Next Article in Journal
Quantitative Analysis of Carbon Emissions from Highway Construction Based on Life Cycle Assessment
Next Article in Special Issue
Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity
Previous Article in Journal
Looking for Environmental Scoring: A Case Study of a Portuguese Cotton White T-Shirt Made with Recycled Fiber
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 14
10.3390/su16145894
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Examining Commuters’ Intention to Use App-Based Carpooling: Insights from the Technology Acceptance Model

by
Wei Kang
1,
Qun Wang
1,
Long Cheng
1,2,* and
Meng Ning
1
1
Anhui Research Center of Construction Economy and Real Estate Management, Anhui Institute of Real Estate and Housing Provident Fund, School of Economics and Management, Anhui Jianzhu University, Hefei 230022, China
2
School of Transportation, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 5894; https://doi.org/10.3390/su16145894
Submission received: 6 June 2024 / Revised: 4 July 2024 / Accepted: 8 July 2024 / Published: 10 July 2024
(This article belongs to the Special Issue Transportation and Sustainable Mobility; Users’ Transitions for a Greener Future)
Browse Figures
Versions Notes

Abstract

:
App-based carpooling is recognized as a solution for sustainable commuting. However, there is currently no widespread acceptance and adoption of app-based carpooling services among urban commuters. The study aims to predict residents’ intention to use app-based carpooling services for commuting trips based on the extended Technology Acceptance Model, focusing on perceived risk, social influence, and environmental awareness, and further explore whether there are significant gender differences among these influential factors. A questionnaire was created to empirically test the model and a total of 392 valid surveys were collected in Hefei, China. The results confirm that commuter intention was positively affected by perceived usefulness, social influence, and environmental awareness, while it was negatively influenced by perceived risk. Although the effect of perceived ease of use on intention was not significant, it played a role in enhancing commuters’ perceived usefulness of the service. Moreover, gender differences exist regarding the strength of the relationship between environmental awareness and commuter intention. These findings provide practical insights for app-based carpooling providers and transportation departments aiming to promote their services and foster sustainable commuting practices.

1. Introduction

Commuting represents the most fundamental and important daily activity for urban residents. The choice of commuting mode not only directly affects residents’ schedules but also influences their commuting satisfaction and overall well-being [1]. Over the past few decades, the use of private vehicles for commuting has been increasing, leading to various adverse effects on society and the environment. Therefore, there is an urgent need to deal with the growing problems of urban transportation.
Internet-based shared mobility services provide new ideas for sustainable transportation, including ride-hailing, bike-sharing, and car-sharing. Among these, app-based carpooling has gradually attracted attention as an alternative and supplement to traditional taxis and emerging ride-hailing services. In the present study, app-based carpooling refers to individual travelers with similar or partially overlapping travel schedules and routes who match with the same vehicle via smartphone apps to commute to work or school and share travel costs [2,3].
App-based carpooling makes full use of spare vehicle seats, which can provide many benefits. Firstly, it can help passengers and drivers reduce travel expenses and enhance travel convenience [4]; secondly, regarding the environment, the enhanced vehicle utilization efficiency reduces the number of cars on the road, thus alleviating traffic congestion and reducing greenhouse gas emissions [5]; lastly, passengers engaging in conversations during carpooling alleviate boredom and foster social interaction, contributing to enhanced societal communication among members [6].
Despite the many benefits generated by app-based carpooling, the widespread acceptance and use of this shared mobility service, especially in commuting trips, remains limited [7]. As a matter of fact, research on carpooling for commuting trips has received extensive attention in developed countries, including the identity characteristics of carpoolers [8,9], their intention to carpool [10,11], and the motivations of commuters in carpooling [4,12]. However, these studies mainly focused on traditional carpooling arrangements organized informally among acquaintances, such as coworkers and friends (excluding family members), without relying on Internet-based platforms. Moreover, traditional commuting carpooling has witnessed a decline in popularity due to challenges in finding suitable carpooling partners and a lack of flexibility [13].
Based on the discussion above, this study aims to employ structural equation modeling (SEM) to investigate factors influencing residents’ intention to use app-based carpooling services for commuting trips, and to explore whether there are significant gender differences among these influential factors using multi-group analysis (MGA).
This study makes some contributions. On the one hand, this study is an early attempt to explore the role of app-based carpooling in commuting trips and proposes practical suggestions, helping transportation sectors and app-based carpooling providers develop strategies for promoting sustainable commuting. On the other hand, this study develops a novel theoretical framework for understanding commuters’ use intention by considering additional factors, including perceived risk, social influence, and environmental awareness, complementing the literature on shared mobility from the perspective of technology acceptance.
The next section reviews the literature. The study hypotheses are presented in Section 3, along with an introduction to the theoretical framework. The research methods and data collection are highlighted in Section 4. Section 5 presents the data analysis and the resulting outcomes. Section 6 delves deeper into the discussion of significant findings. Finally, we draw conclusions on the paper’s key findings, policy implications, and limitations in the last section.

2. Literature Review

To the best of our knowledge, no direct reference to the use intentions regarding app-based carpooling in commuting trips was found in the literature review. This article therefore mainly summarizes studies on traditional carpooling and app-based carpooling in commuting and the application of the technology acceptance model (TAM) to shared mobility, which can provide a foundation and reference for this study.

2.1. Carpooling and App-Based Carpooling in Commuting

The definition of carpooling varies in the literature, but consensus can be found in its broadest definition, where carpooling is described as “an agreement in which two or more persons travel together in the same private vehicle for a trip (or part of a trip) and share travel costs” [14]. Defined in this way, carpooling has attracted the interest of scholars for decades due to its numerous benefits. To be specific, when compared to public transportation and solo driving, carpooling has the advantage of both reducing trip expenses and increasing travel convenience for drivers and passengers [4]. Moreover, carpooling plays an important role in alleviating traffic congestion, reducing energy usage, and cutting carbon emissions [5]. While not extensively documented, some authors have highlighted social benefits, such as the opportunity for individuals to improve their socialization skills during carpooling [6].
The formal appearance of carpooling dates back to the 1973 oil crisis in the United States, when it became increasingly costly to use a private vehicle for single-passenger trips. At that time, coworkers who knew each other from the same workplace sharing rides was one of the most common forms of carpooling [4]. The reason for the popularity of this arrangement is that coworkers have a certain level of social connection, which fosters trust among them, further contributing to the success of carpooling [15]. Additionally, the organization of carpooling among coworkers can be facilitated by the regularity of the spatial and temporal patterns in the commuting trips [6]. The academic research on carpooling behaviors in commuting has mainly focused on incentives [16], characteristics [4], and commuters’ attitudes [17]. However, traditional carpooling has become less popular due to the difficulties in finding suitable carpooling partners with consistent schedules [13].
In recent years, an evolution in carpooling has been facilitated by mobile information and communication technologies (ICT) services [18]. App-based carpooling, which relies on cloud computing, GPS, and advanced algorithms to match drivers and passengers via smartphone apps [19], is a combination of Internet technology and traditional carpooling. There are two main forms of app-based carpooling. The first form, known as a “Dynamic Ridesharing Service”, involves drivers sharing rides with others who have similar travel plans through online matching on a mobile app with the aim of reducing travel costs [20]. In addition, ride-hailing apps such as “Lyft Shared”, “Uber Pool” and “Didi Hitch” offer carpooling features, which are called “Ride-splitting” or “Shared Ride-hailing”, representing the second form of app-based carpooling [21], where drivers are profit-oriented.
Compared to traditional carpooling, app-based carpooling has more advantages since it employs algorithms rather than social networks to enable on-demand matching [22]. A quantitative study based on two service tests conducted in Barcelona and Hanover indicated that shared ride-hailing could be a suitable mode of transport for commuting trips, as was perceived by users [23]. However, the research conducted on app-based carpooling behaviors has not focused on the commuting use case [24,25,26,27]. Therefore, this study focuses on the factors influencing residents’ intention to use app-based carpooling services for commuting trips. Prior related studies are presented in Table 1.

