031 | −0.040 |
| Industry | 0.040 | 0.054 | 0.006 | 0.041 | 0.049 | 0.083 | 0.016 | 0.081 | 0.025 |
| Size | 0.066 | 0.072 | 0.102 | 0.089 | 0.100 | 0.057 | 0.070 | 0.109 | 0.096 |
| Ownership | 0.100 | 0.042 | 0.072 | 0.047 | 0.034 | 0.044 | 0.030 | 0.043 | 0.010 |
| Independent variable | |||||||||
| RE | −0.376 | 0.195 ** | |||||||
| RE2 | 0.657 * | ||||||||
| SE | −0.623 | ||||||||
| SE2 | 0.163 ** | 0.471 *** | |||||||
| PE | 0.350 *** | 0.375 *** | |||||||
| EE | 0.462 *** | 0.455 *** | |||||||
| Adjustment variables | |||||||||
| TEA | 0.099 | −0.092 | 0.107 | 0.126 * | |||||
| Interaction items | |||||||||
| TEA*RE | 0.214 ** | ||||||||
| TEA*SE | |||||||||
| TEA*SE2 | 0.219 *** | ||||||||
| TEA*PE | 0.340 *** | ||||||||
| TEA*EE | 0.137 * | ||||||||
| R2 | 0.019 | 0.107 | 0.264 | 0.132 | 0.277 | 0.273 | 0.253 | ||
| Adjusted R2 | −0.004 | 0.078 | 0.240 | 0.099 | 0.249 | 0.245 | 0.225 | ||
| F | 3.606 ** | 3.606 | 10.773 *** | 3.969 | 9.998 *** | 9.806 *** | 8.859 *** | ||
| DW | 1.915 | 2.037 | 1.836 | 2.020 | 2.037 | 1.914 |
Note: *** means significant at the level of 0.001 (p < 0.001), ** means significant at the level of 0.01 (p < 0.01), and * means significant at the level of 0.05 (p < 0.05).
Results of regression analysis of the moderating effect (n = 305).
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 |
|---|---|---|---|---|---|---|---|---|---|
| Control variables | |||||||||
| Year | 0.016 | 0.027 | −0.037 | 0.054 | 0.023 | 0.047 | −0.036 | 0.070 | 0.033 |
| Stage | −0.042 | 0.006 | −0.023 | −0.029 | −0.026 | 0.015 | −0.008 | −0.017 | −0.022 |
| Industry | 0.042 | 0.041 | 0.017 | 0.038 | 0.030 | 0.063 | 0.031 | 0.068 | 0.015 |
| Size | 0.048 | 0.048 | 0.096 | 0.060 | 0.073 | 0.033 | 0.059 | 0.070 | 0.071 |
| Ownership | 0.118 | 0.080 | 0.096 | 0.081 | 0.072 | 0.094 | 0.054 | 0.073 | 0.067 |
| Independent variable | |||||||||
| RE | −0.300 | 0.241 *** | |||||||
| RE2 | 0.621 ** | ||||||||
| SE | −0.709 * | ||||||||
| SE2 | 0.175 ** | 0.464 *** | |||||||
| PE | 0.284 *** | 0.295 *** | |||||||
| EE | 0.437 *** | 0.429 *** | |||||||
| Adjustment variables | |||||||||
| TEA | 0.102 | −0.061 | 0.106 | 0.114 * | |||||
| Interaction items | |||||||||
| TEA*RE | 0.159 ** | ||||||||
| TEA*SE | |||||||||
| TEA*SE2 | 0.185 * | ||||||||
| TEA*PE | 0.322 *** | ||||||||
| TEA*EE | 0.114 * | ||||||||
| R2 | 0.020 | 0.129 | 0.261 | 0.098 | 0.208 | 0.141 | 0.268 | 0.225 | 0.230 |
| Adjusted R2 | 0.004 | 0.109 | 0.244 | 0.080 | 0.192 | 0.118 | 0.249 | 0.204 | 0.209 |
| F | 1.246 | 6.311 | 15.017 | 5.385 *** | 13.042 *** | 6.091 *** | 13.577 *** | 10.727 | 11.039 *** |
| DW | 1.878 *** | 1.940 | 1.917 | 1.944 | 1.836 | 1.930 | 2.033 | 1.936 |
Note: *** means significant at the level of 0.001 (p < 0.001), ** means significant at the level of 0.01 (p < 0.01), and * means significant at the level of 0.05 (p < 0.05).
