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Article

Selection Attributes of Integrated Mobility Apps on Affecting Users’ Intention to Use: A Case of Republic of Korea

by
Il Joon Tae
1,
Alexandra Broillet-Schlesinger
2 and
Bo Young Kim
1,*
1
Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
2
Business School Lausanne, 1022 Chavannes, Switzerland
*
Author to whom correspondence should be addressed.
Future Transp. 2024, 4(4), 1205-1222; https://doi.org/10.3390/futuretransp4040058
Submission received: 22 July 2024 / Revised: 3 October 2024 / Accepted: 9 October 2024 / Published: 14 October 2024
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Abstract

The innovative trend of “as a service” due to digital development and the rise of issues such as air pollution and traffic congestion led to the emergence of Mobility as a Service (MaaS) in the transportation sector. Companies and governments are experimenting to create a sustainable and efficient transportation future with MaaS. However, MaaS realization and business success from MaaS are still in their growing phase, making this study particularly relevant and timely. This study aims to identify the attributes of users’ selection of integrated mobility app services and the MaaS attributes that affect the behavioral intention to use through the mediation of perceived usefulness and perceived ease of use. This study marked four selection attributes—habit-congruence, information accuracy, relative advantage on efficiency, and IT system quality—for the integrated mobility app service, and 315 actual users of integrated mobility apps in Republic of Korea were sampled and analyzed. In terms of influence, information accuracy, relative advantage on efficiency, and habit-congruence significantly impacted perceived usefulness, in which habit-congruence had the most significant impact on perceived ease of use. In addition, habit-congruence and information accuracy were found to positively affect the behavioral intention to use, mediated by perceived usefulness and perceived ease. We also found that IT system quality was not a user selection attribute where this study was conducted. By providing empirical findings, this study can give management guidelines to companies and researchers in developing integrated mobility app service strategies to increase the number of users and maintain long-term customer relationships.

1. Introduction

Rapid population growth in large cities, the change in mobility requirements, climate change, and pollutant emissions contribute to the need for innovative and sustainable mobility solutions [1,2]. With the development of the sharing economy in recent years, customers have received value for use as an “as a service” without owning assets, such as accommodation rental services, shared offices, and cloud data centers, with solutions provided by global IT companies. These methods are also policy tools to solve urban or community issues [3]. The proliferation of mobile phones and digital technologies is shifting transportation to the use of consumer-centered services rather than owning modes of transportation, and in this process Mobility as a Service (MaaS) has emerged to maximize customer convenience by integrating various modes of transportation [4,5]. Therefore, the importance of integrated mobility services for customers is being emphasized, and the concept of MaaS is being further strengthened [6,7].
MaaS integrates various transportation services, providing users with transportation options from origin to destination within a single app [8]. Customers perceive MaaS as an MaaS app or an integrated mobility app [9]. Integrated mobility apps provide access to various forms of transportation, including cars, taxis, rental cars, trains and buses, bicycles, and kickboards, providing users with comprehensive services from travel planning to payment. In addition, the app serves as a centralized platform between various modes of transportation and consumers, providing individual-level optimization and tailoring products and services to consumers’ transportation preferences. The use of an integrated mobility app is a significant environmental and social advantage as it increases the efficiency of transportation and can also enhance the commuting experience, which is considered the least hedonistic movement among customers’ daily activities [10,11].
MaaS is evolving by reflecting the characteristics of each region. In the U.S. and Canada, the proliferation of ride-hailing services such as Uber, which has 137 million subscribers, and Lyft, which has 21.4 million subscribers, has transformed the taxi transportation market into an “as a service” market [12,13,14]. Japan is trying to solve the differences between cities and regions in transportation services and the unequal transportation benefits for the aging population through MaaS [15]. China is researching MaaS to solve environmental and urban transportation problems to achieve low-carbon emission, traffic reduction, and energy efficiency [16]. In addition, Korea continues to develop MaaS to resolve transportation issues in dense urban cities and increase rural welfare. As of July 2023, T map’s Monthly Active Users (MAUs) were 14.5 million, Kakao T’s were 12.1 million, and T-money GO’s were about 4 million; that is, more than half of Republic of Korea’s total transportation population had used at least one of the apps [17,18]. As such, the demand for integrated mobility apps is increasing in each country, and consumer analysis is becoming more and more necessary.
Looking at the previous studies, Bae et al. [19] discussed the services recommended based on analyzing users’ behavior when using integrated mobility apps. By providing users with information-based mobility services that reflect the needs of users identified through data analysis, the business–customer relationship shifts from a relationship based on a transaction to a relationship of continuous mutual engagement that emphasizes trust and continuity. For example, long-term customer care of business fulfills the customers’ actual requirements by knowing customers, which ultimately builds trust. Schikofsky et al. [20] and Tomaino et al. [21] presented the results of their literature on the psychological perspective of users that can build a long-term relationship with a company based on the attribute factors of integrated mobility apps that users finally choose. Jittrapirom et al. [22] presented the necessity and approach of service development to enhance integrated mobility apps’ marketability and business sustainability.
As such, today’s MaaS market has begun focusing on new business models and product development beyond the past method of focusing on transportation policies. However, although mobility apps and related platforms are ultimately end-user services, many studies have not been concerned with important app attributes and utilization readiness. Therefore, this study defines the user selection attributes of integrated mobility apps based on the technology acceptance models (TAMs) and presents empirical results on the effect of these selection attributes on the behavioral intention to use the apps [23,24]. In particular, this study contributes by conducting research on customers who have used MaaS apps for years, and it is expected to serve as a reference for future studies.

