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Article

Towards Sustainable Mobility: Factors Influencing the Intention to Use Ride-Sharing in the Post-Pandemic Era

by
Kun Wang
1,2,
Linfeng Qi
1,
Shuo Yang
1,2,
Cheng Wang
3,*,
Rensu Zhou
1 and
Jing Liu
4
1
College of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China
2
National-Local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology, Hefei 230601, China
3
School of Architecture & Urban Planning, Anhui Jianzhu University, Hefei 230601, China
4
School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(18), 8343; https://doi.org/10.3390/su17188343
Submission received: 10 June 2025 / Revised: 9 September 2025 / Accepted: 16 September 2025 / Published: 17 September 2025
(This article belongs to the Special Issue Sustainable Transport and Land Use for a Sustainable Future)
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Abstract

As a key element of the sharing economy, ride-sharing plays a vital role in promoting sustainable urban mobility by optimizing vehicle utilization rates, lowering carbon emissions, and alleviating traffic congestion. Despite its cost-efficiency and sustainability benefits, ride-sharing adoption remains limited in the post-pandemic period due to behavioral changes and safety concerns. Accordingly, using survey data from 425 commuters in Hefei, concerns about COVID-19 and satisfaction with ride-sharing services were integrated into the theory of planned behavior framework. Structural equation modeling was applied to examine the relationship between ride-sharing intention and actual usage behaviors. The results indicated that ride-sharing intention was significantly positively affected by subjective norms (β = 0.428 ***), service satisfaction (β = 0.315 ***), and perceived behavioral control (β = 0.162 *), but significantly negatively affected by concerns about COVID-19 (β = −0.183 **). Concerns about COVID-19 significantly negatively affected travelers’ actual ride-sharing behaviors (β = −0.2 **). Furthermore, ride-sharing intention was identified as a significant positive predictor of travelers’ behaviors: specifically, their likelihood of accepting a ride-sharing order (β = 0.395 ***). These findings offer transport authorities evidence-based strategies for designing targeted interventions during health crises, particularly through reinforcing social norms, improving service quality, and implementing transparent health protocols to ensure both user safety and sustainability.

1. Introduction

As an emerging mode of shared mobility facilitated by third-party platforms, ride-sharing enables travellers with similar origins and destinations to share a vehicle through intelligent matching. It can be classified into two main modes based on user dynamics: (1) the hitch mode, in which drivers share pre-determined routes with passengers while splitting basic costs, and (2) the online ride-hailing mode, the focus of this study, where drivers respond to passenger requests through platform-dispatched orders to provide real-time mobility services [1,2,3]. In recent years, ride-sharing has gained increasing popularity, particularly among younger populations, owing to its cost-effectiveness, time efficiency during peak hours, and convenience [4]. Moreover, it supports sustainability goals by alleviating urban parking shortages and reducing the overall carbon footprint of urban travel [5].
Despite these advantages, ride-sharing faces substantial challenges that threaten its potential as a sustainable mobility solution. Safety risks, data vulnerabilities, market distortions, and regulatory non-compliance all constrain its market expansion [6]. Following the outbreak of COVID-19 in 2019, infection risks became more pronounced in shared mobility contexts, particularly in workplaces, schools, and multi-occupant vehicles. To mitigate viral transmission, epidemic control strategies prioritized restricting non-essential mobility through travel limitations, the promotion of private vehicle use, and enhanced commuter distancing protocols. While effective in curbing the spread of COVID-19, such restrictions critically hindered the development of public transit—especially ride-sharing—as evidence suggests that travellers increasingly turned to private or non-motorized modes to reduce infection risks [7,8], thereby undermining progress toward sustainable urban mobility.
Against this backdrop, the present study examines the influence of psychological perceptions, concerns about COVID-19, and the dimensions of ride-sharing satisfaction on travel behavior choices in the post-pandemic era. By disentangling the underlying mechanisms, this study provides empirically grounded strategies for transportation authorities to design precision interventions. The findings aim to support policymakers and transport authorities in designing targeted interventions that reinforce ride-sharing’s role in achieving sustainable urban mobility, while also providing a methodological framework for future public health-related transportation research.

2. Literature Review

Comparing with traditional taxi services, ride-sharing caters to user groups with more sophisticated mobility requirements, involves a greater number of participants per trip, and consequently presents a significantly higher probability of service contingencies [9]. These factors collectively amplify the operational and managerial complexity of ride-sharing services beyond that of conventional taxi operations. Previous research has examined ride-sharing impacts and service adoption determinants along two dimensions: the impact of ride-sharing services (traditional taxi, the labor market, urban mobility) and determinants of ride-sharing service adoption (socio-demographic attributes, psychological factors, travel characteristics, pandemic environment).