2.2. TAM in Shared Mobility

The TAM, a popular theoretical model for understanding a person’s acceptance of a specific innovative system or technology, was originally introduced by Davis in 1989 [28]. TAM proposed that perceived usefulness (PU) and perceived ease of use (PEU) are two key factors when analyzing consumers’ attitudes and intentions toward using new technology. According to TAM, PEU and PU are influenced by external variables; PEU not only directly impacts consumers’ attitude towards technology usage, but also indirectly affects their attitude through PU. Behavior intention (BI) is determined by PU and attitude, ultimately leading to actual behavior.
TAM has been demonstrated to be the most significant theoretical framework for understanding technology acceptance behavior and its influential factors [29]. Originally, it was used to predict a person’s adoption and usage of a specific information system and it is now one of the most extensively employed theoretical frameworks in this field. The extended TAM is further used to investigate public acceptance of a new technology or service, playing an important role in e-commerce platforms, network tools, user services, and other research fields [30].
In the context of shared mobility, the validity of this theoretical model has been verified by researchers through empirical studies. For instance, Chen and Lu (2016) demonstrated that TAM is a valuable framework for understanding the factors influencing the adoption intention of public bike-sharing, particularly in relation to environmental protection [31]. Zhang and Liu (2022) applied TAM to explore potential users’ intention toward adopting ride-sharing services in the COVID-19 context [32]. Another study investigated Egyptian consumers’ continued usage intentions regarding ride-hailing apps by employing the TAM [33]. App-based carpooling represents an innovative form of shared mobility, and TAM provides a good theoretical foundation for relevant research.

3. Research Model and Hypotheses Development

In this study, TAM was employed as the fundamental theoretical model to comprehend residents’ intention to use app-based carpooling for their commuting trips. The attitude construct, which reflects the user’s subjective feelings in the original TAM, poses challenges in measurement [34] and has a weak mediating role between users’ perceptions and behavior intentions [35]. Therefore, in the present study, the attitude construct was removed to simplify the TAM. While TAM has garnered strong support regarding its ability to explain technology acceptance, some scholars have argued that it is incomplete due to its neglect of negative factors [36]. Given that app-based carpooling represents an innovative commuting mode, the risks related to its use are often major barriers for users, making it essential to incorporate perceived risk (PR) into TAM [24]. Moreover, when considering the social orientation of collectivist cultures in Asian countries [37], the impact of others cannot be ignored. Therefore, this study also focused on the impact of social influence (SI) on residents’ intention to use app-based carpooling services for commuting trips. In addition, one of the important drivers motivating people to participate in shared mobility services is environmental awareness (EA) [38]. Thus, it is necessary to understand the influence of residents’ environmental awareness on their use of app-based carpooling services.

3.1. Perceived Ease of Use (PEU)

According to TAM, the PEU is defined as how easy and effortless it is for an individual to use a new technology [28]. Further, in this study, PEU refers to the extent of the ease and flexibility an individual perceives when using app-based carpooling services for commuting via mobile apps. Though the contribution of PEU in explaining technology acceptance has been confirmed by many studies [39,40], a few other studies have demonstrated a non-significant impact of PEU on the adoption intention of app-based carpooling [24,27]. Because of the differences in the research contexts, the literature remains inconclusive about the impact of PEU on the intention to adopt app-based carpooling. Hence, we are interested in further exploring the relationship between the two constructs under the prevailing condition. Building upon these points, the following hypotheses are put forth:
H1. 
PEU positively affects residents’ intention to use app-based carpooling services for commuting trips.
H2. 
PEU positively affects PU.

3.2. Perceived Usefulness (PU)

PU, another key construct of TAM, is considered to be the degree to which using a certain technology is thought to enhance job performance. When individuals are choosing whether or not to adopt new technologies, they are more likely to embrace them if they are seen as beneficial to achieving a goal. PU significantly positively influences the adoption intention of app-based carpooling, as numerous studies have demonstrated [40,41]. In this study, PU is regarded as the belief held by a resident regarding the effectiveness of app-based carpooling services in helping them to meet their commuting goals. Commuters are inclined to use the service when they perceive that using app-based carpooling services for commuting can reduce travel expenses, enhance trip convenience, and offer other advantages. Thus, the following hypothesis is presented:
H3. 
PU positively affects residents’ intention to use app-based carpooling services for commuting trips.

3.3. Perceived Risk (PR)

PR is commonly understood to be the individual’s belief regarding the likelihood of suffering a loss while using a product or service to achieve a desired goal [25]. If greater attention is paid to PR, the possibility of service adoption reduces, as the service is perceived as useless [42]. Prior research about shared mobility services has noted that PR negatively impacts the intention to adopt bike-sharing [43], car-sharing [44], ride-hailing [45], and ride-sharing [40]. Meanwhile, some studies have also shown that PR negatively affects users’ PU of app-based carpooling services [24,39]. In this context, PR represents residents’ uncertainty and perception of the likelihood of negative outcomes during commuting trips using app-based carpooling services. When residents think that commuting with an app-based carpooling service may lead to risks in terms of their privacy, physical and financial safety, and so on, they will tend to be reluctant to use the service. In light of these points, the proposed hypotheses are as follows:
H4. 
PR negatively affects residents’ intention to use app-based carpooling services for commuting trips.
H5. 
PR negatively affects PU.

3.4. Social Influence (SI)

SI is considered to be the extent to which a person’s attitude or behavior is influenced as a result of pressure exerted by important others around them (e.g., family, friends, colleagues) [46]. It has been demonstrated by many previous studies that SI is a critical factor influencing the attitude toward or intention to use shared mobility services [26,27]. In this study, when residents perceive that family, friends, and colleagues are increasingly choosing to use app-based carpooling services for their commuting trips, they are likely to perceive this mode of transportation as being relatively easy to use. Furthermore, if residents receive positive feedback about app-based carpooling services from important others around them, then they will enhance their PU of the service, while reducing their PR regarding using the travel service. Hence, the next hypotheses are as follows:
H6. 
SI positively affects residents’ intention to use app-based carpooling services for commuting trips.
H7. 
SI positively affects PEU.
H8. 
SI positively affects PU.
H9. 
SI positively affects PR.

3.5. Environmental Awareness (EA)

EA has been described as people’s overall comprehension and perception of environmental issues, as well as their commitment to take action for environmental protection [47]. Several studies have emphasized that environmentally friendly behaviors are more likely to be adopted by individuals with higher EA [24]. Furthermore, it has been established by scholars that EA is one of the main drivers that leads to people’s participation intention in some sustainable transportation modes, like electric vehicles, biking and walking [48,49]. App-based carpooling services, which make full use of the spare car seats in the traveling vehicle, are a sustainable transportation mode that is more resource-efficient than driving alone. Given the environmental benefits of the service, it is reasonable to assume that residents with high EA will be more likely to use app-based carpooling services. The positive effect of EA on the intention to adopt app-based carpooling services has been widely considered [27,50]. Drawing on the arguments mentioned above, the hypothesis put forward is as follows:
H10. 
EA positively affects residents’ intention to use app-based carpooling services for commuting trips.

