Performance Enhancement Pathways for Electric Vehicle Manufacturing Companies Driven by Digital Transformation—A Configuration Analysis Based on the TOE Framework

Abstract

Digital transformation has brought unprecedented transformation and opportunities in manufacturing enterprises. Focusing on 65 listed companies in the electric vehicle sector as the research objects and drawing on the “Technology–Organization–Environment” (TOE) framework, this study selects three dimensions—technology, organization, and environment—and six antecedent conditions. Using fsQCA configurational analysis, this research explores diverse paths to improving corporate performance, identifying five pathways. Among these, digital transformation and operational efficiency consistently serve as pivotal bridging conditions across multiple configurations. Furthermore, when enterprises demonstrate strong capabilities in both the technological and organizational dimensions, other conditions tend to act as substitutes, interacting synergistically with these core strengths to enhance overall firm performance. This study organically combines the TOE framework and fsQCA, deepening the application of the TOE theory in the field of electric vehicle manufacturing enterprises. Additionally, based on the configurational paths derived from the research, it provides differentiated countermeasure suggestions for electric vehicle manufacturing enterprises, offering practical guidance for enhancing their performance in the context of digital transformation.

1. Introduction

In the midst of global automotive industry transformation, China’s electric vehicle (EV) industry is rapidly emerging at an astonishing pace, becoming a significant force and leading the global EV market. In the first half of 2024, China’s automobile production and sales reached 13.891 million and 14.047 million units, respectively, representing year-on-year growth of 4.9% and 6.1%. Production and sales of EVs reached 4.929 million and 4.944 million units, respectively, with year-on-year growth of 30.1% and 32% and a market share of 35.2%. This demonstrates that electric vehicles are increasingly gaining consumer recognition.

However, alongside the rapid development of China’s EV industry, manufacturing firms also confront a series of complex, multidimensional challenges. These challenges significantly impact the performance and market competitiveness of automotive manufacturers, exerting profound impacts on automakers’ performance and market competitiveness and ultimately on the sustainable development of the EV sector as a whole.

Firstly, market competition has become increasingly fierce: a flood of new entrants has intensified price wars and squeezed profit margins. As the industry leader, Tesla implemented substantial price cuts to swiftly capture market share, and domestic mainstream manufacturers promptly followed suit. This downward pricing strategy not only affected sales of comparable models but also set off a chain reaction throughout the EV market. The escalation of price competition directly compresses automakers’ profitability. On the one hand, reduced prices diminish per-vehicle profit; on the other hand, in order to maintain market share and brand image, firms must continuously ramp up resource allocations to R&D, marketing, and after-sales services, all of which require substantial financial support.

Secondly, technological innovation, which is now the core driving force behind the development of the electric vehicle industry, is advancing rapidly. Emerging technologies such as autonomous driving and intelligent connectivity are continuously emerging. Once a manufacturer achieves a technological breakthrough, its market advantage and brand premium rise markedly [1], compelling firms to sustain heavy R&D investment to remain competitive. However, in the EV sector, high technological barriers and substantial R&D expenditure have become defining features. This stems from the industry’s relentless pursuit of innovation and performance optimization: whether in battery energy density, charging efficiency and safety, or motor drive efficiency, noise control, and lightweight design, firms must possess deep technical expertise and commit to ongoing research. Furthermore, with the accelerating trends of intelligentization and connectivity, EV makers must continuously push the envelope in autonomous driving, human–machine interaction, and vehicle-to-everything communication—factors that further elevate the sector’s technological thresholds and impose significant cost burdens on manufacturers.

Thirdly, supply chain stability has become a critical challenge [2,3,4], particularly because the secure supply of core components such as batteries directly affects production. As important components of electric vehicles, battery performance and quality determine driving range, safety, and user experience. However, the complexity of battery technology, long production cycles, and significant fluctuations in raw-material prices, coupled with the rapid growth of the electric vehicle market, have intensified the demand pressure on core components like batteries, making their supply a major challenge. Once the battery supply chain experiences disruptions or instability, it will directly hinder the production of automobile manufacturers, subsequently affecting market supply and consumer confidence.

Finally, changes in the policy environment, increasingly stringent laws and regulations, and the complex and volatile international trade landscape have introduced uncertainties related to the performance of EV manufacturing enterprises. Currently, government support policies aim at encouraging the development of electric vehicles, such as purchase subsidies and tax incentives [5,6]. Specifically, in terms of subsidies, the early stage (2009–2015) primarily based subsidies on the vehicle’s battery capacity, with a standard subsidy of 3000 CNY/kWh (not exceeding a certain percentage of the vehicle price, such as 50%). In the mid-stage (2016–2018), the focus of subsidies shifted from batteries to driving range and energy consumption levels, implementing a tiered subsidy system. In the later stage (after 2019), subsidies have further evolved towards more detailed technical specifications, involving energy consumption thresholds, battery energy density, intelligence, and safety. While the requirements have increased, the subsidy amounts have also been continuously reduced. In terms of taxation, since 2014, electric vehicles have been exempt from vehicle purchase tax (approximately 10% of the vehicle price). The policy has been extended multiple times, with the latest round extending until 2027, setting different caps. Additionally, electric vehicles also enjoy non-financial incentives, such as access, parking, and registration, during their use phase. These measures collectively and effectively reduce the cost of purchasing vehicles for consumers. In addition to central policies, local governments also provide additional subsidies or policy conveniences based on their own financial capabilities and industrial layout. For example, Shanghai offers financial subsidies no less than the central standard and exempts electric vehicle owners from license plate fees (which usually cost around CNY 80,000 to 100,000), an important incentive measure. However, relevant policies may be adjusted or canceled at any time, posing a significant challenge for enterprises reliant on policy support. At the same time, ever-tightening safety and environmental standards force companies to continuously seek a dynamic balance between regulatory compliance and technological innovation [7,8]. Additionally, factors such as tariff fluctuations, trade barriers, and geopolitical risks may impact enterprises’ import–export operations and market strategies.

From this, it is evident that how EV manufacturing enterprises can enhance their performance has become a realistic yet complex issue. Digitalization offers significant opportunities for their development. Through the deep application of advanced technologies—such as big data analytics [9,10]; artificial intelligence [11,12], and the Internet of Things [13,14,15]—firms can accurately capture and respond to dynamic market demands. This enables accelerated R&D cycles, personalized customization, and efficient production, allowing companies to stand out in fierce competition.

Meanwhile, digital transformation helps enterprises by optimizing the coordination of nodes in the complex supply chain network, thereby improving supply chain management [16], reducing production costs while improving product quality and operational efficiency [17]. Through intelligent and integrated service platforms, enterprises can accurately reach their target customers; strengthen brand influence; and deliver more convenient, intelligent service experiences, further consolidating their market position. In this way, digital transformation not only provides strong technical support for electric vehicle companies but also opens up new development models and market spaces, helping these companies achieve leapfrog development. Consequently, against the background of digital transformation, it is crucial to elucidate the mechanisms of performance enhancement and to investigate the various configurational pathways through which EV manufacturers can improve their performance.

This study aims to address the following core questions: (1) Based on the TOE (Technology–Organization–Environment) framework, which antecedent conditions in the technological, organizational, and environmental dimensions influence corporate performance? (2) Do individual elements constitute necessary conditions for high performance? (3) What are the pathways to achieving high performance, and how can differentiated strategic recommendations be provided for enterprises?

To explore potential answers, this study employed the method of fuzzy-set qualitative comparative analysis (fsQCA). The specific research findings include the following: (1) the identification of multiple conditions affecting the high performance of electric vehicle manufacturing enterprises, (2) the revelation of five paths for improving enterprise performance, and (3) the provision of differentiated countermeasures and suggestions for different types of electric vehicle manufacturing enterprises based on the results.

2. Theoretical Foundation and Research Model

2.1. Literature Review

Regarding the performance issues of electric vehicle manufacturing enterprises, scholars have conducted a series of studies from various perspectives.

Firstly, in terms of digital transformation, Liu et al. [18] conducted an empirical analysis of China’s electric vehicle companies and found that digital transformation significantly enhances an organization’s absorptive capacity, which in turn further improves the company’s performance, with absorptive capacity acting as a mediator between the two . Based on dynamic capability theory, Panichakarn et al. [19] constructed a structural equation model for empirical analysis and found that digital transformation not only directly enhances the performance of electric vehicle companies but also indirectly promotes performance improvement by enhancing agility and innovation capabilities. Therefore, they recommend that companies in the electric vehicle industry should vigorously pursue digital transformation and foster an innovative cultural atmosphere. Based on the perspective of the innovation ecosystem, Men et al. [20] studied the mechanism by which digital transformation affects the R&D performance of automotive companies. The results show that digital transformation significantly enhances performance by improving a company’s digital technology innovation capabilities. At the same time, the innovation ecosystem positively moderates this mediating effect. Atienza-Barba et al. [21] constructed a structural equation model to conduct an empirical analysis of 403 Spanish companies, finding that in the context of digital transformation, employees’ positive attitudes towards artificial intelligence, supported by management, can significantly optimize internal business processes, thereby enhancing the company’s operational efficiency and performance. Llopis-Albert C et al. [22] believe that in the context of digital transformation, the traditional business models of the automotive industry are being disrupted. Therefore, automotive companies must undergo digital transformation through necessary investments to achieve greater profits.

