Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis

Schandl, Franziska; Fischer, Peter; Hudecek, Matthias F. C. Article — Published Version Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis Transportation Provided in Cooperation with: Springer Nature Suggested Citation: Schandl, Franziska; Fischer, Peter; Hudecek, Matthias F. C. (2023) : Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis, Transportation, ISSN 1572-9435, Springer US, New York, NY, pp. 1-24, https://doi.org/10.1007/s11116-023-10447-4 This Version is available at: https://hdl.handle.net/10419/307488 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Transportation https://doi.org/10.1007/s11116-023-10447-4 1 3 Predicting acceptance ofautonomous shuttle buses bypersonality profiles: alatent profile analysis FranziskaSchandl1· PeterFischer1· MatthiasF.C.Hudecek1 Accepted: 13 November 2023 © The Author(s) 2023 Abstract Autonomous driving and its acceptance are becoming increasingly important in psychological research as the application of autonomous functions and artificial intelligence in vehicles increases. In this context, potential users are increasingly considered, which is the basis for the successful establishment and use of autonomous vehicles. Numerous studies show an association between personality variables and the acceptance of autonomous vehicles. This makes it more relevant to identify potential user profiles to adapt autonomous vehicles to the potential user and the needs of the potential user groups to marketing them effectively. Our study, therefore, addressed the identification of personality profiles for potential users of autonomous vehicles (AVs). A sample of 388 subjects answered questions about their intention to use autonomous buses, their sociodemographics, and various personality variables. Latent Profile Analysis was used to identify four personality profiles that differed significantly from each other in their willingness to use AVs. In total, potential users with lower anxiety and increased self-confidence were more open toward AVs. Technology affinity as a trait also contributes to the differentiation of potential user profiles and AV acceptance. The profile solutions and the correlations with the intention to use proved to be replicable in cross validation analyses. Keywords Autonomous driving· Artificial intelligence· Latent profile analysis· Autonomous vehicle acceptance· User groups Introduction In recent years, psychological research on the acceptance of autonomous driving has increased significantly. In local public transport, the so-called micro-transit, autonomous buses are expected to be part of everyday life as early as 2030 (Litman 2022). Currently, autonomous micro transit pilot systems are being tested in various fields of application worldwide (e.g., Bernhard etal. 2020). In these test projects, in addition to the technological component, the psychological perspective is increasingly becoming the focus of * Franziska Schandl [email protected] 1 Department ofExperimental Psychology, University ofRegensburg, Universitätsstraße 1, 93040Regensburg, Germany Transportation 1 3 interest. With their experience and acceptance, the passenger is crucial for establishing autonomous micro-transit systems. The more autonomous driving is adapted to user needs and interests, the easier it can develop into an attractive alternative to non-autonomous driving (Haboucha etal. 2017). It is becoming increasingly clear that personality traits, e.g., extraversion or self-efficacy of potential users are significant factors influencing the intention to use (ITU, Du etal. 2021; Qu etal. 2021; Venkatesh etal. 2012). To map the complexity of personality factors and thus respond best to the needs of potential users, we aim to analyze patterns in personality characteristics and identify potential user profiles from them. To the best of our knowledge, there are no extensive empirical studies on this topic until now. The goal of this study is therefore the explorative analysis and identification of profiles for potential users of autonomous vehicles (AV) based on selected personal characteristics and dispositions. Literature review andresearch framework The characteristic of the potential passenger must be taken into account when promoting the acceptance of autonomous driving systems. A vast body of research shows that intrapersonal factors contribute to AV acceptance and ITU. For example, acceptance of autonomous vehicles is significantly related to sociodemographic variables, such as gender (Lemonnier etal. 2020), age (Qu etal. 2021), region of living (Lemonnier etal. 2020), education (Yuen etal. 2022), and income (Ding etal. 2022). Several previous studies consistently show that males and younger subjects are more receptive to AVs (e.g., Ding etal. 2022; Dong etal. 2019). Gender effects may be due to men reporting a higher general affinity for technology and being more likely to pursue technical careers (Trapani and Hale 2019). Women, on the other hand, attribute greater discomfort and uncertainty with technology to themselves, possibly due to stereotypical biases (Blasko etal. 2020; Koch etal. 2008). However, acceptance of new technologies such as AVs also appears to be a generational issue. Younger individuals are less concerned about this change in transportation (Charness etal. 2018), but on the other hand have greater concerns about hacking attacks (Garidis etal. 2020). Bonem etal. (2015) found that older individuals rate risks particularly high when the risk addresses health or ethics. It is possible that AV technology, in which artificial intelligence (AI) is responsible for