3.6. The Moderating Effect of Gender

Groups with different socio-demographic characteristics differ in their behavior [51]. Gender serves as an important reference variable for market segmentation. As we all know, a service may be interpreted differently by individuals of different genders, thus resulting in varying behavioral intentions. When it comes to transportation behavior, studies indicate that sustainable transportation modes are more commonly used by women than by men [52] and women are more likely to carpool [53]. A multi-group analysis can be employed to explore whether there are significant differences among groups with different demographic characteristics [54]. Many previous studies on shared mobility services have tested the moderating role of users’ gender [33,55,56]. Therefore, we employed a multi-group analysis to investigate how gender moderates the relationships between PEU, PU, PR, SI, EA, and BI, aiming to provide further insights into the psychological distinctions in the intention to use app-based carpooling services for commuting among males and females. In light of the above discussion, we hypothesize that
H11a. 
Gender has a moderating role between PEU and BI.
H11b. 
Gender has a moderating role between PU and BI.
H11c. 
Gender has a moderating role between PR and BI.
H11d. 
Gender has a moderating role between SI and BI.
H11e. 
Gender has a moderating role between EA and BI.
The theoretical framework based on the above analysis is depicted in Figure 1.

4. Research Methodology

4.1. Questionnaire Design

To better understand app-based carpooling practices and investigate the influential factors on the use of this shared mode for commuting, a questionnaire, named “Questionnaire Survey of Hefei Residents’ Use of App-based Carpooling Services for Commuting Trips”, was designed by the “Low-carbon Transportation” team of the School of Economics and the Management of Anhui Jianzhu University. Hefei, a new first-tier city in central China, was selected as the research setting for several reasons. First, according to “Commuter Monitoring Report of Major Cities in China in 2023”, the commuting distance of Hefei residents has been increasing in recent years, indicating that commuting is an increasingly serious problem in the city. Second, the number of vehicles in Hefei exceeded 3 million in 2023, bringing substantial pressure to its urban transportation infrastructure and the environment. Third, existing studies related to app-based carpooling mainly focus on developed countries, and the research results are difficult to apply to cities in developing countries, which have great differences in their transportation infrastructure and social culture.
There are three parts to our questionnaire. After introducing the research background and providing an explanation of app-based carpooling services to provide respondents with a preliminary understanding of the questionnaire, the first part comprises participants’ current commuting behaviors and the use of app-based carpooling in their commuting trips. The second part consists of questions regarding the general and sociodemographic details of respondents. The third part contains a set of measurement items about PEU, PU, PR, SI, EA, and the intention to use app-based carpooling services for commuting trips. This study used the third part of the questionnaire to test the proposed theoretical model.
The questionnaire adopted measurement items from the existing literature. To be specific, PEU was assessed using items from Garcia’s (2023) studies [57], while items measuring PU were drawn from Wang et al. (2020) [24]. PR measurement items were based on the scales developed by Shao et al. (2022) [58]. SI construct measurements were largely adapted from Lowe and Piantanakulchai (2023) [21], and EA was assessed using items inspired by Ahn et al.’s (2016) scale [59]. Items modified from Schierz et al. (2010) were used to test BI [60]. On a Likert 5-point scale, the measurement items were scored ranging from 1 (strongly disagree) to 5 (strongly agree). Table 2 details the measurement items for each construct.

4.2. Sample and Data Collection

In order to ensure the questionnaire’s validity, three research experts in the field of shared mobility services were consulted to assess the reasonableness of each question. Suggestions provided by the experts were taken into consideration. Subsequently, pre-testing was conducted with 40 participants to gather crucial feedback. The feedback information was then discussed, and the questionnaire was modified accordingly. Then, the formal survey was administered using Wen Juan Xing, a prominent online survey platform in China, from 31 January to 24 March 2024. The study used snowball sampling to construct the sample. Initially, residents were randomly invited to participate in the survey. Subsequently, these initial participants were asked to send a questionnaire link to their acquaintances employed in Hefei, who were then encouraged to further disseminate the survey among their social networks and repeat the process. A total of 529 respondents participated; 137 questionnaires were excluded due to their short completion time, missing values, or uniform responses across all questions. Finally, 392 valid questionnaires (74.1%) were collected for the research, exceeding the recommended sample size of 300 (392 > 20 × 15 = 300), as suggested by scholars [61].
Table 3 outlines the demographic characteristics of the sample. Male participation accounted for 49.2%, slightly lower than female participation, at 50.8%. Most respondents were aged 18 to 30 years (62.5%), indicating a young age distribution. The proportion of unmarried respondents (56.1%) was higher than the proportion of married respondents (43.4%). Educationally, the majority of respondents were highly educated, with approximately 84.1% holding a bachelor’s degree or higher, while only 5.9% had a senior high school education or below. Regarding monthly income, 46 (11.7%) respondents reported incomes below CNY 4000, while 204 (52%) belonged to the income group of CNY 4000 to 8000, followed by 111 (28.3%) in the CNY 8000 to 15,000 range, and 31 (7.9%) were in the income group of above CNY 15,000. The majority of respondents (79.1%) had used app-based carpooling services during their commuting trips, but most of them used them infrequently. The results indicate that younger and well-educated commuters with moderate income levels are pioneering adopters of app-based carpooling services. The socio-economic characteristics of our sample closely resemble the study of Wang et al. (2019) on the use intention regarding ride-sharing [62].

4.3. Statistical Technique

The research hypotheses of the theoretical framework were examined using structural equation modeling (SEM), a multivariate statistical method for exploring the relationships between variables, with the assistance of AMOSS 26.0 software. The SEM analysis consisted of two steps. Initially, the measurement model was tested to ensure its reliability and validity. Subsequently, the proposed relationships among the constructs in the theoretical model were investigated through pathway analysis. Additionally, moderating effects were also investigated in this study using a multi-group analysis (MGA).

5. Results

5.1. Measurement Model Testing

Before the data analysis, the normality of the data was assessed through the measures of skewness and kurtosis, and the results were found to be within the acceptable range according to the recommended criteria [61]. The measurement model was tested using a confirmatory factor analysis (CFA), which includes both reliability and validity analyses. Reliability, used to estimate the internal consistency of the measurement items, was evaluated through Cronbach’s alpha coefficients and composite reliability (CR). In Table 4, the Cronbach’s alpha coefficients of the constructs ranged from 0.845 to 0.939, with all exceeding the minimum threshold of 0.70. Similarly, the CR values ranged from 0.852 to 0.939, which were also all greater than the recommended level of 0.70. Thus, these results confirm the satisfactory reliability of this study.
The next step is to assess the validity of the constructs, which represents the level of the measurement scale that can reflect the constructs, including convergent and discriminant validity. Convergent validity was evaluated using factor loadings and average variance extracted (AVE). As illustrated in Table 4, the AVE values were found to be within the range of 0.592 and 0.837, achieving the recommended level of 0.50. The standardized factor loadings of all measurement items also exceeded the suggested cut-off of 0.60. In Table 5, the square roots of the AVE values of each construct were all greater than the correlation between the constructs, indicating excellent discriminant validity for the measurement scale. Consequently, the measurement model achieved adequate reliability and validity. In addition, there was no multicollinearity in the model, as the variation inflation factor (VIF) values for all measured constructs were within the acceptable threshold of 0.2 to 5 [61].

