1. Introduction
According to rapid urbanization and policy guidance, China currently promotes regional coordination with urban agglomerations as the main engines. Urban agglomerations are formed by a certain number of neighboring cities and their surrounding areas gathering together for development with one or two core cities [1,2,3]. As a result, connections between cities are increasingly closer and important [1,2], and therefore, “intercity travel” is promoted, that is, people travel between nearby cities more frequently [2]. Diversified intercity commuting, tourism, and other travel demands have raised higher requirements for the level of intercity transportation services [3]. Efficient transportation services are important means to promote the regional sustainable development [3]. In recent years, advanced technologies such as information and communication technology (ICT), big data, cloud computing, GPS, etc., have been applied in transportation systems to provide travelers with smart travel services through real-time travel information, integrated ticketing/payment, shared mobility, and personalized customized travel services, etc. [4,5]. In 2023, the total number of mobile phone users in China had grown to 1.727 billion households, with a penetration rate of 122.5 devices per 100 people, and the total number of mobile internet users reached 1.517 billion [6]. The popularity of smartphones and the growth in mobile internet users in China make it possible to find and use a variety of smart travel services at all stages of travelers’ intercity travel nowadays [7,8].
Apparently, the scope of intercity travel services and the way in which they are provided have reshaped accordingly. Travelers are increasingly dependent on smart travel technologies during intercity travel, and smart travel services have gradually become a rigid demand during the whole journey in intercity travel [9]. Travelers can apply integrated travel platforms to check train schedules and ticket availability; itinerary planning and different route recommendations can be obtained according to real-time travel information; mobile payment makes the whole journey of intercity travel more efficient, and on-board mobile internet enables travelers to allocate travel time and resources more effectively [7,8]. Therefore, passengers’ demand for and evaluation of intercity travel services are undergoing significant changes with the increased use of smart travel applications [10,11]. Satisfaction is the most direct indicator for passengers to evaluate travel services and it plays a crucial role in monitoring service quality [12]. Scholars have conducted extensive research on the satisfaction evaluation of traditional intercity travel services, such as passenger security, vehicle cleanliness, crowding on board, etc. [13]. It is evident that under the current trend of technological development, evaluation indicators on smart travel service need to be updated and added, and their impact on travel satisfaction needs to be explored [9]. Furthermore, segmenting travelers can better reflect the complexity of transportation system and explain the impact mechanism [14]. Different passenger groups often have different needs, and their evaluations on quality of the same service tend to be various. Heterogeneity study also contributes to the development of targeted and refined market strategies [14].
Therefore, this paper aims to select appropriate and comprehensive indicators to study the impact mechanism of smart travel service attributes on intercity travel satisfaction, especially within potential categories. Due to the lack of large-scale and comprehensive intercity travel survey data in China, this study conducts a survey in the Guanzhong Plain urban agglomeration to obtain data for empirical analysis. Moreover, this study is expected to propose measures to improve smart travel services based on passenger satisfaction and heterogeneity, which will help to enhance the level of intercity travel services and promote sustainable development in the intercity transportation system.
The remainder of this paper is organized as follows: The previous relevant literature is summarized in Section 2. Section 3 presents the conceptual model and research hypotheses. Data collection and the analysis of the sample are described in Section 4. Section 5 shows the methods used in this study and the model results. Moreover, implications are further discussed in Section 6. Finally, Section 7 summarizes the research findings and provides recommendations for future research.
2. Literature Review
Travel satisfaction is defined as the overall level of realization of travelers’ expectations, the fulfillment of their needs, and the outcomes of individual travel experiences [15]. Previous studies usually focus on exploring travel satisfaction of a single intercity long-haul travel mode and access mode [16,17,18,19], from the traditional aspects of vehicle speed, transfers, in-vehicle environment, service personnel, etc. [20,21]. With the application of emerging smart travel technologies, some “intelligent” indicators have been added. In 2009, Eboli et al. found that transit riders perceive accessing travel information through mobile phones/the Internet was less important than most other services [22]. Echaniz et al. evaluated public transport information services by combining information provided on digital platforms, bus stops, and on board [23]. Shen et al. selected intelligent indicators such as mobile signal strength and automated ticket vending machines to investigate passenger satisfaction with a metro line [21]. Additionally, Kong et al. found that using ICT for route inquiries, mode selection, and departure time determination has a significant positive impact on pre-travel satisfaction with high-speed rail journeys [24]. Wang et al. found that using ICT for work and leisure activities during the journey enhances travel satisfaction [25]. Barreto et al. demonstrated that the utilization of AI speakers in trip planning significantly improves travelers’ overall travel experience [26]. However, with the widespread coverage of smart travel technology throughout the entire intercity journey and across various modes, smart travel services are becoming more diverse and multi-scenario-oriented. It is difficult to select indicators by listing all the services. Therefore, further consideration is needed on representative indicators that can effectively evaluate smart travel services as a whole.
