Factors affecting the decision to use autonomous shuttle services: Evidence from a scooter-dominant urban context

Transportation Research Part F 67 (2019) 195–204

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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

Factors affecting the decision to use autonomous shuttle services: Evidence from a scooter-dominant urban context Ching-Fu Chen Department of Transportation and Communication Management Science, National Cheng Kung University, 1, University Road, Tainan 701, Taiwan

a r t i c l e

i n f o

Article history: Received 3 June 2019 Received in revised form 22 October 2019 Accepted 30 October 2019 Available online 14 November 2019 Keywords: Smart mobility Autonomous shuttle services Use intention, attitude TAM Trust Perceived enjoyment

a b s t r a c t The evolutionary applications of autonomous vehicles (AVs) to serve as part of public transport systems deserve more attention from the urban transport perspective. This study thus views AV as a novel smart mobility technology and proposes an extended model of the Technology Acceptance Model (TAM) with additional variables to investigate the effects of factors influencing people to use autonomous shuttle services. We utilize a sample of 700 passengers who took a test-ride of autonomous shuttle services in a scooter-dominant urban mobility context for model estimations. Results show that both perceived ease of use and perceived usefulness positively correlate to attitude, in turn leading to use intention. Trust is positively related to attitude, but not to use intention, while perceived enjoyment is positively related to both attitude and use intention. Results of multi-group analyses indicate the moderating roles of age and gender in the estimated models. Overall, respondents are satisfied with the shuttle service in terms of the five attributes of speed, stability and comfort, safety, convenience, and information clarity. However, the speed of shuttle service is the one attribute to which respondents are most concerned. Implications and suggestions for future research are discussed. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Smart mobility has been one of major issues in mobility innovation and thus smart cities. The literature has argued that smart mobility helps improve the efficiency of public transport services and bring out sustainable mobility performance, which can enhance quality of life (Olaverri-Monreal, 2016). Due to rapid advances and development in autonomous driving technologies that enable smart mobility in cities, autonomous vehicles (AVs) play an important role in ‘‘revolutioniz(ing) mobility by turning cars into mobility robots and allowing more dynamic and intelligent forms of public transportation” (Bösch, Becker, Becker, & Axhausen, 2018, p. 76). It is expected that AVs will significantly impact travel and the urban form of cities. Although various transport services with AVs have been designed and discussed, it is still unclear how to pinpoint the certain type of workable service in reality. The potential benefits of improving traffic safety and efficiency of transport systems have spurred transport policy-making authorities to welcome AVs. Over the next two decades, cities have committed to instituting changes that have set AV markets, regulation, and planning in motion. Several pieces of evidence on AV-related policy can be found such as testing within a restricted area or committing to drawing up rules and regulations for AVs used in a mixed traffic environment (Madigan et al., 2016; Nordhoff et al., 2018).

E-mail address: [email protected] https://doi.org/10.1016/j.trf.2019.10.016 1369-8478/Ó 2019 Elsevier Ltd. All rights reserved.

