The public sector presents important steps for digital transformation. Digital transformation uses a series of tools and methods to improve the relationship with citizens and improve benefits. This paper explores the role of artificial intelligence (AI) in governance processes and provides public institutions with insight regarding the impact of integrating chatbot communication tools when interacting with citizens. The present research provides an analysis of the socio-economic factors that determine the use of artificial intelligence tools, i.e., the propensity to interact more with the public administration as a result of improved communication through virtual assistants, and highlights the implications of AI in improving services towards civil society by determining the degree of satisfaction on aspects such as reduced waiting times in queues, access to information regardless of the traditional working hours of civil servants, quicker execution of operations, et al. The results, derived from an analysis of 507 sets of responses obtained from an online questionnaire, indicate that a number of variables, such as residential environment, employment status, household income and education level, significantly impact the effectiveness of artificial intelligence in mediating citizen communication with government.

In the current context, where environmental concerns are gaining increased attention, the transition toward sustainable urban mobility stands out as a necessary and responsible step. Technological advancements over the past decade have brought private autonomous vehicles, particularly those defined by the Society of Automotive Engineers Levels 4 and 5, into focus as promising solutions for mitigating road congestion and reducing greenhouse gas emissions. However, the extent to which Autonomous Vehicles can fulfill this potential depends largely on user acceptance, patterns of use, and their integration within broader green energy and sustainability policies. The present paper aims to develop an integrated conceptual model that links behavioral determinants to environmental outcomes, assessing how individuals’ intention to adopt private autonomous vehicles can contribute to sustainable urban mobility. The model integrates five psychosocial determinants—perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control—with contextual variables such as energy source, infrastructure availability, and public policy. These components interact to predict users’ intention to adopt AVs and their perceived contribution to urban sustainability. Methodologically, the study builds on a narrative synthesis of the literature and proposes a framework applicable to empirical validation through structural equation modeling (SEM). The model draws on established frameworks such as Technology Acceptance Model (TAM), Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology, incorporating constructs including perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control, constructs later to be examined in relation to key contextual variables, including the energy source powering Autonomous Vehicles—such as electricity from mixed or renewable grids, hydrogen, or hybrid systems—and the broader policy environment (regulatory frameworks, infrastructure investment, fiscal incentives, and alignment with climate and mobility strategies and others). The research provides relevant directions for public policy and behavioral interventions in support of the development of clean and smart urban transport in the age of automation.

Autonomous vehicles constitute a major innovation in the field of transportation, redefining mobility and providing solutions to many of the existing traffic problems, which is why literature seeks to identify the factors influencing their perception and acceptance by the general public. The present research analyzes the impact of demographic factors such as age, education level, professional status on the perception and trust in autonomous vehicles. Existing research studies largely explore technical challenges, empirical studies on the influence of these factors remain limited. The methodology underlying the study is a quantitative one, based on a structured survey applied to a representative sample. Hypotheses were formulated and tested on the basis of a questionnaire answered by 530 respondents. The questionnaire included questions on demographics, familiarity with autonomous vehicles, sources of information used, confidence in safety and cybersecurity as well as concerns about automation and its impact on the labor market. The research results identified a significant negative correlation between age and familiarity with autonomous vehicles, demonstrating that younger people are more informed and more open to this technology. Educational level also influences information sources, as respondents with higher education prefer academic and professional sources. In terms of professional status, the results show that certain professional categories are more concerned about the impact of autonomous vehicles on jobs. Last but not least, the research shows that cybersecurity concerns are not determined by age. This article emphasizes the importance of demographic factors in the public’s perception of AVs and offers guidelines for strategies to inform and adopt this technology.

The rapid advancement of artificial intelligence and automation has led to significant developments in autonomous vehicle technology. However, public trust remains a crucial factor influencing the adoption and widespread acceptance of self-driving cars. This study explores the relationship between driver experience, cybersecurity concerns, and trust in autonomous vehicles, aiming to identify key factors that shape consumer perception. Existing research highlights that familiarity with emerging technologies positively impacts trust, while cybersecurity threats are often cited as a major barrier to adoption. However, empirical evidence on how these factors interact remains inconclusive. This paper investigates whether individuals with a driver’s license exhibit greater confidence in autonomous vehicles and whether concerns about cyberattacks significantly reduce trust. A quantitative research methodology was employed, using a structured survey distributed to 530 respondents. Three hypotheses were tested through statistical analysis, including the Spearman’s rank correlation test and the Mann-Whitney U test. The results indicate a strong positive correlation between familiarity with autonomous vehicles and trust in their ability to reduce accidents. Furthermore, individuals with a driver’s license were found to have significantly higher trust levels than those without. Surprisingly, cybersecurity concerns did not exhibit a meaningful correlation with trust in autonomous vehicles, suggesting that public perception is shaped more by exposure and experience than by theoretical risks. This research contributes to the field of artificial intelligence applications in business and mobility by providing empirical evidence on the psychological and experiential factors influencing trust in autonomous vehicles. The research findings bring to the forefront the need for educational campaigns and hands-on exposure programs aimed at increasing public confidence, especially among people who do not have a driving license. The information this study reveals can help policy makers and impact automotive companies and technology developers, providing support in increasing public acceptance and integration of autonomous vehicles into modern transportation systems.