2. Literature Review and Hypothesis Development

2.1. Integrated Mobility Application and Selection Attributes

MaaS has the property of a platform (One Platform) that supports providing route and transportation information and integrated payments in a telecommunication environment [25]. Thus, it allows users to travel and perform connected services, such as planning their travels, making reservations, making payments, and retrieving information in real-time. In addition, it is a personalized service that provides mobility solutions that meet the needs and characteristics of users [25]. Integrated mobility apps have the common attributes of providing and integrating multiple modes of transportation [26,27]. Zimmermann et al. [28] explained that integrated mobility apps with high user ratings offer benefits such as integrated infrastructure resources, high-quality monitoring capabilities, problem reporting, and ticket purchases. In addition, Jo et al. [29] suggested relative time efficiency, payment convenience, accurate vehicle and traffic information, and habitual destination suggestions as the user’s selection attributes for integrating mobility apps. On the other hand, inaccuracies in the provided information and the poor quality of the app system, such as slow search functions, lead people to leave the integrated mobility app. Matyas et al. [30] found that customer usage behaviors and preferences can be used to design MaaS. Since actual users use the mobility service package through their personal mobile app, they can flexibly use the integrated mobility service according to their needs [31,32].
In addition, users’ selection is choosing the most ideal solution from several alternatives to solve a problem [33]. Therefore, selection attributes mean the properties of a particular service that can directly shift customers’ use or purchase behavior decisions and intention to long-term use, which ultimately creates a positive impact on the user [34]. Moreover, Bertan et al. [35] and Fang et al. [36] argued that using mobile apps for travel and mobility provides a comparative advantage over online and offline services. Jain et al. [37] proposed system quality, such as adaptability, flexibility, security, ease of use, productivity, and retention of information, as critical attributes for the sustainable development of mobile applications. Kim [38] found that habituality is a more prevalent predictor of actual mobile app use than conscious intent as an essential attribute of mobile application selection. Han et al. [39] studied the impact of service quality, the propensity to innovate, user experience, usability, ease of use, compatibility, price, and self-efficacy on mobile application purchases based on technology acceptance theory to suggest an acceptance model for which factors influence consumers’ selections.
As a result, the quality of the apps had a positive impact on the users’ perceived usefulness, and the customers’ experience positively impacted both perceived usefulness and perceived ease of use. In the light of the theory of technology acceptance models (TAMs), the selection attributes of mobile applications are defined as habit consistency, information accuracy, relative advantage on efficiency, and IT system quality [23,29,40].
Because habit is not the attribute to store information about a particular experience or a particular behavior, but rather information about repetitive behavior [41,42,43], the mental structure of the individual, envisioned in the habit, lowers the behavioral barrier to take the new technology when the transportation service that the customer is familiar with is matched to the integrated mobility app service. Schikofsky et al. [20] argued that individuals are likelier to adopt integrated mobility apps when they perceive an alignment between the services provided by the apps and their habits. In particular, if the congruence with customers’ habits is high, the customers can better adapt their existing habit pattern to the new area of consumption [44,45,46].
Information accuracy, one of the essential elements of mobility, is the attribute of mobile applications to promptly provide accurate information the user requests [3,47,48]. Siuhi et al. [49] suggested that the accuracy of mobility apps’ information refers to the accuracy of collected traffic data, traffic information, ride-hailing/car-sharing data, traffic safety information, travel speed, travel time, number of vehicles, route planning, parking locations, accident status, and travel package information. Since transportation began, providing up-to-date traffic information, accurate location information, and departure and arrival times have been the most critical factors in movement and safety. Integrating mobility apps that provide precise information reduces travel time, costs, and pollution, allowing customers to enjoy a safer and healthier commuting environment. [49].
In many studies, the relative advantage over legacy service (i.e., the perceived new changes such as mobile services over alternative means) have consistently been identified as an essential attribute in predicting use [50]. Wang et al. [51] defined relative advantage as “the degree to which the use of a particular technology or service is perceived as more efficient in terms of job performance improvement than the use of antecedent/competing technology”. In the integrated mobility app service, providing more up-to-date transportation services and structured pricing plans can be considered a relative advantage in terms of efficiency in improving time management and reducing the effort required for travel planning [52].
IT system quality refers to the app’s security, stability, and connectivity. DeLone et al. [53] explained that the system quality of an information system is a factor that comprehensively evaluates the performance or effectiveness of the system and is a major factor in the intention to use mobile apps and customer satisfaction. The previous literature reviews suggested that the quality of the mobile IT system, such as stability and continuous connectivity, were key factors in mobile communication [54,55,56]. When potential subscribers classify all mobile apps based on security, ease of processing, visual appeal, app awareness, and amount of information to decide on a subscription, they tend to evaluate the IT system quality and service quality of the apps beforehand [57,58].