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The evolutionary applications of AVs to serve as part of public transport systems certainly deserve more attention from the urban transport perspective. For instance, one declaration of The Kaohsiung Strategies for the Future of Urban Mobility (ICLEI, 2017) commits to ‘‘support that autonomous vehicles (AVs) in urban areas should be operated only in shared fleets” to achieve the goal of sustainable urban mobility and ecomobility. Various projects of autonomous shuttles in a prototype have been implemented around the world. Most current shuttles with a capacity of 8–10 passengers run on specific routes at limited speeds and require a certain degree of supervision by having a steward on board or by an external control room (Nordhoff et al., 2018). As a matter of fact, the development in AV technology has gone faster than local governments can react to, plan for, and regulate as appropriate. Public sector and/or governments need to prepare for such developments and potential impacts, both positive and negative, in advance. Any innovative smart mobility system cannot become a reality without the acceptance of its targeted users. Hence, a better understanding of public attitude towards autonomous shuttle services as well as intentions of the public to use autonomous shuttle services is imperative. While a growing number of survey studies exist that explore people’s opinions and attitudes towards various autonomous driving concepts (e.g. Bansal, Kockelman, & Singh, 2016; Hohenberger, Spörrle, & Welpe, 2016; Liljamo, Liimatainen, & Pöllänen, 2018; Pettigrew, Dana, & Norman, 2019; Schoettle & Sivak, 2016), most studies’ data on respondents’ attitude are obtained from scenario and imagination instead of from reflections on actual physical ride experiences of autonomous shuttles (Nordhoff et al., 2018, 2019). Without personal experience, it is not easy for respondents to provide actual opinions in the case of emerging and innovative technologies. In reality, enabling people to undergo trial behavior works as a critical factor for evaluating their level of technology acceptance and further encouraging their adoption of the new technology (Strömberg, Rexfelt, Karlsson, & Sochor, 2016). In the literature, just a few studies use survey data based upon respondents’ test ride experience of autonomous shuttles (e.g. Madigan et al., 2016; Madigan, Louw, Wilbrink, Schieben, & Merat, 2017; Nordhoff et al., 2018; Nordhoff, de Winter, Payre, van Arem, & Happee, 2019; Portouli et al., 2017). It is thus important to collect attitudes and perceptions from respondents who have physically experienced such a shuttle to avoid having them provide opinions based on an unrealistic understanding of the technology and its current state of development associated with AVs (Nordhoff et al., 2019). Besides, most of the existing research evidences are obtained from the car-dominant urban context such as Germany (Nordhoff et al., 2018), Greece (Madigan et al., 2017), To fill the gap, we particularly focus on a scooter-dominant urban context that is different from the car-dominant urban context in terms of traffic composition and complexity. Specifically, the present study empirically examines the factors affecting the decision to use autonomous shuttle services by using survey data of a test-ride project in Taiwan. We expect the findings to contribute to providing a more comprehensive picture of how people perceive and accept autonomous shuttle services in different urban contexts. 2. Conceptual development and hypotheses Behavioral models have been commonly used to explore the acceptance of autonomous shuttles in past studies. For example, Madigan et al. (2016) and Madigan et al. (2017) apply the unified theory of acceptance and use of technology (UTAUT, Venkatesh, Morris, Davis, & Davis, 2003) framework to investigate the factors that influence users’ acceptance of automated public transport, while others by and large use descriptive statistics to analyze respondents’ opinions and attitudes. More specifically, five factors based upon the UTAUT2 model (Venkatesh, Thong, & Xu, 2012), including performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation, are specified by Madigan et al. (2016, 2017) to examine their effects on intention to use automated public transport vehicles. Their findings indicate that effort expectancy is not significantly related to behavioral intention, while the other four factors demonstrate their strong impact on behavioral intention. While the UTAUT framework is originally applied to understand people’s intentions to use information systems with regards to employees in an organization (i.e. the UTAUT model) and consumers in general (i.e. the UTAUT2 model), there is concern on the adequacy of directly applying them to the case of autonomous shuttle use intention. Since AV is viewed as a novel smart mobility technology in this study, we argue that the Technology Acceptance Model (TAM, Davis, Bagozzi, & Warshaw, 1989), a commonly employed behavioral model in transport studies from a technology acceptance perspective (Jen, Lu, & Liu, 2009), is an adequate theoretical base to explore the effects of factors influencing people’s acceptance of autonomous shuttle services. TAM, one of the most influential extensions of the theory of reasoned action (Ajzen & Fishbein, 1975), suggests perceived usefulness and perceived ease-of-use are the main determinants of influencing users’ attitude toward and acceptance of a new technology (such as AV shuttle services). Perceived usefulness relates to the degree to individual’s belief in enhancing his/her task performance when using a specific technology system, while perceived ease-of-use pertains to the degree to individual’s perception of being free from effort or difficulty when using a specific technology system (Davis, 1989). Attitude represents the degree of an individual’s positive or negative evaluation of performing a behavior or using a specific technology system (Ajzen, 1991; Davis et al., 1989). According to TAM, perceived ease-of-use is hypothesized to positively relate to perceived usefulness. Both perceived ease-of-use and perceived usefulness are hypothesized to predict attitude toward using the technology, which in turn predict behavioral intention to use and actual behavior. In addition, perceived usefulness is hypothesized to influence the behavioral intention to use (Ajzen, 1991; Chen & Chao, 2011; Davis, 1989). Despite TAM provides a concise, clear, and reasonable theory basic from sociology and psychology perspective, there exists a call for integrating additional factors depending on certain context to improve model’s explanatory power and

