Factors Influencing the Acceptance of Robo-Taxi Services in China: An Extended Technology Acceptance Model Analysis

Liu, Mingyu; Wu, Jianping; Zhu, Chunli; Hu, Kezhen

doi:https://doi.org/10.1155/2022/8461212

Journal of Advanced Transportation

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Abstract Introduction Results Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 8461212 | https://doi.org/10.1155/2022/8461212

Factors Influencing the Acceptance of Robo-Taxi Services in China: An Extended Technology Acceptance Model Analysis

Mingyu Liu,¹Jianping Wu,¹Chunli Zhu,²and Kezhen Hu³

Academic Editor: Yuchuan Du

Received13 Jul 2021

Revised07 Mar 2022

Accepted24 Mar 2022

Published09 Apr 2022

Abstract

With the development of autonomous driving technologies, robo-taxis (shared autonomous vehicles) are being tested on real roads. In China, in particular, people in some cities such as Beijing and Shanghai can book a robo-taxi online and experience the service. To examine the influential factors on user acceptance of robo-taxi services, this study proposes and employs an extended technology acceptance model (TAM) with four external factors: perceived trust, government support, social influence, and perceived enjoyment. Data were collected through an online questionnaire in China, and responses from 403 respondents were analyzed using structural equation modeling. Both typical TAM factors—including perceived ease of use, perceived usefulness, and attitude—and external factors were found to play significant roles in predicting users’ intention to use robo-taxis. The four external factors influenced the user acceptance indirectly via typical TAM factors. Improving users’ perceived trust is important for increasing public adoption. A greater emphasis by manufacturers on safety concerns, wider dissemination of information on data protection and safety systems, and government support through incentives for manufacturers and users can help improve public adoption of robo-taxi services.

1. Introduction

Recently, autonomous driving technologies have been studied by many researchers. Autonomous vehicles (AVs) have become a major challenge for manufacturers. Many companies and research groups, such as Google, Daimler, and Deutsche Bahn, have focused on the design and testing of AVs [1]. However, compared with manually driven vehicles, AVs involve high costs, with up to 50,000 USD per vehicle, which has become a considerable challenge [2]. Therefore, robo-taxis have been considered an effective approach to accelerate the industrialization of AVs. This study considered SAE L5 AVs with sharing services [3], robo-taxis that can meet travel demand with an autonomous driving system. Similar to ride-hailing services and taxis, robo-taxis are intended to provide traffic services to consumers. Sharing services can save money for users, including the cost of purchase, maintenance, repair, and insurance [4]. Moreover, an agent-based study showed that, in a system of shared AVs (SAVs), each SAV can replace 11 conventional vehicles [5]. The robo-taxis can thus alleviate parking problems in modern cities by reducing private car ownership levels.

China has made significant progress in the development of robo-taxis. On June 21, 2019, Baidu’s Apollo robo-taxis obtained 45 licenses for testing in Changsha City, Hunan Province [6]. In August 2019, testing of Apollo robo-taxis began on actual roads in Changsha City [7]. On June 27, 2020, Didi, a vehicle-for-hire company providing bike-sharing and ride-hailing services in China, started to offer robo-taxi services for testing in a specific area in Shanghai [8]. Users can book robo-taxi services for free on their mobile phones. As a measure against the COVID-19 pandemic, these robo-taxis have devices that can recognize whether users have worn a mask before they board the robo-taxi. During the trip, users can interact with the robo-taxi system. Moreover, they are accompanied by two people, one to ensure the users’ safety and the other to make sure the robo-taxi is driven normally. In recent years, robo-taxis have undergone various developments, and they will likely be widely introduced soon. Therefore, before their arrival on the market, the factors that influence consumers’ acceptance of robo-taxi services must be explored.