2.2. Selection Attributes, Perceived Usefulness, and Perceived Ease of Use

The four selection attributes of the technology acceptance model—habit-congruence, information accuracy, relative advantage on efficiency, and IT system quality—directly impact perceived usefulness and perceived ease of use, which are cognitive factors in technology adoption. First, habit-congruence refers to an attribute factor consistent with the user’s daily habits by providing transportation based on the user’s life behavioral pattern, familiar method of commute, and potential traffic behavior when the user chooses a transportation method.
Second, information accuracy is an attribute that provides the information the user requests accurately and promptly. It refers to the accuracy of the information provided online and its consistency with the information experienced offline after the decision to use it. Up-to-date traffic information, location accuracy, traffic safety conditions, and the provision of accurate information such as transfer, departure, and arrival times were defined as the selection attributes of information accuracy.
Third, the relative advantage in efficiency is an attribute that provides more benefits or reduces the user’s effort using the app service compared to other methods. It refers to factors such as being relatively faster to call a taxi, long-term fare discounts, door-to-door mobility guidance services, and discriminatory kindness for app customers, which make customers of mobility apps feel the improvement in the quality of their lives.
Fourth, IT system quality is defined in three ways: security, stability, and connectivity. It means preventing mobile payment transactions and location information from being exposed, maintaining a stable operation performance, and continuously providing the latest traffic information.
As Marangunić et al. [59] described, the technology acceptance model (TAM) focuses primarily on two cognitive factors: perceived usefulness and perceived ease of use. Perceived usefulness refers to expectations of how a particular skill will contribute to an individual’s improved performance. On the other hand, perceived ease of use relates to the expectation of the minimum physical and mental effort required to utilize a particular technology [23]. These perceived factors are closely related to the user’s intention to adopt a given technology [60]. Davis [23] argued that selection influencing factors for using technology-based services positively affect behavioral intentions to use through the mediation of perceived usefulness and perceived ease of use. Tornatzky et al. [61] argued that perceived usefulness and perceived ease of use play a role in accepting innovative products.
Much research has been carried out on the association between perceived usefulness and the four optional attributes. Users find it helpful to adapt habit patterns to new services more quickly when they align with their existing habits [46]. Forgas et al. [62] explain that consumers feel satisfied when their habits toward an app align with reference patterns and form comfortable usage habits. In addition, several service and mobile media studies have proven that accurate information acquisition leads to users perceiving that information is valuable, which leads to real-world usage behaviors, such as making purchases [63,64,65,66]. Wang et al. [51] explained that the relative advantage on efficiency is an inseparable function of perceived usefulness for new or existing technologies. IT system quality is a factor that comprehensively evaluates the performance or effectiveness of the system, and it is a significant factor in judging the app’s usefulness and the user’s intention to use it [67].
Based on these literature reviews, when choosing an integrated mobility app, the information is accurate if the service usage is congruent with habits. The relative advantages of efficiency and IT system quality are superior to other alternatives, and the integrated mobility app will be perceived as valuable. As a result, the choice and usage behavior will be positively affected. Accordingly, the following hypotheses can be established:
H1: 
Habit-congruence will have a positive (+) impact on the perceived usefulness of integrated mobility apps.
H2: 
The accuracy of the information will have a positive (+) impact on the perceived usefulness of integrated mobility apps.
H3: 
The relative advantage on efficiency will have a positive (+) impact on the perceived usefulness of integrated mobility apps.
H4: 
IT system quality will have a positive (+) impact on the perceived usefulness of integrated mobility apps.
The perceived ease of use when using an integrated mobility app means the expectation of reducing the physical and mental effort required to find proper transportation and use the MaaS technology. Schikofsky et al. [20] suggested that the habit-congruence attribute of the app services positively affects perceived ease of use. Machdar [68] argued that the accuracy of information is an important influencing factor in perceived ease of use, and McCloskey [69] and Rogers et al. [70] insisted that users perceive ease of use only when there is a relative advantage over traditional products. In addition, Chen et al. [71] argued that the quality of the IT system of personalized application services has a significant effect on perceived ease of use.
As with literature reviews, when integrated mobility mobile apps conveniently form individual habits and provide accurate information, a relatively better efficiency over existing services, and a high IT system quality, the app’s ease of use will be positively perceived. Accordingly, the following hypotheses were designed:
H5: 
The habit-congruence will have a positive (+) impact on the perceived ease of use of integrated mobility apps.
H6: 
The accuracy of the information will have a positive (+) impact on the perceived ease of use of integrated mobility apps.
H7: 
The relative advantage on efficiency will have a positive (+) impact on the perceived ease of use of integrated mobility apps.
H8: 
IT system quality will have a positive (+) impact on the perceived ease of use of integrated mobility apps.