2.1. The Impact of Ride-Sharing Services

It is widely acknowledged that traditional taxi industries constitute the primary casualties of ride-sharing service expansion, with ride-hailing progressively displacing conventional taxi operations [10]. This market penetration correlates with a documented 10% reduction in taxi driver earnings [11]. Against this backdrop, traditional taxi services actively integrate online hailing platforms to enhance competitiveness and revenue streams [12]. In the labor market domain, despite creating employment opportunities, ride-sharing raises substantive concerns regarding systematic gaps in labor rights protection, occupational instability, and income volatility. These factors potentially drive workforce transitions from stable employment toward high-mobility, low-security work arrangements [13]. Regarding urban mobility impacts, ride-hailing services strengthen transportation functionality by serving as spatiotemporal complements: they effectively address coverage gaps in low-density areas while bridging supply–demand disparities across peak/off-peak hours, day/night cycles, and weekday/weekend periods. Concurrently, these services exert competitive pressure by displacing the shares of public transit, walking, and cycling modes [14].

2.2. Determinants of Ride-Sharing Service Adoption

Socio-demographic attributes critically shape ride-sharing adoption patterns. Empirical analyses confirm that core determinants—gender, age, education, occupation, and income—exhibit differential influences on service selection [15]. Female riders are often more cautious in adopting ride-sharing because of safety concerns when traveling with strangers, particularly male drivers or passengers [16]. Simultaneously, non-linear adoption patterns are also seen with respect to age, with youth cohorts demonstrating a heightened ride-sharing propensity compared to other age groups [17]. Furthermore, higher education levels and occupations in technical or senior management positions are positively associated with ride-sharing use [18,19]. Attitudes towards ride-sharing trips vary considerably according to the income bracket of the commuter. Previous studies have indicated that the use of ride-sharing services is more prevalent among commuters with lower incomes [20].
Psychological determinants substantially influence ride-sharing adoption, with perceived benefits, risks, and value serving as primary predictors of behavioral intention [15]. Personal space valuations, preferences for social interaction, trust in strangers, and concerns about safety and privacy significantly hinder acceptance [21].
Travel attributes also shape ride-sharing behavior. Key factors include travel cost, time efficiency (waiting and in-vehicle duration), and trip distance, especially in high-demand areas where these variables are optimized [22,23,24].
The COVID-19 pandemic severely disrupted urban transportation systems, presenting unprecedented challenges for high-contact modes such as ride-sharing. Studies demonstrate that the combined effects of travel anxiety, income reduction, and safety concerns significantly reduced adoption intention [25]. Concerns about infection risk when sharing rides with strangers led many travellers to abandon ride-sharing altogether [26]. In response, COVID-19-related variables have been systematically incorporated into analytical models of ride-sharing behavior, giving rise to new frameworks that integrate public health considerations [26,27,28].
Synthesizing these findings highlights three important dimensions of ride-sharing behavior—attitudes (ATT), social norms (SN), and perceived behavioral control (PBC)—which align with the core constructs of the Theory of Planned Behavior (TPB) [29,30,31,32,33]. Building on this foundation, additional elements such as risk perception, environmental awareness, and personality traits have been incorporated to further illuminate behavioral mechanisms [34,35]. Moreover, TPB can be integrated with complementary theoretical models—including the Technology Acceptance Model (TAM) [36], the Norm Activation Model (NAM) [37], and the Cumulative Perceived Value (CPV) framework [38]—to enhance explanatory power in understanding ride-sharing behavior.
In summary, while prior research has explored ride-sharing choices from multiple perspectives, the pandemic’s impact remains underexplored. Existing post-pandemic studies are often country-specific and case-based, limiting their generalizability. To address this gap, the present study extends the TPB framework by incorporating two novel dimensions—COVID-19 concerns and service satisfaction—to systematically examine the intrinsic links between ride-sharing behavior and its determinants, thereby providing a more comprehensive understanding of post-pandemic travel decision-making.

3. Research Hypothesis

3.1. Original Variables Related to the Theory of Planned Behavior

Attitude (ATT) reflects individuals’ value judgments towards ride-sharing behavior. Subjective norm (SN) captures the perceived social pressures from one’s social network regarding ride-sharing adoption. Perceived behavioral control (PBC) denotes the perceived ease or difficulty of performing the behavior. Ride-sharing intention (INT) represents the behavioral motivation and level of commitment.
Empirical studies confirm that the TPB model effectively explains ride-sharing intention and behaviors [38], with INT strongly predicting actual participation [39]. For example, Jain et al. (2014) applied the TPB to examine British commuters’ choice between private vehicles and public transportation, showing that attitudes, normative perceptions, and perceived behavioral control significantly shaped travel mode preferences [40].
Accordingly, the following hypotheses are proposed:
H1: 
ATT has significant positive effects on ride-sharing intention among users of ride-sharing software.
H2: 
SN has significant positive effects on ride-sharing intention among users of ride-sharing software.
H3: 
PBC has significant positive effects on ride-sharing intention among users of ride-sharing software.
H4: 
Ride-sharing intention has a significant positive effect on ride-sharing behavior.