Results of regression analysis of the moderating effect (n = 370).
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 |
|---|---|---|---|---|---|---|---|---|---|
| Control variables | |||||||||
| Year | 0.032 | 0.035 | 0.013 | 0.046 | 0.050 | 0.044 | 0.021 | 0.046 | 0.048 |
| Stage | 0.003 | 0.018 | 0.000 | 0.002 | −0.009 | 0.017 | −0.003 | −0.001 | −0.007 |
| Industry | 0.009 | 0.039 | 0.008 | −0.002 | 0.016 | 0.044 | −0.004 | 0.000 | 0.000 |
| Size | 0.034 | 0.026 | 0.017 | 0.009 | 0.053 | 0.010 | 0.020 | 0.015 | 0.052 |
| Ownership | 0.024 | 0.012 | 0.022 | 0.014 | 0.016 | 0.022 | 0.017 | 0.006 | 0.005 |
| Independent variable | |||||||||
| RE | −0.027 | 0.350 *** | |||||||
| RE2 | 0.423 * | ||||||||
| SE | −0.377 | ||||||||
| SE2 | 0.844 ** | 0.420 *** | |||||||
| PE | 0.358 *** | 0.341 *** | |||||||
| EE | 0.427 *** | 0.414 *** | |||||||
| Adjustment variables | |||||||||
| TEA | 0.082 | −0.048 | 0.096 * | 0.090 | |||||
| Interaction items | |||||||||
| TEA*RE | 0.102 * | ||||||||
| TEA*SE | 0.148 ** | ||||||||
| TEA*SE2 | 0.165 * | ||||||||
| TEA*PE | 0.201 *** | ||||||||
| TEA*EE | 0.128 ** | ||||||||
| R2 | 0.003 | 0.159 | 0.228 | 0.130 | 0.184 | 0.164 | 0.254 | 0.178 | 0.206 |
| Adjusted R2 | −0.011 | 0.143 | 0.213 | 0.115 | 0.171 | 0.145 | 0.236 | 0.160 | 0.188 |
| F | 0.220 | 9.782 *** | 15.261 *** | 9.022 *** | 13.675 *** | 8.823 *** | 13.650 *** | 9.783 *** | 11.699 *** |
| DW | 1.980 | 2.091 | 1.973 | 2.073 | 1.978 | 2.066 | 2.042 | 2.070 |
Note: *** means significant at the level of 0.001 (p < 0.001), ** means significant at the level of 0.01 (p < 0.01), and * means significant at the level of 0.05 (p < 0.05).
References
1. Sia, S.K.; Weill, P.; Zhang, N. Designing a future–ready enterprise:the digital transformation of DBS bank. Calif. Manag. Rev.; 2021; 63, pp. 35-57. [DOI: https://dx.doi.org/10.1177/0008125621992583]
2. Lasi, H.; Hans Kemper, G.; Fettke, P.; Feld, T.; Hoffmann, M. Industry 4.0. Bus. Inf. Syst. Eng.; 2014; 6, pp. 239-242. [DOI: https://dx.doi.org/10.1007/s12599-014-0334-4]
3. Brettel, M.; Klein, M.; Friederichsen, N. The relevance of manufacturing flexibility in the context of Industrie 4.0. Procedia CIRP; 2016; 41, pp. 105-110. [DOI: https://dx.doi.org/10.1016/j.procir.2015.12.047]
4. Blichfeldt, H.; Faullant, R. Performance effects of digital technology adoption and product & service innovation—A process–industry perspective. Technovation; 2021; 105, 102275.
5. Tang, X.W.; Sun, Y.; Tang, X.B. An evalution of technological innovation capability of the advanced equipment manufacturing industry in China. Sci. Res. Manag.; 2021; 42, pp. 1-9.
