Who is inclined to buy an autonomous vehicle? Empirical evidence from California

Abstract

This study aims to investigate people’s perceptions and opinions on Autonomous Vehicles (AVs) and the key factors that influence their Behavioral Intention (BI) to purchase and use AVs. Data were sourced from the 2019 California Vehicle Survey to explore the determinants of AV purchase. A Structural Equation Model (SEM) of stated intentions is estimated to validate a theoretical framework drawn on relevant bodies of literature. The descriptive statistics show that many people are already aware of AVs. Many people also think that traveling by AVs is enjoyable, safe, and effective, although some of them would miss the joy of driving and would not entrust a driverless AV to shuttle their children. Results from the SEM indicate that being working-age adults, having children, household income, per capita income, and educational attainment are attributes positively associated with AV purchase intention. Similarly, psychological factors (e.g., perceived enjoyment, usefulness, and safety), prior knowledge of AVs, and experience with emerging technologies (e.g., electric vehicles) significantly enhance BI to purchase AVs. This study finds that family structure and psychological factors are the most influential factors of AV purchase intention, and more so than the built environment, transportation, and other socioeconomic factors.

Introduction

Connected and autonomous vehicles will reshape the automobile industry and recast how humans travel in cities in the near future (Acheampong and Cugurullo 2019; Losada-Rojas and Gkritza 2021; Sharma and Mishra 2022). Many high-tech and automobile companies are determined to introduce the new mobility option of Autonomous Vehicles (AVs) to modern societies (Kim 2018; Moorthy et al. 2017). It is estimated that the rapid progress in the research and development of the constellation of technologies that enable AVs will shepherd the rise of their share of the global private vehicle market to 25% by 2040 (Yuen et al. 2020). However, the impacts of AVs on peoples’ travel patterns, transportation systems, and physical and built environments are still largely unknown. Moreover, people’s acceptance of this new technology is essential for the successful distribution of AVs (Becker and Axhausen 2017; Mara and Meyer 2022; Shammut et al. 2023) and will condition the nature and scale of these impacts. Given the knowledge gap, this study aims to enhance our understanding of the key determinants of people’s propensity to purchase and adopt personal AVs, looking at a range of factors, including socioeconomic, demographic, transportation, technology, the built environment, and vehicle-specific (i.e., safety, convenience, usefulness) elements.

AVs may be capable of driving and navigating without direct human input by using sensing technologies (e.g., radar, Global Positioning System (GPS), and computer vision) and various advanced control systems based on artificial intelligence (Narayanan et al. 2020). The adoption of AVs would benefit people and society by reducing energy use, parking demand, vehicle ownership, travel costs, congestion, and by increasing traffic safety, convenience, accessibility, and roadway capacity, despite some potential drawbacks such as high travel demand, empty VMT, and vehicle cost, breach of personal privacy, system failure, and urban sprawl (Bansal et al. 2016; Rahman and Thill 2023a; Sparrow and Howard 2017). According to the Society of Automotive Engineers (SAE) (SAE International 2018), AVs have six levels of autonomy ranging from Level 0 (No autonomy to assist drivers or replace drivers to control the vehicle) to Level 5 (Full autonomy). Many new cars are already equipped with cameras and sensors to avoid potential crashes (Kim 2018; Van Brummelen et al. 2018). Researchers have predicted that Level 5 AVs would be available commercially in the 2020s to 2030s (Litman 2017; Trommer et al. 2018). However, most benefits of AVs will become prominent in the 2050s to 2060s when these vehicles would be common and affordable (Litman 2017). In this study, we will investigate the determinants of Level 5 AV buying.

Despite tremendous advances in research and development, the implementation of this novel technology is still in its infancy and the presence of AVs on public roads is yet to materialize. Consequently, most people have very limited knowledge of AVs, which could curb the introduction of AVs and slow their widespread availability. Previous studies have provided conjectures about people’s perceptions and AV purchase intentions, without clearly indicating the role of shared mobility on the purchase and use of AVs (Hinda Salum et al. 2022). Moreover, there is significant uncertainty in the rate of adoption of this novel technology to which potential users have had very limited exposure so far (Rejali et al. 2023). Findings on the behavioral intention of people to adopt AVs and on associated socioeconomic, urban, and technological factors are far from conclusive at this time and have not fully accounted for the complex interplay between personal preferences and influences from the broader community and socio-spatial environment. Thus, it is timely to study the factors that influence people’s intentions to purchase and use AVs. The following research questions frame this study:

• 1) What are the perceptions, opinions, and expectations of people about AVs (Bansal and Kockelman 2018; Rahimi et al. 2020; Schoettle and Sivak 2014b)?

• 2) How would people’s socioeconomic and demographic characteristics influence BI to purchase AVs for their travel purposes (Nazari et al. 2018; Nordhoff et al. 2020; Rahimi et al. 2020)?

• 3) How would awareness of AVs, and perception of their convenience, comfort, and safety influence people’s BI to purchase and use AVs (Bansal et al. 2016; Kapser and Abdelrahman 2020; Nordhoff et al. 2020)?

• 4) How would factors of the built environment, transportation, and technology influence people towards purchasing and using AVs for meeting their travel demand (Bansal et al. 2016; Gurumurthy and Kockelman 2020; Laidlaw et al. 2018)?

This study uses data from the 2019 California Vehicle Survey (Transportation Secure Data Center 2019); by design, the sampling scheme of this household survey includes responses from actual users of electric vehicles. This survey comprises data on opinions and perceptions of people about self-driving vehicles through a 12-question survey instrument, which allows to address some of the stated research problems. The research questions of this study are partially imposed by the scope of these secondary survey data. Additionally, they are formulated to achieve our objectives which have been conceived after a comprehensive literature review.

The current study significantly contributes to literature by conducting empirical research in California. The main contributions are fivefold. First, the paper critically reviews the state-of-the-art literature on the people’s acceptance of AV and presents a succinct synopsis of the key findings. Second, the paper proposes a conceptual framework based on the findings from the literature and on existing behavioral theories to investigate the factors that influence people’s BI to adopt and use AVs. Third, thanks to a comprehensive and multivariate empirical base, the study evaluates key socioeconomic and demographic, built environment, transportation, and technological factors that influence the adoption of AVs by applying a structural equation model (SEM). Fourth, it clearly indicates the role of shared mobility on AVs purchase and use. Finally, the paper identifies relevant avenues for future research by identifying remaining gaps in literature.