5.2. Structural Model Testing

A hypotheses test relies on a good-fitting structural model. In the present study, several commonly used goodness-of-fit indices were found to be satisfactory, meeting the threshold limits recommended by Hair et al. (2009) (χ2/df = 2.601 < 3.00, GFI = 0.908 > 0.90, NFI = 0.933 > 0.90, IFI = 0.957 > 0.90, TLI = 0.949 > 0.90, CFI = 0.957 > 0.90, RMSEA = 0.064 < 0.08). Therefore, these results indicate the good fit of the structural model in the study.
After confirming the satisfactory fit of the structural model, a path analysis was conducted to examine the proposed hypotheses. Figure 2 depicts the path analysis and hypothesis testing results, while Table 6 provides a summary of them. The model accounted for 50.4% of the variance in behavior intention (R2 = 0.504), indicating that the five constructs (PEU, PU, PR, SI, EA) substantially explain 50.4% of the variance in intention to use app-based carpooling services for commuting. The path coefficients of beta weight (β), which show how strongly the independent and dependent variables are related to one another, were as follows.
Most of the hypotheses were consistent with the expected direction at different significance levels. Specifically, PEU had no effect on BI (β = 0.062, p > 0.05), while it significantly affected PU (β = 0.291, p < 0.001), so H1 was rejected and H2 was accepted. Meanwhile, PU positively directly affected BI and had a strong explanatory power (β = 0.306, p < 0.001), so H3 was established. PR showed a significant negative impact on BI (β = −0.125, p < 0.01), but had no influence on PU (β = 0.006, p > 0.05), which implies the acceptance of H4 and rejection of H5. SI proved to have a significant positive impact on PR (β = 0.122, p < 0.05), PU (β = 0.496, p < 0.001), PEU (β = 0.221, p < 0.001), and BI (β = 0.377, p < 0.001). Therefore, both H6 and H9 were accepted, which reflects the important role of SI in explaining residents’ intention to adopt app-based carpooling for commuting trips. In addition, EA (β = 0.182, p < 0.001) also had a positive effect on BI, leading to the establishment of H10.

5.3. Multi-Group Analysis (MGA)

The moderating effect of gender on the relationship between PEU, PU, PR, SI, EA, and BI was investigated by MGA. The sample met the criteria of a minimum of 100 elements per subgroup, with 193 males and 199 females [56]. The group variances in the path coefficients were assessed using the critical ratio of differences between parameters in order to test the proposed moderating effect hypotheses. As indicated in Table 7, the critical ratio of the path from EA to BI was −2.035 (p < 0.05), indicating significant gender-based differences in the EA–BI relationship. Compared to females (β = 0.100, p > 0.05), males (β = 0.293, p < 0.001) with higher environmental awareness were more likely to commute using app-based carpooling services, supporting the validation of H11e. The remaining hypotheses (H11a~H11d) suggested that gender did not moderate the relationships between PEU, PU, PR, SI, and BI.

6. Discussion

Based on the TAM, this study offers empirical insights into the significance of PEU, PU, PR, SI, and EA in shaping residents’ intentions to use app-based carpooling services for commuting trips. The only significant moderating effect of gender was found in the relationship between EA and residents’ intentions.
The findings suggest that PEU has no significant impact on residents’ intention to use app-based carpooling services for commuting trips, contrary to some studies [24] but in corroboration with a few others conducted in emerging economies [55]. Previous research has reported that the role of PEU in using new technologies or services may diminish over time [63]. TAM was developed in the late 1980s when Internet technology was not widespread in society. If a technology-based service does not take much effort for users to operate, this will increase people’s intention to use the service. However, in today’s digital era, individuals are accustomed to using a variety of online services (e-shopping, e-banking, e-payment, etc.) and are proficient in using smartphones. Consequently, it is not too difficult for residents to use app-based carpooling services, a kind of mobility service based on Internet technology, and the ease of using the services is not a motivation for residents. Although the ease of use may not directly stimulate the adoption intention for app-based carpooling services in commuting, it can help residents increase their perception of the service’s usefulness. This is consistent with findings from prior studies related to technology acceptance [64].
PU, another key construct of TAM, proved to be a significant factor affecting the use intention of app-based carpooling services for commuting, with a high degree of explanatory power. This result demonstrates that efficiency, usefulness, and convenience are the crucial reasons driving residents to accept app-based carpooling as their commuting mode, which is partly in line with previous studies [41]. For commuters, when it is difficult to get a taxi during rush hour, hard to park in the workplace, and inconvenient to access public transportation, their travel-related anxiety will increase. App-based carpooling services address these challenges by providing easy access to vehicles, eliminating parking issues, offering convenient booking processes, etc. Moreover, app-based carpooling makes full use of spare seats, which can minimize the travel costs of commuters. Hence, residents applaud this service as helpful and valuable.
In line with findings from numerous studies [40,55], PR has a significant negative impact on residents’ intentions. Commuter’s concern about the possible adverse consequences of using app-based carpooling services is a major barrier to their adoption of these services for commuting purposes. To be specific, app-based carpooling relies on a mobile platform, meaning that the private information, such as payment details, location data, and personal identity, collected by the platform may be misused without the commuter’s consent. Furthermore, commuters find themselves in close proximity with unfamiliar drivers and other passengers in confined spaces during the ride, heightening the uncertainty of their immediate surroundings. Additionally, commuters with inflexible work schedules are nervous and worried when using app-based carpooling services, feelings primarily stemming from concerns about the uncertainty of their arrival times when drivers take detours to pick up other carpool passengers. Nevertheless, it is noteworthy that PR has no effect on PU. It is not surprising that, although the use of app-based carpooling services in commuting involves various risks, residents’ perception of the service’s usefulness is mainly shaped by personal attributes and social influences.
SI was the most important determinant influencing the intention to adopt app-based carpooling services for commuting trips, while also exerting effects on PEU, PU, and PR. This finding is congruent with some recent studies [33,65]. In a collective society, the majority of people are influenced by family members, friends, and coworkers. The experiences and opinions of those in our social circle regarding app-based carpooling play an instrumental role in establishing our attitude toward using this service for commuting trips. According to the descriptive statistical analysis presented in Section 3, the use of app-based carpooling services for commuting trips remains relatively low among commuters. The reasons for this may be that commuters may not be familiar with this innovative service and may worry about the suitability of app-based carpooling as a mode of commuting. Therefore, the positive word-of-mouth from commuters who already use app-based carpooling services motivates others to embrace this service. Moreover, this impact enhances confidence levels and reduces risks perceived by residents regarding the use of this innovative mobility service.
Further, the significant effect of EA on residents’ intention to use app-based carpooling services for commuting was also confirmed in the study, indicating that the environmental benefits generated by app-based carpooling are a vital factor driving residents to embrace this mobility solution for their daily trips. Previous studies have reported similar results [24,66]. EA is a comprehensive concept that reflects individual’s perceptions, concerns, and sensitivities about current environmental issues, as well as their attitudes towards solving these problems. In general, the adoption of pro-environmental services is more likely among those with higher EA levels. The carbon emissions caused by the increase in the number of vehicles in urban areas has been proven to be a major source of air pollution. There is no denying the fact that app-based carpooling is a pro-environmental mobility service because of its environmental benefits, including alleviating traffic congestion and reducing carbon emissions. For commuters, engaging in app-based carpooling presents an opportunity to contribute to environmental conservation without compromising their travel comfort requirements. In addition, the results also reveal an interesting finding that the influence of EA on BI is more significant among male commuters. Although many studies have shown that females are more concerned than males about the environment [52,67], female commuters may prioritize factors such as safety over environmental benefits when it comes to using app-based carpooling services for commuting trips. Moreover, compared to females, males are more aware of the consumption of fuel resources caused by the use of cars. They may see app-based carpooling as a practical solution aligned with their environmental concerns and commuting needs.