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specificity (Hu, Chau, Sheng, & Tam, 1999). Referring to previous studies, we include two additional psychological factors: trust (Hegner, Beldad, & Brunswick, 2019; Kaur & Rampersad, 2018) and perceived enjoyment (Hegner et al., 2019; Kyriakidis, Happee, & de Winter, 2015; Pettigrew et al., 2019), in our model to better reflect the features of AV shuttle service. Trust pertains to ‘‘the attitude that an agent will help achieve an individual’s goal in a situation characterized by uncertainty and vulnerability” (Lee & See, 2004, P.51) and has been identified as a key construct which may determine individual attitudes towards and intention to use AV technology (Buckley, Kaye, & Pradhan, 2018; Nordhoff et al., 2019; Zhang et al., 2019). Perceived enjoyment refers to ‘‘an intrinsic belief or motive, which is formed from the individual’s experiences of enjoyment” (Chen, 2016, P.152) and is found to have a positive influence on attitudes towards and intention to use automated public transport (Madigan et al., 2017). Both trust and perceived enjoyment are assumed to positively relate to attitude as well as intention to use based upon previous research evidence. Fig. 1 presents the proposed model with the following hypotheses. H1: H2: H3: H4: H5: H6: H7: H8: H9:

Perceived ease of use is positively related to perceived usefulness Perceived ease of use is positively related to attitude Perceived usefulness is positively related to attitude Perceived usefulness is positively related to intention to use Attitude is positively related to intention to use Trust is positively related to attitude Perceived enjoyment is positively related to attitude Trust is positively related to intention to use Perceived enjoyment is positively related to intention to use

3. Method 3.1. Study site and context The test ride of an autonomous shuttle as a project on future urban smart mobility was conducted by the Transportation Bureau, Kaohsiung City Government in Taiwan at Kaohsiung Software Park during the period from November 6 to December 7, 2017. The project aims to explore the possibility of autonomous shuttle services in urban areas and evaluate its level of service in terms of integration with public transport systems. The route goes through the Software Park area and connects two stations of the Light Rail System to test the last mile scenario for autonomous shuttle services under a restricted mixed traffic scenario. The route length is 1100 m with three stops to link up two terminal stops of tram stations and a stop at the Software Park in-between. During the 25 operation days, the shuttle services (on a 6-h per day basis due to recharging needs) ran a total of 280 services at a speed of 15 km/h and took on 3587 passengers. The Easymile EZ10 autonomous shuttle operated by 7Starlake (http://7starlake.com) was used in this project. This autonomous shuttle can detect surroundings by using a variety of techniques such as radar, Lidar, GPS, odometry, and computer vision. This shuttle can work in a 100% self-driving

Trust H8

Perceived ease of use

H6

H2

H5 H1

Intention to use

Attitude H3

Perceived usefulness

H4

Hĸġ

Perceived enjoyment

Fig. 1. The proposed model.