The rest of the paper is organized as follows: Section Two discusses findings from the relevant literature, presents the theoretical framework, and outlines the hypotheses of the study. The research design is presented in Section Three. The main results of our analysis are reported in Section Four. Section Five articulates the discussion of these results. Section Six concludes the study and points to directions for future research.

Literature review and theoretical framework

Synopsis of literature

A considerable number of empirical studies have evaluated the factors that influence people’s decision to purchase and use AVs (Rahman and Thill 2023b). A summary of the findings from the extant literature is presented in Table 1. In substance, the intention of consumers to purchase and use AVs is strongly influenced by their socio-economic and demographic features. For example, working-age adults, elderly and disabled persons, males, married persons, Asians, Hispanics, or Latinos, people with bachelor’s education, high income earners, people with children, residents of single-family house, townhouse, duplex, triplex, and vehicle owners are more interested in purchasing and adopting AVs. Similarly, prior knowledge of AVs and their features positively influences people to purchase and use AVs. In contrast, being Black or African Americans, with educational attainment limited to high school, having low household income, in a household without private vehicles, and possessing a driver’s license all reduce the propensity towards AV purchase and adoption.

Besides a variety of user attributes, psychological and social factors also affect AV adoption tendency. For example, people’s perception of AVs’ usefulness, ease of use, perceived benefits, trustworthiness, safety, the influence of their social networks, their engagement with technology, their desire to drive less, and their environmental leaning and lifestyle increase the willingness to purchase and use AVs. On the other hand, the perceived risk associated with AV technologies, people’s technological anxiety and traditional values versus altruistic values negatively affect the tendency of people to purchase and use AVs. Researchers have mentioned that different psychological and social factors can explain 43.70–76.00% of people’s intention to adopt AVs (Kapser and Abdelrahman 2020; Panagiotopoulos and Dimitrakopoulos 2018; Rahman and Thill 2023b). Thus, various psychological and social factors significantly influence people’s BI towards AVs, and more so than socioeconomic and demographic, built environment, transportation factors, and institutional settings.

Table 1 Concepts describing AV ownership in the literature

People’s preference for AVs also depends on the context provided by their built environment (e.g., density, land-use diversity). For example, high population and employment density, mixed land use, and short travel distance to destinations increase people’s willingness to use AVs. Additionally, urban residents are more interested in AVs than rural ones.

Furthermore, the rate of AV adoption depends on various transportation factors (e.g., travel mode, distance, and time) and institutional supports. For example, people who mostly use public and active transportation and ride-sharing services are interested in using shared AVs, while people who drive to work are more interested in owning personal AVs. People’s affinity towards new technologies also influences them towards AVs. In this respect, people are interested in purchasing and using vehicles that are equipped with cutting-edge technologies (e.g., automated speed control, braking and parking, collision warning, blind-spot detection, lane-changing warning). In contrast, they are less likely to use AVs when they make shopping or recreational trips. Similarly, a high vehicle price reduces the willingness to purchase AVs. However, strict traffic regulations and active support from central and local governments (e.g., financial assistance, a price rebate, tax reduction, and subsidy, research and development, dedicated infrastructure, expert human resources, and organizational support) strengthen people’s intentions to purchase and use AVs.

Previous studies have used a variety of methodological techniques to evaluate the key determinants of AV use. Many have used statistical methods (e.g., descriptive statistics, ANOVA, Pearson correlation, factor analysis) to understand AV adoption scenarios in different study contexts (Clark et al. 2019; Penmetsa et al. 2019; Schoettle and Sivak 2014a; Xu and Fan 2019). Some studies also used a diversity of more advanced statistical and econometric models such as linear regression model, logit and probit models, seemingly unrelated model, mixed-integer programming, and SEM to explain the key factors of AV adoption and use (Bansal and Kockelman 2017; Castritius et al. 2020; Chaveesuk et al. 2023; Gkartzonikas et al. 2022; Kenesei et al. 2022; Zhang et al. 2018). The extant literature also demonstrates that researchers have mainly evaluated the impacts of people’s psychological factors on AV adoption intentions without accounting for some important internal (socio-economic and demographic features) and external factors (e.g., the built environment, transportation, and institutional aspects). Thus, concomitant effects are typically not treated, and control modeling strategies are not resorted to. Hence, many results may exhibit spurious relationships, which calls for further investigation that more effectively controls for the multiplicity of effects.

The following key findings can be drawn from the extant literature:

• 1. People’s socio-economic, demographic, and psychological factors are key determinants to mediate AV adoption intentions of households.

• 2. Prior basic knowledge on AVs and familiarity with AVs would significantly motivate people to adopt and use AVs.

• 3. Psychological and social factors have more predictive power in explaining people’s BI towards AVs than socioeconomic and demographic, built environment, transportation factors, and institutional settings.

• 4. Among the psychological factors, perceived usefulness, perceived trust, perceived ease to use, perceived risks, social influence, technology anxiety, and engagement in technology are the key factors that influence people’s AV adoption tendency.

• 5. The built environment, transportation features, and institutional aspects have a role in deciding the AV adoption intentions of households.

• 6. Previous studies have used various statistical methods, statistical and econometric models to evaluate the key determinants of AVs. However, integrative modeling frameworks handling a board variety of factors are seldom used, particularly where psychological aspects of AV adoption are concerned, disregarding some important internal and external factors in the process.

Theoretical framework

Several behavioral theories have been advanced to explain how human actions stem from the interaction of intentions, motivations, and attitudes within a particular choice context. These theories posit that human behavior is shaped by a range of internal (e.g., personal attitudes, norms) and external (e.g., incentives, institutional constraints, surrounding environment) factors (Adjei and Behrens 2012). Some well-established theories are presented hereafter.