7. Conclusions

7.1. Key Findings and Implications

In emerging economies, app-based carpooling is regarded as an effective solution for alleviating transportation congestion and reducing carbon emissions caused by the increasing number of vehicles in urban areas. It is necessary to investigate residents’ motives regarding using or not using app-based carpooling services for commuting with the aim of promoting commuting sustainability. Based on TAM, the present study discussed the relationships between PEU, PU, PR, SI, EA, and residents’ intention towards the adoption of app-based carpooling services for commuting trips, and also tested the moderating effects of gender on these relationships. The key conclusions and policy implications of the study are outlined as follows.
Firstly, PU is considered a key factor affecting residents’ intention to use app-based carpooling services for commuting trips. Thus, targeted strategies for different users can be implemented by app-based carpooling providers to enhance the attractiveness and usefulness of the services. Specifically, for potential users who have not yet tried app-based carpooling, providers can offer free carpooling for the first ride and high discounts for the initial month, allowing commuters to familiarize themselves with the carpooling platform. For users who rarely use app-based carpooling services for commuting purposes, the carpooling platform can set up a dedicated section where these users can input their regular commute address and receive a coupon for a better deal than a single carpool. For users who frequently engage in app-based carpooling for commuting, the platform can offer attractive discounts based on their usage frequency and offer special discounts to users on monthly/annual anniversaries. Moreover, app functionalities should be improved continuously to enhance their convenience; for example, by providing real-time matching algorithms, flexible scheduling options, and reliable payment systems. In addition, the local government can adopt supportive policies including dedicated carpool lanes, parking incentives, and regulatory flexibility for app-based carpooling platforms.
Secondly, PR has a significant negative effect on residents’ use intention. Some measures should be taken to strictly control the various risks that passengers may encounter during carpooling. For instance, the criteria for accessing and using user information should be timely, accurately, and transparently communicated to users to enhance the security of their personal information. Meanwhile, platform operators should strictly supervise the access certification of drivers and vehicles, conducting random inspections periodically to protect the well-being of passengers and their property. Mandatory comprehensive insurance can be provided for both drivers and passengers participating in carpooling to protect against accidents and property damage. Furthermore, regulating driver behavior is also crucial. The platform should develop a scoring system to adjust the score of drivers according to complaints received through the user feedback system, thereby incentivizing drivers to maintain a high standard of service. In addition, addressing commuters’ concerns about the risk of uncertain arrival times, the platform should also enhance its technological investment to achieve effective matching between drivers and passengers.
Thirdly, EA positively affects residents’ intention to use app-based carpooling services for commuting, and this effect is particularly significant among male commuters. This result indicates that the environmental advantages of adopting app-based carpooling services should be emphasized by both the platforms and policymakers. App-based carpooling service providers and government departments can strengthen the promotion of the environmental advantages of app-based carpooling by using social media such as the WeChat Official Account, Weibo, and Douyin. Additionally, the platform can introduce real-time “carbon credits” that show the extent to which a user reduced their carbon emissions through the use of app-based carpooling services. Users can then redeem their accumulated “carbon credits” for coupons. Additionally, to effectively address the gender differences discussed above, the platform can develop app interfaces that cater to diverse user concerns and tailor marketing campaigns for male commuters to highlight specific benefits, like the environmental benefits of app-based carpooling.
Finally, SI is the most vital factor affecting residents’ intention to use app-based carpooling services for commuting trips in this study. Word-of-mouth regarding app-based carpooling services from influential individuals can directly affect residents’ intention to adopt this service. Therefore, the social impact of app-based carpooling can be increased by making full use of social networks. First, the service providers can recruit community ambassadors who promote app-based carpooling within their neighborhoods, workplaces, or social circles. They should be provided with resources to organize local events, share success stories, and educate others about the benefits of shared rides. Second, the platform can develop app features that facilitate social connections, such as friend lists and group chats. Third, incentive programs can be implemented to encourage commuters to recommend app-based carpooling services to their family members, friends, and colleagues.

7.2. Limitations and Further Studies

Although this study has obtained intriguing findings that can benefit the carpooling industry and contribute to the sustainable development of commuting, there are limitations that can be further refined in future research. Firstly, this study exclusively focuses on behavioral intention. However, the intention to use app-based carpooling services for commuting trips does not always directly translate into actual usage. Future studies can further explore this transformation. Secondly, the study is only a basic investigation aimed at understanding commuters’ use of app-based carpooling services using cross-sectional data. Future research could employ a subset of samples to track changes in commuter intentions over a one-year period. Lastly, this study represents an early effort to explore shared mobility services for commuting in China using the TAM. While this theoretical framework is robust enough to draw some meaningful conclusions, alternative theories should be considered to examine more variables to comprehensively explain residents’ intention to use app-based carpooling services for commuting.

Author Contributions

W.K. and L.C. managed the research project; W.K., Q.W. and M.N. collected and analyzed the questionnaire data; W.K., Q.W. and M.N. wrote and revised the original manuscript; W.K. and L.C. refined the original manuscript and polished the language. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Department of Education of Anhui Province (2022AH050265), Anhui Jianzhu University (2021QDR09), and Anhui Research Center of Construction Economy and Real Estate Management/Anhui Institute of Real Estate and Housing Provident Fund (2024JZJJ02, 2024JZJJ03).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

All subjects participating in the study provided informed consent.