H9

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mode, but a steward was deployed on board to explain the autonomous shuttle to passengers during the test ride. As a part of the test ride project, we collected questionnaire data from the passengers who finished their test ride at the ending stop. Two research assistants approached passengers based upon the convenience sampling method and asked their willingness to participate in the survey after a brief introduction of the survey aims. Passengers were ensured about anonymity in their answers. Once participation willingness was obtained, passengers were given the self-administrated questionnaire to answer. From a total of 1658 questionnaires collected, after deleting those with incomplete responses, 1498 useable responses were obtained, yielding a response rate of 90.34%. To avoid the sampling bias at our best, we use random number to randomly select 700 respondents for the model estimation in current study. Table 1 lists the sample characteristics of the randomly selected respondents. Among the respondents, 57.7% of them were female. Most respondents are aged between 41 and 65 (40.4%) followed by the group aged between 26 and 40 years (29.4%). A private vehicle is the main transport mode for the vast majority of the sample, including those using a scooter (47.7%) and a car (28.7%). Frequent public transport users account for around 40% (i.e. every day users account for 13.7% and 2–3 times per week are 26.3%). The majority of respondents (66.6%) report novelty motivation as the reason to take the autonomous shuttle. 3.2. Measures We amend the measurement scales for all constructs from the existing literature in order to fit the research context. Since we extend the TAM model by including additional constructs of trust and perceived enjoyment, the constructs of perceived ease of use, perceived usefulness, and intention to use directly refer to the TAM scales (Davis, 1989). Each item is measured by a five point Likert-type scale (1 = ‘strongly disagree’ and 5 = ‘strongly agree’). Specifically, both perceived ease of use and perceived usefulness are measured by four items each adapted from Madigan et al. (2017). Attitude and intention to use are measured by four items (Moon & Kim, 2001) and two items (Madigan et al., 2017), respectively. Trust is measured by three items adopted from Möhlmann (2015). Perceived enjoyment is measured by three items adopted from Sun and Zhang (2006). To understand respondents’ trial-ride experience, we also ask five items of perceived satisfaction related to the service attributes of the autonomous shuttle by using a five-point Likert-type scale (1 = ‘strongly unsatisfied’ and 5 = ‘strongly satisfied’). Information of respondent characteristics such as gender, age, and travel characteristics such as transport mode use, use frequency of public transport, and motivation of using the autonomous shuttle were also surveyed. 3.3. Data analysis This study analyzes the data following the two-stage approach proposed with the maximum likelihood method (Anderson & Gerbing, 1988). The first stage conducts confirmatory factor analysis (CFA) to test convergent reliability and dis-

Table 1 Sample profile. Characteristics Gender Male Female Age Below 18 18–25 26–40 41–65 Above 65 Main transportation mode Car Scooter Bus Metro Bike Walk Frequency of using public transportation Almost every day Two to three times a week Several times a month Hardly Motivation of using autonomous shuttle Novelty Pro-environment Transport fans Technology fans Others

Number of respondents (n = 700)

Percentage (%)