Some studies have investigated public adoption of SAVs using discrete choice experiments (e.g., [4, 9, 10]). However, major roadblocks to the widespread adoption of AVs are not technical but psychological in nature [11]. Therefore, user acceptance of robo-taxis must be investigated from a psychological perspective. To examine user acceptance of information technologies from such a perspective, researchers have proposed many models and frameworks, such as the theory of planned behavior (TPB) [12], technology acceptance model (TAM) [13], unified theory of acceptance and use of technology (UTAUT [14]), and UTAUT2 [15]. TPB was developed based on the theory of reasoned action (TRA), and it can be applied to explain many types of behaviors, such as pedestrian behavior. To understand users’ acceptance on technologies specifically, TAM was proposed. In fact, typical TAM is highly extendable; UTAUT and UTAUT2 are extended theories based on typical TAM. Compared with TAM, although UTAUT and UTAUT2 consider more factors, some factors are not suitable to learn robo-taxi services, such as the factor of habit in UTAUT2, which requires experience on robo-taxis. Many researchers have thus investigated users’ adoption of various technologies based on the TAM framework. For example, Vanduhe et al. extended TAM with task technology fit, social influence (SI), and social motivation to investigate the factors that influence employees’ intention to use gamification for training [16]. Salloum et al. proposed an extended TAM to investigate e-learning acceptance with eight external variables, which were summarized into two constructs: system characteristics and individual factors [17]. Rahman et al. developed a model of driver acceptance of driver support systems by combining TAM with TPB; the model provides an improved understanding of the formation of driver acceptance [18].

Social-psychological models have also been used to examine AVs. Madigan et al. proposed a UTAUT model—which included the factors of performance expectancy, effort expectancy, and SI—to investigate people’s acceptance of automated road transport systems (ARTS) [19]. Respondents in Lausanne (Switzerland) and La Rochelle (France) had used autonomous vehicles as part of the CityMobil2 trials. Wu et al. introduced environmental concerns into TAM, replacing perceived usefulness (PU) with green PU, to study public adoption of autonomous electric vehicles (AEVs) and the effects of environmental factors on AEVs [20]. Xu et al. explored the influence of the direct experience of an automated vehicle (Level 3) and explained and predicted public acceptance of AVs through an extended TAM, which included perceived trust (PT) and perceived safety [21].

However, limited studies have used a social-psychological perspective to examine the adoption and acceptance of robo-taxi services or SAVs, especially in China. Liu et al. investigated the effects of SI, PU, and perceived ease of use (PEU) on public adoption of robo-taxis [22]. These factors were found to be strong predictors of consumers’ behavioral responses to IU robo-taxis. Yuen et al. combined TPB with UTAUT2 to analyze the factors influencing the adoption of SAVs in Vietnam [23]. UTAUT2 factors, including performance expectation, effort expectation, habit, price value, and hedonic motivation, were mediated by attitudes toward SAVs, a TPB factor. All TPB and UTAUT2 factors were effective predictors of the intention to use SAVs.

Government support (GS) is an important factor that influences people’s acceptance of new technologies. However, few studies have explored its effects using a social-psychological model. Khoo and Ong introduced a government policy factor into the subjective norm construct, a basic part of TPB [24]. They demonstrated this factor to be a strong predictor of users’ intention to use sustainable transport. However, the factor’s items were designed to understand the acceptance behavior of sustainable transport, which was already on the market. Although robo-taxis have been tested in several countries worldwide, they are not yet a mature product. Therefore, new items for the GS factor, specifically related to robo-taxis, should be designed.

This study, therefore, developed robo-taxi-related items for the GS factor, which can also be used for other products in development. To the best of the authors’ knowledge, this study is the first to introduce GS into the TAM framework to investigate the role of government policy on public adoption. The proposed extended TAM includes four external variables: GS, PT, PE, and SI. Structural equation modeling (SEM) was conducted to test several hypotheses from the extended TAM to explain the effect of these factors on public adoption of robo-taxi services. The results can be good references for manufacturers, and some policies were also provided for governments and manufacturers to improve public adoption of robo-taxi services.

Following this introduction, which presents a literature review of works relating to robo-taxis and using social-psychological models, the remainder of this paper is organized as follows. Section 2 describes the research model and various proposed hypotheses. Subsequently, the study methodology—including survey design, data collection, participants, and statistical analysis methods—is presented. Thereafter, the results of the reliability and validity and SEM analyses are reported. Lastly, and before concluding the paper, the effects of the investigated factors and some related implications are discussed.