The Theory of Reasoned Action (TRA) is a widely recognized model in social psychology that aims at explore the core determinants of individual BI towards an action (Ajzen and Fishbein 1980; Madden et al. 1992). According to TRA, BI for a specific action is jointly determined by one’s attitude (i.e., positive or negative) towards the behavior and by subjective norms (i.e., the influence of other people on behavioral action). Attitude towards a behavioral choice is determined by the user’s salient beliefs or information about the likelihood that engaging in a certain behavior has a consequence and leads to a specific outcome (Davis et al. 1989; Madden et al. 1992). Subjective norms are determined by individual normative beliefs (i.e., perceived expectation of the individual or their social group) and their drive to comply with these expectations.

Researchers have used the Theory of Planned Behavior (TPB) to investigate the factors that influence people’s travel mode choice behaviors (Bamberg 2006; Bamberg et al. 2003; Conner and Armitage 1998; Heath and Gifford 2002). They particularly investigated the psychological factors of travel mode choice. However, the surrounding physical environment (i.e., urban form) also influences travel behaviors. Ajzen (1985) first introduced the TPB theory based on TRA to investigate the influence of external factors on behavioral actions. The TPB explains that human behavior is dependent on the intention to alter one’s behavior (Morris et al. 2012). These intentions are influenced by attitudes, subjective norms, and perceived behavioral control measures (e.g., ability, opportunity, resources, skill).

The Technology Acceptance Model (TAM) is widely used to understand how users accept and use a technology (Lee et al. 2003; Zhang et al. 2020). Davis (1985) originally proposed it as an evolved variant of the TRA (Fisbein and Ajzen 1975). According to the initial version of TAM, users’ attitude is the main determinant to understand whether they will accept a technology or not. Defined as the positive or negative feelings of an individual about the performance of a technology, the Attitude Towards Technology (ATT) specifically depends on two major beliefs: Perceived Usefulness (PU) and Perceived Ease of Use (PEU) (Davis 1985; Davis et al. 1989). The PU of some technology is the degree to which it can enhance the task performance of the users. On the other hand, the PEU is defined as the degree to which the technology can reduce the physical and mental effort of users; it has a direct causal effect on PU. The model also demonstrates that external features (e.g., socio-economic features, nature of the behavioral outcomes, prior behavior, and persuasive communication) have direct effects on internal factors of PU and PEU. In parallel, these external features indirectly influence people’s attitude and belief by directly affecting PU and PEU. The model indicates that positive ATT and high PU significantly influence people to use technology. The earlier version of TAM includes core variables of user motivation (i.e., PU, PEU, and ATT) and outcome variables (i.e., BI, actual technology use) along with some external factors (Scherer et al. 2019).

Proposed theoretical framework for the factors affecting BI to use AV

Many previous studies have developed their conceptual framework based on TRA, TPB, and TAM or some extended version of them (Acharya and Mekker 2022; Chaveesuk et al. 2023; Farzin et al. 2023; Huang and Qian 2021; Kapser and Abdelrahman 2020; Panagiotopoulos and Dimitrakopoulos 2018). These behavioral theories largely focus on people’s internal factors and neglect external factors to assess people’s acceptance of AVs (Farzin et al. 2023; Gkartzonikas et al. 2023; Jing et al. 2020; Tan et al. 2022). For example, these traditional models rarely incorporate socioeconomic features of people, transportation, and built environment factors to estimate the determinants of AV purchase intention. Consequently, these studies may inadequately estimate the effects of psychological factors on acceptance of AVs, despite their crucial role in determining people’s AV adoption tendency. Thus, a comprehensive conceptual framework encompassing various internal and external factors such as socioeconomic and demographic, psychological, the built environment, transportation, and technological factors is essential to understand complex human behaviors in the decision making process towards AVs (Farzin et al. 2023; Golbabaei et al. 2020).

Based on the findings from the literature and on existing behavioral theories (e.g., TRA, TPB, and TAM), a theoretical framework dubbed the Integrated Technology Acceptance Model (ITAM) is proposed to investigate the factors that influence people’s BI to adopt and use AVs. The new model is more aligned with the updated version of TAM where Davis (1989) argued that PU and PEU have direct effect on BI, rather than ATT. Figure 1 shows the proposed ITAM featuring the behavioral control factors, objective factors, and people’s attitudes towards AVs that influence AV purchase and use intention of the people. It is expected that ITAM will guide researchers to understand the key drivers of AV adoption in the US and other parts of the world by considering different internal and external factors of AV purchase intentions. In the figure, the expected associations between internal and external factors, BI, and actual system use are depicted by positive (+) and negative (-) signs. Based on these associations, the impacts (?) of different factors on actual system use are determined.

Fig. 1

Integrated technology acceptance model (ITAM).

According to the ITAM, human BI towards actual AV use and purchase is directly influenced by behavioral control factors (e.g., socioeconomic and travel factors), objective factors (i.e., urban form), and psychological factors (i.e., PU and PEU). Additionally, the model indicates that the actual use of AVs also depends on the availability of novel technologies (e.g., EV, solar panel) and the affinity of people towards them. Socioeconomic factors also indirectly affect AV use by influencing objective factors, psychological factors, and the affinity of the people towards technology. Similarly, novel technologies indirectly influence actual AV use by mediating behavioral intentions of people.

Based on the conceptual framework of ITAM and on the in-depth review of the literature, a number of research hypotheses are formulated as follows.

a) Socioeconomic and demographic factors

• 1) Young, working-age adults, and households with children have a positive BI to purchase AVs (Hypothesis 1 (H1) (Panagiotopoulos and Dimitrakopoulos 2018; Piao et al. 2016).

• 2) Educational attainment is positively associated with BI to purchase AVs (H2) (Haboucha et al. 2017; Piao et al. 2016).

• 3) People with higher household income are more interested to purchase AVs compared to their counterparts (H3) (Bansal and Kockelman 2018; Bansal et al. 2016).

b) Travel factors

• 1) Preference for ride-hailing and ride-sharing services is negatively associated with BI to purchase AVs (H4) (Bansal et al. 2016; Krueger et al. 2019).

