Attitudes Toward the Use of Conditional Automated Vehicles in the Technology Acceptance Model Framework: Evidence from an Italian Sample

Abstract

Automated vehicles (AVs) have the potential to significantly enhance road safety, traffic efficiency, and mobility access by supporting or replacing human control during driving. However, the success of this transition hinges on people’s attitudes. This study explores the dimensionality and factor interactions in the adaptation of the technology acceptance model (TAM) to conditional AVs applied to an Italian sample, to provide a new perspective on the acceptance of the intermediate level of driving automation. After evaluating the content and relevance of the subscales, we analyzed responses from 201 Italian-speaking individuals collected through a questionnaire. Structural equation modeling was employed to examine the scale’s factor structure and interrelations. Results indicate that most items demonstrated satisfactory fit and the questionnaire maintained its overall factor structure in the Italian context. Interestingly, an unmediated effect of the Perceived Utility of AVs—and, to a lesser extent, concerns with the manufacturer’s compliance with ethical data treatment procedures—were found on the intention to use AVs. These findings contribute to the understanding of factors influencing the acceptance of AVs. They underscore the importance of users’ perceptions of both practical utility and proper privacy safeguarding in determining the acceptance of the described AV technology. The implications for the development of TAM extensions and future research directions are also discussed.

Introduction

Technological innovations in mobility and road use are in the spotlight nowadays, especially due to the development of automated driving systems.^1,2 Automated vehicles (AVs) are expected to support (Levels 1 and 2 of SAE—Society of Automotive Engineers—levels of driving automation),³ assist (Levels 3 and 4), and eventually completely replace (Level 5) human control in the execution of regular and critical driving functions.^4,5 Fully AVs will be able to monitor the road environment and perform driving maneuvers such as steering, braking, and overtaking, through the collection of large amounts of traffic information.⁶ The potential impact of these AVs has been widely deepened, highlighting potential benefits, such as the improvement of road safety,^7,8 traffic efficiency,^9,10 and accessible mobility.^11,12 The literature has also discussed nontrivial barriers to AVs implementation, such as the expensiveness of the technological transition,^8,13 the need for a new traffic regulation and the reconsideration of individual liability in the unlikely event of an accident.^7,14 To date, automated driving systems are available in the market up to Level 3 of automation (i.e., conditional AVs),^15,16 which requires drivers to rely on automated driving maneuvers, but also monitor the driving activity and actively intervene when the system reaches its performance limit.^17,18 Dealing with this level of automation seems not trivial, with a number of potential consequences in terms of performance and cognitive load.¹⁹

In this layered framework, general attitudes toward AVs play a fundamental role in the spread of AV technology, acting on the final intention to use it.^7,20 In fact, integrating AVs into the transport system is not only a matter of technological development, but also demands for a positive attitude of potential adopters and stakeholders.^21,22 Attitudes toward the use of new technologies have been extensively deepened in the literature;²³ in the context of automated driving, a fair amount of research has investigated AVs user acceptance through behavioral models accounting for their antecedents. While a diversity of models of users’ attitudes/behaviors have been applied in the context of nonautomated²⁴ and automated²⁵ cars, a great number of these applications (e.g.,^25–29) have conceptually referred to the technology acceptance model (TAM), a well-established model to describe users’ acceptance toward technologies. The TAM—built upon widely known social psychology motivational theories^30,31—was first proposed in the eighties by Davis^32,33 and then reviewed and enriched in the early 2000s.^34,35 It revolves around two key concepts: Perceived Usefulness (PU), denoting the extent to which an individual believes that using a particular technology will improve their performance or simplify their tasks, and Perceived Ease of Use (PEOU), representing the degree to which a person perceives the use of technology as effortless, understandable, and manageable. According to the TAM, both PU and PEOU would directly impact an individual’s attitude toward using the technology (ATT), which, in turn, would influence their Behavioral Intention (BI) and the actual use of the system. The present contribution delves into the so-called AV-TAM, proposed by Zhang and colleagues.²⁹ This model is a reconceptualization of TAM in the context of driving technologies, specifically on Level 3 of automation, the highest currently available in the market. The novelty of AV-TAM is that it expands the original TAM structure by integrating additional constructs^27,36 namely, Initial Trust (i.e., TRU), Perceived Safety Risk (PSR), and Perceived Privacy Risk (PPR) (see Table 1). Particularly, trust was defined by Lee and See³⁷ as an “attitude that will help an agent achieve an individual’s goals in a situation characterized by uncertainty and vulnerability” (Table 1). In studying human–automation relationship,³⁸ trust is often referred to as “Initial Trust,” as a large section of the population did not have the opportunity to actually use the technology yet.^36,39 Taking advantage of the AV-TAM structure proposed by Zhang and colleagues,^29,40 this contribution thus aims at deepening the understanding of the relationship between the antecedents of behavioral use and the actual intention to use AVs itself. The present proposal involves the translation, adaptation, and psychometric assessment of the AV-TAM scale in an Italian sample. This investigation is particularly timely and relevant, as SAE Level 3 automation is not yet available in the Italian market. However, discussions regarding its future integration into the traffic system are ongoing at both the behavioral and legislative levels.^41,42 Together with the well-known cross-cultural variations in ethical evaluation of autonomous systems,⁴³ this underlines the need for more cross-cultural studies on the topic.^44,45 The present contribution proposes a renewed conceptualization of the AV-TAM model whose factor structure is partially in line with Zhang’s work, even highlighting interesting differences in the relational structure. Assuming the emerging interest in individual attitudes toward AV’s driving behavior in the Italian context (e.g., refs.^46–49), this work provides new insights into the investigation of the interaction between AVs and individuals, as former drivers, and future stakeholders of this technology.

