1. Introduction
Artificial intelligence (AI) uses hundreds of digital videos and images to simulate human intelligence [
1]; AI is about building smart machines that can perform human tasks. One application of AI in this regard is autonomous vehicles (AVs), which can drive themselves like humans do [
2]. To provide a realistic context on how AI is influencing AVs globally, autonomous driving and autonomous cars are currently among the most intensely researched and publicly watched technologies in the transportation field [
2]. AVs solve several driving issues, with benefits such as safety, effectiveness, and mobility [
1]. AVs are constructed to improve vehicle control by minimizing human mistakes and by providing safety and superior driving [
3] by using various innovations, including autonomous cruise control, adaptive high beam, collision avoidance, automatic parking, automotive navigation, driver drowsiness detection, wrong-way driving warning, and intelligent speed adoption [
2]. The Saudi Vision 2030 agenda aims to embrace every upcoming innovation in automation and robotics [
4]. As reported in the Dubai News [
5], “Autonomous automobiles are on experimentation in Dubai as the population of Saudis exist there,” while Mideast Beast News [
6] commented on Google selecting Saudi Arabia as the preferred nation in which to launch the Google car. In June 2018, the Saudi government finally lifted the driving ban on Saudi females [
7]. However, because the ban has been in place for so long, the Kingdom is currently dealing with a large number of inexperienced drivers of various ages [
8]. The adoption of AVs could help beginner motorists to overcome their panic and drive with safety [
9]. However, intelligent technologies are incapable of performing intelligent activities such as ethical judgments, reasoning, situation management, or ideation [
1]. Furthermore, there are other possible obstacles to KSA’s adoption of AVs, including traffic management, infrastructure, and liability insurance [
4]. As a result, there is a chance that Saudi females, who represent a very big sample of novice drivers in Saudi Arabia [
7], might reject the adoption. Therefore, this study aimed to measure to what extent novice drivers are willing to adopt AVs in the future. In this study, a novice driver was defined as a driver who had a year or less of driving experience [
10]. The details regarding the requirements for individuals and the AVs selected for this study were as follows: (1) participants needed to be female novice drivers who had a year or less of driving experience and they needed to have experienced AVs prior to this study taking place; (2) the AVs required for this paper were level 4, which are able to move between two different points with human interference only in the case of an emergency.
A thorough review of the literature on AV adoption revealed several research gaps. First, most research focused on those who already had sufficient driving experience (e.g., [
1,
3]) or already held a valid driver’s license (e.g., [
9,
11]). However, to the authors’ best knowledge, no publications addressed AV adoption from the point of view of novice drivers. Second, research into AV adoption is still in its infancy such that this study is at the forefront in exploring the adoption of this technology in developing countries (e.g., KSA). Third, this study focused on Saudi women drivers, whereas in prior research on AV adoption, most of the participants were male (e.g., [
11,
12,
13]), which may bias their results. Fourth, the extant literature on AV adoption is largely based on the dominant technology adoption theories, examining the general factors that may influence users regarding adopting AVs (e.g., [
1,
3,
9,
11]); no studies identified the benefit/risk factors. To overcome these existing research gaps, a new adoption model was developed by adapting the net valence model (NVM). NVM is one of the social science models that is used to identify the benefit/risk factors that influence the adoption of new technology, such as AVs [
14]. The developed model aimed to understand what influences female novice drivers in Saudi Arabia regarding adopting AVs.
The remainder of the paper is organized into seven sections. The second section reviews the literature on AV adoption and explains the NV model. The third section gives the proposed hypotheses. The fourth section describes the procedure for building the proposed model and hypotheses, the sample, and the data collection procedure. The fifth section covers the data analysis, the results of the study, and the new NVM, along with the new constructs. The discussion takes place in the sixth section, while the conclusion, theoretical implications, practical implications, limitations, and future work are given in the seventh section.
6. Discussion
This research evaluated the influence of different constructs on individuals’ intent to adopt AVs. This research applied a net valence model to identify the significant positive and negative constructs, also enhancing it with new extra constructs: alternatives, personal innovativeness, and social influence.
The main goal of the study was to produce a model to explain the factors that influenced the intention of Saudi female novice drivers to adopt AVs. After conducting the assessment of the measurement and structural models, the adoption model was established, as shown in
Figure 2. The model included three dependent constructs—perceived advantages, alternatives, and intention to adopt—and five independent constructs—performance expectancy, enjoyment, effort expectancy, personal innovativeness, and social influence. Nine relationships were proposed to explain the model. After the data analysis process, eight insignificant relationships were eliminated from the adoption model. The nine remaining significant relationships were found to be appropriate for explaining the adoption model for AVs, as shown in
Figure 4.
