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A structural equation modeling approach to understand users' perceptions of app-based CNG auto-rickshaw services in Dhaka City

Abstract

The use of CNG auto-rickshaws is increasing in Dhaka, the capital of Bangladesh. CNG auto rickshaws are a crucial component of the transportation system of Dhaka City. These offer the dwellers a quick and affordable way to navigate the city’s congested streets. This study aims to determine how commuters in Dhaka City perceive the quality of app-based CNG auto rickshaw service. A structured questionnaire is developed based on the users’ reported attitudes and perceived risks. In order to comprehend how users feel about an app-based CNG auto rickshaw service, collected data were used to create a Structural Equation (SE) model. The responses are also evaluated using the Relative Importance Index (RII) method, which validates the SE model. The study shows that women’s safety and better safety experience are the most significant parameters while accepting app-based CNG auto rickshaw services. The results of this study will help the service providers involved in this operation better understand the best course of action to be taken in order to provide better service to commuters.

Introduction

The exponential expansion of the population in Dhaka City, with the escalating traffic congestion caused by unregulated vehicle density, underscores the imperative of providing adequate mobility facilities to accommodate the substantial populace. The current traffic situation calls for an additional supply of public transportation in Dhaka City. However, the unreliability of the existing public transport system can be substantiated by several factors, including overcrowding, mixing of motorized and non-motorized transport, longer waiting and travel times, insufficient stoppage, and inadequate safety measures. Regarding the safety and security conditions, the three main reasons for the dissatisfaction of passengers are unsafe driving practices, poor boarding and alighting facilities, and lack of law enforcement agencies surveillance. Reasons which make the service unsatisfactory include irregular service provided by buses, regular overcrowding, lack of good quality buses and lack of cleanliness. The occurrence of traffic suspensions, discomfort, inadequate traffic monitoring, overcrowding, a high incidence of accidents, and air pollution problems can characterize the state of public transportation in Dhaka [1]. Moreover, the Public transit environment is not safe for women as they face sexual harassment and security threats often in Public transit [2, 3].

Current service quality scenario

In any service user perception about the quality factors is really crucial. The level of service quality directly influences the level of satisfaction among commuters and bus users. Quality of Services has a great influence on the usability and attractiveness of transport modes [4]. The quality of service has a strong and direct correlation with the perceived ease of use, particularly in terms of efficiency [5]. Over the period, many researchers identified crucial service quality factors of different transportation mediums and transportation infrastructures (i.e., Public buses, railway services, ride-sharing services, sidewalks, and foot-over bridges) [6] –[7]. Better service quality can contribute to the customer satisfaction [8]. Pasharibu et al. (2018) explored that service quality and trust significantly affected customer satisfaction [9]. Wong and Szeto, (2018) identified the service quality of any ride-sharing service as unique and hybrid as it involves not only transportation medium service quality but also the quality attained from mobile app services [10]. The capital of Bangladesh, Dhaka, is seeing a high land use pattern due to the expanding economy, which results in a huge number of trips [11]. In the current globalization scenario, the provision of public transport services requires reflective sensitivity to the SQ provided. Consequently, the examination of SQ has emerged as a crucial matter in contemporary research on public transport [12]. Researchers have measured the SQ of public transport systems using a variety of approaches over the years. Among these, customer satisfaction surveys—which are typically conducted using a questionnaire—are the method most frequently employed to find out how customers view the services [13]. According to studies, there are roughly 75 million cars on Dhaka’s roads, with a road density of 65 km per 100 sq km. When motorized and non-motorized vehicles (NMV) share a road, the situation becomes more critical due to the distinct pattern of modes. Numerous studies demonstrated how users were satisfied when appealing public transport was developed. The average speed of passenger cars in Dhaka is therefore between 15 and 20 km/h. The absence of dependable public bus services and mass rapid transit increases travel times significantly [14]. Furthermore, a lack of appropriate planning techniques is evident in the 10% yearly increase in new cars that must fit into the same amount of space on the road. For most passengers in Dhaka, buses are the main mode of transportation, but the quality of the service is subpar. Despite being an essential component of any urban area, buses usually don't offer enough services to meet demand. Furthermore, the majority of the facilities are underutilised. 75% of comments in a Bashar et al. (2022) study on customer satisfaction with Dhaka City's bus service were negative or extremely negative, while 25% were positive or good [15]. In addition, the buses are not suitable, and the service is unsafe, especially for female passengers at night [16]. Rahman et al. (2020) identified factors influencing the service quality perceptions of road users including cost, time, waiting, comfort, and availability [11]. Service quality analysis conducted by Silalahi et al. (2017) found multiple components that influence the perception of E-platform transportation facility users. perceived cognitive, perceived website innovativeness, ease of use, billing, valence, accessibility, and reliability/fulfillment were included in them [17]. Jahan (2019) explored that speed, cost, and convenience of ride influence the level of customer satisfaction of shared mobility services [18]. The main characteristics that have the greatest positive impact on the quality of shared mobility services, such as car sharing, ride hailing, and bike sharing, are comfort, cost savings, and time savings [19]. User perception of the service quality for intelligent transportation facilities was satisfactory in Taiwan [20]. Rahman, (2022) analyzed the service quality of paratransit system from female perspective and found that women who use paratransit in Dhaka City categorize safety performance movement flexibility ,expenditure and quality of drivers are the key factors that influence their perception [21]. GITHUI et al. (2009) identified level of satisfaction significantly influenced by the fare collection system [22]. Using SEM to understand the user perception, Kumar et al. (2022) found reliability is the most critical factor that influencing that influence service quality of ride-hailing service and safety is the impactful factor influence ride-sharing service quality [23].