Secondly, in terms of organizational strategy, Roscoe et al. [23] analyzed the impact of operational capabilities on the performance of smart manufacturing enterprises. They found that in the context of digital transformation, the operational and decision-making capabilities of enterprises have become key factors in enhancing performance. Rotjanakorn et al. [24] conducted an empirical study on the automotive industry from the perspective of dynamic capabilities. They found that in the current innovation environment, a company’s dynamic capabilities can enhance its performance through the mediating mechanisms of competitive advantage and innovation capability. Therefore, they emphasize that the capabilities of automotive companies in the early strategic stages play a crucial role in their future sustainable performance. Yang et al. [25], based on case studies, found that electric vehicle manufacturing companies can optimize their operational processes through lean production and smart manufacturing and that improvement in operational efficiency is a key factor in enhancing corporate performance. Tsou and Kim [26] focus on China’s automobile manufacturing enterprises, and their research found that the synergy between operational capabilities and dynamic capabilities can significantly enhance the adaptability and performance of enterprises in the market, thereby achieving sustainable competitiveness. Hou et al. [27] discovered three innovation models while studying the collaborative innovation models of electric vehicle companies, and all three models can enhance the performance of companies. At the same time, the dynamic knowledge management capability of an enterprise plays a fully mediating role in this process. Li et al. [28] approached the topic from the perspective of middle-level managers in enterprises and found that when managers possess high digital management capabilities, there is a significant improvement in the operational efficiency and innovation output of an enterprise. At the same time, this has a direct promoting effect on the improvement of the company’s performance. M Beheshti et al. [29] focused on the mediating role of corporate management capabilities in supply chain integration, finding that a company’s supply chain management capabilities can enhance supply chain coordination, thereby improving corporate performance.

Finally, in terms of policy and external environment, Yu F et al. [30] analyzed the differential impact of pre- and post-subsidy government policies on the performance of Chinese electric vehicle companies using a panel regression model. Zhang H et al. [31] found that the government can enhance the development level of electric vehicle companies, expand their market share, and improve their performance by dynamically adjusting subsidy policies in different ways. Wang X et al. [32] studied the operational data of listed companies in the upstream, midstream, and downstream sectors of the electric vehicle industry and found that subsidies have a greater impact on improving the performance of upstream companies. Qiu et al. [33] found that the current industrial policy for electric vehicles is shifting from being government-led to market-led. As government subsidies decrease, this will have a significant impact on corporate performance. Li H et al. [34] believe that government subsidies, by increasing R&D investment, alleviating financing constraints, and enhancing external attention to enterprises, have increased the number of technological innovations in the electric vehicle industry, further promoting performance improvement. Li X et al. [35] believe that government subsidies are crucial for enhancing the consumption resilience of electric vehicles, with sales of subsidized car companies significantly increasing.

In summary, although scholars have conducted extensive research on the factors influencing the performance of EV manufacturers, there remain several areas for deeper investigation. Existing studies tend to examine the impact of individual variables—such as government subsidies, policies, or R&D investment—on firm performance. However, as a multi-factor coupled complex adaptive system, an EV manufacturer’s performance is jointly shaped by the nonlinear interactions and dynamic feedback among technological, organizational, and environmental dimensions. These factors often exhibit intricate interdependencies and interactive effects, and different combinations of these factors may give rise to multiple performance outcomes—an instance of causal complexity. Therefore, research on EV manufacturer performance should move beyond single-factor analyses to systematically investigate the synergies and complex configurational patterns among multiple factors.

Moreover, current investigations into antecedent conditions for performance are scattered and have not fully revealed why performance varies across firms within a complex ecological network. Configurational methods offer unique advantages for addressing causal complexity [36]. By applying a configurational perspective to explore the antecedents and pathways of performance enhancement in EV manufacturers, one can identify which factors, through synergistic interactions, drive performance improvements, which factors are core, and which configurational pathways lead to superior performance.

Additionally, the Technology–Organization–Environment (TOE) framework—recognized for its ability to uncover the generative mechanisms of socio-technical phenomena from a multi-level perspective—has proven well suited for complex systems research [37]. Drawing on this, this study first constructs a set of antecedent indicators spanning the technological, organizational, and environmental dimensions against the background of digital transformation and proposes a multidimensional driving model for EV manufacturer performance enhancement under the TOE framework. Next, it introduces fuzzy-set qualitative comparative analysis (fsQCA) into the research design to clarify the configurational effects of each element within a multi-level coupled network through comparative analysis of different configuration paths. Finally, from a complex systems perspective, it offers pathway choices and policy implications for promoting performance enhancement in EV manufacturers.

2.2. Research Framework

The TOE framework, which stands for Technology–Organization–Environment, is a theoretical framework proposed by Tornatzky and Fleischer in 1990. It is an important theory in organizational research that is used to analyze how enterprises or organizations adapt to changes in the external environment through adjustments in internal technology and organizational structure. The framework is divided into three interrelated dimensions: a technological dimension, an organizational dimension, and an environmental dimension. The factors included in the technological dimension are the advancement and complexity of technology, compatibility and adaptability, innovation and transformational potential, etc. The organizational dimension includes factors such as organizational structure, organizational culture, organizational resources, and organizational readiness. The environmental dimension includes factors such as market environment, industry environment, policy environment, and socio-cultural environment. The TOE framework emphasizes the interaction of these three dimensions, which together influence enterprises’ technology adoption behaviors and performance outcomes. Its systematic and multidimensional characteristics make it suitable for various research contexts of technology adoption and organizational change, and the issues faced by the electric vehicle industry, such as technological innovation, production process optimization, and market environment adaptation, can all be encompassed by the TOE framework. Through the TOE framework, it can more comprehensively help people understand the complexity of technology application in electric vehicle manufacturing enterprises and help enterprises make scientific and reasonable strategic decisions in a dynamic and highly competitive industry environment, enhancing their technological innovation capabilities and market competitiveness. For better understanding, Figure 1 shows the theoretical model structure of the TOE framework.

2.2.1. Technological Dimension

This paper incorporates R&D intensity and digital transformation as antecedent conditions within the technological dimension. According to Schumpeter’s innovation theory, innovation is key for enterprises to address the inevitable decline predicted by the enterprise life-cycle theory. By increasing R&D intensity, enterprises can continuously engage in innovative activities and enhance the number of patents and labor productivity per unit time, thereby reducing costs, improving product quality and performance, attracting more customers, and ultimately boosting enterprise performance [38,39,40]. The electric vehicle industry relies even more on continuous technological innovation and digital upgrades. The intensity of research and development primarily reflects the company’s investment in the development of new technologies and products, which is a core indicator of a company’s innovation capability. Digital transformation, on the other hand, reflects a company’s ability to use smart manufacturing, big data, and information technology to optimize production processes and management, enhance market response speed, and improve competitiveness. This is particularly important for coping with rapidly changing market environments. Shao et al. [41] also found that in the field of electric vehicles, for every 1 percentage point increase in a company’s R&D intensity, it can achieve an improvement of about 0.5 percentage points in overall vehicle performance and a 3-percentage-point increase in profit growth. Meanwhile, digital transformation significantly improves the production efficiency and innovation capability of electric vehicle companies by integrating advanced technologies such as the Internet of Things, big data, and artificial intelligence. It optimizes supply chain management, accelerates market responsiveness, and improves operational management efficiency. The deep integration of these two factors can drive technological innovation and product upgrades, enabling enterprises to achieve strategic implementation more rapidly and enhance performance levels.

2.2.2. Organizational Dimension

This paper incorporates the proportion of R&D personnel and operational efficiency as two antecedent conditions in the organizational dimension. In the process of achieving high-quality development in manufacturing enterprises, comprehensive R&D personnel are first needed. They inject continuous momentum into the development of the enterprise through enhancing innovation capabilities, optimizing products and services, improving economic efficiency, strengthening sustainable development capabilities, and supporting strategic adjustments [42,43,44]. At the same time, the organizational capability of a company is not only reflected in its talent structure but also in the efficiency of its internal resource allocation and operational processes. In the manufacturing of electric vehicles, due to the complex product structure, long supply chain system, and rapid technological updates, companies require higher collaborative management capabilities in production, inventory, and sales processes. Compared to the subjective evaluation of executive quality, operational efficiency, as an objective reflection of the organizational operation level of an enterprise, can better reflect the optimization effects of the internal management system and processes of the enterprise. Recently, some scholars have pointed out that a company’s operational efficiency significantly affects its financial performance and market competitiveness, reflecting the organization’s internal coordination, execution, and resource allocation capabilities [23,25].

