Will the perceptions to incentive policies have any effect on users' willingness to participate in Mobility-as-a-Service?

Rongxuan Zhang; Lianqun Ouyang; Lingling Xiao; Rongxuan Zhang; Lianqun Ouyang; Lingling Xiao

doi:10.48130/dts-0024-0006

With the increasing severity of urban traffic congestion and environmental pollution issues, Mobility-as-a-Service (MaaS) has garnered increasing attention as an emerging mode of transportation. Thus, how to motivate users to participate in MaaS has become an important research issue. This study first classified the incentive policies into four aspects: financial incentive policy, non-financial incentive policy, information policy, and convenience policy. Then, through online questionnaires and field interviews, 456 sets of data were collected in Beijing, and the data were analyzed by the structural equation model and latent class model. The results show that the four incentive policies are positively correlated with users' participation in MaaS, among which financial incentive policy and information policy have the greatest impact, that is, they can better encourage users by increasing direct financial subsidies and broadening the information about MaaS. In addition, Latent Class Analysis was performed to class different users and it was found that the personal characteristics of users had some influence on willingness to participate in MaaS. Therefore, incentive policies should be designed to consider the needs and characteristics of different user groups to improve their willingness to participate in MaaS. The results can provide theoretical suggestions for the government to promote the widespread application of MaaS in urban transportation.

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Introduction

With the development of the economy, people's demand for high-quality living is constantly increasing. In terms of transportation, there is a growing dependence on private cars, leading to an annual increase in car ownership. As a result, congestion and environmental pollution problems are becoming increasingly prominent, becoming a contradiction in urban transportation development. Although various shared modes of transportation have been widely mentioned, their effects have been minimal^[1]. To alleviate related issues, Mobility as a Service has emerged and has become a new development in urban transportation in recent years. This service integrates public transportation with various shared modes of transportation, providing one-stop travel information services. The emergence of MaaS not only reduces the frequency of private car usage for travelers, but also reduces congestion, improves traffic efficiency, and thus protects the environment^[2]. Internationally, Western countries have proposed new goals for MaaS in terms of integrating green transportation, based on intelligent and digital transportation. At the same time, many provinces and cities in China actively utilize the development ideas of MaaS to solve urban transportation problems, taking into account their urban transportation development status. In 2022, Beijing held a MaaS Ecosystem Development Forum to discuss the 'Beijing MaaS 2.0 Work Plan', which plans to include new modes of transportation such as 'rail+' in the Beijing MaaS plan; Shanghai aims to comprehensively promote MaaS and achieve 'one code access' for rail transit, ground buses, and ferries. However, due to imperfect governance mechanisms and low levels of travel privacy and security^[3], the participation of MaaS still needs to be improved. Therefore, it is still a research focus for scholars to call for more travelers to participate in MaaS travel in a targeted manner.

Carbon inclusivity refers to the specific quantification of energy-saving and carbon-reduction behaviors of small and micro-enterprises, community households, and individuals. It assigns a certain value to these efforts and establishes a positive guiding mechanism that combines commercial incentives, policy encouragement, and verified emission reduction trading. In the context of MaaS, transportation service apps like Ubigo and Whim in various countries have designed goals for low-carbon travel. In 2019, Beijing collaborated with Amap, launching the first domestic integrated green travel service platform. It implemented carbon incentives such as taxi fare refunds and discounted vouchers to encourage travelers in choosing green travel options. Furthermore, in terms of travel incentives, scholars have expressed diverse opinions on the effectiveness of different measures. Researchers believe that the incentive approach is related to the individual characteristics of travelers^[4] and their socio-economic attributes^[5]. Different incentive measures can alleviate peak travel demand from different perspectives. For example, financial incentives not only encourage people's participation in non-motorized travel but also have long-term effects on travelers' travel behavior^[6,7]. Information incentives can promote sustainable low-carbon consumption among individuals^[8] and so on.