In addition to the changes in the influencing factors that affect travel satisfaction, emerging scholars applied the structural equation model approach to study the mechanism of how perceived attributes of smart technologies affect satisfaction, especially in the field of smart tourism [7,27,28,29,30,31]. Studies revealed that smart tourism technology can enhance memorable experiences and significantly influence tourists’ happiness [7,27] and revisit intention [27,28]. Travel is a major component of tourism, and the direct and indirect impacts of smart tourism technology on travel were also discussed as part of the model. According to the path results of most studies, smart tourism technology experience is shown to be significantly associated with travel experience satisfaction [7,29,30]. Goo et al. examined how smart tourism technology attributes influence travel experience satisfaction [29]. Huang et al. explored the relationships between the attributes of smart tourism technologies, explorative and exploitative use of smart tourism, and overall travel experience satisfaction [30]. However, there are few studies that discuss the influencing mechanisms of smart travel satisfaction alone. Intercity travel covers various travel purposes, and it is necessary to explore the relationship between smart travel service and intercity travel satisfaction, which is of great significance to the transportation field.
There are also significant differences among travelers due to factors such as socioeconomic attributes, subjective cognition, etc., and single or combined indicators are also the basis for exploring the unobserved heterogeneity in satisfaction. Choi et al. identified two latent groups of commuters based on their attitudes towards commuting [31]. According to travel attributes, personal socioeconomic attributes, and personality traits, Gao et al. studied the impact of travel satisfaction on overall life satisfaction among three potential categories [32]. Shin et al. divided the travelers into more-tourism clustered and less-tourism clustered areas based on number of employees of specific industries and found differences in the impact of smart tourism technologies on the memorability of the tourism experience [33]. Yuan et al. applied the FIMIX-PLS model to identify heterogeneity in the psychological–behavioral relationships among three groups of passengers and found the differences in their satisfaction with air and rail travel integrated service-related latent variables in terms of psychological and behavioral perceptions [16]. Allen et al. classified air transport passengers based on sociodemographic characteristics, attitudes, and travel habits, and identified differences in service evaluation [34]. Some scholars believe segmenting travel groups is an effective strategy to increase the share of public transportation modes [35,36]. Khan et al. used latent category clustering analysis (LCCA) to make the segmentation and help the planners to improve the efficiency of transit services [37]. Eldeeb and Mohamed investigated the heterogeneity in passenger expectations for the quality of public transportation services and proposed measures to improve services [38]. Research on the heterogeneity study related to smart travel is more limited but remains necessary both theoretically and practically.
3. Research Hypotheses
This study focuses on investigating the contributions of smart travel service and smart travel experience to intercity travel satisfaction. Therefore, this paper proposes an integrated concept model that considers the five attributes of smart travel service—accessibility, informativeness, personalization, interactivity, and security—and perceived service quality of smart travel (PSQST), smart travel service satisfaction (STSS), perceived value of smart travel service (PVSTS), smart travel experience satisfaction (STES), and overall intercity travel satisfaction (ITS). The research hypotheses are illustrated in Figure 1. Widely recognized attributes of accessibility, informativeness, personalization, interactivity, and security are adopted to assess smart travel service quality, and they can enhance the perceived usability and usefulness of the smart travel technologies [7,26,28,29,39]. Accessibility refers to the ease of accessing and using various smart mobility facilities and applications, which is an important measure of smart travel services. Simple and operable smart travel services will be favored by travelers. Informativeness means that reliable and timely travel information can be obtained from the smart travel services during the whole travel process, which is the basis for decision making. Personalization of smart travel products or services is one effective way to meet various needs of travelers and enhance their individual experiences. Interactivity is the ability to actively communicate and receive feedback from providers when using smart travel services. Travel experience can also be shared and utilized by other travelers through the platform. Moreover, smart travel services usually extract the individual information of travelers; therefore, smart travel services’ ability to protect privacy of personal information and prevent information leakage is a key factor [27,28,29,32,40]. PSQST is a second-order variable constructed by the above five first-order constructs representing characteristics of smart travel service.
Previous studies have shown that the above attributes consider all aspects of service quality comprehensively and are used to study the impact of travel service quality on user satisfaction [7,41]. Smart travel service satisfaction refers to the level of cognition and evaluation of the smart travel services that can be provided during intercity travel. Perceived service quality has been confirmed as the most important variable closely related to satisfaction [41]. Research results indicate that the perceived attributes of the above dimensions have a significant positive impact on satisfaction with smart tourism [7]. Thus, the research hypothesis is proposed that the PSQST has a significant positive impact on STSS, where a higher perception level of smart travel service quality leads to greater satisfaction with it.
PSQST has a significant positive impact on STSS.