Second, this research established positive and negative constructs that were based on previous studies from various disciplines and clarified the influence of these constructs on individuals’ risk and benefit perceptions of AV adoption. To the best of the authors’ knowledge, past studies failed to examine the entire range of possible perceived benefit and risk constructs of using AVs. The outcomes revealed that during the consideration of the possible benefits that are linked to AVs, individuals concentrated on the degree to which they enhanced achievement, reduced effort, and minimized people’s worries during driving.
In the field of smart homes, the design and mobility of a briefcase house as an IoT space allowed it to interact with the user and the surrounding space [
15]. AVs use basic technologies similar to smart homes, such as an Internet connection, user interfaces, and wireless networks [
28]. Therefore, most of the benefits and risks that were measured in this study were basically guided by the field of smart homes as evidence that both fields are emerging technologies and face similar benefits/risks [
33]. Like smart homes, this study’s results indicated the significant association of perceived advantages (H1) with AVs adoption; however, it is very important to be aware of the different kinds of risk. Moreover, like [
15], this study revealed the significant association of performance expectancy (H1a) and effort expectancy (H1c) regarding AV adoption. In conclusion, the field of smart homes can support the field of AVs with a variety of factors in future studies.
Although the outcomes revealed an insignificant association between perceived risk (H2) and AV adoption, consumers should be vigilant when deciding to adopt AVs. Hence, AV providers need to minimize the risks that are linked to using their vehicles. The outcomes can be beneficial to providers in making decisions about which kinds of risk to concentrate on. For instance, because time (H2c) and financial risks (H2d) were the only risks that had a significant impact on perceived risk, marketers need to concentrate on the price of AVs and the length of time required to successfully establish and operate the vehicles.
Among the five suggested measurements of risk, finance (H2d) and time (H2c) were the only two that had a significant impact on the perceived risks that prospective users faced regarding successfully buying and operating AVs. Unlike [
15], who found a negative association between financial and time risks and the intention to adopt new technologies, this research confirmed that there was a strong association, with perceived cost being a constituent of the overall perceived risk. The impact of time risk shows consistency in practice, as individuals worry about potential time loss, such as disruption to daily activities, and expect products to have time warranties. Providers also need to concentrate on designing their products to be time-saving (H2c) and needing less time to become familiar, further lowering the perception of time risk. Individuals were also concerned about money (H2d).
Security risk (H2a), performance risk (H2b), and psychological risk (H2e) were found to be insignificant. It may be the case that, with the maturity of the AV technology, individuals consider it to be less vulnerable to be hacked such that security (H2a) is not significant in their risk perceptions, despite [
29] discovering that security is the most commonly perceived risk of smart technologies. Alternatively, clients may be insufficiently aware of potential security risks and their consequences. Future researchers could examine the awareness and knowledge of particular forms of security. AVs also appear to be less affected by total risk perceptions than in many studies. Finally, unlike [
11], the outcomes of this research implied that psychological risk (H2e) had a negative impact on perceived drawbacks. Similarly, although [
15] found a positive influence of performance risk (H2b) and security risk (H2a) on perceived drawbacks in the field of smart homes, this study revealed a negative association toward the perceived drawbacks of AVs.
The outcomes of this research also revealed a direct link between the intent to adopt AVs and three new constructs: alternatives (H8), social influence (H7), and personal innovativeness (H3, H5). The impact of these constructs on individuals’ intention to adopt AVs has largely been ignored [
1,
3,
10].
Unlike [
21], who found a negative influence of alternatives (H8) on the adoption intention of innovation, this study revealed a positive association of alternatives with AV adoption. Many customers may compare the benefits and sacrifices of a new technology with those of alternatives [
31]. Hence, competitive alternatives to AVs, such as hiring personal drivers or taking taxis, can affect adoption by individuals. Although the outcomes revealed a positive association of alternatives with AV adoption, marketers need to be aware that the existence of different driving alternatives is a major challenge. From research on tablet PCs [
21], alternatives were indicated as the initial and key construct that negatively impacted their adoption. However, the outcomes of this research implied that the alternatives had a positive impact on AV adoption. The absence of a link could be because the increase in the use of smartphones and other innovations strengthened an assumption that driving AVs is straightforward and, therefore, the personal energy required appeared to be of little impact.
This study revealed a significant association of social influence (H7) with AV adoption. According to [
19], social influence can change people’s opinions on the usefulness of new technology. Hence, marketers need to concentrate on the overall social perception of AVs. Social advertising and promotion can explain how AVs connect and protect families and friends. According to [
40], “consumers are going to influence other consumers by writing reviews and giving suggestions by using face-face and social media tools”; hence, social influence plays a critical role regarding adopting AVs.
Finally, this study revealed the significant association between personal innovativeness (H5) and AV adoption. Like [
29] with ATM fingerprinting, personal innovation was found to have a favorable impact on both present and prospective adopters’ adoption intentions. Hence, marketers may facilitate the adoption of AVs throughout the early phases of the technology’s marketing by targeting customers with high levels of personal innovativeness. These consumers have a more positive attitude toward the adoption of AVs than other customers.