In order to promote the participation of travelers in MaaS, previous studies have mainly explored the preferences and willingness of travelers to use MaaS, as well as the factors behind these preferences. Several reports suggests that, user participation in MaaS travel and their travel habits^[9], environmental awareness and the awareness of innovation^[10] and other personal characteristics have significant relationships, besides, In recent years, some scholars have combined the satisfaction of MaaS participation with gender, age, and occupation^[11] and other combination of personal characteristics. Some scholars have also conducted research from the perspective of a specific population, for example, van't Veer et al.^[12] analyzed the acceptance attitude of the commuter population towards MaaS, and Kriswardhana et al.^[13] explored the preference of college students for MaaS. With the development of intelligent transportation, the related research on MaaS at home and abroad is gradually increasing. However, most of the existing studies only focus on the acceptance of MaaS by different populations and lack the discussion of how to motivate more different groups of users to participate in MaaS. Therefore, this paper comprehensively considers the following three directions: First, explore the willingness of users with different characteristics to participate in MaaS under the incentive policies; next, according to the existing literature, the personal characteristics of users were included in the study of behavioral intention; lastly, as MaaS services are just emerging in China, to explore the intention of different populations to participate in MaaS^[14]. The potential category analysis of the above influencing factors, to study the willingness of populations with different social attributes and travel characteristics to participate in MaaS, it provides a targeted reference basis for the application and promotion of MaaS travel in China.

Literature review

Research on the incentive policies

While MaaS is considered a new direction for future urban transportation, whether it can undermine the dominance of private car travel largely depends on the level of participation by travelers^[15]. Therefore, governments and operators have to formulate related policies to promote travelers' participation in MaaS from different angles^[16,17]. Relevant research has also confirmed the positive effects of financial and non-financial incentives on travel behavior. For example, direct price subsidies and exemption from purchase taxes can incentivize travelers to use electric vehicles^[18]; financial incentives are more effective in encouraging travelers to accept smart mobility services compared to other approaches^[19]. Additionally, the Chinese Amap platform has incorporated both financial and non-financial incentives into Beijing MaaS platform design. It assigns corresponding carbon energy values based on individuals' level of participation in MaaS, providing incentives such as discounted ride vouchers and discounts from relevant third-party partner businesses to encourage traveler participation^[20]. Besides, according to the discussion on the integration level^[21], information integration functionality is the first-level requirement of an integrated platform. As a mobility integration service platform, MaaS also provides travelers with one-stop travel information services, offering convenient services. In existing services, platforms like Whim in Finland, Ubigo in Sweden have incorporated real-time travel information into their MaaS services^[22]. They provide travelers with information such as intelligent trip planning, travel times, and distances^[23], thereby saving travelers' time spent waiting for transportation and queuing. Additionally, to encourage low-carbon travel, some platforms also provide real-time information on carbon emissions and combine it with carbon-inclusive incentives. At the same time, to improve the design of the MaaS platform and encourage more travelers to choose non-private car travel modes, various local governments have issued different policies to enhance travel convenience. According to the annual report released by 'Suishenxing' (Shanghai MaaS service platform) in 2022, Shanghai's government-developed MaaS platform integrates various travel modes and services, including ferries, ride-hailing, shared bicycles, maglev trains, and smart parking^[24]. Furthermore, the inclusion of electric shared scooters and carpooling into the MaaS platform provides travelers with more options^[25].

Research on the personal traits
Many scholars believe that the heterogeneity of travelers has a significant impact on their participation in MaaS. Therefore, scholars have expanded models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand the heterogeneity of users' participation in MaaS is based on different habits and personality traits^[26,27]. Hence, this study refers to existing literature on the impact of individual traits on MaaS participation and discusses the influencing factors in four aspects: Innovation trait, Habit schema congruence, Environment trait, and Social influence.

