Should I Travel by Metro? Analyzing the Service Quality Perception of Elderly and Physically Disabled Passengers in Delhi,India

Abstract

Transit officials worldwide continuously learn from passenger perception surveys to improve service quality and enhance transit ridership. However, many elderly and physically disabled (E&PD) passengers are transit users with special needs whose service requirements have received insufficient attention in the past. In fact, providing satisfactory transit services to E&PD passengers is often a critical challenge for transit officials, since a theory of interrelations among service factors is not yet established from E&PD passengers’ perspectives. Therefore, the present study aims to establish interrelations among service quality factors and identify service improvements for E&PD passengers using satisfaction data from 254 E&PD passengers of Delhi Metro. For this purpose, this study has employed an integrated Bayesian networks and partial least squares path modeling approach. The obtained results have shown some remarkable and meaningful interpretations. For instance, a one-unit increase in satisfaction with the “safety & security” factor would induce a 68% increase in their satisfaction with overall service quality. Besides, importance–performance map analysis showed that Delhi Metro must consider “safety and security,”“passenger ease,” and “seamless connectivity” as high-priority areas needing improvements to promote E&PD-friendly transit services. Overall, the findings of this study suggest several policy measures that would enrich the service quality and achieve universal design standards in metro rail transit systems.

Keywords

elderly physically disabled service quality public transport metro rail passenger satisfaction

Some elderly and physically disabled (E&PD) people need special mobility assistance; they may also have limited access to personal vehicles. For them, transit mobility may play a crucial role and help to maintain autonomy, manage self-care, and travel independently ( 1 ). As the share of elderly group (aged 60 and above) is growing rapidly worldwide, the need for elderly-friendly transit systems will be substantial ( 2 , 3 ). At the same time, the increased “universal design” transit regulations anticipate broader service provisions for physically disadvantaged passengers ( 4 , 5 ). More recently, providing “hassle-free” transit services has become an outstanding issue for many transit agencies in large cities ( 6 , 7 ). However, with the increased mobility concerns of E&PD passengers, transit agencies now realize that transit services (as commonly designed for typical passengers) have not reached a satisfactory level for this important group of riders ( 8 ). Therefore, transit agencies must focus on E&PD passengers, whose service requirements are yet understudied. In this case, special efforts should be made to deeply investigate the service quality of transit systems for providing seamless mobility of this vulnerable group.

Existing studies rely on passenger perceptions as a powerful tool for evaluating service quality and realizing service improvements of transit systems ( 8 – 10 ). These studies measure service quality as a function of several factors, such as comfort, convenience, availability, accessibility, information, customer service, reliability, safety and security, which can be derived from their corresponding attributes ( 11 – 13 ). Researchers have often investigated the interrelationships among service factors and their effects on overall service quality ( 12 – 14 ). They have found that each factor can have a direct or indirect (through other factors) effect on the overall service quality of transit systems ( 13 ). To explore these interrelationships, many studies have employed statistical methods such as regression analysis, the decision tree approach, and bivariate correlation analysis ( 12 , 15 ). However, findings based on these methods can limit the interpretations to direct relationships only ( 12 , 14 ). To overcome this limitation, recent studies have used structural equation modeling (SEM) to develop the theory of interrelationships among service factors (14 –16). Nevertheless, the application of the SEM method for exploring interrelations is questionable because of its assumptions of normality and homogeneity in perception data, which is impractical ( 13 , 17 , 18 ).

To address these above-mentioned gaps in the literature, the present study unveils interrelations among service quality factors using satisfaction data from 254 E&PD passengers of metro rail transit in Delhi, India. This study employed an integrated Bayesian networks and partial least squares (PLS) path modeling methodology to identify the influence of different service quality factors on overall service quality. This integrated methodology can handle heterogeneity and non-normality in perception data. Besides, an importance–performance map analysis was conducted to identify the service improvements of Delhi Metro and assist transit officials in serving barrier-free transit services to E&PD passengers. The findings of this study contribute to E&PD-related studies in three ways. Firstly, this study adds evidence to the service quality literature through reliable, sufficient, and comprehensive E&PD passenger satisfaction data for a metro of a high-density Indian city, Delhi, a context extremely different from existing studies. Secondly, this study establishes interrelations among service quality factors from E&PD passengers’ perspectives. As per the authors’ knowledge, no study has focused on service quality interrelations based on perceptions of E&PD passengers. Thirdly, this study presents a systematic analytical framework employing Bayesian networks and PLS path modeling, the results of which can be easily transferable to other cities in developing nations while identifying effective policy measures for E&PD passengers.

