Mediating Role of 6V-Based SBMI Between Competitive Strategies and Firm Performance: An Empirical Study of China’s Electric Vehicle Industry

Abstract

Strategic management and sustainable business model innovation (SBMI) are widely recognized important firm performance. This study develops a theoretical framework that integrates competitive strategy, SBMI, and performance, with SBMI conceptualized through the multidimensional 6V model. While the model is broadly applicable across industries, this study focuses on the electric vehicle (EV) sector in China as an empirical case to test the proposed relationships. Using survey data from 261 managerial respondents across nine major Chinese EV brands, PLS-SEM is employed to examine both direct and mediated effects of differentiation and cost leadership strategies. The results confirm that both strategies positively influence firm performance; however, the mediating roles of SBMI dimensions vary. This study contributes to the literature by demonstrating the explanatory power of the 6V-SBMI framework and offering practical insights for firms seeking to align strategic choices with sustainability-oriented innovation.

1. Introduction

The rapid expansion of the electric vehicle (EV) industry is a transformative force in the global automotive market, with China emerging as a dominant player, accounting for approximately 60% of global EV production. China’s growth trajectory has been driven by supportive government policies [1,2] and rising consumer demand for cost-effective and environmentally sustainable transportation solutions. While established brands such as BYD, Xpeng and NIO have gained global recognition [3], a multitude of smaller firms are also competing across various market segments. As market competition intensifies, Chinese EV companies must prioritize both technological and business model innovation (BMI) to sustain competitive advantage. In this context, sustainable business model innovation (SBMI) has emerged as a pivotal strategy for achieving long-term growth and differentiation [4,5].

BMI refers to the process of transforming a firm’s value proposition, value creation, delivery, and capture mechanisms to enhance competitiveness [6]. Within the EV sector, sustainable business models (SBMs) are particularly relevant due to the industry’s emphasis on environmental sustainability and resource efficiency [7]. SBMI enables firms to align innovation with both economic and sustainable objectives, thereby fostering long-term sustainability [8]. Examples of SBMI practices include the development of circular battery supply chains, the provision of vehicle-to-grid services, and the integration of ”Mobility-as-a-Service” platforms. A notable case is NIO’s “Battery-as-a-Service” model, wherein customers lease batteries separately from the vehicle. This subscription-based approach reduces initial purchase costs while facilitating efficient battery reuse and upgrades. Despite this strategic importance, research on SBMI’s mediating role in linking competitive strategy to firm performance has received limited scholarly attention, highlighting a significant research gap in the existing literature [9].

The role of competitive strategies in fostering firm performance has been extensively documented in strategic management research, particularly within Porter’s framework. According to Porter, a differentiation strategy emphasizes unique value propositions, enabling firms to command premium prices, while cost leadership strategy focuses on operational efficiency and cost minimization to compete on price, and the focus strategy represents a niche variation in differentiation, targeting specific market segments [10]. While these strategies have been widely examined in traditional business contexts [11], their interaction with SBMI in shaping firm performance within the EV industry has received limited scholarly attention. For instance, an EV firm pursuing a differentiation strategy may prioritize premium features and advanced services, whereas a cost leadership-oriented firm may concentrate on operational efficiencies and economies of scale—each strategic orientation potentially giving rise to distinct SBMI initiatives.

In response to increasing pressures for sustainable transformation in China’s EV sector, this study investigates how competitive strategies influence firm performance through the mediating mechanism of SBMI conceptualized via the 6V framework [12]. Accordingly, the study addresses the following research question: How do competitive strategies influence firm performance via the mediating role of 6V-based SBMI in the context of China’s EV industry?

To explore this question, the study proposes a theoretical framework that links two widely recognized competitive strategies—differentiation and cost leadership—with firm performance, both directly and indirectly through SBMI. SBMI is conceptualized using the six dimensions of the 6V model: “Value Proposition to Customer” Innovation (VPCI), Value Creation Innovation (VCrI), Value Delivery Innovation (VDI), Value Capture Innovation (VCaI), “Value of After-Sales” Innovation (VSI), and “Value of Residual” Innovation (VRI) [3]. Each of these dimensions is examined as an independent mediator, offering a more granular understanding of how distinct innovation mechanisms reinforce strategic positioning and influence firm performance.

While previous studies have separately examined the relationships between competitive strategy and firm performance, or between BMI and performance, few have integrated these constructs into a unified framework. Moreover, to the best of our knowledge, little prior empirical study has tested the full set of relationships—including the multidimensional mediating role of 6V-based SBMI—within a single model. To fill this gap, the proposed framework is applied to the context of China’s EV industry, and the hypothesized relationships are empirically tested using survey data.

By integrating competitive strategy, 6V-based SBMI, and firm performance into a unified analytical framework, this study advances both theoretical and practical understanding of how strategy execution is mediated through multidimensional innovation mechanisms in emerging industries. The Chinese EV industry serves as a suitable empirical context for validating this mediation pathway and underscores the strategic role of SBMI in achieving competitive advantage in emerging, innovation-intensive markets.

The remainder of this study is structured as follows. Section 2 reviews the relevant literature and develops the research hypotheses. Section 3 outlines the research methodology, including data collection procedures and measurement model specification. Section 4 presents the empirical results derived from partial least squares structural equation modeling (PLS-SEM). Section 5 discusses the theoretical and managerial implications of the findings, as well as a summary of contributions, practical recommendations for EV firms, and directions for future research.

2. Literature Review and Hypotheses Development

2.1. Competitive Strategies and Sustainable Business Model Innovation

SBMs have increasingly been recognized as a vital source of competitive advantage in today’s dynamic business environment [5,13] such as electric vehicles (EVs). SBMI, therefore, refers to the processes through which companies either create new BMs or transform existing ones to achieve sustainable development [4]. These processes involve the reconfiguration of key elements, including value proposition, value creation, value delivery, and value capture [14,15,16]. In this research, SBMI is conceptualized as a 6V model, a multidimensional construct comprising the following six dimensions [12]: (1) VPCI, (2) VCrI, (3) VDI, (4) VCaI, (5) VSI, and (6) VRI.

In the Chinese EV industry context, these dimensions capture the breadth of innovation required to deliver sustainable value while addressing the complex challenges posed by rapidly evolving market and environmental conditions. For example, residual value innovation has become increasingly relevant for domestic brands as they seek to build long-term trust with consumers.

The literature suggests that successful SBMI not only enables companies to meet evolving customer needs but also fosters long-term economic, environmental, and social sustainability [6,17]. BMs, as the embodiment of a firm’s strategic orientation, reflect the competitive strategies that a company adopts [18]. In this regard, a company’s ability to innovate in value creation, value delivery, and other mechanisms is often influenced by the competitive strategy it employs.