296 404

42.3% 57.7%

67 57 206 283 87

9.6% 8.1% 29.4% 40.4% 12.4%

201 334 39 81 22 23

28.7% 47.7% 5.6% 11.6% 3.1% 3.3%

96 184 292 128

13.7% 26.3% 41.7% 18.3%

466 129 26 64 15

66.6% 18.4% 3.7% 9.1% 2.1%

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criminant validity for the measurement model. The second stage estimates the hypothesized structural model to test the hypothesized relationships. To assess model fit, we use indices of model estimation including CMIN/DF (ratio of chi-square statistics over degrees of freedom, v2/d.f.), the goodness-of-fit index (GFI), the normed fit index (NFI), the comparative fit index (CFI), and the root mean square of approximation (RMSEA). CMIN/DF (v2 / df) is the minimum discrepancy divided by its degrees of freedom; a ratio of approximately five or less is suggested as a reasonable fit (Wheaton, Muthen, Alwin, & Summers, 1977). The values of all indices except RMSEA are greater than 0.9, indicating a good model fit. An RMSEA value less than 0.08 means a reasonable model (Hair, Anderson, Babin, & Black, 2010). Multi-group SEM analyses were also conducted to explore the differences in estimated results by age and gender. 4. Results 4.1. Descriptive statistics Table 2 presents the descriptive statistics including mean and standard deviation for the measurement items of each construct of the conceptual model. 4.2. Measurement model To check convergent validity, we conduct a confirmatory factor analysis on all latent constructs in the measurement model. The estimated model shows an acceptable fit (v2/df = 2.77, CFI = 0.99, NFI = 0.99, GFI = 0.95, RMSEA = 0.055, SRMR = 0.028). We check the convergent validity of the measurement model by the strength and significance of the factor loadings (i.e. item reliability), construct reliability (CR), and the average variance extracted (AVE) for each construct (Hair et al., 2014). As shown in Table 3, all items load significantly on their latent construct and all factor loadings are greater than 0.50, supporting convergent validity of the constructs. All AVEs are larger than 0.50 and construct reliability estimates of all the constructs are higher than 0.70, providing additional evidence of convergent validity. As shown in Table 4, discriminant validity is confirmed by the results that the square root of AVE for each latent construct is larger than its correlation with other constructs (Fornell & Larcker, 1981). 4.3. Structural model and hypothesis test We subsequently estimate the structural model to examine the causal relationships between constructs. The hypothesized model shows a good fit on the sample data according to the goodness-of-fit indices: v2 (123) = 368.795 (p = 0.00), Table 2 Descriptive statistics of measurement items. Constructs and items

Mean

SD

Perceived ease of use (PE, Cronbach’s a = 0.849, mean = 4.37, SD = 0.59) PE1: My interaction with the autonomous shuttle is clear and understandable. PE2: I find the autonomous shuttle easy to use. PE3: Learning to use an autonomous shuttle is easy for me.

4.35 4.40 4.37

0.65 0.66 0.70

Perceived usefulness (PU, Cronbach’s a = 0.842, mean = 4.07, SD = 0.68) PU1: I find the autonomous shuttle a useful mode of transport. PU2: Using the autonomous shuttle to travel helps me to achieve things important to me. PU3: Using the autonomous shuttle will help me reach my destination more quickly.

4.36 4.03 3.82

0.67 0.79 0.88

Attitude (ATT, Cronbach’s a = 0.90, mean = 4.40, SD = 0.58) ATT1: Using an autonomous shuttle is a good idea. ATT2: Using an autonomous shuttle is a pleasant idea. ATT3: Using an autonomous shuttle is a wise idea. ATT4: Using an autonomous shuttle is a positive idea.

4.35 4.47 4.33 4.43

0.68 0.63 0.69 0.64

4.14 4.27

0.77 0.68

Trust (TR, Cronbach’s a = 0.886, mean = 4.24, SD = 0.64) TR1: An autonomous shuttle provides a robust and safe environment in which I can use the service. TR2: I trust that the autonomous shuttle provider has enough safeguardsto protect me from liabilityfor damage I am notresponsible for. TR3: Overall, an autonomous bus is trustworthy.

4.30

0.68

Perceived enjoyment (EN, Cronbach’s a = 0.933, mean = 4.52, SD = 0.56) EN1: I find using the autonomous shuttle to be enjoyable. EN2: The actual process of using the autonomous shuttle is pleasant. EN3: I have fun using the autonomous shuttle.

4.51 4.52 4.53

0.59 0.58 0.60

Intention to use (IN, Cronbach’s a = 0.902, mean = 4.50, SD = 0.57) IN1: If the autonomous shuttle became available permanently, I intend to use it. IN2: If the autonomous shuttle became available permanently, I will recommend friends and relatives to use it.

4.48 4.53

0.61 0.59

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Table 3 CFA results of the measurement model.