3.2. Concerns About COVID-19

Concerns about COVID-19 (CAC) reflect travelers’ prioritization of pandemic-related risks. Prior studies show that perceived transmission likelihood is associated with proximity, crowd density, and regional health protocols. Highly cautious travelers tend to rigorously evaluate ride-sharing partners and routes, whereas less cautious individuals adopt more relaxed travel strategies [41].
Based on these findings, the following hypotheses are proposed:
H5: 
CAC has a significant negative effect on ride-sharing intention among users of ride-sharing software.
H6: 
CAC has a significant negative effect on ride-sharing behavior.

3.3. Ride-Sharing Service Satisfaction

Ride-sharing service satisfaction refers to travelers’ positive evaluations of service quality relative to price, convenience, and overall experience. It is generally divided into satisfaction with software operation (SSO) and satisfaction with service quality (SSQ) [42,43]. Travelers with high satisfaction consistently prefer ride-sharing for its reliability, comfort, and cost-effectiveness, while dissatisfaction may drive modal shifts or service abandonment [44]. For example, Donald found that ride-sharing software helps passengers easily identify vehicle type, waiting time, and cost, thereby enhancing user experience.
Accordingly, the following hypotheses are proposed:
H7: 
SSO have significant positive effects on ride-sharing intention among users of ride-sharing software.
H8: 
SSO have significant positive effects on ride-sharing behavior.
H9: 
SSQ have significant positive effects on ride-sharing intention among users of ride-sharing software.
H10: 
SSQ have significant positive effects on ride-sharing behavior.
In light of the aforementioned ten hypotheses, a theoretical framework is proposed, as illustrated in Figure 1.

4. Methodology

4.1. Development and Validation of Survey Instruments

This study developed the ‘Questionnaire on Influencing Factors of Ride-Sharing Intention in the Post-COVID Era’ through a systematic literature review and cross-disciplinary expert validation. A pilot test was conducted via a leading Chinese online survey platform in August 2021. Preliminary data analyses employed SPSS 25.0 for reliability and validity assessments, while AMOS 26.0 was used to apply structural equation modeling (SEM) and verify theoretical pathways, thereby ensuring measurement robustness prior to large-scale implementation.

4.2. Sample and Process

The dataset was collected from Hefei, Anhui Province (September–October 2021). With accelerating urbanization and the growing emphasis on green travel, ride-sharing has become a prevalent mode of urban mobility in Hefei. As a nationally recognized ‘sub-center of the Yangtze River Delta urban agglomeration’ and a new first-tier city, Hefei embodies the transportation challenges faced by other central Chinese cities (e.g., Wuhan, Zhengzhou, and Changsha) under the Rise of Central China strategy. Its ride-sharing market—featuring platforms such as Didi, Hello Hitchhiker, and Caocao—has expanded rapidly, with wide service variety and high penetration rates, but also challenges such as multi-platform order acceptance, pricing opacity, and safety concerns. Thus, Hefei provides a representative case with transferable insights for other urban ride-sharing contexts.
To ensure broad coverage and high representativeness, both online and offline survey methods were adopted. Online questionnaires were distributed via a well-known Chinese survey platform to leverage internet convenience and reach. Offline surveys were conducted in public spaces, busy commercial districts, and transportation hubs, allowing direct and interactive feedback collection from respondents. Prior to participation, all respondents were fully briefed on the research methodology and relevant precautions. To encourage participation and ensure response quality, each respondent received a 10 RMB remuneration.
The questionnaire consisted of two major sections:
  • Demographic information, including sex, age, education, marital status, occupation, and monthly income, as well as ride-sharing software usage frequency.
  • Extended TPB scale, measuring constructs such as ATT, SN, PBC, INT, CAC, SSO, and SSQ.
The distribution of the sample characteristics is presented in Table 1.
To ensure the quality and reliability of the data, strict screening criteria were implemented. Based on the exclusion of duplicate IP address submissions, completion times deviating from the predetermined range (4–10 min), and data that frequently exhibited responses such as omissions or irrational answers (e.g., missing entries, violating common sense). Ultimately, 425 valid responses were secured from a total distribution of 505 questionnaires, attaining an effective collection rate of 84.16%. This included 166 male participants (39.06%) and 259 female participants (60.94%). The analytical process is illustrated in Figure 2.