2.2.3. Environmental Dimension

This paper incorporates two antecedent conditions, namely, economic development level and government subsidies [45], into the environmental dimension. According to organizational dependency theory, organizations must exchange resources and information with their environment to survive and operate effectively. This theory discusses the synergistic relationship between organizations and their external environment. For enterprises, the external environment includes political, economic, market, and industry factors, among others. Government subsidies, as an additional source of funding, can directly increase a company’s revenue, thereby improving its financial condition and overcoming resource constraints. Some subsidies may target specific operational costs of enterprises, such as R&D subsidies or environmental protection subsidies. These subsidies can directly reduce related operational costs, thereby enhancing the profitability of enterprises. The electric vehicle industry is highly dependent on policy support, and government subsidies are an important external driving force for promoting industry development and market cultivation. On the other hand, companies can rely on the natural resources, human resources, and financial resources of the economic environment in their region to obtain the necessary raw materials, labor, and capital, thereby supporting their production and operational activities. Economically developed regions usually have a well-established supporting infrastructure, which helps enterprises reduce costs and accelerate technology diffusion [46]. At the same time, economically developed regions have higher consumption levels and broader market demand [47]. This has a significant impact on electric vehicle companies. The electric vehicle industry is characterized by high capital and technology intensity, with a strong reliance on upstream raw materials, advanced manufacturing infrastructure, and highly skilled talent, all of which are often concentrated in economically developed regions. As a mid-to-high-end consumer product, the market demand for electric vehicles is closely related to residents’ income levels, which are also typically concentrated in more economically developed areas. Yi et al. [48] found that for electric vehicle companies, the differences in economic and policy environments between regions will have different impacts on their technological innovation, and through the synergy with policies and regional economies, their technological innovation capabilities and production efficiency will be significantly improved, so as to better promote the growth of enterprises.

2.2.4. The TOE Alignment and Interaction Mechanism of Antecedent Conditions

The above three sections respectively define the antecedent condition variables of this study in the dimensions of technology (R&D intensity and digital transformation), organization (proportion of R&D personnel and operational efficiency), and environment (regional economic development level and government subsidies). However, these condition variables do not operate in isolation, so it is necessary to further clarify their alignment and interaction mechanisms within the TOE framework.

Within the internal structure of electric vehicle manufacturing companies, enterprises utilize efficient operational systems for lean production and resource optimization, establishing a foundational guarantee for the effective conversion of future technological research and development investments. This enables the efficient implementation of research and development intensity in areas such as battery management systems and intelligent production lines [49]. Meanwhile, through digital transformation, enterprises can leverage digital and intelligent platforms to grasp real-time data from production lines and the current market situation. The data obtained can be analyzed and integrated into the operational processes, allowing these processes to be continuously improved based on actual conditions [50]. This not only enhances resource utilization but also indirectly supports the effective allocation of R&D personnel and improves their research and development capabilities.

Outside the electric vehicle manufacturing company, when a company is located in economically developed areas, local resources and government subsidies jointly reduce the costs of technological innovation and R&D, making companies more willing and proactive in investing resources and efforts in core technologies and service models [51]. When these technological and organizational capabilities are enhanced, they can further attract policy support and consolidate the local industrial ecosystem. These three dimensions form a holistic and synergistic closed loop within the TOE framework, collectively driving the performance improvement and sustainable development of electric vehicle manufacturing enterprises. Moreover, this multidimensional interaction characteristic is precisely what distinguishes EV manufacturing enterprises from traditional manufacturing enterprises.

Therefore, this study constructs the theoretical model shown in Figure 2.

3. Research Design

3.1. Methodology

Qualitative comparative analysis (QCA) is a research method that combines the strengths of qualitative and quantitative analysis, proposed by the sociologist Charles C. Ragin in 1987. This method aims to explore the different configurations of antecedent conditions and their complex relationships with outcomes through case comparisons, thereby identifying the configurations of conditions that can lead to the expected outcomes. This study employs fuzzy-set qualitative comparative analysis (fsQCA), primarily based on the following considerations. First, compared to traditional regression analysis methods, fsQCA does not aim to analyze “net effects” but rather focuses on the combinatorial effects and causal complexity among multiple conditions. It is more suitable for exploring nonlinear and asymmetric causal relationships, which helps reveal the heterogeneous impacts of different conditional paths on corporate performance. Secondly, compared to csQCA (set-theoretic QCA) and mvQCA (multi-value QCA), fsQCA introduces the gradation and continuity of variables through fuzzy sets, making it more suitable for handling non-binary variables such as R&D intensity, digitalization level, and government subsidies (0 or 1) [52]. This approach retains the complexity and precision of the data while enhancing the flexibility and practical interpretability of the analysis. Dynamic QCA emphasizes the temporal dimension, thereby capturing the dynamic changes in the causal relationship between conditions and outcomes. Therefore, its research data must have a time-series structure, meaning that each case is observed at multiple time points. In contrast, this study uses cross-sectional data, making fsQCA more suitable. Moreover, fsQCA has relatively lenient requirements regarding sample size, maintaining the robustness of results even in small to medium-sized samples, making it particularly suitable for the analysis based on a sample of 65 EV manufacturing companies in this study. Therefore, fsQCA not only more effectively identifies configurational patterns of multiple conditions but also captures complex causal mechanisms overlooked by traditional methods, aligning better with the research needs of EV manufacturing companies, which are typical complex systems.

Additionally, Ragin [53] emphasizes that to avoid “limited diversity,” researchers should ensure that the sample size, N, is at least 4k when selecting k antecedent conditions, thereby guaranteeing enough cases to fill possible combinations of conditions and maintain the stability of the solution. This study selected one outcome variable and six antecedent variables, using 65 companies as the research sample. The characteristics of the sample and the types and quantities of variables all meet the above requirements. Therefore, this paper employed fsQCA for analysis. The specific research process is shown in Figure 3.

3.2. Data Collection

The data sample for this study was based on the list of stocks of electric vehicle concept companies listed on the main board published by Huaxi Securities, as this is a recognized financial institution and market research basis within the industry. The initial sample included a total of 180 companies, and the following processing was carried out on the sample: companies with an industry code of C36 (automobile manufacturing) were selected to ensure that the companies in the sample were indeed electric vehicle manufacturers. Next, companies marked with ST and *ST (financial anomalies) and those with missing data were removed to ensure the quality of the sample and the scientific nature of the analysis. After processing, samples from 65 electric vehicle manufacturing companies were retained. These 65 companies are distributed across several provinces and cities in China (such as Guangdong, Jiangsu, Shanghai, Anhui, and other core regions of the electric vehicle industry), which highly coincides with the regional distribution of China’s electric vehicle manufacturing industry. In addition, these companies include leading enterprises (such as Changan Automobile, JAC Motors, etc.) as well as medium-sized enterprises, representing a certain level of hierarchization. As they are listed on the main board, these companies generally have good information disclosure systems, large operational scales, and strong innovation capabilities, such that they can well represent the overall characteristics of Chinese electric vehicle manufacturing companies. Therefore, the selected sample has relatively good representativeness and analyzability and can to some extent reflect the overall trends and patterns of China’s automotive manufacturing companies.

The data used in this study came from the CSMAR database, which was developed by the Shenzhen Guotai An Information Technology Co., Ltd. It is a leading economic and financial research database in China and is widely used in academic research by universities and research institutions. The database includes multidimensional data, such as financial indicators of listed companies, corporate governance, and industry classification, and it is highly authoritative and reliable. Moreover, its data have undergone strict cleaning and standardization processes, providing data support for this study. In order to further ensure data quality, this study supplemented some missing data in the database by consulting the annual reports published by the companies. At the same time, it compared and checked the data from CSMAR and the annual reports to ensure data accuracy.

3.3. Variable Description

3.3.1. Outcome Variable

Corporate Performance: This is a comprehensive reflection of a company’s economic efficiency and management level, demonstrating its strength and potential in market competition. According to the research by Nwude et al. [54], return on equity (ROE) is a core financial indicator for measuring a company’s profitability. Listed companies often adjust this indicator through various strategies to enhance market valuation, reduce external financing pressure, and meet equity incentive conditions, among other objectives. Accurately identifying the extent and methods of ROE manipulation is crucial for investors to reasonably assess a company’s true value and for regulatory authorities to effectively monitor market misconduct. Therefore, this study used the 2022 ROE of 65 electric vehicle manufacturing companies as the measurement indicator. The calculation formula used is ROE = Net Profit/Average Shareholders’ Equity, with the relevant data sourced from the annual reports of the respective companies.