Building on Porter’s seminal work, this research categorizes competitive strategies into two primary dimensions: differentiation strategy and cost leadership strategy, with focus strategy often regarded as a variation in differentiation [10]. According to Porter, organizations can secure a competitive edge by pursuing one of these two approaches. Differentiation strategy emphasizes the development of unique products or services that provide superior value to customers through advanced technology, innovative features, and high-quality customer experiences. For instance, in the EV industry, differentiation may be achieved through product innovations such as extended battery life or distinctive branding—strategies that typically require substantial investment in research and development (R&D) and customer-centric innovation.

In contrast, cost leadership strategy focuses on minimizing production and operational costs to offer competitively priced products [10]. This can be achieved through scalable production, lean supply chains, or localized manufacturing within the EV industry. While cost leadership strategy is primarily associated with cost reduction, it can also stimulate SBMI by enabling firms to reallocate resources toward process innovations and efficiency improvements that support sustainable business practices. For instance, Clauss et al. [19], who found that strategic agility positively influences value proposition innovation, value creation innovation, and value capture innovation.

Given these insights, this study proposes that both differentiation and cost leadership strategies influence SBMI in distinct ways, thereby shaping how companies create, deliver, and capture sustainable value. Based on the foregoing discussion, the following hypotheses are formulated:

H1a–H1f: 

Within the context of China’s EV industry, differentiation strategy positively influences the six dimensions of SBMI based on the “6V” model: VPCI (H1a), VCrI (H1b), VDI (H1c), VCaI (H1d), VSI (H1e), VRI (H1f).

H2a–H2f: 

Within the context of China’s EV industry, cost leadership strategy positively influences the six dimensions of SBMI based on the “6V” model: VPCI (H2a), VCrI (H2b), VDI (H2c), VCaI (H2d), VSI (H2e), VRI (H2f).

2.2. Competitive Strategies and Firm Performance

For decades, the relationship between competitive strategies and firm performance has been a central focus in the scholarly discourse of strategic management. Porter’s seminal framework posits that organizations typically adopt one of two core competitive strategies—differentiation strategy or cost leadership strategy—to establish and sustain a competitive advantage [10].

Pioneering research by Hambrick [20] and Dess and Davis [21] laid the foundation for understanding the relationship between competitive strategy and firm performance through rigorous analyses of the industrial manufacturing sector. Their findings underscored that firms adhering to either a differentiation strategy or a cost leadership strategy consistently outperformed competitors trapped in ambiguous “middle-ground” positions. Furthermore, these studies identified cost leadership strategy as particularly effective in driving firm performance. This pattern has demonstrated remarkable temporal stability, with subsequent large-scale investigations reinforcing these initial conclusions.

However, more recent studies have challenged the notion that cost leadership alone is superior. Akan et al. [22] identified that both differentiation and cost leadership strategies exert a positive impact on firm performance through regression analysis of data collected from 226 employees across various organizations. Similarly, Pulaj et al. [23] further explored the relationship between Porter’s generic strategies and organizational performance, concluding that both differentiation and cost leadership strategies significantly enhance firm performance. Furthermore, Islami et al. [24] empirically confirmed a positive correlation between differentiation strategy, cost leadership strategy, and firm performance.

In the context of China’s EV industry, where competition is intense and consumer expectations are rising, both cost efficiency and technological leadership have become critical determinants of success. Collectively, the extant literature suggests that firms may achieve performance improvements by adopting either a single focused strategy—differentiation or cost leadership—or by integrating elements of both.

Based on the above discussion, the following hypotheses are proposed:

H3: 

Within the context of China’s EV industry, differentiation strategy has a positive direct influence on firm performance.

H4: 

Within the context of China’s EV industry, cost leadership strategy has a positive direct influence on firm performance.

2.3. The Mediating Role of Sustainable Business Model Innovation Between Competitive Strategies and Company Performance

While the relationship between competitive strategies and firm performance has been extensively examined in strategic management literature, the mediating role of SBMI remains underexplored, and even fewer have adopted a multidimensional framework such as the 6V model, particularly within the context of the EV industry.

Although prior studies have investigated the mediating effects of BMI in various strategic settings, explicitly addressing SBMI as a crucial link between competitive strategies and firm performance remains limited. For instance, Bhatti et al. [13] demonstrated that BMI mediates the relationship between knowledge absorptive capacity and firm performance in the IT sector, highlighting its pivotal role in bridging organizational capabilities and performance outcomes. Similarly, Clauss et al. [19] and Jatra and Giantari [25] provided empirical evidence reinforcing the mediating function of BMI in the relationship between strategic agility, market orientation, and firm performance.

Building on this foundation, SBMI, which an extension of BMI that integrates sustainability principles, serves as a crucial mechanism through which competitive strategies translate into enhanced firm performance [5]. By embedding sustainability-oriented innovations into BMs, firms can not only strengthen their competitive positioning but also generate long-term value that aligns with environmental, social, and economic imperatives.

2.3.1. The Mediating Role of Sustainable Business Model Innovation Between Differentiation Strategy and Firm Performance

A differentiation strategy seeks to establish a distinctive market position by offering superior product attributes, enhanced quality, and strong branding [10]. However, achieving a sustainable differentiation advantage depends on a firm’s ability to continuously innovate its BM.

In recent years, scholars have extended this argument to the sustainability context, positing that SBMI can reinforce differentiation by integrating environmental and social dimensions into the value proposition [7,26]. Firms that embed sustainable practices within their value creation and delivery processes are better positioned to address evolving consumer demands and comply with regulatory pressures. Empirical evidence suggests that BMI mediates the relationship between strategic agility and firm performance [19]. Specifically, innovations in value proposition, creation, and capture have been shown to enhance brand competitiveness and increase perceived customer value [27].

Accordingly, the following hypothesis is proposed:

H5a–H5f: 

Within the context of China’s EV industry, the relationship between differentiation strategy and firm performance of Chinese EV brand company is mediated by each dimension of SBMI based on “6V” model: VPCI (H5a), VCrI (H5b), VDI (H5c), VCaI (H5d), VSI (H5e), VRI (H5f).

2.3.2. The Mediating Role of Sustainable Business Model Innovation Between Cost Leadership Strategy and Firm Performance

A cost leadership strategy focuses on achieving cost efficiencies and economies of scale, enabling firms to offer lower-priced products than competitors [10]. While this strategy has been demonstrated to enhance firm performance through cost minimization and market share expansion [24], its long-term sustainability depends on the continuous evolution of the underlying BM.

BMI, particularly within sustainability, enables cost leaders to rethink value capture and resource utilization by adopting innovative approaches, such as circular economy practices and lean production methods [26]. By integrating SBMI, firms pursuing cost leadership strategies can transform traditional cost-cutting initiatives into sustainable competitive advantages, ensuring that efficiency gains do not compromise long-term value creation [9].

Accordingly, the following hypothesis is proposed:

H6a–H6f: 

Within the context of China’s EV industry, the relationship between cost leadership strategy and firm performance of Chinese EV brand company is mediated by each dimension of SBMI based on “6V” model: VPCI (H6a), VCrI (H6b), VDI (H6c), VCaI (H6d), VSI (H6e), VRI (H6f).