2.3. Perceived Usefulness, Perceived Ease of Use, and Behavioral Intention to Use Integrated Mobility Apps

Many literature reviews on technology acceptance models suggest that perceived usefulness and perceived ease of use induce positive attitudes toward new technologies and information systems, which in turn inspire the behavioral intention to use and have a positive impact on actual use and selection [23,40,72,73,74]. Given the weak mediating role of attitudes as a parameter in the early days, Davis and Venkatesh [75] proposed a technology adoption modification model in which perceived usefulness and perceived ease directly affect behavioral intentions to use. Since then, the TAM has been established as the most frequently used model for information technology acceptance research [76,77]. Lee et al. [78] examined the effects of perceived usefulness and perceived ease of use of Self-Service Technology on the customer behavioral intention to use. In the Papakostas [79] study, the behavioral intention to use map-based mobile Augmented Reality (AR) apps was directly and positively impacted by quality output, perceived usefulness, and perceived ease of use.
Behavioral intention to use is a measure that evaluates the degree of strength of the intention to perform a particular action using an information system such as a mobile application [40]. Gefen and Straub [60] also argue that TAMs provide a high explanatory power in understanding users’ technology adoption behaviors and intentions. Users who perceive new technologies, such as “as a service”, as easy to use and helpful are likelier to adopt and engage with them [80]. Rahman et al. [81] used TAMs to prove the individual adoption intention for the cloud computing model, Software-as-a-Service (SaaS), while Seo [82] demonstrated the behavioral intention of adopting the technology for Infrastructure-as-a-Service (IaaS).
Similarly, Schikofsky et al. [20] suggested that perceived usefulness and perceived ease of use influence behavioral intentions to use integrated mobility apps. Mola et al. [83] found that in MaaS perceived usefulness and perceived ease directly and indirectly influence the behavioral intention to use, respectively. In addition, Ahn [84] suggested that in the behavioral intention to use integrated mobility apps, perceived ease of use is a catalyst to increase the perceived usefulness further. Therefore, we came up with the following hypothesis, which is directly linked to the behavioral intention to use MaaS:
H9: 
The perceived usefulness of an integrated mobility app will have a positive (+) effect on the behavioral intention to use it.
H10: 
The perceived ease of use of an integrated mobility app will have a positive (+) effect on the behavioral intention to use it.

3. Research Method

3.1. Research Model

This study aimed to empirically analyze the effect of the customer’s selection attribute factors on the behavioral intention to use the integrated mobility app. Customers’ selection attributes to use were set as the independent variable. In contrast, the dependent variable was the behavioral intention to use integrated mobility. We conducted literature reviews from global perspectives, a group of MaaS experts’ pilot surveys, and accurate integrated mobility app user surveys based on research methods of previous research [20,57]. A research model was designed by selecting perceived usefulness and ease of use as parameters, as shown in Figure 1.
The operational variables of the survey components were implemented to compose this research survey. As the independent variables, the selection attributes refer to the main features of integrated mobility apps that allow customers to select and use the apps when going to the destination. Based on the technology acceptance model, integrated mobility app selection attributes have been classified into classified integrated mobility app selection attributes: habit-congruence, information accuracy, relative advantage on efficiency, and IT system quality.
The parameter “perceived usefulness” refers to the degree to which the integrated mobility app improves the customer’s transportation use, while “perceived ease of use” means it is efficiently utilized with minimal effort based on the potential core structure of TAMs [23,24,40,85]. Lastly, the dependent variable, behavioral intention to use integrated mobility apps, is the user’s willingness to use the service. A positive response to the last item is likelier to lead to actual use.

3.2. Measurement Variable and Data Collection

A survey was conducted to collect data to analyze the research model. For the composition of the questionnaire, the survey items shown in Table 1 below were constructed through a review of the previous literature [28,29,30]. As shown in Table 1, these variables consisted of 21 questions in a questionnaire. Based on the literature of Schikofsky et al. [20] and Sohn et al. [86], the habit-congruence attribute factor was composed of three items based on conformity, convenience, and familiarity. According to Cheng et al. [48], the information accuracy attribute factor consists of three items: timeliness, accuracy, and information provisionability. The relative advantage of the efficiency attribute factor is based on the previous research of Sahin [87] and Rogers et al. [70] and consists of three items: efficiency, life improvement, and convenience. Based on the literature review of Horng [88], the IT system quality attribute factor consists of three items: system stability, connectivity, and security. Based on the literature review of Schikofsky et al. [20], the perceived usefulness was composed of three items focusing on factors related to practical usefulness in daily life. The perceived ease of use was composed of three items based on the literature review of Davis [23] and Schikofsky et al. [20]. In contrast, the behavioral intention to use integrated mobility apps was composed of three items based on the study of Kim et al. [57].
Survey item measurements consisted of responses ranging from 1 (strongly disagree) to 5 (strongly agree) on a 5-point Likert scale. To obtain factor composition reliability, a factor analysis was conducted to remove items that did not meet the composition requirements for the measurement variable and then the final measurement variable was constructed. In this study, one of the questionnaire items about constructed usefulness and ease of use was rejected. The other questionnaire items were used as they were because there was no problem with their significance as a measurement variable. SPSS 26.0 was used for data analysis to assess demographic characteristics, descriptive statistics, and exploratory factor analysis [89,90]. For the path analysis of the hypothesis, AMOS 27.0 was used to conduct confirmatory factor analysis, path analysis, and direct and indirect effect analysis based on the structural equation model [89,91].
Table 1. Variable definitions and measurement items.
Table 1. Variable definitions and measurement items.
FactorsMeasurement ItemsReferences
Habit-Congruence
-
IMA * provides taxis, buses, and other means of transportation that I habitually use.
-
IMA is as easy to use as the other apps I often use.
-
I can use the means of transportation habitually through IMA.
Schikofsky et al. [20]
Sohn et al. [86]
Information Accuracy
-
The IMA provides up-to-date traffic information.
-
The IMA provides accurate location information.
-
The IMA’s information on transfers, arrival times, and other information is timely.
Cheng et al. [48] Choi [92]
Relative Advantage on Efficiency
-
I can make quick moves conveniently by using IMA.
-
I feel that my quality of life is improving by using IMA.
-
I can use transportation more efficiently by using IMA.
Sahin [88]
Rogers et al. [70]
IT System Quality
-
The system is stable without stutters and errors.
-
The process of access and use is easy and convenient.
-
Privacy is well protected.
Horng [89] Kim et al. [57]
Perceived Usefulness
-
IMA is helpful in my daily life
-
It is fast to move around using IMA in daily life.
-
With IMA, you can move more effectively.
Schikofsky et al. [20]
Davis [23]
Perceived Ease of Use
-
It is easy to learn how to use IMA.
-
It is easy to use IMA on a day-to-day basis.
-
It is not difficult to use the IMA.
Schikofsky et al. [20]
Davis [23]
Behavioral Intention to Use Integrated Mobility Apps
-
I am willing to use the new IMA.
-
I want to experience the various services of IMA.
-
I am willing to pay for IMA to use it.
Kim et al. [57]
* IMA (integrated mobility app).