6. Troise, C.; Corvello, V.; Ghobadian, A.; O’Regan, N. How can smes successfully navigate vuca environment: The role of agility in the digital transformation era. Technol. Forecast. Soc. Chang.; 2022; 174, 121227. [DOI: https://dx.doi.org/10.1016/j.techfore.2021.121227]
7. Ghosh, S.; Hughes, M.; Hodgkinson, I.; Hughes, P. Digital transformation of industrial businesses: A dynamic capability approach. Technovation; 2022; 113, 102414. [DOI: https://dx.doi.org/10.1016/j.technovation.2021.102414]
8. Lin, C.; Kunnathur, A. Strategic orientations, developmental culture, and big data capability. J. Bus. Res.; 2019; 105, pp. 49-60. [DOI: https://dx.doi.org/10.1016/j.jbusres.2019.07.016]
9. Ardolino, M.; Rapaccini, M.; Saccani, N.; Gaiardelli, P.; Crespi, G.; Ruggeri, C. The role of digital technologies for the service transformation of industrial companies. Int. J. Prod. Res.; 2017; 56, pp. 2116-2132. [DOI: https://dx.doi.org/10.1080/00207543.2017.1324224]
10. Helfat, C.E.; Raubitschek, R.S. Dynamic and integrative capabilities for profiting from innovation in digital platform–based ecosystems. Res. Policy; 2018; 47, pp. 1391-1399. [DOI: https://dx.doi.org/10.1016/j.respol.2018.01.019]
11. Sedera, D.; Lokuge, S.; Grover, V. Innovating with enterprise systems and digital platforms: A contingent resource–based theory view. Inf. Manag.; 2016; 53, pp. 366-379. [DOI: https://dx.doi.org/10.1016/j.im.2016.01.001]
12. Günther, W.A.; Mehrizi, M.H.R.; Huysman, M. Debating Big Data: A Literature Review on Realizing Value from Big Data. J. Strateg. Inf. Syst.; 2017; 26, pp. 191-209. [DOI: https://dx.doi.org/10.1016/j.jsis.2017.07.003]
13. Wu, L.F.; Sun, L.W.; Chang, Q.; Zhang, D.; Qi, P.X. How do digitalization capabilities enable open innovation in manufacturing enterprises? A multiple case study based on resource integration perspective. Technol. Forecast. Soc. Chang.; 2022; 184, pp. 122019.1-122019.17. [DOI: https://dx.doi.org/10.1016/j.techfore.2022.122019]
14. Furr, N.; Shipilov, A. Digital Doesn’t Have to Be. Harv. Bus. Rev.; 2019; 95, pp. 94-103.
15. Oesterreich, T.D.; Teuteberg, F. Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Comput. Ind.; 2016; 83, pp. 121-139. [DOI: https://dx.doi.org/10.1016/j.compind.2016.09.006]
16. Dalenogare, L.S.; Benitez, G.B.; Ayala, N.F.; Frank, A.G. The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ.; 2018; 204, pp. 383-394. [DOI: https://dx.doi.org/10.1016/j.ijpe.2018.08.019]
17. Nambisan, S.; Lyytinen, K.; Majchrzak, A.; Song, M. Digital innovation management: Reinventing innovation management research in a digital world. MIS Q.; 2017; 41, pp. 223-238. [DOI: https://dx.doi.org/10.25300/MISQ/2017/41:1.03]
18. Birkel, H.; Veile, J.W.; Müller, J.M.; Hartmann, E.; Voigt, K. Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers. Sustainability; 2019; 11, 384. [DOI: https://dx.doi.org/10.3390/su11020384]
19. Chi, R.Y.; Zheng, R.Y.; Ruan, H.P. A study on the dual digital transformation of enterprise manufacturing process and business model. Stud. Sci. Sci.; 2022; 40, pp. 172-181.
20. Yoo, Y.; Boland, R.J.; Lyytinen, K.; Majchrzak, A. Organizing for Innovation in the Digitized World. Organ. Sci.; 2012; 23, pp. 1398-1408. [DOI: https://dx.doi.org/10.1287/orsc.1120.0771]
21. Rai, A.; Tang, X. Leveraging IT Capabilities and Competitive Process Capabilities for the Management of Interorganizational Relationship Portfolios. Inf. Syst. Res.; 2010; 21, 516. [DOI: https://dx.doi.org/10.1287/isre.1100.0299]
22. Nylén, D.; Holmström, J. Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation. Bus. Horiz.; 2015; 58, pp. 57-67. [DOI: https://dx.doi.org/10.1016/j.bushor.2014.09.001]
23. Gupta, R.; Mejia, C.; Kajikawa, Y. Business, innovation and digital ecosystems landscape survey and knowledge cross sharing. Technol. Forecast. Soc. Chang.; 2019; 147, pp. 100-109. [DOI: https://dx.doi.org/10.1016/j.techfore.2019.07.004]
24. Chae, B.K. A general framework for studying the evolution of the digital innovation ecosystem: The case of big data. Int. J. Inf. Manag.; 2019; 45, pp. 83-94. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2018.10.023]
25. Beltagui, A.; Rosli, A.; Candi, M. Exaptation in a digital innovation ecosystem: The disruptive impacts of 3D printing. Res. Policy; 2020; 49, 103833. [DOI: https://dx.doi.org/10.1016/j.respol.2019.103833]
26. Wamba, S.F.; Gunasekaran, A.; Akter, S.; Ren, S.J.F.; Dubey, R.; Childe, S.J. Big data analytics and firm performance: Effects of dynamic capabilities. J. Bus. Res.; 2017; 70, pp. 356-365. [DOI: https://dx.doi.org/10.1016/j.jbusres.2016.08.009]