**

Construct

Item

Standard factor loading

Standard error

T-value

CR

AVE

Perceived usefulness

PU1 PU2 PU3

0.79 0.85 0.79

0.37 0.28 0.38

23.70** 26.43** 24.36**

0.851

0.657

Perceived ease of use

PE1 PE2 PE3

0.80 0.86 0.77

0.35 0.26 0.41

24.91** 26.97** 23.22**

0.853

0.659

Attitude

ATT1 ATT2 ATT3 ATT4

0.81 0.83 0.86 0.83

0.34 0.31 0.26 0.31

25.63** 26.12** 28.25** 26.45**

0.901

0.695

Trust

TR1 TR2 TR3

0.77 0.91 0.88

0.41 0.16 0.23

23.51** 30.55** 28.88**

0.891

0.733

Perceived enjoyment

EN1 EN2 EN3

0.91 0.94 0.87

0.17 0.11 0.24

30.88** 32.94** 28.73**

0.934

0.826

Intention to use

IN1 IN2

0.90 0.91

0.19 0.17

28.81** 29.02**

0.901

0.820

Denotes p > 0.01.

Table 4 Results of discriminant validity. Construct

(1)

(2)

(3)

(4)

(5)

(6)

Perceived usefulness (1) Perceived ease of use (2) Attitude (3) Trust (4) Perceived enjoyment (5) Intention to use (6)

0.810 0.608** 0.637** 0.589** 0.455** 0.566**

0.812 0.708** 0.633** 0.562** 0.537**

0.833 0.669** 0.642** 0.602**

0.856 0.641** 0.529**

0.909 0.535**

0.905

Notes: 1. The square roots of AVE are shown on the diagonal of the matrix. ** 2. Denotes p < 0.01.

Trust

Perceived ease of use

0.038 (0.745) 0.427*** (6.142)

0.133*** (2.847) 2

R =0.747 0.764*** (16.860)

Attitude

0.245*** (3.636)

Intention to use

2

R =0.523

0.215*** (4.426) Perceived usefulness 2

R =0.562

0.347*** (6.434)

0.237*** (5.913)

Perceived enjoyment

Fig. 2. The estimated model.

0.219*** (4.247)

Note: ***p<0.001; **p<0.01; *p<0.05

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v2/d.f. = 2.998, GFI = 0.944, CFI = 0.975, NFI = 0.963, RMSEA = 0.053. SRMR = 0.017. Fig. 2 shows the estimated model with the standardized path estimates and t-value in parentheses. Eight out of nine hypotheses in the model are supported, except for H8: the path from trust to intention to use. Overall, the results are consistent with the TAM theory. Perceived ease of use (b = 0.764, t = 16.860) has a significantly positive effect on perceived usefulness, thus supporting H1. Perceived ease of use (b = 0.427, t = 6.142) and perceived usefulness (b = 0.215, t = 4.426) has a significantly positive effect on attitude respectively, indicating support for H2 and H3. Perceived usefulness (b = 0.347, t = 6.434) also has a significantly positive effect on intention to use, hence supporting H4. Attitude (b = 0.245, t = 3.636) has a significantly positive effect on intention to use, hence supporting H5. Regarding the effects of additional factors, trust (b = 0.133, t = 2.847) and perceived enjoyment exhibit a significantly positive effect on attitude respectively, supporting H6 and H7. While its direct effect on intention to use is not evident, perceived enjoyment (b = 0.219, t = 4.247) has a significantly positive effect on intention to use, supporting H9. 4.4. Multi-group analyses by age and gender Age and gender are two demographic variables commonly used as moderators to explore the differences in technology acceptance as well as autonomous vehicles among groups (Bansal et al., 2016; Dong, DiScenna, & Guerra, 2019; Pettigrew et al., 2019). In this study, we also explore the moderating role of age and gender in the proposed model by conducting multi-group analyses. Table 5 and Table 6 report the restless of multi-group analyses by age and gender, respectively. Significant differences in two paths, i.e. perceived ease of use ? attitude (Z-score = 3.044) and trust ? attitude (Zscore = 2.744) are found between Group 1 (age below 40) and Group 2 (age over 40) in Table 5. Significant differences in three paths, i.e. perceived ease of use ? perceived usefulness, perceived usefulness ? attitude (Z-score = 2.323) and trust ? attitude (Z-score = 2.837) are found between males and females in Table 6. 4.5. Satisfaction with test-ride experience The respondents offer high ratings to satisfaction levels with regards to five service attributes of the autonomous shuttle. The mean of overall satisfaction level is 4.30 on the scale from 1 (strongly disagree) to 5 (strongly agree). More specifically, the respondents are the most satisfied with the safety perception of the shuttle service, with a mean of 4.45. The respondents also feel rather satisfied with the clarity of information (mean = 4.38), stability and comfort (mean = 4.36), and convenience (mean = 4.32) of the shuttle service. The lowest satisfaction rating pertains to the speed of shuttle service (mean = 4.01). We further compared the satisfaction level with speed among various age groups by one-way ANOVA and found that older respondents report higher satisfaction ratings. In Fig. 3, respondents over 65 years have a satisfaction rating over 4, while the ratings for the other groups are less than 4. 5. Discussion and conclusion This study proposes and tests an integrated model for autonomous shuttle use intention that is hypothesized to be affected by TAM factors and two psychological factors. The findings of this paper show that attitude and perceived enjoyment have direct positive effects on use intention, while trust has an indirect effect through the mediation of attitude. In line with TAM, attitude demonstrates the strong predictor of behavioral intention, implying users who hold a greater attitude towards the autonomous shuttle as an alternative of public transport are more likely to accept and use it in the future.