accident-free driving and ethical decision-making, is experienced as more threatening due to its novelty (Cui etal. 2019; Sankeerthana and Raghuram Kadali 2022). In addition, relevant differences in AV acceptance also emerge in relation to region of living. Thus, individuals from urban regions are more likely to adopt AVs than individuals from rural regions (Deb etal. 2017). This is plausible in that people in urban regions may be less likely to own a car or parking may be more difficult in cities (Nielsen and Haustein 2018; Nordhoff etal. 2018a). Therefore, Avs may appear attractive especially for people who have their center of living in a city. High levels of education and income are also associated with higher technology acceptance (Yuen etal. 2022). Individuals with higher education are in many cases more familiar with new technologies such as AVs due to broader knowledge of technical functions and developments (Yuen etal. 2022). In addition, high levels of education are often associated with higher socioeconomic status and income (Rojas-Méndez etal. 2017). Moreover, the often expensive technological innovations, e.g., the newest smartphones or laptops, can often only be financed if income permits. As a result, individuals with high levels of education Transportation 1 3 and income often have better access to technology, which in turn favors familiarity and adoption (Rojas-Méndez etal. 2017). In addition to sociodemographics, there is now particularly insightful evidence on personality variables in relation to the adoption of autonomous driving technology. Various studies show significant relationships between classic personality traits such as the Big Five (neuroticism, extraversion, openness, agreeableness, and conscientiousness; Costa and McCrae 1989) and attitudes toward AVs (e.g., Zhang etal. 2020). As demonstrated in a study by Qu etal. (2021), individuals with high scores in extraversion and openness are more open to AVs, whereas high neuroticism scores negatively affect acceptance. However, Charness etal. (2018) also showed that particularly open-minded users are more willing to relinquish control to the AI in an autonomous vehicle. Particularly conscientious and agreeable users showed more concern in this regard, e.g., regarding the reliability and usability of AVs (Charness etal. 2018; Qu etal. 2021). In addition to these classical personality traits, constructs related to one’s attribution of control seem to have an impact on AV acceptance. However, a look at the studies on control beliefs and self-efficacy reveals partly contradictory results. Control beliefs can be located as a construct on a dimension whose extremes are internal and external control beliefs. People differ individually in whether they generally attribute control over situations or facts to themselves (internal) or to external factors (external; Rotter 1966). According to Choi and Ji (2015), an external control belief contributes positively to ITU. The authors explain this by the fact that, for example, people who do not feel able to participate in traffic under their control or responsibility (e.g., due to physical impairment) prefer to use autonomous vehicles as a means of transportation. Another reason for this could be that people with external control beliefs generally attribute low levels of their control to themselves and thus experience the relinquishment of control to AI as less drastic (Takayama etal. 2011). This is contrasted with a finding by Du etal. (2021) showing that high selfefficacy has a positive effect on trust in AVs and thus ITU. The authors explain this result by the fact that people with high self-efficacy prefer to accept challenges rather than avoid them and thus react more openly to AVs (Graham 2011). Since high self-efficacy is associated with internal rather than external locus of control beliefs, the results contradict the finding of Choi and Ji (2015), who found external locus of control beliefs to be a predictor of ITU (Chen and He 2014). A low general need for control also contributes positively to the ITU (Garidis etal. 2020). One reason for the contradictory results on own control attribution might be the interaction with other personality traits. Among other things, the acceptance of AVs is also determined by the general disposition to trust (Benleulmi and Blecker 2017). It is plausible that individuals who have a fundamentally higher level of trust also trust AVs more strongly without needing a high level of their own experience of control. Thus, people with high general trust are more willing to use AVs (Benleulmi and Blecker 2017). In addition, technology affinity contributes positively to trust in new technologies, which in turn lowers perceptions of potential risks (Choi and Ji 2015). High technology confidence, in the sense of confidence in one’s technological capabilities, is in turn considered a basis for trust in human–machine interaction (Jian etal. 2000). According to Venkatesh (2000), this type of trust also influences the perceived ease of use, which in turn favors the ITU of AVs (Jing etal. 2020). Another major determinant of AV acceptance is anxiety, although the study results still differ regarding the direction of the relationship. For example, contact with AV technology can create anxiety among potential users due to the novelty of the technology (Fraedrich and Lenz 2016). Fears about AVs can also reduce the willingness to use AVs Transportation 1 3 (Hohenberger etal. 2017). Based on these results, it would be plausible to assume that high trait anxiety as a stable personality trait is also associated with low AV acceptance. In contrast, the