Literature Review

Transit service quality is best measured through the perceived performance of service quality attributes from passengers’ point of view ( 13 , 14 ). For the past two decades, numerous researchers have examined key service quality attributes/factors that influence the overall service quality of or overall satisfaction with public transit ( 15 – 18 ). For instance, Stuart et al. ( 14 ) found that service quality attributes, such as reliability, safety, and cleanliness, significantly influence overall satisfaction with transit services in New York. Dell’ Olio et al. ( 15 ) revealed that waiting time and vehicle occupancy are the most and least valued service attributes, respectively, among bus transit passengers of Santander, Spain. Similarly, Shen et al. ( 17 ) found that passengers expect improvements in services such as information distribution, equipment, and facilities to enhance the overall service quality of Suzhou rail transit in China. In India, Mandhani et al. ( 18 ) reported that daily passengers value passenger ease and amenities as the most and least influential factors, respectively, of overall service quality for Delhi Metro. Interestingly, the findings from many studies evidence that the degree and influence of different attributes/factors on overall service quality vary among contexts, transit systems, and individual characteristics ( 9 , 16 , 19 ).

Demographic characteristics of passengers induce them to perceive transit services differently ( 18 , 20 , 21 ). A plethora of studies showed distinction in perceptions of service quality for different passenger groups with respect to gender, age, income, travel frequency, or trip purpose ( 18 , 20 , 22 ). For instance, Iseki and Smart ( 20 ) found that occasional and low-income passengers of bus services in San Francisco are less concerned about safety during the daytime. In California, Barajas et al. ( 23 ) revealed that low-income immigrants consider affordability rather than crime as a major issue to travel by public transit. For light rail transit in Spain, de Oña et al. ( 21 ) unveiled that work-trip and education-trip passengers perceive regularity and fare as the most important service attributes explaining overall satisfaction. More recently, Mandhani et al. ( 18 ) found that male passengers value passenger ease, whereas female passengers value service availability the most in explaining the overall service quality of Delhi Metro. These studies highlighted that each passenger group has varying expectations, utilities, and perceptions of available transit services. However, very few studies have captured the service requirements and perceptions of E&PD passengers ( 5 , 7 , 24 ).

Among the few, Kerschner and Aizenberg ( 25 ) emphasized five A’s for elderly-friendly transit, namely, availability, accessibility, acceptability, affordability, and adaptability. Ritter et al. ( 26 ) identified that dissatisfaction with reliability, accessibility, availability, and safety factors were the main reasons for infrequent use of public transit among the elderly in the U.S.A. Besides, Burkhardt ( 24 ) unveiled that public transit in the U.S.A. needs significant improvement in service quality attributes such as on-time performance, staff attitude, cleanliness, and comfort, to increase elderly patronage. According to the Transport Department of Hong Kong, service quality attributes such as seat availability, comfort, and waiting time need to be improved for providing age-attuned transit systems ( 3 ). More recently, Yuan et al. ( 27 ) indicated that safety and security, convenience, and driver services are influential factors for elderly passengers of bus services in Harbin, China.

Further, O’Neill and O’Mahony ( 28 ) revealed that passengers with disabilities perceive the rail transit system as the most inaccessible one amongst all the public transit modes in Dublin, Ireland. Lubin and Deka ( 29 ) reported that disabled passengers value accessibility and travel-related information while commuting by public transit. Verbich and El Geneidy ( 5 ) highlighted that disabled passengers in London seek the presence of bus shelters most while valuing their overall satisfaction. Some researchers observed differences in perceptions of disabled groups based on their type of disability ( 7 , 30 ). Recently, transit agencies in developed nations such as the U.S.A., England, Germany, Sweden, Canada, and Japan have mandated guidelines and design standards for providing barrier-free transit systems, keeping E&PD passengers’ needs in mind ( 31 ). However, transit agencies in developing nations such as India are still in the process of drafting guidelines to provide E&PD-friendly transit systems.

