Researchers Michael A. Erskine, Stoney Brooks, Timothy H. Greer and Charles Apigian from Jones College of Business, Middle Tennessee State University, Murfreesboro, Tennessee, USA, published an article regarding consumer acceptance of autonomous vehicle technologies (From driver assistance to fully-autonomous: examining consumer acceptance of autonomous vehicle technologies). The article provides an up- to-date review on this issue. Here are some of the key issues.
Technological innovations, entrepreneurial activities, capital investments and infrastructure improvements have created an environment primed for the development and introduction of autonomous vehicles (AV). Alone in the USA, over 160 companies are developing AV technologies. AV are defined as driving automation systems that perform part or all dynamic driving tasks of a motor vehicle on a sustained basis. It is projected that 8 million consumer vehicles will feature some autonomous capabilities in the next five years.
Nevertheless, even with technological advances and optimism, it is widely accepted that fully-AV will not be commonplace on public roads in the immediate future. For the acceptance of fully-AV to occur, not only will the technology, infrastructure improvements and related public policy need to be developed but also consumer perceptions and attitudes will have an important role. Factors that may impact acceptance include trust, control, enjoyment and skepticism.
Therefore, the paper points out that identifying key consumer characteristics will help predict consumer attitude toward AV, a predictor of adoption intention and adoption. Even if consumers perceive benefits, engagement is essential for acceptance. Thus, developing a model to predict AV adoption can elicit guidelines for such consumer engagement.
Theoretical framework and hypotheses
Much of the technology acceptance research is grounded in social psychology or sociology theories, such as the theory of planned behavior (TPB). A widely examined extension of the TPB is the unified theory of acceptance and use of technology (UTAUT). UTAUT included several constructs found to influence behavioral intention and actual usage, moderated in part by gender, age, experience and voluntariness of use. Lauded for its predictive power, UTAUT suggests that numerous factors contribute to usage intention and actual usage of technology.
While UTAUT was focused on organizational technology adoption, UTAUT2 was established to examine technology adoption in the consumer context. UTAUT2 expands upon UTAUT to posit that hedonic motivations, perceptions of price-value and the formation of habits further influence consumer technology adoption behaviors.
By extending UTAUT2, the authors expect to elucidate how perceptions of intelligent-agent and human agent control influence the effects of individual characteristics on AV adoption.
Performance expectancy: Performance expectancy (PE) is the degree to which an individual believes that using a particular system will improve task performance. Scholars have empirically validated the predictive relationship of PE on attitude and found that PE predicts behavioral intention. As it is often envisaged that fully-AV will become a space where individuals can work or rest while commuting, AV should elicit perceptions of greater efficiency in the lives of consumers. The authors postulate.
H1. PE will positively influence attitude toward AV.
Effort expectancy: Effort expectancy (EE) is a predictor of technology adoption. The effects of EE are significant early in the adoption process and have been found to predict attitudes and purchase intentions. As an AV is to perform more or all of the dynamic driving tasks, operating an AV should be perceived as nearly effortless. As improved EE is positively associated with technology adoption, the authors postulate:
H2. EE will positively influence attitude toward AV.
Social influence: Social influence (SI) is the degree that an individual perceives that other, who are important to them, believe they should perform a specific behavior. As the opinions of significant others regarding AV will likely have effects on the attitude toward AV, the authors postulate:
H3. SI will positively influence attitude toward AV.
Hedonic motivation: Hedonic motivation (HM) describes enjoyment derived from using technology and is an influencing factor of technology adoption). Intrinsic motivations, such as pleasurable experiences, are better predictors of behavior than extrinsic motivations. hedonic perceptions had the most substantial influence on attitude. For instance, passengers in fully AV could enjoy a variety of entertainment options while commuting. Therefore, developers and marketers should leverage the hedonic capabilities unique to AV to influence attitudes and adoption intentions. As HM influences technology adoption, the authors postulate:
H4. HM will positively influence attitude toward AV.
Behavioral intent: In the context of this study, behavioral intention (BI) is the subjective probability that AV will be adopted provided a theoretical justification and empirical findings concerning the role of attitude to mediate the effect of beliefs on behavioral intentions. In automotive marketing research, attitude is frequently considered as a predictor of purchase intent. While not included in the UTAUT2 model, researchers argue that removing attitude without theoretical justification has placed limits on the ability to develop a comprehensive understanding of technology acceptance.
The study stresses that BI may only capture a near-future intent, while attitude is a long-term evaluation. This is an important consideration as it is unclear how AV capabilities may evolve.
H5. Attitude toward AV will positively influence BI.
Autonomous capability: It is often thought that the level of autonomy may influence perceptions of autonomous systems. Furthermore, other studies found that attitudes toward AV decreased based on the level of autonomy. Therefore, the authors postulated:
H6. The autonomous capability of a vehicle (MODE) will moderate the effect of consumer perception (PE, EE, SI and HM) on attitude toward AV.
As increased congestion leads to longer commutes, frustrated consumers may find added productivity during a commute appealing. Thus, increased PE may improve commuter attitudes. If the EE needed to learn and use an AV is relatively easy, the consumer’s attitude may also improve. Therefore, according to the study, marketing AV based on improved productivity and ease-of-use may lead to a higher level of attitude and increased BI.
A vehicle with autonomous capabilities can be considered a status symbol. Therefore, it is not surprising that SI has a significant positive relationship with attitude. Consumers may consider owning an AV to symbolize affluence and importance by projecting a need for chauffeured driving. Understanding the relevant perceptions of status in the context of AV will be essential to influence attitude and BI.
As attitude leads to BI, this article stresses how essential it is to focus on factors that form attitudes toward AV. The researchers also indicate that their findings also demonstrate that each of the exogenous UTAUT2 constructs (PE, EE, SI and HM) leads to a more positive attitude.
This is the first study to examine attitudes toward AV through the theoretical lens of UTAUT2.
This study reveals several practical implications. First, hedonic perceptions had the most substantial influence on attitude. This may be because of the novelty of AV technology or additional entertainment capabilities not available to drivers of manually operated vehicles. For instance, passengers in fully AV could enjoy a variety of entertainment options while commuting. Therefore, the research concludes that developers and marketers should leverage the hedonic capabilities unique to AV to influence attitudes and adoption intentions. Similarly, demonstrating the ability to accomplish work while commuting could fundamentally change how consumers, particularly professionals, perceive AV.
Multiple factors may contribute to positive consumer perception and a willingness to accept AV. The researchers emphasize that their findings suggest that attitude toward AV is primarily formed through performance expectancy, effort expectancy, social influence and hedonic motivation.
The authors underscore that the conceptual model can be used to examine the adoption of other nascent technologies.