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Article

Understanding Individuals’ Continuance Intention to Use Advanced Driver Assistance Systems: An Integrated Application of Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis

by
Huijun Xiao
1,2,3,
Weisheng Chiu
2,* and
Shenglun Shen
1
1
Department of Business Administration, Dongguan City University, Dongguan 523419, China
2
Lee Shau Kee School of Business & Administration, Hong Kong Metropolitan University, Hong Kong, China
3
School of Journalism and Communication, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Systems 2024, 12(12), 589; https://doi.org/10.3390/systems12120589
Submission received: 6 November 2024 / Revised: 20 December 2024 / Accepted: 22 December 2024 / Published: 23 December 2024
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)
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Abstract

:
This study aimed to understand the factors that influence individuals’ intention to continue using advanced driver assistance systems (ADASs) through an integrated approach that extends the technology acceptance model (TAM). First, perceived safety, perceived quality, and satisfaction were incorporated into the traditional TAM framework as additional constructs to address the complexities of ADAS usage. Second, an approach that combines partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) was employed to identify both the sufficient and necessary conditions for the continuous intention to use ADASs. This combined approach was directed toward data collected from 843 drivers hailing from the Greater Bay Area of China and experienced with ADAS usage. The findings revealed that perceived usefulness, perceived quality, perceived safety, and satisfaction significantly influenced continuance intention, while perceived ease of use indirectly affected it through perceived usefulness and satisfaction. This study underscores the paramount importance of safety and quality perceptions in ADAS adoption and offers practical insights that can help product design and marketing professionals enhance the acceptance and sustained use of ADAS technologies.

1. Introduction

The automotive industry is undergoing a transformative shift with advanced technologies that enhance vehicle safety, vehicle efficiency, and driver experience. One such technology is the advanced driver assistance system (ADAS), which represents a significant milestone toward fully autonomous vehicles, offering features such as adaptive cruise control, lane departure warnings, automatic emergency braking, and blind spot detection [1,2]. Recent market analyses highlight the rapid growth of the global ADAS market. This market, valued at about $40 billion in 2022, is projected to exceed $130 billion by 2032, with a compound annual growth rate of approximately 13% [3]. By 2027, a significant proportion of new vehicles are expected to include ADAS technologies, such as rear cameras, parking sensors, and front crash prevention systems [4], indicating the anticipated ubiquity of ADASs in the near future.
More recently, the automotive industry has been advancing toward higher levels of intelligent driving. Automated driving systems depend on ADAS, and the growing trend toward intelligent driving is also helping to popularize these technologies [5]. ADAS technology has many advantages, providing important information to drivers, reducing the burden of driving by occasionally taking over some driver tasks, and sometimes providing additional control to assist drivers in critical situations [6]. However, automated systems are often seen as potentially demanding and distracting. When decision support tools are incomplete, automation bias can lead to omissions and delegation errors [7]. The use of ADAS adds complexity to driving tasks, requiring drivers to supervise its functions and manage its limitations during emergencies [8].
However, despite the potential benefits of ADASs and the technological progress made in this regard, the widespread acceptance and continuous use of such systems by drivers have yet to be fully realized [9,10,11]. User acceptance remains a critical determinant of the successful implementation and adoption of ADAS technologies [10,12]. Challenges such as concerns over system reliability, ease of use, perceived usefulness, and safety perceptions can hinder drivers’ intentions to adopt and continually use ADASs [13,14,15]. These challenges must, therefore, be understood to promote the integration of ADASs into mainstream automotive offerings and maximize their potential for enhancing road safety.
Adoption behaviors toward new technologies have been extensively studied using the technology acceptance model (TAM), which posits that perceived usefulness and perceived ease of use are primary determinants of technology acceptance [16,17]. The TAM has been validated for the analysis of various use contexts, including those involving mobile applications [18,19,20] and wearable devices [21]. Nevertheless, its application to research on ADASs may not fully capture all relevant factors [1,22], as the high-stakes driving environment surrounding these innovations and the complexities of ADAS interactions necessitate the consideration of factors that go beyond traditional TAM constructs [23,24]. For example, the literature has highlighted the importance of users’ perception of quality and safety as well as their satisfaction with ADAS functionalities as potential determinants of drivers’ continued use of these systems [13,15,25]. These ADAS-specific factors have not been integrated with conventional TAM constructs to comprehensively elucidate the determinants of continuance intention for ADASs [10,11]. Individual aspects have been explored in previous studies, but there is a need for a holistic examination of perceived ease of use, perceived usefulness, perceived safety, perceived quality, and satisfaction in order to fully grasp the complexities influencing drivers’ intentions to continuously use the technologies of interest.
Accordingly, the present research developed and empirically tested an integrated model that extends the TAM by incorporating perceived safety, perceived quality, and satisfaction into analysis to better understand individuals’ continuance intentions for ADASs. Additionally, partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) were carried out in combination to identify both the sufficient and necessary conditions for the continuous intention to use ADASs. This study contributes to both theoretical knowledge and practical application in the automotive industry. Illuminating the aforementioned determinants is expected to help manufacturers and policymakers design strategies for enhancing ADAS acceptance, ultimately promoting safer and more efficient driving experiences.