A review of existing studies on service quality and E&PD have led to two important indications. Firstly, E&PD passengers are vulnerable users of public transit. Limited researchers have focused their work on unveiling service improvements of public transit for E&PD passengers. However, these studies are mostly based on perceptions of E&PD passengers for bus transit services in developed nations. For developing nations such as India, where transit shares of the elderly have been declining, it is paramount that transit agencies must examine the shortcomings in service aspects to retain E&PD passengers and attract new ones. Secondly, the majority of E&PD-related studies have reported limited evidence based on descriptive analysis ( 7 , 24, 28, 29), logistic regression ( 5 , 32 ), and ordered probit ( 3 ) models to determine the direct influence of specific service quality factors on overall service quality or overall satisfaction for E&PD passengers. For exploring and establishing interrelations among service quality factors from E&PD passengers’ perspective, a systematic, integrated, and comprehensive methodology is required.

Recent studies suggest the use of integrated Bayesian networks and PLS path modeling approach as a robust methodology to establish the interrelations among service quality factors ( 13 ). PLS path modeling is an attractive alternative to SEM in exploring interrelationships between observed and unobserved variables in a real-world scenario. Unlike traditional SEM, PLS path modeling can address the non-normal and heterogeneous nature of passenger perception data ( 33 ). Also, it provides efficient results with small sample sizes and is most preferred for exploratory analysis ( 18 ). Besides Bayesian networks, a data-mining approach complements PLS path modeling in learning and exploring the interrelations among influencing factors. Therefore, the present study employed an integrated Bayesian network with PLS path modeling as the most suitable methodology for exploring interrelations among transit service factors. Before implementing this integrated methodology, a confirmatory factor analysis (CFA) was used to test and confirm the dimension reduction of service attributes to service quality factors, as pre-defined by Mandhani et al. ( 13 , 18 ). CFA allows researchers to verify the researcher’s prior theory/knowledge on measurement properties of latent factors and their underlying observed variables (attributes).

Context and Survey Data

The present study chose Delhi Metro as a case study to analyze service quality perceptions of E&PD passengers. The metro rail transit system in Delhi is a travel lifeline for its citizens, carrying nearly 2.65 million passengers each day. Most passengers of Delhi Metro use a “smart card” for entry to/exit from the stations. This smart card facility saves time from the daily purchase of tickets and also charges 10% less on every journey made using tickets. However, it offers no special discounts for E&PD passengers. Besides, Delhi Metro is deemed to be one of the disabled-friendly transit systems in India through “universal and inclusive design” and its hassle-free services in and around transit stations for persons with disabilities ( 34 ). Most metro stations have E&PD-exclusive service provisions, such as lifts, ramps, tactile pathways, extra-wide automated flap gates for wheelchairs, handrails, disabled-friendly toilets, escalators, push buttons, and first-aid corners. For E&PD passengers, each six-coach train includes 66 reserved seats (11 seats a coach). In addition, each station has dedicated staff for providing personal assistance to help the needy in reducing travel difficulties while boarding the train. Despite all these efforts, recent statistics indicate that only 2% of metro users were E&PD passengers in 2017, and this proportion has seen a gradual decrease from 6% in 2011 ( 34 ). Therefore, it is important to investigate the interrelations among service quality factors and identify key service aspects that need immediate attention for improving services to E&PD passengers and increasing transit ridership.

In this study, satisfaction data on service quality attributes was collected from E&PD passengers using a tablet-based face-to-face structured questionnaire survey along nine corridors of Delhi Metro during August–December 2019. E&PD passengers (elderly 60 and above, and persons with disabilities of any age) were intercepted on-board or at metro stations and requested to participate in the survey. This study has ensured simplicity, generosity, logicality, content validity, and reliability of the survey questionnaire through a pilot survey. The survey questionnaire includes three major sections. The first section includes the travel information of E&PD passengers. The second section elicits the level of satisfaction of E&PD passengers toward 41 service quality attributes on a five-point Likert scale, ranging from “1” as “highly dissatisfied” to “5” as “highly satisfied.” Besides, E&PD passengers express their satisfaction level for two global attributes, that is, “overall level of service” and “overall level of satisfaction with the metro trip,” using the same scale. The third section captures the demographic characteristics of E&PD passengers. In addition, a section was dedicated to note their short comments on existing services.

