Technology Acceptance Modelling for Investigating the Uptake of

Electric, Connected, Autonomous, and Shared Mobility Technologies

Konstantina Karathansopoulou, Despoina Mitsiogianni, Eleni Tsaousi,

George Dimitrakopoulos and Dimitris Georgiadis

Department of Informatics and Telematics, Harokopio University of Athen, Greece

Keywords: QETAM, Technology Acceptance, Modeling, Adoption, FMEA, TAM, Risk, Electric, Connected,

Autonomous, and Shared Mobility.

Abstract: This paper introduces QETAM (Quantitative Effect and Technology Acceptance Modelling), the first

quantitative user acceptance model for evaluating the impact and adoption of Electric, Connected,

Autonomous, and Shared (ECAS) mobility technologies. Developed in Python, QETAM leverages data

collected through specifically designed questionnaires to assess key adoption factors, including technological

reliability, user attitudes, infrastructure, and environmental considerations. The model accounts for the

interconnected nature of ECAS technologies, emphasizing synergies between electric propulsion,

connectivity, autonomy, and shared mobility services. Utilizing advanced statistical techniques, it analyzes

large-scale datasets to provide a data-driven understanding of user behavior. Beyond academic contributions,

QETAM offers practical insights for policymakers and industry stakeholders, supporting the transition toward

sustainable and user-centric mobility solutions.

1 INTRODUCTION

In the contemporary landscape of urban mobility, the

integration of electric, connected, autonomous, and

shared (ECAS) mobility technologies marks a

paradigm shift, offering transformative potential in

addressing environmental concerns, enhancing

connectivity, and revolutionizing transportation

systems (Society of Automotive Engineers, 2014).

The rapidly evolving technologies in this field aim to

create numerous benefits for both society and

individuals including improved traffic safety, higher

fuel economy, and reduced emissions. Therefore, as

we stand at the crossroads of technological innovation

and sustainable urban development, understanding

such technologies' intricate dynamics, risks, and

acceptance patterns becomes imperative. In other

words, the intention of consumers to adopt ECAS

mobility technologies is critical for forecasting

adoption rates and aiding policymakers and

implementers (Becker et al., 2020)

(Karathanasopoulou et al., 2022).

Up to today, researchers have extensively

examined the factors influencing end users'

behavioral intention to use and adopt new

technologies. While prominent models like the

Technology Acceptance Model (TAM) (Davis.,

1989) and the Unified Theory of Acceptance and Use

of Technology (UTAUT) (Venkatesh et al., 2003)

have provided frameworks for understanding user

adoption, recent studies emphasize the importance of

user trust and psychological factors. In the particular

domain of mobility, the issue of the public acceptance

of ECAS mobility solutions is attracting scholarly

attention such that extensive discussions have been

conducted on the factors that influence the acceptance

of ECAS vehicles, while the biggest obstacle to the

popularization of such technologies is related less to

the technical aspect and more to the low

psychological acceptance of the public (Yuen et al.,

2021).

While numerous studies have explored user

acceptance in the context of highly automated

vehicles, the majority of these investigations have

predominantly relied on theoretical models to assess

the factors influencing the behavioral intention to use

such vehicles. In our proposed model, we seek to

integrate both theoretical and practical perspectives,

providing a comprehensive framework to not only

identify obstacles but also to offer guidance for future

advancements within this domain. In particular, we

390

Karathansopoulou, K., Mitsiogianni, D., Tsaousi, E., Dimitrakopoulos, G. and Georgiadis, D.

Technology Acceptance Modelling for Investigating the Uptake of Electric, Connected, Autonomous, and Shared Mobility Technologies.

DOI: 10.5220/0013235500003941

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2025), pages 390-398

ISBN: 978-989-758-745-0; ISSN: 2184-495X

propose a comprehensive model that systematically

analyzes the multifaceted effects and acceptance

nuances surrounding ECAS mobility

solutions.(Wang et al., 2025) here's a breakdown of

the specific challenges you've outlined:

1. Lack of Specificity for ECAS: Traditional

models might not capture the unique features and

concerns surrounding electric, connected,

autonomous, and shared mobility solutions.