Innovation trait is considered one of the influencing factors for individuals' willingness to accept new products and technologies^[28]. As an integrated platform, MaaS not only innovates users' travel methods but also introduces a brand-new 'one-stop' platform technology into the urban transportation system. Compared to traditional travel methods, MaaS can provide not only point-to-point travel recommendations but also various travel modes supplementary information^[29]. Technophilia^[30] refers to a group of people who have a preference for new technologies or products. These individuals often exhibit more enthusiasm for engaging with new technologies^[31,32]. They are also an important target audience for promoting MaaS, as they tend to have a positive impact on the usage intention and participation level of MaaS^[33]. Since MaaS requires people's participation through mobile devices, travel habits not only include users' choice of transportation modes but also their usage habits of mobile devices. Under the concept of MaaS, which may disrupt traditional travel patterns, users with consistent travel habits are considered to be more receptive to MaaS services and they may have a higher willingness to pay for it^[34]. According to existing literature, environmentalists are often more inclined toward public transportation travel^[35]. As MaaS is considered to encourage low-carbon travel and maximized social welfare associated with travel impacts^[36,37], individuals with high environmental awareness are more likely to prefer participating in MaaS. In addition to individual characteristics, social networks are often considered an important factor influencing people's behavior. Previous studies have also shown that social influence has a positive impact on people's acceptance of automated public transport^[38], driverless cars^[39], and other new travel modes. Therefore, social influence factors are frequently included in adoption studies of new transportation technologies^[40,41]. The results consistently show that social influence factors have a positive impact on user participation in new travel modes.

Research gap
Based on the literature reviewed above, it is evident that MaaS has also received a lot of attention in China. With the emphasis on intelligent transportation, related research on MaaS has gradually increased. However, a majority of the existing research is derived from foreign investigations and empirical studies on the acceptance of MaaS, while research on this topic in China remains limited. Furthermore, most of the studies only focus on the acceptance of MaaS by different populations, with a lack of relevant discussion on the related policies for MaaS. Therefore, this paper focuses on the incentives that affect travelers, and takes into account personal characteristics in combination with the theoretical basis of TAM and UTAUT models to identify policies that encourage or discourage consumer adoption of MaaS.

Conclusions

Based on different incentive policies, combined with TAM, UTAUT and TRA theory, and comprehensively considering the personal characteristics of users, this paper constructs the MaaS travel intention model under the incentive policy, to reveal the incentive policies that affect users' participation in MaaS, and analyzes the structural equation model. On this basis, the groups are grouped according to the travel characteristics of different users, and the latent category model. The main conclusions are summarized as follows:

The study found that financial incentive policies, information policies and convenience policies have an obvious direct impact on users 'participation in MaaS travel, indicating that the promotion of more users' participation in MaaS is inseparable from the joint efforts of direct financial subsidies and more convenient travel information. Users with innovation and environmental awareness, or travel habits similar to MaaS, are more willing to try to participate in MaaS, while social influence can indirectly affect users' participation behavior through intermediary variables.

As can be seen from the latent category model, different types of users have different preferences for MaaS participation as well as related incentive policies. The 18−34 age group and higher education also showed higher motivation for participating in MaaS, similar to the findings of Narayanan & Antoniou^[45]. In addition, among the three categories with the highest participation, female members are higher than male members, so this paper believes that women prefer and participate in MaaS more than men, which may result from the heterogeneity of different gender preferences for MaaS platform design, content services, etc. In addition, those users with longer travel distance and more travel purposes, despite showing interest in MaaS, may still be a hindering factor in actual participation because information policy and facilitation policy have no significant incentive effect on this part of the group.

Therefore, in the future, on the one hand, the travel platform can expand the way of financial incentives, and provide more diverse exchange options for travelers who prefer public transportation. To encourage more potential users to join, the platform suppliers can provide more favorable direct financial relief for users who participate in MaaS travel for the first time, and establish the connection between the platform and travelers. On the other hand, in terms of information policy, the platform can cooperate with the government. In addition to incorporate more travel modes into MaaS, the MaaS travel platform can also be combined with scenic spots, ticketing, parking and even cross-city tourism to create a more comprehensive information platform and provide one-stop information services for travelers^[46]. Finally, because the user's personal characteristics are closely related to the participation of MaaS, the government or suppliers need to try to promote the concept and service of MaaS, but also need to explain its green, environmental protection and other characteristics, to stimulate the participation of potential users with environmental awareness and expand the audience range of MaaS services.