Table 1.

Automated Vehicle-Technology Acceptance Model Factors, Their Acronyms, and Definitions

	Factor	Definition
BI	Behavioral intention to use	Extent to which an individual intends to use a technology
ATT	Attitude toward technology	An individual’s favorable or unfavorable feelings regarding the use of a technology
PEOU	Perceived ease of use	“To what degree people believe that using a particular system or technology could be free from physical and mental effort”⁴¹
PU	Perceived usefulness	Level at which “a person believes that using a particular system would enhance his or her job performance”⁴¹
TRU	Trust	“Attitude that will help an agent achieve an individual’s goals in a situation characterized by uncertainty and vulnerability”.⁴² In AV adaptations, it is often referred to as Initial Trust, as a large section of the population did not have the opportunity to actually use the technology yet.
PSR	Perceived safety risk	Potential safety-related concerns associated with adopting a particular technology. It represents the extent to which users perceive potential safety issues or hazards related to the use of the technology.
PPR	Perceived privacy risk	Potential risk that drivers’ data might be transmitted to government entities, vehicle developers, and insurance companies without prior notice, possibly being used against the users or of being vulnerable to hacking by unauthorized parties.

Materials and Methods

Participants

A convenience Italian-speaking and Italy-based sample (N = 201; 53.23% females) was recruited through a random snowball sampling, controlling for gender balance. The recruitment was advertised online on social media and university networks and took place from May 2022 to July 2022, and from December 2022 to January 2023. Participation was voluntary and unremunerated. Participants’ mean age was 30.45 years (standard deviation [SD] = 8.66, range: 19–51), in line with the profile of future users of conditional AVs. They attained on average 17 years of formal education (SD = 2.95), with 42.79% (N = 86) being university students. Most of them (65.17%, N = 131) reported driving frequently, with an average number of road accidents experienced during the lifetime of 1.74 (SD = 1.79) from both the driver and the passenger perspective. The complete demographics of the sample are reported in the Supplementary Materials.

Experimental materials

Informative materials

A description of a SAE Level 3 conditional AV was given to the participant, introducing the technology as follows: “An autonomous vehicle is a vehicle that takes on a range of driving functions, replacing the driver in most of their tasks (acceleration, lane keeping, braking, etc.). However, the driver is required to take back control of the vehicle in situations that the system is unable to handle (e.g., road construction).”

TAM items

Zhang and colleagues’ TAM Scale for AVs (AV-TAM)²⁹ was translated into Italian according to current guidelines.⁵⁰ Participants were asked to express their level of agreement with each sentence (i.e., item) on a five-point Likert scale (1 = “Strongly Disagree”; 5 = “Strongly Agree”). This version included the subscales PEOU (four items); PU (five items); PPRs (three items), PSR (two items), Initial Trust (TRU; three items), and ATT (three items). Since the Behavioral Intention to Use (BI) scale featured highly redundant items, we retained in the questionnaire one single item, which showed the highest factor loading in the original paper (BI2: “Overall, I expect to be using automated vehicles in the future”; answers from 1 = “Highly unlikely” to 5 = “Highly likely”). The complete item list is retrievable from Supplementary Appendix.

