Master Thesis
University of Amsterdam
The acceptance of the Internet of Things in vehicles: A cross-sectional study on the adoption of technologies in vehicles and the influence of sustainability
E x e c u t i v e P r o g r a m m e i n B u s i n e s s S t u d i e s D i g i t a l B u s i n e s s T r a c k
Date: August 2021
Author: Michael Mackie-Kwist (12944572) Supervisor: Andrea Ganzaroli
Statement of Originality
This document is written by Michael Mackie-Kwist who declares to take full responsibility for the contents of this document. I declare that the literature in this document is original and that sources have been credited adequately. The Faculty of Economics and Business is responsible solely for the supervision of the completion of the work, not for the contents.
Table of Contents
1. Introduction ... 5
2. Literature Review ... 9
2.1 Transition to Circular Economy ... 9
2.2 Environmental Behavior ... 10
2.2.1 Pro Environmental Identity ... 10
2.2.2 Pro Environmental Strivings ... 11
2.3 Internet of Things ... 12
2.3.1 The Internet of Things in Vehicles ... 13
2.3.1.1 Sustainable Advantages to IoT in Vehicles ... 13
2.3.1.2 Issues to IoT in Automobiles ... 15
2.4 Theory Frameworks ... 17
2.4.1 Technology Acceptance Model ... 17
2.5 Research Framework/Conceptual Framework ... 22
3. Methodology ... 29
3.1 Research Design ... 29
3.2 Sample ... 30
3.3 Survey Instrument ... 31
3.4 Limitations of The Design ... 32
3.5 Data Collection ... 32
4. Results ... 34
4.1 Measurement Model (Reliability & Validity) ... 34
4.2 Structural Model ... 37
4.3 Summary of the Conceptual Model ... 38
5. Discussion ... 41
6. Conclusion ... 45
6.1 Managerial Implications ... 45
6.2 Policy Implications ... 47
6.3 Limitations ... 48
Abstract
In recent decades the world has seen an ever-growing influence of technology in more and more aspects of individual’s everyday lives. Starting with a desktop computer to the technologies fitting into your pocket being used in most products. However, new
technologies are being realized as a difference maker in the worlds new battle to become more sustainable. However, are consumers willing to accept all new technologies and to what extent. Transportation, specifically vehicles have been labeled as significant obstacle in the fight against saving the environment do consumers feel the same and will technologies convince or distraught individuals in making vehicle purchasing decisions.
The process of technology acceptance has developed models and are present in a broad range of research. Birthed as the TAM model and further developed to forms of the UTAUT model. These models measure factors, and the influence of these factors have on decision to accept new technologies. This study uses UTAUT model to research and study the intention to adopt new IoT technologies in vehicles and does sustainability influence an individual’s decision.
Data was collected from a variety of respondents from different areas and ages in the Netherlands. This data was used to test hypothesizes and find conclusions. From these conclusions suggestions have been made for managers working in the automobile industry and policy makers in governments.
1. Introduction
For the first time in history, the traditional linear business model in means of production and consumption is being pressured to change. The ecological system in which resources are limited, the amount of waste and consumption is growing together with the population, can only last so long. Businesses have been challenged to accept and focus on becoming more sustainable and eco-friendlier in terms of production (Circular Economy Action Plan, 2020), and consumers are becoming more aware of their consumption habits and their environmental impact (Panda et al, 2020). The Circular Economy is a concept which has grown in popularity and interest within businesses, institutions, and governments (Merli et al., 2018). The model focuses on closing the gap by looping the linear business model through Reuse, Restoration, and Recycling (Ellen Macarthur Foundation, 2016). All industries and sectors will ultimately be directly or indirectly affected by this transformation to become more circular or sustainable.
One of the industries that is already experiencing different innovative transformations is the transportation industry, specifically the automotive sector. The transportation sector is one of the largest emitters of greenhouse gases on a sector specific level and therefore a sector of importance to make impact in the transition to become more sustainable (Ritche, 2020).
One of the popular components to fast-forward the transformation of the economy, and specifically the automotive industry, is the influence of digital technologies. In the transition away from fossil fuels, technology is allowing the automotive industry to change with the purpose of becoming more sustainable or eco-friendly. Falling under the umbrella of new technologies is the Internet of Things (IoT) and a new development which is enhancing the ability of collecting data and the communication of devices across numerous products, sectors, and industries (Pagoropoulos, Pigosso, & McAloone, 2017). On the list of top ten applications
is defined as the networking capability that allows information to be sent to and received from objects and devices using the internet (Nizetic et al, 2020). The general perception of technology, and the IoT, is seen as having a positive impact on society, but the further implementation of technology into more and more aspects of life is a rather new phenomenon.
The digitalization of society and the implementation of technology into the automotive sector or cars specifically will inevitably have an impact on consumers. More information can be gathered from the use of specific transportation methods, durability of parts, or even to give consumers or suppliers a transparent and accurate representation of how sustainable drivers are (Ucar et al, 2020). However, to what extent are consumers willing to accept the implementation of the IoT into vehicles, and will consumer behavior be affected by the interpreted risks of the transparency about their personal use, preferences, or habits with cars consumers purchase and use?
There has been extensive research done on the Circular Economy and technology from a macro level perspective or business model focus (Marrucci et al. 2019) More recent research, with focus on technology as an important influence on the Circular Economy, presented results about the numerous opportunities and solutions, as well as showing the challenges associated with the implementation of technology in the race to be more sustainable (Okorie, et al., 2018).
Nizetic’s (2020) research highlighted social acceptance of the IoT as one of the issues and challenges for the IoT as a tool for sustainability. How there has been limited to no research on a sector level, especially from a consumer perspective.
Numerous consumer behavior frameworks have been developed with a focus on the different perspectives on the factors that consumers base their decisions on (Ajzen, 1985). The Technology Acceptance Model (TAM) is an appropriate model because it doesn’t place heavy
influence on the technology itself but more about the perception or believe this technology is to be (Chuttur M. , 2009). Ultimately, the consumer perception of the IoT and the results of its influences in their vehicles could have an influence on consumer purchasing behavior. If the perception for certain consumers is that the risks of IoT in vehicles outweigh the benefits of driving sustainability, there could be a negative effect on purchasing intentions and ultimately the sector becoming more sustainable and eco-friendlier.