Based on the above analysis, the research framework of this study is illustrated in Figure 1. This framework presents the hypothesized relationships between two types of competitive strategies—differentiation and cost leadership—and firm performance. These strategies are proposed to influence performance both directly and indirectly through six dimensions of SBMI, based on the 6V model. This structure enables a nuanced analysis of how each strategic orientation affects firm performance directly and indirectly through innovation pathways. Hypotheses H1a–H2f test the effect of strategies on SBMI dimensions, H3–H4 test direct effects on firm performance, and H5a–H6f examine the mediating role of SBMI dimensions.

3. Research Method

3.1. Research Design

This study adopts a quantitative research design to empirically investigate the relationships between competitive strategies, SBMI, and firm performance within the context of China’s EV industry. Specifically, it examines both the direct and indirect effects of differentiation strategy and cost leadership strategy on firm performance, with SBMI—operationalized through the 6V model—acting as a mediating variable. The six dimensions of SBMI considered in this research are VPCI, VCrI, VDI, VCaI, VSI, and VRI.

To test these relationships, this study employs Structural Equation Modeling (SEM), a robust statistical approach that allows for the simultaneous modeling and estimation of complex relationships among multiple independent and dependent variables [28]. SEM consists of two primary approaches: covariance-based SEM (CB-SEM) and variance-based SEM (VB-SEM) utilizing the Partial Least Squares (PLS-SEM) technique [28,29]. CB-SEM is primarily used to confirm (or reject) theories and their underlying hypotheses, while PLS-SEM focuses on explaining variance in the dependent variables and is particularly useful for predictive modeling and exploratory research [29].

Given the exploratory nature of this study and the complexity of the proposed model, PLS-SEM is selected as the preferred analytical technique. PLS-SEM is widely recognized for its effectiveness in handling models with multiple constructs and mediation effects [30], making it particularly well-suited for this research. Moreover, PLS-SEM is advantageous in cases involving smaller sample sizes, non-normal data distributions, and high model complexity [29], which align with the characteristics of this study.

By employing PLS-SEM, this study can simultaneously assess both the direct and indirect effects of differentiation strategy and cost leadership strategy on firm performance, while also examining the mediating role of the six SBMI dimensions based on the 6V model.

3.2. Measures

The research model of this study comprises nine primary constructs: differentiation strategy, cost leadership strategy, VPCI, VCrI, VDI, VCaI, VSI, VRI, and firm performance. Each construct is measured using reflective indicators adapted from prior literature to ensure content validity and reliability.

The measurement items for differentiation strategy are adapted from Anwar and Shah, Li and Li, and Navaia et al. [31,32,33]. The items for cost leadership strategy are similarly adapted from Anwar and Shah, and Li and Li [31,32]. The measurement of the six dimensions of SBMI based on the 6V model are adapted and modified from Clauss et al. [19] and have been validated and applied by Zang et al. [3]. Firm performance is assessed using both financial and non-financial indicators, following the framework proposed by Venkatraman and Ramanujam [34]. The specific measurement items include profitability, market share, and customer satisfaction, adapted from Anwar and Shah [31], and Clauss et al. [19].

All measurement items can be found in Appendix A 

Table A1. Measurement Items of Construct in This Research.

Construct	Measurement Item
Cost Leadership Strategy	CLS1	Our company purchases raw materials and components costs are lower than competitors.
	CLS2	Our company manufacturing costs are lower than competitors.
	CLS3	Our company products’ prices are lower than competitors.
	CLS4	Our company gaining economies of scale.
Differentiation Strategy	DS1	Our company emphasizes brand building and identification.
	DS2	Our company adjusts the product to meet the customer’s changing needs.
	DS3	Our company’s products have more advanced technology and configuration than competitors.
	DS4	Our company products’ quality is higher than our competitors.
“Value Proposition to Customer” Innovation	VPCI1	Our company regularly address new, unmet customer needs.
	VPCI2	Our company products or services are very innovative as compared to our competitors.
	VPCI3	Our company products or services regularly solve customer needs, which were not solved by competitors.
	VPCI4	Our company regularly take opportunities which arise in new or growing markets.
	VPCI5	Our company regularly address new, untouchable market segments.
	VPCI6	Our company are constantly seeking new customer segments and markets for our products and services.
Value Creation Innovation	VCrI1	We permanently reflect which new competencies need to be established in order to adapt to changing market requirements.
	VCrI2	We regularly utilize new technical opportunities in order to extend our product and service portfolio.
	VCrI3	We are constantly searching for new collaboration partners.
	VCrI4	New collaboration partners regularly help us to further develop our business model.
	VCrI5	We utilize innovative procedures and processes during manufacturing of our products.
	VCrI6	Existing processes are regularly assessed and significantly changed if needed.
Value Delivery Innovation	VDI1	Our company regularly utilize new distribution channels for our products and services.
	VDI2	Constant changes in our channels led to improved efficiency of our channel functions.
	VDI3	Our company consistently changed our portfolio of distribution channels.
	VDI4	Our company often use new exchange and interactive platforms to transmit information to consumers.
	VDI5	Our company often uses new ways to advertise our products or services.
	VDI6	Our company constantly changed the information of our products or services delivery ways.
Value Capture Innovation	VCaI1	Our company recently developed new revenue opportunities, such as trading-in old motor vehicles for new one.
	VCaI2	Our company increasingly offer integrated services such as auto finance services available, in order to realize long-term financial returns.
	VCaI3	Our company regularly reflect our price-quantity plan.
	VCaI4	Our company actively seek for opportunities to save manufacturing costs.
	VCaI5	Our company constantly examine purchase cost and if necessary amended according to market prices.
	VCaI6	We utilize opportunities, which arise through price differentiation.
“Value of Service” Innovation	VSI1	We try to increase customer retention by new service offerings.
	VSI2	We emphasize innovative actions to increase customer retention.
	VSI3	We recently took many actions in order to strengthen customer relationships.
	VSI4	Our company constantly change the way how to service customers.
	VSI5	Customers can always feel impress though our service.
	VSI6	Our company have many new customers referred by used customers.
“Value of Residual” Innovation	VRI1	Our company constantly take various measures to raise the price of second-hand products.
	VRI2	Our company often provides customers with a channel to trade second-hand products.
	VRI3	Our company always pays close attention to the second-hand products market in this industry.
	VRI4	Our company provides recycling channels for end-of-life product.
	VRI5	Our company offers reprocessing of end-of-life product to reduce the negative impact on the environment.
	VRI6	Our company can benefit financially from the end-of-life product reprocessing.
Firm Performance	FP1	Our company’s brand is gradually becoming better known.
	FP2	Relative to our competitors, our company had a greater market share.
	FP3	Relative to our competitors, the sales growth of our company was growing faster.
	FP4	Relative to our competitors, the product development speed of our company was faster.
	FP5	Relative to our competitors, our company financial performance was much better.
	FP6	Relative to our competitors, the development of our company was much better.
	FP7	Employee loyalty in our company is high.
	FP8	Customer loyalty in our company is high.