2. Difficulties with Dynamic Nature: Rapid

advancements and the evolving nature of ECAS

technologies can make it challenging for existing

models to provide accurate long-term predictions.

3. Inadequate Trust Focus: Existing models may

not fully address the intricacies of how users develop

trust in autonomous systems, which is crucial for

widespread acceptance.

4. Missing Regulatory Considerations: The legal

and regulatory landscape surrounding autonomous

driving significantly influences user acceptance, and

current models may not sufficiently consider these

factors.

5. Gaps in Safety Perception: Safety concerns are

paramount. Existing models may not fully capture

how users perceive the safety and reliability of

autonomous vehicles.

6. Cultural Variations Omitted: Technology

acceptance is influenced by cultural factors, and

existing models may not adequately consider this

diversity in user attitudes.

7. Ethical Concerns Unaccounted for: Ethical

concerns around decision-making algorithms in

autonomous vehicles pose challenges to user

acceptance, and current models often don't address

these ethical dimensions.

In light of the above, the contribution of this paper

lies exactly in addressing these gaps and challenges,

by proposing a novel technology acceptance model,

building on previous research efforts, and enhancing

them by adding the ability to accurately predict and

understand the factors influencing the acceptance of

autonomous driving technology, more quantitatively.

This paper is structured as follows: The next

section (II) delves into an exploration of the related

work that has been conducted within the field of

ECAS vehicle acceptance and the methodologies

employed in previous research. Section IIII describes

the proposed model, the hypotheses, the research

methodology and the profile of the participants.

Details of the survey results on general attributes,

factors associated with intent to use ECAS vehicles,

and data analysis with hypotheses testing are reported

in Section IV. The most important implications of the

present study which showcase the benefits of the

proposed model, are discussed in Section V. Last,

concluding remarks are drawn in Section VI, along

with an outlook on future work.

2 RELATED WORK

2.1 Technology Acceptance Models

The Technology Acceptance Model (TAM),

introduced by Davis (Davis., 1989), has been a

cornerstone in understanding user acceptance of

information systems and technologies. TAM focuses

on two core beliefs: Perceived Usefulness (PU) - the

degree to which users believe technology will

enhance their job performance, and Perceived Ease of

Use (PEU) - the perceived effort required to learn and

use the technology. While TAM has been widely

applied, it may not fully capture the nuances of user

acceptance for Electric, Connected, Autonomous, and

Shared (ECAS) mobility solutions. For instance,

TAM's focus on general-purpose technologies might

not account for the unique features and concerns

surrounding ECAS, such as trust in autonomous

systems or cultural variations in user attitudes

(Alfadda and Mahdi, 2021). As evidenced by the

studies exploring user reactions to Zoom for language

learning (Alfadda and Mahdi, 2021) and healthcare

IT adoption (Kamal et al., 2020), the Technology

Acceptance Model (TAM) has been a versatile tool

for understanding user acceptance across various

domains. However, limitations have been identified,

such as infrequent measurement of variables and a

potential lack of detailed theoretical explanation for

the constructs used in the model (Kamal et al., 2020).

Furthermore, the TAM-TOE model, which integrates

TAM with the Technology-Organization-

Environment framework, offers a broader perspective

by considering social, environmental, and

technological factors influencing technology

adoption, as seen in the research by Sheshadri

Chatterjee et al. (Chatterjee et al., 2021). While

valuable, TAM-TOE might still lack the specific

focus needed to fully understand user acceptance of

Electric, Connected, Autonomous, and Shared

(ECAS) mobility solutions. Building upon these

insights and addressing the limitations of existing

models, this research proposes a novel user

acceptance model specifically tailored to the

complexities of ECAS technologies (Ferran et al.,

2024). The proposed model offers a more

comprehensive framework to not only identify the

factors influencing user acceptance but also to

provide a quantitative understanding of these

Technology Acceptance Modelling for Investigating the Uptake of Electric, Connected, Autonomous, and Shared Mobility Technologies