Procedure

Participants partook in a broader online study involving attitudes toward AVs on the Qualtrics XM platform.⁵¹ Informed consent and debriefing were implemented respectively before and after the study completion. Socio-demographic information and driving habits were collected before any other activity, followed by the AV-TAM, while unrelated experimental tasks concerning the framework study were presented afterward. Participants were not compensated for participation and were free to drop out of the survey any time. The protocol was approved by the University of Granada’s Institutional Review Board (IRB approval #1528/CEIH/2020).

Data analysis

The factor structure of the AV-TAM scale was assessed to find out whether the latent structures found by Zhang and colleagues²⁹ held in the Italian sample. A structural equation model (SEM) was tested, using a Maximum Likelihood estimator. Upon model estimation, the following indices were computed to estimate the fit: the chi-squared to degrees of freedom ratio, the root mean square error of approximation, its 90% confidence interval, and the probability of it being less than or equal to 0.05, the standardized root mean square residual, and the comparative fit index.^52,53 Data are available at the following OSF project: https://bit.ly/3TxwYKH.

Results

Factor structure

The following results describe the fitted SEM model based on the measurement structure resulted by Zhang and colleagues,²⁹ which was selected to ensure reliable parameter estimation. The estimated model features acceptable-to-good indices assuming the reference thresholds (cf. Table 2).^52,53 The relationships between latent factors in the estimated model are reported in Table 3 and graphically depicted in Figure 1B; on the other hand, Figure 1A exemplifies the original findings by Zhang and colleagues²⁹ and Figure 1C graphically summarizes the differences between the latter and the present findings. Some deviations from the original model resulted in the Italian sample (cf. Figure 1C, colored lines), specifically: a significant negative effect of PPR on BI (r = –0.188, p = 0.019); positive albeit nonsignificant effect of PEOU on PU (r = 0.445, p = 0.179); a nonsignificant impact of PEOU on ATT (r = 0.114, p = 0.728) and of PU on TRU (r = 0.055, p = 0.718). Results are interpreted and discussed in the next section.

Table 2.

Fit Indices of the Italian Adaptation of the Automated Vehicle-Technology Acceptance Model Factors Scale and Its Acceptability

	χ² (DoF)	χ²/DoF	RMSEA [90% CI]	P (RMSEA ≤0.05)	SRMR	CFI
SEM Model	308.665 (180)	1.715 (good)	0.060 [0.048–0.071] (acceptable)	0.079 (acceptable)	0.074 (acceptable)	0.942 (acceptable)

Acceptability thresholds for fit indices in the evaluation of the model.^52,53 Acceptable fit: 2 < χ²/DoF < 3; 0.06 < RMSEA < 0.08; 0.05 ≤ P (RMSEA ≤0.05) ≤ 0.10; 0.06 ≤ SRMR < 0.08; 0.90 ≤ CFI < 0.95. Good fit: 0 < χ²/DoF < 2; 0 < RMSEA < 0.06; P (RMSEA ≤0.05) > 0.10; 0 ≤ SRMR ≤ 0.06; CFI >0.95.

χ², chi-squared; CI, confidence interval; CFI, comparative fit index; DoF, degrees of freedom; RMSEA, root mean squared error of approximation; SEM, structural equation model; SRMR, standardized root mean square residual.

Table 3.

The Correlation Indexes Between Latent Factors and Their Respective Significance in the Structural Equation Model of the Italian Adaptation of the Automated Vehicle-Technology Acceptance Model Factors Scale

	PEOU	PU	PPR	PSR	TRU	ATT
PU	0.445^ns	—
PPR	—	—	—
PSR	0.059^ns	—	—	—
TRU	—	0.055^ns	—	—	—
ATT	0.114^ns	—	—	–0.609^***	0.832^***	—
BI	—	0.921^***	–0.188^***	—	—	0.925^***

***

p < 0.001; ns = p ≥ 0.05.

FIG. 1.

(A) Path diagrams of the Chinese version of the AV-TAM scale;²⁹ (B) path diagram of the Italian adaptation of the AV-TAM scale; (C) path diagram of the Italian adaptation of AV-TAM compared with the model proposed by Zhang and colleagues.²⁹ Relations to previous findings and theoretical models are highlighted in different colors. AV, automated vehicle; TAM, technology acceptance model.