Data Availability Statement

The data can be requested from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chatterjee, K.; Chng, S.; Clark, B.; Davis, A.; De Vos, J.; Ettema, D.; Handy, S.; Martin, A.; Reardon, L. Commuting and Wellbeing: A Critical Overview of the Literature with Implications for Policy and Future Research. Transp. Rev. 2020, 40, 5–34. [Google Scholar] [CrossRef]
  2. Lokhandwala, M.; Cai, H. Dynamic Ride Sharing Using Traditional Taxis and Shared Autonomous Taxis: A Case Study of NYC. Transp. Res. Part C Emerg. Technol. 2018, 97, 45–60. [Google Scholar] [CrossRef]
  3. Si, H.; Shi, J.; Hua, W.; Cheng, L.; De Vos, J.; Li, W. What Influences People to Choose Ridesharing? An Overview of the Literature. Transp. Rev. 2023, 43, 1211–1236. [Google Scholar] [CrossRef]
  4. Shaheen, S.A.; Chan, N.D.; Gaynor, T. Casual Carpooling in the San Francisco Bay Area: Understanding User Characteristics, Behaviors, and Motivations. Transp. Policy 2016, 51, 165–173. [Google Scholar] [CrossRef]
  5. Minett, P.; Pearce, J. Estimating the Energy Consumption Impact of Casual Carpooling. Energies 2011, 4, 126–139. [Google Scholar] [CrossRef]
  6. Aguiléra, A.; Pigalle, E. The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges. Sustainability 2021, 13, 11824. [Google Scholar] [CrossRef]
  7. Molina, J.A.; Giménez-Nadal, J.I.; Velilla, J. Sustainable Commuting: Results from a Social Approach and International Evidence on Carpooling. Sustainability 2020, 12, 9587. [Google Scholar] [CrossRef]
  8. Benita, F. Carpool to Work: Determinants at the County-Level in the United States. J. Transp. Geogr. 2020, 87, 102791. [Google Scholar] [CrossRef]
  9. Brown, A.E. Who and Where Rideshares? Rideshare Travel and Use in Los Angeles. Transp. Res. Part Policy Pract. 2020, 136, 120–134. [Google Scholar] [CrossRef]
  10. Bachmann, F.; Hanimann, A.; Artho, J.; Jonas, K. What Drives People to Carpool? Explaining Carpooling Intention from the Perspectives of Carpooling Passengers and Drivers. Transp. Res. Part F Traffic Psychol. Behav. 2018, 59, 260–268. [Google Scholar] [CrossRef]
  11. Waerden, P.; Lem, A.; Schaefer, W. Investigation of Factors That Stimulate Car Drivers to Change from Car to Carpooling in City Center Oriented Work Trips. Transp. Res. Procedia 2015, 10, 335–344. [Google Scholar] [CrossRef]
  12. Vanoutrive, T.; Van De Vijver, E.; Van Malderen, L.; Jourquin, B.; Thomas, I.; Verhetsel, A.; Witlox, F. What Determines Carpooling to Workplaces in Belgium: Location, Organisation, or Promotion? J. Transp. Geogr. 2012, 22, 77–86. [Google Scholar] [CrossRef]
  13. Neoh, J.G.; Chipulu, M.; Marshall, A. What Encourages People to Carpool? An Evaluation of Factors with Meta-Analysis. Transportation 2017, 44, 423–447. [Google Scholar] [CrossRef]
  14. Olsson, L.E.; Maier, R.; Friman, M. Why Do They Ride with Others? Meta-Analysis of Factors Influencing Travelers to Carpool. Sustainability 2019, 11, 2414. [Google Scholar] [CrossRef]
  15. Correia, G.; Viegas, J.M. Carpooling and Carpool Clubs: Clarifying Concepts and Assessing Value Enhancement Possibilities through a Stated Preference Web Survey in Lisbon, Portugal. Transp. Res. Part Policy Pract. 2011, 45, 81–90. [Google Scholar] [CrossRef]
  16. Bulteau, J.; Feuillet, T.; Dantan, S.; Abbes, S. Encouraging Carpooling for Commuting in the Paris Area (France): Which Incentives and for Whom? Transportation 2023, 50, 43–62. [Google Scholar] [CrossRef]
  17. Dinesh, S.; Rejikumar, G.; Sisodia, G.S. An Empirical Investigation into Carpooling Behaviour for Sustainability. Transp. Res. Part F Traffic Psychol. Behav. 2021, 77, 181–196. [Google Scholar] [CrossRef]
  18. Shaheen, S.; Cohen, A. Shared Ride Services in North America: Definitions, Impacts, and the Future of Pooling. Transp. Rev. 2019, 39, 427–442. [Google Scholar] [CrossRef]
  19. Adelé, S.; Dionisio, C. Learning from the Real Practices of Users of a Smart Carpooling app. Eur. Transp. Res. Rev. 2020, 12, 39. [Google Scholar] [CrossRef]
  20. Si, H.; Duan, X.; Cheng, L.; Zhang, Z. Determinants of Consumers’ Continuance Intention to Use Dynamic Ride-Sharing Services. Transp. Res. Part Transp. Environ. 2022, 104, 103201. [Google Scholar] [CrossRef]
  21. Lowe, W.U.A.; Piantanakulchai, M. Investigation of Behavioral Influences of Carpool Adoption for Educational Trips—A Case Study of Thammasat University, Thailand. Case Stud. Transp. Policy 2023, 12, 100970. [Google Scholar] [CrossRef]
  22. Créno, L. User Experience of Dynamic Carpooling: How to Encourage Drivers and Passengers? In Proceedings of the Energy Consumption and Autonomous Driving, Paris, France, 3–4 December 2014; pp. 71–81. [Google Scholar]
  23. Gilibert, M.; Ribas, I.; Rosen, C.; Siebeneich, A. On-Demand Shared Ride-Hailing for Commuting Purposes: Comparison of Barcelona and Hanover Case Studies. Transp. Res. Procedia 2020, 47, 323–330. [Google Scholar] [CrossRef]
  24. Wang, Y.; Wang, S.; Wang, J.; Wei, J.; Wang, C. An Empirical Study of Consumers’ Intention to Use Ride-Sharing Services: Using an Extended Technology Acceptance Model. Transportation 2020, 47, 397–415. [Google Scholar] [CrossRef]
  25. Nguyen-Phuoc, D.Q.; Su, D.N.; Nguyen, M.H.; Vo, N.S.; Oviedo-Trespalacios, O. Factors Influencing Intention to Use On-Demand Shared Ride-Hailing Services in Vietnam: Risk, Cost or Sustainability? J. Transp. Geogr. 2022, 99, 103302. [Google Scholar] [CrossRef]
  26. Akbari, M.; Moradi, A.; SeyyedAmiri, N.; Zúñiga, M.Á.; Rahmani, Z.; Padash, H. Consumers’ Intentions to Use Ridesharing Services in Iran. Res. Transp. Bus. Manag. 2021, 41, 100616. [Google Scholar] [CrossRef]
  27. Goel, P.; Haldar, P. Shared Ride-Hailing Service in India: An Analysis of Consumers’ Intention to Adopt. Int. J. Bus. Emerg. Mark. 2020, 12, 336–353. [Google Scholar] [CrossRef]
  28. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  29. Xia, M.; Zhang, Y.; Zhang, C. A TAM-Based Approach to Explore the Effect of Online Experience on Destination Image: A Smartphone User’s Perspective. J. Destin. Mark. Manag. 2018, 8, 259–270. [Google Scholar] [CrossRef]
  30. Verma, P.; Sinha, N. Integrating Perceived Economic Wellbeing to Technology Acceptance Model: The Case of Mobile Based Agricultural Extension Service. Technol. Forecast. Soc. Chang. 2018, 126, 207–216. [Google Scholar] [CrossRef]
  31. Chen, S.Y.; Lu, C.-C. A Model of Green Acceptance and Intentions to Use Bike-Sharing: YouBike Users in Taiwan. Netw. Spat. Econ. 2016, 16, 1103–1124. [Google Scholar] [CrossRef]
  32. Zhang, W.; Liu, L. Exploring Non-Users’ Intention to Adopt Ride-Sharing Services: Taking into Account Increased Risks Due to the COVID-19 Pandemic among Other Factors. Transp. Res. Part Policy Pract. 2022, 158, 180–195. [Google Scholar] [CrossRef] [PubMed]