391

factors.(Chatterjee et al., 2021). Indeed, TAM shows

several similarities with the Unified Theory of

Acceptance and Use of Technology (UTAUT),

having the same primary constructs (perceived ease

of use and perceived usefulness), as the latter was

created based on TAM and seven other theoretical

frameworks. Nevertheless, UTAUT examines the

acceptance of technology, determined by the effects

of performance expectancy, effort expectancy, social

influence, and facilitating conditions. TAM and

UTAUT has been used in different fields to assess

user acceptance of specific technologies. For

instance, it has been applied to identify the main

factors that determine students. acceptance of

MOOCs in higher education in Saudi Arabia (Altalhi

et al., 2021). Also, UTAUT with core constructs such

as social influence, enabling conditions, etc. has been

used by researchers Novianti Puspitasari et al. (2019)

to identify variables that influence users to use the

Integrated Licensing Services Information System

(Puspitasari et al., 2019).

2.2 Failure Mode and Effect Analysis

An important factor that can negatively affect the

successful execution or performance of a process or a

project is risk, which can manifest itself as

uncertainties. For this reason, effective risk

management is vital, as it helps mitigate potential

challenges.

The Failure Mode and Effects Analysis (FMEA)

(Sharma and Srivastava, 2018) can be characterized

as a risk management tool and is an engineering

method that helps to identify weak points during the

concept and design phase of all kinds of products

(hardware, software) and processes. It is mainly a

qualitative analysis, which shows how reliable the

designed system is (Liu et al., 2013). FMEA can be

also used to implement the analysis of component

failure modes, their resultant effects, and secondary

influences on both local component function and the

performance of the whole system (Carlson., 2012).

Essentially, the purpose of FMEA is to take steps to

eliminate or reduce failures, starting with those that

have the highest priority, and more specifically those

that cause the most serious consequences, or that

occur frequently and can be identified most easily.

By combining FMEA with the TAM model,

which is a theoretical approach, we leverage the

strengths of both models to obtain quantitative results

and to provide a more comprehensive and robust

framework for evaluating the acceptance and impact

of emerging ECAS mobility technologies.

2.3 Studies on Technology Acceptance

of ECAS Mobility Solutions

User acceptance is paramount for the success of any

new technology. It serves a two-fold purpose, firstly

allowing developers to monitor potential acceptance

during the priori development phase ("a priori") and

by providing valuable feedback to the industry that

can influence product development. This is crucial for

Electric, Connected, Autonomous, and Shared

(ECAS) mobility solutions. While public perception

of autonomous vehicles is gradually becoming more

positive, a deeper understanding of user acceptance is

essential for widespread adoption. Social and

psychological factors significantly influence how

societies respond to new technologies. Research has

identified several key factors impacting public

acceptance of ECAS technologies, including:

Perceived Risk: 1. Concerns about safety and

potential for accidents with autonomous vehicles. 2.

Trust: The level of trust users has in the technology's

ability to function safely and reliably. 3. Perceived

Benefit: The perceived advantages and improvements

to transportation that ECAS solutions offer.

Existing models like the Unified Theory of

Acceptance and Use of Technology (UTAUT) and

the Car Technology Acceptance Model (CTAM) by

Osswald et al. (Osswald et al., 2012; Sithanant et al.,

2023) have provided valuable insights into user

acceptance. CTAM, for example, incorporates

UTAUT's framework along with additional

constructs like safety to understand user attitudes

towards driving information technology systems.

However, Madigan et al. (Madigan et al., 2016)

highlight that CTAM's investigation did not extend to

behavioral intentions towards using such systems.

Further research (mention a recent study if possible)

emphasizes the need for models that specifically

address the unique features and concerns surrounding

ECAS technologies. Recent studies have focused on

enhancing existing models (TAM and UTAUT) to

account for the specific attributes of automated

driving, but there is still a gap in understanding user

perceptions of usefulness and trust in these novel

technologies (Panagiotopoulos et al., 2018). This

paper proposes a novel user acceptance model

specifically tailored to ECAS technologies to address

these limitations. Our model leverages the strengths

of existing models and incorporates Failure Mode and

Effect Analysis (FMEA) to identify potential

"acceptance failures" that could hinder user adoption.

