With the global emphasis on environmental protection and low-carbon development, the adoption of electric cars has become a critical strategy in the transportation sector. This study investigates how family structure differences influence consumers’ purchase intentions toward electric cars, utilizing a value-based adoption model. By analyzing factors such as perceived usefulness and perceived enjoyment, we explore the direct and indirect effects of situational and psychological factors on electric car adoption. The findings reveal that urban families, unmarried individuals, and households with multiple driver’s licenses prioritize the entertainment value of electric cars, while married couples with children and families with fewer cars focus more on practical utility. This research provides insights for targeted marketing strategies in the electric car industry.
The rapid growth of the electric car market underscores the importance of understanding consumer behavior. In 2022, global electric car production and sales saw explosive increases, highlighting a shift from policy-driven to market-led adoption. However, consumer decisions are often influenced by family dynamics, making it essential to examine how family structures affect purchase intentions. This study employs structural equation modeling to analyze data from 372 valid questionnaires, assessing the role of perceived value in electric car adoption. The results demonstrate that perceived usefulness and perceived enjoyment serve as key mediators, with family characteristics moderating these relationships. This approach helps identify potential electric car buyers and tailor strategies to diverse household needs.
Literature Review
Previous research on electric car purchase intention has focused on individual attributes, external factors, and psychological dimensions. Studies indicate that young consumers, particularly women, show higher willingness to adopt electric cars, while high-income groups prefer them over traditional vehicles. Factors such as charging infrastructure, government policies, and product performance significantly impact adoption. For instance, Cecere et al. (2018) found that performance improvements and price reductions are primary drivers of electric car purchase intention, with home charging availability being crucial for highly motivated consumers. Similarly, Ali et al. (2021) emphasized that environmental awareness and knowledge of electric car performance play vital roles in developing countries.
Family structure has gained attention as a determinant of consumer behavior. Traditional models of family decision-making have evolved, with modern households exhibiting diverse forms, such as single-parent or childless families. These differences affect how families evaluate products like electric cars. For example, Lin et al. (2018) noted that marital status influences collective decision-making in households, while Ma et al. (2013) highlighted that family car ownership and driver’s license numbers significantly impact electric car adoption. Despite this, few studies integrate family structure with the value-based adoption model to explain electric car purchase intentions.
The value-based adoption model (VAM) provides a framework for understanding how perceived value drives technology adoption. Kim et al. (2007) developed VAM by combining the technology acceptance model and perceived value theory, positing that consumers weigh perceived benefits against costs. In the context of electric cars, perceived usefulness refers to the practical advantages, such as improved efficiency and cost savings, while perceived enjoyment relates to emotional satisfaction, like the fun of driving an electric car. Research by Lee et al. (2021) confirmed that environmental concerns and perceived usefulness positively influence electric car adoption intentions. This study extends VAM by incorporating family structure as a moderating variable.
Theoretical Framework and Hypotheses
The theoretical model for this study is based on VAM, with perceived usefulness and perceived enjoyment as core mediators. Situational factors, including charging infrastructure and product performance, are hypothesized to affect perceived usefulness, while psychological factors, such as environmental consciousness and symbolic traits, influence perceived enjoyment. These perceptions, in turn, impact electric car purchase intention. Family structure differences—such as residence location, marital status, number of driver’s licenses, and car ownership—are proposed as moderators. The structural model can be represented as follows:
Let \( PU \) denote perceived usefulness, \( PE \) denote perceived enjoyment, and \( PI \) denote purchase intention. The relationships are expressed as:
$$ PU = \beta_1 CF + \beta_2 PP + \beta_3 PE + \epsilon_1 $$
$$ PE = \beta_4 EC + \beta_5 ST + \epsilon_2 $$
$$ PI = \beta_6 PU + \beta_7 PE + \epsilon_3 $$
where \( CF \) is charging facilities, \( PP \) is product performance, \( EC \) is environmental consciousness, and \( ST \) is symbolic traits. The coefficients \( \beta_1 \) to \( \beta_7 \) represent the path effects, and \( \epsilon \) denotes error terms.
Based on this, the following hypotheses are proposed:
- H1: Charging facilities have a positive effect on perceived usefulness of electric cars.
- H2: Product performance has a positive effect on perceived usefulness of electric cars.
- H3: Environmental consciousness has a positive effect on perceived enjoyment of electric cars.
- H4: Symbolic traits have a positive effect on perceived enjoyment of electric cars.
- H5: Perceived enjoyment has a positive effect on perceived usefulness of electric cars.
- H6: Perceived enjoyment has a positive effect on electric car purchase intention.
- H7: Perceived usefulness has a positive effect on electric car purchase intention.
- H8: Family structure differences moderate the relationships in the model.
These hypotheses are tested through empirical data analysis to provide a comprehensive understanding of electric car adoption.
