Article

In with the new? Generational differences in technology adop differences in population technology adoption in the age of self-driving vehicles

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Abstract

With rapid growth of self-driving vehicle technologies, policymakers and industry are actively engaging the public to understand attitudes toward smart mobility. As public officials explore implementing connected systems, they may find diverse reactions. We present an important insight using precise technology adoption curves for three age groups within a major initiative in the United Kingdom, going beyond theoretical expectations. Specifically, the adaptation of self-driving cars reflects the patterns of adaptation to previous technologies. Furthermore, older participants were more likely to be late adopters of the technology than younger participants. Implications from these insights offer the opportunity to enhance public engagement and optimize the implementation of such systems, thereby maximizing population benefits.

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... Divergent needs and motivations are underlying the adoption of new technologies and there are findings that age has a moderating effect on new technology adoption (e.g. mobile health service adoption studied by Zhao, Ni, and Zhou (2018) or AV adoption by Ruggeri et al. (2018). Hence, separate studies are required for different age groups. ...
... The model was compared with UTAUT and nine other information technology adoption alternatives and UMEGA was empirically shown to explain significantly more variability as well as have better fit indices. Ruggeri et al. (2018) and Haboucha et al. (2017) argued that there is a positive relationship between the general attitude to the technology and perceptions regarding the adoption of AV and their benefits. The inclusion of attitude as a mediator was also proposed by Dwivedi et al. (2017) in the revised UTAUT model, which was empirically validated for the context of electronic government. ...
... Hossain, Quaresma, and Rahman (2019) proved that personal innovativeness, i.e. the individual's intention to adopt new technology, was a significant determinant for an electronic health record in the healthcare system by physicians concluding that those more open to new technologies will adopt them earlier. Besides, Ruggeri et al. (2018) in their study distinguished between five groups of adopters and showed that the biggest share of early adopters is amongst the age group between 18 and 25 years while the age group above 65 years was most likely among the groups to be the avoiders or late adopters. Thus, we hypothesise that people who are more open to new technologies are also more likely to be early AV adopters. ...
... Technologically, automated car is built on the combined technologies of machinery, sensor, computer and communication networks to identify the surroundings and move the vehicle onto the proper paths. The automobile manufacturers (like GM and BMW) and emerging IT corporates (like Google and Uber) are developing and testing fully automated vehicle (Anania et al., 2018;Hopkins & Schwanen, 2018;Cohen & Hopkins, 2019 Advances in autonomous vehicles place us on the cusp of a revolution in the transportation system (Sanbonmatsu, Strayer, Yu, Biondi, & Cooper, 2018;Ruggeri et al., 2018;Wei, Chen, Guo, Chen, & Ma, 2020). The automation technology could result in less traffic crashes, relieved congestion, mitigated air pollution and improved energy efficiency related to transportation (Rafael et al., 2020;Wang et al., 2020). ...
... 3.3. Product determinants: perceived usefulness and perceived risks Ruggeri et al. (2018) summarized that the benefits of AVs are generally categorized as (1) improved safety owing to reducing risk of human error; (2) comfort and increased mobility for those who need assistance; (3) better economic outcomes and (4) improved environmental sustainability. Even though these benefits have been completely perceived through media, people's psychology is still contradictory because of customer expectation and perceived risks. ...
... Recent surveys showed that young people and people living in urban areas are the most progressive adopters of new technologies (Ruggeri et al., 2018;Hulse, Xie, & Galea, 2018). Considering that young generations represent a critical group of early adopters of AVs, a convenience sample was drawn from undergraduate students in Beijing. ...
... Divergent needs and motivations are underlying the adoption of new technologies and there are findings that age has a moderating effect on new technology adoption (e.g. mobile health service adoption studied by Zhao, Ni, and Zhou (2018) or AV adoption by Ruggeri et al. (2018). Hence, separate studies are required for different age groups. ...
... The model was compared with UTAUT and nine other information technology adoption alternatives and UMEGA was empirically shown to explain significantly more variability as well as have better fit indices. Ruggeri et al. (2018) and Haboucha et al. (2017) argued that there is a positive relationship between the general attitude to the technology and perceptions regarding the adoption of AV and their benefits. The inclusion of attitude as a mediator was also proposed by Dwivedi et al. (2017) in the revised UTAUT model, which was empirically validated for the context of electronic government. ...
