An Empirical Study on the Impact of Key Technology Configurations on Sales of Battery Electric Vehicles: Evidence from the Chinese Market

Abstract

In the global automotive industry’s transition towards electrification and intelligence, the influence of key technology configurations of battery electric vehicles (BEVs) on consumer purchasing decisions and market sales has become increasingly prominent. This paper empirically investigates the impact of BEVs’ key technology features—specifically, driving range, Advanced Driver-Assistance Systems (ADASs), and intelligent cockpits—on sales, with a particular focus on the interaction effect between ADAS score and price. Employing panel data from the Chinese market spanning January 2023 to March 2025, this study analyzes 783 observations across 29 models and 13 brands using a multilevel mixed-effects model (MEM). The results indicate that driving range and intelligent cockpit score (ICS) are significantly and positively associated with sales growth, whereas price has a significant negative effect. More importantly, a significant interaction effect exists between the ADAS score and price, which implies that the impact of ADASs on sales varies across different price levels. Specifically, in lower-priced models, a high ADAS score corresponds to a decrease in sales, while its effect trends toward positive in higher-priced models. Furthermore, a high ADAS score significantly reduces consumers’ price sensitivity.Compared with prior macro-level studies, our contribution is jointly quantifying (i) the main effects of range and ICS and (ii) a price-contingent ADAS effect within a model-within-brand MEM, revealing that higher ADAS scores attenuate price sensitivity in premium segments. These findings offer actionable guidance for configuration bundling and pricing across market segments.

1. Introduction

In recent years, with the growing severity of global climate change and the environmental pressures from fossil fuel consumption, BEVs have been recognized as a key pathway to achieving carbon peak and carbon neutrality goals, receiving strong policy support from countries worldwide [1,2]. Although governmental policies such as subsidies played a crucial role in the early cultivation of the BEV market [3], as these policies are adjusted and market mechanisms mature, the true consumer-pull effect, driven by the inherent advantages of a product’s technology configuration, is becoming the core factor in sustained market growth [4]. Therefore, an in-depth investigation into the quantitative relationship between different technology features and sales volume is of considerable theoretical and practical importance for enterprises seeking to optimize product development strategies, accurately target market demand, and enhance their core competitiveness.

The Global Electric Vehicle Outlook 2024 report indicates that global electric vehicle sales surpassed 17 million units, with the Chinese market accounting for over 11 million, representing nearly two-thirds of the global share. As the mainstream of NEVs, the market penetration of BEVs continues to climb in China [5], accounting for 27.8% of total passenger vehicle retail sales in 2024. Against the backdrop of the rapidly developing global BEV industry, market competition has intensified, with manufacturers engaging in multidimensional competition around technological innovation, cost control, and market share. The level of technology implementation has become a key determinant of a product’s market competitiveness [6]. With the accelerated pace of technological iteration and evolving consumer demands, EV manufacturers must seek an optimal balance between maintaining technological leadership and ensuring cost controllability to sustain a competitive market advantage [7].

As the market transitions from its initial development phase to maturity, consumer focus has shifted from the basic question of “whether it can meet travel needs” to “how it can better meet and enhance the travel experience”. In this context, key technology features such as driving range [8], ADASs [9], and intelligent cockpits [10] are no longer mere add-ons but have become key factors that directly influence consumer purchasing decisions and constitute a product’s core competitiveness. For instance, driving range directly addresses consumers’ “range anxiety” [11], while Advanced Driver-Assistance Systems (ADASs) and intelligent cockpits represent the future direction of automotive intelligence and connectivity [12], significantly enhancing driving safety and convenience. These technologies are no longer exclusive to high-end models but are gradually penetrating broader market segments. This shift reflects that intensifying competition compels manufacturers to attract consumers through product differentiation, rather than merely satisfying basic electrification needs [13].

