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11 May 2022

Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment

,
,
and
1
Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
2
Faculty of Digital Technology and Media, Universiti Melaka, Melaka 78200, Malaysia
*
Author to whom correspondence should be addressed.
Future Internet2022, 14(5), 148;https://doi.org/10.3390/fi14050148
This article belongs to the Section Big Data and Augmented Intelligence
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Abstract

Driver acceptance studies are vital from the manufacturer’s perspective as well as the driver’s perspective. Most empirical investigations are limited to populations in the United States and Europe. Asian communities, particularly in Southeast Asia, which make for a large proportion of global car users, are underrepresented. To better understand the user acceptance toward in-vehicle applications, additional factors need to be included in order to complement the existing constructs in the Technology Acceptance Model (TAM). Hypotheses were developed and survey items were designed to validate the constructs in the research model. A total of 308 responses were received among Malaysians via convenience sampling and analyzed using linear and non-linear regression analyses. Apart from that, a mediating effect analysis was also performed to assess the indirect effect a variable has on another associated variable. We extended the TAM by including personal characteristics, system characteristics, social influence and trust, which could influence users’ intention to use the in-vehicle applications. We found that users from Malaysia are more likely to accept in-vehicle applications when they have the information about the system and believe that the applications are reliable and give an advantage in their driving experience. Without addressing the user acceptance, the adoption of the applications may progress more slowly, with the additional unfortunate result that potentially avoidable crashes will continue to occur.

1. Introduction

Car manufacturers, throughout recent years, have been focusing on delivering new car models equipped with in-vehicle or pre-install applications [1] which support car navigation, maneuver and stabilization. Typically, the applications involve either a camera, sensors or a millimeter wave radar, or a combination of these, to support the driver and ease the driver’s driving experience rather than annoy [2,3] which may cause the system to be turned off or its disuse. Human limitations in assessing the current driving context may be lifted via an extension of these in-vehicle applications (see Table 1). Generally, these applications were designed for the global market. However, users’ acceptance toward technology has been known to be influenced by cultural differentiation. Overlooking this factor may cause the technology to receive poor attitudes and acceptance rates from the users which eventually may lead to poor or nonactual use of the application [2].
Table 1. In-vehicle applications.
Driver acceptance studies are vital from the manufacturer’s perspective as well as the driver’s perspective. Generally, these vehicle manufacturers have included these systems in their manufactured cars to promise an improved and safer driving experience for both vehicle drivers and passengers. Understanding consumers’ acceptance is key for effective implementation and the actual use of advanced driver-assistance systems. There is limited research on understanding the acceptance process of in-vehicle applications from the driver’s viewpoint [4].
Most empirical investigations are limited to populations in the United States, Europe, Korea, China and Taiwan, with a different culture than Malaysia [5]. The user perspective on new and unfamiliar technology will vary and will normally be driven by personal characteristics and locality. A technology developed based on European culture may not directly fit to users residing in Asian countries [6]. As a result, it is critical to assess the responses of people from a certain background as part of the global market user acceptance studies of the technology [7,8] to acquire the full potential of innovations that cannot be optimally reached until they are well-received by society. Asian communities, particularly in Southeast Asia, which make for a large proportion of global car users, are underrepresented. Even though most modern vehicles nowadays are equipped with in-vehicle applications (see Table 2), these applications are considered new among local drivers. Furthermore, Malaysians generally prefer vehicle-resident features that help them avoid accidents [9], such as collision warnings with auto-braking systems and blind-spot information systems. These vehicle safety features could influence the vehicle buying behavior of urban buyers in Malaysia [10]. However, not all drivers have a propensity or desire to implement the technology. Hence, we intend to explore their acceptance and intention to use the applications should they be made available.
Table 2. Available advanced driver-assistance systems based on vehicle models.
Using the results of this study, along with other available information, the decision-makers may decide how to proceed with additional activities involving in-vehicle technologies. To our best knowledge, we have not found any further study investigating drivers’ acceptance, specifically addressing Malaysian drivers. The output of this study may also provide input to the implementation of the Malaysia Intelligent Transport System (ITS) Blueprint, which aims to help drivers make informed decisions by outlining three Focus Areas, namely Automated Enforcement, Weigh-in-Motion and Emergency Management [11]. The remainder of this paper is organized as follows. Section 2 describes road safety and advanced driver systems applications focused on Malaysia. The works directly related to this study are discussed in Section 3. Section 4 discusses the methodology approach used for this study. The findings and discussions for this study are provided in Section 5. Finally, Section 6 presents the conclusions.