Regardless of the purpose of travel, our intercity travel is no longer about how to reach the destination, but about how to get there better. With the support of smart travel technology, travel experience during intercity travel could be different, and the measurement of smart travel experience satisfaction is employed to evaluate fulfillment of their needs during intercity travel. The key attributes of smart travel allow for travelers to travel efficiently and effectively. Travel service experience can be boosted through high-quality smart travel services. Therefore, service quality is critical to satisfaction with the service, which in turn leads to satisfaction with the overall experience when the service is performed [28]. It is generally believed that the higher the satisfaction with the smart travel service, the higher the satisfaction with the intercity travel experience. Therefore, this paper proposes the following hypothesis:
STSS has a significant positive impact on STES.
Moreover, tourists usually want their choices to have more value, such as functional, emotional, and social value [7]. Perceived value in the travel domain is the overall evaluation of the travel services provided, specifically the balance between perceived benefits and perceived costs [41]. Travelers can obtain the services they need through smart travel applications to replace manual services, which saves on physical cost [27]. Smart travel technologies can facilitate the traveler deriving greater value from the service while traveling, which can greatly improve their travel experience [27,28,29]. In this study, perceived value is reflected in the value added by smart travel services in terms of convenience, comfort, safety, and the environmental friendliness of intercity travel. The higher the perceived value gain in the various aspects listed above, the better the travel experience for passengers using intercity travel. Therefore, the following hypothesis is proposed:
PVSTS has a significant positive impact on STES.
Previous studies have shown that higher satisfaction with service leads to higher travel satisfaction [7]. It was also found that a high-quality smart travel experience makes tourists feel positive about their travel [27]. Overall intercity travel satisfaction is defined as the rating of well-being across the entire intercity trip. Therefore, when travelers are satisfied with the smart travel services they use, they will also tend to have a positive perception of the whole intercity journey. At the same time, intercity travel satisfaction can also be achieved through satisfaction with an unforgettable travel experience. Therefore, the following research hypotheses are proposed:
STSS has a significant positive impact on ITS.
STES has a significant positive impact on ITS.
4. Data Collection and Analysis
4.1. Data Collection
Based on the above research hypotheses and latent constructs, 28 related attitudinal measurement items were designed and can be found in Table 1. A five-point Likert scale was used for evaluation, ranging from “strongly disagree” to “strongly agree”. In addition, usage of smart travel during intercity travel and attitudes towards smart travel were also included in the questionnaire.
A self-administrated survey was conducted in Guanzhong Plain urban agglomeration in western China, with Xi’an as the core city. The survey targeted individuals who had traveled between cities within the Guanzhong Plain urban agglomeration in the last year and who were at least 18 years old. Pre-surveys were conducted in August 2021 in advance, and adjustments and modifications were made to the survey according to the feedback. The official on-site survey began on 21 September 2021, at Xi’an Railway Station, Xi’an North Railway Station, and Xi’an Intercity Bus Station. The respondents were selected randomly to complete the questionnaire by scanning a QR code. For respondents who had difficulty filling out the questionnaire on their mobile phones, investigators provided assistance in filling out the paper questionnaire. Although intercity travel had already resumed during the recovery period, travelers were still cautious. Most respondents were unwilling to communicate too much, which is also the reason for the low response rate. In October 2021, due to the impact of the COVID-19 pandemic, activities not related to travel were not encouraged at transportation stations. Considering the safety of the investigators (from being infected), the survey switched to an online mode. Online questionnaires were randomly distributed to residents of major cities in the Guanzhong Plain urban agglomeration and the survey was ended in November 2021. The survey link was accessed a total of 7592 times, and 1483 samples were collected, with a response rate of 10%. The low response rate is due to the fact that not all online respondents had participated in an intercity travel experience in the past year. Moreover, survey data were examined by minimum answering time, validation questions, and scope of survey (whether the intercity trip was in the Guanzhong Plain urban agglomeration and whether the respondent was over 18 years old). After removing samples that were too incomplete or obviously inconsistent, 902 valid samples were finally obtained, with an effective rate of 60.8%.
Table 2 presents the statistical analysis on the selected socioeconomic attributes of the sample. Of the total, 55.9% of the respondents were females. The majority of respondents (69%) were aged between 18 and 35, and those aged 36 to 55 accounted for about 26%; 45% of the respondents had an annual income of less than 50,000 yuan. From the distribution of education levels, half of the respondents (50%) had four-year college degree(s). Moreover, 75% of the respondents had obtained their driver’s licenses and 65% of the respondents had at least one car in their household. From the sample characteristics shown above, it can be seen that there was a slightly higher proportion of young respondents, which has a certain deviation from the actual basic population structure. This was mainly due to the high response rate of young people. This study mainly analyzed influencing factors and heterogeneity; therefore, the effective samples in this study met the research needs.