2. Literature Review and Hypothesis Development

2.1. Technology Acceptance Model

The TAM is a foundational framework for understanding how users accept and use new technologies. The model, developed by Davis [16], posits that perceived usefulness and perceived ease of use are the primary determinants of technology adoption [17,26]. It has been widely recognized for its simplicity and effectiveness, leading to numerous extensions and adaptations over the years, including the incorporation of factors such as trust and satisfaction [27,28,29,30]. The importance of the TAM lies in its effectiveness in providing insights into user behavior, which is critical for the successful implementation of technology across various domains, including education, healthcare, and the consumer market [31,32,33].
The application of the TAM also extends beyond traditional information systems to encompass a variety of technologies, including wearable devices and mobile health applications. For instance, studies have used this model to assess user acceptance of sports bracelets [34] and mobile health technologies [19]. These applications highlight the model’s versatility and relevance in contemporary technological contexts, particularly in clarifying how users interact with ADASs and autonomous driving technologies. The acceptance of such systems is crucial and considerably influenced by user trust and perceived usefulness [12,17,35].
Nevertheless, although the TAM is a solid theoretical foundation for analyzing ADAS usage, it may inadequately capture the multifaceted nature of factors that contribute to users’ continuance intentions for these systems, as adoption in this context is influenced by determinants that go beyond perceived usefulness and ease of use [1,22]. Recent investigations specific to driver-assistance technologies have emphasized the need to incorporate factors, such as perceived safety and perceived quality of automation, if the unique high-risk environment of driving is to be fully captured [23,24,36]. Research has indicated that users are more likely to accept autonomous driving technologies if they perceive them as enhancing safety and reducing the cognitive load associated with driving [10,11]. Moreover, the literature is deficient when it comes to the emotional aspects of technology acceptance, particularly in demanding settings such as driving. The traditional TAM focuses on cognitive evaluations, but recent studies have suggested that affective responses (e.g., satisfaction) also substantially impact user acceptance [37,38,39].
In this study, TAM was selected as a theoretical framework because of its parsimonious structure and well-documented robustness in capturing key cognitive factors associated with technology usage decisions. A recent study highlights that, compared to other theories such as the Theory of Planned Behavior and the Unified Theory of Acceptance and Use of Technology, the TAM is particularly effective in explaining how perceived usefulness and ease of use influence acceptance behaviors in the context of ADASs [6,36,40,41]. Hence, the current study developed an integrated framework that combines the TAM with additional factors relevant to ADAS acceptance, such as perceived safety, perceived quality of automation, and satisfaction, to guarantee a more exhaustive explanation of individuals’ intention to continuously use these systems.

2.2. Perceived Quality

Perceived quality is a critical construct in understanding consumer behavior and technology acceptance. It refers to a consumer’s assessment of the overall quality or superiority of a product or service based on their perceptions rather than objective measures [42]. This subjective evaluation is influenced by various factors, including brand reputation, personal experiences, and contextual cues [25]. The importance of perceived quality lies in its direct impact on customer satisfaction, loyalty, and, ultimately, purchase decisions [43,44,45,46].
Perceived quality in the realm of ADAS extends beyond basic functionality to include drivers’ judgments of performance consistency, reliability across different driving conditions, and the system’s ability to maintain consistent support over time [18]. It is particularly relevant to the ADAS context given the complexities involved in user interactions with these technologies. This perception is also instrumental in shaping user acceptance and usage intentions. Research has shown that users’ perceptions of the quality of these systems can markedly affect their trust and willingness to adopt them [9]. For instance, if people perceive ADASs as reliable and effective, they are more likely to accept and use these technologies in their vehicles [25]. Conversely, negative perceptions regarding the quality of these systems can lead to resistance and skepticism toward their adoption [9].

2.3. Perceived Safety

Perceived safety is another critical factor in understanding user acceptance of ADASs. It refers to the degree to which an individual believes that using a technology will enhance their personal safety and reduce the risk of accidents or injuries [2,14,47]. It is an important issue with respect to ADASs, as these systems are designed to improve driving safety and assist individuals in navigating various driving scenarios. It directly impacts user trust, acceptance, and the overall willingness to engage with autonomous technologies [14]. Understanding perceived safety is thus paramount for manufacturers and policymakers aiming to foster public confidence in these systems.
Perceived safety has consistenly been highlighted as a central driver of ADAS acceptance because, unlike entertainment or convenience-oriented technologies, driver-assistance systems operate in life-critical environments [13,15,48]. For instance, a study found that over half of the participants in a survey attached importance to the highest possible level of safety in automated cars, highlighting the necessity for manufacturers to demonstrate that the use of these vehicles is not a risky endeavor [48]. Perceived safety also influences users’ positive experiences, which can either facilitate or hinder the acceptance of these technologies. A lack of perceived safety may lead to skepticism and resistance, while a strong perception of safety can enhance user trust and encourage adoption [13,49]. By addressing real or anticipated safety outcomes—such as error prevention in high-speed driving or reduced reaction time in emergency situations—ADAS developers can better align their designs with drivers’ trust requirements [13].

2.4. Satisfaction

Satisfaction refers to an individual’s overall contentment with a service, product, or experience [50,51]. Satisfaction is a crucial factor in understanding user acceptance and intention to continuously use technologies [52] because it not only influences immediate user experiences but also affects long-term adoption and continued use [52,53,54]. Its importance in technology acceptance is underscored by its role as a mediator in the relationship between user perceptions and behavioral intentions, making it a vital aspect for investigation across various fields, including healthcare, education, and consumer technology [53,55,56,57,58].
While satisfaction has been widely examined in traditional technology acceptance research, studies on automotive technology emphasize the cumulative effect of real-world performance, perceived safety, and perceived usefulness on sustained positive evaluations [53,59]. In the milieu of ADAS, the link between satisfaction and behavioral intention can also be influenced by whether the system meaningfully reduces driver workload or elevates overall driving performance. Thus, bringing together performance-related perceptions (e.g., quality, usefulness) with trust-related perceptions (e.g., safety) can yield a more comprehensive understanding of how satisfaction shapes long-term technology usage decisions [60].