27. Zittrain, J.L. The generative Internet. Harv. Law Rev.; 2006; 119, 1974.
28. Jovanovic, M.; Sjodin, D.; Parida, V. Co–evolution of platform architecture, platform services, and platform governance: Expanding the platform value of industrial digital platforms. Technovation; 2021; 118, 102218. [DOI: https://dx.doi.org/10.1016/j.technovation.2020.102218]
29. Gupta, A.K.; Smith, K.G.; Shalley, C.E. The interplay between exploration and exploitation. Acad. Manag. J.; 2006; 49, pp. 693-706. [DOI: https://dx.doi.org/10.5465/amj.2006.22083026]
30. Price, D.P.; Stoica, M.; Boncella, R.J. The relationship between innovation, knowledge, and performance in family and non–family firms: An analysis of SMEs. J. Innov. Entrep.; 2013; 2, 14. [DOI: https://dx.doi.org/10.1186/2192-5372-2-14]
31. Chatterjee, S.; Moody, G.; Lowry, P.B.; Chakraborty, S.; Hardin, A. Information Technology and organizational innovation: Harmonious information technology affordance and courage–based actualization. J. Strateg. Inf. Syst.; 2020; 29, 101596. [DOI: https://dx.doi.org/10.1016/j.jsis.2020.101596]
32. Trocin, C.; Hovland, I.V.; Mikalef, P.; Dremel, C. How Artificial Intelligence affords digital innovation: A cross–case analysis of Scandinavian companies. Technol. Forecast. Soc. Chang.; 2021; 173, 121081. [DOI: https://dx.doi.org/10.1016/j.techfore.2021.121081]
33. Sestino, A.; Prete, M.I.; Piper, L.; Guido, G. Internet of Things and Big Data as enablers for business digitalization strategies. Technovation; 2020; 38, 102173. [DOI: https://dx.doi.org/10.1016/j.technovation.2020.102173]
34. Henfridsson, O.; Yoo, Y. The liminality of trajectory shifts in institutional entrepreneurship. Organization. Science; 2014; 25, pp. 932-950.
35. Tsou, H.T.; Chen, J.S. How Does Digital Technology Usage Benefit Firm Performance? Digital Transformation Strategy and Organisational Innovation as Mediators. Technol. Anal. Strateg. Manag.; 2021; pp. 1-4. [DOI: https://dx.doi.org/10.1080/09537325.2021.1991575]
36. Pereira, J.; Tavalaei, M.M.; Ozalp, H. Blockchain–based platforms: Decentralized infrastructures and its boundary conditions. Technol. Forecast. Soc. Chang.; 2019; 146, pp. 94-102. [DOI: https://dx.doi.org/10.1016/j.techfore.2019.04.030]
37. Hart, S.L.; Dowell, G. Invited editorial: A natural–resource–based view of the firm: Fifteen years after. J. Manag.; 2011; 37, pp. 1464-1479. [DOI: https://dx.doi.org/10.1177/0149206310390219]
38. Coreynen, W.; Vanderstraeten, J.; Witteloostuijn, A.V.; Cannaertse, N.; Lootse, E.; Slabbinckf, H. What drives product–service integration? An abductive study of decision–makers’ motives and value strategies. J. Bus. Res.; 2020; 117, pp. 189-200. [DOI: https://dx.doi.org/10.1016/j.jbusres.2020.05.058]
39. Adamides, E.; Karacapilidis, N. Information technology for supporting the development and maintenance of open innovation capabilities. J. Innov. Knowl.; 2020; 5, pp. 29-38. [DOI: https://dx.doi.org/10.1016/j.jik.2018.07.001]
40. Nambisan, S. Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrep. Theory Pract.; 2017; 41, pp. 1029-1055. [DOI: https://dx.doi.org/10.1111/etap.12254]
41. Liu, Y.; Dong, J.Y.; Mei, L.; Shen, R. Digital innovation and performance of manufacturing firms: An affordance perspective. Technovation; 2022; 102458. [DOI: https://dx.doi.org/10.1016/j.technovation.2022.102458]