4.2. Reliability Test and Factor Analysis
Cronbach’s α was used for reliability analysis of the survey data. It is generally recommended that the Cronbach’s α coefficient be greater than 0.7. The higher the Cronbach’s α coefficient, the better the reliability. The overall Cronbach’s α coefficient of the scale is 0.955 for this study, indicating ideal reliability. This study used confirmatory factor analysis to obtain potential variables. Firstly, measurement results of a Kaiser–Meyer–Olkin (KMO) test (0.963) and Bartlett sphericity tests (p value was 0.000) demonstrate that the samples are suitable for factor analysis. In addition, application of the most commonly used criteria proposed in previous studies—(1) factor loading > 0.5, (2) composite reliability (C.R.) > 0.5, and (3) average extraction value (AVE) > 0.7—indicates high internal consistency and convergent validity [42,43]. The above testing indicators are calculated in Table 3. Furthermore, the Cronbach’s α coefficient values of each latent variable are between 0.761 and 0.888, which all meet the standard, indicating strong reliability and good internal consistency [42]. All the results indicate that the measurement indicators have high accuracy and can effectively achieve the expected results.
5. Methods and Results
5.1. Latent Class SEM
This study employs the latent class SEM (structural equation model) method to conduct a comprehensive investigation into traveler heterogeneity. Latent class SEM is a statistical model employed for investigating the variations in the relationship between latent classes within multivariate data. It simultaneously considers the correlation between causal variables and latent variables, enabling analysis of distinctions among different classes. The specific advantages of latent class SEM are as follows: (a) when the differences between groups are not readily apparent and a priori segmentation is not feasible, the utilization of the classification method based on latent classes becomes imperative; (b) it enables simultaneous estimation of specific path coefficients for each group within a predefined number of segments, while inferring the segment membership for each observed value; (c) it can also be employed to examine whether unobserved moderators account for heterogeneity phenomena in structured data. The measurement model of latent variables in latent class SEM can be expressed by the following equation:
(1)
(2)
Equations are customized for the multiple-group situation and thus the notation includes the group indicator, = 1, 2, …, G, in which and are vectors of measurement intercept terms; is the vector of exogenous observation variables, and is the vector of endogenous observation variables; and are the factor loading vectors of (, ); is the vector of exogenous latent variables, and is the vector of endogenous latent variables; is the measurement error of exogenous variables, and is the measurement error of endogenous variables.
The structural model can be expressed as follows:
(3)
in which is the structure coefficient matrix between exogenous and endogenous latent variables; is the matrix of the structure coefficients among endogenous latent variables. represents the residual term [41].Latent class SEM is calculated using the responses of individuals in a sample to observe indicators. From a survey in the previous literature, it can be seen that the heterogeneity of travelers is reflected in various aspects such as individual characteristics and subjective variables such as preferences, attitudes, perceived value, etc., and some recent studies attempt to add the behavior variable for the segmentation [27,29,32,33,44,45]. To measure the heterogeneity construct of intercity travelers, this study constructed measurement items for traveler heterogeneity in two dimensions—usage characteristics (frequency of use of smart travel service (SERFRE)) and attitude preferences (including travelers’ preference for new technologies (LTECH), perceived importance of smart travel service (OEIMP), and travelers’ loyalty to smart travel service (CONTIUE))—and used these four aspects as input variables for latent class classification.
5.2. Model Results
In order to test the appropriate number of latent categories of the model, one to four potential classes were set successively to determine the optimal number of latent groups. Test indicators for latent class classification mainly include the Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted Bayesian information criterion (aBIC), entropy index, Lo–Mendell–Rubin (LMR), and bootstrapped likelihood ratio test (BLRT) [46]. The results of various fit indices are shown in Table 4. It can be observed that when there are three latent classes, the AIC, BIC, aBIC, and entropy index all reach their optimal values. Additionally, both LMR and BLRT achieve a significance level of 95% (p < 0.05), indicating the best model fit and classification accuracy. Therefore, three-class is considered as the optimal classification for dividing the intercity travelers in Guanzhong Plain urban agglomeration in this study.
As shown in Figure 2, compared with the other two classes, travelers in Class 1 not only had the lowest frequency of using STS, but also gave the lowest ratings on preference for new technology, perceived importance of STS, and willingness to continue using STS, so Class 1 is named the “cold” smart travel service user group; travelers in Class 2 use STS slightly more frequently than those in Class 1, but their attitudes towards STS have significantly positively increased, indicating their recognition of STS during intercity travel and their willingness to use STS according to their own needs. Therefore, Class 2 is defined as a “rational” group. Latent Class 3 showed the highest values of behavior and attitude variables towards STS among the three groups, indicating their preference and dependence on smart travel, and it is considered as an “enthusiastic” group accordingly.