2.5. Hypothesis Development

Building on the theoretical foundation of the TAM and the literature review on perceived quality, perceived safety, and satisfaction in connection to ADASs, we developed a research model with corresponding hypotheses (Figure 1) intended to cast light on continuance intention for these technologies. First, the TAM maintains that perceived ease of use influences perceived usefulness, suggesting that when users find a technology easy to operate, they are likely to recognize its benefits [16,17]. Numerous meta-analyses have confirmed that perceived ease of use exerts a strong influence on perceived usefulness, affirming its central role in shaping users’ positive cognitive evaluations of new systems [26,31,33]. This relationship has been supported in various technological contexts where ease of use enhances the perceived utility of a system [26,31].
In the context of ADASs, if drivers perceive these systems as user-friendly innovations, they are inclined to acknowledge their usefulness in enhancing driving performance and efficiency [6]. Recent automotive-related studies also highlight that a user-friendly interface and straightforward operation can heighten the sense of tangible benefits in ADASs [40,61,62]. Indeed, individuals who find it simpler to engage with system features (e.g., lane-keeping assistance, adaptive cruise control) are more inclined to view them as beneficial in streamlining or improving the driving experience [63]. Accordingly, we put forward Hypothesis 1:
H1: 
Perceived ease of use positively affects the perceived usefulness of ADASs.
Satisfaction with ADASs arises when essential user expectations—ranging from smooth usability to tangible functional advantages—are consistently met or exceeded [60,64]. First, perceived ease of use influences satisfaction by reducing frustration; for instance, if an adaptive cruise control system is overly complicated, dissatisfaction can occur even the technical benefits are high [23,59]. In contrast, systems that deliver a straightforward interaction style can evoke a feeling of brand competence, which then magnifies users’ general contentment [15,59]. Second, in prior TAM-based studies, perceived usefulness was found to reinforce satisfaction when the ADAS unequivocally demonstrated time-saving and performance-improvement features, such as precise parking assistance that spares the driver repeated trial-and-error attempts [17]. Third, perceived quality is often singled out in automotive research for its role in determining whether users deem these systems well-made, robust, and worth returning to over time. When sensor accuracy and reliability match up to marketing promises, the resulting psychological comfort translates into higher satisfaction [25]. Finally, perceived safety is paramount: since accidents can have severe consequences, seeing advanced sensors, collision alerts, or lane-departure warnings operate seamlessly prompts a strong sense of security—reinforcing satisfaction in ways simpler consumer technology might not [13,15]. Thus, the following supposition was established:
H2: 
Satisfaction is positively influenced by the (a) perceived ease of use, (b) perceived usefulness, (c) perceived quality, and (d) perceived safety of ADASs.
Behavioral intention for ongoing ADAS adoption can hinge on the interplay of cognitive beliefs (usefulness, quality) and risk-based assessments (safety perceptions) [17]. According to the TAM, perceived ease of use and perceived usefulness are fundamental determinants of users’ intentions to continue using a technology [16,17,33,35]. When individuals find ADASs useful and easy to use, they tend to integrate such usage into their regular driving habits. Meanwhile, advanced vehicle research underscores how perceived quality—including the system’s hardware performance, crash avoidance capabilities, and stable driving behavior—can make systems more attractive for long-term adoption [44,46]. Perceived safety is another critical factor, with individuals who believe that ADASs enhance safety being predisposed to carry on using them [14,49]. That is, a driver’s confidence in the protective elements of ADASs is a strong determinant of sustained usage. Therefore, we crafted the supposition below:
H3: 
The intention to continuously use ADASs is positively influenced by (a) perceived ease of use, (b) perceived usefulness, (c) perceived quality, and (d) perceived safety.
Finally, satisfaction has been identified as a significant predictor of users’ continual usage intentions [53,56]. In the realm of ADAS usage, satisfaction serves as a bridge between a user’s positive experiences and their long-term behavioral intentions, reinforcing the notion that repeated exposure to a “problem-free” and beneficial system nurtures loyalty [58]. Beyond the classical TAM lens of perceived usefulness and ease of use, satisfaction infuses an emotional component—drivers may continue using certain ADAS features, such as automatic emergency braking, because such features consistently promote peace of mind and reduce driving stress. This relationship underscores the importance of user satisfaction in the sustained acceptance of advanced technologies. In the context of ADASs, satisfied drivers often show more consistent trust in ADAS features, which translates to repeated, vigorous usage over time. This relationship further indicates that satisfaction is not merely an end-state of positive experiences but also an ongoing driver of behavioral intention in high-stakes automotive contexts [60]. In line with these observations, we developed Hypothesis 4:
H4: 
Satisfaction positively affects the intention to continuously use ADASs.