Methodology
Data were collected via a questionnaire distributed in shopping malls, residential areas, and electric car dealerships. The questionnaire consisted of two sections: demographic information and scales measuring purchase intention factors. Demographic variables included family residence, marital status, income, number of driver’s licenses, and car ownership. The scales were adapted from established literature to ensure reliability and validity, with items rated on a Likert scale. After pretesting with 24 participants, the final questionnaire was administered, yielding 372 valid responses.
Descriptive statistics of the sample are summarized in Table 1. The sample comprised 59.95% urban households and 40.05% rural households. In terms of marital status, 47.85% were unmarried individuals, and 36.29% were married with children. Most respondents had household incomes between $10,000 and $20,000 annually, and over 80% possessed at least one driver’s license. Additionally, 62.64% had experience riding in an electric car, indicating familiarity with the product.
| Characteristic | Category | Frequency | Percentage (%) | Mean | Standard Deviation |
|---|---|---|---|---|---|
| Marital Status | Unmarried, living alone | 178 | 47.85 | 1.68 | 0.732 |
| Married with children | 135 | 36.29 | |||
| Other | 59 | 15.86 | |||
| Residence | Rural | 149 | 40.05 | 1.60 | 0.491 |
| Urban | 223 | 59.95 | |||
| Household Income | Below $10,000 | 80 | 21.51 | 2.19 | 0.875 |
| $10,000–$20,000 | 173 | 46.50 | |||
| $20,000–$30,000 | 86 | 23.12 | |||
| Above $30,000 | 33 | 8.87 | |||
| Driver’s Licenses in Family | 0 | 72 | 19.35 | 2.37 | 0.789 |
| 1 | 89 | 23.92 | |||
| 2 or more | 211 | 56.73 | |||
| Cars Owned by Family | 0 | 79 | 21.24 | 2.21 | 0.768 |
| 1 | 137 | 36.83 | |||
| 2 or more | 156 | 41.93 | |||
| Experience with Electric Car | Yes | 233 | 62.64 | 1.37 | 0.484 |
| No | 139 | 37.36 |
Reliability and validity were assessed using Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). As shown in Table 2, all constructs had Cronbach’s alpha values above 0.8, CR values exceeding 0.8, and AVE values greater than 0.5, indicating high internal consistency and convergent validity. Bartlett’s test of sphericity was significant at the 0.001 level, confirming the suitability for structural equation modeling.
| Variable | Composite Reliability (CR) | Cronbach’s Alpha | Average Variance Extracted (AVE) | Bartlett’s Test Significance |
|---|---|---|---|---|
| Charging Facilities | 0.906 | 0.904 | 0.709 | 0.981 |
| Product Performance | 0.902 | 0.899 | 0.697 | |
| Environmental Consciousness | 0.901 | 0.898 | 0.694 | |
| Symbolic Traits | 0.920 | 0.920 | 0.742 | |
| Perceived Usefulness | 0.880 | 0.874 | 0.709 | |
| Perceived Enjoyment | 0.876 | 0.866 | 0.702 | |
| Purchase Intention | 0.873 | 0.871 | 0.697 |
Results
The structural equation model was fitted using maximum likelihood estimation. Model fit indices, including CMIN/DF = 2.288, RMR = 0.023, NFI = 0.939, CFI = 0.965, PNFI = 0.820, PCFI = 0.842, and RMSEA = 0.059, indicated a good fit between the model and the data. Path coefficients were estimated to test the hypotheses, as summarized in Table 3.
| Hypothesis Path | Standardized Coefficient | Standard Error | Critical Ratio (C.R.) | P-value | Conclusion |
|---|---|---|---|---|---|
| H1: Charging Facilities → Perceived Usefulness | 0.291 | 0.118 | 2.740 | 0.006 | Supported |
| H2: Product Performance → Perceived Usefulness | 0.376 | 0.119 | 3.001 | 0.003 | Supported |
| H3: Environmental Consciousness → Perceived Enjoyment | 0.633 | 0.104 | 5.728 | <0.001 | Supported |
| H4: Symbolic Traits → Perceived Enjoyment | 0.363 | 0.093 | 3.342 | <0.001 | Supported |
| H5: Perceived Enjoyment → Perceived Usefulness | 0.365 | 0.085 | 4.547 | <0.001 | Supported |
| H6: Perceived Enjoyment → Purchase Intention | 0.644 | 0.161 | 3.883 | <0.001 | Supported |
| H7: Perceived Usefulness → Purchase Intention | 0.336 | 0.149 | 2.084 | 0.037 | Supported |
The results show that all hypotheses are supported. Charging facilities and product performance significantly positively affect perceived usefulness of electric cars, with product performance having a stronger effect (0.376 vs. 0.291). Environmental consciousness and symbolic traits positively influence perceived enjoyment, with environmental consciousness being more impactful (0.633 vs. 0.363). Perceived enjoyment directly enhances perceived usefulness and purchase intention, while perceived usefulness also directly boosts purchase intention. The total effect of perceived enjoyment on purchase intention is 0.840, with a direct effect of 0.434 and an indirect effect through perceived usefulness of 0.405, accounting for 49% of the total effect. This mediation effect was confirmed using bootstrap analysis with 5000 resamples (95% CI [0.328, 0.502]).