results of Qu etal. (2021) showed a positive correlation between trait anxiety and the acceptance of autonomous driving systems. Anxious people rate the reliability of AVs higher. The authors explain these expected findings by arguing that anxious people would rather hand over control to an autonomous system because they are more afraid of human errors than AI errors (Qu etal. 2021). Regardless of the direction of the association, trait anxiety seems to play a role in AV acceptance. Similar findings also emerged for the so-called technology anxiety. Kopeć etal. (2022) found that higher technology anxiety impairs the acceptance of an autonomous working environment. This association can also be applied to AVs. Keszey (2020) found that both fears of technology in general and specific technological fear (e.g., related to hacking attacks) have a negative impact on AV adoption. The answer to the question which needs are important for the potential users of autonomous driving systems and how these can be satisfied is correspondingly complex and cannot be given in a generalized way. Previous research has already identified some personality traits that are predictive of ITU. As described before, it was found that both classic personality traits such as the Big Five (i.e., neuroticism, extraversion, openness, agreeableness, conscientiousness), as well as traits related to technology affinity (e.g., technology competence, acceptance, confidence, and anxiety), are positively related to the acceptance of AVs. In addition, especially variables related to self-confidence (e.g., self-efficacy expectancy, control belief), the disposition to trust, and trait anxiety have a significant effect on the acceptance of AVs. However, to the best of the authors’ knowledge, no attempt has yet been made to combine these characteristics and to investigate whether typical response patterns for different types of potential users can be identified. To address interindividual requirements and expectations in AV development and to further adapt AVs to potential passengers, it is important to analyze patterns in selected characteristics of potential users and thus identify profiles. These profiles can present the complex set of characteristics and needs of potential users abstractly and at the same time allow AV providers a more differentiated perspective on their potential passengers. In other contexts, e.g., general public transport (Shrestha etal. 2017) or Bitcoin (Kang etal. 2020), user profile analysis has already been successfully applied to better understand target groups from a marketing point of view and thus to better target their needs. Thus, the analysis of different profiles is also desirable in the context of AVs, especially because this technological innovation is expected to affect the general population (Litman 2022). The aim of this study is, therefore, to identify and exploratively analyze profiles of potential AV users with respect to the ITU AVs. Personality, in particular, which also proved to be crucial for the acceptance of AVS in our research, is widely used for the identification of person profiles within a society (e.g., Perera and McIlveen 2017; Rzeszutek and Gruszczyńska 2020). Due to its relative stability, it allows reliable and consistent predictions of distal outcomes, as in our case of ITU (Diener and Lucas 2019). Therefore, the analysis is based on variables found to be relevant to AV acceptance in previous research: the Big Five, the dispositional technology affinity variables, the self-confidence variables, disposition to trust and trait anxiety. Following the approach of Spurk etal. (2020), our study addresses the following research questions: 1. What is a meaningful and useful number of personality profiles based on which to examine the ITU of potential AV users? 2. How can the different profiles be characterized? Transportation 1 3 3. How big are the profiles? 4. To what extent is profile affiliation predictive for ITU of AVs? 5. How valid are the results? Method Sample A sample of 388 volunteers (111 male, 276 female, 1 diverse) aged between 18 and 64 was recruited via different online platforms of universities, social media, and personal approach. Therefore, when we refer to bus users in our study context, we always refer to potential users, since the data were collected online and independently of actual bus use. At the same time, this allows us to identify groups of people who are less willing to use AVs. To provide the participants with a vivid and detailed idea of the ride in an autonomous bus the participants watched a video of an autonomous bus and then answered the questionnaire. Two people were pre-excluded because they had processed less than 80% of the questionnaire. Table1 shows the sociodemographic characteristics of the final sample. Participation in the study was without payment; students received course Table 1 Sample characteristics based on gender a The terms correspond to German school diplomas. Mittelschulabschluss and Realschulabschluss are equivalent to a High School diploma after nine and ten years Characteristics Total Male Female Divers (N = 388) (n = 111) (n = 276) (n = 1) Average Age (SD) 26.19 (7.25) 26.67 (6.05) 26.02 (6.69) 22.00 (0.00) Training (%) No degree 1 (0.3%) 1 (0.9%) – High School Diploma, i.e., German Mittelschulabschlussa1 (0.3%) – 1 (0.4%) High School Diploma, i.e., German Realschulabschlussa13 (3.4%) 6 (5.4%) 7 (2.5%) University of applied sciences entrance qualification, i.e., German Fachhochschulreife 49 (12.6%) 17 (15.3%) 32 (11.6%) University entrance qualification, i.e., German Abitur 274 (70.6%) 76 (68.5%) 197 (71.4%) 