It is necessary to note several limitations. Firstly, the development of MaaS, brings new issues and solutions such as platform privacy and security, fairness, and monopolies^[47]. These aspects were not considered in this study. Therefore, future research can further investigate the impact of new incentive policies on user participation in MaaS. Secondly, the sample of this study was limited to Beijing, China. Future research can consider larger samples and broader geographical regions, while also accounting for geographical effects. Another limitation of this study is that it only explores users' willingness to participate in MaaS under incentive policies. Although some groups show enthusiasm for MaaS participation, they may not engage in it. Therefore, in the future, it would be beneficial to conduct research and analysis on the actual participation of users under incentive policies.

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: Zhang R, Xiao L; data collection: Zhang R, Ouyang L; analysis and interpretation of results: Zhang R, Xiao L; draft manuscript preparation: Zhang R, Ouyang L, Xiao L. All authors reviewed the results and approved the final version of the manuscript.

[1]	Miskolczi M, Földes D, Munkácsy A, Jászberényi M. 2021. Urban mobility scenarios until the 2030s. Sustainable Cities and Society 72:103029 doi: 10.1016/j.scs.2021.103029 CrossRef Google Scholar
[2]	Hensher DA, Nelson JD, Mulley C. 2022. Electric car sharing as a service (ECSaaS) – Acknowledging the role of the car in the public mobility ecosystem and what it might mean for MaaS as eMaaS? Transport Policy 116:212−16 doi: 10.1016/j.tranpol.2021.12.007 CrossRef Google Scholar
[3]	Huang S. 2022. Listening to users' personal privacy concerns. The implication of trust and privacy concerns on the user's adoption of a MaaS-pilot. Case Studies on Transport Policy 10:2153−64 doi: 10.1016/j.cstp.2022.09.012 CrossRef Google Scholar
[4]	Lopez-Carreiro I, Monzon A, Lois D, Lopez-Lambas ME. 2021. Are travellers willing to adopt MaaS? Exploring attitudinal and personality factors in the case of Madrid, Spain. Travel Behaviour and Society 25:246−61 doi: 10.1016/j.tbs.2021.07.011 CrossRef Google Scholar
[5]	Wang S, Li J, Zhao D. 2017. The impact of policy measures on consumer intention to adopt electric vehicles: Evidence from China. Transportation Research Part A: Policy and Practice 105:14−26 doi: 10.1016/j.tra.2017.08.013 CrossRef Google Scholar
[6]	Hu X, Wang S, Zhou R, Gao L, Zhu Z. 2023. Policy driven or consumer trait driven? Unpacking the EVs purchase intention of consumers from the policy and consumer trait perspective. Energy Policy 177:113559 doi: 10.1016/j.enpol.2023.113559 CrossRef Google Scholar
[7]	Zeiske N, van der Werff E, Steg L. 2021. The effects of a financial incentive on motives and intentions to commute to work with public transport in the short and long term. Journal of Environmental Psychology 78:101718 doi: 10.1016/j.jenvp.2021.101718 CrossRef Google Scholar
[8]	Wei J, Zhang L, Yang R, Song M. 2023. A new perspective to promote sustainable low-carbon consumption: The influence of informational incentive and social influence. Journal of Environmental Management 327:116848 doi: 10.1016/j.jenvman.2022.116848 CrossRef Google Scholar
[9]	Matowicki M, Amorim M, Kern M, Pecherkova P, Motzer N, et al. 2022. Understanding the potential of MaaS – An European survey on attitudes. Travel Behaviour and Society 27:204−15 doi: 10.1016/j.tbs.2022.01.009 CrossRef Google Scholar
[10]	Ye J, Zheng J, Yi F. 2020. A study on users' willingness to accept mobility as a service based on UTAUT model. Technological Forecasting and Social Change 157:120066 doi: 10.1016/j.techfore.2020.120066 CrossRef Google Scholar
[11]	Aman JJC, Smith-Colin J. 2022. Application of crowdsourced data to infer user satisfaction with mobility as a service (MaaS). Transportation research interdisciplinary perspectives 15:100672 doi: 10.1016/j.trip.2022.100672 CrossRef Google Scholar
[12]	van't Veer R, Annema JA, Araghi Y, Homem de Almeida Correia G, van Wee B. 2023. Mobility-as-a-Service (MaaS): A latent class cluster analysis to identify Dutch vehicle owners' use intention. Transportation Research Part A: Policy and Practice 169:103608 doi: 10.1016/j.tra.2023.103608 CrossRef Google Scholar