3. Methods

3.1. Participants and Data Collection

Drivers experienced with ADAS usage were recruited via purposive sampling in the Greater Bay Area in China. This location was selected, as it comprises a rapidly growing population and economy, making it an increasingly important market for the adoption of advanced automotive technologies, including ADASs [65]. In particular, the research team visited local automotive dealerships in the Greater Bay Area and recruited participants who owned vehicles equipped with ADAS features, such as lane departure warnings, adaptive cruise control, and automatic emergency braking. The prospective participants were reached through social media platforms (i.e., WeChat, Weibo). After they provided informed consent, they were administered an online survey containing measures of the constructs encompassed in the proposed research model.
A total of 942 valid questionnaires were collected. Further screening for invalid responses left us with a final sample of 843 respondents. As shown in Table 1, among the participants, 57.8% and 42.2% were male and female, respectively, and the majority belonged to the age groups of 30 to 39 years (38.4%) and 20 to 29 years (38.2%). Most of the respondents (63.8%) had at least a bachelor’s degree. In terms of vehicle brands, 58.3% of the respondents owned Chinese vehicles, while 41.7% owned foreign-made automobiles. Vehicle prices ranged primarily from CNY 210,000 to CNY 300,000 (27.3%).

3.2. Survey Instrument

To collect data, we developed a survey instrument based on the research model and existing validated scales. The questionnaire consisted of scales revolving around the following constructs: perceived ease of use, perceived usefulness, perceived quality, perceived safety, satisfaction, and continuance intention. The scales intended to measure perceived ease of use and perceived usefulness were adopted from Davis [16] and were designed to capture users’ cognitive evaluations of ADASs [6]. The scale ascertaining perceived quality, taken from Chi [18], was meant to measure users’ perceptions of ADAS performance. The scale measuring perceived safety was developed based on the literature to assess users’ beliefs about the safety enhancements provided by ADASs [13,66]. The satisfaction scale, adapted from the information system literature, was intended to determine the participants’ overall evaluations of ADASs [56], and the continuance intention scale, adapted from the ADAS literature, was designed to measure the respondents’ intention to use ADASs in a sustained manner [6,60]. The items constituting the scales were modified to fit the ADAS context and were rated by the respondents using a five-point Likert scale ranging from 1 (highly disagree) to 5 (highly agree). All scale items are shown in Appendix A.

3.3. Data Analysis

Combined PLS-SEM and NCA were conducted to identify the necessary and sufficient conditions that influence an individual’s intention to continuously use ADASs. First, PLS-SEM was run on SmartPLS 4.0 [67] to evaluate the measurement and structural models. More specifically, the measurement model was assessed in terms of the reliability (indicator reliability, internal consistency, and composite reliability) and validity (convergent validity and discriminant validity) of the constructs. The structural model was then evaluated to determine the significance and strength of the relationships proposed in the research model via PLS-SEM bootstrapping with 5000 subsamples [68].
Next, NCA, which is a relatively new method developed by Dul [69], was carried out to identify the necessary and sufficient conditions that motivate continuance intention. Unlike traditional correlation-based analyses, NCA can shed light on the minimum requirements (i.e., necessary conditions) and the combined conditions that produce a desired outcome (i.e., sufficient conditions) in relation to target outcome variables (i.e., satisfaction and continuance intention). More specifically, NCA is performed to determine the upper limit lines and effect sizes of necessary factors and establish bottleneck tables to show the mutually necessary conditions for achieving an outcome variable [70].
Combining PLS-SEM and NCA is a comprehensive approach to understanding the complexity of causal relationships in research models. PLS-SEM effectively identifies relationships based on sufficiency—determining how independent variables adequately explain the variations in dependent variables—but it does not reveal whether certain conditions are indispensable for an outcome to occur [71,72]. This deficiency is compensated for by NCA, which uncovers the critical prerequisites to realizing a desired outcome, regardless of the presence of other factors [71,72]. Integrating NCA with PLS-SEM ensures more robust theoretical insights and practical implications [72].

4. Results

4.1. Assessment of the Measurement Model

The assessment of the measurement model pointed to its satisfactory reliability and validity. All constructs surpassed the recommended thresholds for indicator reliability (loadings > 0.70) and internal consistency reliability (Cronbach’s alpha and composite reliability > 0.70), indicating adequate reliability (Table 2). Convergent validity was established by the average variance extracted (AVE) of all latent variables exceeding the recommended threshold of 0.5 [68]. Discriminant validity was confirmed, as the square root of the AVE of each construct was greater than its correlations with other constructs and the Heterotrait-Monotrait (HTMT) ratio was below the threshold of 090 [68,73,74] (see Table 3).

4.2. Assessment of the Structural Model

Prior to structural model assessment, we examined the variance inflation factor (VIF) values for each construct to assess multicollinearity. All VIF values were below 5.0, indicating that multicollinearity was not a concern in the research model [68]. The evaluation of the structural model began with an examination of the explanatory power of the exogenous variables using the coefficient of determination (R2). The R2 values of the endogenous constructs in this research fell within the acceptable range, indicating moderate to substantial explanatory power. Specifically, the model explained 57.4%, 63.8%, and 58.4% of the variations in perceived usefulness, satisfaction, and continuance intention, respectively. Additionally, the predictive relevance of the structural model was evaluated using Stone–Geisser Q2 values [75,76]. The Q2 values of perceived usefulness, satisfaction, and continuance intention all exceeded zero, suggesting predictive relevance in explaining the endogenous latent variables under evaluation.
The evaluation then proceeded to the exploration of the significance levels and magnitudes of the hypothesized relationships via PLS bootstrapping with 5000 subsamples. As shown in Table 4, perceived ease of use significantly affected perceived usefulness and satisfaction (β = 0.785, p < 0.001 and β = 0.209, p < 0.001, respectively), supporting H1 and H2a. Satisfaction was influenced by perceived usefulness (β = 0.178, p < 0.001), perceived quality (β = 0.202, p < 0.001), and perceived safety (β = 0.306, p < 0.001), supporting H2b, H2c, and H2d, respectively. Furthermore, perceived usefulness (β = 0.178, p < 0.001), perceived quality (β = 0.207, p < 0.001), and perceived safety (β = 0.189, p < 0.001) positively influenced continuance intention, confirming H3b, H3c, and H3d. However, perceived ease of use did not significantly impact continuance intention (β = 0.066, p = 0.113), indicating the rejection of H3a. Finally, satisfaction significantly predicted continuance intention (β = 0.295, p < 0.001), validating H4.
Mediation analysis was performed to evaluate the indirect effects of perceived ease of use, perceived usefulness, perceived quality, and perceived safety on continuance intention. Following the procedure of Nitzl, et al. [77], we found significant total indirect effects, suggesting that perceived usefulness and satisfaction partially mediated the relationship between the aforementioned variables and continuance intention. We examined specific indirect effects, whose results empirically supported the mediating roles of perceived usefulness and satisfaction (Table 5).