By combining these approaches, our model offers a

more comprehensive framework for evaluating user

acceptance of ECAS solutions.

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While the original TAM, as proposed by Davis et

al. in 1989, has been widely used to understand user

acceptance of various technologies, including

information systems, health informatics, and

educational platforms, has also several limitations, as

discussed previously. Adding three new factors,

“Perceived Trust (PT)”, “Social Influence (SI)”, and

“Facilitating Conditions (FC)” to the original TAM,

addresses some of the limitations and enriches the

framework with a more nuanced understanding of

factors affecting user acceptance of ESAC

technologies. The integration of TAM with FMEA

bridges the gap between technology reliability and

user acceptance, recognizing that these elements are

interrelated. This integration allows us to not only

assess the acceptance of ECAS technologies but also

identify and prioritize potential system design

failures, leading to more informed decision-making

and risk mitigation strategies.

Leveraging these foundations, the following

section delves into our user acceptance model for

highly automated vehicle (HAV) technologies. We'll

explore the model's structure, the Python code behind

its implementation, the questionnaire design used for

data collection, the obtained results, and finally, the

conclusions drawn from this initial evaluation.

3 CONCEPTUAL MODELING

3.1 Overview

The speed at which consumers embrace advancing

technologies is influenced by factors like technology

availability, convenience, consumer needs, and trust.

Various theories and models, as proposed so far (as

indicated in the previous section), aim to elucidate

consumers’ inclination toward adopting new

technologies. Examining the intention of consumers

to use ECAS mobility solutions is crucial, given that

this emerging technology is gradually penetrating the

global market.

To investigate this, the Technology Acceptance

Model (TAM) has provided valuable insights into

factors influencing user adoption of new

technologies. Concomitantly, Failure Mode and

Effect Analysis (FMEA) offers a systematic approach

to identify and assess potential challenges that may

arise during user interaction. This study builds upon

these established methodologies to create a

comprehensive user acceptance model based on the

key indicators of TAM and the computational method

of FMEA.

3.2 Framework Description

In our approach, we leverage a combination of 2

methods, by exploiting the quantitative nature of

(FMEA) with the modeled social analysis advantages

of the Technology Acceptance Model (TAM) to

assess the User Acceptance of HAV In this context, it

is essential to meticulously identify and measure the

key parameters outlined in the TAM. This integrated

methodology enables a comprehensive evaluation of

potential failure modes while concurrently gauging

user acceptance factors, ensuring a robust and holistic

assessment of the new technologies' viability and user

satisfaction. More specifically the main components

of the derived FMEA-based acceptance model

QETAM (Quantitative Effect and Technology

Acceptance Modelling) are structured as follows:

• Perceived Trust (PT) - Measure of a person’s

trust in a particular technology

• Perceived Usefulness (PU) - Measure of the

usefulness of a particular technology

• Perceived Ease to Use (PEU) - Measure of

the usability of a particular technology.

• Social Influence (SI) - The degree to which

someone is influenced by social norms and their

social environment.

• Facilitating Conditions (FC) - The degree to

which an individual believes that an organizational

and technological infrastructure exists and also the

degree to which they have the appropriate knowledge

and resources to use the system.

The main research hypotheses, upon which the

analysis is focused, are the following: H1: The overall

impact of Perceived Trust (PT), Perceived Usefulness

(PU), Perceived Ease to Use (PEU), Social Influence

(SI), and Facilitating Conditions (FC) on Behavioral

Intention to Use (BIU) H2: The Correlation of

Perceived Trust (PT) with Perceived Usefulness (PU)

and their impact on Behavioral Intention to Use (BIU)

H3: The Correlation of Perceived Trust (PT) with

Perceived Ease to Use (PEU) and their impact on

Behavioral Intention to Use (BIU) H4: The

Correlation of Perceived Trust (PT) with Social

Influence (SI) and their impact on Behavioral

Intention to Use (BIU) H5: The Correlation of

Perceived Trust (PT) with Facilitating Conditions

(FC) and their impact on Behavioral Intention to Use

(BIU).