3.3. Demographic Information of the Data

As shown in Table 2, the integrated mobility apps used in this study are T map, Kakao T, and T-money GO, Korea’s top three MaaS app market brands. Since 2010, SK Telecom of the Korean conglomerate SK has been operating T map, and it has evolved based on navigation for drivers. Kakao T of the mobile Internet company Kakao Group has evolved based on taxi calling services, while the government’s T-money GO has evolved around public transportation, such as buses and subways. Now, all three apps support a variety of modes of transportation and mobile payments [93].
This study conducted an online survey among college students and office workers through a random survey sample. Before conducting the survey, 63 people were surveyed and piloted sampled, including a group of mobility experts, digital smart learning instructors, deep learning experts, and IT department graduate students. The pilot survey was conducted 8th–15th August 2022. They were well aware of the existence and functionality of the target service apps and were selected as a population of customers.
The respondents were provided with the definition of an integrated mobility app (MaaS) and a conceptual explanation of the functions of each integrated mobility app in Korea. Questionnaire verification was conducted for 7 days to increase the reliability and validity of the questionnaire. Since then, a survey has been conducted for users of the integrated mobility app. In this study, the “hierarchical random sampling” method was used among probability sampling methods for data collecting. The integrated mobility app experience group and the non-use group were divided into hierarchical groups and randomization within the experience group was applied.
The sample size was calculated to be appropriate for 300 people based on a confidence level of 95%, error margin of 5.0%, population ratio of 0.5, and population size of 120,000 using the “small population sample size determination formula”, and the study was conducted with 315 people considering the sample size. The survey was conducted from 23 August 2022 to 9 July 2022, and 345 responses were collected and analyzed, and 315 valid samples were extracted and 30 invalid responses were excluded. The validity of the sample was selected based on a value of 5.0 or less by adopting the verification method according to the significance level comparing occupied areas in the normal distribution. The participants’ demographic characteristics are shown in Table 2. The demographic characteristics collected in this way were defined as control variables and adjusted so as not to affect the analysis results.

4. Results

4.1. Analysis Results of Reliability and Validity

As shown in Table 3, the analysis of the reliability and intensive validity of the measurement model were both found to be good. The factor load was 0.643–0.909, both good at 0.6 or higher, while the internal reliability (CR) was 0.682–0.887, which secured significance. The t-value was at least 4.475, which was statistically significant. The mean extract variance (AVE) value was 0.502–0.725, and the Cronbach α value was 0.628–0.887. Based on these results, the reliability and validity of the research variables for analysis are secured.
The analysis of the fit-to-fit model of the structural equation found that χ2 (pdf) was 182.429(131), and χ2/degree of freedom was 1.393. The Goodness-of-Fit Index (GFI) value was 0.941, the Adjusted Goodness-of-Fit Index (AGFI) was 0.914, the Normal Fit Index (NFI) was 0.926, and the Root Mean Square Error of Approximation (RMSEA) was 0.035. Accordingly, it was possible to proceed with research analysis by obtaining the suitability of the research model for structural equation analysis.
Upon analyzing the AVE value and correlation coefficient between the latent variables of this study, it was found that the value of the AVE square root of each latent variable was greater than the correlation coefficient between them, as shown in Table 4, confirming the discriminant validity. Each selection attribute factor in this study was confirmed to be valid for this study as a differentiated and independent question without overlapping with other motivation factors. As a result, discriminant validity could be secured and hypothesis analysis could be conducted.