  33. Elnadi, M.; Gheith, M.H. What Makes Consumers Reuse Ride-Hailing Services? An Investigation of Egyptian Consumers’ Attitudes towards Ride-Hailing Apps. Travel Behav. Soc. 2022, 29, 78–94. [Google Scholar] [CrossRef]
  34. Chen, L. A Model of Consumer Acceptance of Mobile Payment. Int. J. Mob. Commun. 2008, 6, 32–52. [Google Scholar] [CrossRef]
  35. Lee, D.Y.; Lehto, M.R. User Acceptance of YouTube for Procedural Learning: An Extension of the Technology Acceptance Model. Comput. Educ. 2013, 61, 193–208. [Google Scholar] [CrossRef]
  36. Park, C.-K.; Kim, H.-J.; Kim, Y.-S. A Study of Factors Enhancing Smart Grid Consumer Engagement. Energy Policy 2014, 72, 211–218. [Google Scholar] [CrossRef]
  37. Sama, R.; Trivedi, J.P. Factors Affecting Consumers’ Loyalty towards Halal Cosmetics: An Emerging Market Perspective. Int. J. Bus. Emerg. Mark. 2019, 11, 254–273. [Google Scholar] [CrossRef]
  38. Luri Minami, A.; Ramos, C.; Bruscato Bortoluzzo, A. Sharing Economy versus Collaborative Consumption: What Drives Consumers in the New Forms of Exchange? J. Bus. Res. 2021, 128, 124–137. [Google Scholar] [CrossRef]
  39. Mehrab Ashrafi, D.; Habiba, W.; Alam, I. An Assessment of the Behavioural Intention for Using Ride-Sharing Services: Empirical Evidence from a Developing Country. Malays. J. Consum. Fam. Econ. 2020, 24, 33–62. [Google Scholar]
  40. Raza, S.A.; Khan, K.A.; Salam, J. Impact of Environmental Triggers on Students’ Behavior to Use Ride-Sharing Services: The Moderating Role of Perceived Risk. Curr. Psychol. 2023, 42, 11329–11343. [Google Scholar] [CrossRef]
  41. Werth, O.; Sonneberg, M.-O.; Leyerer, M.; Breitner, M. Examining Customers’ Critical Acceptance Factors toward Ridepooling Services. Transp. Res. Rec. 2021, 2675, 1310–1323. [Google Scholar] [CrossRef]
  42. Jaiswal, D.; Kaushal, V.; Kant, R.; Kumar Singh, P. Consumer Adoption Intention for Electric Vehicles: Insights and Evidence from Indian Sustainable Transportation. Technol. Forecast. Soc. Chang. 2021, 173, 121089. [Google Scholar] [CrossRef]
  43. Kim, B.; Kim, D. Exploring the Key Antecedents Influencing Consumer’s Continuance Intention toward Bike-Sharing Services: Focus on China. Int. J. Environ. Res. Public Health 2020, 17, 4556. [Google Scholar] [CrossRef] [PubMed]
  44. Zhang, R.; Zhao, L.; Wang, W.; Zhou, A. Analysis on Influencing Factors of Car-sharing Choice Behaviors. J. Highw. Transp. Res. Dev. 2022, 39, 143–151. [Google Scholar] [CrossRef]
  45. Ma, L.; Zhang, X.; Ding, X.; Wang, G. Risk Perception and Intention to Discontinue Use of Ride-Hailing Services in China: Taking the Example of DiDi Chuxing. Transp. Res. Part F Traffic Psychol. Behav. 2019, 66, 459–470. [Google Scholar] [CrossRef]
  46. Venkatesh, V.; Morris, M.; Davis, G.; Davis, F. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
  47. Lembcke, T.-B.; Herrenkind, B.; Nastjuk, I.; Brendel, A.B. Promoting Business Trip Ridesharing with Green Information Systems: A Blended Environment Perspective. Transp. Res. Part Transp. Environ. 2021, 94, 102795. [Google Scholar] [CrossRef]
  48. Asiamah, N.; Frimpong Opuni, F.; Aidoo, I.; Benyi Ansah, N.; Lomatey, T.; Muhonja, F.; Agormeda-Tetteh, K.; Koranteng Kumi, P.; Eku, E.; Osumanu Doumbia, M.; et al. Effects of Climate Change Awareness on Green Purchase Behaviour, Biking, and Walking Time: Moderated Mediation by Sustainability Knowingness. Transp. Res. Interdiscip. Perspect. 2024, 25, 101134. [Google Scholar] [CrossRef]
  49. Wang, S.; Li, J.; Zhao, D. The Impact of Policy Measures on Consumer Intention to Adopt Electric Vehicles: Evidence from China. Transp. Res. Part Policy Pract. 2017, 105, 14–26. [Google Scholar] [CrossRef]
  50. Burghard, U.; Scherrer, A. Sharing Vehicles or Sharing Rides—Psychological Factors Influencing the Acceptance of Carsharing and Ridepooling in Germany. Energy Policy 2022, 164, 112874. [Google Scholar] [CrossRef]
  51. Hsu, C.H.C. Reference Group Influences among Chinese Travelers. J. Travel Res. 2006, 44, 474–484. [Google Scholar] [CrossRef]
  52. Polk, M. Are Women Potentially More Accommodating than Men to a Sustainable Transportation System in Sweden? Transp. Res. Part Transp. Environ. 2003, 8, 75–95. [Google Scholar] [CrossRef]
  53. Ayaz, A.; Waheed, A.; Saleem, H.; Abid, M.M. Travelers’ Attitude towards Carpooling in Islamabad. J. Eng. Appl. Sci. 2021, 68, 23. [Google Scholar] [CrossRef]
  54. Chin, W.; Marcolin, B.; Newsted, P. A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study. Inf. Syst. Res. 2003, 14, 189–217. [Google Scholar] [CrossRef]
  55. Goel, P.; Haldar, P. Does India Need a Shared Ride-Hailing Now More than Ever? Understanding Commuter’s Intentions to Share Ridesailing Now than Ever? Understanding Commuters’ Intention to Share Rides. Asian J. Bus. Account. 2020, 13, 277–305. [Google Scholar] [CrossRef]
  56. Li, L.; Zhang, Y. An Extended Theory of Planned Behavior to Explain the Intention to Use Carsharing: A Multi-Group Analysis of Different Sociodemographic Characteristics. Transportation 2023, 50, 143–181. [Google Scholar] [CrossRef]
  57. Garcia, M. Factors Affecting Adoption Intention of Productivity Software Applications Among Teachers: A Structural Equation Modeling Investigation. Int. J. Hum.-Comput. Interact. 2023, 40, 2546–2559. [Google Scholar] [CrossRef]
  58. Shao, Z.; Zhang, L.; Li, X.; Zhang, R. Understanding the Role of Justice Perceptions in Promoting Trust and Behavioral Intention towards Ride-Sharing. Electron. Commer. Res. Appl. 2022, 51, 101119. [Google Scholar] [CrossRef]
  59. Ahn, M.; Kang, J.; Hustvedt, G. A Model of Sustainable Household Technology Acceptance. Int. J. Consum. Stud. 2016, 40, 83–91. [Google Scholar] [CrossRef]
  60. Schierz, P.G.; Schilke, O.; Wirtz, B.W. Understanding Consumer Acceptance of Mobile Payment Services: An Empirical Analysis. Electron. Commer. Res. Appl. 2010, 9, 209–216. [Google Scholar] [CrossRef]
  61. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis, 7th ed.; Pearson: New York, NY, USA, 2015. [Google Scholar]
  62. Wang, Y.; Gu, J.; Wang, S.; Wang, J. Understanding Consumers’ Willingness to Use Ride-Sharing Services: The Roles of Perceived Value and Perceived Risk. Transp. Res. Part C Emerg. Technol. 2019, 105, 504–519. [Google Scholar] [CrossRef]
  63. Venkatesh, V. Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Inf. Syst. Res. 2000, 11, 342–365. [Google Scholar] [CrossRef]
  64. Amin, M.; Rezaei, S.; Abolghasemi, M. User Satisfaction with Mobile Websites: The Impact of Perceived Usefulness (PU), Perceived Ease of Use (PEOU) and Trust. Nankai Bus. Rev. Int. 2014, 5, 258–274. [Google Scholar] [CrossRef]