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393

Figure 1: “QETAM Conceptual Modelling”.

According to the above, the outcome is the

identification of several user Acceptance, based on

the aforementioned Behavioral Intention to Use

(BIU), For each distinct category within the

administered questionnaire, participant responses

will be subjected to a ranking procedure. This ranking

will utilize a standardized 1-to-10 scale. Following

the ranking of responses, a Batch Index Unit (BIU)

number will be calculated using Equation 1 Once the

BIU number is obtained, it will be used to categorize

based on Table 1

BIUi = PUi × PEUi × PTi × SIi × FCi (i = 1, 2, . . . , N)

(1)

BIU Number

Behavioural Intention

to Use

80.001-100.0000

Very High

50.001-80.000

High

20.001-50.000

Medium

5.001-20.000

Low

0 - 5.000

Improbable

4 RESULTS AND DISCUSSION

4.1 Implementation

For the quotative approach of the QETAM model, a

code was created in the programming language

Python. In this code, five categories were created,

which are „Perceived Usefulness (PU)”, “Perceived

Ease to Use (PEU)”, “Perceived Trust (PT)”, “Social

Influence (SI)” and “Facilitating Conditions (FC)”.

For each category individually, the inappropriate

acceptance was found according to the tables x1, x2,

x3, x4, and x5. The responses provided indicate that

users’ overall acceptability for each category was

7.694 for Perceived Usefulness (PU), 7.548/10 for

Perceived Ease to Use (PEU), 6.858/10 for Perceived

Trust (PT), 7.492/10 for Social Influence (SI) and

7.314/10 for Facilitating Conditions (FC).

Then another piece of code was added that

concerned the BIU. The code creates a histogram

based on table x6 and takes values from 0 to 100,000.

The Total BIU score is calculated as the result of the

five BIU parameters for each category found.

Furthermore, the code categorizes the results into five

categories: Improbable, Low, Medium, High, and

Very High. Each category corresponds to a different

value range. Then, the histogram displays the total

BIU score in a bar, using different colors for each

category, with the color representing the category of

each value.

Finally, a correlation matrix was created to

respond to the assumptions defined above, to find out

which combination affects user acceptance positively

and negatively. This code analyzes correlations

between different lists of data. Initially, it calculates

the means of each list’s values and then computes the

correlation coefficient between one list (PT) and the

rest of the lists (PU, PEU, SI, FC). Then, it displays

the correlation values in an image, using color to

represent the level of correlation, with blue indicating

high correlation and lighter shades indicating low

correlation

4.2 Survey Design

To gain comprehensive insights into user perspectives

on autonomous driving technologies, we have

designed a targeted survey structured around three key

components aimed at assessing user

acceptance.Firstly, demographic data including age,

gender, education, and work location are collected to

explore potential correlations between user

characteristics and their acceptance of Electric,

Connected, Autonomous, and Shared (ECAS)

technologies. This foundational information is crucial

for capturing the diverse viewpoints that influence

user comfort with autonomous vehicles. Secondly, we

evaluate user awareness of highly autonomous

vehicles through two general knowledge questions.

These questions assess familiarity with classification

criteria such as SAE levels and any personal

experience users may have had with driving such

vehicles. This approach enables us to gauge user

awareness, knowledge levels, and direct interaction

with autonomous driving technology. The core of our

survey comprises 25 meticulously crafted questions,

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categorized into five key areas aligned with our newly

developed acceptance model: Perceived Trust (PT),

Perceived Usefulness (PU), Perceived Ease of Use

(PEU), Social Influence (SI), and Facilitating

Conditions (FC). Each category is designed to probe

user perceptions and attitudes towards ECAS mobility

solutions through targeted inquiries. For example,

questions under Perceived Trust explore user comfort

levels with autonomous vehicles handling

emergencies or navigating complex road condition.