At the factorial level, item-factor loadings are reported in Table 4. All of the items showed loading on their respective and expected factors, which were statistically significant (p < 0.001). Most of the loadings fell above the rule-of-thumb threshold of 0.45 for acceptability,⁵⁴ with the only exception being PU5 [“I find automated vehicles to be useful when I am impaired (e.g., drowsy, drunk, drugs)”; λ = 0.355]. This is consistent with the original model, in which the same item was removed from the final AV-TAM alongside PU2 (“Autonomous vehicles will let me do other tasks, such as eating, watching a movie, being on a cell phone on my trip.”) because of low loadings. However, in the present model, PU2 showed a satisfactory although lower loading (λ = 0.521), and since dropping PU5 would negatively impair the model fit, the authors decided to keep all the items of the scale, deeming PU5 and PU2 less central although still informative facets of the factor.

Table 4.

Factor Loadings for Each Item of the Italian Adaptation of the Automated Vehicle-Technology Acceptance Model Factors Scale in the Structural Equation Model

	PEOU	PU	PSR	PPR	TRU	ATT	BI
PEOU1	0.832^***	—	—	—	—	—	—
PEOU2	0.521^***	—	—	—	—	—	—
PEOU3	0.815^***	—	—	—	—	—	—
PEOU4	0.828^***	—	—	—	—	—	—
PU1	—	0.650^***	—	—	—	—	—
PU2	—	0.504^***	—	—	—	—	—
PU3	—	0.707^***	—	—	—	—	—
PU4	—	0.711^***	—	—	—	—	—
PU5	—	0.355^***	—	—	—	—	—
PSR1	—	—	0.783^***	—	—	—	—
PSR2	—	—	0.576^***	—	—	—	—
PPR1	—	—	—	0.917^***	—	—	—
PPR2	—	—	—	0.919^***	—	—	—
PPR3	—	—	—	0.919^***	—	—	—
TRU1	—	—	—	—	0.827^***	—	—
TRU2	—	—	—	—	0.857^***	—	—
TRU3	—	—	—	—	0.837^***	—	—
ATT1	—	—	—	—	—	0.816^***	—
ATT2	—	—	—	—	—	0.848^***	—
ATT3	—	—	—	—	—	0.610^***	—
BI2	—	—	—	—	—	—	0.609^***

***

p < 0.001.

Discussion

Investigating the determinants behind AV acceptance before their actual use is crucial for understanding future interactions and preventing the negative consequences of slow market adoption.^9,55 This study explores how an Italian sample perceives forms of mobility where cooperation between humans and intelligent systems is required, identifying novel relationships with acceptance determinants. As for the AV-TAM measurement structure, Zhang and colleagues’ model was partially confirmed (Figure 1C, black lines), and three important differences may be highlighted and further discussed.

First, the positive impact of PU on BI is evident in both samples, though it emerges as substantially stronger in the Italian sample compared with the Chinese one (r = 0.921 vs. r = 0.335). Conversely, the expected mediating role of TRU in this relationship was not confirmed (r = 0.055, p = 0.718, compared with Zhang’s r = 0.604, p < 0.001), nor was the indirect pathway through ATT. The difference in the magnitude of the PU-TRU path between the Italian and Chinese samples points to a potential cultural influence on how individuals cognitively and affectively process AV adoption. Interestingly, trust was confirmed to fully mediate only the impact of PSR on BI, as evidenced by Zhang and colleagues.²⁹ This evidence suggests that Italian consumers’ initial trust in AVs is primarily driven by safety concerns, while other determinants have a less prominent role in shaping trust. Notably, interpreting Trust in AV-TAM as Initial (or Perceived) Trust—formed in the absence of actual interaction with AVs—raises important questions about how trust is conceptualized in the early stages of technology acceptance. Further exploration is needed of how individuals form expectations about emerging technologies and how these expectations evolve once real-world interactions become possible.^38,56

Second, in the Italian sample, PEOU appears to be isolated from the rest of the model, while prior research—also in the automation domain^29,57–59—has consistently demonstrated its positive associations with ATT and PU. Given that PEOU is a well-established determinant of technology acceptance across different sectors and cultural contexts, it is unlikely that its relevance does not extend to the Italian population. Instead, the observed lack of significance in our study may be better explained by methodological factors, such as sample size and the higher standard error in our Italian sample compared with the Chinese sample. Indeed, while the strength of correlations between these pairs of latent factors was comparable to previous findings, they did not reach statistical significance (PEOU to ATT: r = 0.114, p = 0.728 vs r = 0.164, p < 0.01; PEOU to PU: r = 0.445, p = 0.179 vs r = 0.578, p < 0.001). Therefore, caution is warranted when interpreting the factor as nonrelevant; rather, the results suggest a decreased centrality.