. They are rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The selected measurement scales ensure construct validity, reliability, and comparability with existing studies, thereby enhancing the robustness of the research model.

3.3. Survey Administration and Data Collection

This study employed a self-administered questionnaire, which was meticulously translated from English to Chinese to ensure clarity and accessibility for managers from Chinese EV brands. To further enhance comprehension, the survey was presented in both languages, accommodating the target respondents during the data collection phase.

Before initiating the formal data collection process, the survey underwent a rigorous evaluation to establish content validity and reliability through two key phases: expert review and pilot study. First, the researchers consulted three managers from the Chinese automotive industry and two academic experts from Chinese universities to assess the questionnaire’s clarity, relevance, and applicability. Second, a pilot test was conducted with 40 respondents from a Chinese automobile brand company that was not part of the primary research target. The results of the pilot study confirmed the reliability and effectiveness of the questionnaire, ensuring the efficiency of the data collection process.

The empirical data for this study were collected from the top nine Chinese automobile brand companies with EV brands, identified based on their production and sales volumes in 2023. The data collection was conducted in two main stages. In the first stage, the online questionnaire was distributed via a secure online survey platform, targeting employees in managerial positions within these firms. In the second stage, for companies with lower initial responders, the researchers conducted on-site visits to personally invite managerial personnel to complete the questionnaire. This dual approach contributed to a relatively high and diverse response rate across different levels of management.

A total of 294 responses were received during the survey period. To ensure data accuracy and reliability, the rigorous data screening procedures were conducted, ultimately retaining 261 high-quality and valid responses for final analysis. The sample included participants from various management levels—senior, middle, and frontline—as well as a range of functional departments such as R&D, procurement, marketing, and operations. All participants voluntarily completed the questionnaire, and anonymity was ensured in accordance with ethical research guidelines.

4. Results

4.1. Demographic Profiles

The collected data were analyzed using SPSS 26, and the demographic characteristics of the 261 valid respondents are summarized in

Table 1. Demographic Profiles.

Variables	Category	Frequency	%
Gender	Male	177	67.8
	Female	84	32.2
Age	Under 25 years	21	8
	26–35 years	64	24.5
	36–45 years	153	58.6
	Over 46 years	23	8.8
Educational level	Below undergraduate	25	9.6
	Undergraduate	187	71.6
	Postgraduate	41	15.7
	Ph.D. graduate and above	8	3.1
Position level	Top manager	15	5.7
	Middle manager	59	22.6
	Under the middle manager	187	71.6
Department	Marketing department	48	18.4
	R&D department	67	25.7
	Purchasing department	61	23.4
	Production and manufacturing department	31	11.9
	Human resource department	9	3.4
	Finance department	6	2.3
	Others	39	14.9

Note, N = 261.

. The demographic profile reveals a workforce primarily consisting of male employees (67.8%), while female respondents comprise 32.2% of the sample. Regarding age distribution, most participants are aged 36–45 years (58.6%), followed by 26–35 years (24.5%), while smaller proportions are over 46 (8.8%) or under 25 (8.0%). With respect to educational background, the majority hold an undergraduate degree (71.6%), while 15.7% have a postgraduate degree and 3.1% have attained a Ph.D. or higher. Only 9.6% have an education level below undergraduate. Regarding position level, 71.6% of respondents belong to the category of “under the middle management”, which includes frontline and first-line managers who are responsible for overseeing specific tasks, teams, or business units. An additional 22.6% of respondents hold middle management roles (e.g., department heads), and 5.7% occupy top-level managerial positions. This classification aligns with common management hierarchies within Chinese automotive enterprises. The R&D department has the highest representation (25.7%), followed by purchasing (23.4%) and marketing (18.4%). Production and manufacturing (11.9%), human resources (2.3%), and finance (2.3%) have relatively lower participation. Additionally, 14.9% of respondents work in other unspecified departments.

Although managerial levels and departmental backgrounds may lead to differences in strategic insight and operational focus, this study’s measurement constructs are designed to capture perceptual evaluations rather than objective or confidential company data. Therefore, respondents across different managerial levels and functional departments can provide valuable and valid input. The inclusion of diverse perspectives—ranging from frontline and middle managers to senior executives—offers a more holistic understanding of strategic practices and firm performance. This diversity not only enhances the comprehensiveness of the empirical data but also improves the overall robustness and generalizability of the research findings.

To analyze the collected data and test the hypotheses, this study employed PLS-SEM using SmartPLS v.4 software. The data analysis process comprises two key components: (1) measurement model assessment and (2) structural model assessment [28].

4.2. Measurement Model Evaluation

According to Hair et al. [28], the evaluation of a reflective measurement model follows four sequential steps: (1) assessing indicator reliability, (2) assessing internal consistency reliability, (3) assessing convergent validity, and (4) assessing discriminant validity. These evaluations are conducted using the PLS-SEM algorithm, as illustrated in Figure 2.

4.2.1. Indicator Reliability, Internal Consistency Reliability, and Convergent Validity

Indicator reliability is evaluated using outer loadings (standardized loading factors), which measure the strength of the relationship between each indicator and its respective construct [28,29]. The general thresholds for outer loading values are 0.7 or higher, which is considered ideal as it indicates strong reliability and validity of the indicator [28]. Outer loading values between 0.50 and 0.70 are still acceptable in empirical research [29,35].

Internal consistency reliability evaluates the degree of coherence among multiple indicators measuring the same construct. This reliability is commonly assessed using Composite Reliability (CR) (including rho_a and rho_c) and Cronbach’s Alpha (α), following established recommendations [28]. Higher CR values indicate greater reliability. Values between 0.70 and 0.90 are considered satisfactory to good, whereas values exceeding 0.95 may indicate redundancy or measurement issues [28,36]. Cronbach’s Alpha follows the same thresholds as CR [28].

Convergent validity assesses the extent to which multiple indicators of a construct are correlated and jointly explain its variance. It is evaluated using Average Variance Extracted (AVE) [28]. The minimum acceptable AVE threshold is 0.50, indicating that the construct explains at least 50% of the variance in its indicators [28,36].

Table 2. The Result of Indicator Reliability, Internal Consistency Reliability, and Convergent Validity.