Moreover, the distribution statistics of socioeconomic attributes of three latent classes are examined and presented in Table 5, and a chi-square test is applied to test the significant differences in the socioeconomic characteristics among the different groups. From the results in Table 3, it can be seen that “age”, “education”, “annual income”, and “whether hold a driver’s license not” have differences at significance levels among different groups. By examining the characteristics of socioeconomic attributes of each class, the “cold” group can be identified by low income, low educational level, and higher average age; in contrast, the “rational” group and “enthusiastic” group can be characterized as high-income and highly educated groups. Additionally, the proportion of travelers with a driver’s license in the “rational” group is the highest, and the “enthusiastic” group has a large proportion of young people.
Estimated results of pooled model and latent classes are shown in Table 6. It can be seen from the pooled model that all the hypotheses are accepted. Compared with the results of pooled model, the results of latent class model have some differences among the three classes. This suggests heterogeneity among travelers. For the latent class model, H1, H3, and H5 are valid in all three classes. The results imply that perceived service quality of smart travel has a significant positive effect on smart travel service satisfaction (Class 1: β = 0.959, p < 0.001; Class 2: β = 0.972, p < 0.001; Class 3: β = 0.899, p < 0.001), and perceived value of smart travel has a significant positive effect on smart travel experience satisfaction (Class 1: β = 0.594, p < 0.01; Class 2: β = 0.787, p < 0.001; Class 3: β = 0.644, p < 0.001); smart travel experience satisfaction also has a significant positive effect on the overall intercity travel satisfaction (Class 1: β = 0.477, p < 0.01; Class 2: β = 0.405, p < 0.001; Class 3: β = 0.567, p < 0.001). The coefficients of the five attributes of the perception of smart travel service quality are significant, indicating that the accessibility, informativeness, personalization, interactivity, and security of the smart travel service considered by travelers of all three groups can be good measures of the current quality of smart travel service.
Moreover, the results of the differences among the three classes are as follows: (1) Among the three groups, hypothesis H2 is shown significantly only in latent Class 3 (β = 0.231, p < 0.01), that is, the satisfaction of smart travel service significantly affects the satisfaction with smart travel experience in the “enthusiastic “group, while this hypothesis was rejected in both Class 1 and Class 2. (2) H4 is supported in both Class 2 (β = 0.55, p < 0.001) and Class 3 (β = 0.343, p < 0.001), but is rejected in the “cold” group only, indicating intercity travel satisfaction would not be significantly affected by smart travel service satisfaction for this group.
6. Discussion and Implications
According to the above results, it can be seen that the five dimensions of smart travel service quality outlined in this study—accessibility, informativeness, personalization, interactivity, and safety— all well reflect the perceived quality of smart travel service. Therefore, it is necessary to optimize smart travel service in the above aspects to enhance satisfaction. Smart travel services mainly provide travelers with comprehensive travel information so that they can choose the most suitable travel plan for themselves. The timeliness and effectiveness of information provided by smart travel services need to be improved, and further construction of a real-time big data processing platform should be carried out. Personalized travel demand is an important trend in current intercity travel and travelers obtain personalized services through interaction with applications. Smart travel solutions for individuals should be continuously optimized based on historical travel data and the user preferences of travelers. In addition, the ease of use of smart travel services is also an important factor affecting service satisfaction. The construction and upgrading of relevant intelligent systems and devices should be strengthened; the technical limitations of smart travel services should be reduced, for example, Wi-Fi connections should be provided throughout the journey; the interface and content of smart services should be made more user-friendly; and appropriate functions and expressions should be designed especially for vulnerable groups (such as the elderly). The research results also show that travelers have a positive attitude towards highly interactive services, and therefore the efficiency and effectiveness of real-time communication and service evaluation feedback in smart travel services need to be continuously strengthened. It is also very important to establish trust in the security of services. The scope of information acquisition and collection by smart travel service providers should be restricted from a legislative perspective; relevant departments should strengthen supervision and ensure smooth reporting channels for terminal service providers involved in the illegal collection of privacy information.
The perceived value of smart travel has a significant positive effect on smart travel experience satisfaction for all the groups, indicating that the more value added that travelers perceived by using smart travel services, the better their intercity travel experience. Smart travel services should enable intercity travelers to not only reach their destinations, but also add more value to travel. The perceived value discussed in this study is mainly reflected in four aspects: convenience, comfort, safety, and environmental protection. Compared to traditional travel service, the use of smart travel services helps travelers to easily obtain more value during their intercity travel. For example, travelers can use smart travel services such as ride hailing to increase the convenience of transfers, increase resource sharing rates, and benefit environmental protection. In addition, the use of intelligent gates and security check facilities, as well as real-time information, have reduced waiting time for intercity travelers and increased travel comfort, and map navigation software reduces the risk of getting lost for travelers and increases the safety of intercity travel. Therefore, the upgrade of smart travel services should not only improve the quality of the aforementioned services themselves, but also aim to make intercity travel more convenient, comfortable, safe, and environmentally friendly. Furthermore, in this study, hypothesis H5 was also accepted in all three models, demonstrating that travel satisfaction can be improved by enhancing the smart travel experience satisfaction of all intercity travelers in the Guanzhong Plain urban agglomeration. This further indicates that for any group, satisfaction with the smart travel experience is an important factor affecting intercity travel satisfaction.