4.3. Necessary Condition Analysis

The NCA results complemented the PLS-SEM findings given the identification of the necessary conditions for satisfaction and continuance intention. Following the guidelines, the latent variable scores of all the constructs obtained from the PLS-SEM were used as input for the NCA implemented on SmartPLS 4.0 [70,72]. A ceiling envelopment-free disposal hull (CE-FDH) was applied to the analysis to determine ceiling lines (upper limits) or necessary conditions. This approach was used due to the discreteness of the data, which had a relatively small range and a limited number of levels (i.e., the five-point Likert scale).
The NCA was carried out to investigate the effect sizes (d) of the latent variable scores and their significance levels. An effect size threshold >0.1 was used to determine the necessary conditions. According to Dul [69], an effect size of 0.1 ≤ d < 0.3 is indicative of a moderate effect, 0.3 ≤ d < 0.5 points to a large effect, and d ≥ 0.5 reflects a very large effect. Table 6 shows that the necessary effect sizes of perceived ease of use, perceived usefulness, perceived quality, and perceived safety on satisfaction were statistically significant (p < 0.001) and fell within the range of large effects. The necessary effect sizes of all the antecedents on continuance intention were statistically significant and ranged from 0.169 to 0.429, indicating moderate to large effect sizes.
Subsequently, bottleneck analysis was performed to identify the mutually necessary conditions for satisfaction and continuance intention. To attain high satisfaction (80% or above), the minimum levels required for perceived ease of use, perceived usefulness, perceived quality, and perceived safety were 2.687, 3, 3, and 3.24, respectively (Table 7). For the realization of high continuance intention (80% or higher), the minimum levels required for perceived ease of use, perceived usefulness, perceived quality, perceived safety, and satisfaction were 2, 2.69, 3.32, 2, and 2, respectively.

5. Discussion

This study examined the factors influencing individuals’ continuance intention for ADASs by extending the TAM through the incorporation of perceived safety, perceived quality, and satisfaction into the analysis. This expansion, along with the integration of PLS-SEM and NCA, not only revealed the relative importance of these factors but also identified the necessary conditions for the achievement of high satisfaction and continuance intention.
The PLS-SEM results largely supported the proposed hypotheses, highlighting the significant relationships among the constructs in the extended TAM framework. Specifically, perceived ease of use significantly influenced perceived usefulness (H1) and satisfaction (H2a), indicating that when users find ADASs easy to interact with, they perceive the systems as more useful and are more satisfied with them. This aligns with the foundational propositions of the TAM [16,17], reinforcing the importance of ease of use in shaping perceived usefulness and satisfaction in technology acceptance.
Notably, perceived usefulness, perceived quality, and perceived safety emerged as significant predictors of both satisfaction and continuance intention (H2b, H2c, H2d, H3b, H3c, H3d), emphasizing the critical role of these factors in the adoption and sustained use of ADASs. Users who perceive the usefulness and quality of the ADAS as high and believe that it enhances safety are more satisfied and more inclined to continue using the technology. This suggests that in high-stakes environments like driving, perceptions of system reliability and safety are paramount [13,15,25]. These findings corroborate prior research indicating that safety and quality perceptions are crucial in the acceptance of advanced driving technologies [13,15,48]. Additionally, satisfaction also had a significant positive effect on continuance intention (H4), indicating that users who are satisfied with their experience using ADASs are more likely to continue using them. This aligns with the findings of prior studies that highlight satisfaction as a key determinant of continuance intention in technology adoption contexts [52,53,56].
However, perceived ease of use exerted no significant direct effect on continuance intention (H3a). This contrasts with some previous studies, where perceived ease of use directly influenced behavioral intention [26,31]. This discrepancy may stem from the unique context of ADAS usage, in which the complexity and critical functionalities of the systems give rise to increased concerns regarding effectiveness and safety compared with those centering on simplicity. Nastjuk, et al. [24] suggested that in autonomous driving technologies, trust and perceived safety outweigh the ease of use in determining user acceptance. This assertion is supported by our findings, which indicated that while ease of use enhanced perceived usefulness and satisfaction, it did not directly drive the intention to continue using ADASs. Instead, its effect was mediated through perceived usefulness and satisfaction, consistent with the findings of Detjen, et al. [23], who emphasized the indirect role of ease of use in complex technological contexts.
Complementing the PLS-SEM results, the NCA results provided additional insights by identifying the minimum threshold levels required for perceived ease of use, usefulness, quality, and safety to lead to the achievement of high satisfaction and continuance intention. The NCA revealed that although perceived ease of use may not have directly influenced continuance intention, a certain level of ease was still a necessary condition for considerable satisfaction and sustained use. Specifically, the bottleneck analysis uncovered specific minimum requirements: As shown in Table 7, to attain high satisfaction (80% or above), perceived ease of use, perceived usefulness, perceived quality, and perceived safety were required to reach values of 2.687, 3, 3, and 3.24, respectively. For high continuance intention, minimum levels of these perceptions were similarly critical: perceived ease of use (2), perceived usefulness (2.69), perceived quality (3.32), perceived safety (2), and satisfaction (2). This emphasizes that while high levels of these perceptions contribute to user satisfaction and continuance intention, they must at least meet certain minimum standards for them to effectively advance the desired outcomes [69].
The NCA findings highlight that even if perceived ease of use does not have a significant direct effect on continuance intention, it remains a necessary condition for achieving high satisfaction and continuance intention. This suggests that users require a minimum level of ease in using ADASs to be satisfied and to continue using the systems, even if other factors (like perceived quality and perceived safety) are more influential in driving their sustained usage.
In summary, the combined use of PLS-SEM and NCA in this study offers a holistic understanding of the factors influencing continuance intention for ADASs. While PLS-SEM identifies the strength and significance of relationships (sufficiency analysis), NCA identifies the necessary conditions that must be present for high levels of satisfaction and continuance intention to occur. This dual approach reveals that constructs like perceived quality and perceived safety are not only significant predictors of CI but also necessary conditions for high satisfaction and continuance intention. It adds depth to our understanding by indicating that certain variables must meet minimum thresholds to enable desired outcomes, even if they are not the most influential factors in the sufficiency analysis.