This work has contributed to research by using the NVM in this context for the first time. Enjoyment, effort expectancy, performance expectancy, time risk, privacy risk, social influence, personal innovativeness, and alternatives seem to possess harmonious effects across various technology environments [
15,
21,
29,
40], whereas the effects of security risk, performance risk, and psychological risk appear to be more background-specific.
Generally, the contributions of this research to the literature are the clarification of the impact of positive and negative constructs on the adoption of AVs, and expanding the NVM with the three extra constructs: alternatives, social influence, and personal innovativeness. Although not every hypothesis was supported, the NVM indicated that some variance in every dependent variable was explained by the model (69% of perceived advantages, 24% of perceived risk, and 48% of intention to adopt AVs), giving powerful approval for the model.
In addition, the results gave significant insight into the impact of alternatives, personal innovativeness, and social influence on the adoption of AVs. Nevertheless, using the NVM meant that some benefit/risk constructs may not be significant. For instance, as psychological risk appeared not to be a problem during the adoption of AVs, its insignificant impact showed consistency with the perception of clients. However, previous studies that used the NVM revealed the opposite; for instance, [
22] discovered that only psychological risk had a significant impact on Italians’ health information seeking.
Finally, unlike previous studies on AVs, which found trust (as a single construct) to be the most significant factor that influenced the adoption negatively [
1,
3,
10], this study measured five different kinds of risk (five different constructs) to calculate exactly which risk was the real reason that prevented consumers’ trust. As clarified throughout this discussion, of the five suggested measurements of risk, only financial (H2d) and time risks (H2c) had a significant impact on the perceived drawbacks.
7. Conclusions
The main contribution of the study was the development of the proposed model, which represented female novice drivers from the biggest driving school in Riyadh, Saudi Arabia. Furthermore, the adoption of AVs by novice drivers could provide ways in which to drive safely, avoid accidents, and feel more confident. Meanwhile, the recommendations to developers and manufacturers of AVs could result in them attracting more novice drivers to their market, increasing sales, improving the services provided, creating a competitive atmosphere with companies, and identifying the benefits and risks that are evaluated by individuals regarding AV adoption. The findings confirmed the value of extending NVM to determine novice drivers’ intentions to adopt AVs. This study made significant theoretical and practical contributions.
7.1. Implications for Theory
The major theoretical contribution of this study was the formation of a new NVM model by extending the original NVM with three more constructs (social influence, personal innovativeness, and alternatives attractiveness). According to a recent comprehensive review, the NVM model has not been applied in the field of AVs.
The current research has many significant theoretical implications, the first of which is its use of the NVM to evaluate individuals’ intention to adopt AVs for the first time. The outcomes revealed that perceived advantages were strongly linked to this intention, while perceived risk failed to have a significant impact. Individuals had a tendency to accept the possible risks and concentrate instead on the possible benefits resulting from AVs. Hence, this theoretical model helped to explain the likelihood of individuals adopting AVs.
7.2. Implications for Practice
First, this study has practical implications for novice drivers to drive safely using AVs, avoid accidents, and feel safer and more confident. Second, it provides recommendations for car manufacturers and AV developers regarding how to attract novice drivers to their market, with the advantages delineated in the previous section.
This research also has key practical implications: first, some perceived advantages were significant in the adoption of AVs and others are not. Hence, during campaigns to encourage the use of AVs, marketers should concentrate on the benefits outlined above. Specifically, performance expectancy had a strong effect on perceived benefit; hence, marketers should emphasize how AVs can help to bring convenience to individuals’ driving. Enjoyment and effort expectancy also had a significant impact; therefore, marketers should emphasize that their AVs will reduce stress and bring more fun into people’s lives.
7.3. Limitations and Future Work
Gathering data from the whole population of Saudi female novice drivers was not possible, and the data from only 1400 respondents were examined. By the beginning of 2019, AVs were available in Saudi Arabia from only a single source, namely, the STC (Saudi Telecommunication Company); therefore, most of the targeted population had not yet experienced AVs. Even with the inclusion of participants from different backgrounds, this sample could show some bias, as it included women from only one area of Saudi Arabia, meaning that the outcomes may fail to satisfy individuals from other cultural backgrounds. Future research should consider variations in cultural background.
Nonetheless, the limitations open avenues for several intriguing possibilities for future research. For instance, researchers might evaluate the significance of other risk constructs in individuals’ overall perceived risk, as the dimensions that were evaluated in this research explained less than half of the variance. Further exploration might include the influence of other constructs in moderating the link between the perceived benefit/risk and adoption intention. According to [
15], subdivided participants into various groupings grounded on culture and established the difference arising from the impact of perceived enefits/risk on BYOD adoption intention were distinct across subgroups. As such, there is a possibility that individuals from different cultural backgrounds have distinct perceptions of benefits and risk, which, in turn, may impact their intention to adopt AVs. Research can also be undertaken to evaluate individuals’ post-adoption satisfaction