To test H8, multi-group analysis was conducted based on family structure variables. The path coefficients for different groups are presented in Table 4. Urban households showed significant effects of charging facilities on perceived usefulness (β = 0.373, p < 0.01) and perceived enjoyment on purchase intention (β = 0.692, p < 0.001), whereas rural households did not. Unmarried individuals exhibited significant effects of perceived enjoyment on purchase intention (β = 0.780, p < 0.001), while married couples with children had significant effects of perceived usefulness on purchase intention (β = 0.526, p < 0.05). Households with two or more driver’s licenses demonstrated significant impacts of product performance on perceived usefulness (β = 0.745, p < 0.001) and symbolic traits on perceived enjoyment (β = 0.622, p < 0.001). Families with one or fewer cars showed significant effects of charging facilities on perceived usefulness (β = 0.443, p < 0.05) and perceived usefulness on purchase intention (β = 0.464, p < 0.05), unlike families with more cars.
| Hypothesis | Rural Households | Urban Households | Unmarried Individuals | Married with Children | ≤1 Driver’s License | ≥2 Driver’s Licenses | ≤1 Car Owned | ≥2 Cars Owned |
|---|---|---|---|---|---|---|---|---|
| H1: CF → PU | 0.097 | 0.373** | 0.084 | 0.410** | 0.520** | 0.155 | 0.443* | 0.088 |
| H2: PP → PU | 0.529* | 0.408** | 0.616** | 0.289 | 0.117 | 0.745*** | 0.226 | 0.623*** |
| H3: EC → PE | 0.566** | 0.643*** | 0.783*** | 0.501*** | 0.859*** | 0.384** | 0.573*** | 0.705*** |
| H4: ST → PE | 0.418* | 0.365** | 0.177 | 0.508*** | 0.135 | 0.622*** | 0.422** | 0.279 |
| H5: PE → PU | 0.400*** | 0.264* | 0.397*** | 0.326* | 0.612*** | 0.245* | 0.362** | 0.385*** |
| H6: PE → PI | 0.441 | 0.814*** | 0.780*** | 0.456 | 0.211 | 0.805*** | 0.544* | 0.864*** |
| H7: PU → PI | 0.540 | 0.161 | 0.158 | 0.526* | 0.780* | 0.103 | 0.464* | 0.031 |
Note: CF = Charging Facilities, PP = Product Performance, EC = Environmental Consciousness, ST = Symbolic Traits, PU = Perceived Usefulness, PE = Perceived Enjoyment, PI = Purchase Intention. *p < 0.05, **p < 0.01, ***p < 0.001.
These findings highlight the moderating role of family structure. For instance, urban and unmarried consumers are more influenced by the entertainment aspects of electric cars, whereas married couples with children and those with fewer cars prioritize practical benefits. This suggests that marketing strategies for electric cars should be tailored to specific family profiles to enhance adoption.
Discussion
The study confirms that perceived usefulness and perceived enjoyment are critical drivers of electric car purchase intention, acting as mediators between external factors and adoption decisions. The positive effects of charging infrastructure and product performance on perceived usefulness align with previous research, emphasizing the importance of tangible benefits in electric car adoption. Similarly, environmental consciousness and symbolic traits enhance perceived enjoyment, reflecting the emotional and social dimensions of consumer behavior. The mediation analysis reveals that perceived usefulness partially explains the relationship between perceived enjoyment and purchase intention, underscoring the interconnectedness of practical and emotional values in the decision to buy an electric car.
Family structure differences significantly moderate these relationships. Urban households, with better access to charging facilities and greater exposure to electric car marketing, are more sensitive to entertainment values. In contrast, rural households may face infrastructure barriers, reducing the impact of perceived usefulness. Unmarried individuals, often younger and less burdened by family responsibilities, prioritize the fun and innovation associated with electric cars. Married couples with children, however, focus on reliability and cost-effectiveness, aligning with their practical needs. Households with multiple driver’s licenses may value electric cars for their symbolic status and performance, while those with fewer cars consider charging convenience and utility as paramount.
These insights have important implications for the electric car industry. Companies should segment markets based on family characteristics and design targeted campaigns. For example, promoting the environmental benefits and stylish design of electric cars could appeal to urban and unmarried consumers, while emphasizing performance and charging infrastructure might resonate with families. Policymakers can support this by investing in rural charging networks and offering incentives tailored to different household types. Future research could explore additional moderators, such as cultural factors, to further refine electric car adoption models.
Conclusion
This study demonstrates that family structure differences play a crucial role in shaping electric car purchase intentions through perceived value mechanisms. By integrating the value-based adoption model with multi-group analysis, we provide a nuanced understanding of how situational and psychological factors influence adoption across diverse households. The findings advocate for personalized marketing strategies and policy interventions to accelerate electric car adoption. As the electric car market continues to evolve, accounting for family dynamics will be essential for sustainable growth and environmental goals. Further studies should expand on these results by incorporating longitudinal data and cross-cultural comparisons to enhance generalizability.