Third, another evident difference between the Chinese and Italian models concerns the role of potential consumers’ concerns about privacy (PPR). While PPR remains unrelated to other factors consistently with original findings, the Italian sample appears reluctant to use AVs when manufacturers are suspected of mishandling data (PPR to BI: r = –0.188, p = 0.019). Notably, this relationship was not hypothesized in the original study, in which the PPR was only expected to reduce BI indirectly through the mediation of perceived trustworthiness (TRU), but found reliable support from the literature.^60–62 The direct, strong, and negative impact of PPR on BI suggests a conceptual distinction between propensity toward the technology itself and toward the ethical rule guiding the vehicle impacting on privacy and data security.⁶³ This makes transparency about data management crucial for establishing an accountable relationship between manufacturers and AVs’ users. This is supported by the literature,^60,64 which highlighted the importance of ensuring future users of the ability of AVs to provide a secure personal space and a strong level of privacy. This result suggests that the insurance of individual data privacy might hold considerable importance for AVs adoption among Italian future users (and Europeans, in general),⁶⁵ and suggests that addressing both safety and privacy issues in and after the trust-building processes regarding AVs is fundamental.^66,67

Finally, the chain of influence from TRU to BI exhibits stronger correlations in the Italian sample than in the Chinese sample (TRU to ATT: r = 0.832, p < 0.001 vs. r = 0.670, p < 0.001; ATT to BI: r = 0.925, p < 0.001, vs. r = 0.527, p < 0.001). These results suggest that, for Italian consumers, trust, attitudes, and BI toward AVs form a more tightly interconnected net. This could indicate a more affect-driven approach, where once trust is established, it strongly reinforces both positive attitudes and the willingness to adopt the technology.⁶⁸ Such a pattern highlights the importance, specifically for the Italian population, of promoting interventions to foster trust at the very early stages, even before the introduction of AVs, as it may have a cascading positive effect on overall acceptance and readiness to embrace this innovation.

Limitations and future perspectives

The evidence collected in the present study is centered on a sufficient albeit limited sample of Italian adults. Specifically, while the current sample size (N = 201) is comparable to that of the original study by Zhang and colleagues²⁹ (N = 216), the use of randomized snowball sampling limits control over demographic variables and affects the generalizability of the findings. Future application will aim to improve the sample size and possibly control for age groups and potential accessibility to technology, which may impact the relationship among antecedents of AV use.^69,70 Additional investigation would also benefit from a comparative cross-cultural analysis to distinguish between differences due to cultural characteristics, social norms, and general/specific technological literacy.^71,72 Alternative methodological applications can also be covered in future studies, exploring further features and hidden relations within the AV-TAM factorial model (e.g., Explorative SEM), and stressing the conceptualization and, therefore, the investigation of Trust as a fundamental mediator toward AV use.

Conclusions

Our study offers a new perspective on applying a well-known TAM model adapted to the context of conditional autonomous driving (SAE Level 3). It encourages future research to explore the relationships among these factors in shaping the intention to accept, share, and interact with AV technology. A multidisciplinary approach to the impact of future mobility could pave the way for a deeper understanding of both general and individual attitudes toward AVs, ultimately facilitating the effective integration of this cutting-edge technology into our culture and daily lives. AV technology holds the potential to bring significant benefits to road safety, traffic regulation, and mobility. However, its success depends not only on technical advancements but also on societal acceptance and widespread adoption. This research aims to provide valuable insights for policymakers, stakeholders, and practitioners, supporting a smoother transition to autonomous driving and fostering better human-AV interaction.

Authors’ Contributions

G.B.: Conceptualization, investigation, methodology, project administration, validation, and writing—original draft. A.M.: Investigation, data curation, formal analysis, visualization, and writing—original draft. F.N.: Supervision and writing—original draft. C.D.-P.: Conceptualization, methodology, project administration, resources, supervision, and writing—review and editing. A.S.: Methodology, resources, supervision, and writing—review and editing.

Transparent Use of AI Statement

The authors acknowledge that artificial intelligence was employed to enhance the readability of the text. All AI-generated content was carefully reviewed and verified by the authors, who take full responsibility for the final written material.

Ethics of Research Statement

The protocol was approved by the University of Granada’s Institutional Review Board (IRB approval #1528/CEIH/2020).

Footnotes

Data Availability Statement

Data and scripts in support of the present findings are available at the following Open Science Framework project: .

Author Disclosure Statement

The authors report there are no competing interests to declare.

Funding Information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Supplementary Material

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