2. Research Model

2.1. Typical TAM

A typical TAM includes PU, PEU, attitude (ATT), and intention to use (IU). PU can be defined as the extent to which users believe that a specific technology or service can enhance overall work performance [13]. PEU can be defined as the degree to which users believe that employing a specific technology or service could be free from mental and physical effort [13]. ATT can be defined as users’ preference when using specific technologies or systems [25]. IU can be defined as users’ desire to use particular services and technologies [13]. Previous research has found positive relationships between PEU and PU, PU and ATT, PEU and ATT, and ATT and IU, as shown in Figure 1. Therefore, the following hypotheses are proposed: H1: PU (of robo-taxis) is positively associated with user ATT (toward robo-taxis) H2: PU is positively associated with IU (robo-taxis) H3: PEU (of robo-taxis) is positively associated with user ATT H4: PEU is positively associated with PU H5: ATT is positively associated with IU

2.2. Perceived Trust

PT indicates the degree to which consumers generally trust a particular technology system [26]. Many studies on the public adoption of AVs have focused on the effect of PT or perceived risk on user acceptance. Safety factors have been demonstrated as strong predictors of public adoption [26–28]. In fact, consumers have always been concerned about the safety of robo-taxis and AVs. This safety concern includes data safety concerns and body safety concerns. On the one hand, users may be concerned about personal data collected illegally by others; on the other hand, whether robo-taxis can perform better and safer than normal vehicles requires to be ensured by users themselves. Therefore, PT can be regarded as an essential factor in investigating user acceptance of robo-taxis.

2.3. Government Support

GS indicates the extent to which users’ willingness to use is promoted when the government implements supportive policies or provides incentives for a particular technology. The Chinese government has supported new developments in many technologies. For example, in 2010, the Central People’s Government of the People’s Republic of China (CPGPRC) selected five cities, including Shanghai, Changchun, Shenzhen, Hefei, and Hangzhou, for a pilot project on electric and hybrid vehicles. Each plug-in hybrid electric vehicle can cost up to 50,000 yuan, while each pure electric vehicle can cost up to 60,000 yuan [29]. One study showed that government incentives can significantly promote users’ intention to purchase; when people knew more about this policy, this effect was found to be more significant [30]. Besides, some other policies can also be regarded as government support. For example, if robo-taxis can use bus lanes or autonomous vehicle lanes, they will save travel time for users. Therefore, users will be more likely to accept robo-taxi services. In the present study, GS is hypothesized to be an external TAM factor that can influence public adoption of robo-taxi services.

2.4. Social Influence

SI indicates the degree to which an individual perceives that important people believe he or she should use a particular technology [14]. SI plays an important role in public acceptance of new technologies, such as AVs [19]. Acceptance of AVs by friends or family members increased users’ confidence in the technology as well as their intention to purchase or actual purchase [26, 31]. SI has also been tested in other psychological models, such as UTAUT [14] and UTAUT2 [15]. It has been regarded as a basic factor when exploring users’ acceptance of new technologies. Therefore, SI was added to TAM as an external factor to explore its effect on user acceptance of robo-taxi services.

2.5. Perceived Enjoyment

PE here is similar to hedonic motivation in UTAUT2; it indicates the perceived degree of fun or pleasure experienced by users when using a particular technology [15]. With the development of economics and technology, traffic tools can meet people’s travel demands more easily. Furthermore, comfort and fun during a trip are being increasingly considered by users. However, PE or hedonic motivation has only been studied under the UTAUT2 model. Hedonic motivation has been found to be a strong predictor of user acceptance of ARTS [32] and autonomous delivery vehicles [33]. Besides, hedonic motivation was validated to be a good predictor in other products, such as ride-hailing services [34], bike-sharing [35],and social networks for education [36]. Therefore, PE from robo-taxis can be hypothesized to be influential in the context of robo-taxi acceptance.