4.2. Analysis Results of the Structural Model

As shown in Table 5, the goodness-of-fit analysis of the structural model showed that χ2(df) was 221.231(136), and χ2/degree of freedom was 1.512. The Goodness-of-Fit-Index (GFI) was 0.944, and the Normal Fit Index (NFI) was 0.907. The Root Mean Square Residual (RMR) was 0.031, the Adjusted Goodness-of-Fit-Index (AGFI) was 0.912, and the Root Mean Square Error of Approximation (RMSEA) was 0.025, indicating that the goodness-of-fit components were found to be excellent, and the model fit was also significant. Although it is not affected by the sample, the comparative fit index (CFI) that indicates the model’s explanatory power was 0.936, and the Tucker–Lewis index (TLI) that determines the explanatory power of the structural model was 0.942, indicating that the basic model is highly suitable.
As a result of hypothesis testing through the path analysis of structural equation models, 3 out of 10 hypotheses were rejected, as shown in Table 5. Habit-congruence (2.020, p < 0.05) and information accuracy (2.809, p < 0.001) showed a positive (+) effect on perceived usefulness. The relative advantage on efficiency (2.454, p < 0.01) also had a positive (+) effect on perceived usefulness. However, IT system quality did not affect perceived usefulness, so Hypothesis 4 was rejected. Habit-congruence (3.148, p < 0.01) had a positive (+) effect on perceived ease of use, and information accuracy (2.380, p < 0.05) also had a positive (+) effect on perceived ease of use. On the other hand, the relative advantage on efficiency and IT system quality did not appear to affect perceived ease of use. Thus, Hypotheses 7 and 8 were rejected. Finally, perceived usefulness (3.788, p < 0.001) and perceived ease of use (4.243, p < 0.001) were found to have a positive (+) effect on the behavioral intention to use integrated mobility apps.
Among the selection attributes, habit-congruence, information accuracy, and relative advantage on efficiency have a positive (+) effect on perceived usefulness, while habit-congruence and information accuracy have a positive (+) effect on perceived ease of use. Cheng et al. [48] showed the same results: the accuracy and timeliness of traffic information in integrated mobility apps positively affected usefulness and ease of use. MaaS studies in Japan and China have also argued that the performance, such as the accuracy and habitual effect of MaaS apps, determines consumers’ willingness to use MaaS [94,95]. In particular, among the selection attributes of integrated mobility apps, the factors that most affected the perceived usefulness were information accuracy and habit-congruence, which had the greatest influence on the perceived ease of use. As Schikofsky et al. [20] pointed out, providing accurate information and improving habit-congruence are the most important things that reinforce users’ intention to use the MaaS Apps.
The relative advantages of efficiency were found to have a positive (+) effect on perceived usefulness but not on perceived convenience. Eventually, the relative benefits of integrated mobility apps of receiving discounts on fees by using app services, obtaining more detailed service information, and being able to use transportation services quickly show that they are considered useful but not perceived to help increase transportation convenience.
On the other hand, IT system quality did not affect perceived usefulness and perceived ease of use. These results contradict the literature review by DeLone and McLean [53] and Hanjaya [67] that the quality of IT systems in application services positively affects the perceived usefulness and perceived ease of use of users. However, this can be interpreted as a result attributable to the characteristics of integrated mobility app services related to transportation. In the case of mobility app services, IT systems between competing apps are often not differentiated because the system must be built on the basis of education regulations and institutions in different countries and regions. Therefore, customers also often perceive integrated mobility apps as public goods, and often do not require better usefulness or convenience.
Therefore, in the case of mobility app services, this result can be due to the low need for service selection through IT system quality or usefulness or convenience. Since the integrated mobility app service in Korea, where this study was conducted, is provided specifically in accordance with the traffic regulations and systems of Korean information, it can be confirmed that customers using public transportation already understand the integrated mobility app service as a public service and do not recognize the quality of IT service as a personal optional attribute.

4.3. Analysis Results of the Mediated Effect

The significance of indirect effects was verified by deriving direct effects, indirect effects, and total effects using bootstrapping methods (see Table 6). Among the selection attributes, habit matching (0.064, p < 0.01) and information accuracy (0.065, p < 0.05) influenced perceived usefulness mediation and perceived ease of using integrated mobility apps. This is consistent with many literature reviews that have successfully applied TAMs. As An [84] argues, integrated mobility app services, like other innovative technology services, support the results that users’ intention to accept technology is influential. Ultimately, we find that integrated mobility apps are related to embracing new technologies.
However, it was found that the relative benefits of efficiency influence the intention to use the integrated mobility app by mediating the perceived usefulness, but not the perceived ease of use. This confirmed that the factors of the relative advantages such as price and benefits are not the factors that influence the behavioral intention as perceived convenience of the service to the users of the mobility app service, as explained earlier. In the end, the relative advantages show that it can be more advantageous to be considered in terms of enhancing the usefulness of the service.
Finally, IT system quality had no indirect effect mediated by perceived usefulness or perceived ease of use. As explained above, for mobility app service users IT system quality has a strong unrecognized characteristic in connection with service use attributes in terms of public service, so it was confirmed that other variables such as publicity or safety should be considered rather than perceived usefulness or convenience in the process of IT system quality affecting the behavioral intention.