Consumer behavior in relation to sustainability or eco-friendly choices has been broadly researched and has the acceptance of new technologies by Panda et al (2020). Nonetheless, since the phenomenon of sustainability and the use of technology to drive sustainability is relatively new, research related to consumer behavioral effects is relatively new and limited.
The research has shown that sustainability can have an influence on consumers purchasing behavior (Da Costa et al, 2000). However, the impact of the IoT as a driver of sustainability on consumer behavior has been under researched. Aspects of businesses transparency, as well as consumer’s behavior, habits, security, and especially privacy are all affected by the implementation of the IoT into society.
The aim of this paper is to investigate to the attitudes and behavior consumers have towards new technologies, specifically the IoT, into vehicles with focus to enhance sustainability and help protect the environment. The intertwining of the concepts of the IoT for sustainability purposes, and the effect of its implementation on consumer behavior, is an untouched gap that is yet to be researched. What factors are of importance to car owners, what risks do they perceive and are there trends for different consumer groups? It is important for manufacturers to understand what a consumer’s threshold is for acceptance of new innovations or changes, especially with a target to become more sustainable. If a manufacturer is seeking to implement
changes in order to drive sustainability, it is important to know what the consumer is willing to accept to maximize the effect on sustainability. Are consumers concerned with the IoT and the sharing, what in the past was private information with third parties for environmental purposes.
If consumers are not willing to accept these changes, then the goal of becoming more sustainable will not be met. Therefore, other solutions and methods must be developed in order to counter the negative effect automobiles have had on the planet in the past.
Investigating which factors and characteristics influence the intention to adopt new technologies in vehicles, specifically IoT in vehicles. Whether sustainable/environmental factors influence in order to develop further insights on who, why and whether individuals are willing to accept the IoT in vehicles. This analysis will lead to answering the research question:
What factors influence adoption of new IoT technologies in vehicles and what effect does the influence on an individual willingness to be sustainable effect that adoption.
This study is structured as follows, chapter two is an introduction and analysis of previous literature and research in which the individuals’ variables are introduced. This chapter includes an analysis and explanation of the variables and relationship between these variables ending with the hypotheses. The third chapter describes the methodology used for this study, how the relevant data is collected and how this is analyzed. Chapter four presents the statistical analysis resulting from the data collected. Chapter fives consists of the discussion section of this study.
The final chapter presents an overall conclusion.
2. Literature Review
Starting with section 2.1, past literature and research relevant to this study of the circular economy and the worlds focus on becoming more sustainable will be discussed to gain a background in the subject of this study. Section 2.2 will introduce the identity and striving of individuals willingness to become sustainability. Section 2.3 introduces the IoT technologies and influence on vehicles. Section 2.4 outlines theories and models for research into acceptance of new technologies will be presented. Section 2.4 includes the additional factors used in this study. In the last section, 2.5, the theoretical framework is proposed in a conceptual framework including its hypotheses.
2.1 Transition to Circular Economy
The Circular Economy is a term and idea that was first discussed in a study by Pearce and Turner (1989) but further defined as an economic system of closed loops in which raw materials, components and products lose their value as little as possible, renewable energy sources are used and systems thinking is at the core (Ellen Macarthur Foundation, 2016). The current socioeconomic system is linear, in which producers make products, consumers use them, and at the end of a product’s life cycle, it is considered waste. In turn, this leads to environmental degradation and to exploitation of natural resources which are not abundant, and which are becoming scarcer and more expensive (Lieder & Rashid, 2016). At a sector level, the top three economic sectors that have the largest emission of greenhouse gases are transportation, energy/electricity production, and industry or the production of goods from raw materials (US EPA, 2018) (Ritche, 2020). Society is being pressured to adapt to a more sustainable and environmentally friendly model and the Circular Economy seems to be the most popular and adaptable model. Of the three sectors mentioned above, the transportation
or automotive sector can be directly influenced by consumer behavior, while the others have a more indirect relationship. The influence of the Circular Economy will bring change not only to business models but also to the experience in which consumers purchase and use products.
2.2 Environmental Behavior
In current society there is a significant focus towards acting in a more environmentally friendly way. It is recognized that there is a broad spectrum of environmental behaviors, or lifestyles that need to be adopted in order to enhance the transition to a more sustainable future (Whitmarsh & O’Neill, 2010). There are a number of mechanisms that drive environmental behaviors and realizing these mechanisms can help facilitate a more environmentally friendly lifestyle, so that these mechanisms will encourage citizens to move a more environmentally friendly direction. The mechanisms of environmental identity and strivings that have been previous developed by Whitmarsh & O’Neill (2010) and further studied by Kashima et al (2019). will be used to understand relevant determinants of behavior and thus improve its predictive power. There is an overlap and expected correlation of these two concepts on the perceived ease of use and usefulness of the internet of things in vehicles.
2.2.1 Pro Environmental Identity
Whitmarsh & O’Neills uses socio-cultural forces to provides strong evidence for a persons environmental identity and its ability to predict a wide range of pro-environmental behaviors.
The instrument developed called the pro-environmental self-identity scale, asked respondents to indicate whether they agree or disagree to multiple statements ranging from “I think of myself as an environmentally-friendly consumer” to “being embarrassed to be seen as having an environmentally-friendly lifestyle”. Those who affirm themselves as “environmentally friendly person” place themselves into a social category, whose content is largely determined by what it means to be environmentally friendly (Whitmarsh & O’Neill, 2010). The scale seeks to compare cultural conception of an environmentally friendly person with themselves.
This will highlight social and cultural understandings of what it means to be environmentally friendly.
Kashimar et al calls the everyday understandings of “environmental friendliness” mundane environmentalism. This study differs from environmentalism in the sense of environmental activism. Environmental activism seeks to change a cultures status quo and sits outside the mainstream, while mundane environmentalism is a everyday notion of understanding and living a pro environmental lifestyle. For this paper the identity of a mundane
environmentalist as environmentalists and are defined as “persons high in the environmental engagement”.
2.2.2 Pro Environmental Strivings
For this research, the aspect of environmental striving will be implemented as an aspect of environmental identity. Environmental striving is aligned with personal strivings, which is defined as “what individuals are characteristically aiming to accomplish through their behavior or the purposes that a person is trying to carry out.” (Kashimar et al, 2014).
Otherwise conceptualized as an individual’s important personal goals or what that person is trying to do. An individuals environmental striving is based on his or her personal
conviction, views of the world, and drives an individuals actions regardless of what their culture may permit.