1 (100%) Academic degree, i.e., bachelor, master or higher 19 (4.9%) 3 (2.7%) 16 (5.8%) No answer 22 (5.7%) 5 (4.5%) 17 (6.2%) Annual income (%) 9 (2.3%) 3 (2.7%) 6 (2.2%) < 20 000 € 20 000 €–30 000 € 124 (32.0%) 22 (19.8%) 101 (36.6%) 1 (100%) 30 000 €–40 000 € 80 (20.6%) 25 (22.5%) 55 (19.9%) 40 000 €–50 000 € 71 (18.3%) 27 (24.3%) 44 (15.9%) 50 000 €–60 000 € 37 (9.5%) 14 (12.6%) 23 (8.3%) > 60 000 € 17 (4.4%) 7 (6.3%) 10 (3.6%) No answer 13 (3.4%) 5 (4.5%) 8 (2.9%) Transportation 1 3 credit for participation (students must take part in studies and experiments carried out by researchers of the universities). Instrument andprofile indices Based on the current state of research, we selected 16 variables as possible indices for personality profiles by which the ITU is to be predicted: 15 of the indices refer to personality, and one variable to age. Age has a significant effect on the acceptance of AVs (Charness etal. 2018). We, therefore, consider it useful to include age when analyzing potential user groups, because it can contribute to a deeper understanding of characteristics of potential users. This combination should later enable us to place the ITU of potential customers on AVs in the context of individual personality characteristics. The questionnaires and instruments used are shown in Table2. A detailed overview of all items and scales used in the study is available in the OSF repository, https:// osf. io/ 87vr4/. The basis for the present study was the data of a larger survey on the first impression of autonomous vehicles. Therefore, in addition to the variables mentioned, the following variables were collected: education, area of work, working hours, income, political orientation, neighborhood, motivation for AV use, AV knowledge, expectations, and suggestions for improvement (all self-developed), transport usage habits (adapted from Nordhoff etal. 2019), Satisfaction-with-Travel-Scale (Ettema etal. 2011), facilitating conditions (van der Laan etal. 1997), performance expectations (based on Nordhoff etal. 2018b), effort expectations (based on Venkatesh etal. 2012), service and vehicle characteristics (based on Nordhoff etal. 2019), social influence (based on Venkatesh etal. 2012), hedonic motivation (based on Venkatesh etal. 2012), the perceived benefits and risks (Liu etal. 2019), the willingness to share (Nordhoff etal. 2019) and the perceived safety (based on Xu etal. 2018). Procedure The data was collected via an online questionnaire using the soscisurvey online application. Driverless buses are too rare in Germany to assume that the respondents have any experience in this area. The video format has already proven to be a useful alternative to the presentation of AV technology in previous studies (e.g., Bjørner 2015). For this reason, participants who were interviewed online watched a video of 4.5min of a trip with the autonomous bus before answering the questionnaire to get the most comprehensive first impression of the bus possible. This video provides the perspective of a passenger boarding an autonomous bus with other passengers, looking around the shuttle, sitting down, riding in it through several stops, getting off, and watching the autonomous bus drive away. The video is accessible in the online repository. Before processing the actual questionnaire, all participants were informed about the study objective and the protection of their data and then had to confirm their consent for participation. The datasets generated during the current study are available in the OSF repository, https:// osf. io/ 87vr4/. Transportation 1 3 Table 2 Used constructs and inventories with item characteristics, Cronbach’s Alpha and source Note. α: Cronbach´s Alpha Construct/inventory Item count Range α Source Example item Age 1 Neuroticism 6 1 (strongly disagree) to 5 (strongly agree) .84 NEO-FFI-30, Körner etal. (2008) I often feel tense and nervous Extraversion 6 .75 I am a cheerful, good-humoured person Openness 6 .80 I often enjoy playing with theories or abstract ideas Agreeableness 6 .70 I always try to act considerate and sensitively Conscientiousness 6 .78 I keep my things neat and clean Self-efficacy 3 .85 Allgemeine Selbstwirksamkeitskurzskala (ASKU), Beierlein etal. (2012) I can cope well with most problems by my own efforts Internal Control Belief 3 .62 Jakoby & Jacob 1999 I like to take responsibility External Control Belief 3 .47 Success often depends less on performance and more on luck Trait Anxiety 3 1 (strongly disagree) to 7 (strongly agree) .78 Skalen zur Messung manifester Angst (MAS); Lück and Timaeus, (1969) I am almost always afraid of something or someone Disposition to Trust 6 .91 Gefen and Straub (2004) I generally trust other people Technology Acceptance 4 .92 Kurzskala Technikbereitschaft, Neyer etal. (2012) I am very curious about new technical developments Technology Competence 4 .91 When dealing with modern technology, I am often afraid of failing Technology Control Belief 4 .81 Whether I am successful in using modern technology depends mainly on me Technology Anxiety 3 .63 Based on Venkatesh (2000) New technology doesn’t scare me at all Trust in Technology 2 .81 Based on Jian etal. (2000) I trust new technologies Intention to Use 1 Self-developed based on Venkatesh etal. (2012) I plan to use autonomous shuttles like the People Mover in the future if they were available to me Transportation 1 3 Statistical analysis The focus of this study is on a Latent Profile Analysis with subsequent analysis of the relationship between profile affiliation and ITU as well as a validation of the results. For preliminary and descriptive analyses, we used SPSS (version 26). The LPA was conducted in R (Version 4.1.3; R Core Team 2022) with the tidyLPA- and the caret-package via Gaussian mixture modelling (Rosenberg etal. 2018). Possible outliers were checked in advance in boxplots. We did not exclude outliers because the values were within the plausible range, did not represent error outliers, and thus are part of the normal distribution in the population (Leys etal. 2019; Wiggins 2000). The graphical analysis indicated the normal distribution of the residuals. All data were z-standardized in advance to determine the interpretability of the profiles. We opted for an LPA followed by regression to investigate differences in ITU in the identified profiles. LPA is a person-centered procedure that identifies latent profiles based on similar response patterns. In contrast to factor or regression analytic methods, LPA focuses on relationships between individuals rather than relationships between variables (Bauer and Curran 2004). This enables the probabilistic assignment of each potential user to the profile with the best fit based on the individual response pattern (Tein etal. 2013). Thus, LPA provides a differentiated insight into profile-specific characteristics within a diverse network of variables. The method is therefore particularly well suited for our goal of identifying and distinguishing personality profiles of potential users (Howard and Hoffman 2018; Woo etal. 2018). The approach allows a subsequent description of various empirically determined personality profiles in relation to the ITU. Our study thus contributes to mapping the knowledge about the characteristic of potential users as well as their needs about AVs in a differentiated and multidimensional manner and on this basis to be able to respond more purposefully to their needs, e.g., in the marketing of AVs. For later validation of the profile solution, we randomized the dataset into a training dataset (80%, n = 315) and a test dataset (20%, n = 73). To identify the correct number of profiles, we calculated several models in R based on the training data set, each with a different number of profiles. We followed the recommendation of Nylund-Gibson and Choi (2018) and started with the model calculation for a single latent profile, after which we gradually increased the number of profiles. We ended this increase after the four-model solution when the profile size fell below the limit of 5% of the data set for the first time. This procedure, which is common in LPA research (e.g., Kircanski etal. 2017; Ricketts etal. 2018), preserves the practical applicability and interpretability of the profiles because small profile sizes are considered difficult to replicate. We compared the resulting four profile solutions based on predefined criteria with regard to their model fit (Nylund-Gibson and Choi 2018; Ricketts etal. 2018). We followed the recommendation of Lubke and Neale (2006) and considered the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) adapted to the sample size in the form of the Sample-Size-Adjusted Bayesian Information Criterions (saBIC), where low values suggest a better model fit. We also examined the Lo Mendell Rubin Likelihood Test (LMR; Lo etal. 2001). This compares the solution of k profiles with a solution with k − 1 profiles. A significant test indicates a better fit of the model with k profiles (Lo etal. 2001; Pastor etal. 2007). The entropy was additionally tested as a measure of the separation reliability of the profiles (Clark and Muthén 2009). It reflects the mean probability that a person can be correctly classified based on their response pattern within the model with values from 0.80 being considered very Transportation 1 3 profile1 and 2 attributed less control to the external AI technology than to themselves and were therefore less convinced of AVs. To give potential users control options in the autonomous bus, warning systems could be installed in buses, for example, so that passengers can contact the transport operations center in an emergency (Dong etal. 2019). Nordhoff etal. (2020) have shown in a qualitative setting that an emergency button inside the vehicle contributes to the perceived safety in autonomous buses. Information on driving safety and AV functionality could also be helpful for potential users who are most likely to be associated with profile2. Manufacturers could provide training to help users to better understand AVs and reduce potential technology anxiety. Compared to the profile1 group, however, the focus in this group should be on providing simple and understandable information, taking into account the low affinity for technology. Manufacturers should also make the handling of AVs as intuitive as possible due to the lower level of technical competence. They should also counter the low confidence in new technologies by making AVs as predictable as possible for this group, e.g., by using monitors inside the vehicle that transmit the stimulus detection and response of the AV sensors in real-time (Yuen etal. 2022). In addition, transit agencies should focus primarily on reliable, trusted manufacturers to increase the AV trust of the technology-critical group (Yuen etal. 2022). For profile3, a relatively average response pattern emerged across the variables, with slightly increased technology competence and slightly decreased technology anxiety. People who are most probably to be classified to profile3 showed an average ITU, although they were less likely in general to use public transport. It is possible, therefore, that the ITU for autonomous cars would be even higher than in our study related to autonomous buses. This