4.3. Measurement Questionnaire

The TPB scale comprises the following dimensions: ATT, SN, PBC, INT, COVID-19, SSO, and SSQ. Each dimension was assessed through the use of a series of questions. The participants responded to these items on a 5-point Likert scale, with scores ranging from 1 (indicating a high level of non-compliance and dissatisfaction) to 5 (indicating a high level of compliance and satisfaction). A higher score on the scale indicates greater consistency between the travelers’ stated feelings and their actual situation. This scale provides a comprehensive and in-depth analysis of the psychological motivations behind travelers’ ride-sharing travel behavior. The original scale is summarized in Table 2.

5. Results

5.1. Normality Test

A normality test of each measurement item was conducted via the skewness and kurtosis coefficients, and according to the criteria proposed by Kline et al. (2008) [50], the data satisfy the requirements of a near-normal distribution if the absolute value of the skewness coefficient is less than 3 and if the absolute value of the kurtosis coefficient is less than 8. The absolute values of the skewness and kurtosis coefficients of the individual measurement items occurred within the standard ranges. Consequently, each measurement item satisfied the criteria for a near-normal distribution.

5.2. Reliability and Validity Analysis

A confirmatory factor analysis (CFA) was conducted to assess measurement models, including reliability and convergent validity. The reliability analysis evaluated the internal consistency of measurement items through Cronbach’s alpha and Cronbach’s alpha (CR) values. The deletion of certain measurement items was implemented during structural equation modeling development, whereby all initial items with standardized factor loadings below 0.40 were systematically removed. This refinement process enhances the measurement model by simultaneously satisfying core reliability and validity thresholds (Composite Reliability ≥ 0.7; Average Variance Extracted ≥ 0.5) while improving goodness-of-fit indices [51]. Table 3 shows that the Cronbach’s alpha values for all items in the table ranged from 0.710 to 0.837, exceeding the minimum threshold of 0.70; CR values also ranged between 0.785 and 0.881, surpassing the recommended level of 0.70. The average variance explained (AVE) values for convergent validity assessment were all above the recommended threshold of 0.5. These findings collectively confirm the data reliability of this study [52].

5.3. Structural Equation Modelling

To explore the correlation between these independent variables and INT in depth and to rigorously verify the validity and accuracy of the theoretical assumptions, an SEM was constructed to assess the hypothesized model. The final SEM is shown in Figure 3. The results revealed that the constructed model matched the data well (CMIN/DF = 2.260, RMSEA = 0.055, CFI = 0.909, GFI = 0.907, and ILI = 0.910). The model explained 43.2% of the total variance in travelers’ INT.
For the estimated model of ride-sharing behavior, the standardized path coefficients and the statistical significance results for the latent variables are shown in Figure 3 and Table 4. These results provide notable data support for an in-depth understanding of the psychological mechanisms behind ride-sharing behavior, as well as a systematic basis for formulating relevant policies and strategies. The results revealed that SN (β = 0.428, p < 0.001), PBC (β = 0.162, p < 0.050), CAC (β = −0.183, p < 0.010), and SSQ (β = 0.315, p < 0.001) exerted a significant effect on INT, which supports hypotheses H2, H3, H5, and H9. Furthermore, INT (β = 0.395, p < 0.001) and CAC (β = −0.200, p < 0.010) significantly affected ride-sharing behavior, supporting hypotheses H4 and H6. The remaining hypotheses could not be supported.