The definition of Environmental strivings for this specific research is the extent to which the maintenance and improvement of the natural environment is a personal important personal goals in life (Kashimar et al 2014). The environmental striving of an individual is to motivate behaviors that seek to maintain or improve the natural environment.
2.3 Internet of Things
The term “Internet of Things” was first introduced by Ashton (1999) and has since evolved to and developed for applications in a variety of industries and products. Defined as the networking capability that allows information to be sent to and received from objects and devices using the internet (Nizetic et al., 2020). Small computers attached a growing number of products and devices, exchanging information by themselves all connected to the internet.
(Fleisch, 2010) This exchange, and collection of data is seeking to bring benefits including to monitor and evaluate usage from an environmental point of view more accurately.
Most of the research that focuses on the IoT for sustainable purposes comes from a theoretical or business model viewpoint. Nizetic et al. (2020) is his research summarized the opportunities, issues and challenges with the IoT as a tool for sustainability. A few of the challenges found with the development of IoT technologies for sustainable purposes is the fast consumption of raw materials for new devices, rebound effect, electronic waste, and the socials impacts including security and adoption by consumers (Nizetic et al., 2020). Other research touches on the impact on supply chain, manufacturing, and economic sectors. All previous research has found that there are opportunities for the IoT to influence a sustainable future.
Research which focuses on the practical use and acceptance of the IoT for sustainable purposes in untouched and seen as a gap in the academic literature, which this thesis will contribute towards. Physical things can now be connected to the virtual world; can be controlled remotely, connected to access points to internet services broadcasting information to numerous channels.
However, with any innovation, it also involves risks including technical and socials challenges (Mattern & Floerckmeier, 2010).
2.3.1 The Internet of Things in Vehicles
Cutting-edge technologies, specifically the Internet of Things can play an integral role in the
“rollout” of CE concepts by institutions and governments in the adoption of CE concepts in society as a whole (Demestichas & Daskalakis, Information and Communication Technology Solutions for the Circular Economy, 2020). The rapid development of IoT technologies has allowed for possibilities with respect to ecological advancements in different aspects of life, including that of vehicles (Kanagachidambaresan & Maheswar, 2020) In the past decade, vehicles have become more software driven and are continually developing the ecosystems of connected devices and information being shared. The further implementation of IoT into vehicles will allow monitoring and controlling, which could result in improved accuracy, efficiency and security (Panga et al, 2016).
Goldman Sachs developed a framework which outlined the key attributes of how to make S- E-N-S-E of the IoT and it’s difference to “regular” internet. SENSE is an acronym standing for Sensing, Efficient, Networked, Specialized, Everywhere, outlining the direction of technology development and adoption through the IoT. This same report also directly highlights the implementation to automobiles, which is second in the five key verticals of adoption next after “wearables” and before “connected homes” (Jankowski et al, 2014).
2.3.1.1 Sustainable Advantages to IoT in Vehicles
Whether this be the use of GPS, emergency roadside assistance, or the linking of your smartphone. Vehicles today are already to an extent connected and users experience the continuous advancements in technology that automakers develop. (Krasniqi & Hajrizi, 2016) Most believe that the future of the car industry leads to completely autonomous/self-driving car and a road structure in which the driver is limited in his or her ability to control the cars
every movement and therefore mitigating risks (Krasniqi & Hajrizi, 2016). The IoT is the foundation in the development of autonomous vehicles, and up until that fully autonomous point is reached new functions, abilities and features will be developed by car manufacturers for consumers.
The IoT will allow all aspects of the car to be monitored and analyzed. This monitoring and sharing of information will make it easier to detect any problems in advance as technician or engineers will be able to find and predict failures (Panga et al, 2016). This constant monitoring of diagnostics of parts in the vehicle through the IoT will allow for more efficient use and also be proactive in repairing and therefore expanding the life of a part or vehicle as a whole. This extension of life will enhance sustainability and ultimately make a vehicle eco-friendlier as less parts or cars will have to be made.
Besides the diagnostics part, the IoT in vehicles will also help drivers keep track of the yearly maintenance, analyze fuel consumption, program service appointments, or give advice for a more eco-friendly driving style (Panga et al, 2016). The safety surrounding driving a vehicle will also be enhanced as risks will be mitigated through assisting in predicting driving situations, receiving real time traffic alerts, and informing emergency rescue service when an accident occurs (Krasniqi & Hajrizi, 2016). Vehicles being able to communicate with each other and react to more energy efficient methods depending on surroundings (temperature, altitude, etc.). Not only this impact vehicles itself but also its surrounding stakeholders. The potential impact on more dynamic insurance which is not only dependent on how far you drive (“pay-as-you-drive”) but also you individual risk, speeding, overtaking, dangerous driving (Mattern & Floerckmeier, 2010). These are all capabilities that the IoT will allow or enhance which will have impact on the sustainability of a vehicle.
2.3.1.2 Issues to IoT in Automobiles
“The biggest risk is not taking risk” – Mark Zuckerberg.
A Consumers behavior is influenced by the trade-offs between what one gives up and what one gains. There will always be risks or negative aspects when developing innovative ideas or products and this is also applicable to the implementation of IoT into vehicles. Considering the contrarian viewpoint from all the positive things that the IoT will bring to a vehicle for sustainability purposes, there are also risks or consumers will have to “give-up” that need to be considered.
Trust
Trust is multidimensional and important in any type of transaction or agreement between two or more parties. Trust, from the social phycologist perspective, is characterized in terms of the expectation and willingness of the trusting party engaging in a transaction (Roca et al, 2008).
Mayer (1995) defined trust to be behavioral and based on an individual’s beliefs about the characteristics of another person. Trust should be a critical factor in purchasing vehicles with innovative technologies, IoT, in which consumers don’t have a complete understanding of the technology and the implications of it. Lack of trust is one of the reasons for consumers not to engage in transactions or use of certain products (Roca et al, 2008). Therefore, an individual feeling of trust toward a technology or vehicle is an important determinant when considering an individual’s intentions to use and behaviors towards that product.
Can consumers trust the intentions of the manufacturer and the technologies implemented to have sustainable impact? “Trust is multidimensional and based on the rational appraisal of an individual ability and integrity, and on feelings of concern and benevolence” (Roca et al, 2008).