In addition, the impact of smart travel service satisfaction varies among different potential groups. In the “cold” category and “rational” category, smart travel service satisfaction has no significant impact on smart travel experience satisfaction, and only smart travel service satisfaction in the “enthusiastic” group can affect smart travel experience satisfaction. Different groups may have different preferences and inherent attitudes towards smart travel and new technologies. The “enthusiastic” group has a greater interest in and demand for smart travel services. Therefore, the comprehensive functionality and high quality of smart travel services have become an indispensable part of their smart travel experience. However, the “cold” group and “rational” group may have relatively low preferences for new technologies, and the experience of intercity travel is less affected by the smart travel services themselves. In addition, among the “cold” group, the impact of smart travel service satisfaction on intercity travel satisfaction is not significant, indicating that the use of smart travel services during intercity travel is not necessary for them, which to a certain extent shows that their perceived importance of smart travel services is relatively low.
From this perspective, it is useful to strengthen the positive promotion of smart travel services through various channels, enhancing awareness of the importance of smart travel services among the “cold” group. Service trial activities can also be held to improve their use of smart travel, and thus increase their satisfaction with smart travel services. For the “rational” group, more interesting or comprehensive smart travel services should be further developed to stimulate their new interests and challenges. In addition, the “enthusiastic” group frequently uses various smart services and may have a better understanding of service improvement than service providers. Therefore, receiving regular consultation and feedback from this group can effectively improve smart travel services and increase their satisfaction with smart travel.
7. Conclusions
This study constructs multidimensional indicators and proposes conceptual model and research hypotheses to explore the impact mechanism of intercity travel satisfaction against the background of smart travel from two perspectives: smart travel service quality and smart travel experience. Using survey data on the smart travel behavior of intercity travelers in the Guanzhong Plain urban agglomeration, a latent class SEM was constructed. The dual dimensions of behavior and attitude preference of smart travel services (STSs) were selected to explore the potential categories of intercity travelers, and the samples were divided into “cold” users, “rational” users, and “enthusiastic” users of STSs.
Overall, this study confirms that smart travel services and travel experience satisfaction have significant positive impacts on the intercity travel satisfaction of travelers. In addition, it also indicates a differential impact of group heterogeneity on smart travel satisfaction. Based on the results of the latent class model, this study shows that for all three groups, perceived smart travel service quality has a significant positive impact on smart travel service satisfaction; perceived value of smart travel service has a significant positive impact on smart travel experience satisfaction; and among the two paths, only smart travel experience satisfaction has a significant impact on intercity travel satisfaction. However, the impact of smart travel service satisfaction varies among different groups. For travelers in the “cold” group, the impact of smart travel services on overall intercity travel satisfaction is not statistically significant. In addition, smart travel service satisfaction has no significant impact on smart travel experience satisfaction in both the “cold” group and the “rational” group.
However, due to the impact of the pandemic, field surveys were often interrupted, which brought challenges to the design of longer questionnaires and the low response rate. For subsequent studies, it is possible to consider conducting a survey to explore more indicators related to intercity smart travel and compare the impact of smart travel services on travel satisfaction with different intercity transportation modes. The indicators selected in this study are mainly related to the subjective evaluation of smart travel. Some objective indicators can be discussed in future studies. Despite the limitations of this study, this paper systematically and comprehensively analyzes the heterogeneous characteristics of smart travel and its impact mechanism on intercity travel satisfaction, which has theoretical and practical value.
The theoretical contributions of this paper are an exploration of impact mechanisms from the perspectives of smart travel service quality and smart travel experience and a study on the influences of diverse STS attributes on intercity travel satisfaction using empirical data. At the same time, this paper proposes that in the Guanzhong Plain urban agglomeration, the quality of smart travel services should be improved regarding aspects such as accessibility, informatization, personalization, interactivity, safety, and convenience. Moreover, smart travel services should be committed to increasing the added value of travel and improving the travel experience by enhancing the convenience, comfort, safety, and environmental protection of travel. It is also necessary to raise awareness of smart travel services among the “cold” group through publicity in order to increase their opportunities for and satisfaction with using smart travel; for the “rational” group, the practicality and fun of smart travel service functions should be improved, and the comments and feedback from “enthusiastic” travelers are also of great reference value for improvement. The theoretical framework and proposed measures in this study can also be applied to future research in other regions and provide a basis for the improvement of intercity travel.
The authors confirm their contributions to this paper as follows: study conceptual design: Z.D., X.G. and L.W.; data collection: Z.D. and X.G.; analysis and interpretation of results: Z.D., J.Z. and X.G.; draft manuscript preparation: Z.D., J.Z. and X.G. All authors have read and agreed to the published version of the manuscript.