3. Research Model and Hypotheses Development

According to the literature, Technology Acceptance Model (TAM) is a reliable model for exploring the acceptability of new technologies [20,39] and describing user behavior and technology usage [42]. However, because TAM was commonly explored in the context of other technologies that are different to in-vehicle applications, we aim to investigate other constructs related to driver acceptance by extending the basic TAM model.
The basic independent variables, i.e., perceived usefulness (PU) and perceived ease of use (PE), were first selected from the basic TAM model to represent users’ intention to use the in-vehicle applications. We assumed that if user-perceived in-vehicle applications are useful, the user would also perceive that the applications are easy to use and eventually will intend to use the applications. AT and PU are the most important factors influencing the user acceptance [21]. Moreover, if a user perceives that the in-vehicle applications are easy to be used, i.e., users can easily activate the application when they are in the driver’s seat, and if the interaction process through the human–machine interface is simple and useful for a driving experience, the user will indicate a positive attitude (AT) and a high probability of accepting the applications. However, we removed the actual use variable because the in-vehicle applications are momentarily not essential components of all vehicle models, and it is beyond the scope of this study. The intention to use in-vehicle applications (BI) is set as the target variable of the model to represent users’ or drivers’ actual system use.
Three other variables relevant to the context of drivers were included, including trust (T), system characteristics (SCs) and social influence (SI). SC refers to the preferable design or features of the in-vehicle applications, whereas SI represents social factors that influence a user’s opinion on aspects of the in-vehicle applications. A study among licensed drivers from United States and Canada revealed that drivers who are not vehicle owners but have better knowledge of system capabilities have lower trust level. However, the trust level of drivers who are also vehicle owners does not seem to be influenced by the knowledge of system capabilities [40]. In another study, Chan et al. studied the effect of trust in the user acceptance of 5G-connected autonomous vehicles. Authors concluded that trust has mediating effect on PU, PE and SI with BI [43]. Apart from that, ref. [35] mentioned that users are more accepting toward the technology if they are certain of the recommendation provided by the system. This may be reflected using T and PU.
In addition, personal characteristics (PCs) of users refer to gender, age, prior knowledge about in-vehicle applications, self-capabilities and involvement in accidents as well as driving distance per week which may influence a user’s trust, the expectation of system characteristics and impact of social influence toward the in-vehicle applications. Individuals who are highly educated and earn a good paycheck may be more willing to use the in-vehicle applications [15]. The older a driver is, the more concerned they are regarding the PU, T and SC [41]. Furthermore, ref. [39] concluded that drivers in Jakarta community are positively and significantly influenced by only AT, PU and subjective norms to adopt FCW and LDW. Hence, based on our literature, the research model is designed as in Figure 2 and the research questions of this study are listed in Table 5 with corresponding hypotheses.
Figure 2. Research model.
Table 5. Research questions and hypotheses.

4. Questionnaire Design and Data Collection

We collected data from 308 respondents using a questionnaire which was designed based on previous studies. The items in the questionnaire or survey for each construct are shown in Table 6. Respondents are required to rate the items on a scale of 1 (totally disagree) to 7 (totally agree).
Table 6. Survey items.
The sociodemographic details of each respondent include their gender and age as other previous work. In addition, we require information whether they are licensed driver or not, involvement in road accidents, driving distance per week, locality as well as knowledge about in-vehicle applications. Respondents were also required to self-declare their hearing, vision and motor skills. The survey items categorized under the personal characteristics’ variable are presented in Table 7.
Table 7. Details of respondents (n = 308).

5. Data Analysis and Results

The user responses are made available in Zenodo, an open-access repository under the Creative Commons Attribution 4.0 International license. The data analysis was performed using the Real Statistics Resource Pack software (Release 7.6) for MS Excel [44], including items consistency, correlation analysis, variance inflating factor, regression analysis and mediation analysis. Moreover, the results are presented either in tabular form or figures, and findings are discussed accordingly.

5.1. Construct Items

In this study, a total of seven constructs, i.e., the SC, SI, T, PU, EU, AT and BI, were involved, and the responses were collected based on the survey items as in Table 6. The Cronbach’s Alpha tests were performed to assess the reliability of the multiple-question Likert scale used to measure the latent variable structure of psychological measures as single items derived from the respondents. The Cronbach’s Alpha value presents how reliable a set of test items are to validate and authenticate the response for the TAM constructs, i.e., 0.91–1.00 (excellent), 0.81–0.90 (good), 0.71–0.80 (good and acceptable), 0.61–0.70 (acceptable) and 0.01–0.60 (not acceptable). We found that the scale system is highly reliable for all constructs where the average is 0.7872. All variables have a value of Cronbach’s Alpha higher than 0.7 except for the target variable, i.e., BI, which is 0.6294 (Table 8). Nevertheless, the items for the BI are still acceptable. Increasing the number of items under this construct may increase the internal consistency.
Table 8. Reliability and validity analysis on variables.
The Kaiser–Meyer–Olkin (KMO) test value of 0.791 indicates that the sample is adequate and has sufficient information to estimate factor solutions. In addition, Bartlett’s test p-value is less than 0.001 which is significant to reject the null hypothesis. Hence, there exists some level of correlation among the items to estimate the factor loadings. A Confirmatory Factor Analysis was performed using JASP 0.16.1. The factor loadings of each item are shown in Figure 3.
Figure 3. Factor loadings.
In addition, we calculated the Average Variance Extracted (AVE) and determined the convergent validity as well as the discriminant validity. We conclude that there is a convergent validity when the AVE is 0.5 and above. Moreover, the discriminant validity exists when the square root of AVE is more that the correlation value. The results are shown in Table 9. Evidence of both the convergent and discriminant validity demonstrates the constructs’ validity.
Table 9. Convergent and discriminant validity.

5.2. Correlation Analysis

The Pearson product–moment correlation coefficient r was calculated between the variables. The r value is interpreted as 0.0–0.1 (negligible), 0.10–0.39 (weak), 0.40–0.69 (moderate), 0.70–0.89 (strong) and 0.90–1.00 (very strong). Based on Table 10, in general, positive relationships exist among the TAM variables. Strong relationships exist between the PU and T and also the SC and SI with r values more than 0.7. In addition, strong relationships also exist between the AT and BI as well as the SI and T. However, the EU and BI are the only variables with weak relationships compared to the other variables with moderate relationships.
Table 10. Correlation between TAM variables.
Furthermore, the relationship among the personal characteristic (PC) variables was also assessed and illustrated in Figure 4. There is no correlation (negligible) among most of the variables. However, weak relationships exist between a few variables. This shows that although the response of one variable may change the other correlated variables, the relationship is not strong and can be ignored.
Figure 4. Relationship among PC variables.

5.3. Multicollinearity

When there is a relationship among the exploratory or control variables, there is a possibility of multicollinearity. In regression analysis, the first step is to detect multicollinearity. Generally, a variance inflating factor (VIF) above four indicates that multicollinearity might exist, and further investigation is required. When the VIF is higher than 10, there is significant multicollinearity that needs to be corrected. Because only the VIF for trust (T) is slightly above four, it can be safely ignored without suffering from multicollinearity. The regression coefficients are not impacted and only exist in the control variable but not in the variables of interest (PU, PE and BI). The VIF of each variable is shown in Table 11.
Table 11. Variance Inflating Factor (VIF).

5.4. Causal Relationship

The causal relationships between the constructs PU, EU, AT, BI, DC, SC and SI are investigated using regression analysis. The results are shown in Table 12. The hypotheses are validated at 95% confidence level. If the null hypothesis is rejected, it is statistically significant that there is a non-zero correlation among the variables, and it can be modeled with the regression equation. Furthermore, the correlation between the predicted value of Y generated in the equation and the actual Y value for each unit refers to the multiple R. The coefficient values provide the impact or weight of a variable toward the entire regression model.
Table 12. ANOVA results.
The assumption is that the combination of independent variables will generate a larger multiple R or correlation than any single variable used as a predictor variable. When single predictors were used (T, SC, SI) to predict the PU, the multiple R values were 0.73987, 0.74992 and 0.72057, respectively. The multiple R value increased to 0.82550 when the predictors were combined. Moreover, the Adjusted R2 indicates the amount of variability being explained by the regression model. Any field that attempts to predict human behavior, such as psychology, typically has R-squared values lower than 0.5. For instance, the value of the Adjusted R2 is 0.6783 when the T, SC and SI were assigned to predict the PU. This shows that the explanatory power of trust, system characteristics and social influence to perceived usefulness is 67.83%. Because the value is more than 0.5, the variables are a good fit to predict the perceived usefulness of in-vehicle applications. This means that the combination of the variables has a significant positive effect on the user-perceived usefulness of in-vehicle applications. When trust, system characteristics and social influence have high values, users are much more likely to perceive that an in-vehicle application is useful. The multiple regression analysis results of different models are summarized in Table 13.
Table 13. Multiple Regression Analysis Results.

5.5. Mediating Effect Analysis

This section uses the hierarchical regression method as suggested by [44] to verify the mediating effect of attitude between different factors. First, we analyzed the mediating effect when (i) AT is the mediator between the PU and BI and (ii) AT is the mediator between the EU and BI. The results are shown in Table 14. When the partial correlation analysis is performed i.e., without the AT as the mediator, the correlation value between the PU and BI dropped to −0.0355, and the correlation value between the EU and BI also dropped to −0.0271.
Table 14. Mediation Analysis (PU affects the outcome of BI indirectly through mediator AT).
Moreover, to determine the significance of the relationship with the mediator AT, the Sobel test was applied. Because the p-value is less than 0.05 (95% confidence level), both paths, i.e., PU→AT→BI and EU→AT→BI, are significant and this confirms that mediating relationships exist between the PU and EU with the BI where the AT is the mediator variable. Additionally, we analyzed the mediating effects considering the EU as the mediator variable between the SI, SC and T with the AT and PU as the mediator variable between the SI, SC and T with the AT as in the research model. Similarly, the semi correlation between the mediator variable and the target variable was weakened by the direct variables. This indicates a mediating effect. We confirm the findings with the Sobel Test as in Table 15. The p-values are significant at a 95% confidence level, ensuring that the PU and EU are the mediator variables for the SC, SI and T, leading to the AT. A user with high values of SC, SI and T will perceive the in-vehicle application as useful and easy to use in their driving experience, which will lead to the user having a positive attitude toward the application and eventually having a high intention to use the application.
Table 15. Results of Sobel Test.

5.6. Linear and Non-Linear Relationship

A correlation shows the relationship between two variables, while regression allows us to see how one affects the other. Based on the correlation analysis results, we examine the type of regression involving the personal characteristics variables, including gender, age, locality, involvement in accidents, knowledge about in-vehicle applications, locality, self-reported capabilities (vision, hearing, mobility and dexterity) and driving distance per week as shown in Table 16. Non-linear regressions were found for age and intention to use the in-vehicle application, self-reported capabilities and system characteristics, gender and attitude. We compared the standard error values of linear and non-linear regression models. A lower standard error value indicates a better fit model for the variables.
Table 16. Personal characteristics variable relationships.
Table 17 summarizes our findings when we further investigate the personal characteristics variables, which may have a direct influence or moderate the investigated relationships. Knowledge about in-vehicle applications positively influences trust, social influence and system characteristics, which eventually fosters a positive attitude and higher intention to use the application in their driving experience. Because the paper focuses on individual acceptance, locality and knowledge contribute to social influence. Individuals who reside in sub-urban and urban areas may have more exposure to the automotive changing landscape and information, allowing them to react better toward their social cycle views and comments.
Table 17. Models with personal characteristics variable.

6. Discussions

This study investigates the user acceptance of in-vehicle applications where recent vehicle models are equipped with the application and automotive aftermarket service vendors offer the application as a vehicle accessory that can provide advanced driver assistance to users. Due to the variety of vehicle owners and users, this paper aims to answer several research questions by modeling and quantifying the user acceptance of in-vehicle applications based on survey responses. A research model based on the integration of the TAM, trust, system characteristics, social influence and personal characteristics is presented. In addition, the empirical results on the user intention to use in-vehicle applications in a driving experience are provided. Trust and system characteristics are important to Malaysian users. Users will simply turn-off or ignore the warnings from the application which annoy them. They are keener on trusting their instincts then letting technology influence their driving decisions. Their biggest worry is that the application may be faulty and tempered by unauthorized personnel. However, if the users are getting enough information and have seen or observed their close contact using the application before, they have a more positive attitude toward the technology.
The results of this study are limited to users in Asian countries that are in the early phase of implementing autonomous vehicles, specifically Malaysia. Because previous studies focused mostly on European countries, which have a different culture, there may be a variation of the results when it comes to investigating the user acceptance of the technology. We assumed that in-vehicle applications would be made available to users in recent vehicle models and be available as vehicle accessories to be installed in earlier vehicle models. We did not consider the variety of in-vehicle applications and providers. Hence, the respondents’ opinions are based on the general usage of in-vehicle applications.
Additionally, because attitude and behavior are important in marketing in-vehicle applications, the TAM has been adopted to investigate the underlying relationship between attitudes and the behavior or intention to use in-vehicle applications. The attitude–behavior relation is not always straightforward and linear but may display non-linearities. Weak attitude evaluations might not have much of an effect on the user’s intention to use the in-vehicle applications. Additionally, an attitude change will not necessarily be followed by an equal change in the user intention or actual application use. Thus, segmenting users based on their attitude extremity before designing a marketing strategy can be valuable.