5.1. Theoretical Implications

This study makes significant contributions to the existing literature by extending TAM in the context of ADAS. By integrating perceived safety, perceived quality, and satisfaction into the traditional TAM framework, the research addresses limitations in explaining user acceptance of complex and safety-critical technologies. The findings demonstrate that while perceived ease of use and perceived usefulness remain important, factors such as perceived safety and quality have a more pronounced influence on users’ continuous intention to use ADAS [13,15]. This suggests that in high-stakes environments like driving, users prioritize the reliability and protective benefits of technology over its simplicity, indicating the need for context-specific adaptations of TAM [23,24].
Furthermore, our findings suggest that while PEOU significantly affects perceived usefulness and user satisfaction, it does not have a significant direct effect on continuous intention to use ADAS. This indicates that in the context of ADAS, users may place greater importance on factors such as perceived usefulness, safety, and quality rather than on how easy the system is to use. This highlights the need to reconsider the role of PEOU in complex technological contexts where the system’s effectiveness and safety are paramount concerns for users [24].
Finally, the study’s innovative methodological approach of combining PLS-SEM with NCA offers a novel way to examine both sufficient and necessary conditions influencing technology acceptance. PLS-SEM is adept at identifying sufficiency relationships and determining how independent variables sufficiently explain variations in dependent variables. However, it does not reveal whether certain conditions are indispensable for achieving an outcome. NCA complements this by uncovering necessary conditions, i.e., critical prerequisites without which the desired outcome cannot occur, regardless of the presence of other factors. This dual approach enhances theoretical rigor by demonstrating that certain factors are not only influential but also indispensable for the adoption of advanced technologies. Such methodological integration can be valuable for future research aiming to uncover complex causal relationships in technology acceptance studies [69,70,71].

5.2. Practical Implications

The findings also present several actionable insights for ADAS developers and automakers seeking to promote sustained user adoption. From the perspective of product design, manufacturers should focus on enhancing the perceived usefulness, quality, and safety of ADAS features to foster continued usage among drivers. This involves developing systems that not only perform reliably but also demonstrably improve driving performance and safety in real-world conditions [59]. Integrating advanced safety functions, such as collision avoidance and adaptive cruise control, can boost perceived safety. Ensuring exceptional components and robust engineering practices will enhance the perceived quality of these systems. While perceived ease of use does not directly influence continuance intention, it indirectly affects user satisfaction and perceived usefulness. Therefore, designers should create intuitive interfaces and provide a seamless user experience to increase overall satisfaction with ADASs.
From a market promotion perspective, strategies should emphasize the tangible benefits of ADASs in terms of usefulness, quality, and safety to motivate continuance intention among drivers. Marketing campaigns can highlight how ADAS features contribute to safer and more efficient driving experiences, addressing potential concerns about system reliability and effectiveness. Sharing testimonials and endorsements from satisfied users can build trust and showcase the excellent satisfaction associated with ADASs [60]. Educational initiatives, such as tutorials and demonstrations, can help users understand how to effectively utilize ADAS features, indirectly enhancing perceived usefulness and satisfaction [61]. Although perceived ease of use is not a direct influencer of continuance intention, these educational efforts can improve user perceptions and encourage the sustained adoption of ADAS technologies.
Moreover, cultivating user engagement through participatory design methodologies can further drive ADAS acceptance in the long run. By involving drivers in iterative testing, co-creation workshops, or feedback sessions, automakers and ADAS developers can identify potential usability pitfalls, integrate user-driven suggestions for interface improvements, and align system enhancements with customer expectations [6]. Such a user-centered design approach plays a pivotal role in optimizing perceived quality and novelty, ultimately fostering more favorable attitudes toward high-technology product innovations [44,46]. Indeed, prior studies have shown that when consumers perceive tangible improvements in functionality and safety, they are more inclined to adopt and remain loyal to advanced vehicle technologies [78,79]. From an organizational standpoint, systematically integrating this feedback loop can also unveil additional cross-promotional strategies (e.g., loyalty programs, long-term warranty bundles) that underscore ADAS reliability and user satisfaction, reinforcing continuous acceptance and adoption.

