Introduction

The high-end equipment manufacturing industry represents a pinnacle of advanced manufacturing and plays a crucial role in national economic development and strategic national defense. It currently serves as the primary vehicle for promoting the deep integration of digital and physical technologies in China. Driven by a series of policy initiatives, an increasing number of high-end equipment manufacturing enterprises are adopting digital technologies across various stages of their product life cycles, gradually transitioning from informatization to digitalization1. However, because digital transformation is a complex and systematic process, and due to a lack of practical experience, many enterprises have not achieved significant results, with some even facing failure2. As the new wave of technological revolution and industrial transformation advances, digital transformation has become an essential choice for China’s high-end equipment manufacturing industry to achieve high-quality development3. Thus, what is the current level of digital transformation among China’s high-end equipment manufacturing enterprises? What are the existing constraints? A thorough understanding of these issues will provide valuable insights for these enterprises as they engage in practical explorations of digital transformation.

Research on evaluating the digital transformation of enterprises primarily addresses two aspects: defining evaluation dimensions and selecting evaluation methods. In terms of evaluation dimensions, most studies construct index systems from multiple perspectives. Regarding evaluation methods, mainstream approaches include questionnaire surveys4,5, expert scoring6,7, entropy weighting8, principal component analysis9,10, and text analysis11,12. Although existing research has laid an important foundation for assessing digital transformation, several areas still require improvement. First, in the setting of evaluation dimensions, current research predominantly focuses on technology-driven, business integration, and input–output perspectives, lacking a“process view”that fully captures the digital transformation process. Enterprise digital transformation is a dynamic and evolving process, characterized by continuous systematic change rather than a one-time application or upgrade of technology. It begins with the recognition of the need for transformation and progress through organizational adaptation, the integration of digital technologies, and ongoing iterative development, ultimately resulting in value creation and a shift in operational paradigms. Therefore, evaluating enterprise digital transformation should adopt a “process-oriented” perspective to establish a comprehensive evaluation index system, thereby unveiling the internal mechanisms—the“black box”—of the transformation process. Second, in terms of evaluation methodology, most existing studies employ static evaluation approaches that overlook the temporal influence on indicator weights, resulting in evaluation outcomes that lack dynamic comparability. Consequently, the scientific rigor and comparability of indicator weight determination remain suboptimal. To address this issue, this paper adopts the I-P-O model—following the logic of “input-process-output”—to construct an evaluation index system for assessing the digital transformation level of high-end equipment manufacturing enterprises. This system encompasses three dimensions: digital transformation awareness (input), digital transformation process (process), and digital transformation benefits (output), thereby operationalizing the “process-oriented” perspective. Furthermore, the VHSD-EM model13,14, which accounts for the temporal variation in indicator weights, is employed to evaluate the digital transformation levels of 124 high-end equipment manufacturing enterprises in China over the period from 2016 to 2021. Based on this evaluation, the primary barriers to digital transformation in these enterprises are further analyzed using the obstacle degree model. The high-end equipment manufacturing industry is a typical representative of advanced manufacturing and differs markedly from small and medium-sized equipment manufacturing enterprises in terms of strategic orientation, technological capabilities, and R&D resources. Therefore, while the conclusions of this study may not be directly applicable to promoting the digital transformation of small and medium-sized enterprises, they nonetheless offer valuable reference points for informing their transformation strategies.

The remainder of the paper is organized as follows: Section "Analysis of the digital transformation process and construction of an evaluation index system" provides a literature review, while Section "Evaluation and analysis of the level of digital transformation" outlines the research hypotheses based on relevant theories and research objectives. Section "Impediment factor analysis" details the research methodology, including variables and data sources. Section "Conclusion" presents the results of the empirical analysis, and Sect. 6 discusses and summarizes the findings from both longitudinal and cross-sectional studies.

Analysis of the digital transformation process and construction of an evaluation index system

Digital transformation process analysis

The digital transformation of enterprises is a process in which digital technology progressively integrates into various aspects of enterprise operations, leading to a comprehensive transformation of business processes, reshaping of business models, and the creation of new formats and models15. Accordingly, this paper adopts a“process view”as the foundation and, based on the I-P-O model, analyzes the specific process of digital transformation in high-end equipment manufacturing enterprises from three dimensions: digital transformation awareness, digital transformation implementation, and digital transformation outcomes (Fig. 1)16.