Constructs	Items	Outer Loadings ≥0.5	Cronbach’s Alpha ≥0.7	CR (rho_a) ≥0.7	CR (rho_c) ≥0.7	AVE ≥0.5
CLS	CLS1	0.818	0.854	0.871	0.900	0.693
	CLS2	0.834
	CLS3	0.844
	CLS4	0.833
DS	DS1	0.677	0.747	0.761	0.839	0.567
	DS2	0.736
	DS3	0.778
	DS4	0.815
FP	FP1	0.805	0.910	0.917	0.928	0.617
	FP2	0.783
	FP3	0.786
	FP4	0.800
	FP5	0.821
	FP6	0.785
	FP7	0.858
	FP8	0.628
VCaI	VCaI1	0.683	0.882	0.901	0.910	0.632
	VCaI2	0.640
	VCaI3	0.834
	VCaI4	0.864
	VCaI5	0.869
	VCaI6	0.846
VCrI	VCrI1	0.776	0.800	0.819	0.857	0.503
	VCrI2	0.808
	VCrI3	0.703
	VCrI4	0.563
	VCrI5	0.683
	VCrI6	0.697
VDI	VDI1	0.773	0.879	0.881	0.908	0.623
	VDI2	0.770
	VDI3	0.759
	VDI4	0.822
	VDI5	0.799
	VDI6	0.812
VPCI	VPCI1	0.811	0.911	0.912	0.931	0.694
	VPCI2	0.825
	VPCI3	0.824
	VPCI4	0.897
	VPCI5	0.843
	VPCI6	0.795
VRI	VRI1	0.766	0.872	0.879	0.904	0.611
	VRI2	0.855
	VRI3	0.761
	VRI4	0.760
	VRI5	0.761
	VRI6	0.781
VSI	VSI1	0.774	0.885	0.886	0.913	0.635
	VSI2	0.821
	VSI3	0.805
	VSI4	0.839
	VSI5	0.758
	VSI6	0.783

presents the results of indicator reliability, internal consistency reliability, and convergent validity for the constructs used in this study. The table includes outer loadings, Cronbach’s Alpha, CR (rho_a and rho_c), and AVE values for each construct.

The criteria of indicator reliability, internal consistency reliability, and convergent validity have been met due to the following variables. First, all factor loadings for the measurement items in the model exceed the minimum acceptable threshold of 0.50 and remain below 0.95, indicating satisfactory indicator reliability. Second, Cronbach’s Alpha and CR (rho_a and rho_c) values for all constructs exceed 0.70, demonstrating strong internal consistency reliability. Third, AVE values for all constructs exceed 0.50, confirming robust convergent validity, as each construct accounts for a substantial proportion of variance in its respective indicators.

4.2.2. Discriminant Validity

Discriminant validity evaluates whether a construct is empirically distinct from other constructs in the structural model [28]. It is assessed using two primary methods: the Heterotrait–Monotrait (HTMT) ratio and the Fornell–Larcker criterion. An HTMT value exceeding 0.90 suggests a lack of discriminant validity, indicating potential construct overlap [29,36]. The Fornell–Larcker criterion requires that a construct’s AVE square root be greater than its squared inter-construct correlations, thereby confirming its distinctiveness [28,36].

Table 3. HTMT and Fornell–Larcker Criterion.

	CLS	DS	FP	VCaI	VCrI	VDI	VPCI	VRI	VSI
CLS	0.832	0.519	0.527	0.436	0.359	0.467	0.506	0.288	0.267
DS	0.440	0.753	0.737	0.660	0.783	0.588	0.661	0.621	0.695
FP	0.486	0.628	0.786	0.517	0.771	0.764	0.725	0.714	0.700
VCaI	0.390	0.544	0.482	0.795	0.662	0.637	0.482	0.545	0.621
VCrI	0.310	0.620	0.673	0.573	0.709	0.740	0.714	0.679	0.785
VDI	0.411	0.491	0.690	0.580	0.627	0.789	0.581	0.669	0.708
VPCI	0.458	0.547	0.670	0.457	0.620	0.525	0.833	0.562	0.687
VRI	0.259	0.524	0.643	0.475	0.575	0.592	0.511	0.781	0.710
VSI	0.242	0.568	0.634	0.558	0.671	0.629	0.622	0.630	0.797

Note: Diagonal bolded values represent square roots of AVEs; below the diagonal are correlations between constructs; above the diagonal are HTMT values.

presents the HTMT values (above the diagonal) and Fornell–Larcker results (diagonal, bolded values as AVE square roots; below the diagonal as correlations between constructs).

As shown in Table 3, all HTMT values remain below 0.90, while the diagonal AVE square roots surpass their respective inter-construct correlations, confirming robust discriminant validity. This indicates that all constructs in the research model are empirically distinct and do not exhibit substantial conceptual overlap.

4.3. Structure Model Assessment

The structural model estimates the relationships between constructs through a series of regression equations. However, strong correlations among predictor constructs can result in biased point estimates and standard errors, necessitating an assessment of potential collinearity concerns [28].

4.3.1. Assess Collinearity Issues of the Structural Model

To evaluate collinearity, the variance inflation factor (VIF) values were calculated based on the construct scores of the predictor variables in each regression equation within the structural model. According to established guidelines, a VIF value exceeding 5 suggests possible collinearity issues among the predictor constructs [28].

The outer model collinearity statistics are presented in

Table 4. The outer model Collinearity statistics.

	VIF <5		VIF <5		VIF <5		VIF <5
CLS1	2.018	FP6	2.406	VCrI5	1.514	VPCI6	2.666
CLS2	2.587	FP7	2.779	VCrI6	1.565	VRI1	2.179
CLS3	2.504	FP8	1.538	VDI1	1.838	VRI2	2.538
CLS4	1.852	VCaI1	2.323	VDI2	1.774	VRI3	2.135
DS1	1.436	VCaI2	2.150	VDI3	1.749	VRI4	1.938
DS2	1.536	VCaI3	2.300	VDI4	2.493	VRI5	2.513
DS3	1.537	VCaI4	3.250	VDI5	2.286	VRI6	2.260
DS4	1.659	VCaI5	3.264	VDI6	2.141	VSI1	2.013
FP1	2.798	VCaI6	2.661	VPCI1	2.101	VSI2	2.358
FP2	2.502	VCrI1	1.684	VPCI2	2.919	VSI3	2.078
FP3	2.592	VCrI2	1.837	VPCI3	2.594	VSI4	2.347
FP4	2.620	VCrI3	1.479	VPCI4	3.592	VSI5	1.800
FP5	2.497	VCrI4	1.250	VPCI5	3.024	VSI6	1.951

, where all VIF values are below 5, indicating the absence of multicollinearity concerns at the indicator level. Similarly, the inner model collinearity statistics presented in

Table 5. The inner model collinearity statistics.

	VIF <5		VIF <5
CLS → FP	1.543	DS → VDI	1.240
CLS → VCaI	1.240	DS → VPCI	1.240
CLS → VCrI	1.240	DS → VRI	1.240
CLS → VDI	1.240	DS → VSI	1.240
CLS → VPCI	1.240	VCaI → FP	1.889
CLS → VRI	1.240	VCrI → FP	2.594
CLS → VSI	1.240	VDI → FP	2.303
DS → FP	2.080	VPCI → FP	2.173
DS → VCaI	1.240	VRI → FP	1.974
DS → VCrI	1.240	VSI → FP	2.692

reveal that all VIF values remain below the threshold of 5, confirming that collinearity does not present a significant issue in the structural model. These results confirm that the estimated path coefficients remain reliable and unbiased, supporting the robustness of the structural model.

4.3.2. Hypothesis Testing

To assess the statistical significance and robustness of the structural model, PLS-SEM bootstrapping was performed with 10,000 resamples at a significance level of 0.05 (one-tailed). This non-parametric resampling method provides standard deviations, t-values, and confidence intervals, facilitating the assessment of path coefficient significance and hypothesis validation. The results of the bootstrapping procedure are presented in Figure 3.

The results of the hypothesis testing are summarized in

Table 6. The results of Hypothesis.

H	Path	β	SD	T	p	LLCI	ULCI
H1a	DS → VPCI	0.429	0.058	7.440	0.000	0.336	0.526
H1b	DS → VCrI	0.599	0.047	12.762	0.000	0.524	0.678
H1c	DS → VDI	0.385	0.059	6.573	0.000	0.291	0.486
H1d	DS → VCaI	0.462	0.050	9.247	0.000	0.379	0.545
H1e	DS → VSI	0.572	0.050	11.392	0.000	0.490	0.657
H1f	DS → VRI	0.509	0.051	9.883	0.000	0.425	0.594
H2a	CLS → VPCI	0.269	0.046	5.872	0.000	0.194	0.346
H2b	CLS → VCrI	0.047	0.057	0.815	0.207	−0.046	0.143
H2c	CLS → VDI	0.242	0.069	3.484	0.000	0.124	0.354
H2d	CLS → VCaI	0.186	0.057	3.294	0.000	0.093	0.280
H2e	CLS → VSI	−0.010	0.059	0.162	0.436	−0.104	0.091
H2f	CLS → VRI	0.035	0.069	0.503	0.307	−0.078	0.151
H3	DS → FP	0.155	0.058	2.694	0.004	0.058	0.247
H4	CLS → FP	0.151	0.042	3.577	0.000	0.083	0.221
H5a	DS → VPCI → FP	0.084	0.022	3.784	0.000	0.050	0.124
H5b	DS → VCrI → FP	0.096	0.042	2.290	0.011	0.028	0.166
H5c	DS → VDI → FP	0.104	0.025	4.109	0.000	0.063	0.146
H5d	DS → VCaI → FP	−0.057	0.025	2.245	0.012	−0.098	−0.016
H5e	DS → VSI → FP	0.033	0.038	0.865	0.193	−0.026	0.098
H5f	DS → VRI → FP	0.098	0.030	3.309	0.000	0.054	0.151
H6a	CLS → VPCI → FP	0.053	0.017	3.093	0.001	0.028	0.084
H6b	CLS → VCrI → FP	0.007	0.010	0.749	0.227	−0.008	0.024
H6c	CLS → VDI → FP	0.065	0.024	2.693	0.004	0.028	0.107
H6d	CLS → VCaI → FP	−0.023	0.012	1.916	0.028	−0.044	−0.005
H6e	CLS → VSI → FP	−0.001	0.005	0.103	0.459	−0.011	0.006
H6f	CLS → VRI → FP	0.007	0.014	0.480	0.316	−0.016	0.030

Note: β, Original sample; SD, Standard deviation (STDEV); T, T statistics (|O/STDEV|); p values, significance value; LLCI, Lower Limit Confidence Interval, 5%; ULCI, Upper Limit Confidence Interval, 95%.

, with a focus on statistical significance and confidence intervals. Hypotheses are considered supported if their path coefficient (β) is significant, the t-value exceeds 1.96, the p-value is less than 0.05, and the confidence intervals do not cross zero.

The results of the hypothesis tests in this study are analyzed as follows:

First, Hypotheses H1a–H1f, which posited that differentiation strategy positively influences all six dimensions of SBMI based on the 6V model, were fully supported. The findings indicate that differentiation strategy has a statistically significant positive impact on VPCI (β = 0.429, p < 0.05), VCrI (β = 0.599, p < 0.05), VDI (β = 0.385, p < 0.05), VCaI (β = 0.462, p < 0.05), VSI (β = 0.572, p < 0.05), and VRI (β = 0.509, p < 0.05). The fact that none of the confidence intervals cross zero confirms the significance of these relationships. This suggests that differentiation strategy is a critical driver of all dimensions of SBMI within China’s EV industry.

Second, Hypotheses H2a–H2f, which examined the influence of cost leadership strategy on the six dimensions of SBMI, received mixed support. The results indicate that cost leadership strategy positively influences VPCI (β = 0.269, p < 0.05), VDI (β = 0.242, p < 0.05), and VCaI (β = 0.186, p < 0.05). However, the influence of cost leadership strategy on VCrI (β = 0.047, p > 0.05), VSI (β = −0.010, p > 0.05), and VRI (β = 0.035, p > 0.05) was not statistically significant. These results suggest that, within China’s EV industry, while cost leadership strategy positively affects certain dimensions of SBMI, its influence is not consistent across all dimensions.

Third, Hypotheses H3 and H4, which posited direct positive influences of differentiation strategy and cost leadership strategy on firm performance, were both supported. The findings indicate that both differentiation strategy (β = 0.155, p < 0.05) and cost leadership strategy (β = 0.151, p < 0.05) have statistically significant positive direct effects on firm performance, highlighting the importance of both constructs in driving firm performance within China’s EV industry.

Fourth, Hypotheses H5a–H5f, which examined the mediating role of the six dimensions of SBMI in the relationship between differentiation strategy and firm performance, received mixed support. The results indicate that VPCI (β = 0.084, p < 0.05), VCrI (β = 0.096, p < 0.05), VDI (β = 0.104, p < 0.05), and VRI (β = 0.098, p < 0.05) mediate the relationship between differentiation strategy and firm performance. However, the mediating effect of VCaI (β = −0.057, p < 0.05) was negative, and the mediating effect of VSI (β = 0.033, p > 0.05) was not statistically significant. These findings suggest that, within China’s EV industry, while some dimensions of SBMI mediate the relationship between differentiation strategy and firm performance, others do not.

Finally, Hypotheses H6a–H6f, which examined the mediating role of the six dimensions of SBMI in the relationship between cost leadership strategy and firm performance, also received mixed support. The results indicate that VPCI (β = 0.053, p < 0.05) and VDI (β = 0.065, p < 0.05) mediate the relationship between cost leadership strategy and firm performance. However, the mediating effect of VCaI (β = −0.023, p < 0.05) was negative, and the mediating effects of VCrI (β = 0.007, p > 0.05), VSI (β = −0.002, p > 0.05), and VRI (β = 0.007, p > 0.05) were not statistically significant. These results suggest that, within China’s EV industry, while some dimensions of SBMI mediate the relationship between cost leadership strategy and firm performance, others do not.

R² measures the variance explained in each of the endogenous constructs, and it is also referred to as the predictive power in the sample [29,37]. Generally, R² values of 0.67, 0.33, and 0.19 are considered substantial, moderate, and weak, respectively [29]. However, a value of R² greater than 0.2 can be considered high in the behavioral sciences [38]. The R² values in this study are presented in

Table 7. The Results of R² Values.

	β	SD	T	p	LLCI	ULCI
FP	0.697	0.029	24.434	0.000	0.660	0.755
VCaI	0.324	0.045	7.232	0.000	0.258	0.406
VCrI	0.386	0.049	7.884	0.000	0.315	0.476
VDI	0.288	0.047	6.146	0.000	0.222	0.376
VPCI	0.358	0.056	6.439	0.000	0.276	0.459
VRI	0.276	0.047	5.807	0.000	0.209	0.366
VSI	0.322	0.050	6.386	0.000	0.250	0.417

Note: β, Original sample; SD, Standard deviation (STDEV); T, T statistics (|O/STDEV|); p values, significance value; LLCI, Lower Limit Confidence Interval, 5%; ULCI, Upper Limit Confidence Interval, 95%.

.

Table 7 shows the R² values for firm performance and the six dimensions of SBMI (VPCI, VCrI, VDI, VCaI, VSI, and VRI), reflecting the explanatory power of the model. The results indicate that 69.7% of the variance in firm performance is explained by the predictors (R² = 0.697), demonstrating strong explanatory power. Among the SBMI dimensions, VCrI (R² = 0.386) exhibits the highest explained variance, followed by VPCI (R² = 0.358), VCaI (R² = 0.324), and VSI (R² = 0.322), suggesting that these dimensions are significantly influenced by the independent variables. Meanwhile, VDI (R² = 0.288) and VRI (R² = 0.276) also show meaningful levels of explained variance. Overall, these findings confirm the robustness of the model in explaining firm performance and SBMI, reinforcing the critical role of differentiation strategy and cost leadership strategy in driving business model innovation and enhancing firm performance.

5. Discussion and Conclusions

This research empirically investigates the mediating role of SBMI in the relationship between competitive strategies—specifically, differentiation strategy and cost leadership strategy—and firm performance within the Chinese EV industry. By applying the 6V model and analyzing the full value chain innovation, this study provides critical insights into how competitive strategies and SBMI drive corporate success in a rapidly evolving market.

5.1. Discussion

5.1.1. Relationship Between Competitive Strategies and Sustainable Business Model Innovation

BMs reflect the competitive strategy implemented by a company [18]. Based on this premise, this study hypothesized that differentiation strategy would positively influence SBMI based on the 6V model. However, the findings reveal interesting patterns.

The results indicate that differentiation strategy exerts a robust and positive influence on all six dimensions of the SBMI (VPCl, VCrl, VDl, VCal, VSl, and VRl). In contrast, cost leadership strategy has a mixed impact; while it significantly enhances dimensions such as VPCI, VDI, and VCaI, it does not significantly affect VCrI, VSI, and VRI. These findings suggest that cost leadership strategies may not uniformly drive innovation across all aspects of SBMI. This aligns with Albayraktaroglu [39], who found that the relationship between the underlying elements of strategic agility and BMI is highly complex and, at times, reciprocal, with strategic agility and BMI switching roles in the causal relationship.

Moreover, although Clauss et al. [19] found that strategic agility positively influences value proposition, value creation, and value capture innovations, this research reveals that differentiation strategy is more effective in stimulating these innovation dimensions than cost leadership strategy within Chinese EV brand companies. This discrepancy may be attributed to the unique characteristics of the EV industry; for instance, many aspects of the EV value creation process are shared with traditional fuel vehicles, which have long benefited from extensive standardization and automation, limiting the potential for further innovation under a cost leadership strategy.

Additionally, this research’s inclusion of the VSI and VRI dimensions further demonstrates that differentiation strategy positively impacts these areas, whereas cost leadership strategy does not yield significant effects. It appears that while operational efficiency and cost minimization remain important, they might not be sufficient to stimulate comprehensive innovation in areas critical for long-term sustainability, such as value creation, after-sales services value, and residual value. Future research can delve deeper into these industry-specific dynamics to better understand the mechanisms underlying SBMI in different competitive contexts.

5.1.2. Relationship Between Competitive Strategies and Firm Performance

The empirical findings of this research reinforce the well-established relationship between competitive strategies and firm performance, as originally posited by Porter. In this study, both differentiation strategy and cost leadership strategy exhibit significant direct positive effects on firm performance of Chinese EV brand companies, aligning with earlier empirical evidence from Islami et al. [24], Pulaj et al. [23], and Akan et al. [22].

Notably, our data indicate that, within the context of China’s (EV) industry, the impacts of differentiation strategy (T = 2.694) and cost leadership strategy (T = 3.577) on firm performance differ, but remain remarkably similar. This suggests that both strategies are equally critical for driving corporate success in this dynamic and competitive market. In practical terms, these findings remind managers of Chinese EV brand companies that relying solely on low cost—often manifested through aggressive pricing strategies—may not be sufficient to secure long-term competitive advantage. Instead, companies should also invest in product and service differentiation to meet evolving consumer demands and to build robust brand equity.

The balanced effect of both strategies implies that EV companies can benefit from a dual approach that leverages cost efficiencies while simultaneously differentiating their offerings to create unique value propositions. This dual strategy can help mitigate the risks associated with intense price competition and foster sustainable growth in an industry characterized by rapid technological advancements and shifting regulatory landscapes.

5.1.3. The Mediating Role of Sustainable Business Model Innovation Between Competitive Strategies and Firm Performance

The findings of this research indicate that SBMI plays a critical mediating role in translating competitive strategies into enhanced firm performance. This is consistent with prior studies that have demonstrated the mediating function of BMI in enhancing firm performance. For instance, Bhatti et al. [13] found that BMI mediates the relationship between knowledge absorptive capacity, organizational agility, top management mindfulness, and firm performance in IT firms in Pakistan. Similarly, Jatra and Giantari [25] found that innovation strategy mediates the relationship between market orientation and business performance, while Clauss et al. [19] provided empirical support for the mediating role of BMI between strategic agility and firm performance. Collectively, these studies underscore BMI’s crucial role as an intermediary mechanism that converts various organizational capabilities and strategic orientations into improved performance outcomes.

Extending this discourse, this research further disaggregates the mediating effects of SBMI based on the 6V model into its six dimensions—VPCI, VCrI, VDI, VCaI, VSI, and VRI—to examine their differential mediating roles in the relationship between competitive strategies to firm performance within the Chinese EV industry. The results reveal that the mediating effects vary across these dimensions.

Specifically, VPCI and VDI demonstrate significant positive mediating effects on the relationship between both differentiation and cost leadership strategies and firm performance. In contrast, VCrI and VRI significantly mediate the relationship between differentiation strategy and firm performance, but their mediating effects in the context of cost leadership are not statistically significant. Furthermore, VSI does not significantly mediate the relationship between either competitive strategy (differentiation or cost leadership) and firm performance. Conversely, VCaI exhibits a negative mediating effect on the relationship between both strategic orientations and firm performance, suggesting that an excessive focus on value capture may actually hinder performance improvements. This result is consistent with findings by Clauss et al. [19] and Zang et al. [3], who also reported a negative impact of VCaI on firm performance, providing a plausible explanation for the observed negative mediation effect.

These nuanced insights challenge the assumption of uniform SBMI effects and underscore the necessity of adopting a dimension-specific perspective. Within China’s EV industry, where competition hinges on balancing technological differentiation (e.g., battery innovation, autonomous driving) and cost efficiency (e.g., scale economies, supply chain optimization), the results emphasize that strategic success depends on selectively activating SBMI dimensions aligned with organizational priorities.

5.2. Contributions

This research, by examining the Chinese EV industry, provides robust empirical evidence regarding the mediating role of SBMI in the relationship between competitive strategies and firm performance. It makes a significant contribution to the growing body of literature on competitive strategies, SBMI, and firm performance. Unlike traditional research that either focuses exclusively on the direct relationship between competitive strategies and firm performance or treats BMI as a monolithic mediating variable, this study refines the analysis by disaggregating SBMI into its six distinct dimensions based on the 6V model. This dimension-specific approach not only enhances our theoretical understanding of how different aspects of SBMI drive performance outcomes but also offers nuanced insights for managers in the EV industry.

5.2.1. Theoretical Implications

This research makes several important contributions to the existing body of competitive strategic management and SBMI literature. First, it demonstrates that SBMI serves as a critical mediating mechanism linking competitive strategies to firm performance. By disaggregating SBMI into six distinct dimensions based on the 6V model, this research offers a more nuanced understanding of BMI than previous studies that treated BMI as a monolithic construct. This dimension-specific approach challenges the notion of a uniform effect and aligns with recent findings that underscore the complex interplay between strategic and innovation [19,39]. Furthermore, by integrating sustainability into Porter’s framework [10] of competitive strategies, this research provides a comprehensive model that captures how strategic initiatives can be effectively transformed into sustainable competitive advantages in the context of the Chinese EV industry.

5.2.2. Practical Implications

From a managerial perspective, the findings of this study offer several actionable insights for practitioners in the EV industry. First, the evidence that both differentiation and cost leadership strategies have significant direct positive effects on firm performance suggests that EV companies should adopt a balanced strategic approach. Managers should not rely exclusively on low-cost tactics; rather, they must also invest in product and service differentiation to build robust brand equity and meet evolving consumer demands. Second, the results indicate that specific dimensions of SBMI, such as VPCI and VDI, play a pivotal role in mediating the effects of competitive strategies on performance. This implies that managers should prioritize initiatives that enhance these innovation dimensions to drive long-term growth. Finally, the mixed mediating effects observed for dimensions such as VCrI and VRI caution managers to tailor their innovation efforts to the specific context of their operations rather than adopting a one-size-fits-all approach. Moreover, the negative mediating effect observed for VCaI underscores the importance of avoiding an overemphasis on cost-cutting. Instead, firms should strive to balance efficiency with innovative practices that foster comprehensive improvements across the entire value chain. Collectively, these insights can guide strategic planning and resource allocation, enabling EV companies to navigate competitive pressures and rapidly evolving market conditions more effectively.

5.3. Limitations and Future Research

While this study offers valuable insights into the mediating role of SBMI in the relationship between competitive strategies and firm performance within the Chinese EV industry, it is not without limitations. Recognizing these limitations is essential for contextualizing the findings and guiding future research directions.

First, this research focuses exclusively on the Chinese EV industry, which is shaped by unique regulatory, technological, and market conditions—such as government subsidies and strong policy-driven development. These industry-specific factors may limit the generalizability of the findings to other sectors or geographic contexts. Future research should explore the applicability of the proposed framework in different industries and countries to enhance the external validity and robustness of the conclusions.

Second, the data employed in this study are cross-sectional in nature, capturing organizational perceptions at a single point in time. This design restricts the ability to draw causal inferences or to observe how the mediating effects of SBMI may evolve as firms or industries transition through different life cycle stages. For example, the negative role of VCaI may evolve as the industry transitions from growth to maturity, thus necessitating temporal analysis. Future studies could adopt longitudinal designs, which would provide deeper insights into how competitive strategies and SBMI influence firm performance over time.

Third, the study relies on self-reported data from firm managers and executives, which may be subject to perceptual or social desirability bias. While appropriate steps were taken to ensure data quality and relevance, future research could benefit from employing more objective measures, such as financial performance data or third-party evaluations, to validate the findings.

Fourth, this study adopts a quantitative approach using survey data and structural equation modeling to examine SBMI’s mediating role. Incorporating qualitative methods in future studies could help uncover deeper mechanisms and managerial interpretations behind SBMI practices.

Finally, although the sample includes respondents from various departments and managerial levels, the model results are not disaggregated by these demographic factors. Given that strategic perception and innovation insights may vary across functional areas or organizational hierarchies, future research may consider stratified sampling or multi-group analysis to examine potential perceptual differences and further validate the framework’s robustness across internal stakeholder groups.

5.4. Conclusions

This study empirically investigates the mediating role of SBMI in the relationship between competitive strategies—specifically, differentiation strategy and cost leadership strategy—and firm performance within the Chinese EV industry. By applying the 6V model to conceptualize SBMI across the full value chain, this study offers valuable insights into how firms can leverage strategic positioning and innovation to enhance performance in a rapidly evolving and sustainability-driven market.

The results indicate that differentiation strategy consistently and positively influences all six dimensions of SBMI, whereas cost leadership strategy exerts a mixed impact—significantly enhancing certain dimensions while leaving others unaffected. Furthermore, both strategies demonstrate significant direct positive effects on firm performance, with SBMI acting as a critical mediator that translates these strategies into improved performance. Notably, the disaggregation of SBMI into its six constituent dimensions reveals that each dimension plays a distinct mediating role in linking competitive strategies to firm performance.

These findings contribute to the literature by extending classical competitive strategy theory through the integration of sustainability-oriented BMI. The study enriches theoretical understanding by offering a dimension-specific view of SBMI’s mediating function and advances practical knowledge by highlighting the need for firms to adopt a balanced strategic approach that combines cost efficiency with innovation-led differentiation. Such an approach enables organizations to achieve a more sustainable competitive advantage.

Future research could build upon these findings by examining the proposed framework in different industrial and geographic contexts to assess its generalizability. Longitudinal research designs are also encouraged to capture temporal dynamics in the relationships between strategy, SBMI, and performance. Moreover, disaggregated analyses based on managerial levels or functional departments could further uncover how internal organizational roles influence perceptions of strategy implementation and innovation effectiveness.