Benevolence is defined to which a trustee, manufacturer, is believed to do good to the truster, consumer, beyond the profit motives (Mayer et al, 1995). Integrity in this research would be
defined as the consumers perception that the manufacturer will adhere to a set of principles acceptable to the consumers during and after the transaction (Roca et al, 2008). These factors therefore have an effect on the consumer’s confidence to use of a vehicle with new IoT technologies and in the abilities of the vehicle to be more sustainable. Customers who trust are more likely to buy or use products and therefore have an effect on the transition to become more sustainable.
Privacy
Perceived privacy is the confidence or possibility that companies, specifically for this research vehicle manufacturers, collect data about individuals and use that data inappropriately (Roca et al, 2008). To what extent information can be shared as a plethora of information is being sent to manufacturers and potentially other parties (Kirk, 2015). As mentioned above, the IoT will communicate vehicle and driver information about diagnostics, driving habits, and location. There is a common growing concern regarding the use of personal information and uses of that information. It is common for consumers to understand that in the digitalization of society that everything is able to be tracked and traced. Information is being used to attract or gain more insights about each specific user. Is that information always being used for the good of that specific user? This is a common trend in digital business and in marketing. The selling of private data is already a large market that is not transparent to most consumers and to whom the information is being sold to and what information is being shared (Roca et al., 2009) To what extent are consumers willing share this information and with whom are they willing to share this information with?
Security
The IoT implemented into vehicles is also increasing the amount of software that is being placed in all aspects of the car and therefore increase threats (Krasniqi & Hajrizi, 2016).
Perceived security is defined as the consumers beliefs in the ability and willingness of the
technology to keep users and their information from security breaches (Salisbury et al, 2001).
Software must consistently be updated in order to be able to use the software but also defend from security threats (Kirk, 2015).
Falling under security, specifically with the implementation of IoT in vehicles, is the continuous threat of surveillance and the further abilities of vehicles. With more information being shared about the vehicle and driver, third parties are now able to see and measuring exactly what they are doing. The Dutch government in recent years has debated to change
“wegenbelasting”, a tax to the sustainability of your car based on size and engine type. Instead of tax on the standard sustainable measurements of a vehicle some parties now want to tax based on kilometers drive (Wingerden, 2021). The IoT will now give other parties the ability to track this more easily. The Chinese state is experimenting cities in which all aspects of life are tracked and citizens are constantly under surveillance (Romaniuk & Burgers, 2018). The IoT in vehicles will enhance the ability of governments to constantly track driving behavior, and even from a distance see if speed limits are exceeded or other traffic violations occur. To what extent are consumers willing to give up rights/privacy or allow increased surveillance in order to reach a more sustainable goal?
2.4 Theory Frameworks
2.4.1 Technology Acceptance Model
Consumer behavior is defined as all the aspects that affect consumers’ search, selection, and purchase of products (Nelson, 1970). In recent times, purchasing decisions by consumers are more likely now to be influenced by the increasing awareness and inclination towards sustainable consumption (Medeiros & Ribeiro, 2017). In 1989, Fred Davis proposed the Technology Acceptance Model (TAM), in which “that system or technology use is a response
that can be predicted or explained by user motivation, which, in turn, is directly influenced by an external stimulus consisting of the actual systems features and capabilities” (Chuttur M. , 2009). The TAM’s main goal is “to provide an explanation of the determinants of technology acceptance that is general, capable of explaining user behaviour across a broad range of end- user computing technologies and user populations, at the same time being parsimonious and theoretically justified” (Davis et al, 1989)
Davis in his study, concluded that people tend to use or not use a specific technology or type of system based on their perceived usefulness and also the perceived ease of use. The TAM model is used to explain the use of technology, most commonly in the workplace, but the variables can be also adopted to predict consumers’ acceptance. Therefore the TAM is used as a tool to measure the adoption of technologies including the IoT as a new type of IT in automobiles.
TAM has been applied to a variety of studies and researches, from adoption of e-learning (Lee et al, 2012), specific devices, and the adoption Internet banking (Al-Ajam & Nor, 2013). The TAM model has evolved into a variety of versions which have its own strengths and focuses, but the popularity and strength of the original TAM it only employs two behavioral beliefs that a behavioral intention towards a technology: Perceived Usefulness & Perceived Ease of Use.
The definitions for perceived usefulness and perceived ease of use are as follows:
Perceived Usefulness: The degree to which an individual or user believes that the technology will help to improve the performance/efficiency.
Perceived Ease of Use: The degree to what extent an individual or user is comfortable in using the features of the technology.
Figure 1: Technology Acceptance Model
These two variables determine the attitude of the user towards using the technology. The TAM has evolved into other forms in which certain variables are analyzed or specific variables base on what is of importance. The TAM will be used for this research because it is the most widely applied model of user acceptance and usage of technology but also leave space for specific interpretation which is important for the specific research of this paper (Venkatesh, 2008). The TAM says very little about the technology itself but more about what individuals perceive this technology to be (Chuttur M. , 2009). The importance of this research is to measure and see if the perception of the IoT in vehicles differs between different types of potential consumer and to what extent they are willing to accept this technology to become sustainable.
2.4.2 Theory of Planned Behavior
Theory of Planned Behavior (TPB) frameworks a variety of constructs with focus on decision making factors which helps to predict the performance and intentions of consumers. In the framework of TPB, behavior is determined by intention and intention is predicted by three factors; Attitude Towards the Behaviour (ATB), Subjective Norms (SN), Perceived Behavioral Control (PBC). There is a variety of differences when comparing to the TAM model but most relevant to this study is the influence of social factors. A consumer’s belief towards a
technologies. The behavioral belief and evaluation of outcomes is Attitude Towards Behaviour (ATB), defined as “A person’s general feeling of favorableness or favorableness for a specific behavior” (Ajzen, 1989). Subjective norm (SN) is defined as “An individual’s perception of social pressure to perform the behavior.” (Mathieson, 1991). Subjective norm are the normative beliefs and motivation to comply. Finally control beliefs and perceived facilitation as the perceived behavioral control (PBC). Perceived behavioral control is defined as “An individual’s perception of the difficulty in performance the interest.” (Mathieson, 1991).
2.4.3 UTAUT – Unified Theory of Acceptance and Use of Technology Model
The Unified Theory of Acceptance and Use of Technology Model (UTAUT) was developed by Venkatesh et al (2003) to predict user adoption of technology. UTAUT was derived from integrating eight previous theories, which include the TAM, the theory of reasoned action (TRA), IDT, motivational model, social cognitive theory (SCT) model of PC utilization, theory of planned behavior (TPB, and a model combing TPB and TAM (Alghazi et al, 2021). The UTAUT was found to outperform previous models. A recent review focusing on technology and acceptance models between 2010-2020 was conducted, the UTAUT was found to be the most used model (Alghazi et al, 2021). Venkatesh et al further developed the model into the UTAUT 2 and used context but for this specific research the UTAUT is most applicable because the variables of context are not relevant. The variables include Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC).
Performance Expectancy is defined as the degree to which using a technology will provide benefits to a consumer while performing an activity (Venkatesh et al, 2013). Performance Expectancy is based on perceived usefulness from the TAM model and outcome expectations from the TBP. Effort Expectancy (EE) is defined as the degree of ease associate with
consumers use of technology. (Venkatesh et al, 2013) Further development of the perceived ease of use from the TAM model. Social Influence (SI)is defined as the degree to which potential users are concerned with the opinion of other people in their network, community, or society. A conclusive concept also evolving and including the TPB specifically subjective norm. Facilitating Conditions (FC) refers to the support and resources needed to perform a specific behavior. In other words barriers that would prevent a user from using new technology.
Figure 2: UTAUT Model
2.4.4 Perceived Risk & Security
During any type of decision-making process, the evaluation of risk is always included and that is no different when adopting new IT. Perceived Risk is defined by Summer (1976) as “an assessment of the possibility of an event taking place that may impact the achievement of his objective” and initially introduced by Ostlund (1974). For example, the fear of misuse of information by information receivers or failure to safeguard information; this specifically when looking at IT technologies and the sharing or transferring of information (Luo et al, 2010). For this specific research Perceived Risk will focus on the trust and privacy of which information is being shared. Therefore, the definition developed by Luo et al. (2010) will be maintained for this research in which the perceived risk pertains to “probability or fear that the information transmitted may be compromised by the information receiver”. Security can be perceived in a
Perceived security focuses more on the technology and perceived risk on the information shared from the technology.
2.5 Research Framework/Conceptual Framework
The Theories and models mentioned above contribute to the research framework in this study.
The proposed conceptual model of consumer adoption of the IoT in vehicles for sustainability purposes in this study is shown in Figure 3.
As previously mentioned, there is extensive research on the CE, the IoT for sustainable purposes, and consumer behavior. Technology in cars is not a new phenomenon but the implementation of IoT is a new concept. The IoT implemented in vehicles will theoretically allow for extensive advancements in capabilities for users and manufacturers but also allow for more information to be collected, which will ultimately feed these advancements. From a theoretical standpoint there is existing research on IoT and but not extensively on the possibilities it will allow in the future for the automotive industry, including increased sustainability or eco-friendliness. However, research regarding the consumer perspective and their willingness to accept the IoT in vehicles is limited. The TAM model can be used to measure the adoption of technologies and the UTAUT is a further developed model of the TAM which will be used as a framework for this research. The UTAUT model has been used in previous studies to explain adoption of different information technologies, including mobile banking (Zhou et al, 2010) health information technologies (Kijsanayotin et al, 2009), and location-based services (Xu and Gupta, 2009). However, it has seldom been used with focus on sustainability technologies.
The UTAUT transforms the TAM’s perceived usefulness into performance expectancy (PE)
of the concepts of the IoT for sustainability purposes, and the effect of its implementation on consumer behavior, is an untouched gap that is yet to be researched. This study is pursuing to investigate what factors effect consumer behavior and adoption in relation to the implementation of IoT technologies in vehicles.
For this study, the conceptual model in Figure 3 outlines the structural basis for this research.
The basis of this conceptual model combines three different concepts: TAM (Davis et al 1989), TPB (Ajzen, 1985), UTAUT (Venkatesh et al, 2003). In this study there is an expectation that the intention to adopt IoT in vehicles will be affected by other factors and therefore additional factors were included in the conceptual model. The TPB model was implemented, specifically the Perceived Behavioral Control (PBC), because it is believed to have an impact on the intent adopt as necessary resources, skills, and surroundings are necessary (Mathieson, 1991). In recent times decisions regarding sustainability and decisions regarding the environment are sensitive to opinions of others (Kashima et al, 2014). Therefore, it is important to consider opinions of other important persons and its factor in the intention to adopt. This factor will be labeled as social influence. Because this research has a focus on environmental/sustainability, the social influence will also focus specially on sustainability social influences.
An extra measurement in adoption of IoT is added to this study which was earlier introduced by Ostlund (1974), Perceived Risk and Perceived Securuity. It is proposed that there is a risk realized by consumers when adoption vehicles with IoT technologies. There are purposed risks or threats with any advancement and to what extent are consumers willing to accept the IoT in vehicles for sustainability purposes. Having a better understanding from the consumer perspective and the trade-offs they are willing to make to become more sustainable is essential to understand how best to shift towards a Circular Economy, which is socially and
economically beneficial. As mentioned before, a risk is an obstacle in the intention to adopt new technologies, because a potential lack of trust, privacy, or security in the devices.
Conceptual Framework
Figure 3: Conceptual Model
2.5.1 Hypothesis
In the previous section, the models of adoption and acceptance are described in which the conceptual model was visualized. The conceptual model has four factors that predict the intention to use and adopt IoT in vehicles: performance expectancy, social influence, perceived risk and perceived security. Environmental striving is to be an important interacting effect on the relation of these factors and ultimately the intention to adopt the new technology. These hypothesis will help answer the research question of: What factors influence adoption of new IoT technologies in vehicles and what effect does the influence on an individual willingness to be sustainable effect that adoption.
Performance Expectancy (PE)
Performance Expectancy is defined as “is the degree to which using a technology will provide benefits to a consumer while performing an activity” (Venkatesh et al, 2013). The probability that an individual is going to adopt or use a new technology is if he or she believes that that technology will benefit in its performance. For example, a potential user might perceive IoT useful because it will give her more information regarding efficiency of energy use and how he/she can drive more sustainable. Performance Expectancy develops from perceived usefulness in the TAM, which is the most commonly used instrument for predicting technology usuage (Palau-Saumell et al, 2019). Balta-Ozkan et al, (2013) states that the acceptance of technology will be enhanced if the benefits and usefulness of the new technologies are clear and demonstrated. For this research it is expected that performance expectancy has a positive effect on the intention to adopt IoT technologies in vehicles.
Hypothesis 1. PE positively affects the intention to adopt IoT technologies in vehicles.