[13]	Kriswardhana W, Esztergár-Kiss D. 2024. University students’ adoption of mobility as a service with respect to user preferences and group differences. Journal of Public Transportation. 26:100079 Google Scholar
[14]	Alonso-González MJ, Hoogendoorn-Lanser S, van Oort N, Cats O, Hoogendoorn S. 2020. Drivers and barriers in adopting Mobility as a Service (MaaS) – A latent class cluster analysis of attitudes. Transportation Research Part A: Policy and Practice 132:378−401 doi: 10.1016/j.tra.2019.11.022 CrossRef Google Scholar
[15]	Christensen TH, Friis F, Nielsen MV. 2022. Shifting from ownership to access and the future for MaaS: Insights from car sharing practices in Copenhagen. Case Studies on Transport Policy 10:841−50 doi: 10.1016/j.cstp.2022.02.011 CrossRef Google Scholar
[16]	Fenton P, Chimenti G, Kanda W. 2020. The role of local government in governance and diffusion of Mobility-as-a-Service: exploring the views of MaaS stakeholders in Stockholm. Journal of Environmental Planning and Management 63:2554−76 doi: 10.1080/09640568.2020.1740655 CrossRef Google Scholar
[17]	Vij A, Dühr S. 2022. The commercial viability of Mobility-as-a-Service (MaaS): What's in it for existing transport operators, and why should governments intervene? Transport Reviews 42:695−716 doi: 10.1080/01441647.2022.2028032 CrossRef Google Scholar
[18]	Khan U, Yamamoto T, Sato H. 2020. Consumer preferences for hydrogen fuel cell vehicles in Japan. Transportation Research Part D: Transport and Environment 87:102542 doi: 10.1016/j.trd.2020.102542 CrossRef Google Scholar
[19]	dos Reis RA, Grant-Muller S, Lovelace R, Hodgson F. 2022. Different people, different incentives? Examining the public acceptance of smartphone-based persuasive strategies for sustainable travel using psychographic segmentation. International Journal of Sustainable Transportation 16:1−21 doi: 10.1080/15568318.2020.1836693 CrossRef Google Scholar
[20]	Chen Y, Acheampong RA. 2023. Mobility-as-a-service transitions in China: Emerging policies, initiatives, platforms and MaaS implementation models. Case Studies on Transport Policy 13:101054 doi: 10.1016/j.cstp.2023.101054 CrossRef Google Scholar
[21]	Sochor J, Arby H, Karlsson ICM, Sarasini S. 2018. A topological approach to Mobility as a Service: A proposed tool for understanding requirements and effects, and for aiding the integration of societal goals. Research in Transportation Business & Management 27:3−14 doi: 10.1016/j.rtbm.2018.12.003 CrossRef Google Scholar
[22]	Li W, Shibasaki R, Zhang H, Chen J. 2022. MaaS system development and APPs. In Big Data and Mobility as a Service, eds. Zhang H, Song X, Shibasaki R. Netherlands: Elsevier. pp. 1−24. https://doi.org/10.1016/b978-0-323-90169-7.00002-6
[23]	Sakai K. 2020. Public transport promotion and mobility-as-a-service. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103.A:226−30 doi: 10.1587/transfun.2019tsi0001 CrossRef Google Scholar
[24]	Yang Y, Lin S. 2023. External pressures and internal dynamics of the implementation of MaaS: Case study of Shanghai City Government. Insights from ongoing Arctic cooperation, Seminar “Norway-China Academic Cooperation connected to Arctic issues", Shanghai, China, 2023. Academic lecture. Nord University and High North Center.
[25]	Brezovec P, Hampl N. 2021. Electric vehicles ready for breakthrough in MaaS? consumer adoption of E-car sharing and E-scooter sharing as a part of mobility-as-a-service (MaaS). Energies 14:1088 doi: 10.3390/en14041088 CrossRef Google Scholar
[26]	Kriswardhana W, Esztergár-Kiss D. 2023. Exploring the aspects of MaaS adoption based on college students' preferences. Transport Policy 136:113−25 doi: 10.1016/j.tranpol.2023.03.018 CrossRef Google Scholar
[27]	Lopez-Carreiro I, Monzon A. 2023. Exploring travellers' willingness to adopt MaaS in two European metropolitan areas. Transportation Research Procedia 72:1885−93 doi: 10.1016/j.trpro.2023.11.667 CrossRef Google Scholar
[28]	Roos JM, Sprei F, Holmberg U. 2022. Traits and transports: The effects of personality on the choice of urban transport modes. Applied Sciences 12:1467 doi: 10.3390/app12031467 CrossRef Google Scholar