group is thus likely to be more of a target group for autonomous cars. Overall, people who are most likely to be classified to profile3 can nevertheless be expected to adopt autonomous buses. From a marketing perspective, little consideration of potential fears or skepticism is necessary according to our model. Rather, this group could be further encouraged in their motivation to use AVs by highlighting possible benefits and the fun of driverless driving, e.g., in advertising. However, no in-depth knowledge of AV technology should be assumed. Profile4 differed from profile1 in almost all variables. People most likely to be associated this profile are characterized by pronounced self-confidence, low anxiety, and a high affinity for technology in every respect. It seems plausible that members of profile4, i.e., people who are more likely to have higher self-confidence on average attribute better coping skills to themselves and are less anxious. As a result, they may be more open to new technology. Accordingly, this profile group was most likely to use AVs. Complementary to profile1, which had a comparatively low ITU with the lowest proportion of car and driver’s license owners, we found the highest ITU for profile4 with the highest proportion of car and driver’s license owners. It is possible that people most likely to be assigned to profile4 (in line with our rationale for profile1) have more experience with autonomous driving assistance systems and are therefore more open to autonomous driving. Another explanation for the comparatively high ITU may be that people most likely to be associated with profile4 had on average the highest income and thus have better access to (often expensive) new technologies or are more familiar with them (Gallo etal. 2022; Yuen etal. 2022). Given their high extraversion and openness, persons with profile4 could be further encouraged to use AVs by highlighting social and sustainable aspects of autonomous ridesharing services. With their openness to technologies and AVs, this group also has great potential to act as a multiplier for AVs. Sharma and Mishra (2022) showed in their study that peer influence can have an even greater impact on AV adoption than media marketing. Accordingly, individuals who are most likely to be assigned to profile4 could be suitable Transportation 1 3 for introducing skeptical target groups, such as people who are most probably associated with profiles1 and 2, to AVs and motivating them to use it. This could be both, for example, in private or via public reports of positive experiences in social media. It is noteworthy that our profiles, taken individually and also in their overall constellation, provide a thoroughly consistent, coherent picture. The characteristic response patterns of the single profiles follow a logical, reasonable constellation, e.g., for the positive association between anxiety- and insecurity-related variables. Overall, the four profiles form a holistic pattern in that they complement each other in a meaningful way and can be clearly differentiated from each other. Thus, our profiles are not only plausible in their respective logic, but they also complement each other to form a comprehensive overall concept. Overall, the four profiles showed a very heterogeneous pattern of characteristics and willingness to use AVs. Specially for profiles1 and 2, anxiety was still associated with a low ITU. These results are a sign that the transition from conventional vehicles to AVs must be gradual to pick up AV-skeptical groups and get them accustomed to the new technology. A too abrupt changeover could lead to overwhelming people most likely to be associated with profile1 or 2 and thus frustrating them right from the start. Manufacturers and public transport operators should therefore not implement the system too quickly and should define specific measures in advance to meet the needs of each of the four profile groups. Limitations andoutlook To be able to classify the results of our study in a well-founded manner, possible limitations of our study must be reflected, too. First, it should be noted that our sample was not balanced in terms of gender, age, or experience with AVs. To ensure a meaningful analysis of potential users, we aimed for the largest possible sample, for which we were dependent to a significant extent on the recruitment of students who completed the study participation as part of their studies. This is due to the relatively young sample and is presumably also responsible for the predominance of female participants due to the focus on psychology Particularly due to the limited age variance, our profiles show relatively homogeneous age structures. This made it difficult for us to interpret the profiles in terms of age differences. We were therefore not able to address differences, e.g., in technology affinity, which may have been caused by age (Blut andWang 2020). To further deepen the research on profiles of potential AV users, the profiles should be considered in future studies in samples with greater age variance. Due to the high proportion of participants with a comparatively high level of education, it can also be assumed that the sample tends to have more knowledge or experience with new technologies and was therefore relatively open to AVs (Ding etal. 2022). In addition, several studies showed that different cultures differ in their AV acceptance. For example, Asian areas show higher acceptance of AVs than European areas, possibly due to a higher willingness to accept circumstances (e.g., AV adoption), especially if they benefit society (Potoglou etal. 2020; Yun etal. 2021). We, therefore, consider