A large portion of the population shows a propensity to use private transport (cars, motorbikes, CNG auto-rickshaws, etc.), and public transport such as buses, trams, and BRT. for their daily commute. Most people in Mega cities depend on car trips [24]. Speed, comfort, convenience, and freedom are the key factors influencing people to use private transport [18] –[20]... Additionally, Dhaka’s citizens face problems of not having a personal car, where quality public transport facilities are also very fragile [25]. The living standards and well-being of individuals who do not possess an automobile experience a deterioration [26, 27]. Simultaneously rapid propagation of private cars or transport also warrants massive gridlocks. Twenty percent of private car commuters to public transport results in a 10.6% rise in average traffic speed and a 12.8% decrease in average travel delay [11]. However, a modal shift of 40% leads to a 21.0% gain in speed and a 23.2% reduction in trip delay. As a result, there exists a possibility for savings of 1.41 and 2.57 million crucial work hours. The present public transportation condition is the fundamental hindrance behind the shifting not being applicable. So, policymakers’ primary concern is to provide sustainable transportation facilities that will provide convenience and safety and mitigate the current traffic conditions. Moreover, whether it is a public or private transportation system is considered the most significant source of pollution in a Country. Vehicular ownership exacerbates vehicular emissions [28].

Motor vehicles account for extreme SO2 and NOx emissions in Dhaka City [29, 30]. Under the air quality improvement management project (AQIM), to improve air quality in Dhaka BD, the government banned two-stroke three-wheelers and replaced them with four-stroke and compressed natural gas (CNG) engines in 2002. Until then, CNG-powered vehicles are emerging as one of Dhaka’s most reliable, emission-free, and affordable private transport facilities. Other factors CNG-powered vehicles are prevalent in Bangladesh are their speed, size, and availability of fuel. Consequently, the air quality visibly improved in the capital of Bangladesh, Dhaka, after implementing programs switching the engines of three-wheeled taxis (auto-rickshaws) from two-stroke to four-stroke and increasing the use of CNG [30] [31]. CNG conversion prevented more than 2000 premature deaths, resulting in a saving of around USD 400 million [32]. Environmental benefits of transformation ultimately compensate for the congestion cost. Still, CNG-powered vehicles have some demerits, such as “drivers not using meters, unjustified fares for short trips, longer waiting lines in front of pumps, and no parking places for these vehicles. Considering the effectiveness and good reputation among all socio-economic class different application-based Companies include CNG-powered into their Services. App-based service is a contemporary service that utilizes smartphone technology to facilitate real-time ride pooling, with distinct roles of drivers and passengers [33]. App-based companies extending their services in Dhaka City include Uber, Pathao, Amarbike, Taxiwala, Garivara, Chalo, Sohoj Ride, Obhai, Obon, and others. The service of these companies mainly involves the best possible usage of several modes of transportation, typically comprising cars, bikes, and CNG-powered three-wheeler vehicles. The transportation market is witnessing a rise with the emergence of various app-based transport services [34]. App-based services are gradually becoming prominent in low- and middle-income countries with insufficient public transportation investment [35]. App-based services save a lot of consumer time by instantly sending idle vehicles to customers in need [36]. Motorcycles are one of Dhaka City’s predominant modes of transportation for app-based purposes. However, users often avoid them due to their susceptibility to accidents and concerns regarding the safety of women and children. Considering the socio-demographic conditions of people, CNG-powered vehicles have more affordability than cars. The app-based service exhibits the quality of accessibility, interactivity, informativeness, and effectiveness adjusted to the user’s particular needs [37]. In the context of an app-based service, passengers are provided convenient access to CNG-powered auto-rickshaws based on their preferences. When the projected arrival time exceeds the user’s anticipated timeframe, they can cancel their trip and interact with other available drivers. Before embarking on the journey, individuals can acquire essential details regarding drivers that offer adequate safety. During the trip, passengers can track their whereabouts through GPS and send updates to friends and families. After the trip, ratings can be given to the drivers. The advantages of app-based service include savings in travel costs for the passengers and increased total revenue for drivers, reduced travel time, mitigation of traffic congestion, fuel conservation, and decreased air pollution [38]. Public transit is unsuitable for disabled people, but disabled people take more trips using applications [39]. Unlike private transport, app-based CNGs may not face parking problems. Overall, app-based services can aid public transport effectively. In fact, many researchers stated that consumers have started preferring app-based services over other traditional forms of public transport [40]. The consideration of socioeconomic factors plays a part when selecting an app-based service. In this regard, a CNG-powered auto-rickshaw may be a viable choice for middle- and lower-income individuals. Moreover, app-based CNG auto-rickshaw services provide employment opportunities to a lot of people.

This study aims to analyze the service quality factors of app-based CNG vehicle services that influence the acceptance of the service. Service quality is perceived as an essential determinant of users’ travel demand. Over the period, many researchers identified crucial service quality factors of different transportation mediums and transportation infrastructures (i.e., Public buses, railway services, ride-sharing services, sidewalks, and foot-over bridges) [41] –[7]. Better service quality can contribute to the customer satisfaction [42] [9]. explored that service quality and trust significantly affected customer satisfaction. The service quality of any ride-sharing service is unique and hybrid as it involves not only transportation medium service quality but also the quality attained from mobile app services [10]. Previous studies identified different dimensions while analyzing app-based ride-sharing services’ service quality. But in the context of Dhaka City, it is necessary to examine the factors impacting the user’s choice of app-based CNG auto-rickshaw facilities. The outcomes of this study will provide a proper scenario of existing Service quality and user’s perception towards app-based CNG auto-rickshaw facilities to companies like Pathao, Uber, and Obhai. Overall, this study is expected to guide service providers in developing a sustainable, pollution-free, less congested transportation system.