[29]	Hensher DA, Ho CQ, Mulley C, Wong Y, Nelson JD, Smith G, et al. 2020. Understanding Mobility as a Service (MaaS): Past, present and future. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/C2019-0-00508-0
[30]	Seebauer S, Stolz R, Berger M. 2015. Technophilia as a driver for using advanced traveler information systems. Transportation Research Part C: Emerging Technologies 60:498−510 doi: 10.1016/j.trc.2015.10.009 CrossRef Google Scholar
[31]	Fioreze T, De Gruijter M, Geurs K. 2019. On the likelihood of using Mobility-as-a-Service: A case study on innovative mobility services among residents in the Netherlands. Case Studies on Transport Policy 7:790−801 doi: 10.1016/j.cstp.2019.08.002 CrossRef Google Scholar
[32]	Schikofsky J, Dannewald T, Kowald M. 2020. Exploring motivational mechanisms behind the intention to adopt mobility as a service (MaaS): Insights from Germany. Transportation Research Part A: Policy and Practice 131:296−312 doi: 10.1016/j.tra.2019.09.022 CrossRef Google Scholar
[33]	Dastjerdi AM, Kaplan S, de Abreu e Silva J, Nielsen OA, Pereira FC. 2019. Use intention of mobility-management travel apps: The role of users goals, technophile attitude and community trust. Transportation Research Part A: Policy and Practice 126:114−35 doi: 10.1016/j.tra.2019.06.001 CrossRef Google Scholar
[34]	Tsouros I, Tsirimpa A, Pagoni I, Polydoropoulou A. 2021. MaaS users: Who they are and how much they are willing-to-pay. Transportation Research Part A: Policy and Practice 148:470−80 doi: 10.1016/j.tra.2021.04.016 CrossRef Google Scholar
[35]	Kamargianni M, Li W, Matyas M, Schäfer A. 2016. A critical review of new mobility services for urban transport. Transportation Research Procedia 14:3294−303 doi: 10.1016/j.trpro.2016.05.277 CrossRef Google Scholar
[36]	Pritchard J. 2022. MaaS to pull us out of a car-centric orbit: Principles for sustainable Mobility-as-a-Service in the context of unsustainable car dependency. Case Studies on Transport Policy 10:1483−93 doi: 10.1016/j.cstp.2022.08.00 CrossRef Google Scholar
[37]	Zong F, Li YX, Zeng M. 2023. Developing a carbon emission charging scheme considering mobility as a service. Energy 267:126495 doi: 10.1016/j.energy.2022.126495 CrossRef Google Scholar
[38]	Madigan R, Louw T, Wilbrink M, Schieben A, Merat N. 2017. What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation research part F: traffic psychology and behaviour 50:55−64 doi: 10.1016/j.trf.2017.07.007 CrossRef Google Scholar
[39]	Ghasri M, Vij A. 2021. The potential impact of media commentary and social influence on consumer preferences for driverless cars. Transportation Research Part C: Emerging Technologies 127:103132 doi: 10.1016/j.trc.2021.103132 CrossRef Google Scholar
[40]	Manca F, Sivakumar A, Polak JW. 2022. Capturing the effect of multiple social influence sources on the adoption of new transport technologies and services. Journal of choice modelling 42:100344 doi: 10.1016/j.jocm.2022.100344 CrossRef Google Scholar
[41]	Zhu C, Shou M, Zhou Y, Li W. 2023. Modeling the effect of social media on older adults' usage intention of public transport. Economic Analysis and Policy 77:239−50 doi: 10.1016/j.eap.2022.11.014 CrossRef Google Scholar
[42]	Hair JF Jr, Black WC, Babin BJ, Anderson RE. 2010. Multivariate data analysis: a global perspective. Hoboken, NJ, USA: Pearson Prentice Hall.
[43]	Hu Lt, Bentler PM. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal 6:1−55 doi: 10.1080/10705519909540118 CrossRef Google Scholar
[44]	Lanza ST, Rhoades BL. 2013. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevention science 14:157−68 doi: 10.1007/s11121-011-0201-1 CrossRef Google Scholar
[45]	Narayanan S, Antoniou C. 2023. Shared mobility services towards Mobility as a Service (MaaS): What, who and when? Transportation Research Part A: Policy and Practice 168:103581 doi: 10.1016/j.tra.2023.103581 CrossRef Google Scholar
[46]	Chen C-F, Fu C, Chen Y-C. 2023. Exploring tourist preference for Mobility-as-a-Service (MaaS) – A latent class choice approach. Transportation Research Part A: Policy and Practice 174:103750 doi: 10.1016/j.tra.2023.103750 CrossRef Google Scholar
[47]	Enoch M, Potter S. 2023. MaaS (Mobility as a Service) market futures explored. Transport Policy 134:31−40 doi: 10.1016/j.tranpol.2023.02.007 CrossRef Google Scholar