The demographic part of the survey includes basic

information about age, gender, education level, and

work location. These serve as critical variables in our

analysis, allowing us to explore potential associations

between user characteristics and acceptance of ECAS

technologies. Understanding demographic factors'

influence is integral to capturing the different

perspectives that may shape user attitudes toward

autonomous vehicles.

In addition, participants were asked if they were

familiar with the criteria that classify vehicles as

highly autonomous, such as those meeting a Society

of Automotive Engineers (SAE) level of more than 3,

and if they have personally driven highly autonomous

vehicles. Thus, we were able to understand better

whether there is an awareness and understanding and

personal experience of the specific vehicles from the

users.

Finally, the core of our survey consists of 25

questions divided into five 5 categories, namely

“Perceived Trust (PT)”, “Perceived Usefulness

(PU)”, “Perceived Ease to Use (PEU)”, “Social

Influence (SI)”, and “Facilitating Conditions (FC)”.

Each category includes five questions tailored to

capture nuanced insights into user perceptions and

attitudes toward ESAC mobility technologies.

This survey was designed to gain valuable

insights into user acceptance of autonomous driving

technologies, ultimately strengthening the

competitive advantage of the European Union's

autonomous vehicle industry and achieving user-

driven market adoption. Conducted between

September 2023 and January 2024, the survey

targeted adult participants. Notably, it employed a

two-stage approach: an initial survey and a follow-up

survey to be conducted later. This design will enable

us to assess the impact of future developments on user

perceptions We utilized Google Forms to create the

questionnaires, with the resulting data set delivered in

an .xls file format. To ensure a representative sample

of the general population, we employed a random

sampling approach and disseminated the survey

electronically through mailing lists, social media

platforms, and QR codes distributed at relevant

events. In total, we received responses from 128

individuals.

The subsequent figures (Figures 2, 3) illustrate the

distribution of various demographic attributes among

the respondents who completed the questionnaires.

Figure 2: ‘Age groups’.

Figure 3: Education.

To understand user understanding of the

technology behind autonomous vehicles, we included

two questions assessing their fundamental knowledge

of highly automated vehicles. Analyzing the

responses reveals that while a majority of participants

grasp the concept, many lack firsthand experience

with the technology. This finding aligns with our

expectations, considering the survey targeted

European adults and widespread autonomous vehicle

deployment might not be as prevalent compared to

other regions. This lack of experience could

potentially influence user attitudes, such as leading to

a more cautious or apprehensive perspective towards

autonomous vehicles.

4.3 Hypothesis Testing

This study investigates the impact of various factors

on individuals' intention to use (BIU) the

technologies adopted in Highly Automated Vehicles

(HAVs). We leverage an acceptance model that

builds upon the well-established Technology

Acceptance Model (TAM) Our first hypothesis (H1)

examines how user perceptions, such as Perceived

Trust (PT), Perceived Usefulness (PU), and Perceived

Ease of Use (PEU), along with external factors like

Social Influence (SI) and Facilitating Conditions

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395

(FC), contribute to the formation of a behavioral

intention to use HAV technology. Prior research has

established the positive influence of PU and PEU on

BIU. H1 extends this understanding by incorporating

the effects of PT, SI, and FC.

Utilizing data from Figure 4, our analysis reveals

a Behavioral Intention to Use (BIU) score of

21,823.9963, placing HAV technology within the

"medium" category of user acceptance. Interestingly,

the question on HAVs knowledge indicates that while

76.6% of participants are aware of HAVs, only 25%

have used them. This suggests a potential for

significant growth in user acceptance as experience

with the technology increases. Our detailed analysis

of H1 revealed that each factor (PT, PU, etc.) had a

significant positive impact on BIU, with Perceived

Trust being the strongest factor influencing user

intention to use HAVs.

Figure 4: “Behavioral Intention to Use Outcome”.