• 2) People who prefer public transport for their daily travel are less willing to purchase AVs (H5) (Krueger et al. 2019; Narayanan et al. 2020).

c) The built environment

• 1) High population and employment density and a high walkability score are positively associated with BI to purchase AVs (H6) (Bansal et al. 2016; Gurumurthy and Kockelman 2020).

• 2) Mixed land uses and short travel distance to workplaces are positively associated with BI to purchase AVs (H7) (Bansal and Kockelman 2018; Bansal et al. 2016).

• 3) People who live in neighborhoods with a higher rate of democratic supporters are interested to purchase AVs (H8) (Etienne 2021; Maranges et al. 2022).

d) Psychological factors

• 1) Perceived usefulness, safety, and effectiveness are positively related to BI to purchase AVs (H9) (Panagiotopoulos and Dimitrakopoulos 2018; Rahman et al. 2017).

• 2) People having familiarity with advanced automated technologies are likely to purchase AVs (H10) (Bansal and Kockelman 2018; Nazari et al. 2018).

• 3) Age, income, and education positively mediate people’s psychological attributes to purchase AVs (H11) (Panagiotopoulos and Dimitrakopoulos 2018; Rahman et al. 2017).

e) Technological development

• 1) Experience with alternative fuel vehicles (e.g., electric vehicles, hybrid electric vehicles, fuel cell vehicles) is positively associated with BI to purchase AV (H12) (Bansal and Kockelman 2018; Bansal et al. 2016).

• 2) Households’ preference for gasoline vehicles is negatively associated with BI to purchase AV (H13) (Bansal and Kockelman 2018; Bansal et al. 2016).

• 3) Working-age adults, high income, and education level mediate people’s technological preference to purchase AV (H14) (Nazari et al. 2018).

Research design

Data

To conduct this study, data are sourced from the 2019 California Vehicle Survey (CVS) undertaken by the California Energy Commission (Transportation Secure Data Center 2019). The survey collected data to assess transportation fuel needs and to provide key policy guidelines for transportation planning in California. It assessed consumer preferences for light-duty vehicles (i.e., personal and commercial) and Electric Vehicles (EVs). This survey collected economic and demographic data, vehicle information including vehicle and fuel types, vehicle choice information using a stated preference approach. Moreover, charging behavior, electricity rates, and main motivations for purchasing EVs were collected from the EV owners. Complementing this information, a total of 13 questions were articulated to know people’s perceptions, opinions, intentions, and motivation towards self-driving cars and ride-sharing facilities.

This study only analyzes the data collected from residential surveys part of the 2019 CVS. A total of 4,248 individuals aged 18 or above, including 718 EV owners, participated in an online-based residential survey. A stratified random sampling technique was used to collect data from each of six regions: San Francisco, Sacramento, Central Valley, Los Angeles, San Diego, and the Rest of California. Households were selected randomly by address at the county level and invited to participate in the survey in such a way to ensure that samples are proportional to the population in each county. The descriptive statistics of the sampled respondents and households are presented in Table 2 and compared with population statistics of the general population of California. The population statistics drawn from the American Community Survey 2015–2019 5-year average data (US Census Bureau 2019). The survey exhibits an oversampling of households with senior respondents in contrast to the general population. However, there is a slight underrepresentation of low income and zero-car households, whereas households residing in single family housing units are overrepresented in the data. Despite the small deviation of the CVS sample from the 2015–2019 ACS population estimates, the sample is deemed adequately representative for statistical model building with control for several socio-economic and demographic features (Xiao and Goulias 2022).

Table 2 Description of the variables

Other data sources include the American Community Survey (US Census Bureau 2019), Environmental Protection Agency (Environmental Protection Agency 2020), and California State Association of Counties (California State Association of Counties 2019). Aggregated at the county level, these data were combined with the 2019 CVS data. Finally, the data were processed (i.e., missing value imputation with the median values, creation of new variables from the original data) and analyzed to test the research hypotheses. A detailed description of the variables used in the study is presented in Table 3.

Table 3 Description of the variables

Some variables require further elaborations. Mixed land use measures the diversity in population count and land-use areas in census block groups (Environmental Protection Agency 2020). Since the residence of respondents is reported in the survey at the resolution of the county, the county is the spatial unit of interest in the study and the county median value is used and related back to each survey response. It uses Shannon’s entropy measure, which ranges from 0 to 1, where 0 denotes the uniformity of attributes across county units and 1 denotes extreme diversity.

The Walkability Index (Walkability) indicates the likelihood or feasibility of walking for utilitarian purposes (Environmental Protection Agency 2020). This composite index is created using four built environment measures, namely the population and land-use entropy measure mentioned earlier, a measure of employment diversity (also using the entropy principle), the street intersection density, and the distance to the nearest transit stop, which are all considered as supporting walking. Similar to the Mixed land use measure mentioned above, the county median values of Walkability, Employment density, and Daily VMT are used and related back to each survey response.

Tables 4 and 5 report the descriptive statistics of dependent and independent variables used for model building. Suffices it here to comment on the dependent variable. Asking the intentions to purchase an AV for household use, the survey found that 53.93% of respondents expressed interest in purchasing AVs (Table 5). In the sample, 8.97% of respondents self-identify as eager to be among the early customers who would purchase AVs, while 44.96% mentioned that they would wait and buy AV when AVs will be commonly in use. In addition, 46.07% of respondents would wait longer before purchasing an AV and even try to avoid buying an AV altogether.

Table 4 Descriptive statistics of continuous variables (N = 4,248)

Table 5 Descriptive statistics on dichotomous and polychotomous variables (N = 4,248)

Considering the enormous possibilities of AVs, many people are interested to adopt and use them in California. The California Department of Motor Vehicles (DMV) has already developed regulations and protocols for the manufacturers to follow during testing and before the deployment of AVs to encourage innovation and promote safety (Department of Motor vehicles 2021). It first permitted Nuro, a robotics company, to test AVs on public roads in 2017 and they received approval from DMV to deploy AVs for commercial use in the San Francisco Bay Area in December 2020 (Klar 2020). Nuro is already operating AVs in partnership with 7-Eleven to deliver convenience store products (Hawkins 2021). Currently, more than fifty robotics and automotive companies are permitted to test full AVs in California including Waymo and General Motors (Subin and Wayland 2021). It is expected that AVs will be common in California in a few years and people will use AVs for their daily travels.