Table 5 Results of multi-group analysis by age. Path

Fully Constrained

Estimate PE ? PU PE ? ATT PU ? ATT TR ? ATT EN ? ATT PU ? IN TR ? IN ATT ? IN EN ? IN

0.771 0.439 0.204 0.135 0.236 0.343 0.025 0.258 0.225

Notes: ns, non-significant. * p-value < 0.05 at one-tail test. ** p-value < 0.01. *** p-value < 0.001.

Unconstrained Group 1 (Age: Below 40)

Group 2 (Age: Over 41)

P

Estimate

P

Estimate

P

***

0.704 0.255 0.268 0.216 0.202 0.320 0.039 0.268 0.244

***

0.833 0.705 0.150 0.066 0.328 0.322 0.120 0.248 0.193

***

*** *** *** *** ***

ns *** ***

*** *** *** *** ***

ns *** ***

*** **

ns *** ***

ns *** **

z-score

1.418 3.044*** 1.203 2.744*** 1.525 0.013 1.518 0.153 0.494

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Table 6 Results of multi-group analysis by gender. Path

PE ? PU PE ? ATT PU ? ATT TR ? ATT EN ? ATT PU ? IN TR ? IN ATT ? IN EN ? IN

Fully Constrained

Unconstrained Group 1 (Gender: Male)

Group 2 (Gender: Female)

z-score

Estimate

P

Estimate

P

Estimate

P

0.756 0.426 0.201 0.130 0.241 0.350 0.037 0.242 0.222

***

0.659 0.428 0.360 0.029 0.300 0.355 0.042 0.255 0.260

***

0.823 0.450 0.113 0.247 0.191 0.345 0.033 0.248 0.175

***

*** *** *** *** ***

ns *** ***

*** ***

ns *** ***

ns ** ***

*** ** *** *** ***

ns *** ***

1.824* 0.149 2.323** 2.837*** 1.349 0.077 0.087 0.053 0.804

Notes: ns, non-significant. * p-value < 0.05 at one-tail test. ** p-value < 0.01. *** p-value < 0.001.

Fig. 3. Satisfaction rating of speed by age groups.