Informed consent was obtained from all subjects involved in this study. Written informed consent was obtained from the participant(s) to publish this paper.
The raw data supporting the conclusions of this article will be made available by the authors on request.
The authors thank Purui Wang, Wenbin Yu, Jiaxing Tang, and Pan Xing from Chang’an University, who made great efforts in organizing the administration of the survey. Zeyuan Tian, Panpan Xu, and Rong Huang from Chang’an University provided assistance in improving this paper. The authors also thank the anonymous reviewers for their valuable comments.
Author Xiaoqi Gong was employed by the company Ningbo Geely Royal Engine Components Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
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Survey items.
| Dimension | Item | Variable | Reference |
|---|---|---|---|
| Accessibility | Smart travel service can be used anytime and anywhere during the journey. | V 11 | [ |
| It is easy to use smart travel service during the journey. | V 12 | ||
| It is easy to find smart travel service during the journey. | V 13 | ||
| Informativeness | Smart travel service can provide useful travel information. | V 21 | [ |
| The information provided by smart travel service has been helpful for my travel. | V 22 | ||
| The information provided by smart travel service is updated timely. | V 23 | ||
| Personalization | I can obtain personalized travel solutions through smart travel service. | V 31 | [ |
| Smart travel service can provide personalized travel information to me | V 32 | ||
| The smart travel service available during the journey meets my needs. | V 33 | ||
| Interactivity | I can interact with smart travel service to obtain information I need. | V 41 | [ |
| I can access real-time travel information and comments shared by others through smart travel service. | V 42 | ||
| I can evaluate travel services and provide feedback through smart travel service. | V 43 | ||
| Security | I am not concerned about smart travel service collecting excessive personal information. | V 51 | [ |
| Smart travel service protects personal privacy information. | V 52 | ||
| Smart travel service will not misuse or access my personal information without authorization. | V 53 | ||
| Smart travel | Overall, I am satisfied with the smart travel service available during this intercity trip. | V 61 | [ |
| Currently, the smart travel service available for intercity travel exceed my expectations. | V 62 | ||
| Currently, the smart travel service available for intercity travel are close to my ideal service. | V 63 | ||
| Perceived value of smart travel | Smart travel service has made intercity travel more convenient. | V 71 | [ |
| Smart travel service has made intercity travel more comfortable. | V 72 | ||
| Smart travel service has made intercity travel safer. | V 73 | ||
| Smart travel has made intercity travel more environmentally friendly | V 74 | ||
| Smart travel | Smart travel service has left me with wonderful memories of this intercity travel. | V 81 | [ |
| Smart travel service has greatly enriched this intercity travel experience for me. | V 82 | ||
| Smart travel service has made this intercity travel experience more fulfilling. | V 83 | ||
| Intercity travel | I am glad that I took this intercity trip. | V 91 | [ |
| This intercity trip has made me feel great. | V 92 | ||
| Overall, I am satisfied with the whole journey of this intercity trip. | V 93 |
Selected characteristics of the sample (total sample size, N = 902).
| Sample Characteristics | Number of | Proportion | |
|---|---|---|---|
| Gender | Male | 398 | 44.1% |
| Female | 504 | 55.9% | |
| Age (years) | 18–25 | 372 | 41.2% |
| 26–35 | 255 | 28.3% | |
| 36–55 | 236 | 26.2% | |
| Above 55 | 39 | 4.3% | |
| Personal annual income (RMB) | Less than 50 thousand | 405 | 44.9% |
| 50–100 thousand | 283 | 31.4% | |
| 100–200 thousand | 175 | 19.4% | |
| Above 200 thousand | 39 | 4.3% | |
| Education | Junior high school | 27 | 3.0% |
| Senior high school | 54 | 6.0% | |
| Junior college | 112 | 12.4% | |
| Four-year college degree | 457 | 50.7% | |
| Completed graduate degree(s) | 252 | 27.9% | |
| Driver’s license | yes | 680 | 75.4% |
| Household vehicle ownership | 1 | 593 | 65.7% |
| ≥2 | 100 | 11.1% | |
Analysis of reliability and convergent validity.