3.3.2. Conditional Variables

R&D Intensity (RDI): Scholars currently believe that the higher the ratio of a company’s R&D expenditure to its operating income, the greater its R&D intensity. Drawing on the research of Yin [55], this paper used the ratio of a company’s R&D expenditure to its operating income as a measurement indicator, based on data collected from corporate annual reports.

Digital Transformation (DT): There is a limited literature on measuring corporate digital transformation, with most studies employing indicator-based or questionnaire-based methods. This paper selected the corporate digital transformation index from the CSMAR database, which comprehensively considers sub-indicators such as digital achievements and digital applications, to measure this variable.

Proportion of R&D Personnel (PRD): The proportion of R&D personnel in a company can, to some extent, reflect its R&D capability. Referencing the methodology of Zhao et al. [56], this paper uses the ratio of the number of R&D personnel to the total number of employees as a measurement indicator, based on data collected from corporate annual reports.

Operational Efficiency (OE): By collecting data from corporate annual reports and referencing the research of Li et al. [57], this paper used the total asset turnover ratio as a measurement indicator.

Level of Economic Development (LED): By collecting the “GDP Data by City” published in CSMAR and following the approach of Yang et al. [58], the value of “(Current Year GDP − Previous Year GDP)/Previous Year GDP” for the city in which each enterprise is located was used as the measurement indicator.

Government Subsidy (GS): By collecting data from corporate annual reports and referencing the research of Luo et al. [59], the proportion of government subsidies in each enterprise’s operating revenue was used as the measurement indicator.

4. Results and Analysis

4.1. Data Calibration

In fsQCA, variable calibration is a critical step, which involves transforming raw data into fuzzy-set membership scores for subsequent analysis. Usually, continuous data are converted into fuzzy-set membership values ranging between 0 and 1 to reflect the degree of membership of a case under certain conditions. The original data in this paper were all continuous variables. Some variables (such as corporate performance and regional economic development levels) may have negative values, so, before calibration, these negative values needed to be linearly transformed to shift their range to the positive interval while maintaining the relative relationships between the values. For example, for corporate performance, the original value range was (−1.271, 0.174), so an overall shift of +2 changed it to (0.271, 2.174). Similarly, the overall level of economic development shifted up by 1. During calibration, this study used the direct calibration method. Based on the distribution of antecedent and outcome variables, full membership (maximum threshold: 95%), full non-membership (minimum threshold: 5%), and crossover point (intermediate threshold: 50%) were selected as the threshold values for anchor points [60]. Additionally, a fine-tuning adjustment of 0.001 was applied to the membership scores at 0.5. This study selected 65 enterprise samples, excluding those labeled ST and *ST, for configuration analysis. The raw data were calibrated into fuzzy-set data using fsQCA 3.0 software, and the results are shown in

Table 1. Calibration of condition variables and outcome variables.

Variable Dimensions	Name of the Variable	Calibration of the Anchor Point			Descriptive Statistics
		Full Membership	Crossover Point	Full Non-Membership	Mean	SD	Min	Max
Outcome variables	Corporate Performance (CP)	2.148	2.052	1.603	1.994	0.220	0.271	2.174
Technical dimensions (T)	R&D Intensity (RDI)	0.118	0.048	0.023	0.059	0.036	0.015	0.208
	Digital Transformation (DT)	52.311	37.844	26.438	38.621	7.932	22.693	57.370
Organizational dimensions (O)	Proportion of R&D Personnel (PRD)	0.295	0.143	0.067	0.155	0.074	0.046	0.467
	Operational Efficiency (OE)	1.005	0.667	0.277	0.645	0.243	0.103	1.411
Environmental dimension (E)	Level of Economic Development (LED)	1.095	1.044	0.963	1.046	0.033	0.950	1.145
	Government Subsidy (GS)	0.033	0.007	0.001	0.012	0.012	0.000	0.056

.

4.2. Necessity Analysis of Individual Conditions

After calibrating the data, the next step was to conduct a necessity test on the condition variables and outcome variables. According to the research findings of Dul [61], the consistency score of necessary conditions should exceed 0.9. The

Table 2. Results of necessity test for antecedent conditions.

Conditional Variables	High Corporate Performance		Non-High Corporate Performance
	Consistency	Coverage	Consistency	Coverage
RDI	0.6337	0.7219	0.6666	0.6472
~RDI	0.6904	0.7084	0.7136	0.6241
DT	0.6135	0.6590	0.7266	0.6653
~DT	0.6884	0.7471	0.6276	0.5806
PRD	0.6265	0.7124	0.6932	0.6718
~PRD	0.7114	0.7312	0.7032	0.6161
OE	0.5977	0.6591	0.7081	0.6655
~OE	0.6967	0.7368	0.6374	0.5746
LED	0.7462	0.7228	0.7515	0.6205
~LED	0.6082	0.7417	0.6643	0.6905
GS	0.6461	0.7310	0.6500	0.6268
~GS	0.6702	0.6920	0.7211	0.6346

shows that the consistency of all antecedent conditions leading to high performance is less than 0.9. Therefore, none of the six antecedent conditions constitute a necessary condition for explaining high corporate performance. This indicates that none of the six antecedent conditions can independently lead to the occurrence of the outcome variable, and it is necessary to consider the synergistic combination of multiple antecedent conditions. Coverage measures the proportion of the result set that can be explained by the condition set, that is, how much of the results are explained by the condition. Generally, the higher the value, the stronger the explanatory power.

4.3. Sufficiency Analysis of Condition Configurations

This paper applied fsQCA to conduct a configurational analysis of the pathways to high performance in the electric vehicle manufacturing industry, as shown in

Table 3. Configuration analysis results for enterprise performance in the electric vehicle manufacturing industry.

Variables	High Performance of Electric Vehicle Manufacturing Enterprises
	C1	C2	C3	C4	C5
R&D Intensity	⨂	⨂	●	⬤	⨂
Digital Transformation		⬤	⬤	⨂	⨂
Proportion of R&D Personnel	⨂	●	⨂	●	⨂
Operational Efficiency	●	⬤	⬤	⬤	⨂
Level of Economic Development	⨂		●		●
Government Subsidy	⨂	⨂		●	⨂
Consistency	0.9438	0.9475	0.9415	0.9582	0.9580
Raw Coverage	0.3626	0.3152	0.2762	0.2668	0.2972
Unique Coverage	0.0789	0.0424	0.0144	0.0548	0.0762
Solution Coverage	0.6094
Solution Consistency	0.9240

Notes: “⬤” or “●” indicates the existence of variables; “⭙” or “⨂” indicates that the variable does not exist; “Blank” indicates that the variable may or may not exist in the combination; “⬤” or “⭙” indicates core variable; “●” or “⨂” indicates auxiliary variable.

. In fsQCA, the analysis of the sufficiency of condition configurations is a core step, used to explore which antecedent conditions or combinations of antecedent conditions can sufficiently lead to the occurrence of specific outcomes. This paper set the raw consistency threshold at 0.80, the PRI consistency threshold at 0.75, and the frequency threshold at 1 [62]. Finally, five configurations explaining high performance in electric vehicle manufacturing enterprises were identified. Each configurational path is represented by an abbreviation indicating the antecedent conditions (i.e., ~ = negation of) [63].

From Table 3, it can be seen that, first, consistency is one of the core indicators for measuring the strength of causal relationships. The consistency of a single configuration solution is used to measure the proportion of cases covered by a specific path that truly exhibit the outcome variable. The higher the consistency, the more reliable the path, representing “if this combination of conditions is met, the outcome is very likely to occur.” Generally speaking, a consistency higher than 0.75 is considered to be interpretable. The overall solution consistency refers to the entire set of identified effective configurational paths, reflecting to what extent these paths as a whole can indicate the occurrence of the outcome variable. High overall solution consistency indicates that the causal explanation of the entire configurational model is robust. Typically, the overall consistency also needs to be higher than 0.75 to demonstrate that the model has strong causal inference validity. In this study, the consistency of both individual configurations and the overall solution exceeds the minimum standard of 0.75 [64]. The overall solution consistency is 0.9240, indicating that 92.40% of the samples demonstrate the impact of these five configurations on high performance in the manufacturing industry. Secondly, the raw coverage indicates the proportion of outcome cases that a path can explain relative to the total outcome cases, that is, the path’s “explanatory power” for the outcome. The raw coverage of the five configurational paths in this study fluctuates between 27% and 36%, with Configuration 1 having the highest coverage at 36% and Configuration 3 having the lowest at 27%. According to the research by Ragin [53] and Fiss [65], coverage between 0.25 and 0.40 is generally considered to indicate a relatively high explanatory power for a configuration, suggesting that the various paths are representative in explaining the formation mechanisms of high-performance electric vehicle manufacturing enterprises. Although there are differences in the original coverage of each path, the overall coverage exceeds the minimum explanatory power threshold (approximately 0.2), which can be regarded as reasonable and valuable for research. The unique coverage refers to the proportion of outcome cases that can be explained solely by a given path, without the influence of other paths. Therefore, unique coverage reflects the “unique contribution” of this path. Additionally, the total coverage reached 0.61, indicating that these paths collectively explain the causes of most high-performance cases, demonstrating that the configuration has strong overall explanatory power. The above content indicates that these configuration analyses are effective and reliable.