6. Discussion and Recommendations

6.1. Influence of the Elements of the Theory of Planned Behavior

SN significantly positively affected travelers’ INT, which notably demonstrates the strong theoretical support provided by the TBS in parsing and investigating people’s ride-sharing behavior [53]. The most significant effect of SN on travelers’ INT was that travelers’ intention to choose a ride-sharing service highly depended on the social pressure perceived from important reference groups. This finding is consistent with previous literature suggesting that Chinese people place high value on self-monitoring and typically adjust their behavioral patterns on the basis of their surroundings and the behavior of others [54]. When family members or friends regularly use ride-sharing services, people are more likely to adopt the same travel mode.
Additionally, PBC significantly affects travelers’ INT. Ciasullo et al. (2018) reported that those who perceived themselves to be better able to cope with the various situations that may arise during ride-sharing were more inclined to travel this way [55]. Bachmann et al. (2018) further emphasized this point by suggesting that, when ride-sharing becomes more convenient, people exhibit greater PBC levels, which increases the likelihood that they will utilize ride-sharing services [37]. Moreover, Zhang et al. (2020) revealed that users who are more familiar with the ride-sharing process typically exhibit higher PBC levels and stronger intention to ride-share [56]. Overall, these findings reveal the important role of social influences and personal confidence in promoting the popularity of ride-sharing services.
Based on these findings, passengers’ intention to adopt ride-sharing can be strengthened by enhancing their SN and PBC through real-time trip tracking systems, rapid emergency response protocols, and pre-trip guidance that clarifies service procedures, fare structures, and key considerations.
This study proposes enhancing passengers’ pre-emptive awareness of service procedures, fare structures, and critical considerations through pre-trip educational initiatives, complemented by real-time journey tracking systems and immediate emergency response channels enabling direct one-touch connections to customer support. These integrated measures optimize behavioral expectations, proactively mitigate potential risks, and ensure the sustained perception of autonomy throughout the ride-sharing process, thereby effectively alleviating psychological distress stemming from perceived loss of control. Overall, these findings reveal the important role of social influences and personal confidence in promoting the popularity of ride-sharing services.
Notably, although existing studies have demonstrated a significant influence of attitude ATT on INT [57], some research has indicated the limited predictive validity of attitude as a determinant of ride-sharing intention [58,59]. People possibly tend to form their intention by relying more on subjective norms, such as what their social environment is doing (descriptive norm) or what their own moral values suggest that they do (personal norm) [37]. Smith et al. (2007) also noted that people tend to use normative information in situations of uncertainty as opposed to more systematic information processing by weighing attitudinal beliefs [60]. In addition, the findings in this study do not negate the importance of attitudes but rather reveal that the COVID-19 may have fundamentally altered the mechanism through which attitudes translate into ride-sharing intentions. In the post-pandemic context, traditional positive attitudes may remain a necessary condition for ride-sharing behavior, but are no longer sufficient, as new behavioral determinants are emerging as more salient drivers of choice.
The results also show a significant positive correlation between INT and actual behavior, consistent with the behavioral science premise that intention precedes action. When travelers exhibit high intention towards ride-sharing, they are more likely to engage in ride-sharing behavior. However, travelers’ intention does not directly translate into actual actions. Although intention is a decisive antecedent of behavior, there is a non-negligible gap, especially in the investigation of consumer behavior [61]. Therefore, ride-sharing behavior can be facilitated by indirectly increasing INT via other factors. This view is also supported by other studies; for example, Gao et al. (2019) reported that travelers’ INT can be greatly enhanced through the government’s formulation and improvement of ride-sharing service regulations, thereby clarifying the legal status, rights and obligations, and dispute resolution mechanisms of ride-sharing services [62]. Guo et al. (2020) reported that platforms provide preferential ride-sharing prices, a wealth of ride-sharing options, and high-quality ride-sharing services to attract more travelers to choose ride-sharing [3].

6.2. Influence of Concerns About COVID-19

The findings of the study indicate that individuals who are more aware of the COVID-19 pandemic are less inclined to utilize ride-sharing services [63]. Specifically, travelers with a higher risk of exposure to the virus exhibit more stringent criteria for the ride-sharing process, including contact distance, ride-sharing route traffic, and regional immunization status. In the event of an outbreak, it is possible that commuters may be compelled to relinquish the use of ride-sharing as a mode of transportation due to the uncertainty surrounding the potential health risks associated with riding with strangers [26]. When households include medically vulnerable individuals (such as seniors above 65 years or children under 12 years), perceived COVID-19 risks exhibit systematic intensification, triggering the fundamental restructuring of decision weight parameters in travel behavior models—where conventional ride-sharing benefits (time efficiency and cost savings) become substantially overridden by safety concerns, consequently shifting travel choices toward low-risk alternatives with spatial autonomy, particularly private vehicles or walking [27]. Conversely, the reduction of the risk of infection during ride-sharing through the implementation of scientific measures may facilitate the selection of ride-sharing as a travel mode. A survey conducted by Lowe et al. (2023) on travelers’ ride-sharing intention during an outbreak revealed that, if safety guidelines are followed correctly, millennials perceive ride-sharing as a safe travel mode during a pandemic such as the current one [64]. Furthermore, commuters are more likely to choose ride-sharing if they are guaranteed that all ride-sharing participants, including drivers, have completed their vaccinations.
The proposed core measures include utilizing in-vehicle sensors to detect mask compliance, establishing a digital platform to share real-time vehicle sanitization records, and integrating GPS data to optimize routing plans that circumvent pandemic high-risk zones. These solutions not only offer a feasible pathway for restoring pooled mobility services as a mainstream transportation mode in the current phase but also establish critical foundational technology and actionable frameworks for addressing future public health emergencies, thereby ensuring the continuity of essential transit operations and reinforcing public confidence in communal transport systems.