5.3. Limitations and Future Research Directions

Despite the insights derived regarding continuance intention for ADASs, this study has several limitations that are worth noting. First, the sample was drawn exclusively from the Greater Bay Area in China, which may limit the generalizability of the findings to other regions or cultural contexts. Future studies could replicate this research in different geographical locations to examine the applicability of the proposed model across diverse populations. Second, the study used a cross-sectional design, capturing participants’ perceptions at a single point in time. Given the rapid adoption of ADASs, longitudinal investigations tracking the evolution of user intentions and behaviors over time would reinforce the insights derived here with respect to the dynamic nature of technology acceptance. Third, this study focused on general ADAS features without differentiating between specific functionalities (e.g., lane keep assist, adaptive cruise control, collision avoidance). Future research should explore how user acceptance varies depending on different ADAS functionalities to better understand the factors influencing continuance intention. Moreover, although the study extended the TAM by incorporating perceived safety, perceived quality, and satisfaction in the analysis, it did not consider other potentially influential factors, such as trust, risk, cost, value, and social influences. Other researchers should inquire into these additional variables for a more exhaustive grasp of the determinants of continuance intention for ADASs. Additionally, as ADAS technology matures, alternative models or theories, such as the Unified Theory of Acceptance and Use of Technology or the Diffusion of Innovation, could serve as theoretical foundations for future research.

Author Contributions

Conceptualization, H.X. and S.S.; methodology and software, W.C; validation and formal analysis, W.C. and H.X.; investigation and resources, H.X. and S.S.; data curation, S.S. and H.X; writing—original draft preparation, H.X and W.C.; writing—review and editing, W.C; visualization, W.C; supervision, H.X.; project administration, H.X.; funding acquisition, H.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Dongguan Municipal Government (2023 Key Consultation Project for Philosophy and Social Sciences Planning in Dongguan City, grant number 2023ZD03) and the Guangdong Provincial Department of Education (2022 Guangdong Provincial University Characteristic Innovation Project, grant number 2022WTSCX186).

Institutional Review Board Statement

This study was granted an exemption of ethical review in accordance with the “Measures for Ethical Review of Life Science and Medical Research Involving Humans” (Article 32, Chapter 3) issued jointly by the China’s National Health Commission, Ministry of Education, Ministry of Science and Technology, and the National Administration of Traditional Chinese Medicine (see https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm (accessed on 28 October 2024)).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data presented in this study are available upon request from the corresponding author. The data are not publicly available because of privacy concerns.

Acknowledgments

We would like to express our gratitude to the Dongguan Municipal Government and the Guangdong Provincial Department of Education and the student team in the School of Business at Dongguan City University for their assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

  • Survey items
  • Perceived ease of use
1.
My interaction with the advanced driver assistance systems would be clear and understandable.
2.
Advanced driver assistance systems are easy to use.
3.
It is easy to interact with advanced driver assistance systems.
  • Perceived usefulness
4.
Using the advanced driver assistance systems would improve my driving performance.
5.
Using the advanced driver assistance systems enhances effectiveness in my driving.
6.
Using the advanced driver assistance systems is useful in my driving.
  • Perceived quality
7.
The advanced driver assistance systems are reliable.
8.
The features of advanced driver assistance systems are effective.
9.
The layout and display of advanced driver assistance systems are clear.
  • Perceived safety
10.
Overall, using the advanced driver assistance systems would help my trip safer than cars without them.
11.
The advanced driver assistance systems would act better than me in complex traffic situations.
12.
The advanced driver assistance systems respond adequately to unexpected situations that commonly require rapid responses from drivers.
  • Satisfaction
13.
I am a real fan of my favorite club.
14.
I am very committed to my favorite club.
15.
There is nothing that could change my commitment to my favorite club.
  • Continuance intention
16.
If the system is available in the market at an affordable price, I intend to purchase the system.
17.
If my car is equipped with a similar system, I predict that I would use the system when driving.
18.
Assuming that the system is available, I intend to use the system regularly when I am driving.