Methods of analysis

A Structural Equation Model (SEM) is employed to find the factors that affect people’s BI towards AVs using the conceptual framework summarized in Fig. 1. SEM is commonly used in various fields to unveil complex relationships between dependent and independent variables by introducing mediators (Bayard and Jolly 2007; Irfan et al. 2020; Janggu et al. 2014). It has been found to be particularly effective in situations where collinearity among identified predictors is large. Instead of controlling for collinearity in regression style models using conventional approaches such as dimensionality reduction with principal components analysis or the use of a subset of uncorrelated predictors, it explicitly models interdependencies among predictors. As a powerful multivariate modeling technique, SEM combines various statistical tools, namely regression, factor analysis, and path analysis (Shen et al. 2016; Wang et al. 2016). The main strengths of SEM include (1) the modeling of intervening indirect effects of explanatory variables on response variables, (2) the estimation of total effects in addition to direct and indirect effects, (3) the estimation of the relationship between latent constructs and their manifest factors, and (4) the correction of measurement errors in all observed variables (Rahman et al. 2021; Acker et al. 2007). Moreover, SEM shows the structural model which represents the theoretical relationships between observed variables and latent constructs. It specifies the directional and potentially causal relationships among the variables based on theory or prior research.

We started by estimating an Exploratory Factor Analysis (EFA) to gauge the interrelationship between observed variables. The results of EFA are presented in the Appendix (Table A1). In Table A1, the highest loading of each observed variable is reported. However, loadings under 0.25 representing weak associations, these variables were excluded from the analysis. Factors with an eigenvalue (sum of squared loadings) greater than one are more effective at explaining the variance in the variables (Najaf et al. 2018). Additionally, the cumulative variation represents the total variation explained by the factors in the EFA model. As reported in Table A1, the eigenvalues and cumulative variance of extracted factors indicate model fitness and validity of the measurement model.

On the basis of the EFA and of existing concepts articulated in the extant literature, eight latent constructs are generated from the variables available in our dataset. A Confirmatory Factor Analysis (CFA) is used to validate this model. Finally, the relationships between the dependent, mediator, and independent variables are estimated with a path analysis after controlling for socioeconomic features. The model is calibrated with Mplus Version 7.4 (Muthén and Muthén 2017). The Weighted Least Squares Means and Variance Adjusted Estimator (WLSMV) approach is used to estimate the model and coefficients of the model given the ordinal nature of the dependent variable. The WLSMV uses the diagonal weight matrix to get the estimates, thus the residual tends to be closer to zero (Muthén 1997). This estimator is widely used when the responses are non-normal and exhibit a high level of skewness or kurtosis (Muthén 1993; Xiao and Goulias 2022). We chose this estimator due to its reduced biased and enhanced accuracy in estimating the factor loadings across various condition compared to other estimators such as a Maximum Likelihood (ML) estimator or the Maximum Likelihood Robust (MLR) estimator, as demonstrated by Li (2016). Several fit measures are used to verify the goodness-of-fit of the calibrated model.

Results

Calibrated model

The complete structure of the calibrated model based on the CFA and path analysis is given in Fig. 2. Some non-significant associations between latent constructs and outcome variables were excluded to achieve a robust final model. The final specification of the model consists of interactions between explanatory and response variables through some mediators. In Fig. 2, the rectangles represent the observed variables and circles indicate latent dimensions. It is worth mentioning that we also tested some important factors of the built environment (e.g., activity density, workers per household, percent of high wage workers, jobs within 45 min auto travel time), transportation factors travel behavior (e.g., gas price, percentage of workers who choose public transport to work), technological factor (e.g., experience of solar panels), and socioeconomic factors (e.g., per capita gross domestic product, household size) in the base model. However, to achieve the best-fit final model, we dropped them during the model calibration process. Several variables (e.g., population and employment density, land-use diversity, VMT, share of registered democrat supporters, per capita income) are log-transformed to linearize the relationships captured in the model.

The overall fit of the calibrated model is assessed with several fit indices (Table 6). All indices are within their acceptable range and thus satisfy the model requirements and confirm the model’s external validity (Hu and Bentler 1999; MacCallum et al. 1996; Rahman et al. 2020, 2021).

Table 6 Goodness-of-fit indices of the calibrated model

Fig. 2

Calibrated model with direct standardized effects. UR1: Log of Population density, UR2: Log of Democrat voter, UR3: Employment density, UR4: Walkability, UL1: Log of Mixed land use, UL2: Log of Daily VMT, FS1: Working adult, FS2: Young adult, FS3: Number of children, Aff1: Highly educated, Aff2: High income, Aff3: Log of Per capita income, RS1: Ride hailing use, RS2: Ride share availability, RS3: Ride share use, RS4: Public transportation, RS5: Public transport prevalence, TA1: PHEV vehicle, TA2: BEV vehicle, TA3: PFCEV vehicle, TE1: EV ownership TE2: Gasoline vehicle, PUS1: Enjoy traveling more, PUS2: Joy of driving, PUS3: Accept longer time, PUS4: Work more in AV, PUS5: Accompany children, and PUS6: Travel more often

Standardized direct effects on the intention to purchase AVs

The standardized coefficients of the calibrated SEM and the direction of modeled direct effects are given in Table 7. These coefficients indicate the direct connections between and among explanatory variables, response variables, and latent dimensions. Most impacts are significant at the P-value of 0.001, 0.01, or 0.05. However, some of the effects with a P-value over 0.05 are kept in the model to better and more comprehensively understand it and to demonstrate a complete set of relationships.

Table 7 Estimated standardized direct effects (N = 4,248)

Supported by the CFA and extant theories, eight latent dimensions are generated as predictors to complement two observed variables (AV familiarity and EV charger):

• 1) Family Structure: Working adult, Young adult, and Number of children.