6.3. Influence of Ride-Sharing Service Satisfaction

Notably, this study highlights the significant positive impact of service satisfaction quality (SSQ) on intention to use ride-sharing (INT) [65]. The impact of SSQ is immediate, with travelers updating their satisfaction ratings after the experience, which influences subsequent decisions [31]. In addition, SSQ includes comprehensive satisfaction with multiple aspects of economic benefits, environmental awareness, and social experience throughout the entire ride-sharing activity process. Economic factors notably influence SSQ and repeat use [66]. Wang et al. noted (2020) that the more environmentally conscious a rider is, the more likely they are to choose a ride-sharing service [67]. After realizing that participating in a ride-sharing trip can provide significant environmental benefits, travelers are more likely to continue using ride-sharing services rather than stopping or adopting other modes of transportation. Interactions between fellow ride-sharing users during travel help create social bonds that go beyond economic exchange [68]. Moreover, research has shown that negative user experiences generate a more drastic impact than positive user experiences do when travelers choose to use ride-sharing services [69]. Specifically, if there is a bad ride-sharing experience, users may stop immediately. In contrast, if ride-sharing with unfamiliar travelers produces a satisfactory social experience, the traveler will develop positive emotions, further enhancing their INT.
Although ride-sharing apps serve as the primary gateway for users to access services, their perceived usability, interface design, and functional completeness, while important, primarily function as supplementary factors rather than direct determinants of core travel experiences [70]. When users prioritize essential service attributes such as cost-effectiveness, driver reliability, and journey efficiency, technological shortcomings in app features or aesthetics are often tolerated or overlooked if the underlying service quality proves sufficiently attractive [71]. Notably, young demographics constitute the majority of ride-sharing users, whose digital nativity diminishes the perceived barriers to app usage; even with dissatisfaction toward specific functionalities like response speed or interface complexity, their familiarity with technology enables seamless completion of transactions, thereby attenuating the impact of SSO on INT.
Notably, while service satisfaction significantly enhances users’ intention to adopt ride-sharing, it fails to effectively catalyze actual behavior, revealing a misalignment in behavioral regulation mechanisms—this is termed the ride-sharing intention–behavior gap [72]. This gap manifests as dual resistance forces: subjectively, deeply entrenched commuting habits perpetuate dependence on fixed travel modes, preventing users from breaking established behavioral patterns despite existing ride-sharing intentions; objectively, structural constraints like insufficient temporal flexibility and peak-hour demand conflicts create tangible barriers. For instance, young commuters with explicit ride-sharing intention frequently default to their original travel modes when real-time matching durations exceed tolerance thresholds during morning trips or unexpected work overtime disrupts schedule controllability, empirically demonstrating conversion attrition.

7. Conclusions

The influence mechanism behind travelers’ intention to accept ride-sharing trips in the post-epidemic era was deeply analyzed by building a modified ride-sharing travel intention choice model with a sample of 425 respondents. Travelers’ intention to choose ride-sharing trips in the post-epidemic era was elucidated, and the effects of travelers’ ATT, SN, PBC, COVID-19, SSQ, and SSO on the intention to ride-share, as well as the effects of the intention to ride-share and COVID-19 on the behavior of ride-sharing, were investigated.
Based on the aforementioned research findings, effectively enhancing ride-sharing intention requires coordinated efforts from both social cognition and perceived behavioral control dimensions. Specifically, ride-sharing platforms can optimize the user experience to foster positive word-of-mouth, leverage social media for service promotion and feedback management, and implement an invitation code system to incentivize new users. Collaboration with traffic management departments for training services and complaint mechanism optimization is also essential to enhance PBC.
On the basis of improving SN and PBC, the systematic optimization of epidemic prevention and control measures should also not be overlooked. The platform needs to establish a dual ‘prevention-emergency response’ protection system: on the one hand, it should enforce policies requiring drivers to be vaccinated and undergo regular epidemic prevention training; on the other hand, the platform should establish standardized vehicle disinfection and safety inspection procedures. In addition, platforms should also focus on service quality, trip planning, user feedback, and green travel initiatives, ensuring safety, efficiency, and user engagement.
This study also has some limitations. For instance, it would be beneficial for future studies to extend the geographic scope to encompass a wider area. More variables such as individual characteristics, trust in drivers/platforms, or environmental attitudes should be also considered in the study. Ultimately, providing a comprehensive understanding of the evolving trajectory of user ride-sharing intention and behavior, establishing a multi-period longitudinal database, and deploying panel data models are essential.

Author Contributions

K.W.: Conceptualization, writing—original draft, funding acquisition, supervision, L.Q.: Data curation, methodology, S.Y.: Methodology, funding acquisition, writing—review & editing, C.W.: Conceptualization, methodology, funding acquisition, R.Z.: Data curation, methodology, J.L.: Data curation, methodology, funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Introduction of Talents and Doctoral Initiation Fund for Universities [Grant no. 2022QDZ25, 2020QDZ37, 2023QDZ31], Open Fund for the Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes [Grant no. JTX202401], and the National Natural Science Foundation of China [Grant no. 52402415].

Institutional Review Board Statement

The study was approved by the Science and Technology Ethics Committee of Anhui Jianzhu University (Approval Code 2025001).