  • The input stage of digital transformation begins with a shift in ideology, encompassing awareness of corporate strategy, management models, production operations, and business models17. This shift is often influenced by a complex ecosystem composed of technology, institutions, markets, and industries. At the technological level, advancements and integrated interactions in digital technologies, such as big data, the Internet of Things (IoT), and artificial intelligence (AI), have significantly transformed how companies acquire and create value. These changes affect corporate digital strategies and decision-making, stimulating awareness of digital transformation18. At the institutional level, policies supporting the digital economy—such as financial subsidies, tax incentives, and credit support—reduce the costs of digital transformation and increase enterprises’ willingness to pursue it19. At the market level, the growing diversification and personalization of user needs compel enterprises to leverage digital technologies to enhance interactivity with users, integrate them into the value creation process, and meet their emotional needs, thereby driving digital transformation. At the industry level, competitive pressure in the high-end equipment manufacturing sector, coupled with the challenge of technological dependence, pushes companies to keep pace with industry transformation. As peer companies use digital transformation to enhance resource integration, collaboration efficiency, and value creation, enterprises are compelled to imitate and learn from the digital practices of others in their field20.

  • Process stage: digital transformation implementation. Increased awareness of digital transformation gradually drives enterprises to adopt digital practices and integrate digital technologies across the entire product life cycle, including design, processing, assembly, and service. At this stage, enterprises formulate action plans to define specific changes and mobilize resources to address the external demands and opportunities identified in the input stage21. This process can be divided into three key areas: Design actions: To successfully implement digital transformation, companies must plan and design new structures, such as product architecture, production processes, and business models, while developing various feasible action plans,Selection of actions: Factors like the external environment, company size, technological capacity, financial resources, and human capital will influence the company’s choices in its digital transformation journey. These constraints require companies to select specific action plans based on their current conditions; Implementation actions: Enterprises will continue to invest resources according to their chosen action plan, ensuring an effective response to a rapidly evolving ecosystem22. This supports the enterprise’s transition to digital practices, accelerating the digital transformation and enhancing its ability to seize market opportunities. The core tasks during the digital transformation implementation phase involve investing in, integrating, and developing new resources. This includes allocating resources to key production factors, repurposing existing resources, leveraging external resources to supplement internal capabilities, and discarding outdated or inefficient resources.

  • Output stage: Benefits of digital transformation. The direct impact of digital transformation on digital product innovation, digital service innovation, digital process innovation, organizational structure innovation, and business model innovation is the enhancement of enterprise efficiency23. On one hand, digital technologies enable enterprises to restructure business processes and value chains—such as research and development, production, and sales—thereby improving operational efficiency. On the other hand, the integration of digital technologies breaks down departmental barriers and enhances organizational agility, allowing companies to respond more rapidly to external changes and further boost efficiency. Additionally, digital transformation enriches the elements within the innovation ecosystem, strengthens the symbiotic relationships between these elements, and stimulates the vitality of various innovation components, leading to improved operational and innovation efficiency.

Fig. 1
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Enterprise digital transformation process framework model.

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Overall, the digital transformation of an enterprise is an iterative process. The input stage (digital transformation awareness) evolves through the digital transformation implementation phase, and the output (digital transformation benefits) is fed back into the input stage, driving the enterprise to continuously adjust, adapt, and improve within the digital environment. This feedback loop perpetually advances the enterprise’s digital transformation24. Enterprise digital transformation begins with managerial awareness. It is a proactive decision made in response to technological, institutional, market, and industry changes. This awareness reflects management’s foresight into the enterprise’s future trajectory and their capacity to drive digital initiatives. A strong managerial awareness not only shapes the strategic orientation of digital transformation but also accelerates the transformation of production management and business models, thereby providing a critical foundation for its successful implementation.

Construction of a digital transformation evaluation index system

Based on the framework model of the enterprise digital transformation process, and with full consideration of the representativeness, scientific rigor, and practicality of the indicators, this paper establishes an evaluation index system for the digital transformation level of high-end equipment manufacturing enterprises across three dimensions: digital transformation awareness, digital transformation implementation, and digital transformation benefits, as shown in Table 1.