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Systems 12 00589 g001
Figure 1. Research model with hypotheses.
Figure 1. Research model with hypotheses.
Systems 12 00589 g001
Table 1. Demographic characteristics of participants (N = 843).
Table 1. Demographic characteristics of participants (N = 843).
Categories N%
GenderMale48757.8
Female35642.2
Age18–19 536.3
20–2932238.2
30–3932438.4
40–4910312.2
50 and above414.9
EducationHigh school and below718.4
Associate degree/Higher diploma23427.8
Bachelor’s degree40548.0
Graduate school13315.8
BrandDomestic brand 49158.3
Foreign brand35241.7
Vehicle price (RMB)100,000 and below677.9
100,001 to 200,00022126.2
200,000 to 300,00023027.3
300,001 to 400,00018321.7
400,001 and above 14216.8
Table 2. Assessment of the measurement model.
Table 2. Assessment of the measurement model.
MeasuresCronbach’s AlphaFactor LoadingCRAVE
Perceived ease of use0.7350.792–0.8370.8500.654
Perceived usefulness0.7320.766–0.8360.8490.652
Perceived quality0.7090.778–0.8160.8370.632
Perceived safety 0.7520.811–0.8260.8580.668
Satisfaction0.7650.813–0.8320.8640.680
Continuance intention0.7530.810–0.8310.8590.669
Table 3. Discriminant validity—Fornell-Larcker Criterion & HTMT.
Table 3. Discriminant validity—Fornell-Larcker Criterion & HTMT.
Constructs123456
1. Perceived ease of use0.809 10.8320.8290.7620.7500.740
2. Perceived usefulness0.7580.807 10.7970.7350.7270.759
3. Perceived quality0.7440.7180.795 10.8020.7630.800
4. Perceived safety 0.7150.6920.7310.817 10.7570.763
5. Satisfaction0.7130.6930.7090.7260.825 10.787
6. Continuance intention0.7010.7130.7320.7260.7500.818 1
1 The bold values are the square roots of the AVEs.
Table 4. Structural model assessment.
Table 4. Structural model assessment.
Hypothesis/PathStandardized
Estimate		Standard
Deviation		t-Statistic
H1: Perceived ease of use → Perceived usefulness0.7850.01743.506 ***
H2a: Perceived ease of use → Satisfaction0.2090.0494.269 ***
H2b: Perceived usefulness → Satisfaction0.1780.0483.689 ***
H2c: Perceived quality → Satisfaction0.2020.0494.116 ***
H2d: Perceived safety → Satisfaction0.3060.0437.410 ***
H3a: Perceived ease of use → Continuance intention0.0660.0551.213
H3b: Perceived usefulness → Continuance intention0.1780.0473.835 ***
H3c: Perceived quality → Continuance intention0.2070.0514.038 ***
H3d: Perceived safety → Continuance intention0.1890.0424.536 ***
H4: Satisfaction → Continuance intention0.2950.0456.589 ***
*** p < 0.001.
Table 5. Results of total and specific indirect effects on continuance intention.
Table 5. Results of total and specific indirect effects on continuance intention.
Path Standardized EstimateStandard
Deviation		t-Statistic
Total indirect effects
  Perceived ease of use → Continuance intention0.2370.0366.522 ***
  Perceived quality → Continuance intention0.060.0183.244 ***
  Perceived safety → Continuance intention0.090.0194.813 ***
  Perceived usefulness → Continuance intention0.0520.0173.097 **
Specific indirect effects
  Perceived ease of use → Perceived usefulness → Satisfaction → Continuance intention0.040.0133.077 **
  Perceived ease of use → Satisfaction → Continuance intention0.0620.0163.942 ***
  Perceived ease of use → Perceived usefulness → Continuance intention0.1350.0353.852 ***
Perceived quality → Satisfaction → Continuance intention0.060.0183.244 **
Perceived safety → Satisfaction → Continuance intention0.090.0194.813 ***
Perceived usefulness → Satisfaction→ Continuance intention0.0520.0173.097 **
*** p < 0.001, ** p < 0.01.
Table 6. NCA effect sizes.
Table 6. NCA effect sizes.
ConstructSatisfactionContinuance Intention
CE-FDHCE-FDH
Perceived ease of use0.394 ***0.250 ***
Perceived usefulness0.461 ***0.429 ***
Perceived quality0.382 ***0.250 ***
Perceived safety0.408 **0.400 ***
Satisfaction 0.169 ***
*** p < 0.001, ** p < 0.01.
Table 7. Bottleneck table for satisfaction and continuance intention.
Table 7. Bottleneck table for satisfaction and continuance intention.
Perceived Ease of UsePerceived UsefulnessPerceived QualityPerceived SafetySatisfaction
Bottleneck for satisfaction
0%NNNNNNNN
10%2222
20%2222
30%2.3132.3262.6452
40%2.3132.3262.6452
50%2.3132.3262.932.34
60%2.6872.6232.34
70%2.6872.6932.34
80%2.687333.324
90%3.6473.693.9983.988
100%4444
Bottleneck for continuance intention
0%NNNNNNNNNN
10%22221.351
20%22221.351
30%22.3262.64521.351
40%22.3262.64521.351
50%22.3262.64521.351
60%22.636322
70%22.69322
80%22.693.3222
90%23.6363.3222
100%243.3222
Percentiles are shown for the dependent variables (satisfaction and continuance intention). Actual values are reported for conditions, and NN = not necessary. The gray shaded area shows the minimum percentage of each condition for high satisfaction and continuance intention.
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Xiao, H.; Chiu, W.; Shen, S. Understanding Individuals’ Continuance Intention to Use Advanced Driver Assistance Systems: An Integrated Application of Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis. Systems 2024, 12, 589. https://doi.org/10.3390/systems12120589

AMA Style

Xiao H, Chiu W, Shen S. Understanding Individuals’ Continuance Intention to Use Advanced Driver Assistance Systems: An Integrated Application of Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis. Systems. 2024; 12(12):589. https://doi.org/10.3390/systems12120589

Chicago/Turabian Style

Xiao, Huijun, Weisheng Chiu, and Shenglun Shen. 2024. "Understanding Individuals’ Continuance Intention to Use Advanced Driver Assistance Systems: An Integrated Application of Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis" Systems 12, no. 12: 589. https://doi.org/10.3390/systems12120589

APA Style

Xiao, H., Chiu, W., & Shen, S. (2024). Understanding Individuals’ Continuance Intention to Use Advanced Driver Assistance Systems: An Integrated Application of Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis. Systems, 12(12), 589. https://doi.org/10.3390/systems12120589

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