Informed Consent Statement

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

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 08343 g001
Figure 1. Expanded theory of planned behavior framework diagram.
Figure 1. Expanded theory of planned behavior framework diagram.
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Figure 2. Flow of data analysis.
Figure 2. Flow of data analysis.
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Figure 3. Structural model testing results. CMIN/DF = 2.260; RMSEA = −0.055;GFI = 0.907; TLI = 0.910; CFI = 0.909. * is significant at the 0.05 level. ** is significant at the 0.01 level. *** is significant at the 0.001 level.
Figure 3. Structural model testing results. CMIN/DF = 2.260; RMSEA = −0.055;GFI = 0.907; TLI = 0.910; CFI = 0.909. * is significant at the 0.05 level. ** is significant at the 0.01 level. *** is significant at the 0.001 level.
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Table 1. Distribution of sample characteristics.
Table 1. Distribution of sample characteristics.
DemographicsDescriptionFrequencyPercentage
GenderMale16639.06%
Female25960.94%
AgeUnder 2510324.24%
26–3016639.06%
31–3510324.24%
36–40307.06%
Above 40235.40%
EducationHigh school and below143.29%
Junior college7417.41%
Bachelor’s degree and above33779.30%
Marital statusUnmarried13130.82%
Married29168.47%
Other30.71%
OccupationStudent5212.24%
Workers276.35%
Service personnel225.18%
Staff/civil servants25560.00%
Private/self-employed workers6214.59%
Other71.64%
Monthly income (unit: RMB)Under 1600378.71%
1601–2500204.71%
2501–50007016.47%
5001–10,00020448.00%
10,001–20,0008319.53%
20,000112.58%
Frequency of ride-sharing use per week2 rides or less30170.82%
3–4 times8520.00%
Over 4 times399.18%
Table 2. Original extended theory of planned behavior scale.
Table 2. Original extended theory of planned behavior scale.
SourceFactorItem
Javid et al. (2022) [30]ATTRide-sharing helps reduce pollution and improve the environment (ATT1)
Ride-sharing can reduce urban traffic congestion (ATT2)
Ride-sharing trips have higher benefits (in terms of time and money level considerations) compared to regular orders (ATT3)
I can accept ride-sharing for traveling (ATT4)
I think that with proper protection, ride-sharing trips carry the same risk of contracting an epidemic as regular trips (ATT5)
Liu et al. (2017) [45]SNMy family and friends support my choice of ride-sharing as a mode of travel (SN1)
My friends support my choice of ride-sharing as a mode of travel (SN2)
My family would also choose a ride-sharing mode of travel (SN3)
My friends and coworkers would also choose the ride-sharing mode of travel (SN4)
Abutaleb et al. (2020) [46]PBCI can have a good conversation with drivers and travelers during a ride-sharing trip (PBC1)
I can be competent enough to deal with problems that may arise while waiting for the driver to pick up travelers (PBC2)
I can handle problems that may arise with drivers and travelers during ride-sharing trips (PBC3)
Ride-sharing incentives offered by the platform would influence me to choose ride-sharing for my trip (PBC4)
Completing a ride-sharing trip on a ride-sharing app is easy for me (PBC5)
Yousefi et al. (2021) [47]CACI have followed the national outbreak daily (CAC1)
I have followed the outbreak in my province and city daily (CAC2)
I will wash my hands frequently with soap and water or with an alcohol-based hand sanitizer (CAC3)
I will remind a ride-sharing traveler to wear a mask when he or she is not wearing one (CAC4)
When a driver is not wearing a mask, I remind him to wear one (CAC5)
I will cancel the ride-sharing order when the driver or traveler refuses to wear a mask (CAC6)
I hoard the relevant anti-epidemic drugs and supplies (Banlangen, Shuanghuanglian oral solution, alcohol, disinfectant water, goggles, etc.) that are rumored on the Internet (CAC7)
I stock up on medical surgical masks (CAC8)
I always wear a mask when I choose to ride-share (CAC9)
I actively cooperate with staff on immunization measures (e.g., checking in, taking my temperature, etc.) when I enter certain areas or establishments (CAC10)
Bachmann et al. (2018) [37]INTThe likelihood that you intend to choose ride-sharing for your trip with proper safety precautions (INT1)
When traveling alone at night, the likelihood that you intend to choose ride-sharing (INT2)