  65. Lu, J.; Yao, J.E.; Yu, C.-S. Personal Innovativeness, Social Influences and Adoption of Wireless Internet Services via Mobile Technology. J. Strateg. Inf. Syst. 2005, 14, 245–268. [Google Scholar] [CrossRef]
  66. Li, H.; Wen, H. How Is Motivation Generated in Collaborative Consumption: Mediation Effect in Extrinsic and Intrinsic Motivation. Sustainability 2019, 11, 640. [Google Scholar] [CrossRef]
  67. Luchs, M.G.; Mooradian, T.A. Sex, Personality, and Sustainable Consumer Behaviour: Elucidating the Gender Effect. J. Consum. Policy 2012, 35, 127–144. [Google Scholar] [CrossRef]
Sustainability 16 05894 g001
Figure 1. Theoretical framework and hypotheses.
Figure 1. Theoretical framework and hypotheses.
Sustainability 16 05894 g001
Sustainability 16 05894 g002
Figure 2. The results of the tested hypotheses. (Notes: * p < 0.05; ** p < 0.01; *** p < 0.001).
Figure 2. The results of the tested hypotheses. (Notes: * p < 0.05; ** p < 0.01; *** p < 0.001).
Sustainability 16 05894 g002
Table 1. Summary of prior related studies.
Table 1. Summary of prior related studies.
ReferenceTopicPlaceSampleMethod
Bulteau et al. (2023) [16]Incentives to encourage carpooling for commutingParis, France2002 workersLogistic Model
Sofi Dinesh et al. (2021) [17]Commuters’ attitude towards carpooling services.Kerala, India400 carpoolersSEM
Shaheen et al. (2016) [4]Characteristics, behaviors, and motivations of casual carpoolers.San Francisco, United States503 participantsLogit Model
Wang et al. (2020) [24]Consumers’ intention to use ride-sharing services.China426 participantsSEM
Nguyen-Phuoc et al. (2022) [25]Factors influencing intention to use on-demand shared ride-hailing services.Vietnam520 passengersSEM
Akbari (2021) [26]Consumers’ intentions to use ridesharing services.Iran318 Iranian usersSEM
Goel and Haldar. (2020) [27]Consumers’ intention to adopt a shared ride-hailing service.India345 consumersSEM
Table 2. Measurement items.
Table 2. Measurement items.
ConstructCodeItem
PEUPEU1Learning to use a carpooling app is easy for me.
PEU2The interface of a carpooling app is clear to me.
PEU3The payment of the order in a carpooling app is simple for me.
PUPU1Using app-based carpooling services can improve the efficiency of my commuting travel.
PU2Using app-based carpooling services is helpful for my commuting trips.
PU3Using app-based carpooling services is convenient for my commuting trips.
PU4Using app-based carpooling services can make my commuting travel easier.
PRPR1I’m worried that my personal information will be disclosed when I use a carpooling app.
PR2I’m worried that there may be life and property risks associated with the use of app-based carpooling services.
PR3I’m worried about the uncertainty that comes with sharing a car with strangers.
SISI1I will use app-based carpooling services for commuting if my friends use them.
SI2I will use app-based carpooling services for commuting if my family uses them.
SI3I will use app-based carpooling services for commuting if someone important to me uses them.
EAEA1I take into account the potential environmental impact of my behavior when making many decisions.
EA2I consider myself environmentally responsible.
EA3My daily behavior is influenced by my environmental awareness.
EA4The waste of resources on our planet worries me.
BIBI1In the future, I will try app-based carpooling as a commuting mode.
BI2I would like to commute via app-based carpooling services if it is possible.
BI3I plan to commute using app-based carpooling services and probably recommend them to others.
Table 3. Demographic profile of respondents (n = 392).
Table 3. Demographic profile of respondents (n = 392).
VariableClassificationFrequencyPercentage (%)
GenderMale19349.2
Female19950.8
Age18~3024562.5
31~4512832.7
More than 45194.80
Educational levelSenior high school or below235.90
Associate or bachelor’s degree22056.1
Master’s degree or Ph.D.14938.0
Marital statusUnmarried22256.6
Married17043.4
Monthly incomeLess than CNY 4000 4611.7
CNY 4000 to 8000 20452.0
CNY 8000 to 15,000 11128.3
More than CNY 15,000 317.90
The frequency of use of app-based carpooling services for commutingNever8220.9
Less than once a month11028.1
One to three times a month13333.9
One to three times a week4411.2
More than four times a week235.90
Table 4. Reliability and convergent validity of the research model.
Table 4. Reliability and convergent validity of the research model.
ConstructItemReliabilityConvergent Validity
Cronbach’s AlphaCRStandardized
Factor Loading		AVE
PEUPEU10.8920.8930.8680.737
PEU20.895
PEU30.810
PUPU10.9190.9220.7750.747
PU20.863
PU30.921
PU40.892
PRPR10.8870.8870.8490.724
PR20.873
PR30.831
SISI10.9390.9390.9070.837
SI20.926
SI30.912
EAEA10.8450.8520.7150.592
EA20.820
EA30.866
EA40.659
BIBI10.9330.9330.8730.823
BI20.923
BI30.924
Table 5. Discriminant validity of the research model.
Table 5. Discriminant validity of the research model.
BIEASIPRPUPEU
BI0.907
EA0.405 0.769
SI0.616 0.390 0.915
PR−0.015 0.318 0.116 0.851
PU0.588 0.312 0.559 0.054 0.864
PEU0.321 0.293 0.217 −0.050 0.401 0.858
Table 6. Results of path analysis and hypothesis testing.
Table 6. Results of path analysis and hypothesis testing.
HypothesisPathEstimatet-ValueResult
H1PEU→BI0.0621.376Rejected
H2PEU→PU0.291 ***6.041Accepted
H3PU→BI0.306 ***5.489Accepted
H4PR→BI−0.125 **−3.007Accepted
H5PR→PU0.0060.129Rejected
H6SI→BI0.377 ***6.842Accepted
H7SI→PEU0.221 ***4.069Accepted
H8SI→PU0.496 ***9.756Accepted
H9SI→PR0.122 *2.206Accepted
H10EA→BI0.182 ***3.863Accepted
Notes: * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 7. The moderating effects of gender.
Table 7. The moderating effects of gender.
PathMaleFemaleCritical RatiosResult
Estimatet-ValueEstimatet-Value
H11a: PEU→BI−0.027−0.4290.1091.7081.479Rejected
H11b: PU→BI0.379 ***4.6280.248 ***3.386−1.410Rejected
H11c: PR→BI−0.069−1.242−0.196 **−3.149−1.625Rejected
H11d: SI→BI0.303 ***3.7080.418 ***5.7601.108Rejected
H11e: EA→BI0.293 ***4.4290.1001.529−2.035 *Accepted
Notes: * p < 0.05; ** p < 0.01; *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kang, W.; Wang, Q.; Cheng, L.; Ning, M. Examining Commuters’ Intention to Use App-Based Carpooling: Insights from the Technology Acceptance Model. Sustainability 2024, 16, 5894. https://doi.org/10.3390/su16145894

AMA Style

Kang W, Wang Q, Cheng L, Ning M. Examining Commuters’ Intention to Use App-Based Carpooling: Insights from the Technology Acceptance Model. Sustainability. 2024; 16(14):5894. https://doi.org/10.3390/su16145894

Chicago/Turabian Style

Kang, Wei, Qun Wang, Long Cheng, and Meng Ning. 2024. "Examining Commuters’ Intention to Use App-Based Carpooling: Insights from the Technology Acceptance Model" Sustainability 16, no. 14: 5894. https://doi.org/10.3390/su16145894

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop