## 2 Literature Review
### Analysis Methods for User Acceptance Evaluation  
Utilizing key terms related to our research topic, we searched through the primary scholarly publication databases--Web of Science, Scopus, and Google Scholar--to retrieve published studies on the acceptance of AVs. The search terms comprised a combination of keywords such as (Automated OR Autonomous OR Driverless OR Self-driving OR Smart Vehicles OR Robot car) AND (Acceptance OR Acceptability OR accept* OR adopt*) AND (HMI OR HCI OR Interaction Design OR User and AVs) AND (test OR experiment). Given the innovative nature of AD technology, our search was confined to the last decade, specifically from 2014 to 2024. This search effort resulted in 472 findings, including academic papers, journal publications, conference proceedings articles, reports, and presentation slides, with duplicates excluded. By reviewing the relevant literature, we found that research in this field usually forms a user acceptance model through development and testing, thus reducing the likelihood of user resistance or rejection [22]. A detailed description of the evolution of user acceptance models will be provided hereafter. The most representative models and those related to autonomous driving technology are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref. ** & **Theory Name** & **Description** & **Influencing Factors** & **Definition** \\ \hline \multirow{6}{*}{[23; 24; 25]} & & & Perceived usefulness (PU) & The extent to which an individual perceives that utilizing a specific system would improve their job effectiveness. \\ \cline{3-4}  & & It is a widely accepted model in information systems and expands in driving environments to predict driver behavior, such as in-vehicle navigation, cruise control, and other assistance systems. & Perceived ease-of-use that using a specific system would require minimal effort. \\ \cline{3-4}  & & It is a widely accepted model in information systems and expands in driving environments to predict driver behavior, such as in-vehicle navigation, cruise control, and other assistance systems. & Attitude Toward Using & An individual evaluates the appeal of utilizing a particular information system application. \\ \cline{3-4}  & & & Behavioral intention to use (BI) & An individual’s likelihood of engaging in certain behaviors. \\ \hline \multirow{6}{*}{[26; 27; 28; 29]} & & & Performance Expectancy (PE) & The extent to which an individual perceives that utilizing a system would contribute to improving job performance. \\ \cline{3-4}  & & It aims to explain user intentions and behavior. This model is frequently used in transport studies from a technology acceptance standpoint. & The connections between the effort exerted in the workplace, the performance attained, and the rewards garnered. \\ \cline{3-4}  & & It is a variation of the UTAUT that specifically targets in-car technology instead of overall car technologies. & The attitudes, beliefs, or behavior of an individual are influenced by the presence or actions of others. \\ \cline{3-4}  & & It is a variation of the UTAUT that specifically targets in-car technology instead of overall car technologies. & The extent to which an individual perceives that the use of AVs will affect his or her well-being. \\ \cline{3-4}  & & It is a variability of the UTAUT that specifically targets in-car technology instead of overall car technologies. & An individual’s belief in the capacity to produce specific performance attainments. \\ \hline \multirow{6}{*}{[30; 31; 32]} & & & One’s positive or negative evaluation towards the introduction of new technologies. \\ \cline{3-4}  & & It is a widely accepted model in information systems and expands in driving environments to predict driver behavior, such as in-vehicle navigation, cruise control, and other assistance systems. & The extent to which an individual perceives that utilizing a specific system would require minimal effort.