7. Conclusions

This study presents an implementation of the basic Technology Acceptance Model (TAM) with additional constructs. In addition to the original TAM constructs, we added three variables which are related to driver context, i.e., system characteristic (SC), trust (T) and social influence (SI). We investigated the relationships of the SC, T and SI to the perceived usefulness (PU) and ease of use (EU) which will influence user attitude and lead to the intention to use the in-vehicle application. Moreover, we examined the relationship between the personal characteristic (PC) variable and the SC, T and SI. Compared to a study among Romanian licensed drivers [21] which highlighted perceived usefulness and attitude as the main factors influencing the user acceptance of in-vehicle applications, our results show that other factors such as knowledge about in-vehicle applications can significantly affect trust and social influence, attitude and usage intentions among Malaysians. Similar to the in-vehicle application users in Czech [28], our findings agree that a user will be able to make an informed decision if they are aware of the advantages and limitations of the applications. Applications which are promoted as safety applications will have a higher acceptance rate. Moreover, gender and driving experience also have a moderating effect on the BI [4]. However, in contrast to [45], which investigates user acceptance among users in Rhodes Island, USA, our study shows age and gender do not have a significant influence on the user acceptance. The driving distance per week or driving experience also has no influence on the user acceptance. In addition, the findings show that trust, system characteristics and social influence may also influence the perceived usefulness and perceived ease of use, which in turn positively affects attitude toward using an in-vehicle application, a significant predictor of usage intentions.
Thus, we summarized the findings from our work in relation to each research question in Table 18. Our work fills the gap in the existing research by extending the basic TAM model with variables which are influential on the user acceptance of the in-vehicle applications in a Malaysia context. Even though our study is not designed to study specific in-vehicle applications, vehicle marketers will generally benefit from understanding the factors which positively influence the user acceptance of in-vehicle applications. Consideration of these factors may provide better insight for the developers to ensure the positive response of users toward the advantages of effectively utilizing the in-vehicle applications for a better and safer driving environment. Communications can be strategically planned to educate the users on the benefits of in-vehicle applications to increase the acceptance level in the nation.
Table 18. Findings based on research questions.

Author Contributions

Conceptualization, S.F.A.R. and A.A.; data curation, S.F.A.R. and A.A.; formal analysis, M.F.A.A.; investigation, S.F.A.R.; methodology, S.Y. and M.F.A.A.; resources, S.Y. and M.F.A.A.; supervision, S.F.A.R.; validation, A.A.; writing—original draft, S.F.A.R. and S.Y.; writing—review and editing, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Research Grant Scheme under the Ministry of Education Malaysia (Grant Number: FRGS/1/2019/TK08/MMU/03/2).

Institutional Review Board Statement

This research obtained ethical approval from the Technology Transfer Office of the Multimedia University (Approval Number: EA0562021).

Data Availability Statement

Abdul Razak, Siti Fatimah; Yogarayan, Sumendra; Abdullah, Mohd Fikri Azli; Azman, Afizan In-vehicle Applications Among Malaysians 2022. https://doi.org/10.5281/zenodo.6393708 (29 March 2022).

Acknowledgments

The authors fully acknowledge the Ministry of Higher Education (MOHE) for the approved fund which makes this important research viable and effective. The authors gratefully acknowledge the use of services and facilities of the Connected Car Services Research Group, Centre of Intelligent Cloud Computing at the Multimedia University. The authors would also like to thank the anonymous reviewers for their constructive comments.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

360 cam360-Degree-Parking Assist
ACCAdaptive Cruise Controls
AEBAutonomous Emergency Braking
A-HoldAuto Hold/Brake Hold
A-ParkAuto Parking
ATAttitude
BIIntention to use the technology
BSMBlind-spot monitor
C-TAM-TPBCombination form of TAM and TPB
EUPerceived Ease of Use
FCWForward Collision Warning
HDCHill Descent Control
HASHill-Start Assist
HUDHead-up Display
IDTInnovation Diffusion Theory
LDWLane Departure Alert/Warning
LKASLane-Keep Assist
MMMotivational Model
MPCUModel of PC Utilization
PEDPedal Misapplication Control
PUPerceived Usefulness
RCTARear Cross Traffic Alert
SCSystem Characteristics
SCTSocial Cognitive Theory
SISocial Influence
TTrust
TAMTechnology Acceptance Model
TIBTheory of Interpersonal Behavior
TJALow-Speed Follow/Traffic Jam Assist
TPBTheory of Planned Behavior
TRATheory of Reasoned Action
U&GUser and Gratification Theory
UTAUTUnified Theory of Acceptance and Use of Technology
VIFVariance Inflating Factor

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