42. Ode, E.; Ayavoo, R. The mediating role of knowledge application in the relationship between knowledge management practices and firm innovation. J. Innov. Knowl.; 2020; 5, pp. 210-218. [DOI: https://dx.doi.org/10.1016/j.jik.2019.08.002]
43. Teece, D.J.; Pisano, G.P.; Shuen, A. Dynamic capabilities and strategic management. Strateg. Manag. J.; 1997; 18, pp. 509-533. [DOI: https://dx.doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z]
44. Annarelli, A.; Battistella, C.; Nonino, F.; Parida, V.; Pessot, E. Literature review on digitalization capabilities: Co–citation analysis of antecedents, conceptualization and consequences. Technol. Forecast. Soc. Chang.; 2021; 166, 120635. [DOI: https://dx.doi.org/10.1016/j.techfore.2021.120635]
45. Malchenko, Y.; Gogua, M.; Golovacheva, K.; Smirnova, M.; Alkanova, O. A critical review of digital capability frameworks: A consumer perspective. Digit. Policy Regul. Gov.; 2020; 22, pp. 269-288. [DOI: https://dx.doi.org/10.1108/DPRG-02-2020-0028]
46. Lenka, S.; Parida, V.; Wincent, J. Digitalization Capabilities as Enablers of Value Co-Creation in Servitizing Firms. Psychol. Mark.; 2017; 34, pp. 92-100. [DOI: https://dx.doi.org/10.1002/mar.20975]
47. Levallet, N.; Chan, Y.E. Role of digital capabilities in unleashing the power of managerial improvisation. MIS Q. Exec.; 2018; 17, pp. 4-21.
48. Byrne, B. Structural Equation Modeling with AMOS: Basic Concepts, Applications, Programming; 2nd ed. Routledge: New York, NY, USA, 2010.
49. Yoo, Y.J.; Henfridsson, O.; Lyytinen, K. The new organizing logic of digital innovation: An agenda for information systems research. Inf. Syst. Res.; 2010; 21, pp. 724-735. [DOI: https://dx.doi.org/10.1287/isre.1100.0322]
50. Takeda, Y.; Aoshima, Y.; Nobeoka, K. The importance of technology integration capabilities: Evaluating the impact of 3D technologies on product development performance in Japan and China. Int. J. Prod. Dev.; 2012; 16, pp. 26-44. [DOI: https://dx.doi.org/10.1504/IJPD.2012.047261]
51. Loebbecke, C.; Picot, A. Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. J. Strateg. Inf. Syst.; 2015; 24, pp. 149-157. [DOI: https://dx.doi.org/10.1016/j.jsis.2015.08.002]
52. Cheah, C.G.; Chia, W.Y.; Lai, S.F.; Chew, K.W.; Chia, S.R.; Show, P.L. Innovation designs of industry 4.0 based solid waste management: Machinery and digital circular economy. Environ. Res.; 2022; 213, 113619. [DOI: https://dx.doi.org/10.1016/j.envres.2022.113619] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35700763]
53. Hader, M.; Tchoffa, D.; Mhamedi, A.E.; Ghodous, P.; Dolgui, A.; Abouabdellah, A. Applying Integrated Blockchain and Big Data Technologies to Improve Supply Chain Traceability and Information Sharing in the Textile Sector. J. Ind. Inf. Integr.; 2022; 28, 100345. [DOI: https://dx.doi.org/10.1016/j.jii.2022.100345]
54. Xie, Z.; Wang, J.; Miao, L. Big Data and Emerging Market Firms’ Innovation in an Open Economy: The Diversification Strategy Perspective. Technol. Forecast. Soc. Chang.; 2021; 173, 121091. [DOI: https://dx.doi.org/10.1016/j.techfore.2021.121091]
55. Zhang, G.S.; Du, P.F.; Chen, M.M. Digital Empowerment and Firm Technology Innovation–Empirical Evidence from China’s Manufacturing Industry. Contemp. Econ. Sci.; 2021; 43, pp. 65-76.