For ease of representation, R&D Intensity is abbreviated as rdi, Digital Transformation is abbreviated as dt, Proportion of R&D Personnel is abbreviated as prd, Operational Efficiency is abbreviated as oe, Level of Economic Development is abbreviated as led, Government Subsidy is abbreviated as gs. Furthermore, the ~ (tilde) sign indicates the negation of the variable. Details of the enterprise can be found in Appendix A 

Table A1. Company information and key indicators.

Company name and business scope	RDI	DT	PRD	OE	LED	GS	ROE
Dongfeng Electronic Technology Co., Ltd.	0.034	38.428	0.145	0.649	1.097	0.020	2.035
R&D, manufacture, and sales of commercial vehicles, passenger cars, engines, and key auto parts
Zhengzhou Yutong Bus Co., Ltd.	0.078	51.279	0.217	0.701	1.019	0.019	2.051
Design and production of city buses, coaches, and related bus components
Dongfeng Technology Co., Ltd.	0.040	43.319	0.134	0.742	1.033	0.006	2.071
Automotive parts distribution, logistics, and supply chain services
SAIC Motor Co., Ltd.	0.029	45.793	0.161	0.756	1.033	0.005	2.069
Manufacture and sale of passenger cars and commercial vehicles; automotive financing and investment
Beiqi Foton Motor CO., Ltd.	0.041	52.324	0.162	0.954	1.033	0.005	2.004
R&D and manufacturing of heavy-duty trucks, light-duty trucks, buses, and powertrain systems
Dong’an Power Co., Ltd.	0.051	32.192	0.145	0.710	1.026	0.011	2.041
Development and production of automotive engines, transmissions, and related components
Anhui Jianghuai Automobile Group Co., Ltd.	0.050	41.808	0.189	0.781	1.053	0.033	1.885
R&D and manufacture of light- and heavy-duty commercial vehicles and passenger vehicles
Lingyun Heavy Industry Co., Ltd.	0.037	45.773	0.164	0.967	1.042	0.004	2.080
Production of heavy trucks, special-purpose vehicles, and key parts
King Long United Automotive Industry Co., Ltd.	0.037	46.218	0.148	0.678	1.109	0.009	1.904
Design and manufacture of buses, coaches, and new energy buses
Hunan Tianyan Automotive Technology Co., Ltd.	0.120	39.540	0.174	0.305	1.065	0.023	1.964
Automotive stamping parts and precision metal component manufacturing
Joyson Electronics Co., Ltd.	0.061	54.025	0.111	0.944	1.076	0.002	2.014
Automotive electronics and safety systems (airbags, sensors, and body control modules)
BAIC BluePark New Energy Technology Co., Ltd.	0.174	41.569	0.467	0.270	1.033	0.009	1.400
R&D and manufacture of new-energy vehicles (NEVs) and related battery systems
Huayu Automotive Systems Co., Ltd.	0.045	42.672	0.195	1.000	1.033	0.004	2.146
Development and production of interior trims, chassis parts, and stamping assemblies
FAW Fuwei Passenger Vehicle Components Co., Ltd.	0.018	34.812	0.046	1.007	0.950	0.001	2.080
Automotive safety systems, brake components, and chassis parts
ACDelco (Aikedi) Automotive Parts Co., Ltd.	0.048	47.698	0.102	0.517	1.076	0.008	2.131
Exterior trims, moldings, and fasteners for passenger vehicles
Bohai Automotive Systems Co., Ltd.	0.022	37.302	0.060	0.525	1.054	0.007	1.986
Production of brake systems, steering parts, and hydraulic components
Seres Group Co., Ltd.	0.091	50.068	0.249	0.863	1.044	0.015	1.404
R&D and production of electric vehicles and connected mobility solutions
Guangzhou Automobile Group Co., Ltd.	0.060	40.118	0.066	0.635	1.022	0.009	2.074
Manufacture of passenger cars, commercial vehicles, and new-energy vehicles
Inno Light Auto Parts Co., Ltd.	0.039	28.602	0.117	0.676	0.950	0.004	2.017
R&D and manufacturing of new-energy vehicle platforms and components
Great Wall Motor Co., Ltd.	0.089	37.110	0.273	0.761	1.042	0.013	2.130
Design and production of SUVs, pickup trucks, and powertrain systems
Ningbo Tuopu Group Co., Ltd.	0.047	43.562	0.181	0.692	1.076	0.004	2.149
Manufacture of precision automotive parts such as shock absorbers and steering knuckles
Zhengzhou Coal Mining Machinery Group Co., Ltd.	0.046	52.260	0.117	0.791	1.019	0.007	2.153
Mining equipment and special vehicles, plus some auto-related machinery
Lifan Technology Co., Ltd.	0.047	41.278	0.166	0.452	1.044	0.001	2.015
R&D and manufacture of motorcycles, small cars, and small-displacement engines
Beijing North Special Technology Co., Ltd.	0.047	37.219	0.202	0.536	1.033	0.010	2.029
Automotive engineering design, testing services, and special-purpose vehicle conversion
Yapp Automotive Parts Co., Ltd.	0.035	35.620	0.102	1.411	1.061	0.003	2.143
Production of fuel injection systems and fuel supply modules
Changshu Automotive Trim Co., Ltd.	0.035	38.897	0.165	0.457	1.055	0.009	2.115
Interior and exterior trim parts for passenger vehicles
Kaizhong Auto Parts Co., Ltd.	0.092	26.374	0.310	0.601	1.033	0.025	2.080
Automotive HVAC (heating, ventilation, and air conditioning) modules and filters
Zhejiang Limin Mechanical & Electrical Co., Ltd.	0.063	26.696	0.090	0.328	1.145	0.015	2.016
Manufacture of engine chains, noise control parts, and powertrain components
Techtron Controls Co., Ltd.	0.066	33.074	0.121	0.574	1.044	0.004	1.720
Development of electric actuators and control modules for vehicles
Zhengyu Industrial Co., Ltd.	0.045	31.280	0.125	0.737	1.044	0.007	2.047
Production of automotive bearings and transmission components
Huapei Power Co., Ltd.	0.055	25.434	0.187	0.480	1.033	0.012	2.075
Design and manufacture of starters, alternators, and electrical components for vehicles
Tenglong Auto Parts Co., Ltd.	0.042	29.855	0.099	0.681	1.084	0.006	2.073
Stamping and welding for automotive chassis and body structures
Kehua Holdings Co., Ltd.	0.034	32.947	0.110	0.578	1.084	0.011	2.014
PCB manufacturing and automotive electronic control units
Fuda Auto Parts Co., Ltd.	0.069	39.346	0.151	0.328	1.054	0.029	2.026
Interior trim and functional plastic parts for passenger cars
Shenlong Automotive Parts Co., Ltd.	0.052	39.173	0.135	0.716	1.076	0.010	2.073
Production of powertrain components and precision machined parts
Xinquan Stock Co., Ltd.	0.044	36.972	0.139	0.830	1.053	0.002	2.120
Elastomeric components (seals and vibration dampers) for automotive applications
Bolong Technology Co., Ltd.	0.068	36.389	0.169	0.812	1.033	0.008	2.087
Automotive engine bearings and bushings
Ningbo Xusheng Auto Technology Co., Ltd.	0.039	33.919	0.127	0.500	1.076	0.004	2.151
Supply chain and distribution of automotive parts and components
Xiangyang Oil Pump Co., Ltd.	0.072	38.756	0.118	0.603	1.065	0.008	2.115
Manufacture of engine oil pumps and fluid control systems
Dissen Power Co., Ltd.	0.021	22.693	0.096	1.124	1.065	0.000	1.953
R&D and production of starters and alternators
Huada Technology Co., Ltd.	0.037	35.957	0.074	0.857	1.063	0.003	2.077
Automotive sensors, instrument clusters, and telematics modules
Bethel Automotive Safety Systems Co., Ltd.	0.068	28.885	0.214	0.744	1.046	0.018	2.174
Precision stamping and suspension components for vehicles
Qin’an Stock Co., Ltd.	0.049	22.731	0.165	0.403	1.044	0.056	2.068
Electronic connectors and wiring harnesses for automotive applications
Zhongma Transmission Co., Ltd.	0.044	37.844	0.147	0.514	1.044	0.006	2.029
Manufacture of drive shafts, transmissions, and related powertrain assemblies
Changqing Stock Co., Ltd.	0.029	32.645	0.204	0.785	1.053	0.007	2.052
Automotive bearings, powertrain components, and industrial bearings
Koboda Automotive Trim Co., Ltd.	0.111	27.959	0.404	0.667	1.033	0.002	2.118
Interior and exterior decorative trims for automotive applications
Ningbo Gaofa Mechanical & Electrical Co., Ltd.	0.054	31.949	0.113	0.455	1.076	0.004	2.059
Fuel injection pumps and rail systems for diesel engines
Haoneng Technology Co., Ltd.	0.068	34.703	0.078	0.339	1.045	0.027	2.106
R&D and production of EV charging equipment and electric motors
Jinhongshun Auto Parts Co., Ltd.	0.051	40.482	0.136	0.411	1.055	0.006	1.989
Automotive lighting modules and electronic control units
Tieliu Stock Co., Ltd.	0.023	40.211	0.080	0.791	1.036	0.007	2.052
Production of brake pads, friction materials, and braking systems
Xuelong Group Co., Ltd.	0.065	27.061	0.126	0.266	1.076	0.021	2.042
New-energy vehicle refrigeration and thermal management systems
QuanFeng Auto Components Co., Ltd.	0.099	34.218	0.127	0.357	1.034	0.025	1.934
CV joint boots, steering bellows, and rubber components
Fu’ao Stock Co., Ltd.	0.035	49.332	0.128	0.835	0.950	0.007	2.055
Automotive sealing strips and rubber profiles
Weichai Power Co., Ltd.	0.051	54.760	0.300	0.596	1.054	0.004	2.054
Design and manufacture of diesel engines, powertrain systems, and clean-energy solutions
Jiangling Motors Co., Ltd.	0.067	36.426	0.190	1.118	1.097	0.031	2.097
R&D and production of light- and medium-duty commercial vehicles
Wanxiang Qianchao Co., Ltd.	0.038	48.400	0.070	0.778	1.036	0.007	2.092
Manufacture of chassis systems, steering systems, and EV batteries
Haima Automobile Group Co., Ltd.	0.092	36.957	0.190	0.328	1.038	0.033	1.581
Design and manufacture of passenger cars
Weifu Gasoline Engine Components Co., Ltd.	0.046	42.664	0.209	0.451	1.034	0.009	2.010
Catalytic converters, sensors, and exhaust emission control systems
Changan Automobile Co., Ltd.	0.047	57.370	0.184	0.862	1.044	0.009	2.130
R&D and manufacture of passenger vehicles and new-energy vehicles
FAW Jiefang Automotive Co., Ltd.	0.076	46.822	0.143	0.606	0.950	0.043	2.015
Production of heavy-duty trucks, special-purpose vehicles, and engines
Ankai Bus Co., Ltd.	0.052	32.403	0.125	0.416	1.053	0.050	0.729
Design and production of city buses, school buses, and coaches
China National Heavy Duty Truck Group Co., Ltd.	0.015	34.256	0.086	0.825	1.054	0.000	2.036
Manufacture of heavy-duty trucks and diesel engines
Zhongtong Bus Holding Co., Ltd.	0.047	43.806	0.158	0.548	1.052	0.006	2.038
R&D and manufacture of city buses, coaches, and special-purpose buses
Zotye International Automobile Trading Co., Ltd.	0.185	37.653	0.108	0.103	1.039	0.006	1.691
Development and sale of passenger vehicles
Shanzi Stock Co., Ltd.	0.208	42.458	0.065	0.190	1.035	0.007	1.763
Automotive electronics, sensors, and power distribution modules