Based on the typical TAM, these external variables may affect users’ intention to use robo-taxis indirectly via PU and PEU. Therefore, PT, GS, SI, and PE were hypothesized to affect PU and PEU (Figure 2). The proposed hypotheses are as follows: H6: PT (in robo-taxis) is negatively associated with PU H7: PT is negatively associated with PEU H8: GS is positively associated with PU H9: GS is positively associated with PEU H10: SI is positively associated with PU H11: SI is positively associated with PEU H12: PE (from robo-taxis) is positively associated with PU H13: PE is positively associated with PEU

3. Methodology

3.1. Survey Design

The data were collected using an online questionnaire that consisted of three sections. The first section was related to demographic information, including age, gender, education, and income, with a total of seven items. The second section included items for all predictive factors of the proposed model (Table 1): PT (three items), GS (three items), SI (three items), PE (two items), PU (three items), PEU (three items), and ATT (three items). Based on different categories of Chinese policies for supporting new technologies, three scenarios were mentioned in the GS construct: providing incentives for users, providing incentives for manufacturers, and providing traffic policy support for robo-taxis. A five-point Likert scale was used in this section, with 1 being “strongly disagree” and 5 being “strongly agree.” The third section included items for IU. This section too employed a five-point Likert scale, with 1 being “extremely unlikely” and 5 being “extremely likely.”

3.2. Data Collection

The survey was conducted using Wenjuanxing (https://www.wjx.cn/), one of the largest online survey platforms in China. A link was disseminated through social networks (e.g., Weibo and Wechat) and the Wenjuanxing platform itself. Respondents from different age groups, education levels, and income groups were included in the sample. Data were collected from February 2020 to March 2020. The initial questionnaire was designed in English by using English references. Several translators were asked to independently translate the questionnaire into Chinese and then translate it back into English with random answers. Respondents were provided with a brief introduction on robo-taxi services to ensure proper understanding of such services. To ensure the effective administration of the questionnaire, the following measures were taken. Respondents with the same IP address or username could only answer the questionnaire once. The response time for each item should be less than 5 minutes. Some attention-check questions randomly appeared while answering the questionnaire, for example, “This is a quality control question. Please select all the options, including the word ‘frog’.” Furthermore, all items in the second section appeared in a random order. However, the output was ordered normally. To encourage more participation in the survey, bonuses were provided for valid answers.

3.3. Participants

A total of 914 questionnaires (540 completed) were received, with 403 valid questionnaires. The demographic information of the respondents is shown in Table 2. Of the valid respondents, 53.3% were women, while 46.7% were men. Furthermore, more than 90% of the respondents were between 18 and 44 years of age, indicating that this study is more useful for understanding young people’s adoption of robo-taxis in China. Most participants were well-educated, with a bachelor’s degree or above (85.6%). Moreover, more than half of the respondents (64.5%) had a personal annual income of between 40,000 and 200,000 yuan. Notably, 18.4% had no income and were likely to be students. Most respondents had driving licenses (73.4%), and more than half had their own vehicles (65%). Lastly, 58.1% chose to drive by themselves during a trip.

3.4. Statistical Analysis

Statistical analyses were performed using SPSS version 23 and AMOS version 22. First, Cronbach’s alpha was calculated using SPSS to determine the reliability of the factor structure. A reliability coefficient of 0.7 or more is generally considered acceptable [38].

Moreover, confirmatory factor analysis (CFA), based on the maximum likelihood estimation method, was used to assess the suitability of the whole scale. Items with factor loadings lower than 0.4 were removed (e.g., [39]). Subsequently, convergent and discriminant validity were calculated to evaluate the construct validity of the measurement instrument. Convergent validity (using average variance extracted, AVE) denotes whether items in the same construct have internal consistency [40], while discriminant validity refers to the degree to which a given construct differs from another construct [41]. Generally, an AVE of more than 0.50 is acceptable [42]. Regarding discriminant validity, the square root of AVE for all constructs should be larger than the correlation coefficients of all constructs [43].