56. Chirumalla, K. Building digitally–enabled process innovation in the process industries: A dynamic capabilities approach. Technovation; 2021; 105, 102256. [DOI: https://dx.doi.org/10.1016/j.technovation.2021.102256]
57. Horvat, D.; Kroll, H.; Jäger, A. Researching the Effects of Automation and Digitalization on Manufacturing Companies’ Productivity in the Early Stage of Industry 4.0—ScienceDirect. Procedia Manuf.; 2019; 39, pp. 886-893. [DOI: https://dx.doi.org/10.1016/j.promfg.2020.01.401]
58. Krner, M.F.; Sedlmeir, J.; Weibelzahl, M.; Fridgen, G.; Heine, M.; Neumann, C. Systemic risks in electricity systems: A perspective on the potential of digital technologies. Energy Policy; 2022; 164, 112901. [DOI: https://dx.doi.org/10.1016/j.enpol.2022.112901]
59. Kallinikos, J.; Aaltonen, A.; Marton, A. The Ambivalent Ontology of Digital Artifacts. MIS Q.; 2013; 37, pp. 357-370. [DOI: https://dx.doi.org/10.25300/MISQ/2013/37.2.02]
60. Paredes–Frigolett, H.; Pyka, A. The global stakeholder capitalism model of digital platforms and its implications for strategy and innovation from a schumpeterian perspective. J. Evol. Econ.; 2022; 32, pp. 463-500. [DOI: https://dx.doi.org/10.1007/s00191-022-00760-z]
61. Still, K.; Seppanen, M.; Korhonen, H.; Suominen, A.; Kumpulainen, M.; Valkokari, K. Business model innovation of startups developing multisided digital platforms. Proceedings of the 2017 IEEE 19th Conference on Business Informatics (CBI); Thessaloniki, Greece, 24–27 July 2017.
62. Schreieck, M.; Wiesche, M.; Krcmar, H. Express: Capabilities for value co–creation and value capture in emergent platform ecosystems: A longitudinal case study of sap’s cloud platform. J. Inf. Technol.; 2021; 36, pp. 365-390. [DOI: https://dx.doi.org/10.1177/02683962211023780]
63. Broekhuizen, T.; Emrich, O.; Gijsenberg, M.J.; Broekhuis, M.; Donkers, B.; Sloot, L.M. Digital platform openness: Drivers, dimensions and outcomes. J. Bus. Res.; 2021; 122, pp. 902-914. [DOI: https://dx.doi.org/10.1016/j.jbusres.2019.07.001]
64. Stanko, M.A.; Calantone, R.J. Controversy in Innovation Outsourcing Research: Review, Synthesis and Future Directions. RD Manag.; 2011; 41, pp. 8-20. [DOI: https://dx.doi.org/10.1111/j.1467-9310.2010.00624.x]
65. Yang, W.; Liu, J.; Wu, J. The impact of ‘population–flow’ conguration on focal rm’s performanceempirical research on digital innovation ecosystemsinarticial intelligence industry Studies. Sci. Sci.; 2020; 38, pp. 2077-2086.
66. Boudreau, K.J. Let a thousand flowers bloom? An early look at large numbers of software app developers and patterns of innovation. Organ. Sci.; 2012; 23, pp. 1409-1427. [DOI: https://dx.doi.org/10.1287/orsc.1110.0678]
67. Romano, A.; Passiante, G.; Vecchio, P.D.; Secundo, G. The innovation ecosystem as booster for the innovative entrepreneurship in the smart specialisation strategy. Int. J. Knowlegement Based Dev.; 2014; 5, pp. 271-288. [DOI: https://dx.doi.org/10.1504/IJKBD.2014.065315]
68. Holland, J.H. Complexity: A Very Short Introduction; Oxford University Press: Oxford, UK, 2014.
69. Martinez, J.J.; Hoyos, M.; Ortega, B.H. Influence of the business technological compatibility on the acceptance of innovations. Eur. J. Innov. Manag.; 2007; 10, pp. 7-24.