it important to replicate the study in different socio-demographic contexts and, in addition, to examine the cultural generalizability through studies in other countries. Furthermore, the study findings should be verified under real-life conditions as soon as autonomous buses are widely available. In our study, the recruitment of test persons under real conditions with the desired sample size proved to be almost impossible due to the anti-Coronavirus measures applicable at the time of the survey and the limited availability of autonomous Transportation 1 3 shuttle buses. For this reason, data was collected online regardless of whether individuals had prior experience with autonomous buses. This enabled us to also survey individuals who would not use autonomous buses and to assess them in terms of their personalities. However, our study thus refers exclusively to potential and not actual users. This must be taken into account when interpreting the results. As soon as autonomous buses are available on a large scale, this study should be conducted with actual users. To provide the participants with an impression of driving an autonomous bus that is as close to reality as possible, we opted for a sample, which was shown a video of the autonomous bus. In previous studies, this type of presentation has also proven to be representative (e.g., Lemonnier etal. 2020) and ensures an equal experience base across all participants (Kettle and Lee 2022). However, we cannot guarantee that relatively abstract technologies such as AI and AVs have been sufficiently illustrated by the videos in this study. It will be the necessary task of future studies to investigate this question. In terms of statistical analysis, our studies have the characteristic limitations of LPA. On the one hand, LPA is a probabilistic procedure. Therefore, the results of the LPA represent probabilities and not absolute values. Our class assignments are highly likely to apply, but LPA does not guarantee the correctness of our solutions. LPA enabled us as a procedure to initially simplify complex personality dimensions by forming profiles to derive practical implications for the introduction of AVs. However, it must also be noted that this procedure entails a loss of information in two aspects: First, in order to perform the regression, it was important to assign each subject to the profile to which he or she belongs with the highest probability based on the personality pattern (Clark and Muthén 2009). However, the profile assignment means that this probability no longer can be taken into account in the subsequent regression. For example, if a person has a probability of 0.55 of belonging to Profile3, he or she will be assigned to that profile in the same way as a person whose probability of belonging to Profile3 is 1.00. In the subsequent regression, both persons are counted identically as belonging to profile3, regardless of what the original assignment probability was (Clark and Muthén 2009). Second, profile affiliation represents a simplification of a previously more complex response pattern. In the regression, we deliberately left the level of personality variables and only considered profile affiliation to meet our demand for complexity reduction. The pure profile affiliation does not reveal the extent to which the respective personality variables influence the ITU. However, this was not the goal of the study because the associations of the selected personality variables with ITU have already been investigated in previous research. Since the specific associations between the personality variables and ITU can be interesting for the interpretation of the results, we have provided a correlation matrix in Table4 in the appendix, from which the associations between the examined constructs can be seen. In this context, we would also like to point out that profile affiliation was indeed predictive of ITU in our study. However, as in any regression, these trends are not exempt from variation. Thus, when we assign an individual to the profile to which he or she is most likely to belong based on his or her response pattern, our results allow us to make predictions but not absolute statements about expected ITU. Regarding the model decision, it must be noted that most our information criteria did not reach a low point for any of the model solutions considered and that the likelihood parameters remained significant. This suggests that each additional profile provided insights. According to Nylund-Gibson and Choi (2018), however, the steady decline can also be an indication that the chosen mixture model is not a perfect model for our data. To make a profile decision, we therefore relied on a combination and best possible Transportation 1 3 matching of the fit indices under consideration of the profile size, which best supported the four-profile solution. It must be mentioned that we selected several parameters for the assessment of the fit and brought them into a decision hierarchy. However, there are no uniform rules for this approach. In this study, we followed current recommendations and best practices from simulation studies. Nevertheless, the profile decision and the interpretation are also subject to a subjective decision-making framework that the LPA entails. The classification of the profiles is also essentially dependent on the separation potential of the used items (Nylund-Gibson and Choi 2018). With our results, we have now made a first contribution to measuring the separation potential of our items. One task of future studies may be to further refine the findings and the item pool. Conclusion Personality