Social Influence (SI)
Social influence is defined as the “degree to which potential users are concerned with the opinion of other people in their network, community, or society” or “people important to a user think he or she should or should not perform” (Cho, 2011). Similar and developed from the subjective norms in the TPB, social influence is widely considered to be an important factor in the adoption of new technologies (Karahanna et al, 1999). The UTAUT model incorporates scoail influence as an important factor in determining whether an individual intends to adopt a new technology (Venkatesh et al, 2003). When a potential user is uncertain on whether to adopt a new technology, potential users have a tendency to involve their “network” for consulting (Karahanna et al, 1999). Other peoples or influences in a user’s network, community or people
of interest may have a positive or negative influence on the adoption of a new technology.
Environmental friendliness and sustainability are traditionally viewed as a positive in society and mainstream influences. Therefore, it is expected SI will have a positive influence on the adoption of IoT in vehicles.
Hypothesis 2. SI positively affects the intention to adopt IoT technologies in vehicles.
Perceived Risk and Perceived Security (PR) (PS)
Perceived Risk is refers to “the extent to which a functional or psychosocial risk a user feels he/she is taking when using a product” (Luo et al, 2010). This study will focus on two aspects of potential obstacles; Perceived Risk (PR) and Perceived Security (PS). For this study, perceived risk is defined as “fear of misuse of information by the information receivers or failure to safeguard information by the information receivers (Luo et al, 2010). A lack of security and privacy is common and a widely recognized obstacle in not only IoT systems but any system which shares personal information. Therefore, the perceived risk can greatly affect a user’s intention towards a particular technology, in this specific can IoT in vehicles. When the perceived risk is higher the user will be less willing to use vehicles with IoT technologies.
If the security of a technology or internet-based system is unsatisfactory to a user’s perception, this can be seen as an important hindering factor. Perceived security refers to the probability which users believe their sensitive information will not be viewed, stored, and manipulated by unauthorized parties (Luo et al, 2010). Perceived Risk focuses on the trust users have in the manufacturers while perceived security focuses on the security of the system and the level of privacy in which their personal information is handled. Weber (2010) argues that there is a serious risk of uncontrolled privacy and security surrounding IoT technologies. Therefore perceived risk and security are important determinants of the intention to adopt IoT
technologies in vehicles. Importantly the relationships of perceived risk and pervceived security and adoption are reversed. The more perceived risk, the less likely users will adopt IoT technologies in vehicles. The better perceived security, the more likely users will adopt IoT technologies in vehicles.
Hypothesis 3: Perceived Risk will have a negative effect on the intention to adopt IoT technologies in vehicles.
Hypothesis 4: Perceived Security will have a positive effect on the intention to adopt IoT technologies in vehicles.
Environmental Strivings
For this research, the aspect of environmental striving will be implemented as a moderator.
Environmental striving is aligned with personal strivings, which is defined as “what individuals are characteristically aiming to accomplish through their behavior or the purposes that a person is trying to carry out.” (Kashimar et al, 2014). Otherwise conceptualized as an individual’s important personal goals or what that person is trying to do. An individual’s environmental striving is based on his or her personal conviction, views of the world, and drives an individual’s actions regardless of what their culture may permit. The definition of Environmental strivings for this specific research is the extent to which the maintenance and improvement of the natural environment is a personal important personal goal in life (Kashimar et al 2014). The environmental striving of an individual is to motivate behaviors that seek to maintain or improve the natural environment. Are users who consider themselves environmental strivers willing to accept the adoption of new technologies. Therefore, willing to accept more risks and therefore finding the new technology of IoT technologies in vehicles more useful and therefore less effort expectancy.
Hypothesis 5: A higher degree of environmental striving will positively affect the positive effect of PE on intention to adopt IoT technologies in vehicles.
Hypothesis 6: A higher degree of environmental striving has a positive effect on the positive relationship between SI and the intention to adopt IoT technologies in vehicles.
Hypothesis 7: A higher level of environmental striving will have a positive effect on the negative relationship between perceived risk and intention to adopt IoT technologies in vehicles.
Hypothesis 8: A higher level of environmental striving will have a positive effect on the positive relationship of perceived security on intention to adopt IoT technologies in vehicles.
3. Methodology
3.1 Research Design
For research purposes there are three classifications which are most often used in reseach literature, exploratory, descriptive, and explanatory (Saunders et al, 2012). An exploratory study is if a scholar wants to clarify the understanding of a problem, in other words an ambiguous problem (Saunders et al, 2012). Descriptive studies focus on answering the who, what, where and how questions without explaining the cause of the findings (Saunders et al, 2012). Explanatory studies study phenomena’s in order to establish causal relationships among variables (Saunders et al, 2012). This research starts with a research problem, will the implementation of IoT in vehicles have an influence on consumers, which guided the literature review in order to establish the research question and the conceptual framework. Therefore, it can be concluding the research purpose and question acknowledge that this study is descriptive.
Quantitative and qualitative methods are the two approaches most commonly used in social science research. Qualitative research is any data collection or analysis which uses non- numerical data (Saunders et al, 2012). Quantitative research is used when there is any data collection or analysis procedure which uses numerical data. The research method best suitable is a quantitative study. The aim of this study is to collect data to find factors influence the adoption of IoT technologies in vehicles and this data will be tested using a quantitative research method.
There are two approaches to collect data: deductive and inductive. Deductive approaches establish a theoretical position prior to collecting data. In retrospect, an inductive approach
develops theory after the data is collected (Saunders et al, 2008). This study is a deductive research and empirical approach.
The research strategy used in this study is in the form of a survey. A survey will be conducted to gather primary data from potential users of vehicles with IoT technologies. Secondary data was collected through research found on Google Scholar and UvA libraries. The survey will collect quantitative data which will be analyzed by descriptive statistics using SPSS. The survey is a questionnaire based on a standardized set of questions to validate a potential connection between factors that can influence the intention to adopt IoT technologies in vehicles.
There are four main types of a research design: case study, experiment, longitudinal, and cross- sectional (Saunders et al, 2012). Dues to resources, time constraints, and appropriateness to the research, a cross-sectional study approach is used to understand a potential user’s intention to accept IoT technologies in vehicles.
3.2 Sample
Although different generations have different experience with technology, the use of IoT technologies in vehicles are a new phenomenon and therefore no specific users will have extensive experience. Therefore, a sample is to measure the findings from a representative population. The population with focus on the Netherlands. Therefore, participants are living all currently living in the Netherlands and no restrictions regarding age, gender, or other categorical characteristics. Not all participants in the survey had an equal chance of partaking in the population so therefore, the sampling technique used to employ this survey is a non- probability sampling (Turner, 2020). Specifically, a convenience sampling technique was used
because of the limited (financial) resources, ease of access, and limited time frame. At least a minimum of 100 responses will be need for as an accepted amount of data in order to measure correlations to the intentions of behavior (Taherdoost,2016).
3.3 Survey Instrument
The collection of data was done through a survey via an electronically spread survey. The survey was in a form of a survey/questionnaire which was made in Qualtrics and sent/filled in electronically by participants. Participation of respondents was voluntary and completely anonymous. The survey was carried out in the period from 23rd of May to the 14th of June 2021.
The survey begins with a brief introduction to the study and the topic. Participants were informed that all information that would be labelled or discussed as private would be held completely anonymous and kept confidential. The first area of topic in the survey was demographic information; Age range, gender, education, income, province of residence and area of living. Following the demographics two questions were asked with regards to vehicle usage and ownership. The other questions focused on the variables/factors for the theory and conducted from Venkatesh & al (2003) for PE, van Osch (2016) for SI, Luo et al, (2010) for Perceived Risk & security and Kashimir et al (2016) for environmental strivings. The questions will be tailored to attain topic specific data using a Likert 7-point scale ranging from 1 to 7 or strongly disagree to strongly agree respectively. Finally, two multiple choice questions were asked specifically to the intentions of the participants to accept IoT in vehicles. Data collection was (will be) done via the University of Amsterdam questionnaire/survey platform Qualtrics.
3.4 Limitations of The Design
The design of the study is not longitudinal but cross-sectional, which means that the limitation or issue of reversed causality can occur. With focus on the sample/participants, the spectrum is not based on the complete population because the respondents will be found based on convenient sampling or snowball sampling. Therefore, a generalization to the entire population of the Netherlands cannot be guaranteed.
Similar to numerous other surveys there is always the potential issue of the common method bias and specifically to environmental sustainability an issue of social desirability. In order to combat and limit this issue there was no private information collected and the survey was completely anonymous which allowed for more honest answers.
3.5 Data Collection
The characteristics of the sample regard gender, age, education, income, and nationality are shown in Table 1. The research sample table show that majority of the respondents were males at 65%. The age distribution consisted of respondents who are older than 18, while the largest age group represented was 26 – 35. This age group of 35 and younger, millennials, was most important and the most attractive age group for the transition of the industry as these are vehicle user for the next decades. Majority of all the respondents were educated at a bachelor’s degree level or more advanced. There is a good balance, which is important for the analysis, of different levels of education. At least 75% of the respondents own a vehicle which states the reliability of experience and knowledge of vehicle usage and preferences. After an analysis of the data, respondents with numerous missing values were deleted and the total response rate was 115.
Table demographics.
Table 1: Statistics (Control Variables) Respondents
4. Results
This chapter explains the procedures that were used in analyzing the data from the research.
The first steps taken were to test the measurement of the model by assessing the validity and the reliability. The procedure will test how well the measurement items relate to the constructs.
Once the measurements’ reliability and validity were tested, the results from the structural model were conducted. The hypothesizes constructed in the conceptual model were tested by performing a regression analysis to investigate the relationship between the factors that were proposed in the literature in relation to the intention to adopt IoT technology in vehicles. As part of the research, the effect of an individual’s “Environmental Striving” identity was conducted as a moderator. Additionally, the model was controlled by gender, age, etc. was measured.
4.1 Measurement Model (Reliability & Validity)
In SPSS and quantitative studies, the Cronbach Alpha is the commonly used indicator which measures the reliability of the instruments in the study. An exploratory factor analysis was conducted to test reliability which examines the shared variance of variables which are then determined to be factors under the hypothesis (Sage, 2019). In this study there were six factors that were measured by variables which were collected in the form of a survey/questionnaire.
In the exploratory factor analysis, there are a few assumptions which are met by this study: 1.
all variables need to be at the interval level, the sample size should be above 100 respondents, and the variables should be normally distributed regarding Skewness and Kurtosis. The data was cleaned by checking if there was missing data, outliers, and if coding needed to be done to reverse the score to ensure that equations were equal and measuring in a consistent way. There
were no outliers found. The KMO test is larger than .60 and the Bartlett’s test is significant being less the .05. The Kaiser-Meyer-Olkin (KMO) test verifies the sampling adequacy and with a score of .716 deemed approval. The Bartletts test resulted in a score of 1099.98 and the P-value was less than .01, which indicates that the correlations between variables were large for principal component analysis. In the methodology, it stated that the model sought out to have 6 factors in the research. This was confirmed by analysis to find the eigenvalues for each component of data. With a Kaiser criterion of greater than 1, a total of six eigenvalues were found which in combination have a total variance of 67%. The scree plot also confirmed that after the sixth factor there was a leveling off revealed. Table 2 shows an overview of the factor loading and other statistics. The items that were loaded together show that factor 1 represents Environmental Striving, factor 2 represents Social Influence, factor 3 represents Performance Expectancy, factor 4 represents Perceived Risk, factor 5 represents Perceived Security, and finally factor 6 represents Intention.
It is widely accepted in quantitative research using Cronbach Alpha that a level of .7 is accepted as a reliable coefficient (Sage, 2019). Four of the six factors have high reliability by Cronbach’s Alpha with scores above .8. These factors were Environmental Striving (.848), Social Influence (.822), Performance Expectancy (.842), and Intention (.814). The factor of perceived risk met the requirements of having high reliability with a score of .761. Perceived Security however did not meet the .7 level which is seen to be accepted. Perceived Security was right below the .7 threshold. Other techniques were performed such as deleting items to reach the score. However, to reach the .7 threshold, to many questions would have to be deleted in which made the test less reliably on the other end. It is acceptable to use a score which has a score of at least .6, although less reliable (Sage, 2019). It was therefore confirmed to continue with the research. The questions of the survey were based on a 7-point Likert Scale and the
data are ordinal, and therefore almost impossible to have a normal distribution. To continue the reliability and validity test, the research continued analyzing by measuring the average variance extracted (AVE). This explains that there is an amount of correlation in construct items but also validates that the items observed in the construct explain each specific factor.