... Hossain, Quaresma, and Rahman (2019) proved that personal innovativeness, i.e. the individual's intention to adopt new technology, was a significant determinant for an electronic health record in the healthcare system by physicians concluding that those more open to new technologies will adopt them earlier. Besides, Ruggeri et al. (2018) in their study distinguished between five groups of adopters and showed that the biggest share of early adopters is amongst the age group between 18 and 25 years while the age group above 65 years was most likely among the groups to be the avoiders or late adopters. Thus, we hypothesise that people who are more open to new technologies are also more likely to be early AV adopters. ...
Article
Smart cities are developing at increasing speed. Smart cities rely on the deployment of information and communication technology (ICT) that is digitally transforming our habitats. Digital transformation affects several areas from transportation, energy, government to the environment. But, primarily, it affects citizens. Therefore, the adoption of all areas of the smart city holds great research potential. The purpose of this paper is thus to provide an insight into millennials’ adoption of autonomous vehicles (AV), which are at the forefront of future transport. Millennials are an important focus group for smart city concepts since they are keen to adopt technology and new transport modes. Our research focuses on technology adoption, perception of the benefits, security, safety, mobility-related efficiencies and concerns as the key AV adoption factors. The impact of these factors is empirically tested with structural equation modelling using data from 382 millennials. We confirm the perceived benefits of AV are vital factors for AV adoption whereas the perceived safety of AV significantly reduces the influence of various concerns regarding AV.
... Age. Previous studies suggest that age is negatively related to an individual's decision to adopt new technology (Morris & Venkatesh, 2000;Ruggeri et al., 2018). This effect is also shown in studies of senior executives (Awa et al., 2015;Pijpers et al., 2001). ...
... Age. Managers' age is not shown to be a significant factor in any of the three countries. Previous studies demonstrate that (higher) age is negatively related to an individual's decision on new technology adoption and use (Morris & Venkatesh, 2000;Ruggeri et al., 2018). However, in this study, this effect does not seem to be significant. ...
... Finally, in many new technology adoption studies (e.g., Gupta et al., 2016;Hoffmann et al., 2022;Melitski et al., 2010), demographic characteristics, such as age and education level, are usually treated as control variables, whereas in this study we included them in the research model and examined them as independent variables because previous research shows that they can have direct effects on individuals' behavioral intention and actual use of new technology (e.g., Ruggeri et al., 2018;Tarhini et al., 2016). ...
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Background: As a breakthrough technology, big data provides an opportunity for organizations to acquire business value and enhance competitiveness. Many companies have listed big data analytics (BDA) as one of their top priorities. However, research shows that managers are still reluctant to change their work patterns to utilize this new technology. In addition, the empirical evidence on what determines their adoption of BDA in management decision making is still rare. Method: To more broadly understand the determinants affecting managers’ actual use of BDA in decision making, a survey was conducted on a sample of 363 respondents from New Zealand, China, and Vietnam who work in different managerial roles. The dual process theory, the technology–organization–environment framework, and the key associated demographic characteristics are integrated to form the theoretical foundation to study the internal and external factors influencing the adoption. Results: The findings illustrate that the common essential factors across countries linking BDA in decision making are technology readiness, data quality, managers’ and organizational knowledge related to BDA, and organizational expectations. The factors that are more situation-dependent and evident in one or two countries’ results are managers’ predilection toward valuing intuition and experience over analytics and organizational size. Conclusion: The findings enrich the current literature and provide implications for practitioners on how they can improve the adoption process of this new technology.
... Age. Previous studies suggest that age is negatively related to an individual's decision to adopt new technology (Morris & Venkatesh, 2000;Ruggeri et al., 2018). This effect is also shown in studies of senior executives (Awa et al., 2015;Pijpers et al., 2001). ...
... Age. Managers' age is not shown to be a significant factor in any of the three countries. Previous studies demonstrate that (higher) age is negatively related to an individual's decision on new technology adoption and use (Morris & Venkatesh, 2000;Ruggeri et al., 2018). However, in this study, this effect does not seem to be significant. ...