• 2) Affluence: Highly educated, High income, Log of Per capita income.

• 3) Ride-share: Ride hailing use, Ride share availability, Ride share use, Public transportation, and Public transport prevalence.

• 4) Urban Structure: Log of Population density, Log of Democrat voter, Employment density, and Walkability.

• 5) Urban Layout: Log of Mixed land use, Log of Daily VMT, and Employment density.

• 6) Perceived Usefulness and Safety: Enjoy traveling more, Joy of driving, Accept longer time, Work more in AV, Accompany children, and Travel more often.

• 7) Tech Affinity: PHEV vehicle, BEV vehicle, and PFCEV vehicle.

• 8) Tech Experience: EV ownership and Gasoline vehicle.

We now proceed to discuss the estimated relationships between the dependent variable and each of its predictors, whether observed independent variables or latent dimensions, as well as select relationships among latent dimensions and between latent dimensions and exogenous independent variables. The analysis of results and following discussions are organized to investigate the research hypotheses identified in section Literature review and theoretical framework.

Family Structure: This exogenous latent dimension represents the demographic structure of the household. As reported in Table 7C, it has a positive association with AV ownership (0.20), after accounting for other factors, which indicates that households with working-age adults and children are more likely to purchase AVs. Their motivations to purchase AVs are grounded in state and federal incentives (e.g., a price rebate, tax reduction, and subsidy), research and development, conducive traffic regulations, and infrastructure, in addition to affinity to advanced technologies. The study also finds that family structure is positively associated with the perceived usefulness and safety of AVs, tech affinity, and tech experience. Households with working-age adults and children lean towards advanced technology and have experience with advanced transportation modes (e.g., EVs), and hence they value the convenience, usefulness, and safety features of AVs (Nordhoff et al. 2020; Piao et al. 2016; Webb et al. 2019).

Affluence: This exogenous latent factor denotes the prosperity of the household in the study context. It is positively associated with AV ownership (0.01), although not with statistical significance (P-value: 0.35), which suggests that people in wealthier areas or having reached higher educational attainment are more likely to purchase AVs. This echoes the findings reported in the literature (Bansal et al. 2016; Daziano et al. 2017; Rahimi et al. 2020). Affluence is also positively associated with the perceived usefulness and safety of AVs, tech affinity, and tech experience. Thus, prosperity in the household motivates people to adopt and experience advanced transportation options and thereby value the convenience, usefulness, and safety features of AVs despite higher purchase and operating prices. However, family composition has significant effects on AV purchase intentions of the households, in comparison to their affluence.

Ride-share: This latent dimension represents the availability and use of public transportation and shared mobility options (i.e., ride-hailing and ride-sharing) in the local area. SEM results (Fig. 2) show that ride-share has a direct negative effect on AV ownership (-0.05) that is statistically significant. Thus, people who have ride-share mobility options in their localities and use them for daily travel are less likely to purchase personal AVs. The calibrated model explains that a one-unit increase in ride-sharing services significantly reduces people’s AV purchase intentions by 0.05 unit, other things being equal. However, they could be interested in using the services of shared AVs (SAVs) motivated by convenience, by a willingness to share vehicles with fellow riders, and by concerns for reducing environmental degradation, congestion, and travel costs (Gurumurthy and Kockelman 2020; Krueger et al. 2016; Nazari et al. 2018).

Urban Structure: This endogenous latent dimension represents the patterns of the built environment and the extent to which it features traits strongly associated with urban living in the study context. The urban structure in California is characterized by high population and employment density, walkability, and a higher share of democrat supporters (Table 7). The calibrated model in Fig. 2 shows that urban structure has a tendency towards a negative effect on AV ownership (-0.01), which may suggest that people who live in urban areas with high population and employment density, walkability, and a democratic mindset are less likely to purchase AVs. The availability of good quality public transportation and ride-sharing services in the urban context may in fact discourage people from purchasing personal AVs. Moreover, they would have the option of using SAVs instead. It is to be noted that the effect of urban structure on people’s intention to purchase personal AVs is minimal and not statistically significant (P-value: 0.27).

Urban Layout: This endogenous latent dimension also represents the built environment of the study context, and it does so in ways that complement the dimension of urban structure. SEM estimation shows (Table 7) that the urban layout is positively associated with mixed land use and negatively associated with vehicle miles travelled. Thus, urban layout in the study context is distinguished by mixed land uses and low travel distance. Figure 2 indicates that urban layout has a weak positive association with AV ownership (0.01), which suggests that people who live in a neighborhood with a diversity of land uses and low travel distance may be inclined to purchase AVs, which points in the same direction as the extant literature (Laidlaw et al. 2018; Nazari et al. 2018). However, as for urban structure, the association is not statistically significant (P-value of 0.31). Thus, this factor of the built environment has no effective influence on people’s AV purchase intention.

Perceived Usefulness and Safety: This endogenous factor is the only latent dimension that encompasses various features (e.g., convenience, usefulness, safety) of AVs. From the results reported in Table 7, we find that people enjoy a less stressed travel experience (i.e., watching the scenery), make use of time by doing work or taking a rest, and accept longer travel time to ensure the safety of pedestrians and bicyclists when traveling by AV. Figure 2 reveals that the perceived usefulness and safety of AVs is positively associated with AV ownership (0.50). Thus, perceived enjoyment and usefulness (e.g., work, talking on the phone, reading, taking a rest) significantly influence the BI of people to purchase AVs. Similarly, the perceived reduced risk for pedestrians, bicyclists, kids, and themselves due to the low speed of AVs influence people to purchase AVs. On the other hand, fear of loss of control of one’s vehicle discourages people from purchasing AVs. Thus, those who enjoy driving are less likely to purchase an AV. The strength of the effect of perceived usefulness and safety indicates that this latent dimension has a greater role in deciding people’s BI to purchase AVs compared to socioeconomic features and to factors of transportation and of the built environment. The study findings are also in line with the literature (Kaye et al. 2020; Rahman et al. 2017; Yuen et al. 2020).