During the survey, the interviewer approached E&PD passengers at transit stations and on-board along the nine Metro lines through a random selection process. More specifically, the interviewer approached E&PD passengers near lifts, help desks, waiting areas at transit stations, and also while traveling on-board. In this study, disabled passengers are those with any type of disability such as vision impairment, mobility impairment, or locomotor disability. Notably, persons with hearing, speaking, or mental disabilities have not been covered in this study. Before initiating the survey, the purpose and motivation of the survey and potential use of the response data were shared with the respondents to ensure confidentiality. With their approval to participate in the survey, the survey questions were solicited. Each survey took 15 min on average to complete the structured questionnaire. During the survey, 28 responses were incomplete or invalid because of time constraints (e.g., fear of being late, in a hurry) of respondents in decision-making. Therefore, from a total of 282 responses collected, 254 responses comprising 148 elderly and 106 disabled passengers were found complete and valid.

Table 1 presents the descriptive statistics of perception data. It reveals that most of the respondents in both the groups are male, middle income (₹50 k–1.5 lakh [$700–2100]), own at least one vehicle, travel daily for mostly work trips, and rely on sustainable travel modes (walking, bus, e-rickshaw, cycle) for their access and egress travel. The respondents in the physically disabled passenger group are mainly in the age group of 20–44 years. Besides, most of the passengers hold a high school degree in the elderly group and a graduate degree in the physically disabled group, respectively. Further, Table 2 provides mean satisfaction toward service quality attributes of Delhi Metro. E&PD passengers are moderately satisfied with metro services (mean satisfaction values > 3). They are mostly satisfied with lighting in the metro system, smart card facility, and cleanliness inside metro, and least satisfied with convenience at metro stations, seat availability at metro stations, and ease of interchange within stations. Mean satisfaction of E&PD passengers with overall level of service and overall satisfaction with the metro trip are found to be 3.87 and 3.71, respectively.

Table 1.

Survey Descriptive of Elderly and Physically Disabled Passengers

Particulars	Distribution
Gender
Elderly	Male = 73.5%; female = 26.5%
Physically disabled	Male = 63.2%; female = 36.8%
Age group (in years)
Elderly	>59 = 100%
Physically disabled	15–19 = 7.5%; 20–29 = 46.2%; 30–44 = 30.3%; 45–59 = 16%
Qualification
Elderly	High school or below = 43.4%; graduation = 30.5%; post-graduation or above = 8.4%; professional course = 2.2%; illiterate = 11.4%; no answer = 4.1%
Physically disabled	High school or below = 26.9%; graduation = 48%; post-graduation or above = 12.2%; professional course = 10.7%; illiterate = 0.9%; no answer = 1.3%
Monthly household income (in ₹^a)
Elderly	<25 k = 4.6%; 25–50 k = 24.5%; 50 k–1 lakh = 34%; 1–1.5 lakh = 15%; >1.5 lakh = 2.7%; no answer = 19.2%
Physically disabled	< 25 k = 12.2%; 25 k–50 k = 25.5%; 50 k–1 lakh = 28.3%; 1–1.5 lakh = 17.9%; > 1.5 lakh = 7.5%; No answer = 8.6%
Vehicle ownership
Elderly	Two-wheeler = 42.5%; car = 19.8%; both = 20.2%; no vehicle = 17.5%
Physically disabled	Two-wheeler = 38%; car = 14.5%; both = 30.6%; no vehicle = 16.9%
Travel frequency
Elderly	Once a week or less = 11%; twice a week = 18%; thrice a week = 17%; daily = 54%
Physically disabled	Once a week or less = 6.8%; twice a week = 14%; thrice a week = 30.2%; daily = 49%
Trip purpose
Elderly	Work = 55%; education = 0.7%; shopping = 7%; social/recreational = 27.3%; other = 10%
Physically disabled	Work = 54.6%; education = 21.3%; shopping = 3.8%; social/recreational = 15.7%; other = 4.6%
Access travel mode
Elderly	Walking = 22.1%; e-rickshaw/cycle = 24.5%; auto-rickshaw = 35.1%; bus = 7.4%; two-wheeler = 7.5%; car = 3.4%
Physically disabled	Walking = 21.6%; e-rickshaw/cycle = 23.7%; auto-rickshaw = 37.7%; bus = 5.8%; two-wheeler = 7.2%; car = 4%
Egress travel mode
Elderly	Walking = 27.3%; e-rickshaw/cycle = 28.6%; auto-rickshaw = 35.3%; bus = 6.4%; car = 2.4%
Physically disabled	Walking = 23.6%; e-rickshaw/cycle = 27.4%; auto-rickshaw = 43.4%; bus = 2.8%; car = 2.8%

1 USD = ₹71.45 as of August 2019.

Table 2.