The AVE is the average of the communalities for each factor items, and these are extracted directly from SPSS. Fornell & Larcker (1981), stated that to have a sufficient convergent validity a value score for AVE must be at least 0.5. All of the factors have an AVE value ranging between .510 to .731. Each of the items which were taken into the measurement of the validity and reliability analysis met a minimum score of 0.5 in the factor loading process. Given the tests and analysis mentioned above, the measurement model meets adequate criteria for reliable and valid factors.
Table 2: Validity Analysis
Table 3: Correlation Matrix
4.2 Structural Model
A path analysis was used to examine the relationship between hypothesizes. In order to investigate the factors that surround the intention to adopt IoT in vehicles for sustainability purposes a hierarchical multiple regression was performed. Included was ES as a moderator and how it interacts with these relations, while the model was controlled by demographic items including Age, Gender, Income, and Education. None of these control variables from respondents had missing data and therefore no respondents were needed to be deleted. The number of respondents again was 115. The first step was test the controlling factors and the results show that they individually did not significantly correlate with the dependent factor, Intention. However, combined they had a significant effect with P value < 0.01, F = 1.412, and explaining 6% of the variance.
Following the control variable analysis the factors PE, SI, PR, and PS were entered with the moderator value of ES to follow. The total variance explained by the model as a whole was 35%, F = 6.369 and P value < 0.01. The additional factors explained an extra 30% percent of the variables after the control variables. Venkatesh et al. (2003) stated that existing technology
acceptance models routinely explain approximately 40% of the variance explained in the intention to use. A variance of 35% is approximately 40% and therefore worthy of testing. In the table below based on the structural/conceptual model, two of the proposed paths are significant. Looking at the path’s values can be both positive and negative relationship with the intention to adopt, in other words the dependent variable. As predicted, all the factors would have a positive effect on Intention except for Perceived Risk. It was predicted in the hypothesis that all the factors would influence intention of individuals to adopt IoT technologies in vehicles. The path coefficients, as seen in Table 4, have significant effect; PE and PR. While the two other paths have an effect but cannot be deemed as significant. Once the moderator effect was implemented, the results came to be different than first expected. The expectation was that an individual environmental striving or willingness to adopt new technologies would have a significant effect on intention to adopt. However, the results deem to show that there is no significant effect with no P values < 0.05, when the moderator is implemented.
Table 4: Path Coefficients
4.3 Summary of the Conceptual Model
The conceptual model, in Table 4, has now been adjusted to include the path coefficients and whether the effects deem to be significant. Only 2 of the 8 hypotheses are regarded as
include factors that would measure the effect on the adoption on new technologies. The factors that were included are Performance Expectancy, Social Influence, Perceived Risk, and Perceived Security. Two of these factors resulted to be significant in the intention to adopt IoT technologies in vehicles. H1 showed significant support, which was the relationship between PE and INT. The other factor being significant is PR and the intention to adopt. The other factors SI and PS deemed not to have a strong or significant relationship with the intention to adopt IoT technologies in vehicles.
The design of the research further proposed that there was a relationship bet a moderator factor being Environmental Striving on the factor’s relationship with intention. However, the results of the analysis state that there were no strong or significant support for these hypotheses.
Although the study must deny hypothesis H5 – H8, there seems to be important information collected with regards to the influence on the intention to adopt IoT technologies in vehicles.
Individuals show that, at least not yet the willingness to be more sustainable or the influence of being environmentally friend had a significant effect on the intention to adopt. Individuals are significantly focused on the performance and risk associated with new technologies. This will be further discussed in the following chapter.
Figure 4: Path Coefficients in Conceptual Model
5. Discussion
This chapter will discuss the results of the findings and provide answers the to the research questions raised in the previous chapters. The goal of this research is to study what factors have an influence on the acceptance of Internet of Things technology in vehicles and whether an individual’s willingness to be sustainable has an effect on that intention to accept this new technology in vehicles. The widely accepted and popular UTAUT model was used to study the if the technology acceptance factors apply to the acceptance of IoT in vehicles. Additional factors were included into the model including perceived risk and perceived security. The moderating effect was “Environmental Striving” and measured its effect on the other factors or paths. This chapter discusses the findings of this study.
The first four hypothesizes in this study tested the relations between the factors in the effect on the intention to accept IoT technologies in vehicles. These elements were derived from existing and widely used models from the TAM model (Davis et al, 1989), TPB model (Mathieson, 1991), and the UTAUT model (Venkatesh et al, 2003). The UTAUT is a further derived model of the TAM model. The factors from the TAM model, perceived usefulness, and perceived ease of use, are further developed and related to the UTAUT model factors of performance expectancy and effort expectancy. The validity of the factors is supported by the results of the research and testing.
It can be seen from the analysis of integration model results that different factors affect the intention to adopt IoT in vehicles to varying degrees. All of the factors, Performance Expectancy, Perceived Risk, Perceived Security, and Social Influence, together have a significant effect on the intention to adopt IoT technologies in vehicles. However only PE and
confidence in their understanding of what IoT technologies are, the information collected and even experience using these technologies in other devices. Therefore, it can be stated with confidence that individuals believe that effectiveness and the risks surrounding the new technology are of most importance.
There was the expectation that an increase in performance would have positive influence on a consumer’s intention to accept IoT technologies in vehicles. The results confirm that there is a significant effect and path coefficient. Therefore, evidence that the increase in performance that an individual expects IoT technologies will increase their intention to adopt the technology in vehicles. This supports previous research recorded by Venkatesh et al, (2003) that performance is a primary determinant of technology adoption. The second factor which confirmed to having a significant effect on the intention to adopt is Perceived Risk. Perceived Risk recognized as a potential obstacle in an individual’s intention to accept IoT technologies in vehicles. In essence the more risk that an individual perceives, the less willing to accept.
Similar to Weber (2010) recorded that a lack of perceived security or a high perception of risk played a role on the intention to accept new technologies but not a significant one. Potential reasoning for this is that individuals don’t have experience and therefore an individuals own perception can be made. This research shows that perceived risk has a significant effect on the intention to adopt IoT technologies in vehicles. IoT technologies is a new technology with majority of users stating to have understanding and experience with IoT technologies. With that experience users seem to have a confidence that the benefits of IoT in vehicles outweigh the risks that users perceive.
Perceived security, similar to perceived risk in focusing on the obstacles of new technologies, did not have a significant effect on the intention to adopt. Perceived Security related to more