plays a significant role in AV acceptance. Our study went beyond the previous findings and integrated them by identifying four profiles based on the most relevant personality traits and related them to the ITU AVs. Our results allow us to draw implications about the characteristics of four profiles of potential users and how to respond to them. These identified profiles differed particularly in variables of self-confidence (i.e., self-efficacy, internal and external control belief), general anxiety (i.e., neuroticism, trait anxiety and technology anxiety), and affinity for technology (i.e., technology acceptance, competence, and control belief): Profiles1 and 2 were characterized by low technology affinity and ITU, which was accompanied by high general anxiety and uncertainty in profile1, and high technology anxiety in profile2. Profile 3 showed average values across all variables, including for the ITU. With high self-confidence and affinity for technology accompanied by low anxiety, profile4 proved to be particularly promising for the intention to use AVs and thus differed considerably from the other three profiles. Manufacturers and transit agencies should take the differences into account in their AV marketing strategies. In particular, people with a low affinity for technology (mainly represented in profile2), but also with general anxiety (mainly represented in profile1), should be approached with special consideration in order to increase their ITU systematically. Complementary to this, people who are particularly affine to technology and have low levels of anxiety (mainly represented in profile4) can be deliberately targeted to serve as multipliers for the idea of autonomous driving. An implementation concept tailored to the profiles can help to meet the individual needs of each of the four profile groups. In summary, our study provides important contributions from a psychological perspective to better define potential AV users in terms of their characteristics and potential needs. Our implications provide initial suggestions on how the different profiles and needs of potential users can be addressed by manufacturers and providers. Future research should follow up on this and examine in more detail how potential users can be approached depending on their personality profile. Appendix See Table4. Transportation 1 3 Table 4 Correlations of the central variables studied N = 388 *p < 0.05 **p < .0.01 Variable M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1. Gender 0.72 0.47 2. Age 26.19 7.25 − .05 3. ITU 5.14 1.28 − .12 .02 4. Neuroticism 1.63 0.79 .24** − .14** − .14** 5. Extraversion 2.69 0.61 .01 .04 .19** − .33** 6. Openess 2.43 0.75 .05 .05 .13** − .04 .09 7. Agreeableness 2.88 0.62 .24** − .03 .00 − .09 .10* .02 8. Conscientiousnes 3.23 0.55 .14** − .02 .08 − .25** .22** .03 .24** 9. Self Efficacy 3.96 0.58 − .07 .05 .17** − .37** .30** .18** .02 .42** 10. Internal Control Belief 3.84 0.62 .15** − .02 .00 .01 .02 − .07 .03 − .02 .− .01 11. External Control Belief 3.39 0.64 − .04 .03 .10* − .03 − .02 .04 − .02 − .04 .05 .25** 12. Trait Anxiety 4.10 1.36 .26** − .21** − .11* .61** − .20** − .05 .07 − .01 − .20** .04 − .03 13. Disposition to Trust 4.50 1.09 .07 .01 .11* − .10 .30** − .00 .27** .08 .10* .05 − .02 − .10 14. Technology Acceptance 4.59 1.43 − .12** − .01 .25** − .17** .24** .08 − .10* .07** .22** .03 .09 − .11* .05 15. Technology Competence 5.78 1.09 − .13** − .09 .19** − .27** .10 .04 .14** .16** .19** .01 .05 − .19** − .01 .35** 16. Technology Control Belief 5.22 0.95 − .11* − .05 .20** − .12 ‘ .06 .07 .02 .12** .24** − .01 .02 − .07 .10 .29** .29** 17. Technology Anxiety 2.64 0.99 .14** .02 − .30** .28** − .22** − .02 − .06 − .16** − .26** − .01 − .02 .22** − .07** − .54** − .63** − .39** 18. Trust in Technology 5.02 1.04 .03 − .01 .36** − .11* .16** .01 .05 .11** .18** 09 .11* − .14** .33** .40** .16** .26** − .41** Transportation 1 3 Author contributions All authors developed the study concept, contributed to the study design, and interpreted the results. Material testing and data collection were performed by FS and MFCH. The data were analyzed by FS. FS drafted the manuscript, and MFCH and PF provided critical revisions. All authors contributed to the article and approved the submitted version. Funding Open Access funding enabled and organized by Projekt DEAL. The research was funded by a grant from the Federal Ministry for Digital and Transport (BMDV), Germany (FKZ: 01MM20003F). Declarations Conflict of interest All authors declare that they have no conflicts of interest. 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Technol. 112, 220– 233 (2020). https:// doi. org/ 10. 1016/j. trc. 2020. 01. 027 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Franziska Schandl is a Ph.D. candidate at the Chair of Social, Work, Organizational and Economic Psychology, University of Regensburg, Germany. Her main research focuses on the acceptance of autonomous systems with a specialization in autonomous driving systems. Peter Fischer is a full professor and Chair of Social, Work, Organizational and Economic Psychology at the University of Regensburg, Germany. His main research areas comprise the psychology of digitalization, blockchain, judgment and decision-making, aggressive behavior and prosocial behavior. Matthias F. C.Hudecek is a postdoctoral researcher at the Chair of Social, Work, Organizational and Economic Psychology, University of Regensburg, Germany. His main research areas comprise the acceptance of autonomous systems with a specialization in autonomous driving systems, personality and narrative identity and self-determination theory.