Following our initial hypothesis on the overall impact

of user perceptions on BIU, we delve deeper into the

interplay between these perceptions, specifically

focusing on the role of trust. The next four key

hypotheses explore how perceived trust interacts with

various user perceptions, ultimately influencing their

intention to use the technology (BIU). The 2nd

Hypothesis focuses on the relationship between

perceived trust (PT) and perceived usefulness (PU),

and we aim to understand how trust interacts with

perceived value to drive user adoption. The 3rd

hypothesis investigates the correlation between

perceived trust (PT) and perceived ease of use (PEU)

and focuses on exploring how trust interacts with

user-friendliness to shape technology acceptance.

From the 4th hypothesis which examines the link

between perceived trust (PT) and social influence

(SI), we will understand how trust interacts with the

influence of a user's social circle on their technology

adoption. Lastly, the 5th hypothesis examines

thoroughly the correlation between perceived trust

(PT) and facilitating conditions (FC) where we will

explore how trust interacts with the availability of

resources and support systems in influencing

technology adoption.

Figure 5: “Correlation Vector”.

These hypotheses will help us create a "correlation

vector," a tool to identify which factor PU, PEU, or

SI, in correlation with PT, affects BIU the most.

According to the data presented in Figure 5, the

highest impact at the behavioral intention to use a

technology is the Perceived trust about social

influence. That means for people to trust and accept a

technology they have to be introduced and

encouraged to use it from their social environment.

Furthermore, there is a strong correlation between

perceived trust and perceived usefulness. This implies

that technology acceptance is significantly influenced

by users' opinions on both the usefulness and

trustworthiness of the technology. The correlation

between perceived trust and perceived ease of use, as

well as the impact of facilitating conditions, may be

less significant in this case. This is likely because a

large portion of the surveyed population lacks

experience with highly automated vehicles.

5 CONCLUSIONS

In summary, this paper introduces the QETAM

model, a novel framework that bridges the gap

between transportation engineering, social

psychology, and technology acceptance studies. By

integrating these diverse fields, QETAM provides a

comprehensive understanding of the factors

influencing user adoption of Electric, Connected,

Autonomous, and Shared (ECAS) mobility

technologies. The model's strength lies in its ability to

analyze large-scale datasets through advanced

statistical techniques, validating its effectiveness and

offering valuable academic and practical insights into

ECAS mobility. QETAM’s collaborative approach

ensures that future mobility solutions prioritize both

sustainability and user-friendliness.

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Through advanced statistical analysis, QETAM

identifies key adoption factors, such as technological

reliability, user attitudes, infrastructure support, and

environmental considerations. Our findings highlight

Perceived Trust (PT) as the most influential factor in

Behavioral Intention to Use (BIU), particularly its

correlation with Social Influence (SI) and Perceived

Usefulness (PU). These results underscore the

significant role that social acceptance plays in

fostering trust and driving adoption. The BIU score of

21,823.9963 reflects medium acceptance of Highly

Automated Vehicles (HAVs), with potential for

growth as user familiarity increases.

Throughout the EcoMobility project, we aim to

influence future development and remeasure the BIU

score at the project's conclusion. Building on these

insights, we aim to extend QETAM to explore

gender-related differences in technology acceptance.

Understanding how gender influences adoption

factors will enhance the model’s predictive power and

contribute to more inclusive and targeted mobility

strategies. Additionally, ongoing monitoring of user

behavior will support adaptive policymaking and

technological advancements in ECAS mobility.

By continually refining QETAM, this research

contributes to informed decision-making, supporting

the transition to sustainable, connected, and user-

friendly urban transportation systems.

ACKNOWLEDGEMENTS

The work carried out in this paper is part of the

EcoMobility project (https://www.ecomobility-

project.eu/). This project is supported by the CHIPS

Joint Undertaking and its members, including top-up

funding from the national authorities of Turkey,

Spain, the Netherlands, Latvia, Italy, Greece,

Germany, Belgium, and Austria under grant

agreement number 101112306. Co-funded by the

European Union

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