Tech Affinity: As reported in Table 7, this endogenous latent dimension is positively associated with the willingness of the respondents to consider PHEV, BEV, and PFCEV vehicles in their future purchase. Tech affinity is negatively associated with AV ownership (-0.06), which contradicts the extant literature (Rahimi et al. 2020; Wang et al. 2020a). This finding indicates that despite a higher tendency to use technology, many people would wait and observe the trend of AV adoption before going to buy this new technology due to risks and uncertainty associated with AVs (Zmud and Sener 2017). However, the association between tech affinity and AV ownership is not statistically significant (P-value: 0.16).

Tech Experience: This endogenous factor illustrates the prior experience of a household with owning or leasing an electric or hydrogen cell vehicle (e.g., HEV, PHEV, BEV, and FCV) and future intention to purchase gasoline vehicles. Table 7 indicates that tech experience is positively associated with AV ownership (0.24). Assuming everything is held equal, a one-unit increase in tech experience increases people’s BI to purchase AVs by 0.24 unit. People who have real experience with EVs and vehicles equipped with smart technologies are more interested to purchase AVs compared to conventional gasoline vehicles (Chen 2019; Shin et al. 2015). Thus, vehicles equipped with improved services for people and capable of enhancing safety, security, and personal privacy are attractive to people in their decision to adopt and use AVs (Daziano et al. 2017; Rahimi et al. 2020).

Figure 2 also indicates that people’s familiarity with AVs (AV familiarity) is positively associated with their BI to purchase AVs (0.11). People who have prior knowledge of AVs are more likely to purchase and use AVs compared to the people who have little knowledge or have never heard of AVs. In the survey, 57.33% of respondents had heard about AVs; hence it can be assumed that these people would be the first to purchase and use AVs. Thus, prior knowledge about AVs is considered one of the main factors that would influence people towards AVs, as mentioned in previous studies (Daziano et al. 2017; Feys et al. 2020; Laidlaw et al. 2018). Similarly, the availability of EV charging stations at the workplace is positively associated with AV ownership (0.02). The convenience afforded to people who have access to EV charging stations at their workplace enhances their likely to purchase and use AVs compared to their counterparts.

Standardized total effects on the intention to purchase AVs

For a full account of the reasons supporting household BI towards AVs, the total effects of latent dimensions considering both direct and indirect pathways are now discussed. In most instances, only direct effects exist, and these were explicitly listed in Table 7; Fig. 2. Family structure and affluence have both direct and indirect effects, which are reported in Table 8.

Table 8 Standardized total (direct and indirect) effects of latent factors on AV purchase

As specified by the calibrated model (Fig. 2), family structure and affluence are the only two latent factors that have indirect effects on people’s intention to purchase AVs by mediating urban structure, urban layout, tech affinity, tech experience, and usefulness and safety of AVs. Considering both direct and indirect effects, family structure has a total effect of 0.603 on people’s AV purchase intentions. Households with working-age adults and children would be the first to purchase and use AVs due to their experience and affinity to advanced technologies, convenience, usefulness and improved safety features of AVs, and their neighborhood selection. Similarly, affluence has a total effect of 0.011, consisting of direct (0.006) and indirect (0.005) effects. Better economic conditions and higher educational attainment in the study context increase the affordability of AV purchase. However, the magnitude of the effect is rather minimal.

After accounting for several built environment attributes, other socioeconomic features, and transportation factors, the family structure remains the most influential factor of AV purchase intention of households. Encompassing working-age adults and children, the family structure is thus the key consideration in households’ intention to purchase AVs. Considering direct effects (Table 7), Usefulness and Safety (0.50), Tech Experience (0.24), and AV familiarity (0.11) have significant total effects on household AV purchase intension. Thus, in addition to family structure, usefulness and safety features of AVs and people’s prior knowledge and experience with advanced technologies are salient factors to regulate household decisions to purchase and use AVs.

Discussion

The study shows that many people are already aware of AVs and of the services they provide, which is vital to increase the market penetration of AVs. A considerable number of people also think that traveling by AVs would be enjoyable, safe, and effective, although some would not dispatch an unsupervised AV to drop-off or pick-up their children due to insecurity and uncertainty concerns. Regardless of personal preference for driving, many people are interested to purchase AVs when they will be available to the public. Additionally, the California state government has already introduced regulations to enable the testing and operation of AVs on public streets. Thus, considering the enormous possibilities and favorable institutional support, many people would purchase and use AVs in California. However, adequate measures (e.g., easy to operate and navigate, onboard driver, sharing option, incentives, collaboration between state agency, tech, and automobile companies) need to be taken to motivate people to adopt and use AVs (Bazilinskyy et al. 2015; Feys et al. 2020; Wang et al. 2020a).

Results from the SEM indicate that households with more working-age adults and more children are more likely to purchase personal AVs when they are commercially available. Similarly, people living in areas with higher household and per capita income, and people with higher educational attainment have higher AV purchase BI. Considering both direct and indirect effects, family structure and affluence of the study context also influence AV purchase of the household by interceding urban structure, urban layout, tech affinity and experience, and usefulness and safety of AVs. However, the family composition has stronger effects on AV purchase intention than affluence. The results also show that the family structure remains the most influential factor after accounting for the built environment, other socioeconomic features, and transportation factors. Thus, controlling for other factors, the family structure is the key consideration in household intention to purchase AVs. Overall, the study supports the hypotheses that younger people, working-age adults, households with children, higher educational attainment, and higher household income have stronger BI to purchase AVs (Hypotheses 1, 2, and 3).

We also observed that people who are interested in public transportation, ride-hailing, and ride-sharing services and use them for daily travel purposes are unlikely to purchase AVs, which supports our hypotheses 4 and 5. Instead, these people demonstrate an interest in adopting SAVs for their daily commuting. Thus, appropriate initiatives should be implemented by transit agencies and other transport providers (i.e., Transport network companies) to provide SAV services for people who are driven to protect the environment and to ensure sustainable urban forms and transportation rather than count on them to embrace private AVs (Narayanan et al. 2020; Sparrow and Howard 2017). The integration of SAVs with public transit could be instrumental in solving the last-mile problem and in increasing transit ridership and reducing transportation costs (Moorthy et al. 2017; Sparrow and Howard 2017). Thus, SAVs should be introduced at a large scale to fully realize the benefits of AVs and eventually encourage people to have an AV sharable among household members.