Constructs	Items	Item descriptions
Financial incentive policy	FIP1	For adopting MaaS, direct financial subsidy is attractive to me.
	FIP2	For adopting MaaS, subsidy for travel is very attractive to me.
	FIP3	For adopting MaaS, getting the discount coupon available to supermarket is very attractive to me.
Non-financial incentive policy	NFIP1	For adopting MaaS, participating in charity donation (Ant Forest from Alibaba) is very attractive to me.
Non-financial incentive policy	NFIP2	For adopting MaaS, getting the carbon coin is very attractive to me.
Information policy	IP1	For adopting MaaS, the information about route planning is useful to me.
	IP2	For adopting MaaS, the information about predicted travel time and cost is helpful.
	IP3	For adopting MaaS, the Information related to the real-time free parking space is helpful to me.
	IP4	For adopting MaaS, the information related to my carbon footprint is valuable to me.
Convenience policy	CP1	For adopting MaaS, one-code pass (a QR code can scan all public transport services) is essential.
	CP2	For adopting MaaS, connect with other travel platform(such as taxi system)is essential.
	CP3	For adopting MaaS, reducing the queuing time makes sense for me.
Innovation trait	IT1	I am always curious about new things.
	IT2	I usually take the lead in trying new technologies compare to people aroud me.
	IT3	I think it's very interesting to try out the new travel service mode.
Habit schema congruence	HSC1	MaaS system is similar with my current way of travel.
	HSC2	MaaS system is similar to the product or service that I am used to.
	HSC3	I am familiar with using my smartphone for payments and I always take it with me outside.
	HSC4	I am familiar with the use of journey planning apps (for example Google Map)
Environment trait	ET1	I am very concerned about the environment, and I feel that environmental problems have become more and more serious.
	ET2	I think the harmony between man and nature can promote sustainable development.
	ET3	I think that everyone has the responsibility to protect the environment.
	ET4	I am worried about the future shortage of natural resources.
Social influence	SI1	I am willing to adopt MaaS if the government evaluation is good.
	SI2	I am willing to adopt MaaS if the media evaluation is good.
	SI3	I am willing to adopt it if MaaS can get support and praise from people around me.
Attitude to MaaS	ATM1	I think it is necessary to travel with MaaS.
	ATM2	I think the government should encourage the use of MaaS mode.
	ATM3	I think once I use the MaaS, I will reduce my car usage.
Behavior to MaaS	BTM1	I plan to be involved in the MaaS travel mode in the future.
Behavior to MaaS	BTM2	I am looking forward to take part in the MaaS travel mode in the future.