.

Configuration 1: Organization-Driven Type (~rdi*~prd*oe*~led*~gs). In this configuration path, the operational efficiency of the enterprise exists as the sole condition, with the rest of the conditions lacking. According to life-cycle-related theories, the development of an enterprise goes through different stages, including the startup phase, growth phase, maturity phase, and decline phase [66]. This configuration can explain manufacturing enterprises in the growth phase. During the growth phase, the enterprise’s products or services gradually gain market acceptance, with rapid growth in sales and profits, and the goal at this stage is to increase market share. At this point, the enterprise’s core technology products or services are relatively mature, but as the enterprise scales up, its organizational structure differs from that of the startup phase.

This leads to an increase in personnel departments, thereby escalating management complexity, resulting in issues such as excessive decentralization of authority, decreased organizational efficiency, difficulties in communication and coordination, and internal conflicts [67]. At this time, the level of operational management within the organization becomes particularly important. If a company continues to focus solely on technological innovation while neglecting other innovative behaviors, it may inadvertently hinder performance improvement [68]. Therefore, at this stage, the company needs to improve internal operational efficiency, strengthen the organization’s capacity for change, and provide a solid organizational foundation for innovation. The consistency, raw coverage, and unique coverage of this configuration are 0.9438, 0.3626, and 0.0789, respectively. This indicates that the path can explain approximately 36.3% of manufacturing enterprises. Additionally, about 7.9% of manufacturing enterprises can only be explained by this path.

Companies belonging to this path include FAW Fuwei Passenger Vehicle Components Co., Ltd.; Inno Light Auto Parts Co., Ltd.; and Dongfeng Electronic Technology Co., Ltd. A typical case is FAW Fuwei Passenger Vehicle Components Co., Ltd. As a component enterprise under the FAW Group, FAW Fuwei has undergone multidimensional reforms since being selected for the “Double Hundred Reform” in 2018. These reform measures include equity structure adjustment, organizational structure optimization, and incentive mechanism innovation, which have effectively stimulated the company’s operational vitality. By introducing a professional manager system, adjusting the executive team, and launching equity incentives, FAW Fuwei has transitioned from a state-owned enterprise to a local state-owned enterprise, further unleashing its operational vitality. FAW Fuwei continuously optimizes its organizational structure to adapt to market changes and business development needs. The company has made multiple adjustments to its internal organizational structure based on business characteristics and market trends to ensure synergy among various departments. At the same time, the company emphasizes the introduction of advanced management concepts and tools to improve internal operational efficiency. Through the optimization of its organizational structure, FAW Fuwei is better equipped to respond to market challenges and achieve sustainable development. Additionally, FAW Fuwei has demonstrated strong organizational transformation capabilities in business upgrading and expansion. Senior management closely follows industry development trends and leverages the company’s strengths to position “providing first-class cabin system solutions” as the strategic development direction for the 14th Five-Year Plan, focusing on three major business segments: smart cabins, cabin derivatives, and high tech. Through intelligent and lightweight transformation and upgrading, the company continuously enhances the core competitiveness of its products.

Configuration 2: Technology-Organization Driven (~rdi*dt*prd*oe*~gs). In this pathway, the digital transformation of electric vehicle manufacturing enterprises and operational efficiency are the core conditions, while the proportion of R&D personnel serves as an auxiliary condition. In this scenario, even with low R&D intensity and limited government subsidies, enterprise performance can still improve. First, companies optimize, automate, and standardize business processes through the active application of digital tools to enhance operational efficiency [69]. Building on this foundation, leveraging the power of data analytics, enterprises gain deeper insights into customer needs and behavioral patterns, enabling them to formulate precise marketing strategies, improve service quality, enhance customer satisfaction, and expand business opportunities. Furthermore, the in-depth implementation of digital transformation has also facilitated a qualitative leap in resource allocation and cost management for enterprises. Through data analysis, enterprises can more accurately predict changes in market demand, allowing them to flexibly adjust production plans and effectively avoid resource waste and cost overruns. Additionally, R&D investment drives industrial upgrading, and the continuous emergence of new technologies and products enables enterprises to transition towards high-tech, high-value-added fields, forming a virtuous cycle of innovation and upgrading [70]. The consistency, raw coverage, and unique coverage of this configuration are 0.9475, 0.3152, and 0.0424, respectively. This indicates that this pathway can explain approximately 31.5% of manufacturing enterprises. Moreover, about 4.2% of manufacturing enterprises can only be explained by this pathway.