... Finally, in many new technology adoption studies (e.g., Gupta et al., 2016;Hoffmann et al., 2022;Melitski et al., 2010), demographic characteristics, such as age and education level, are usually treated as control variables, whereas in this study we included them in the research model and examined them as independent variables because previous research shows that they can have direct effects on individuals' behavioral intention and actual use of new technology (e.g., Ruggeri et al., 2018;Tarhini et al., 2016). ...
Article
Background: As a breakthrough technology, big data provides an opportunity for organizations to acquire business value and enhance competitiveness. Many companies have listed big data analytics (BDA) as one of their top priorities. However, research shows that managers are still reluctant to change their work patterns to utilize this new technology. In addition, the empirical evidence on what determines their adoption of BDA in management decision making is still rare. Method: To more broadly understand the determinants affecting managers’ actual use of BDA in decision making, a survey was conducted on a sample of 363 respondents from New Zealand, China, and Vietnam who work in different managerial roles. The dual process theory, the technology–organization–environment framework, and the key associated demographic characteristics are integrated to form the theoretical foundation to study the internal and external factors influencing the adoption. Results: The findings illustrate that the common essential factors across countries linking BDA in decision making are technology readiness, data quality, managers’ and organizational knowledge related to BDA, and organizational expectations. The factors that are more situation-dependent and evident in one or two countries’ results are managers’ predilection toward valuing intuition and experience over analytics and organizational size. Conclusion: The findings enrich the current literature and provide implications for practitioners on how they can improve the adoption process of this new technology. https://aisel.aisnet.org/pajais/vol14/iss4/3
... Both together make up T-test for equality of means was performed and, according to the table in Appendix, no significant differences were observed between the groups for most of the activities. This is of no surprise since the technology is relatively new and trust into it on a questionable level but being one important factor for AV adoption (Hossain et al. 2019;Ruggeri et al. 2018). Therefore, people have concerns not only to drive in AV but even more so that they give control to AV completely and do other activities instead of driving. ...
... a third of individuals which is consistent with the literature indicating a lower proportion of technologically more and technologically less enthusiastic individuals as the two extremes(Hossain et al. 2019;Ruggeri et al. 2018). ...
... Hardman et al. [65] studied the attitudes of early technology adopters and found that they had positive perceptions of autonomous transportation, and, according to Ruggeri et al. [66], the adoption of driverless cars is a result of the adoption patterns of previous technologies. It is thus reasonable to theorize that early technology adopters will support the use of the ABs [67]. ...
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The topic of emerging mobility services has quickly received attention from scholars and media in recent years. Mobility services employing autonomous buses in transport systems is one such example. Mobility services using emerging technologies are expected to create social, economic, and environmental benefits. However, the potential benefits of emerging mobility services using autonomous technology will not be realized unless self-driving vehicles are accepted and used by many passengers. The recent worldwide pandemic caused us to recognize the benefits of autonomous technologies. This pretest-and-posttest designed research examines the predictors of willingness to ride autonomous buses in a closed environment. The results of this study indicate that a combination of factors, such as societal benefits, attitude and technology adoption, directly and indirectly influence an individual’s acceptance of autonomous buses. This study finds that passengers’ willingness to use emerging mobility services after a sample riding experience is higher than before having a sample riding experience.
... Thence, governments need to work with manufacturers and research organisations to embrace this new mobility and address the arising legislations issues to ensure safety as much as possible [99,100]. Furthermore, policies regarding AVs should be developed neutrally and away from the "bad press" influence [77]. ...
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... There is a growing but still small amount of research around autonomous vehicle adoption [15] [16][17] [18]. Using an adapted Technology Acceptance Model (TAM), [3] postulated that personal driving enjoyment and perceived traffic safety were the two opposing factors that might play a major role in acceptance. ...
... Governments should collaborate with manufacturers and research institutions to welcome this new mobility while ensuring that related regulations are as safe as feasible [66]. To build a legislative framework that provides minimal safety, large-scale AV experiments on public roads must be encouraged. ...
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Adoption of shared autonomous vehicles--a hybrid choice modeling approach based on a stated-choice survey
  • Krueger