Characteristics	Items	Frequency (n = 456)	Percentage (%)
Gender	Male	228	50
Gender	Female	228	50
Age (years)	18−24	142	31.1
	25−34	140	30.7
	35−44	98	21.4
	45−54	54	11.8
	55−64	15	3.2
	≥ 65	7	1.5
Education	High school or below	79	17.3
	Bachelor's degree	227	49.7
	Master's degree or above	150	32.8
Employment status	Enterprise employees	84	18.4
	Administrative staff	75	16.4
	Institution staff	75	16.4
	Students	125	27.4
	Freelancer	47	10.3
	Others	50	10.9
Monthly income (CNY)	< 2,000	80	17.5
	2,000−5,000	92	20.1
	5,001−10,000	129	28.2
	10,001−20,000	108	23.6
	> 20,000	47	10.3
Own private car	Yes	351	76.9
Own private car	No	105	23.1
Trip mode	Public transport	197	43.2
	Private car	137	30
	Taxi	20	4.3
	Carpooling	26	5.7
	Bike-sharing	49	10.7
	Walk or bicycle	17	3.7
	Others	10	2.1

Construct	Item	Estimate	CR	AVE	Cronbach α
Financial incentive policy	FIP1	0.788	0.778	0.540	0.779
	FIP2	0.721
	FIP3	0.692
Non-financial incentive policy	NFIP1	0.778	0.741	0.588	0.74
Non-financial incentive policy	NFIP2	0.756	0.741	0.588	0.74
Information policy	IP1	0.855	0.896	0.682	0.894
	IP2	0.772
	IP3	0.821
	IP4	0.853
Convenience policy	CP1	0.831	0.867	0.686	0.867
	CP2	0.823
	CP3	0.83
Innovation trait	IT1	0.745	0.784	0.548	0.782
	IT2	0.754
	IT3	0.721
Habit schema congruence	HSC1	0.883	0.882	0.652	0.879
	HSC2	0.79
	HSC3	0.783
	HSC4	0.768
Environment trait	ET1	0.838	0.898	0.687	0.897
	ET2	0.829
	ET3	0.835
	ET4	0.812
Social influence	SI1	0.882	0.870	0.691	0.869
	SI2	0.816
	SI3	0.793
Attitute to MaaS	ATM1	0.831	0.836	0.630	0.839
	ATM2	0.789
	ATM3	0.76
Behavior to MaaS	BTM1	0.855	0.796	0.661	0.794
Behavior to MaaS	BTM2	0.769	0.796	0.661	0.794

	FIP	NFIP	IP	CP	IT	HSC	ET	SI	ATM	BTM
FIP	0.735
NFIP	0.473**	0.767
IP	0.532**	0.384**	0.826
CP	0.507**	0.398**	0.436**	0.828
IT	0.429**	0.364**	0.432**	0.414**	0.740
HSC	0.414**	0.350**	0.399**	0.425**	0.417**	0.807
ET	0.535**	0.424**	0.519**	0.506**	0.497**	0.468**	0.892
SI	0.409**	0.357**	0.499**	0.540**	0.315**	0.374**	0.457**	0.831
ATM	0.564**	0.415**	0.574**	0.535**	0.435**	0.520**	0.535**	0.509**	0.794
BTM	0.662**	0.567**	0.616**	0.632**	0.551**	0.584**	0.650**	0.548**	0.708**	0.813
*, , and * represent significance levels of 1%, 5%, and 10% respectively. The diagonal numbers (in bold) represents the value of √AVE.

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Will the perceptions to incentive policies have any effect on users' willingness to participate in Mobility-as-a-Service?

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