Companies belonging to this path include Huayu Automotive Systems Co., Ltd.; SAIC Motor Co., Ltd.; and Ningbo Tuopu Group Co., Ltd. A typical case is Huayu Automotive Systems Co., Ltd., which established 11 digital benchmark factories from 2020 to 2021. Through a series of replication and promotion efforts, this number had reached 24 by the end of 2023. Huayu Automotive adheres to the principles of “strategic orientation, rolling planning; benchmark first, key breakthroughs; classification and grading, replication and promotion; progressive advancement, focusing on effectiveness” to drive its digital transformation. This strategy ensures that the company has a clear direction and goals during the digital transformation process, while also being able to flexibly adjust based on actual circumstances, thereby enhancing operational efficiency. Simultaneously, the company emphasizes technological innovation and R&D, possessing multiple R&D centers and advanced technical talents, enabling it to provide customers with more competitive products and services. In the coming years, the company plans to achieve digital upgrades for more factories and enhance its overall competitiveness and market position through continuous technological innovation and application.

Configuration 3: Three-Dimensional-Driven by Digitalization and Operational Efficiency (rdi*dt*~prd*oe*led). In this pathway, digital transformation and operational efficiency serve as the core conditions, supplemented by R&D intensity and regional economic development level. In this scenario, even a relatively low proportion of R&D personnel can enhance corporate performance. In the wave of digital transformation, operational efficiency has become the core driving force for enhancing corporate performance. Internally, companies continuously optimize processes and improve efficiency by applying digital tools through precise strategies. Simultaneously, the increase in R&D intensity acts as a supplementary force, continuously innovating and guiding the enterprise toward high-tech, high-value-added fields, thereby strengthening market competitiveness. The level of regional economic development provides a favorable external environment for the enterprise, promoting effective resource integration and utilization. The consistency, raw coverage, and unique coverage of this configuration are 0.9415, 0.2762, and 0.0144, respectively. This indicates that this pathway can explain approximately 27.6% of manufacturing enterprises. Additionally, about 1.4% of manufacturing enterprises can only be explained by this pathway.

Companies belonging to this path include Joyson Electronics Co., Ltd. and Shenlong Automotive Parts Co., Ltd. A typical case is Joyson Electronics, which, with the acceleration of digital transformation in the global automotive industry, has established digital production lines across multiple manufacturing bases. By introducing industrial robots, intelligent inspection equipment, and other automated devices, the company has achieved automation and intelligence in its production processes. Through digital transformation, Joyson Electronics has been able to launch new products that meet market demands more quickly, enhancing product competitiveness and added value. As the company’s business continues to expand and market competition intensifies, Joyson Electronics has been continuously optimizing and adjusting its management team structure to improve operational efficiency to adapt to external market changes and the needs of business development. The company places high importance on technological innovation and has consistently increased its R&D investment. In 2023, the company’s total R&D expenditure reached approximately CNY 3.65 billion, with cumulative R&D investment over the past five years exceeding CNY 16.515 billion, ranking among the top in the domestic automotive parts industry. Located in Ningbo, a key city in the integrated development of the Yangtze River Delta region, Joyson Electronics has benefited from the dividends of regional collaborative development. According to the “Outline of the Yangtze River Delta Regional Integration Development Plan,” Ningbo, along with surrounding cities, focuses on key areas, such as electric vehicles, collaboratively building a world-class automotive industry cluster. This provides Joyson Electronics and other automotive parts companies with vast market space and development opportunities. The Ningbo municipal government has placed significant emphasis on the electric vehicle and parts industry, introducing a series of supportive policies. These policies include support in areas such as fiscal subsidies, tax incentives, and land supply, providing strong guarantees for the research and development, production, and market expansion of companies like Joyson Electronics.

Configuration 4: Three-Dimensional-Driven by R&D Intensity and Operational Efficiency (rdi*~dt*prd*oe*gs). In this pathway, R&D intensity and operational efficiency serve as core conditions, while the proportion of R&D personnel and government subsidies act as auxiliary conditions. Even when the degree of corporate digital transformation is not high, this configuration can still enhance corporate performance. Government subsidy policies are typically designed to encourage technological innovation and product development, providing financial support to mitigate the risks and costs associated with R&D, thereby promoting technological upgrades and equipment modernization, and driving industrial upgrading and transformation [71]. The consistency, raw coverage, and unique coverage of this configuration are 0.9582, 0.2668, and 0.0548, respectively. This indicates that the pathway can explain approximately 26.7% of manufacturing enterprises. Additionally, about 5.5% of manufacturing enterprises can only be explained by this pathway.

Companies belonging to this path include Bethel Automotive Safety Systems Co., Ltd.; Jiangling Motors Co., Ltd.; and Great Wall Motor Co., Ltd. A typical example is Bethel Automotive Safety Systems Co., Ltd. The company’s main business covers the fields of automotive braking systems, intelligent driving systems, steering systems, and suspension systems. Bethel places a high emphasis on technological innovation and R&D investment. In 2022, Bethel invested CNY 378 million in R&D, accounting for approximately 6.8% of its revenue. As of April 30, 2023, the company had around 980 R&D personnel and had established R&D centers in multiple cities. Bethel actively promotes the development and industrialization of lightweight components and brake-by-wire systems, further contributing to energy conservation and emission reduction in the automotive industry, enhancing the range of electric vehicles, reducing production costs, and facilitating the widespread adoption of electric vehicles, thereby advancing the goal of carbon neutrality. Additionally, the company continues to optimize energy management methods, increasing the proportion of green electricity in its operations, expanding the application of photovoltaic power generation solutions, and enhancing the procurement and production of green electricity to achieve green production.

Configuration 5: Environment-Driven (~rdi*~dt*~rdp*~oe*led*~gs). In this path, the level of regional economic development appears as a single condition, while other conditions are absent. In such cases, enterprises can rely on the broader economic environment of their region to access various resources, such as funding, while also attracting suppliers and customers [72]. Therefore, even when other conditions may be less favorable, the high level of regional economic development can still lead to improved enterprise performance. The consistency, raw coverage, and unique coverage of this configuration are 0.9580, 0.2972, and 0.0762, respectively. This indicates that this path can explain approximately 29.7% of manufacturing enterprises. Additionally, about 7.6% of manufacturing enterprises can only be explained by this path.

Companies belonging to this path include Ningbo Xusheng Auto Technology Co., Ltd. and Bohai Automotive Systems Co., Ltd. A typical example is Ningbo Xusheng Auto Technology Co., Ltd., which is primarily engaged in the research, development, production, and sales of precision aluminum alloy automotive parts and industrial components. Its products are mainly used in electric vehicles, traditional automobiles, commercial vehicles, and industrial automation. As a leading enterprise in the automotive parts industry, the Xusheng Group has earned the trust and cooperation of numerous renowned domestic and international automotive manufacturers due to its advanced technology and high-quality products. The company is located in Ningbo, Zhejiang Province, where rapid regional economic development, coupled with rising resident incomes and enhanced consumer purchasing power, is expected to drive continuous growth in automotive market demand, providing Xusheng Group with broader market opportunities. Additionally, the region boasts a relatively complete automotive industry chain, encompassing upstream raw material supply, midstream parts manufacturing, and downstream vehicle assembly. This synergistic development of the industrial chain not only reduces procurement costs for enterprises but also enhances production efficiency and product quality. As a crucial link in the industrial chain, the Xusheng Group can fully leverage the advantages of the regional industrial chain to achieve close collaboration and mutual benefits with upstream and downstream enterprises.

4.4. Robustness Test

In order to assess the stability and reliability of the results under different conditions, robustness tests were conducted on the antecedent configurations. This study adopted the standard threshold settings widely used in the mainstream literature to ensure that the results have strong comparability and theoretical consistency [53]. This method is suitable for configuration identification with moderate sample sizes. At the same time, to avoid losing effective configurations with theoretical value, the consistency threshold was not set too high. This study referenced the approach of Zhang et al. [73] and adjusted the original consistency threshold from 0.8 to 0.85. The resulting configurations were consistent with the original results. After adjusting the PRI consistency threshold from 0.75 to 0.8, all configurations except for Configuration 5 remained consistent with the original results. The coverage and consistency showed slight differences but remained stable, as shown in

Table 4. Robustness test.

Variables	High Performance of Electric Vehicle Manufacturing Enterprises
	C1	C2	C3	C4
R&D intensity	⨂	⨂	●	⬤
Digital transformation		⬤	⬤	⨂
Proportion of R&D personnel	⨂	●	⨂	●
Operational efficiency	●	⬤	⬤	⬤
Level of economic development	⨂		●
Government subsidy	⨂	⨂		●
Consistency	0.9438	0.9475	0.9415	0.9582
Raw coverage	0.3626	0.3152	0.2762	0.2668
Unique coverage	0.0936	0.0446	0.0144	0.0565
Solution coverage	0.5332
Solution consistency	0.9215

Notes: “⬤” or “●” indicates the existence of variables; “⭙” or “⨂” indicates that the variable does not exist; “Blank” indicates that the variable may or may not exist in the combination; “⬤” or “⭙” indicates core variable; “●” or “⨂” indicates auxiliary variable.