5. Conclusions

In this study, the relationship between the selection attributes of integrated mobility app users and the intention to use the app was investigated. Above all, this study has academic implications that suggest the importance of a marketing perspective that considers users’ needs and behaviors, away from the research on technology or system development that has dominated research on integrated mobility app services.
Developing an integrated mobility app system is more important than anything else given its practical implications, but increasing the effectiveness of the service requires detailed observations and research on the users they use. As a result, companies and institutions that provide mobility app services will need to strengthen user analysis based on big data. The movement data of users include time, repeatability, route, location, destination, traffic, and selected transportation, which are recorded in the integrated mobility app. We need to continually understand each customer’s usage habits and expand our analysis in a more personalized way. In addition, by analyzing customer movement trends over a year cycle to apply seasonal trends, we can match customer habits and future movement intentions. By establishing a system that automatically analyzes users’ personal traffic information, such as preferred transportation, appropriate fares, and alternative mobility plans, this approach can help the app build long-term customer relationships ultimately by making it intelligent enough to “know the customer well”.
In addition, to increase the accuracy of traffic information provided through the integrated mobility app, there is a need to increase transportation system expansion and integration effectiveness through partnerships with government and competitors. Depending on the transportation asset, the transportation operator or traffic data owner has somewhat different information about traffic conditions, road conditions, vehicles, or infrastructure. This is why we provide different traffic information to our customers. The integrated mobility app service is not just an enterprise product but serves as a service linked to the utilization of social transportation infrastructure and the physical safety of citizens. Sharing real-time information about local traffic conditions, new traffic violation policies, accidental accidents, construction status, and road strikes can show some examples. Active traffic information exchange partnerships with competitors can be a strategic direction to build a virtuous cycle ecosystem where users can receive the most accurate information.
Finally, we recommend that the mobility service companies strengthen their subscription program to highlight the relative benefits of integrated mobility apps. The relative benefits of the efficiency of integrated mobility apps influenced the perceived usefulness. However, the usefulness did not affect the behavioral intention to use the integrated mobility app. The relative benefit properties of current apps also did not affect the perceived ease of use. Subscription programs mean long-term contracts that provide economic benefits and ensure continuous data acquisition by meeting personalized requirements. In addition, subscription programs can increase the number of regular customers and create a better business location to expand exclusive transportation services. Strengthening the subscription model will highlight it as a strong relative advantage of integrated mobility apps.
However, this study has the following limitations: First, this study was conducted on integrated mobility app service consumers in Republic of Korea, so there are limitations in generalizing the research results. The types and characteristics of integrated mobility app services are configured differently according to the regional characteristics of the country and transportation policy and local culture. There are also differences in local users’ selection attributes and cultural characteristics. Therefore, in future research the characteristics of app services by continent and country can be considered, and the research approach through user segmentation can be considered.
Furthermore, the respondents of this study were centered on men. Accordingly, there is a limitation that the analysis results are focused on male-centered opinions. Therefore, in future studies it is necessary to conduct research in consideration of the ratio of male and female and the balance of demographic information.
Finally, this study did not identify the differentiation of each market segment based on the type of integrated mobility app used. It may not fully explain the heterogeneity between target groups because it is difficult to secure a balance for the gender or age group when securing active-user demographic data. Thus, this research recommends that future research identify the market segmentation based on each user group’s characteristics, such as a car owner group, traveler group, daily user group, age group, or gender group.

Author Contributions

Conceptualization, I.J.T.; methodology, I.J.T. and B.Y.K.; software, B.Y.K.; validation, B.Y.K.; formal analysis, I.J.T. and B.Y.K.; investigation, I.J.T.; resources, I.J.T.; data curation, B.Y.K.; writing—original draft preparation, I.J.T.; writing—review and editing, B.Y.K.; visualization, I.J.T. and B.Y.K.; supervision, A.B.-S. and B.Y.K.; project administration, A.B.-S. and B.Y.K.; funding acquisition, A.B.-S. and I.J.T. All authors have read and agreed to the published version of the manuscript.

Funding

This paper is written with support for research funding from aSSIST University.

Institutional Review Board Statement

Ethic Committee Name: The Research Ethics Committee of aSSIST University. Approval Code: The Statistics Act No. 33, 34. Approval Date: 15 July 2024.

Informed Consent Statement

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

Data Availability Statement

Data are not publicly available due to the privacy of respondents.

Conflicts of Interest

The authors declare no conflicts of interest.