Similarly, people who live in urban areas with high population and employment density, high walkability, and strong democratic leaning appear less inclined to purchase personal AVs due to their better access to public and shared transportation. On the other hand, mixed land use and vehicle travel distance encourage private AV purchase. These findings lend support to hypothesis 7, but not to hypotheses 6 and 8. However, the convenience features of AVs (e.g., resting, sleeping, enjoying the scenery and relaxing) may encourage people to live far from their workplaces. Thus, private AVs have the potential to increase urban sprawl (González-González et al. 2019; Meyer et al. 2017). Therefore, it is essential for policymakers to understand the potential side effects of private AVs and formulate policies to protect urban living and the environment.

Various psychological factors such as perceived enjoyment, usefulness, and safety significantly influence people’s BI to purchase AVs. In contrast, people who enjoy driving are less likely to purchase an AV by concern about not being in control of the vehicle anymore. Overall, the latent dimension representing people’s psychological understanding has the greatest direct effect on AV purchase intention compared to socioeconomic features, and the factors of transportation and the built environment. The study also observes that people who have prior knowledge on AVs would be the first to purchase and use AVs compared to the people who have little knowledge or have never heard of AVs. These findings support hypotheses 9, 10, and 11.

The study demonstrates that, in spite of having a higher affinity to technology, many people would wait and watch the trend of AV adoption before going to buy into this novel technology for themselves. However, according to many scholars, Americans would be the first adopters of AVs when they will be available on the road for person use. The study also observes that people who have experience with EVs, FCEVs, and advanced safety equipment onboard the vehicle are more interested to purchase AVs, which supports hypotheses 12 and 13. Finally, the structure and affluence of one’s family affect the tech affinity and experience of the household, which conforms with hypothesis 14.

Conclusions and future research agenda

This study significantly contributes to the literature by empirically investigating public perceptions and opinions on AVs and the salient determinants of household AV purchase intentions within a unified multivariate design. The ITAM conceptual framework proposed and used in this study can effectively identify key internal and external factors of AV adoption and use, which has been too often overlooked in previous studies at the risk of spurious conclusions. Future research can adopt this framework to understand key drivers of AV adoption around the world. Applying SEM, the study evaluated key socioeconomic and demographic, built environment, transportation, and technological determinants of AV adoption with appropriate control of covariates. Results from the SEM indicate that working-age adults, children, household income, per capita income, and educational attainment are positively associated with AV purchase intention. Similarly, psychological factors (e.g., perceived enjoyment, usefulness, and safety), prior knowledge of AVs, and affinity and prior experience of emerging technologies (e.g., electric vehicles) significantly influence BI to purchase AVs. The study clearly indicates that public transportation and shared mobility services have a significant role on AVs purchase and use which is rarely discussed in previous studies. This study found that family structure and psychological factors are the most influential factors of AV purchase intention, and more so than the built environment, other socioeconomic, and transportation factors.

The study findings can advise transportation agencies, professionals, stakeholders, and AV developers to formulate pertinent policy guidelines for designing and implementing AVs (Zou et al. 2022). Since many people are already aware of the usefulness and convenience of AVs, some effective measures could further increase people’s willingness to use AVs. For example, the availability of adequate low-cost SAVs can provide hands-on experience to people, enabling them to assess the anticipated benefits of AVs and consequently motivate people to adopt and use AVs (Bansal et al. 2016; Nazari et al. 2018). Ride-hailing and ride-sharing companies could be the pioneers to launch SAVs and let people gain real-world experience of this efficient and novel transportation mode. Extensive research and testing of AVs combined with public education campaigns demonstrating the benefits of AVs could outweigh perceived risks and uproot psychological barriers, and consequently increase the acceptance of AVs (Gkartzonikas et al. 2023; König and Neumayr 2017).

Nonetheless, the strengths of this study are tempered by some cautionary limitations. These limitations are the results of the unavailability of AVs in the real-world setting at a commercial scale, of the lack of consistent results in previous studies, of unsatisfactory study design and methodologies, and inadequate data collection, which all have the potential to affect study findings. While careful considerations have been taken to mitigate the effects of study limitations, we identify several pertinent research avenues to further the agenda on AV use in contemporary societies.

• 1) There is a lack of consistency in study results regarding people’s perceptions and opinions and the key determinants of AVs. Thus, willingness to use and to pay for AVs needs to be further studied in more diverse settings across the United States and other nations, to achieve a deeper and more contextualized understanding of personal, community, and societal factors of adoption and use of AV technologies as they evolve over time in response to emerging circumstances.

• 2) To estimate the effects of a household’s social and natural environment on their AV purchase and use, we used data at the county level, which is a coarse geographic unit. A finer granularity in the geographic unit should be used in future studies to get additional insights.

• 3) The dependent variable of the study represents household intentions to purchase AV and does not reflect responses of individual family members. Thus, it remains to fully capture the personal preference within the household as far as purchasing and using AVs is concerned (Wali et al. 2021). Thus, future research should be conducted at the level of individual household members to fully understand coupling constraints with households as well as preference heterogeneities.

• 4) This study primarily investigated the factors affecting household intention to purchase personal AVs. However, considering people’s use of public transportation and their interest in shared mobility options (e.g., carshare, bikeshare, ride-sourcing), there is a need to study the factors that influence the AV sharing tendency of people.

• 5) The impacts of different opportunities (e.g., low congestion, emission) and challenges (e.g., legal aspect, breach of privacy, system failure) related to AVs, and institutional arrangement (e.g., incentives, regulations) have not been evaluated, which requires further investigation.

• 6) It has been documented that AVs would increase the mobility of the elderly, children, and disabled persons. However, a study of AV adoption disparities among different income and racial groups is necessary to ensure justice and equity in transportation and formulate appropriate policy interventions to this end.

Appendix A

See Table A1.

Table A1 Factor loadings for exploratory factor analysis with varimax rotation