Furthermore, to obtain a good model fit, a single index cannot provide a reliable measure across situations; multiple indices should be reported [42, 44]. Therefore, the following indices are reported here: normed chi-square, normed fit index (NFI), comparative fit index (CFI), Tucker–Lewis index (TLI), incremental fit index (IFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Acceptable values for a good model fit are as follows: χ²/df < 3, NFI >0.90, CFI >0.90, TLI >0.90, IFI >0.90, RMSEA <0.08, and SRMR <0.10 [37, 44, 45].

4. Results

4.1. Reliability and Validity Analysis

The reliability and validity of the research instrument were calculated before performing SEM. Table 3 presents the fitness of the measurement model and the reliability and validity of each construct. Most constructs had acceptable reliability, with Cronbach’s alphas of more than 0.70, except for PE (α = 0.68). However, Cronbach’s alpha of PE can also be considered acceptable because it was only slightly lower than 0.70. Therefore, all the constructs used in this study can be regarded as reliable.

CFA was used to assess the convergent and discriminant validity of the measurement model. As shown in Table 3, all constructs have composite reliability higher than 0.6 [42]. The AVE of PT, GS, PE, ATT, and IU was higher than 0.50, while that of SI, PEU, and PU were lower than 0.50. Some studies have shown that an AVE higher than 0.45 can also be acceptable [46, 47]. Therefore, all constructs had acceptable convergent validity.

For discriminant validity, the square root of AVE was used to correlate the latent constructs. Table 4 shows that the square root of the AVE for all constructs was higher than the pairwise correlations. Therefore, all constructs also had acceptable discriminant validity.

In addition, a CFA was conducted for PT, GS, SI, and PE; the standardized solution is shown in Figure 3. The model fit indices were acceptable, with χ²/df = 2.365, χ² = 89.883, df = 38, CFI = 0.966, IFI = 0.966, TLI = 0.951, NFI = 0.943, SRMR = 0.047, and RMSEA = 0.058.

4.2. Structural Equation Modeling

SEM was used to test the proposed hypotheses. The results indicated a good model fit: χ²/df = 2.164, χ² = 411.201, df = 190, CFI = 0.946, IFI = 0.947, TLI = 0.935, NFI = 0.905, SRMR = 0.044, and RMSEA = 0.054. The hypothesis testing coefficients are shown in Table 5 and Figure 4.

First, the typical TAM hypotheses were tested. PU was found to significantly affect ATT (β = 0.592, ) and IU (β = 0.494, ), supporting H1 and H2. PEU had a significant effect on ATT (β = 0.405, ), supporting H3. However, no significant relationship was found between PEU and PU; thus, H4 was rejected. Furthermore, ATT had a positive effect on IU (β = 0.452, ), supporting H5.

Second, the hypotheses related to the four external variables were tested. PT had a significant negative effect on PU (β = −0.377, ). However, no significant relationship was found between PT and PEU. Therefore, H6 was supported but H7 was rejected. Furthermore, GS had a positive effect on PEU (β = 0.426, ), but it did not have a significant effect on PU. Therefore, H8 was rejected but H9 was supported. Regarding SI, it had a significant positive effect on PU (β = 0.317, ). However, it did not have a significant effect on PEU. Therefore, H10 was supported but H11 was rejected. Lastly, PE had a significant positive effect on both PU (β = 0.351, ) and PEU (β = 0.586, ), supporting H12 and H13. When we deleted the unsupported hypothesis, the standardized solution for the revised model is shown in Figure 5. All the paths were significant enough ().

5. Discussion

In line with previous research on typical TAM, this study found significant positive relationships between PU and ATT, PU and IU, PEU and ATT, and ATT and IU. However, PEU did not significantly affect PU, which contradicts typical TAM results. Nevertheless, some studies have shown that the effect of PEU on PU is not strong [48].