Confirmatory Factor Analysis Results and Mean Satisfaction Values

Service quality factor with underlying attributes	Mean sat	SL	T	p-value	Rank	CR	AVE	α
Service availability						.89	.58	.86
Metro service frequency	4.11	.76	22.1	***	3
Network coverage	3.75	.79	27.5	***	2
Metro operating time	4.04	.67	17.4	***	4
Waiting time	4.02	.76	18.8	***	3
Service regularity	4.23	.83	33.0	***	1
Service punctuality	4.23	.76	18.1	***	3
Passenger ease						.92	.48	.90
Ease of access to metro stations	3.48	.69	18.8	***	6
Ease of egress from metro station	3.51	.77	28.9	***	1
Availability and operation of lifts and escalators	3.91	.63	15.3	***	9
Ease of interchanging line within metro station	3.43	.74	20.3	***	3
Ease of purchasing ticket/cards	3.78	.52	8.4	***	10
Smooth operation of ticket validators	3.86	.69	21.9	***	6
Staff informative, helpful, and available	4.21	.67	12.3	***	8
Performance of customer services	3.70	.72	22.1	***	5
Attitude of security personnel	3.94	.76	24.9	***	2
Comfort level inside metro	3.67	.73	22.6	***	4
Seat availability at metro stations	3.42	.68	18.2	***	7
Convenience (facilities) at metro stations	3.40	.68	19.0	***	7
Passenger information						.91	.66	.87
Clarity in the travel-related information	3.84	.79	27.9	***	4
Updated and reliable information at station	4.02	.83	35.6	***	2
Updated and reliable information inside metro	3.93	.87	43.1	***	1
Information available on other communication technology	3.86	.82	33.8	***	3
Notification on operational changes	3.92	.75	20.9	***	5
Amenities						.88	.59	.82
Availability of smart card facility	4.47	.61	10.3	***	5
Availability of handrails/grab handles inside metro	4.22	.71	14.5	***	4
Cleanliness at metro stations	4.33	.85	44.1	***	1
Cleanliness inside metro	4.44	.84	39.9	***	2
Lighting in metro system	4.51	.79	23.2	***	3
Safety and security						.89	.67	.84
Security against theft/aggression at metro stations	3.69	.84	34.5	***	1
Security against theft/aggression inside metro	3.60	.79	20.9	***	4
Safety against slipping, falling, and accidents in metro system	3.68	.81	27.8	***	3
Security to women against harassment	3.98	.83	31.8	***	2
Seamless connectivity						.80	.57	.71
Interchange time from one line to another within station	3.49	.76	24.5	***	1
Seat availability for disabled, elderly, and females in metro	4.00	.76	22.6	***	1
Mobile network connectivity inside metro system	3.66	.75	19.7	***	2
Environmental impact						.89	.66	.83
Ecological environment outside metro stations	3.70	.75	22.4	***	3
Reduced air pollutants emissions because of introduced metro	4.09	.75	22.3	***	3
Noise level at metro stations	3.61	.87	43.3	***	2
Noise level inside metro	3.63	.88	52.2	***	1
Overall service quality						.88	.79	.75
Overall level of service	3.87	.85	284.8	***	na
Overall level of satisfaction with the metro trip	3.71	.93	34.8	***	na

Note: Mean sat = mean satisfaction; SL = standard loading; T = T-statistics; CR = composite reliability; AVE = average variance extracted; α = Cronbach’s alpha; na = not applicable.

***

p-value < 0.001.