  • Digital transformation awareness. As decision-makers and executors of corporate strategies, management’s expertise, practical experience, strategic thinking, and exposure to external factors can result in varying perceptions and attitudes toward digital transformation. Their awareness of digital transformation is one of the key factors influencing the enterprise’s digital transformation efforts25. Based on the above analysis, four primary indicators are selected to assess the enterprise’s awareness of digital transformation: awareness of digital production transformation, digital management transformation, digital business model transformation, and digital strategic orientation. Digital production transformation awareness refers to management’s recognition of the need to enhance production processes through internal research and development or the external adoption of digital technologies. In this context, the application of foundational digital technologies and intelligent manufacturing methods are selected as core indicators26. Digital management transformation awareness reflects the adaptive changes in digital management, including shifts in corporate culture, organizational structure, and internal operations to form a management model suited to the digital environment27. The composition of a digital executive team and the adoption of a modern information management system serve as key metrics. Digital business model transformation awareness refers to the use of digital technologies to transform traditional business models, enabling better production guidance and stimulating consumer demand. The adoption of Internet-based business models is used as a measurement indicator28. Digital strategic orientation awareness represents the enterprise’s overall strategic direction in pursuing digital transformation. This direction guides the organization in developing appropriate actions to achieve sustained performance excellence. For this, foresight and sustainability in digital strategic orientation are creatively used as core indicators29.

  • Digital transformation reforms. The focus of digital transformation in enterprises lies in investing, integrating, and developing new resources, which necessitates significant investment in digital technology talent and capital to support various actions implemented during the transformation process. To capture this, two primary indicators are used: digital talent investment and digital capital investment during the transformation phase. Given the knowledge-intensive nature of the high-end equipment manufacturing industry, as well as the critical role of digital technology talent in driving transformation, these enterprises must continuously optimize their human capital structure throughout the digital transformation process30. Drawing on the research of Yan et al31. and others, proxy indicators such as the proportion of R&D personnel, the proportion of R&D personnel salaries, the proportion of technical personnel, and the proportion of technical directors are used to measure digital talent investment. At the same time, enterprises must increase capital investment to support their digital transformation efforts. On one hand, this capital is allocated to build digital components, platforms, and infrastructure, achieving"capital broadening."On the other hand, it is used for R&D and innovation activities, resulting in“capital deepening”32. Referring to the research of Sui et al33. and colleagues, indicators such as the proportion of digital equipment, the proportion of digital projects under construction, the proportion of digital software, and the amount of funds raised for digital projects are selected to represent digital capital investment.

  • Digital transformation benefits. The direct impact of digital product innovation, digital service innovation, digital process innovation, organizational structure innovation, and business model innovation brought about by the digital transformation of enterprises is the improvement of enterprise efficiency34. Drawing on the research of Yan et al31., two primary indicators—operational efficiency and innovation efficiency—are selected to reflect the benefits of digital transformation in enterprises. Enhancing operational efficiency is the most immediate goal of digital transformation, as it enables companies to create value and increase market share35. Revenue, profit, and cost are key economic indicators reflecting an enterprise’s operational efficiency. In this paper, revenue efficiency, profit efficiency, and cost efficiency are used as secondary indicators to measure operational efficiency. In addition to improving operational efficiency through resource optimization, cost reduction, and performance enhancement, digital transformation also enriches the elements of the innovation ecosystem, strengthens the symbiotic relationships between these elements, and stimulates the vitality of various innovation factors36. This process leverages the technological innovation advantages of high-end equipment manufacturing enterprises, ultimately improving innovation efficiency. The output efficiency of digital invention patents is selected as the measurement indicator for innovation efficiency37.

Table 1 Green total factor energy efficiency.
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The application of underlying digital technologies and internet business models is primarily assessed using word frequency statistical methods. The measurement process consists of the following steps: First, a keyword dictionary is developed—typically including terms such as “artificial intelligence technology,” “big data technology,” “cloud computing technology,” and “blockchain technology”—to serve as the basis for evaluating the use of digital technologies. Second, Python is employed to extract relevant sections of annual reports containing these keywords. Finally, the extracted keywords are manually verified against the dictionary, and word frequency statistics are calculated.