Methods

The study was carried out using a two-step process. Data from the respondents are gathered in the first step using a questionnaire survey. SEM analysis estimates the proposed hypothesis framework in the second step. For this study, SEM and RII methods are adopted. The process of Structural Equation Model (SEM) estimation is recurrent and ultimately produces the best-fitted model, which supports the hypothesis. Numerous methods, including maximum likelihood (ML), weighted least squares (WLS), and generalized least squares (GLS), can be used to estimate structural equation models. The most widely used of these three approaches is machine learning. The complexity of the SEM, the sample size, the scale properties of the parameters, and various probability distribution assumptions all play a role in choosing the optimal SEM [43]. The model’s goodness of fit must be described using multiple steps in order to navigate the complexity of the SEM [44]. Any relationship between variables is not just an empirical association; rather, it is an informal connection. SEM is a highly adaptable technique that can be applied to a wide range of relationships, including direct (A→B), indirect (A→C→B), and interactive (A→C→B and D→C→E). To explain the connections between latent and observed variables, SEM employs path analysis [45]. The term “relative importance index” describes how well a variable predicts a result when used alone or with other variables in combination [46]. When the predictors are interrelated, indices frequently obtained by several regression analyses, the variance is not efficiently partitioned [47]. RII analyses aim to divide explained variation among numerous predictors to understand better each predictor’s involvement in a regression equation [48]. In essence, RII describes the contribution of each predictor to the explanation of the variance in the criterion [47]. In some cases, a variable may only account for a very small portion of the predictably observed variance but still be extremely important, and in other cases, it may account for a larger portion of the variance but only have limited practical applicability [49]. Importantly, RII analyses are more effective at showing the true impact of a given predictor than simple correlations or normalized regression coefficients [48].

Data collection and demographics of the respondents

Initially, we carried out a pilot survey of certain variables and reviewed earlier research. Pilot survey identifies particular research topics or questions that require investigation and improvement. A pilot survey is a data collection tool that uses forms for observations, interview protocols, and questionnaires. Typically, a pilot study’s sample size is smaller than the main study’s. Finding patterns, trends, or anomalies in the data is made easier with the use of pilot surveys. Additionally, it can shed light on how well the research tools work. Adjust and improve the research instruments, protocols, or other study design elements as needed in light of the feedback and data analysis. The initial opinions of CNG users were gathered for the pilot survey. The complete questionnaire was developed in response to survey feedback. From then on, we gathered information manually and through the online platform. The target respondents were the users of CNG auto-rickshaw in Dhaka City. Online platforms and direct distribution in person were used to gather a total of 157 data. We looked over the data once it was collected. Using the SEM and RII methods, it was also confirmed that the app-based CNG system was accepted. Seventeen Service Quality variables were used in total to create the intended SE model. The remaining 15 variables were observed, while the other 2 were latent variables that were not observed. After conducting extensive literature reviews and focus groups (FGDs) with professionals, scholars, and policy-makers, the variables listed below are discussed. According to a study, fit indices can be taken into consideration for a better hypothesis for more than 150 samples [50]. For assessing acceptance of the app-based CNG auto rickshaw system following steps are shown in Fig. 1.

Fig. 1
figure 1

Flowchart illustrating the process followed to conduct the research

According to Figs. 2 and 3, Most of the respondents are male, and most of them are in the age range of 20 to 30 years. So, the young generation is mostly using CNG-based automobiles for various purposes.

Fig. 2
figure 2

Gender of respondents

Fig. 3
figure 3

Age of respondents

Independent variables

In total, 15 variables have been used in this study. Variables have been identified from previous studies on CNG auto rickshaw service quality. Variables with their significance are presented in Table 1.

Table 1 App-based CNG auto-rickshaw variables

Fifteen variables have been incorporated into two latent variables; “Convenience (η1)” and “Safety and Security (η2)”. The variables are demonstrated in Table 2.

Table 2 Variables related to app-based CNG auto-rickshaw

Factor analysis

Data reduction is accomplished using factor analysis. Utilizing a minor factor to condense or summarize, a sizable number of variables must be considered [45]. In this study, the Kaiser-Meyer-Olkin test is used to assess the extent of scale unidimensionality in the collected data (Mathur and Dhulla, 2014). The Kaiser-Meyer-Olkin and Bartlett test was used to determine whether the data were adequate for factor analysis (Napitupulu, Abdel Kadar, and Kartika Jati, 2017). The results of the KMO and Bartlett tests in Table 3 demonstrate that the statistics are suitable for factor analysis.

Table 3 KMO and Bartlett’s test

A further reliability test is conducted by determining the Cronbach’s alpha test in Table 4.

Table 4 Cronbach’s alpha test

The Kaiser-Meyer-Olkin measure of sampling’s adequacy score of 0.737 shows that the built model is suitable for factor analysis.

The model demonstrates the relationships between the variables in terms of model fitness. Hooper et al. presented a method for calculating model fit indices Hooper et al. (2008). Absolute fit indices measure the degree to which a given model fits the sample data. The values of the CFI, NFI, RMSEA, and χ2/df are shown in Table 5.

Table 5 Fit indices of SE model

The fit indices in Table 5 demonstrate the model's excellent fit as well as compliance with all fit requirements.

Results and discussion

Results of SE model hypothesis testing

The two latent variables “convenience (1)” and “safety and security (2)” are used to introduce the structural model in Fig. 4. Eight exogenous factors are employed to calibrate the “convenience (η1)” latent variable and seven exogenous variables are employed to calibrate the “safety and security (η2)” latent variable. The latent variable “convenience (η1)” has the highest impact on the acceptance of app-based CNG auto rickshaw service in Dhaka City. The reader would note that the interestingly latent variable “convenience (η1)”, and “safety and security(η2)” positively influence each other. Table 6 displays the results of the respected path's hypothesis testing.

Fig. 4
figure 4

Path co-efficient outcome of structural equation model

Table 6 The verdicts of the esteemed path's hypothesis testing

From the best-fitted model in Fig. 4, it is identified that the observed variable, i.e., the ‘Time affordability (y5)’ variable with path co-efficient 3.11, is the most influencing among the eight convenience-related latent variables. The effectiveness and uptake of the app-based CNG service depend heavily on time affordability. For customer convenience, productivity, traffic reduction, cost savings, and environmental benefits, time affordability in app-based CNG services is essential. Additionally, it can give service providers useful information and a competitive advantage. In contrast, ‘Traditional process (y3)’ is the least significant variable with a path coefficient of 0.12. Long wait times, efficiency issues, safety issues, and a small geographic service area are among the drawbacks of traditional CNG procedures. App-based CNG services attempt to overcome these shortcomings by providing ease, flexibility, and enhanced user experiences, making them a more appealing choice for many CNG consumers. The best-fitted model also identified that ‘Better safety (y11)’ is the utmost influential variable through a path co-efficient of 2.55 among the seven observed variables, whereas ‘Possibilities of accident (y8)’ is the least influence with a path co-efficient of 1.00 from the “safety and security (η2)” latent variable. The acceptance of app-based CNG services depends on their safety. For these services to run smoothly and expand, it is not only morally right to ensure the safety of customers, employees, and the environment, but it is also legally and practically necessary. Accidents in app-based CNG services are undesirable and can have negative effects on operational effectiveness, reputation, and safety. Respondents think that app-based CNG services have fewer chances of accidents.

The output of the SE model is shown in Table 7, and most of the values of the path coefficients are positive. This means that raising a parameter by one unit will have a positive impact on the associated parameter and vice versa [50].

Table 7 Estimated value of different app-based CNG auto-rickshaw variables

Relative Importance Index

The means and standard deviations of each item, when used to determine the overall rank, are unsatisfactory, according to various research, because they don't take the interactions amid the components into account [69] –[70]. The factors that have been discovered are prioritized and the variables are categorized according to priority using the RII approach [4171].

$$\textrm{RII}=\frac{\sum W}{AXN}$$
(1)

Here,

W = Respondents’ estimates of each factor's importance

A = Highest weight

N = Sum of all respondents

The range of the RII value is 0 to 1. Carpooling system adoption is most strongly influenced by RII values close to 1 [70, 71]. Table 8 contains the results of the variables' RII analysis.

Table 8 Variable Ranking and RII Analysis Results

Comparison between SEM and RII analysis

SEM determines the salient characteristics by connecting the variables. The qualities are identified as the respondents’ feedback by RII analysis on the other hand. Rarely do the results of the RII and SEM analyses coincide. However, the service quality of the app-based CNG service is strongly influenced by those matching variables [72].

According to SEM and RII analysis, the ‘Women’s security (y10)’, and “Better safety (y11)” variable is found as the most weighty variable that influences the acceptance of app-based CNG services. The top five significant variables according to SEM and RII are shown in Fig. 5.

Fig. 5
figure 5

Utmost five momentous variables that impact the acceptance of app-based CNG auto-rickshaw service according to SEM and RII (radar chart)

Heterogeneity of the study

This study has been conducted using 157 responses. As the target responses are men and women, their perceptions vary from each other’s. The respondents’ heterogeneity has an impact on the app-based CNG service. The respondents do not perceive every variable in the same way. Respondents are presented as two diverse groups: women and men. It makes sense that the two diverse groups would view the adoption of app-based CNG services differently. As the main analytical technique for heterogeneity analysis, we favored SEM. The data we need is obtained by SEM, which looks into the correlation between the variables after analyzing each stakeholder in heterogeneity separately. To bolster the conclusions even more, we compared the previously mentioned outcomes with RII's analysis. Also, their perception is justified by re-evaluating the SE model. Their variation in perception is shown in Table 9.

Table 9 Perception variation towards app-based CNG variables by various respondents (with path co-efficient value)

Heterogenity-1 (men)

According to men’s perception, the service performance of app-based CNG is more concern than safety. To them, this service should be more easily accessible, time affordable, and less waiting charge. According to them, app-based CNG should be available whenever needed in a very short time. During traffic congestion, the waiting charge should be less as comparing the traffic conditions of Dhaka City. Despite this, men also believe that safety is also a concerning issue for them. They perceive that, if their personal information such as; bank account information and mobile banking information gets leaked then there is a possibility of deception. According to men’s perception, top 5 variables are shown in Table 10.

Table 10 Most significant variables according to men's perceptions

Heterogeneity-1 (women)

On the other side, according to women’s perception, the safety of app-based CNG is more concern than service performance. Women perceive that, while traveling at night, it’s a concern for them. They think their information such as; trip destination, number of passengers, and payment method are disclosed then there arises a safety concern. Women also have concerns about convenient online payment systems. Without a convenient payment system, there arise safety issues. According to women’s perception, the top 5 variables are shown in Table 11.

Table 11 Most significant variables according to women's perceptions

Conclusions

There are many different modes of transportation available in Bangladesh’s public transportation system, from more conventional choices like CNG auto-rickshaws and ridesharing services to more conventional ones like buses, rickshaws, and boats. Due to government incentives and concerns about the environment, CNG is now more widely used in Bangladesh as a cleaner and more environmentally responsible substitute for petrol and diesel. This study examines the acceptance and perceptions of app-based CNG auto rickshaw service in Dhaka City.

The developed structure equation model suggests that convenience in app-based CNG auto rickshaw service is preferable in terms of safety issues. App-based CNG auto rickshaw services are designed to offer a range of conveniences to users, such as simplifying the refueling process, saving time, and providing additional features that enhance the overall user experience.

Among the fifteen observed variables, women’s security (y10), and better safety (y11) are the most significant ones. It is crucial to prioritize women's security in app-based CNG services. For this service to succeed and endure, it is essential to ensure the safety and well-being of female passengers. Maintaining women's safety in CNG services encourages gender equality and makes sure that women have unrestricted access to transportation services. This covers defense against abuse, violence, theft, and other types of harm. If women feel unsafe, they may choose not to use CNG services, which can result in a significant loss of potential economic output. The use of these services may increase if their security is ensured. To ensure women’s safety in transport services, governments and regulatory bodies may impose legal requirements. The provision of such services requires that these obligations be fulfilled.

To make CNG auto rickshaws more acceptable to women and others, the government should verify the credibility of drivers by conducting in-depth background checks on them. For the purpose of thoroughly investigating those requesting CNG driver’s licenses, the government may work with law enforcement organizations and other private sector authorities. Verifying the authenticity of identity documents and looking up driving and criminal histories are all included in this. CNG drivers can receive standardized training and certification through programs established by the public sector in partnership with government agencies. In terms of safety procedures, traffic laws, and customer service, this guarantees that drivers are properly taught. The credibility of CNG drivers can be improved by putting digital identity verification systems in place. Driver identity verification could be verified through biometric verification, facial recognition software, or other safe techniques. Work together to incorporate data about CNG drivers into national databases. This may involve exchanging information in real-time about the status of driver’s licenses and background checks with law enforcement. Create a system wherein CNG drivers and vehicles are regularly inspected and audited. A combined task force made up of public sector representatives and government officials can handle this. Inspections may concentrate on driver compliance, emissions regulations, and vehicle safety. Provide safe and private data-sharing channels that facilitate easy communication between public sector organizations and government agencies. This guarantees that individuals in charge of monitoring and enforcement can access pertinent data regarding CNG drivers. Drivers should receive instructions on how to respect the space between them and their passengers. Offer receptive and trustworthy customer support to help passengers and drivers in case of issues or emergencies, and enforce strict policies against harassment and misconduct with clear consequences for offenders.

App-based CNG service providers can put in place a number of measures to improve safety, including vehicle inspections, real-time tracking, safety education, and training. The success and expansion of the service are ultimately impacted by their ability to produce a safer and more desirable transportation option for both drivers and passengers.

Despite these intriguing results, this study does have some drawbacks. The sample size is low. Although we use the normal distribution to sample the respondents, the SEM's accuracy will be increased by more exact sampling that makes use of census tract data. Further study can be performed using hard data.

Availability of data and materials

Readers should contact the corresponding author for details.

Abbreviations

CNG:

Compressed natural gas

ML:

Maximum likelihood

NFI:

Normal Fit Index

RII:

Relative Importance Index

RMSEA:

Root mean squared error of approximation

SEM:

Structural equation modeling

SE:

Structural equation

SPSS :

Statistical Package for Social Sciences

WLS:

Weighted least squares

References

  1. Mustaqeem MN, Jalaluddin F, Rakibul H (2018) Bus network coverage analysis of Dhaka City along with its service quality

    Google Scholar 

  2. Loukaitou-Sideris A (2014) Fear and safety in transit environments from the women’s perspective. Secur J 27(2):242–256. https://doi.org/10.1057/sj.2014.9

    Article  Google Scholar 

  3. Gekoski A, Gray JM, Adler JR, Horvath MAH (2017) The prevalence and nature of sexual harassment and assault against women and girls on public transport: an international review. J Criminol Res Policy Pract 3(1):3–16. https://doi.org/10.1108/JCRPP-08-2016-0016

    Article  Google Scholar 

  4. Said H (2002) Evaluation method of service quality parameters in transport networks. In: Proceedings of the 5th Biannual World Automation Congress. TSI Press, Orlando, FL, USA, pp 21–26. https://doi.org/10.1109/WAC.2002.1049415

    Chapter  Google Scholar 

  5. Guirao B, García-Pastor A, López-Lambas ME (2016) The importance of service quality attributes in public transportation: narrowing the gap between scientific research and practitioners’ needs. Transp Policy 49:68–77. https://doi.org/10.1016/j.tranpol.2016.04.003

    Article  Google Scholar 

  6. Jahan MI, Mazumdar AAB, Hadiuzzaman M, Mashrur SM, Murshed MN (2020) Analyzing service quality of pedestrian sidewalks under mixed traffic condition considering latent variables. J Urban Plan Dev 146(2):04020011. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000563

    Article  Google Scholar 

  7. Rouf KBA, Hossain DM, Hossain M (2019) Female passengers’ perception on the service quality of public bus services: an exploratory study on Dhaka City, Bangladesh. Int J Gend Stud Dev Soc 3(2):99. https://doi.org/10.1504/IJGSDS.2019.102149

    Article  Google Scholar 

  8. Lee S, Lee WJ, Yoo K-H (2020) Millennial ride-share passengers’ pro-sustainable behaviors: norm activation perspective. Asia Pac J Tour Res 25(1):15–26. https://doi.org/10.1080/10941665.2019.1578811

    Article  Google Scholar 

  9. Pasharibu Y, Paramita EL, Febrianto S (2018) Price, service quality and trust on online transportation towards customer satisfaction. J Ekon Dan Bisnis 21(2):241–266. https://doi.org/10.24914/jeb.v21i2.1965

    Article  Google Scholar 

  10. Wong RCP, Szeto WY (2018) An alternative methodology for evaluating the service quality of urban taxis. Transp Policy 69:132–140. https://doi.org/10.1016/j.tranpol.2018.05.016

    Article  Google Scholar 

  11. Rahman MM, Jahan J, Zhou Y (2020) Alleviating traffic congestion by the strategy of modal shift from private cars to public transports: a case of Dhaka City, Bangladesh, in Proceedings of the 2018 Conference of the Computational Social Science Society of the Americas. In: Carmichael T, Yang Z (eds) Springer Proceedings in Complexity. Cham: Springer International Publishing, pp 101–115. https://doi.org/10.1007/978-3-030-35902-7_7

    Chapter  Google Scholar 

  12. De Oña J, De Oña R, Eboli L, Mazzulla G (2013) Perceived service quality in bus transit service: A structural equation approach. Transp Policy 29:219–226. https://link.springer.com/chapter/10.1007/978-3-030-35902-7_7

    Article  Google Scholar 

  13. Parasuraman A, Zeithaml VA, Berry LL (1985) A conceptual model of service quality and its implications for future research. J Mark 49(4):41–50. https://doi.org/10.1177/002224298504900403

    Article  Google Scholar 

  14. Rahman MS-U, Nahrin K (2012) Bus services in Dhaka City - users’ experiences and opinions. J Bangladesh Inst Plan 5:14

    Google Scholar 

  15. Bashar MA et al (2022) Assessment of the satisfaction of Bus passengers in Dhaka, Bangladesh. Int Res J Eng Appl Sci 10(2):01–08. https://doi.org/10.55083/irjeas.2022.v10i02003

    Article  ADS  Google Scholar 

  16. Subah SK, Tasnim R, Jahan MI, Islam MR (2022) Assessing bus service quality in Dhaka City from the perspective of female passengers, in Advances in Civil Engineering, vol. 184. In: Arthur S, Saitoh M, Pal SK (eds) Lecture Notes in Civil Engineering, vol. 184. Springer, Singapore, pp 339–350. https://doi.org/10.1007/978-981-16-5547-0_32

    Chapter  Google Scholar 

  17. Silalahi SL, Handayani PW, Munajat Q (2017) Service quality analysis for online transportation services: case study of GO-JEK. Procedia Comput Sci 124:487–495. https://doi.org/10.1016/j.procs.2017.12.181

    Article  Google Scholar 

  18. Jahan M (2019) Factors affecting customer satisfaction of the ride-sharing industry in Bangladesh. Bus Ethics Leadersh 3(4):74–80. https://doi.org/10.21272/bel.3(4).74-80.2019

    Article  Google Scholar 

  19. Tyrinopoulos Y, Antoniou C (2020) Review of factors affecting transportation systems adoption and satisfaction. In: Demand for Emerging Transportation Systems. Elsevier, pp 11–36. https://doi.org/10.1016/B978-0-12-815018-4.00002-4

    Chapter  Google Scholar 

  20. Jao-Hong C, Chien Yuan L, Huei-Ping C, Chun-Liang O (2010) The service quality analysis of public transportation system using PZB model–Dynamic bus information system. In: The 40th International Conference on Computers & Indutrial Engineering. IEEE, Awaji City, Japan, pp 1–5. https://doi.org/10.1109/ICCIE.2010.5668302

    Chapter  Google Scholar 

  21. Rahman F (2022) Exploring paratransit service quality based on low-income women’s perception: a case study in Dhaka city by structural equation model (SEM). IATSS Res 46(2):181–192. https://doi.org/10.1016/j.iatssr.2021.11.009

    Article  Google Scholar 

  22. Githui JN, Okamura T, Nakamura F (2009) The structure of users’ satisfaction on urban public transport service in developing country: the case of Nairobi. Eastern Asia Society for Transportation Studies. https://doi.org/10.11175/eastpro.2009.0.232.0

    Book  Google Scholar 

  23. Kumar A, Gupta A, Parida M, Chauhan V (2022) Service quality assessment of ride-sourcing services: A distinction between ride-hailing and ride-sharing services. Transp Policy 127:61–79. https://doi.org/10.1016/j.tranpol.2022.08.013

    Article  Google Scholar 

  24. Anable J (2005) ‘Complacent car addicts’ or ‘aspiring environmentalists’? Identifying travel behaviour segments using attitude theory. Transp Policy 12(1):65–78. https://doi.org/10.1016/j.tranpol.2004.11.004

    Article  Google Scholar 

  25. Andaleeb S, Haq M, Ahmed R (2007) Reforming innercity bus transportation in a developing country: a passenger-driven model. J Public Transp 10(1):1–25. https://doi.org/10.5038/2375-0901.10.1.1

    Article  Google Scholar 

  26. Bostock L (2001) Pathways of disadvantage? Walking as a mode of transport among low-income mothers: Walking as a mode of transport among low-income mothers. Health Soc Care Community 9(1):11–18. https://doi.org/10.1046/j.1365-2524.2001.00275.x

    Article  CAS  PubMed  Google Scholar 

  27. Smith GD, Egger M (1992) Socioeconomic differences in mortality in Britain and the United States. Am J Public Health 82(8):1079–1081. https://doi.org/10.2105/AJPH.82.8.1079

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Colvile RN, Hutchinson EJ, Mindell JS, Warren RF (2001) The transport sector as a source of air pollution. Atmos Environ 35(9):1537–1565. https://doi.org/10.1016/S1352-2310(00)00551-3

    Article  CAS  ADS  Google Scholar 

  29. Azad AK, Kitada T (1998) Characteristics of the air pollution in the city of Dhaka, Bangladesh in winter. Atmos Environ 32(11):1991–2005. https://doi.org/10.1016/S1352-2310(97)00508-6

    Article  CAS  ADS  Google Scholar 

  30. Begum BA, Hopke PK (2018) Ambient air quality in Dhaka Bangladesh over two decades: impacts of policy on air quality. Aerosol Air Qual Res 18(7):1910–1920. https://doi.org/10.4209/aaqr.2017.11.0465

    Article  CAS  Google Scholar 

  31. Hossain I, Gülen G (2007) Lifecycle analysis of different urban transport options for Bangladesh. Energy Policy 35(10):4909–4918. https://doi.org/10.1016/j.enpol.2007.04.036