Likelihood that you plan to ride-share when traveling with a companion at night (INT3)
Likelihood that you intend to ride-share when traveling from a crowded place such as an airport, high-speed rail station, or traveler terminal (INT4)
Likelihood that you intend to choose a ride-sharing trip when you go to a high-speed rail station, airports, etc. The likelihood that you intend to ride-share to transfer to trains or airplanes. (INT5)
Overall, the likelihood that you intend to choose ride-sharing for future trips (INT6)
Likelihood that you intend to choose ride-sharing for future trips in bad weather conditions (INT7)
Likelihood that you intend to choose ride-sharing for future trips when other modes of travel are blocked (INT8)
Likelihood that you intend to choose a ride-sharing trip when there is a COVID-19 case in your city (INT9)
Acheampong et al. (2020) [48]SSOThe interface for using ride-sharing is simple and reasonable (SSO1)
The process of using ride-sharing is simple and easy to operate (SSO2)
The operation of paying for a ride-sharing order is easy and quick (SSO3)
Feedback from the ride-sharing software is easy to operate when there is an emergency (SSO4)
When using the ride-sharing software, the ride-sharing order can be completed quickly (SSO5)
Fileborn et al. (2022) [49]SSQThe ride-sharing driver can arrive at the designated location on time to receive the ride-sharing traveler (SSQ1)
When getting into the car, the ride-sharing driver has standardized service, confirms the order, and reminds to fasten the seatbelt before departing (SSQ2)
The ride-sharing driver can accurately and quickly take me to my destination (SSQ3)
I felt comfortable during the ride-sharing process (SSQ4)
The ride-sharing driver was friendly (SSQ5)
Table 3. Reliability and convergent validity of the research model.
Table 3. Reliability and convergent validity of the research model.
ItemsFactor LoadCronbach’s Alpha CoefficientCumulative Explained Variance (%)CFA Fitting ResultsStandardization CoefficientsCRAVE
ATTATT10.8650.76020.536 0.8650.8410.639
ATT20.7820.782
ATT30.7460.746
ANSN10.7880.81245.695 0.7880.8510.589
SN20.7730.773
SN30.7660.766
SN40.7410.741
PBCPBC10.6940.71065.343 0.6940.8070.583
PBC20.7990.799
PBC30.7930.793
CACCAC10.7350.837 PCMIN/DF = 2.624
RMSEA = 0.043
CFI = 0.998
GFI = 0.986
NFI = 0.981		0.7350.8810.554
CAC20.730.73
CAC30.6810.681
CAC40.7690.769
CAC50.8480.848
CAC60.6920.692
INTINT10.7120.781 PCMIN/DF = 1.430
RMSEA = 0.032
CFI = 0.994
GFI = 0.991
NFI = 0.981		0.7120.8520.49
INT20.600 0.600
INT30.6880.688
INT40.6950.695
INT50.7370.737
INT60.7580.758
SSOSSO10.7510.68627.088 0.7510.8010.572
SSO20.7410.741
SSO30.7770.777
SSQSSQ10.8170.70657.075 0.8170.7950.496
SSQ20.5750.575
SSQ30.7060.706
SSQ40.6980.698
Table 4. Summary of hypothesis testing.
Table 4. Summary of hypothesis testing.
HypothesisPathEstimatepS.E.C.R.Results
H1ATT→INT0.0940.0980.4741.654No
H2SN→INT0.428***0.5176.225Yes
H3PBC→INT0.162*0.4262.410Yes
H4INT→Behavior0.395***0.0058.483Yes
H5CAC→INT−0.183**0.465−3.243Yes
H6CAC→Behavior−0.200**0.053−3.179Yes
H7SSO→INT0.0340.6810.7500.412No
H8SSO→Behavior0.0950.2570.0781.135No
H9SSQ→INT0.315***0.7443.473Yes
H10SSQ→Behavior−0.1300.1670.079−1.381No
Note: * p < 0.05, ** p < 0.01, *** p < 0.001.
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Wang, K.; Qi, L.; Yang, S.; Wang, C.; Zhou, R.; Liu, J. Towards Sustainable Mobility: Factors Influencing the Intention to Use Ride-Sharing in the Post-Pandemic Era. Sustainability 2025, 17, 8343. https://doi.org/10.3390/su17188343

AMA Style

Wang K, Qi L, Yang S, Wang C, Zhou R, Liu J. Towards Sustainable Mobility: Factors Influencing the Intention to Use Ride-Sharing in the Post-Pandemic Era. Sustainability. 2025; 17(18):8343. https://doi.org/10.3390/su17188343

Chicago/Turabian Style

Wang, Kun, Linfeng Qi, Shuo Yang, Cheng Wang, Rensu Zhou, and Jing Liu. 2025. "Towards Sustainable Mobility: Factors Influencing the Intention to Use Ride-Sharing in the Post-Pandemic Era" Sustainability 17, no. 18: 8343. https://doi.org/10.3390/su17188343

APA Style

Wang, K., Qi, L., Yang, S., Wang, C., Zhou, R., & Liu, J. (2025). Towards Sustainable Mobility: Factors Influencing the Intention to Use Ride-Sharing in the Post-Pandemic Era. Sustainability, 17(18), 8343. https://doi.org/10.3390/su17188343

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