Although many studies have investigated the effects of factors such as policy subsidies, charging infrastructure, and consumer preferences on BEV sales [14,15,16,17], a limitation of prior work is that the quantitative impact of key technology elements, particularly the complex interaction between these technologies and key market elements like price, remains an area requiring further exploration. Existing research often concentrates on the macro level or single factors [18,19,20], lacking a granular analysis of micro-level technology features and an exploration of their multidimensional impact mechanisms. Therefore, this study aims to provide new perspectives and empirical support for understanding consumer purchasing behavior, optimizing product strategies, and formulating targeted and evidence-based industry policies through an in-depth analysis of the Chinese BEV market.

To answer the aforementioned questions, this paper will first collect monthly panel data from the Chinese BEV market from January 2023 to March 2025 and define and measure core variables such as driving range, ADAS score, intelligent cockpit score (ICS), and price. Second, this study employs a multilevel mixed-effects model (MEM), which accounts for the nested data structure (models within brands) to control for unobserved individual heterogeneity. The model introduces an interaction term between the ADAS score and price to test its moderating effect. Finally, the model results are interpreted in detail, the marginal effects of the interaction are explored, and corresponding theoretical contributions and decision support are proposed. The remainder of this paper is organized as follows: Section 2 reviews the literature on the influencing factors of BEV sales and technology configuration-related studies, issues in previous research. Section 3 elaborates on the research design, including data sources, variable definitions, and model specification. Section 4 presents and analyzes the empirical results. Finally, the research findings are discussed, and theoretical and practical implications are proposed.

2. Literature Review

2.1. Influencing Factors on BEV Sales

The promotion and adoption of BEVs are an essential component of achieving sustainable development. The academic community has conducted extensive and in-depth discussions on the drivers of BEV sales. In summary, the factors influencing BEV sales can be categorized into macro-environmental and micro-product levels.

Macro-environmental factors include government policies [21], infrastructure development [22], and energy prices [23]. Policy support is widely considered the core driver of early BEV market growth; measures such as purchase subsidies [24], tax incentives [20], and licensing policies [25] significantly reduce consumer purchasing costs and stimulate market demand. The adequacy of charging infrastructure—such as the number, density, and speed of charging piles—directly affects the user experience [26], thereby promoting BEV adoption. Fluctuations in energy prices, particularly gasoline prices, also influence purchasing decisions, as rising gasoline prices highlight the economic advantages of electric vehicles.

At the micro-product level, price, driving range, and consumer preferences are the main considerations. The high initial purchase cost is an obstacle to BEV popularization, but with advancements in battery technology and economies of scale, BEV prices are gradually becoming more reasonable [27]. Driving range, as a core performance metric for BEVs, is directly related to convenience of use; its improvement has effectively mitigated consumers’ “range anxiety” and is a key factor driving sales. Furthermore, consumer concerns for the environment, their acceptance of new technologies, and their preferences for vehicle performance (e.g., acceleration, handling, and safety) also significantly affect BEV sales [28,29].

2.2. Vehicle Technology Configuration and Consumer Choice

Technological innovation is widely regarded as the core engine of progress in the BEV industry. It not only directly enhances product performance and quality but also indirectly promotes sales growth by reducing costs and increasing efficiency. In the automotive sector, consumer preferences for technology features are directly reflected in their purchase decisions. Skippon emphasized that significant improvements in vehicle performance, such as acceleration, top speed, and handling, are crucial for increasing consumer acceptance [30]. The study by Stockkamp et al. further pointed out that model diversity, increased driving range, and enhanced safety features collectively form an important basis for influencing consumer purchase intentions [31].

With the development of intelligent and connected technologies, consumer attention to vehicle technology configurations has extended from traditional power and safety to intelligence and connectivity [32]. As a key technology for enhancing driving safety and comfort, ADASs directly influence perceived value and purchase intentions based on their level and functionality [33]. The intelligent cockpit, which integrates human–computer interaction, in-vehicle connectivity, and infotainment functions, has profoundly changed the driving experience and has become a significant factor in attracting younger consumers [34].

Existing research has accumulated a wealth of findings on the influencing factors of BEV sales, providing a foundation for understanding market dynamics. Nevertheless, certain aspects remain underexplored. Firstly, although technological innovation is widely considered the core driver of BEV development, much of the existing research has focused on its overall impact or on macro-level technological progress [35,36], lacking a quantitative assessment of specific, segmented technology features (such as ADASs and intelligent cockpits) and their concrete impact mechanisms regarding sales. Particularly in the Chinese market, the pace of the development and consumer acceptance of these emerging technologies may have unique characteristics that require more precise analysis. Secondly, little attention has been paid to the interaction effects between technology features and key market factors such as price. As high-value durable goods, vehicles are subject to consumer trade-offs during purchase. The complex relationship of whether the value brought by technology can offset the price increase, and the demand intensity for high-end technology at different price levels, has not been fully revealed. For example, an ADAS may be a bonus feature in high-priced models, but in lower-priced models, its additional cost might instead weaken the product’s appeal. Therefore, this paper aims to, first, use the latest data from the Chinese BEV market to quantify the independent effects of driving range, ADAS score, and ICS on sales, and, second, focus on the interaction effect between ADAS score and price, to deeply analyze the moderating mechanism of this interaction regarding sales and price sensitivity. The findings of this study will provide more refined and targeted strategic recommendations for BEV manufacturers in product planning, technology roadmap selection, and marketing.

3. Materials and Methods

3.1. Data Sources and Variable Descriptions

The data for this study were sourced from the monthly sales data for battery electric vehicle (BEV) models from January 2023 to March 2025, provided by the China Passenger Car Association, and combined with technology configuration data released by manufacturers. To ensure the validity and quality of the research, models with severely missing key variables, those with persistently low sales, or those that had been discontinued were excluded. This resulted in a final panel dataset of 783 observations, covering 29 different models under 13 automotive brands. The unit of observation is monthly. The sample primarily covers mainstream models; emerging start-ups, micro-cars, and some luxury imports are less represented, so external validity beyond these segments should be interpreted cautiously.

The statistical characteristics of the relevant variables are presented in

Table 1. Variable definitions and descriptive statistics.

Category	Variables	Obs.	Mean	Std. Dev.
Dependent Variable	BEV sales (lnsales)	783	8.430	1.079
Independent Variables	Range (Range)	783	485.963	136.899
	ADAS score ($S_{\mathrm{ADAS}}$)	783	0.412	0.369
	Intelligent cockpit score (ICS)	783	0.310	0.164
Control Variables	Price (lnprice)	783	2.693	0.642
	Monthly dummy variables	783	N/A	N/A

Note: Monthly dummy variables represent a set of variables introduced to control for time fixed effects and are thus not shown in the table. As they are categorical indicators, their means and standard deviations are not applicable (N/A).

. The natural logarithm of sales and price was taken to reduce heteroscedasticity, make the data distribution closer to normal, and allow the regression coefficients to be interpreted directly as elasticities or semi-elasticities. The interaction term was centered to reduce multicollinearity between it and the independent variables, and to make the interpretation of the main effects more meaningful, i.e., their effect when the other variable in the interaction term is at its mean value. The sufficient variation exhibited by this dataset in terms of sales, price, and core technology features lays a solid data foundation for this study’s in-depth analysis of the consumer driving factors in the BEV market.

The core explanatory variables for this study are three key technology features: battery performance, intelligent cockpit, and advanced driver assistance. Figure 1 illustrates the indicator system used to evaluate these configurations. Battery performance is directly measured by the official China Light-Duty Vehicle Test Cycle (CLTC) comprehensive driving range (in kilometers) for each model. The intelligent cockpit is a system integrated from multiple technologies. This study selected five representative configurations as secondary indicators to evaluate its level of advancement: Head-Up Display (HUD), voice interaction, gesture interaction, Driver Monitoring System (DMS), and active noise cancellation (ANC) technology. To scientifically consolidate these discrete configurations into a single score, a questionnaire survey was conducted to collect users’ subjective evaluations of the importance of each feature. Based on this, weights for each secondary indicator were calculated, and an ICS reflecting the comprehensive technological level of the cockpit was constructed through weighted summation. Similar to the intelligent cockpit, ADAS capability is also defined by multiple functions. This study selected six core functions as secondary indicators: ADAS level, full-speed adaptive cruise control, automated lane change assist (ALCA), highway navigation-on-autopilot, city navigation-on-autopilot, and automated parking assist. Using the same questionnaire and weighted summation method as for the intelligent cockpit, the secondary indicators were synthesized into a comprehensive ADAS score (see Appendix A for details on the survey and weighting methodology).

Figure 2 illustrates the average monthly sales of BEVs in the dataset, which shows significant seasonal fluctuations and irregular shocks. These graphical features reflect a series of time-varying, unobserved factors that have a common impact on all samples, such as seasonal consumption habits, the macroeconomic environment, industry policies, and promotional activities. If not controlled for, these factors could confound the relationship with our core explanatory variables (e.g., technology configurations), leading to serious omitted variable bias. Therefore, employing month dummy variables effectively absorbs and removes these complex common time trends, allowing for a more precise isolation of the net effects of the core explanatory variables and ensuring the unbiasedness and consistency of the model estimates.

3.2. The Mixed-Effects Model

To systematically investigate the relationship between key technology configurations and the market sales of BEVs, and considering the characteristics of the research data, this study constructed a multilevel mixed-effects model. The choice of this model was based on the following considerations: First, the data have a clear hierarchical nested structure—vehicle models are nested within specific brands. Ignoring this structure could lead to an underestimation of standard errors and biased parameter estimates. Second, there may be a large amount of unobservable or unquantifiable inherent heterogeneity among different brands and models (such as brand reputation, design language, and channel capabilities, i.e., the effectiveness of their sales and distribution network), and a mixed-effects model can effectively control for these unobserved individual effects [37]. The two-level model is specified as follows:

Level 1:

$$\begin{array}{cc}\hfill ln\left({\mathit{Sales}}_{ijt}\right)& ={\beta }_{0ij}+{\beta }_1{\mathit{Range}}_{ijt}+{\beta }_2{S}_{{\mathrm{ADAS}}_{ijt}}+{\beta }_3{\mathit{ICS}}_{ijt}\hfill \\ \hfill & +{\beta }_{4}ln\left({\mathit{price}}_{ijt}\right)+{\beta }_5(c\_S_{{\mathrm{ADAS}}_{ijt}}\times c\_ln\left({\mathit{price}}_{ijt}\right))\hfill \\ \hfill & +\sum _{k=1}^K{\gamma }_k{\mathit{Month}}_{kt}+{\epsilon }_{ijt}\hfill \end{array}$$

(1)

Level 2:

$${\beta }_{0ij}={\beta }_0+{\mu }_i+{\nu }_{ij}$$

(2)

where i denotes the manufacturer (brand), j the vehicle model, and t the month. $ln\left({\mathrm{Sales}}_{ijt}\right)$ is the natural logarithm of sales. The intercept term ${\beta }_{0ij}$ is composed of a fixed intercept ${\beta }_0$ and random effects at the brand level (${\mu }_i$) and model level (${\nu }_{ij}$), allowing it to vary across groups. ${\beta }_1$ through ${\beta }_4$ are the fixed-effects coefficients for the main explanatory variables. ${\beta }_5$ is the coefficient for the interaction term between the centered ADAS score and the centered log of price. ${\mu }_i$ and ${\nu }_{ij}$ are the random effects, assumed to be normally distributed with a mean of zero. ${\gamma }_k$ is the coefficient for the month dummy variables used to control for time fixed effects. ${\epsilon }_{ijt}$ is the residual error term.

4. Results

4.1. Model Applicability Tests

Before conducting the core regression analysis, this study performed several diagnostic tests on the model’s applicability to ensure the reliability of the results.

4.1.1. Necessity Test for the Multilevel Model

Initially, the intraclass correlation coefficient (ICC) was calculated to assess the heterogeneity at the brand and model levels. The results showed that the brand-level ICC was 0.4034, indicating that 40.34% of the total variance in sales can be attributed to brand-level differences. The total ICC for brand and model levels (models nested within brands) was 0.7634, indicating that 76.34% of the total variance can be attributed to differences at the brand and model levels. Both ICC values are significantly greater than zero, strongly supporting the use of a multilevel mixed-effects model to handle the nested structure in the data [38]. Furthermore, a likelihood-ratio test was used to compare the mixed-effects model with random effects against an ordinary linear model without them. The result, ${\chi }^2\left(2\right)=725.55$, $p<0.001$, indicates that the mixed-effects model is significantly superior to the ordinary linear model.

4.1.2. Necessity Test for the Interaction Term

A likelihood-ratio test was conducted to compare the model with the interaction term against the model without it. The result, ${\chi }^2\left(1\right)=3.55$, $p=0.0596$, indicates that the inclusion of the interaction term improves the model’s fit at a 10% significance level. This provides evidence for the existence of an interaction between ADAS score and price that warrants further exploration.

4.1.3. Multicollinearity Test

A variance inflation factor (VIF) test was performed on the independent variables in the model to assess for multicollinearity. As shown in

Table 2. Collinearity analysis results.

Variable	VIF	1/VIF
lnprice	6.88	0.145
ICS	5.43	0.184
Range	4.92	0.203
Interaction Term	2.20	0.455
$S_{\mathrm{ADAS}}$	2.76	0.362
Monthly dummy variables	1.93–2.02	0.495–0.519

, the VIF values for all variables were well below the threshold of 10 (mean VIF = 2.35). This result indicates that the regression model does not suffer from serious multicollinearity, and the coefficient estimates for each variable are stable and reliable for subsequent analysis and interpretation.

4.2. Result Analysis

The estimation results of the mixed-effects model for the factors influencing BEV sales are presented in

Table 3. Mixed-effects model regression results.

Variables	Coefficient	Std. Error	z-Value	p-Value	95% CI
Range	0.0047	0.0014	3.39	0.001	[0.0020, 0.0074]
ICS	1.3895	0.4929	2.82	0.005	[0.4235, 2.3555]
lnprice	−1.3682	0.2671	−5.12	<0.001	[−1.8917, −0.8447]
$S_{\mathrm{ADAS}}$	−0.1471	0.1614	−0.91	0.362	[−0.4634, 0.1692]
Interaction Term	0.6982	0.3638	1.92	0.055	[−0.0148, 1.4112]

. The regression coefficient for Driving Range is positive and statistically highly significant. This indicates that, ceteris paribus, an increase in driving range is significantly and positively associated with BEV sales growth. The ICS also shows a significant positive impact, suggesting that the consumer demand for in-vehicle intelligence and connectivity is growing, making the intelligent cockpit an important arena for product differentiation.

The coefficient for the natural logarithm of Price is negative and statistically highly significant. This is entirely consistent with basic economic principles: price is a decisive factor in sales, with higher prices leading to lower sales. The large absolute value of this coefficient also reflects that consumers in the Chinese BEV market remain highly price-sensitive.

The main effect of the ADAS score alone is not significant ($p=0.362$). This suggests that simply improving the capabilities of an ADAS system does not directly or universally translate into sales growth. However, the interaction term between ADAS score and the logarithm of Price is significantly positive ($p=0.055$). This indicates that the effect of the ADAS score on sales is moderated by the price level. The positive interaction coefficient implies that, the higher the price, the greater the positive impact (or the smaller the negative impact) of the ADAS score on sales. These results clearly reveal that driving range and intelligent cockpit experience are currently universal drivers of sales. In contrast, advanced driver-assistance technology, while a hot topic in the industry, does not have a uniform appeal to consumers; its utility shows a markedly differentiated effect across different price segments of the market.

4.3. Marginal-Effects Analysis of the Interaction

To more intuitively understand the interaction between ADAS score and price, a further analysis was conducted on the marginal effect of the ADAS score as price changes, and on the price elasticity as the ADAS score changes.

4.3.1. The Price-Contingent Marginal Effect of ADAS Score

Figure 3 shows that the effect of ADASs on sales is strongly moderated by price; its direction and magnitude are entirely dependent on the vehicle’s market positioning. For economy models priced below the market average, an improvement in ADASs actually has a significant negative impact on sales. This reveals a “value mismatch”: in this price-sensitive segment, consumers may prioritize practicality and core functions (like range and space), viewing costly or elaborate ADAS features as an unnecessary financial burden. When the vehicle price is at the market average, the marginal effect of ADASs on sales is close to zero and statistically insignificant. This suggests that, in the most competitive mid-range market, ADASs have not yet become a decisive competitive advantage. Consumer preferences are varied, leading to its limited influence in purchase decisions. As the price moves into the premium segment, the technological value of ADASs transitions from negative to positive. High-end consumers show greater acceptance and expectation for advanced features. Here, a powerful and smooth ADAS experience not only helps justify the high price but also becomes a key element of technological appeal and luxury, creating a positive synergy with the vehicle’s premium positioning and effectively attracting target consumers. This result indicates that the impact of ADAS score on sales differs in direction and intensity based on price; it may be a “detractor” in the low-price market due to high costs, while its value is increasingly recognized in the high-price market.

4.3.2. The ADAS Score-Contingent Marginal Effect of Price on Lnsales

Figure 4 illustrates the trend of price elasticity as the ADAS score changes. It indicates that, the higher the level of a vehicle’s ADAS technology, the lower the sensitivity of its sales to price fluctuations. For models with basic ADAS configurations, sales are highly sensitive to price changes. Here, demand is significantly elastic, meaning even a small price increase could lead to a sharp decline in sales. This suggests that, for lower-priced models, consumers may be sensitive to the extra cost of a high-end ADAS, viewing it as an unnecessary premium. For vehicles with market-average ADAS capabilities, as the technology level approaches the mean, the negative impact of price, while still significant and elastic, begins to moderate. For technologically advanced models with ADAS scores significantly above the market average, a qualitative shift occurs. Consumer price sensitivity decreases substantially, and demand becomes inelastic. This means that, even if companies raise the prices of these high-tech models, the resulting decline in sales will be relatively mild. For top-tier models with the highest level of ADAS technology, the negative impact of price on sales can even become statistically insignificant, implying that, for the consumer segment pursuing the ultimate technological experience, price may no longer be a key consideration in their purchase decision.

5. Discussion

This study examined how key technology configurations shape consumer purchase decisions in the Chinese BEV market. Beyond the direct effects, the analysis highlights a dual, price-contingent role for ADASs and a technology-contingent moderation of price sensitivity. Together, these patterns indicate that the market value of a high-end ADAS is not intrinsic but defined by the price segment that a model occupies, while higher ADAS levels systematically dampen the elasticity of demand with respect to price. These insights complement the positive associations observed for driving range and ICS and the negative association for price.

From a product strategy perspective, the results imply that positioning matters as much as the feature list. In budget segments, bundling a costly high-end ADAS may reduce the perceived “value for money”, crowding out attributes that consumers consider fundamental (e.g., usable range or core quality). Manufacturers could therefore prioritize essential safety automation and robust range in entry models, reserving premium ADAS suites for trims where consumers expect and reward them. In premium segments, the evidence supports using advanced ADASs as a differentiator that can sustain pricing power and brand premium; here, communicating experiential quality (smoothness, safety, integration with cockpit functions) may be more effective than purely enumerating functions. For channel execution, staggered OTA feature unlocks could align perceived value with willingness to pay across tiers, and pricing experiments can be structured around technology thresholds identified by the marginal-effect curves. Policymakers aiming to accelerate safe automation uptake may also consider segment-specific incentives that avoid regressive effects in the budget end of the market.

Consistent with prior observations in the Chinese market, China-focused evidence already indicates that longer driving range raises purchase likelihood, whereas price remains the dominant deterrent. For instance, a national stated-choice experiment quantifies substantial willingness-to-pay for range [39], and large-scale Beijing usage records link access to fast charging with reduced range anxiety and higher EV uptake [26]. Recent China-specific preference studies likewise report rising salience of smart and connected features alongside persistent price sensitivity and regional heterogeneity [40,41]; however, most treat automation additively and do not test whether its value depends on price position. By explicitly estimating a cross-level interaction between ADASs and price at the model–month level, our analysis shows that the ADAS–sales association reverses sign across segments and that higher ADAS levels systematically attenuate price elasticity—patterns not identified in prior China-market studies and directly actionable for trim bundling and pricing.

What is new here is the explicit quantification of (i) the price-contingent sign and strength of the ADAS–sales link (detractor in low-price segments, contributor in high-price segments) and (ii) the technology-contingent attenuation of price elasticity as ADAS levels rise. This mapping helps reconcile mixed narratives about “tech for tech’s sake” by showing that the same feature set can be either a perceived premium or a perceived penalty depending on segment. Methodologically, modelling cross-level interactions in a multilevel setting allows segment-conditioned inferences at the model–month level rather than relying only on aggregate correlations.

The scoring framework for ICS/ADASs enables a consumer-centric, comparable measure across heterogeneous spec sheets and ties naturally to marginal-effects interpretation. However, composite indices—by construction—compress heterogeneous user experiences (usability, software maturity, calibration) into single numbers; this may understate the role of perceived quality relative to the mere availability of functions.

Several constraints should be noted. First, the sample of models is limited and may under-represent emerging niches; generalization should therefore be cautious. Second, unobserved, time-varying brand-level factors (e.g., advertising intensity, dealer network breadth, online sentiment) are not directly modelled and could bias estimates despite month controls. Third, estimates are interpreted as associations rather than causal effects; simultaneity between price, trim content, and sales cannot be fully excluded. Future work should incorporate richer time-varying controls, exploit quasi-experimental variation (e.g., firmware releases, recall campaigns, policy shocks) for identification, and disaggregate ADASs into experience-salient bundles (perception, planning, control) to test which elements most reduce price sensitivity. Extending the analysis beyond China and across ownership regimes (private vs. fleet/ride-hailing) would clarify the external validity.

6. Conclusions

Driving range and ICS are positively associated with sales, while price is negatively associated. ADASs exhibit a price-contingent effect—detrimental in budget segments yet turning positive in premium segments—and higher ADAS levels attenuate consumer price sensitivity. These patterns imply that, where a model competes conditions how its technology is valued: in premium tiers, an advanced ADAS functions act as a value amplifier; in entry tiers, it risks value mismatch.

It would be beneficial to avoid costly, comprehensive ADAS bundles in budget segments, and prioritize core range and reliability, with modular pathways for later upgrades. In premium segments, it would be beneficial to highlight the experiential and safety benefits of a high-end ADAS and its integration with cockpit intelligence to support pricing power and differentiation.

Subsequent studies should add time-varying brand and channel covariates, validate the findings across markets and user groups, and, where feasible, employ quasi-experimental designs (e.g., instrumental variables or policy-driven differences-in-differences) to strengthen causal interpretation.