    Article  Google Scholar 

  32. Wadud Z, Khan T (2013) Air quality and climate impacts due to cng conversion of motor vehicles in Dhaka, Bangladesh. Environ Sci Technol 47(24):13907–13916. https://doi.org/10.1021/es402338b

    Article  CAS  PubMed  ADS  Google Scholar 

  33. Geisberger R, Luxen D, Neubauer S, Sanders P, Volker L (2010) Fast detour computation for ride sharing, p 12. https://doi.org/10.4230/OASICS.ATMOS.2010.88

    Book  Google Scholar 

  34. Teubner T, Flath CM (2015) The economics of multi-hop ride sharing: creating new mobility networks through IS. Bus Inf Syst Eng 57(5):311–324. https://doi.org/10.1007/s12599-015-0396-y

    Article  Google Scholar 

  35. Nguyen-Phuoc DQ, Vo NS, Su DN, Nguyen VH, Oviedo-Trespalacios O (2021) What makes passengers continue using and talking positively about ride-hailing services? The role of the booking app and post-booking service quality. Transp Res Part Policy Pract 150:367–384. https://doi.org/10.1016/j.tra.2021.06.013

    Article  Google Scholar 

  36. Shen J, Qiu F, Li W, Feng P (2015) Exploring the effect of the telephone/online booking system on taxi service: case study of Suzhou City in China. In: CICTP 2015. American Society of Civil Engineers, Beijing, China, pp 1201–1212. https://doi.org/10.1061/9780784479292.111

    Chapter  Google Scholar 

  37. Lee S(A) (2018) Enhancing customers’ continued mobile app use in the service industry. J Serv Mark 32(6):680–691. https://doi.org/10.1108/JSM-01-2017-0015

    Article  Google Scholar 

  38. Furuhata M, Dessouky M, Ordóñez F, Brunet M-E, Wang X, Koenig S (2013) Ridesharing: the state-of-the-art and future directions. Transp Res Part B Methodol 57:28–46. https://doi.org/10.1016/j.trb.2013.08.012

    Article  Google Scholar 

  39. Cochran AL, Chatman DG (2021) Use of app-based ridehailing services and conventional taxicabs by adults with disabilities. Travel Behav Soc 24:124–131. https://doi.org/10.1016/j.tbs.2021.02.004

    Article  Google Scholar 

  40. Hall JV, Krueger AB (2018) An analysis of the labor market for uber’s driver-partners in the United States. ILR Rev 71(3):705–732. https://doi.org/10.1177/0019793917717222

    Article  Google Scholar 

  41. Sourav MMI, Afnanin S, Islam MR (2023) Impact of Padma Bridge on transportation system: a case study for Barisal Division. J Eng Appl Sci 70(1):125. https://doi.org/10.1186/s44147-023-00299-1

  42. Lee H, Lee Y, Yoo D (2000) The determinants of perceived service quality and its relationship with satisfaction. J Serv Mark 14(3):217–231. https://doi.org/10.1108/08876040010327220

    Article  Google Scholar 

  43. Golob TF (2003) Structural equation modeling for travel behavior research. Transp Res Part B Methodol 37(1):1–25. https://doi.org/10.1016/S0191-2615(01)00046-7

    Article  Google Scholar 

  44. Hooper D, Coughlan J, Mullen MR (2008) Structural equation modelling: guide- lines for determining model fit. Electron J Bus Res Meth 6(1):53–60

    Google Scholar 

  45. Sourav MMI, Islam MN, Abeer AA (2022) Identifying the key attributes of fresh water crisis in Dhaka city: a structural equation modeling approach. Water Pract Technol 17(10):2113–2124. https://doi.org/10.2166/wpt.2022.126

    Article  Google Scholar 

  46. Johnson JW, Lebreton JM (2004) History and use of relative importance indices in organizational research. Organ Res Methods 7(3):238–257. https://doi.org/10.1177/1094428104266510

    Article  Google Scholar 

  47. Darlington RB (1968) Multiple regression in psychological research and practice. Psychol Bull 69(3):161–182. https://doi.org/10.1037/h0025471

    Article  CAS  PubMed  Google Scholar 

  48. Tonidandel S, LeBreton JM (2011) Relative importance analysis: a useful supplement to regression analysis. J Bus Psychol 26(1):1–9. https://doi.org/10.1007/s10869-010-9204-3

    Article  Google Scholar 

  49. Martell RF, Lane DM, Emrich C (1996) Male-female differences: a computer simulation. Am Psychol 51(2):157–158. https://doi.org/10.1037/0003-066X.51.2.157

    Article  Google Scholar 

  50. Awang Z (2012) A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic, 5th edn. Universiti Teknologi Mara Kelantan, Kota Baru Available: https://www.scirp.org/%28S%28351jmbntvnsjt1aadkozje%29%29/reference/ReferencesPapers.aspx?ReferenceID=2947746

    Google Scholar 

  51. Islam MM, Hossain AF, Abbas SM, Silvy S, Hasan MS (2020) A study on impacts, construction challenges and overcomes of Padma multipurpose bridge, Bangladesh. In 5th International Conference on Civil Engineering for Sustainable Development, ICCESD (pp. 7-9)

  52. Rahman MH, Sadeek SN, Ahmed A, Rifaat SM, Abrar M (2021) Effect of socio-economic and demographic factors on ride-sourcing services in Dhaka City, Bangladesh. Transp Res Interdiscip Perspect 12:100492. https://doi.org/10.1016/j.trip.2021.100492

    Article  Google Scholar 

  53. Shah P, Varghese V, Jana A, Mathew T (2020) Analysing the ride sharing behaviour in ICT based cab services: a case of Mumbai, India. Transp Res Procedia 48:233–246. https://doi.org/10.1016/j.trpro.2020.08.018

    Article  Google Scholar 

  54. Joewono TB, Kubota H (2007) User satisfaction with paratransit in competition with motorization in indonesia: anticipation of future implications. Transportation 34(3):337–354. https://doi.org/10.1007/s11116-007-9119-7