70. Rajaguru, R.; Matanda, M.J. Effects of inter–organizational compatibility on supply chain capabilities: Exploring the mediating role of inter–organizational information systems (IOIS) integration. Ind. Mark. Manag.; 2013; 42, pp. 620-632. [DOI: https://dx.doi.org/10.1016/j.indmarman.2012.09.002]
71. Silva, E.; Shinohara, A.C.; Nielsen, C.P.; Limabc, E.P.; Angelisd, J. Operating Digital Manufacturing in Industry 4.0: The role of advanced manufacturing technologies. Procedia CIRP; 2020; 93, pp. 174-179. [DOI: https://dx.doi.org/10.1016/j.procir.2020.04.063]
72. Ma, B.J.; Qiang, W.; Chen, G.Q.; Zhang, J.; Guo, X.H. Content & structure coverage: Extracting a diverse information subset. INFORMS J. Comput.; 2017; 29, pp. 660-675. [DOI: https://dx.doi.org/10.1287/ijoc.2017.0753]
73. Battleson, D.A.; West, B.C.; Kim, J.; Ramesh, B.; Robinson, P.S. Achieving dynamic capabilities with cloud computing: An empirical investigation. Eur. J. Inf. Syst.; 2016; 25, pp. 209-230. [DOI: https://dx.doi.org/10.1057/ejis.2015.12]
74. Chakravarty, A.; Grewal, R.; Sambamurthy, V. Information technology competencies, organizational agility, and firm performance. Inf. Syst. Res.; 2013; 24, pp. 976-997. [DOI: https://dx.doi.org/10.1287/isre.2013.0500]
75. Ciarli, T.; Kenney, M.; Massini, S.; Piscitello, L. Digital technologies, innovation, and skills: Emerging trajectories and challenges. Res. Policy; 2021; 50, 104289. [DOI: https://dx.doi.org/10.1016/j.respol.2021.104289]
76. Sabai, K.; Theresa, C.F.H. Digital technology, digital capability and organizational performance: A mediating role of digital innovation. Int. J. Innov. Sci.; 2019; 11, pp. 177-195. [DOI: https://dx.doi.org/10.1108/IJIS-08-2018-0083]
77. Jun, W.; Nasir, M.H.; Yousaf, Z.; Khattak, A.; Yasir, M.; Javed, A.; Shirazi, S.H. Innovation Performance in Digital Economy: Does Digital Platform Capability, Improvisation Capability and Organizational Readiness Really Matter?. Eur. J. Innov. Manag.; 2021; 25, pp. 1-19. [DOI: https://dx.doi.org/10.1108/EJIM-10-2020-0422]
78. Soluk, J.; Miroshnychenko, I.; Kammerlander, N.; Massis, A.D. Family Influence and Digital Business Model Innovation: The Enabling Role of Dynamic Capabilities. Entrep. Theory Pract.; 2021; 45, pp. 867-905. [DOI: https://dx.doi.org/10.1177/1042258721998946]
79. Heo, P.S.; Lee, D.H. Evolution Patterns and Network Structural Characteristics of Industry Convergence. Struct. Chang. Econ. Dyn.; 2019; 51, pp. 405-426. [DOI: https://dx.doi.org/10.1016/j.strueco.2019.02.004]
80. Ionescu, A.M.; Clipa, A.M.; Turnea, E.S.; Clipa, C.I.; Bedrule-Grigoruță, M.V.; Roth, S. The impact of innovation framework conditions on corporate digital technology integration: Institutions as facilitators for sustainable digital transformation. J. Bus. Econ. Manag.; 2022; 51, pp. 405-426. [DOI: https://dx.doi.org/10.3846/jbem.2022.17039]
81. Teece, D.J.; Pisano, G. The dynamic capabilities of firms: An introduction. Ind. Corp. Chang.; 1194; 3, pp. 537-556. [DOI: https://dx.doi.org/10.1093/icc/3.3.537-a]
82. Zhou, K.Z.; Li, C.B. How strategic orientations influence the building of dynamic capability in emerging economies. J. Bus. Res.; 2010; 63, pp. 224-231. [DOI: https://dx.doi.org/10.1016/j.jbusres.2009.03.003]
83. Renko, M.; Carsrud, A.; Brannback, M. The effect of a market orientation, entrepreneurial orientation, and technological capability on innovativeness: A study of young biotechnology ventures in the United States and in Scandinavia. J. Small Bus. Manag.; 2009; 47, pp. 331-369. [DOI: https://dx.doi.org/10.1111/j.1540-627X.2009.00274.x]
84. Tian, M. Turning a technology into many solutions: A case study of embedding an information system. J. Bus. Res.; 2019; 101, pp. 23-39. [DOI: https://dx.doi.org/10.1016/j.jbusres.2019.03.053]
85. Tilson, D.; Lyytinen, K.; Sørensen, C. Research commentary–Digital infrastructures: The missing IS research agenda. Inf. Syst. Res.; 2010; 21, pp. 748-759. [DOI: https://dx.doi.org/10.1287/isre.1100.0318]
86. Reuver, M.D.; Wynsberghe, A.V.; Janssen, M.; Poel, I. Digital platforms and responsible innovation: Expanding value sensitive design to overcome ontological uncertainty. Ethics Inf. Technol.; 2020; 22, pp. 257-267. [DOI: https://dx.doi.org/10.1007/s10676-020-09537-z]
87. Bush, A.A.; Tiwana, A.; Rai, A. Complementarities Between Product Design Modularity and IT Infrastructure Flexibility in IT–Enabled Supply Chains. IEEE Trans. Eng. Manag.; 2010; 57, pp. 240-254. [DOI: https://dx.doi.org/10.1109/TEM.2010.2040741]
88. Zhu, Z.; Zhao, J.; Bush, A.A. The effects of e–business processes in supply chain operations: Process components and value creation mechanisms. Int. J. Inf. Manag.; 2020; 50, pp. 273-285. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2019.07.001]
89. Harris, J.; Ives, B.; Junglas, I. IT consumerization: When gadgets turn into enterprise IT tools. MIS Q. Exec.; 2012; 11, pp. 99-112.