Nonetheless, the effect of perceived enjoyment on use intention is slightly less than attitude, indicating that the novelty and fun experienced from the test ride of the autonomous shuttle also attract individuals’ intention to use the autonomous shuttle. The positive effect of perceived enjoyment indicates that the attractiveness of a new mobility technology can be increased by the enjoyment experience provided by the technology. In order to enhance people’s awareness and real experience of an autonomous shuttle, emphasizing the playfulness and fun of the ride experience can be used to increase their use intention in addition to their reasoned reactions to utility perceptions of the autonomous shuttle. Similarly, Madigan et al. (2017) also suggest the significant effect of hedonic motivation on influencing intentions to use an autonomous shuttle due to the novelty and innovativeness of the vehicles. They also recommend that developers keep the factor of perceived enjoyment in mind as the systems become more advanced and easier to be seen in daily life. However, we note that perceived enjoyment may not be necessary to sustain as soon as the autonomous shuttle serves as a type of public transport in practice. In other words, how to enhance the perceived utilitarian value of an autonomous shuttle is crucial to pushing users to adopt it as public transport. The insignificant effect of trust on use intention in our study is understandable, because the test-ride route is in a closed environment. In such a condition, participants who intend to use the autonomous shuttle do not need to worry about the risks associated with confronting other vehicles. However, the positive effect of trust on attitude should be recognized since trust built from the test ride experience can help result in a more positive attitude towards autonomous shuttle usage. Consistent with TAM, our findings show that both perceived ease of use and perceived usefulness are antecedents of attitude. It implies that the degree users feel toward an autonomous shuttle’s ease of use and its functional value will enhance their attitude towards this vehicle and in turn their use intention. However, the magnitude of perceived usefulness on attitude is much less than that of perceived ease of use in our findings. One possible reason might be attributable to the scenario of the test ride being in a restricted mixed traffic area of our study. To actually demonstrate the autonomous shuttle’s usefulness and utilitarian value, we suggest the importance in future projects of making the test ride’s circumstance and experience as real as possible in terms of normal daily commuting. The multi-group analysis results among age groups as well as gender groups in current study provide additional implications. The moderating roles of age and genders are consistent with some previous studies (e.g. Bansal et al., 2016; Dong et al., 2019) but inconsistent with others (e.g. Madigan et al., 2017; Pettigrew et al., 2019). Participants over 40 years of

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old concern the perception of ease of use when shaping their attitude towards AV shuttle service compared to those less than 40 years of old. The effect of trust on attitude is only significantly positive for participants less than 40 years old, implying it is more difficult to build the trust of the older participants than the younger ones. The perception of usefulness of AV shuttle service can make male respondents more preferable attitude than female ones. The perception of trust can only make female respondents but not male ones have favorable attitude toward AV shuttle service. Our findings of satisfaction with an autonomous shuttle suggest that users in general feel highly satisfied with it in terms of speed, safety, stability and comfort, information clarity, and convenience. Similar findings can be found in their survey study by Nordhoff et al. (2018) which shows respondents’ high level of satisfaction with AV shuttle service attributes such as safety, reliability, and atmosphere while speed is the least satisfied among them. We also highlight users’ concern of speed, in particular for younger users who are significantly less satisfied with its slow speed compared to the older users. We note that the speed of our test ride, 15 km/h, is relatively leisurely compared to the speed of daily commuting on other public transport systems like buses or metro. It suggests that an increase in speed to make an autonomous shuttle compatible with public transport must be well considered to help riders perceive better usefulness and to encourage their actual use. There are several limitations of this study, and we also provide potential research directions for future studies. First, since the data analyzed in this study come from one-time test ride project, the results cannot directly apply to other research contexts. However, our findings can provide a useful reference for future studies to accumulate more research evidences to depict a comprehensive picture of user acceptance of AV shuttle services. Second, information on the motivation or purpose to use an autonomous shuttle is a requisite for understanding users’ mobility needs and to then develop corresponding business models. Future studies are recommended to take into account the role of motivation and investigate the link between it and user behavior towards an autonomous shuttle. Third, the complexity level for an autonomous shuttle running in a mixed traffic circumstance like automation at SAE levels 4 and 5 (On-Road Automated Vehicle Standards Committee, 2016) is much higher than in a closed area. Future studies can try and provide more research evidence of autonomous shuttle projects carried out under a mixed traffic scenario. 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