| Dimension | Variable | Factor Loading | C.R. | AVE |
|---|---|---|---|---|
| Accessibility | V11 | 0.800 | 0.862 | 0.676 |
| V 12 | 0.849 | |||
| V 13 | 0.816 | |||
| Informativeness | V 21 | 0.809 | 0.825 | 0.612 |
| V 22 | 0.801 | |||
| V 23 | 0.735 | |||
| Personalization | V 31 | 0.783 | 0.806 | 0.581 |
| V 32 | 0.706 | |||
| V 33 | 0.794 | |||
| Interactivity | V 41 | 0.776 | 0.810 | 0.587 |
| V 42 | 0.777 | |||
| V 43 | 0.745 | |||
| Security | V 51 | 0.744 | 0.859 | 0.671 |
| V 52 | 0.861 | |||
| V 53 | 0.848 | |||
| Smart travel service Satisfaction | V 61 | 0.693 | 0.767 | 0.524 |
| V 62 | 0.706 | |||
| V 63 | 0.770 | |||
| Perceived value of smart travel | V 71 | 0.719 | 0.871 | 0.629 |
| V 72 | 0.819 | |||
| V 73 | 0.840 | |||
| V 74 | 0.788 | |||
| Smart travel experience satisfaction | V 81 | 0.836 | 0.883 | 0.716 |
| V 82 | 0.848 | |||
| V 83 | 0.855 | |||
| Intercity travel satisfaction | V 91 | 0.856 | 0.877 | 0.703 |
| V 92 | 0.840 | |||
| V 93 | 0.819 |
Results of test indicators for latent class classification.
| Number of Class(es) | AIC | BIC | aBIC | Entropy | LMR | BLRT |
|---|---|---|---|---|---|---|
| 1 | 6122.434 | 6122.434 | 6135.464 | ----- | ||
| 2 | 5848.939 | 5935.422 | 5878.257 | 0.646 | 0.0000 | 0.0000 |
| 3 | 5689.839 | 5689.839 | 5711.013 | 1.000 | 0.0000 | 0.0000 |
| 4 | 5859.921 | 5970.427 | 5897.383 | 1.000 | 0.1088 | 0.2593 |
Statistics of socioeconomic attributes of three latent classes.
| Attributes | Class 1 | Class 2 | Class 3 |
|---|---|---|---|
| Gender | |||
| Male | 50.6% | 44.1% | 42.9% |
| Female | 49.4% | 55.9% | 57.1% |
| Age (years) ** | |||
| 18–25 | 23.5% | 39.0% | 48.1% |
| 26–35 | 37.0% | 29.6% | 24.7% |
| 36–55 | 21.0% | 15.1% | 14.0% |
| Above 55 | 11.1% | 12.1% | 9.3% |
| 18–25 | 7.4% | 4.2% | 3.8% |
| Education * | |||
| Junior high school | 4.9% | 3.9% | 1.1% |
| Senior high school | 11.1% | 5.3% | 5.8% |
| Junior college/Four-year college degree | 64.2% | 63.1% | 64.3% |
| Completed graduate | 19.8% | 28.7% | 28.8% |
| Personal annual income (RMB) ** | |||
| Less than 100 thousand | 82.7% | 8.6% | 8.6% |
| 100–200 thousand | 75.7% | 21.0% | 3.3% |
| Above 200 thousand | 77.7% | 20.1% | 2.2% |
| Driver’s license * | |||
| Yes | 66.7% | 78.5% | 73.4% |
Note: * indicates significant values at the 0.05 level; ** indicates significant values at the 0.01 level.
The estimated results of pooled model and the latent class model.
| Hypothesis | Path | Pooled Model | Class 1 | Class 2 | Class 3 | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| β | p | Result | β | p | Result | β | p | Result | β | p | Result | ||
| H1 | STSS←PSQST | 0.945 | *** | Supported | 0.959 | *** | Supported | 0.972 | *** | Supported | 0.899 | *** | Supported |
| H2 | STES←STSS | 0.133 | * | Supported | 0.188 | 0.512 | Rejected | 0.039 | 0.666 | Rejected | 0.231 | ** | Supported |
| H3 | STES←PVSTS | 0.133 | *** | Supported | 0.594 | ** | Supported | 0.787 | *** | Supported | 0.644 | *** | Supported |
| H4 | ITS←STSS | 0.460 | *** | Supported | 0.415 | 0.153 | Rejected | 0.55 | *** | Supported | 0.343 | *** | Supported |
| H5 | ITS←STES | 0.481 | *** | Supported | 0.477 | ** | Supported | 0.405 | *** | Supported | 0.567 | *** | Supported |
| AC←PSQST | 0.869 | — | 0.746 | — | 0.87 | — | 0.858 | — | |||||
| INF←PSQST | 0.972 | *** | 0.934 | *** | 0.977 | *** | 0.963 | *** | |||||
| PE←PSQST | 0.966 | *** | 0.986 | *** | 0.962 | *** | 0.962 | *** | |||||
| INT←PSQST | 0.806 | *** | 0.774 | *** | 0.791 | *** | 0.838 | *** | |||||
| SEU←PSQST | 0.414 | *** | 0.381 | *** | 0.419 | *** | 0.403 | *** | |||||
Note: AC denotes accessibility; INF denotes informativeness; PE denotes personalization; INT denotes interactivity; SEU denotes security; * denotes p < 0.05; ** denotes p < 0.01; *** denotes p < 0.001.
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