90. Maroofi, F.; Ardalan, A.G.; Tabarzadi, J. The effect of sales strategies in the financial performance of insurance companies. Int. J. Asian Soc. Sci.; 2017; 7, pp. 150-160. [DOI: https://dx.doi.org/10.18488/journal.1/2017.7.2/1.2.150.160]
91. Benitez, J.; Arenas, A.; Castillo, A.; Esteves, J. Impact of digital leadership capability on innovation performance: The role of platform digitization capability. Inf. Manag.; 2022; 59, 103590. [DOI: https://dx.doi.org/10.1016/j.im.2022.103590]
92. Hanseth, O.; Modol, J.R. The dynamics of architecture–governance configurations: An assemblage theory approach. J. Assoc. Inf. Syst.; 2021; 22, pp. 130-155. [DOI: https://dx.doi.org/10.17705/1jais.00656]
93. Leong, C.; Pan, S.L.; Leidner, D.E.; Huang, J.S. Platform leadership: Managing boundaries for the network growth of digital platforms. J. Assoc. Inf. Syst.; 2019; 20, pp. 1531-1565. [DOI: https://dx.doi.org/10.17705/1jais.00577]
94. Denning, S. Mastering the challenge of business ecosystems. Strategy Leadersh.; 2021; 49, pp. 9-15. [DOI: https://dx.doi.org/10.1108/SL-06-2021-0057]
95. Taylor, A. Technology innovation and digital ecosystems: Case study analysis and proposal of a lifecycle model. Int. J. Innov. Technol. Manag.; 2022; 19, 2250009. [DOI: https://dx.doi.org/10.1142/S0219877022500092]
96. Henfridsson, O.; Mathiassen, L.; Svahn, F. Managing technological change in the digital age: The role of architectural frames. J. Inf. Technol.; 2014; 29, pp. 27-43. [DOI: https://dx.doi.org/10.1057/jit.2013.30]
97. Iansiti, M.; Lakhani, K.R. Managing our hub economy. Harv. Bus. Rev.; 2017; 10, 117.
98. Vecchio, P.D.; Passiante, G.; Barberio, G.; Innella, C. Digital innovation ecosystems for circular economy: The case of ICESP, the Italian circular economy stakeholder platform. Int. J. Innov. Technol. Manag.; 2021; 18, 2050053. [DOI: https://dx.doi.org/10.1142/S0219877020500534]
99. Xu, Y. Digital innovation ecosystem: Research context, research hotspot and research trends––Knowledge mapping analysis using citespace. J. Electron. Inf. Sci.; 2020; 5, pp. 7-80.
100. Wu, M.L. Statistical Application Practice of SPSS; China Railway Press: Beijing, China, 2000; (In Chinese)
101. Yayavaram, S.; Chen, W.R. Changes in firm knowledge couplings and firm innovation performance: The moderating role of technological complexity. Strateg. Manag. J.; 2015; 36, pp. 377-396. [DOI: https://dx.doi.org/10.1002/smj.2218]
102. Low, C.; Chen, Y.; Wu, M. Understanding the determinants of cloud computing adoption. Ind. Manag. Data Syst.; 2011; 111, pp. 1006-1023. [DOI: https://dx.doi.org/10.1108/02635571111161262]
103. Constantiou, I.D.; Kallinikos, J. New games, new rules: Big data and the changing context of strategy. J. Inf. Technol.; 2015; 30, 4457. [DOI: https://dx.doi.org/10.1057/jit.2014.17]
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