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Futuretransp 04 00058 g001
Figure 1. Research model.
Figure 1. Research model.
Futuretransp 04 00058 g001
Table 2. Demographic information of survey participants.
Table 2. Demographic information of survey participants.
CategoryFrequencyPercentage (%)
GenderMales23875.6
Females7724.4
Age10–19 years of age3410.8
20–298025.4
30–399831.1
40–498226.0
50–60216.7
VocationMiddle/High School students3410.8
College students6821.6
Company employees10633.7
Professionals (medical doctor, lawyer, professor, etc.)3310.5
Owner–operators4614.6
Households113.5
Government employees175.4
Monthly transportation expenditure
($1 = \1310 in August, ’22)		₩50,000–100,000 ($38–76)4313.7
₩100,000–200,000 ($76–153)8928.3
₩200,000–300,000 ($153–229)7423.5
₩300,000–500,000 ($229–382)7724.4
₩500,000 and over ($382–)3210.2
Integrated mobility app experienceKakao T14847.0
T-money GO3812.1
T map family app12941.0
IMA usage intervalAlmost every day12339.0
At least once a week17054.0
At least once a month123.8
At least once a year103.2
Table 3. Results of the reliability and convergent validity tests.
Table 3. Results of the reliability and convergent validity tests.
VariableMeasurement QuestionFactor LoadStandard ErrorT-Valuep-ValueCRBIRDCronbach α
Habit-CongruenceHV30.795 0.8130.5950.808
HV20.8400.093 11.081***
HV10.8510.11111.228***
Information AccuracyIA30.887 0.8610.6770.853
IA20.9090.058 16.994***
IA10.7750.052 12.799***
Relative Advantage on EfficiencyCA30.882 0.8870.7250.887
CA20.8910.055 16.994***
CA10.8900.056 18.320***
IT System QualitySYQ30.643 0.7490.5020.671
SYQ20.8470.293 5.820***
SYQ10.8310.222 6.315***
Perceived UsefulnessPU20.840 0.6820.5170.628
PU10.8350.2194.510***
Perceived Ease of UsePE20.882 0.7760.6420.751
PE10.8630.336 4.475***
Behavioral IntentionBI10.846 0.8520.6580.853
BI20.8440.081 13.437***
BI30.8530.086 14.064***
Measurement model fit: χ2(df) 182.429(131), χ2/degree of freedom 1.393, RMR 0.030, GFI 0.941, AGFI 0.914, NFI 0.926, TLI 0.971, CFI 0.978, RMSEA 0.035; *** p < 0.001.
Table 4. Correlation matrix and AVE.
Table 4. Correlation matrix and AVE.
VariableHCIARAEISQPUPEUBI
Habit-Congruence (HC)0.771
Information Accuracy (IA)0.1390.823
Relative Advantage on Efficiency (RAE)0.2430.2830.851
IT System Quality (ISQ)0.1180.011−0.0030.708
Perceived Usefulness (PU)0.1330.2060.2310.0110.719
Perceived Ease of Use (PEU)0.2280.1790.0790.042 *0.1390.801
Behavioral Intention (BI)0.2910.3180.249−0.0470.3040.3900.811
Note: The square root of AVE is shown in bold letters. * p < 0.05
Table 5. Results of hypothesis test.
Table 5. Results of hypothesis test.
Hypothesis (Path)Standardization CoefficientStandard ErrorT-Value (p)Support
H1Habit-Congruence → Perceived Usefulness0.0890.1022.020 *Y
H2Information Accuracy → Perceived Usefulness0.1910.0792.809 ***Y
H3Relative Advantage on Efficiency → Perceived Usefulness0.1850.0732.454 **Y
H4IT System Quality → Perceived Usefulness−0.0200.192−0.213N
H5Habit-Congruence → Perceived Ease of Use0.2400.0693.148 **Y
H6Information Accuracy → Perceived Ease of Use0.1700.0512.380 *Y
H7Relative Advantage on Efficiency → Perceived Ease of Use0.0030.0460.114N
H8IT System Quality → Perceived Ease of Use0.0070.1230.121N
H9Perceived Usefulness → Behavioral Intention to Use IMA0.3130.0693.778 ***Y
H10Perceived Ease of Use → Behavioral Intention to Use IMA0.3880.0854.243 ***Y
Structural model fit: χ2(df) 221.231(136), χ2/degree of freedom 1.512, RMR 0.031, GFI 0.944, AGFI 0.912, NFI 0.907, TLI 0.942, CFI 0.936, RMSEA 0.025; * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 6. Results of direct and indirect effects.
Table 6. Results of direct and indirect effects.
Dependent VariableExplanatory VariableDirect EffectIndirect EffectTotal Effect
Habit-CongruencePerceived Usefulness2.020 * 2.020 *
Perceived Ease of Use3.148 ** 3.148 **
Perceived Usefulness, Perceived Ease of Use → Behavioral Intention 0.064 **
Information AccuracyPerceived Usefulness2.809 *** 2.809 ***
Perceived Ease of Use2.380 * 2.380 *
Perceived Usefulness, Perceived Ease of Use → Behavioral Intention 0.065 *
Relative Advantage on Efficiency Perceived Usefulness2.454 ** 2.454 **
Perceived Ease of Use0.114 0.114
Perceived Usefulness, Perceived Ease of Use → Behavioral Intention 0.030
IT System QualityPerceived Usefulness−0.213 −0.213
Perceived Ease of Use0.121 0.121
Perceived Usefulness, Perceived Ease of Use → Behavioral Intention 0.001
Note: * p < 0.05, ** p < 0.01, *** p < 0.001.
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Tae, I.J.; Broillet-Schlesinger, A.; Kim, B.Y. Selection Attributes of Integrated Mobility Apps on Affecting Users’ Intention to Use: A Case of Republic of Korea. Future Transp. 2024, 4, 1205-1222. https://doi.org/10.3390/futuretransp4040058

AMA Style

Tae IJ, Broillet-Schlesinger A, Kim BY. Selection Attributes of Integrated Mobility Apps on Affecting Users’ Intention to Use: A Case of Republic of Korea. Future Transportation. 2024; 4(4):1205-1222. https://doi.org/10.3390/futuretransp4040058

Chicago/Turabian Style

Tae, Il Joon, Alexandra Broillet-Schlesinger, and Bo Young Kim. 2024. "Selection Attributes of Integrated Mobility Apps on Affecting Users’ Intention to Use: A Case of Republic of Korea" Future Transportation 4, no. 4: 1205-1222. https://doi.org/10.3390/futuretransp4040058

APA Style

Tae, I. J., Broillet-Schlesinger, A., & Kim, B. Y. (2024). Selection Attributes of Integrated Mobility Apps on Affecting Users’ Intention to Use: A Case of Republic of Korea. Future Transportation, 4(4), 1205-1222. https://doi.org/10.3390/futuretransp4040058

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