. Therefore, the robustness test indicates that the results are robust. Although Configuration 5 did not continue to appear after raising the PRI threshold, we chose to retain this configuration in the main analysis results. On the one hand, this path meets the PRI and consistency standards set in the original configuration, possessing logical reasoning and empirical foundation; on the other hand, the emphasis on “regional economic development level” as the sole core condition in Configuration 5 highlights an important possibility worth noting: that in certain contexts, macro-environmental factors may significantly impact corporate performance independently of internal corporate conditions. This discovery not only helps to enrich the understanding of the mechanisms of the external environment acting alone but also provides insights for regional policy formulation and resource allocation. Therefore, we believe that although Configuration 5 is not robust under high threshold settings, its theoretical and practical significance is still worth further exploration. Future research could combine larger samples or data spanning multiple regions to deeply verify the applicable boundaries and mechanisms of this pathway.

5. Discussion

5.1. Theoretical Implications

The theoretical significance of this study includes the following aspects.

Firstly, regarding the issue of how electric vehicle manufacturing companies can improve their performance in the context of digital transformation, this study first analyzes the current development status, characteristics, and problems and challenges faced by electric vehicle manufacturing companies, addressing the shortcomings of existing research in the exploration of the electric vehicle field. Secondly, current scholarly research mostly focuses on the mechanisms of impact of single or a few factors on the performance of electric vehicle manufacturing enterprises [15,20,23,31,33]. The development process of enterprises is complex, and their performance is often influenced by the nonlinear interactions and dynamic feedback of multidimensional factors, such as technology, organization, and environment. Moreover, there are often intricate relational mechanisms and interactions among these factors [74]. This study delves deeply into the synergistic effects among the factors influencing corporate performance.

Secondly, this study, based on the TOE framework, extracts six key factors influencing corporate performance from the dimensions of technology, organization, and environment and integrates them with fsQCA, breaking through the limitations of the traditional regression model’s “independent effect of each factor” perspective. Five high-performance realization paths, including organization-driven, technology-organization-driven, and three-dimensional-driven paths, were identified, deepening the application of the TOE theory in the field of electric vehicle manufacturing enterprises. At the same time, the interactive relationship mechanisms among various factors also reflect the characteristics of the electric vehicle industry, which aligns with the findings of some scholars [75,76]. Finally, this study demonstrates the heterogeneous causal relationships of these conditions under different combinations through empirical samples, proposing differentiated paths and strategies for enterprises at different development stages. It provides important insights for subsequent theoretical research to further explore the asymmetric causal relationships between multidimensional condition configurations and performance outcomes, considering the industrial life-cycle and policy evolution context. In summary, this study offers a more refined theoretical depiction of the causal mechanisms of enterprise performance in the context of digital transformation for electric vehicle manufacturing enterprises.

5.2. Practical Implications

This study provides some management recommendations for enterprises at different stages of development.

Firstly, for electric vehicle manufacturing enterprises in the growth stage, the primary focus should be on improving organizational conditions. According to the enterprise life-cycle theory, the growth stage is a critical period for rapid development. During this phase, enterprises typically establish their unique product lines, steadily increase their market share, gradually enhance their market competitiveness, and accelerate their performance growth. However, as the enterprise scales up and the market environment evolves, the existing organizational structure may no longer effectively support the rapid development of the enterprise. Therefore, improving organizational conditions becomes crucial for sustained growth. Enterprise managers can enhance operational efficiency by optimizing organizational structure, clarifying departmental functions and divisions, strengthening departmental collaboration and communication, and promoting talent development and incentives, enabling their companies to achieve continuous, stable, and rapid growth.

Secondly, for enterprises operating in relatively insufficient environmental conditions, emphasis should be placed on the improvement of technological and organizational conditions. Such enterprises often face challenges such as limited resources, intense market competition, and fluctuating customer demands. Companies should increase investment in research and development and introduce advanced production technologies and equipment, thereby enhancing production efficiency, reducing production costs, meeting market demands, and gaining competitive advantages. Simultaneously, by improving organizational structures and management processes, adjusting the operating model, rationally allocating resources, promoting communication and collaboration, and enhancing work efficiency, these improvements are interdependent and mutually reinforcing. Technological advancements require corresponding organizational changes to support and implement them, while the optimization of organizational conditions can provide a better environment and conditions for technological innovation. For instance, the introduction of new production technologies may necessitate adjustments to production processes and organizational structures to adapt to new production methods; meanwhile, a flexible organizational structure can encourage employees to propose innovative ideas and solutions, thereby driving technological progress.

Thirdly, for enterprises that excel in both technological and organizational conditions, attention should be paid to improving environmental conditions. These enterprises have undergone the exploratory phase of their inception and the rapid expansion phase of their growth, gradually establishing a relatively stable market position and technological advantages, thereby entering the mature stage. In this stage, their technological and organizational conditions are relatively well-developed. Regional and governmental authorities should guide social capital to support innovation in the real economy, with the key being the effective use of fiscal, financial, and exchange rate policies aimed at reducing the innovation and operational costs of manufacturing enterprises and directing capital precisely towards the manufacturing sector. Simultaneously, innovation-oriented inclusive policies should be implemented to aggregate innovation resources within the manufacturing sector, establishing clear, stable, and predictable incentive mechanisms to encourage long-term investment in technological innovation. Additionally, demand-side policy guidance should be strengthened to streamline market channels, ensuring that the innovative achievements of enterprises can be effectively transformed into market gains. Enterprises should also actively monitor changes and development trends in the policy environment, market environment, and other aspects, taking corresponding measures to adapt to and leverage these environmental changes, thereby creating more favorable external conditions for their development.

5.3. Limitations and Future Research

First, the sample of this study is limited to cross-sectional data from 65 Chinese electric vehicle manufacturing companies. Due to certain factors, there may be missing or erroneous data in the database and a potential lag in data updates, which could lead to some bias in the results. Although this study reveals typical configuration patterns within the industry to some extent, it is difficult to directly generalize the research conclusions to other industries. Future research can include companies from different countries and regions or different types of industries for study and can also expand the sample size to verify the universality of high-performance pathways. Second, in terms of variable measurement, this study uses recognized financial and research indicators to represent technological, organizational, and environmental conditions, and the selection of variables largely depends on previous research. Therefore, some factors such as corporate culture, the depth of supply chain collaboration, and other soft factors, as well as institutional details like policy implementation differences, may not have been considered. These factors, which have not yet been included in the model, could become key driving factors in other contexts. In future research, more influencing factors can be extracted based on fundamental theories, the relevant literature, industry reports, and so on. At the same time, considering that the research sample can be expanded to other countries and regions, future variable sections can also consider international indicators, such as global market share and export ratios, thereby enhancing cross-national applicability and comparative value. Third, the development of a business is a dynamic process, and its performance may be influenced by the combined effects of various internal and external conditions interacting at different times and in different contexts. As market environments, technological conditions, policy support, and the internal resource allocations of businesses continue to change, previously effective resource combinations or management models may gradually become ineffective or even negatively impact performance. This study only selected cross-sectional data from 2022. Future research could collect longitudinal or time-series data to use dynamic QCA to examine the impact of antecedent configurations on the performance improvement of manufacturing enterprises over time.

6. Conclusions

This paper uses the 2022 data of 65 electric vehicle manufacturing companies listed on the main board of the electric vehicle concept stock list published by Huaxi Securities as the research sample. Based on the TOE theoretical analysis framework, the fsQCA method is employed to identify causal factors and conduct a configurational analysis. Based on the TOE theoretical framework, the fsQCA method is employed to identify and analyze the causal factors and configurations. The study explores the combined effects of six conditional variables across three dimensions—technology, organization, and environment—on corporate performance. The causal factors include R&D intensity, digital transformation, the proportion of R&D personnel, operational efficiency, economic development level, and government subsidies. The research findings reveal the following: First, a single conditional variable is not a necessary condition for high performance in manufacturing enterprises; instead, a synergistic combination of multiple conditions is required to enhance corporate performance. Second, there are five configurations that promote improved performance in manufacturing enterprises: organization-driven, technology-organization-driven, three-dimensional-driven by digitalization and operational efficiency, three-dimensional-driven by R&D intensity and operational efficiency, and environment-driven. These configuration paths, formed based on nonlinear causal relationships, provide a basis for proposing reasonable development suggestions and strategies for different manufacturing enterprises. Third, digital transformation and operational efficiency serve as core or peripheral conditions across multiple paths. Moreover, when enterprises excel in both the technology and organization dimensions, the remaining conditions exhibit substitutive relationships. They can synergize with the superior conditions in the technology and organization dimensions, thereby enhancing corporate performance.