Furthermore, the four external variables PT, GS, SI, and PE affected PEU or PU, indirectly influencing ATT and IU. PT indirectly influenced user acceptance via PU, indicating that improving users’ PT in robo-taxis is important for public adoption. In fact, safety has always been an important aspect of AV design [26]. However, people may not know much about data protection and safety measures and may thus have neutral attitudes [27]. Therefore, on the one hand, manufacturers should continue to make efforts to improve the safety of such technologies. On the other hand, whether robo-taxis are as safe or safer than manually driven vehicles should be confirmed. One possible approach to disseminate safety information is that, in the early stage, people could be allowed to use robo-taxis for free or even with some incentives to help them recognize that robo-taxis are safe. Moreover, to reduce losses when accidents do occur, some related insurance can also be provided to users.

In addition, GS indirectly influenced IU through PEU. In China, GS can increase people’s confidence in new technologies and their intention to purchase [30]. Therefore, GS could likely play an important role in increasing public acceptance of robo-taxi services. Governments can provide incentives for users, manufacturers, and service providers to reduce related costs, thereby encouraging more people to accept robo-taxi services. Traffic rules prioritizing robo-taxis can also be implemented. For example, robo-taxis can be permitted to use bus lanes to reduce travel time and improve travel quality.

Regarding SI, it positively and indirectly influenced user acceptance through PU. As SI represents the influence of friends and family members on users [19, 26], this result indicates that the adoption of robo-taxis by friends and family members can promote user acceptance of such services. A motivation mechanism to encourage existing users to invite new users can thus be set to promote greater user acceptance. For example, existing users who invite new users can receive incentives or coupons. Due to the influence of other people, new users will be more willing to accept robo-taxis.

Lastly, PE had an indirect effect on user acceptance of robo-taxis via both PEU and PU, indicating that when people enjoy the convenience and comfort of traffic tools, their demand for such services increased. Therefore, a comfortable and fun design should be adopted to develop robo-taxi services. For example, providing movies and food as an additional service can improve the user experience.

6. Conclusion

As robo-taxis are gradually entering the market in China, this study proposed an extended TAM model to explore the factors that influence user acceptance of robo-taxi services. The analysis validated typical TAM, with PU and ATT having a direct effect on IU, and PEU and PU having an indirect effect on IU via ATT. Moreover, PT, GS, SI, and PE have indirect effects on IU via PEU and PU. All these factors were found to be effective predictors of IU.

Based on these findings, the following suggestions can be made to improve public adoption of robo-taxis. First, as safety is a major concern, manufacturers and service providers should improve the safety of robo-taxi services. Moreover, information on the safety performance of such services should be disseminated to the public through advertisements and other forms of communication to increase their PT. Second, GS can effectively improve robo-taxi adoption. Providing incentives for users and manufacturers can help users to save money and increase their confidence in robo-taxi services. Prioritizing robo-taxis on roads, such as through permission to use them on bus lanes, can help passengers save time. Third, incentives and coupons can be provided to existing users who invite new users to further promote user adoption through SI. Lastly, robo-taxis can provide entertainment services, such as movies, food, and games, to make passengers’ experience more enjoyable.

This study, however, has some limitations, which should be noted here. Data were collected through an online survey, which can lead to nonresponse bias, among other things. Of the respondents, 93.5% were in the 18–44 year age group and 85.6% had a bachelor’s degree or above, thus reducing the generalizability of the results. However, although some companies have tested robo-taxis on actual roads, they will take more time to successfully introduce robo-taxi services into the market. Young and well-educated people are potential groups to first accept such services. Therefore, the findings of this study are still useful for designers of robo-taxis. Future research can survey people who have used robo-taxi services to conduct a more in-depth investigation of how to improve user adoption of robo-taxis.

Data Availability

The data generated during the current study are owned by the Future Transport Research Center, Tsinghua University, and are not publicly available. The data can be obtained from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Acknowledgments

The authors gratefully acknowledge the support provided by the NSFC Zhejiang Joint Fund for the Integration of Industrialization and Informatization (U1709212), and “Research on frontiers of intelligent transport system” funded by the China Association for Science and Technology (2018DX2QY04).

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Copyright © 2022 Mingyu Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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