    Article  Google Scholar 

  55. Noor F (2019) Factors affecting the holistic perception of Uber in Dhaka City transportation. Daffodil International University Journal of Business and Entrepreneurship 12:42–49

    Google Scholar 

  56. Li X, Hu S, Fan W, Deng K (2018) Modeling an enhanced ridesharing system with meet points and time windows. PLoS One 13(5):e0195927. https://doi.org/10.1371/journal.pone.0195927

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Alvarez F, Argente D (2020) On the effects of the availability of means of payments: the case of Uber. National Bureau of Economic Research, Cambridge, MA. https://doi.org/10.3386/w28145

    Book  Google Scholar 

  58. Liu T, Vergara-Cobos E, Zhou Y (2019) Pricing schemes and seller fraud: evidence from New York City Taxi Rides. J Ind Econ 67(1):56–90. https://doi.org/10.1111/joie.12196

    Article  Google Scholar 

  59. Chaube V, Kavanaugh AL, Perez-Quinones MA (2010) Leveraging social networks to embed trust in rideshare programs. In: 2010 43rd Hawaii International Conference on System Sciences. IEEE, Honolulu, Hawaii, USA, pp 1–8. https://doi.org/10.1109/HICSS.2010.255

    Chapter  Google Scholar 

  60. Chaudhry B, Yasar A-U-H, El-Amine S, Shakshuki E (2018) Passenger safety in ride-sharing services. Procedia Comput Sci 130:1044–1050. https://doi.org/10.1016/j.procs.2018.04.146

    Article  Google Scholar 

  61. Young M, Farber S (2019) The who, why, and when of Uber and other ride-hailing trips: An examination of a large sample household travel survey. Transp Res Part Policy Pract 119:383–392. https://doi.org/10.1016/j.tra.2018.11.018

    Article  Google Scholar 

  62. Hoque MM, Saumi BH (2022) Sharing economy services in Dhaka: a change towards women’s perception of commuting. Rajagiri Manag J 16(2):144–163. https://doi.org/10.1108/RAMJ-08-2020-0048

    Article  Google Scholar 

  63. Berneking M et al (2018) The risk of fatigue and sleepiness in the ridesharing industry: an American Academy of Sleep Medicine Position Statement. J Clin Sleep Med 14(04):683–685. https://doi.org/10.5664/jcsm.7072

    Article  PubMed  PubMed Central  Google Scholar 

  64. Yang H, Liang Y, Yang L (2021) Equitable? Exploring ridesourcing waiting time and its determinants. Transp Res Part Transp Environ 93:102774. https://doi.org/10.1016/j.trd.2021.102774

    Article  Google Scholar 

  65. Flohr LA, Schuß M, Wallach DP, Krüger A, Riener A (2023) Designing for passengers’ information needs on fellow travelers: a comparison of day and night rides in shared automated vehicles. https://doi.org/10.48550/ARXIV.2308.02616

  66. Park J, Kim J, Pang M-S, Lee B (2017) Offender or guardian? An empirical analysis of ride-sharing and sexual assault. SSRN Electron J. https://doi.org/10.2139/ssrn.2951138

  67. Joseph S, Dyer C, Coolican H (2005) What does p<.05 mean? Couns Psychother Res 5(2):105–106. https://doi.org/10.1080/17441690500258230

    Article  Google Scholar 

  68. ULFY MARIJE (2020) Examining the antecedents of using ride-hailing services: a study in Dhaka City of Bangladesh. ASIAN J Multidiscip Stud https://www.academia.edu/43515834/Examining_the_Antecedents_of_Using_Ride_Hailing_Services_A_Study_in_Dhaka_City_of_Bangladesh Accessed 19 Jan 2022

  69. Assaf SA, Al-Khalil M, Al-Hazmi M (1995) Causes of delay in large building construction projects. J Manag Eng 11(2):45–50. https://doi.org/10.1061/(ASCE)0742-597X(1995)11:2(45)

    Article  Google Scholar 

  70. Faridi AS, El-Sayegh SM (2006) Significant factors causing delay in the UAE construction industry. Constr Manag Econ 24(11):1167–1176. https://doi.org/10.1080/01446190600827033

    Article  Google Scholar 

  71. Hoque MI, Safayet MA, Rana MJ, Bhuiyan AY, Quraishy GS (2021) Analysis of construction delay for delivering quality project in Bangladesh. Int J Build Pathol Adapt 41(2):401–421. https://doi.org/10.1108/IJBPA-03-2021-0032

    Article  Google Scholar 

  72. Foroutan Mirhosseini A, Pitera K, Odeck J, Welde M (2022) Sustainable project management: reducing the risk of cost inaccuracy using a PLS-SEM approach. Sustainability 14(2):960. https://doi.org/10.3390/su14020960

    Article  Google Scholar 

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Acknowledgements

We want to express our gratitude to each and every respondent who helped us with the data collection. We express our gratitude to the instructors of the Military Institute of Science and Technology’s (MIST) Department of Civil Engineering for their inspiration.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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TJ and MS prepared the questionnaire, collected data, and analyzed the data. MM has also contributed to conceptualization, data collection, writing, and conceptualization. SA has also contributed to its data collection, writing, and reviewing. SM contributed by providing valuable feedback, revising the manuscript, and approving the final version for submission. All authors read and approved the final manuscript.

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Correspondence to Md. Mohaimenul Islam Sourav.

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Salehin, M.S., Jubailee, T., Sourav, M.M.I. et al. A structural equation modeling approach to understand users' perceptions of app-based CNG auto-rickshaw services in Dhaka City. J. Eng. Appl. Sci. 71, 32 (2024). https://doi.org/10.1186/s44147-024-00368-z

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