Compared with previous studies, the evaluation index system constructed in this paper breaks from the traditional perspective and adopts a"process view."The system is designed based on the"input-process-output"model to accurately capture the digital transformation process in high-end equipment manufacturing enterprises.

Digital transformation evaluation method

VHSD-EM model

The digital transformation of high-end equipment manufacturing enterprises is a continuous and dynamic process, and its level measurement requires both vertical and horizontal comparability. However, current academic approaches to measuring enterprise digital transformation tend to be somewhat subjective, making it difficult to assign consistent weights for dynamic evaluation problems that involve panel data, thus complicating cross-period comparative analysis38. In response to this challenge, this paper adopts a dynamic measurement approach: the Vertical and Horizontal Scatter Degree Method (VHSD). This method maximizes the reflection of indicator differences at a specific point in time (horizontal comparability) and captures the overall value characteristics of indicators over time (vertical comparability). The weight coefficient is determined based on both time and data characteristics, effectively avoiding the influence of subjective factors and resolving the issue of inconsistent weights across different years through objective data. The VHSD method has been widely applied in the dynamic measurement of time-series three-dimensional data. However, as this method may reduce the amount of information contained in the indicators, this paper also incorporates the Entropy Method (EM), which fully captures the information within the evaluation indicators13. By combining VHSD with EM, this hybrid evaluation method integrates the strengths of both approaches, improving the credibility of the evaluation results. See the appendix for detailed formulas.

Impediment model

The digital transformation of high-end equipment manufacturing enterprises represents a complex system in which each indicator contributes differently to overall performance. This study develops an obstacle degree model to identify the primary barriers that hinder the digital transformation of these enterprises. See the appendix for detailed formulas.

Evaluation and analysis of the level of digital transformation

Sample selection and data preprocessing

Currently, there is no unified definition of high-end equipment manufacturing enterprises in academia. In this study, we refer to the screening method used by Yan et al.31. and conducted an initial screening, resulting in 209 listed companies in the high-end equipment manufacturing sector that meet the required criteria. Companies with incomplete data, annual reports lacking relevant digital keywords, and those classified as ST and ST* were excluded, leaving a final sample of 124 companies. The data for this study were sourced from the CSMAR database, the Wind database, and the official websites of the listed companies. The introduction of"Made in China 2025"is used as a pivotal point, with 2015 marking the beginning of China’s fourth industrial revolution driven by digital technology. Given the difficulty in assessing the effects of digital transformation during that year and the availability of data, the sample period selected for analysis spans from 2016 to 2021.

During data collection, it was found that some R&D personnel compensation values were missing. This is because the Notice on Amending and Issuing the Format of Financial Statements of General Enterprises for 2018 explicitly required that, starting in 2018, R&D personnel compensation be disclosed separately in the notes to the financial statements. To address the missing values for 2016, this paper supplements the data by using the minimum value of the enterprise’s R&D personnel compensation from 2017 to 2021.

To avoid indicators reflecting redundant information, the correlation between indicators must be tested before calculating the weight, ensuring that relatively unimportant indicators with a correlation coefficient greater than 0.8 are eliminated. After calculating the Pearson correlation coefficient, it was found that the correlation between the indicators of digital strategy orientation and digital strategy orientation continuity was 0.909, indicating a strong correlation. As a result, the digital strategy orientation continuity indicator was eliminated, leaving a total of 18 effective evaluation indicators.

Evaluation result analysis

  • (1) General evaluation.

When using the VHSD-EM model to weight the indicators, a Spearman rank correlation test is required to verify the consistency of the model13. The specific test results are shown in Table 2. The results indicate that the measured outcomes of the VHSD and EM models are strongly positively correlated and significant at the 5% level, demonstrating good consistency in weight measurement between the two models, as well as the stability of the VHSD-EM model. On this basis, the comprehensive weights of each indicator were further calculated. Among these, the structure of the digital executive team, the amount of funds raised for digital projects, and the efficiency of digital invention patent output ranked highest, accounting for 17.48%, 11.70%, and 9.26%, respectively. This suggests that these three indicators are the primary factors currently influencing the level of digital transformation in high-end equipment manufacturing enterprises. Moreover, these three indicators correspond to the input, process, and output stages, respectively, which confirms the rationality of constructing an evaluation index system for the level of digital transformation from a"process perspective."

Table 2 Index weight and correlation coefficient.
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Based on the determined indicator weights, the digital transformation scores of 124 enterprises from 2016 to 2021 were calculated, and the changes in their mean values are shown in Fig. 2. Overall, the digital transformation level of high-end equipment manufacturing enterprises shows an upward trend, albeit with a slow growth rate, increasing gradually from 0.1215 in 2016 to 0.1422 in 2021. However, the outbreak of the Sino-US trade war in 2018 significantly impacted the development of China’s ICT industry, making it difficult for the technology service sector to provide effective support for high-end equipment manufacturing enterprises, leading to a slight decline in digital transformation scores. In response, the Chinese government actively encouraged and guided high-end equipment manufacturing enterprises to pursue independent research and development of core technologies. In 2018, the Ministry of Industry and Information Technology issued the"13th Five-Year Plan for the Development of the High-End Equipment Manufacturing Industry."Subsequently, various provinces and cities introduced relevant policies and measures to accelerate the digital transformation of China’s high-end equipment manufacturing enterprises. From 2019 onward, the digital transformation scores began to rise consistently, reaching a high of 0.1422 in 2021. Using 2016 as the base period, it was found that the digital transformation level of 79.84% of high-end equipment manufacturing enterprises improved significantly by 2021. However, only 24 enterprises experienced a growth rate exceeding 5%, indicating that relatively few enterprises achieved outstanding digital transformation. From 2016 to 2021, companies such as Zoomlion, Xuji Electric, and Aisino ranked among the top five in terms of digital transformation, as shown in Table 3. Notably, most of the top-ranking companies are state-controlled enterprises, which is attributed to their proactive response to policy initiatives promoting digital transformation and their ability to create exemplary transformation models. Several factors may explain this phenomenon. First, the nature of state-owned enterprises (SOEs) necessitates that they actively respond to policy directives and serve as models for digital transformation, making their transformation a national strategic priority. Second, SOEs benefit from substantial resource guarantees and possess extensive control over key industry data resources, which provide critical support for advancing digital transformation. Third, SOEs tend to exhibit greater strategic foresight and organizational coherence, allowing for comprehensive top-level design and long-term strategic planning. This enables them to better navigate the complexity and duration of digital transformation processes and enhances their capacity to absorb the risks associated with potential transformation failures. Additionally, state-controlled enterprises are more likely to access favorable conditions, such as bank credit, government subsidies, and tax incentives, providing them with more resources for digital transformation compared to private enterprises. At the guideline level, the score for digital transformation awareness increased the fastest, reflecting the shift of China’s high-end equipment manufacturing enterprises from passive acceptance to active exploration of digital transformation, driven by a complex ecosystem of technology, systems, markets, and industries. However, despite a relatively high score for digital transformation implementation, its growth rate remains slow. Furthermore, the score for the benefits of digital transformation has even declined in recent years. Possible reasons include the following: first, many companies fail to fully assess their actual circumstances and transformation needs, resulting in a blind pursuit of digital transformation and inefficient use of resources. Second, digital technologies are often not deeply integrated into core business operations, leading to suboptimal transformation outcomes. Third, insufficient attention to cultivating digital talent and building effective teams has caused a mismatch between employee skills and transformation requirements. Finally, delays in business process reengineering and management reforms have created a significant gap between digital investments and expected returns.

Fig. 2
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Score of the digital transformation level of high-end equipment manufacturing enterprises, 2016–2021.

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Table 3 Top 5 high-end equipment manufacturing enterprises in digital transformation.
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  • (2) Sub-field evaluation.

Aviation equipment, satellites and applications, rail transit equipment, marine engineering equipment, and intelligent manufacturing equipment are high-end equipment manufacturing fields that China is focusing on developing. Here, the digital transformation level of high-end equipment manufacturing enterprises in the five key areas from 2016 to 2021 is further analyzed. As can be seen from Table 4, there are significant differences in the digital transformation level scores and the degree of change in the five key areas of high-end equipment manufacturing during the period under review.

Table 4 Digital transformation of enterprises in the five key areas of the high-end equipment manufacturing industry.
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Based on the digital transformation scores, the satellite and application equipment manufacturing industry consistently ranked first from 2016 to 2021, indicating its leading position in digital transformation among high-end equipment manufacturing sectors2. Except for the year 2017, the rail transportation equipment manufacturing industry consistently held second place, while the intelligent manufacturing equipment industry ranked third during the same period. Among the five key sectors of high-end equipment manufacturing, the aviation equipment and marine engineering equipment industries recorded the lowest digital transformation scores. From 2016 to 2020, the aviation equipment industry outperformed the marine engineering sector in digital transformation levels. However, in 2021, the marine engineering equipment industry experienced a significant improvement in its digital transformation score, surpassing the aviation sector and rising to fourth place. Consequently, by 2021, the aviation equipment manufacturing industry ranked lowest in digital transformation among the five major sectors.

In terms of growth in digital transformation scores, the satellite and application equipment manufacturing industry exhibited the highest increase, rising from 0.1590 in 2016 to 0.1958 in 2021—an increase of 23.14%, ranking first. This indicates that the industry not only maintains the highest level of digital transformation but also demonstrates the most rapid advancement. Although the marine engineering equipment manufacturing industry has a relatively lower overall score, it recorded the second-highest growth, increasing from 0.1073 in 2016 to 0.1299 in 2021—an increase of 21.06%. This rapid progress suggests that the industry is gradually narrowing the digital transformation gap with other high-end equipment manufacturing sectors. The rail transportation equipment and intelligent manufacturing equipment industries saw increases of 17.47% and 16.34%, respectively, ranking third and fourth in terms of growth. The aviation equipment manufacturing industry experienced the smallest increase, at only 13.54%. The disparity in growth rates across these five key sectors of high-end equipment manufacturing indicates a widening gap in their respective levels of digital transformation39.

Between 2016 and 2021, the level of digital transformation across the five key sectors of high-end equipment manufacturing improved to varying degrees. However, several challenges persist in advancing digital transformation. Specifically, although the satellite and application equipment manufacturing industry achieved significant progress, it continues to face limitations in enterprise management systems. These systems are often unable to implement integrated planning and execution across associated domains—such as unified management and application—thus hindering the formation of comprehensive operational capabilities. As a result, the sector struggles to meet the complex management demands of Chinese-style modernization40. To address this, the development of intelligent management systems supported by data and artificial intelligence is expected to become a key focus of the industry’s future digital transformation. The rail transportation and intelligent manufacturing equipment industries also face considerable challenges, including underdeveloped scenario-level digital platforms, a shortage of digital R&D talent, and weak collaboration within industrial clusters. These issues have contributed to a slowdown in their digital transformation progress. In particular, the intelligent manufacturing equipment industry is constrained by small enterprise size, limited technological R&D capacity, and relatively low resilience to external risks. For the aviation and marine engineering equipment industries, promoting digital transformation requires building integrated development environments for product design, manufacturing, and service based on industrial internet platforms. Such environments should enable co-development, process optimization, and improved operations and maintenance, which are critical to addressing the core issues impeding digital transformation in these sectors41.

Impediment factor analysis

Analysis of impediment factor at the indicator level

Using the impediment factor diagnosis method, the Impediment factor and their degrees of obstruction affecting the digital transformation levels of high-end equipment manufacturing enterprises from 2016 to 2021 were calculated. Overall, the primary Impediment factor affecting the digital transformation of these enterprises are concentrated in the top five, with their combined degrees of obstruction exceeding 55% over the period under review, showing a year-on-year upward trend. The top four obstacles consistently include the structure of the digital executive team, the amount of funds raised for digital projects, the output efficiency of digital invention patents, and Internet business models, as shown in Table 5. Notably, the degree of obstruction caused by the digital executive team structure is significantly higher than that of the other factors. This highlights the importance of establishing a strong sense of transformation in areas such as digital production, strategic planning, and business models as soon as possible. Doing so will help enterprises actively invest in digital resources, unlock the potential of digital technologies, and achieve transformation in technology, services, management, and more.

Table 5 Top 5 impediment factor and their degree of obstruction (%), 2016–2021.
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In terms of trends, the degree of obstruction for most factors showed a slight upward trend during the study period. However, the obstacle degree of factors such as digital strategic orientation, the proportion of R&D personnel, the proportion of R&D personnel compensation, the proportion of technical personnel, the proportion of digital construction projects, the proportion of digital software, and profit efficiency in relation to the digital transformation of high-end equipment manufacturing enterprises exhibited fluctuating declines, as shown in Fig. 3. Among these, the degree of hindrance caused by the forward-looking orientation of digital strategies decreased the most, from 3.08% in 2016 to 1.54% in 2021. This suggests that with the rapid development of the digital economy, more high-end equipment manufacturing enterprises are integrating digital transformation into their business strategies to adapt to industrial development trends.

Fig. 3
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Impediment factor transformation in high-end equipment manufacturing enterprises, 2016–2021.

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Normative layer impediment factor analysis

The barriers to digital transformation at each guideline level in high-end equipment manufacturing enterprises from 2016 to 2021, along with their changes, are shown in Fig. 4. As illustrated in Fig. 4, the ranking of barrier importance at the guideline level for digital transformation in high-end equipment manufacturing enterprises remained unchanged from 2016 to 2021. The impediment factor at the guideline level are primarily concentrated in the two dimensions of digital transformation awareness and digital transformation implementation.

Fig. 4
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Impediment degree of the guideline layer for the digital transformation of high-end equipment manufacturing enterprises, 2016–2021.

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Specifically, the average barrier levels for digital transformation awareness and digital transformation change were 42.87% and 37.75%, respectively, with only a 5% variation across years and a slight downward trend. Several factors contribute to this decline: under the influence of external drivers such as government policy support, peer effects among enterprises, increasingly personalized and diversified market demands, and the rapid iteration of digital technologies, high-end equipment manufacturing enterprises have progressively strengthened their awareness of digital transformation and increased investments in human, financial, and material resources. These efforts have gradually enabled digitalization and informatization across production, operations, and management processes, leading to a steady reduction in barriers to digital transformation in both dimensions. Despite continuous improvements in awareness and increasing investment in digital initiatives, the change in barrier levels for these two dimensions remains relatively limited. This, from another perspective, underscores the fact that digital transformation is a complex, systematic, high-investment, and long-term process for enterprises. In contrast, the barrier level related to digital transformation benefits remains the lowest but exhibits an upward trend, rising from 18.96% in 2016 to 19.72% in 2021. This trend is primarily driven by a sustained increase in the barrier level associated with the efficiency of digital invention patent output, which has shown a notable year-on-year rise. From the perspective of the law of diminishing marginal efficiency, merely increasing digital investment can reduce the efficiency of digital invention patent output as R&D inputs grow. Additionally, increasing technological complexity, insufficient technological accumulation, and a shortage of skilled R&D personnel further contribute to the decline in patent output efficiency. The sustained inefficiency in digital invention patent output has consequently led to a continuous rise in its associated barrier level.

Conclusion

Conclusion and discussion

Based on the I-P-O theoretical model, this paper constructs an evaluation index system to assess the digital transformation level of high-end equipment manufacturing enterprises across three dimensions: digital transformation awareness, digital transformation implementation, and digital transformation benefits. The VHSD-EM combined model is applied to measure the digital transformation level of 124 listed high-end equipment manufacturing enterprises from 2016 to 2021. On this basis, the barrier model is used to further analyze the key obstacles affecting the digital transformation of these enterprises. The main conclusions of this paper are as follows:

  • Recognizing that enterprise digital transformation is a systematic and dynamic process, this paper constructs a theoretical framework model from the perspective of the “process view.” The proposed model follows an input–process–output structure: input (awareness of digital transformation), process (changes driven by digital transformation), and output (effects or outcomes of digital transformation). This framework addresses a key gap in traditional models of enterprise digital transformation, which often overlook the dynamic “process” phase. By emphasizing the transformational mechanisms that occur between awareness and outcomes, the model offers a more comprehensive understanding of how digital transformation unfolds within enterprises.

  • Overall trend: The digital transformation level of high-end equipment manufacturing enterprises showed an upward trend from 2016 to 2021, though the growth rate was slow, and relatively few enterprises achieved outstanding levels of digital transformation. Across sub-fields, significant differences were observed in both digital transformation scores and the degree of change across the five key areas of the high-end equipment manufacturing industry. The satellite and application field consistently led in digital transformation, while the aviation equipment and marine engineering sectors, though initially lagging, experienced significant acceleration in digital transformation over the past three years.

  • Key indicators: The five key factors affecting the digital transformation of high-end equipment manufacturing enterprises were: the structure of the digital executive team, the amount of funds raised for digital projects, the output efficiency of digital invention patents, the proportion of Internet business models, and digital projects under construction. During the period under review, the combined impact of these barriers exceeded 55%, with an upward trend year by year. In terms of criteria, the main barriers were concentrated in the dimensions of digital transformation awareness and digital transformation implementation, showing a slightly fluctuating downward trend. Conversely, the degree of hindrance related to the benefits of digital transformation remained the lowest, but it exhibited a consistent year-on-year upward trend.

Implications

Based on the above findings, the following policy recommendations are proposed to promote the accelerated digital transformation of high-end equipment manufacturing enterprises:

  • Among the various dimensions of digital transformation, awareness represents the most significant obstacle, particularly at the strategic guidance level. Analysis of key barrier factors reveals that the structure and composition of the digital executive team are the most critical impediments to advancing digital transformation. To address this, high-end equipment manufacturing enterprises should prioritize optimizing their digital executive teams to strengthen leadership awareness and commitment to digital transformation. Specifically, during executive recruitment, greater emphasis should be placed on candidates’ understanding of digital technologies, their capacity for digital application and innovation, and their practical experience in leading digital transformation initiatives. This approach ensures that the executive team possesses the strategic vision and leadership capabilities necessary for driving digital change. Furthermore, it is essential that the executive team collaboratively participates in the formulation of digital strategies. Such involvement clarifies the enterprise’s digital transformation goals and strategic direction, fosters a shared sense of purpose, and deepens the team’s understanding of the transformation’s urgency and significance. This, in turn, enhances their motivation and capacity to effectively lead the digital transformation process.

  • Establish a digital leadership. Digital transformation is also a major obstacle at the policy level. Establishing digital leadership can provide a guarantee for promoting digital transformation. Specifically: Establish a digital strategy committee. Responsible for formulating the company’s digital transformation strategy plan, digital talent recruitment and training plan, and digital transformation investment plan, providing talent and financial support for the company’s digital transformation; improve employee digital literacy. Strengthen publicity and education to promote digital concepts and enhance employees’ understanding of digitalization. Provide employees with digital technology training and guidance to improve their digital skills. Build a digital corporate culture to stimulate digital technology innovation; establish a digital transformation incentive mechanism. Develop a digital assessment system to comprehensively evaluate the company’s digital transformation efforts. Reward employees or teams who demonstrate outstanding performance in advancing digital transformation with bonuses, stock options, certificates of honor, etc., to stimulate their enthusiasm and creativity.

  • Digital infrastructure serves as the foundational backbone of enterprise digital transformation. To facilitate the digital evolution of the high-end equipment manufacturing sector, it is essential to establish a robust digital infrastructure, develop integrated digital platforms, and significantly increase financial investment in related infrastructure projects. The construction of a digital transformation infrastructure platform should prioritize cutting-edge technologies such as 5G, big data, blockchain, cloud computing, the Industrial Internet of Things (IIoT), and artificial intelligence (AI). These technologies should be leveraged to upgrade and modernize traditional infrastructure systems, thereby enabling enterprises to transition toward intelligent and interconnected operations. Specifically, enterprises should focus on developing core components of a digital ecosystem, including but not limited to: Build infrastructure and platforms integrating digital production, management and sales, including 5G network base stations, big data research centers, intelligent production lines, digital twin workshops, data sharing and open platforms, and user personalized experience centers, etc., so as to provide a basic guarantee for the digital transformation of high-end equipment manufacturing enterprises, and then promote the accelerated iteration of Internet-based business model innovation.