Database Preparation

The present study performed CFA in SmartPLS 3.0 to test and confirm the service factors derived from a set of perceived attributes. Notably, CFA is best suited for confirming prior knowledge on measurement relationships between the factors (constructs) and their underlying attributes (observed variables) ( 35 ). In this study, the 41 service quality attributes and two global attributes were hypothesized to reduce to eight service factors based on prior knowledge from Mandhani et al. ( 13 , 18 ). During CFA, two attributes were dropped from the measurement relationships because of small standard loading (<0.5). Table 2 exhibits the CFA results. It is observed that all attributes significantly relate to their respective factors at a 0.001 significance level (p-value < 0.001). All attributes have standard loadings in excess of 0.5, showing satisfactory indicator reliability. All eight factors attain Cronbach’s alpha and composite reliability values higher than the threshold of 0.7, signifying adequate internal consistency reliability. Each factor accounts for average variance extracted value greater than 0.4, indicating acceptable convergent validity. Furthermore, all attributes have standard loadings greater than their cross-loadings, representing suitable discriminant validity among the factors. Thus, CFA tested and confirmed the hypothesis that the 41 underlying attributes can be reduced to eight service factors, namely service availability, passenger ease, passenger information, amenities, safety and security, seamless connectivity, environmental impact, and overall service quality ( 18 ).

Establishing Interrelations among Service Quality Factors

The present study followed the integrated methodological procedure of Mandhani et al. ( 18 ) in establishing interrelations among service quality factors from the available E&PD passenger satisfaction dataset. This methodological procedure employs Bayesian networks to extract an exploratory model and PLS path modeling to analyze the Bayesian networks extracted model. Since the passenger perception data is heterogeneous, non-normal, and fuzzy in nature, this integrated methodology is found to be best suited to explore service quality interrelations in a realistic manner.

Exploratory Model through Bayesian Networks

The Bayesian network learning process initiates with implementation of 16 algorithms on the eight service quality factors having unstandardized factor scores (on a 1–5 scale) using the bnlearn package in R language. Since the literature offers a strong basis for direct influence of service quality factors on overall service quality ( 11 , 12 , 36 ), the direct relations of all seven service quality factors with overall service quality were whitelisted (set as compulsory) in all 16 algorithms. Figure 1 illustrates the most robust Bayesian network structure for the E&PD passengers’ dataset. The most robust network is obtained through hill-climbing with the Akaike information criterion score algorithm. The total score (Figure 1) for each arc has been calculated by summing up its occurrence frequency in all 16 learnt networks. The most robust Bayesian network is the network that has the maximum number of arcs with scores greater than a threshold of 11. This threshold is typically estimated as a two-thirds proportion of total networks learnt. Notably, the arcs directed from all seven service quality factors to overall service quality exhibit a total score of 16 each, as these relations were set as compulsory in all learning algorithms. Besides, there also exist other strong relations (arcs having a total score of more than 11), such as safety and security with service availability, service availability with amenities, seamless connectivity with environmental impact, and so forth. Further, a bootstrap resampling procedure is executed on the initial dataset to examine the robustness of the chosen Bayesian network. This procedure involves learning 1000 networks through the same algorithm (which attained the most robust Bayesian network) on 1000 random datasets having 1000 randomly generated observations. The resulting occurrence proportion for each arc is presented in brackets (see Figure 1). Most arcs have occurrence proportion values greater than 0.5, signifying adequate robustness in the extracted relations. Only two arcs, that is, the relations of safety and security with passenger ease and environmental impact with passenger information, exhibit an occurrence proportion of less than 0.5, needing further checks in PLS path modeling for their relevance and significance in the network.

Figure 1.

Most robust Bayesian network (exploratory model).

PLS Path Modeling

A PLS path model was built in SmartPLS 3.0 using factor relationships extracted from Bayesian networks. The PLS path model represents the interrelations among eight factors, which were explained by 39 service quality and two global attributes. Notably, the observed variance inflation factor values among all constructs and their respective attributes were found to be less than 3, ensuring the absence of multicollinearity issues within the model. Table 3 shows the results of the PLS path model. Most path relations show positive and significant path coefficients at p-values < 0.001, and each of them at p-value < 0.05. Overall, only two path relations, that is, service availability with overall service quality and passenger information with overall service quality, attain insignificant results (having p-values > 0.05). Moreover, the path relations, mainly safety and security with passenger ease and environmental impact with passenger information, which indicated lack of robustness in the Bayesian networks model, attain positive and significant path coefficients in the PLS path model. Concerning the relations of constructs with overall service quality, all the constructs (except service availability and passenger information) exhibit significant path relations, implying direct influence of these constructs on overall service quality. Nonetheless, the influence of service availability and passenger information on overall service quality is found to have indirect relations.

Table 3.

Partial Least Squares Path Model Results

Path relation			Path coefficient	Standard error	T-statistics	p-value
Service availability	→	Passenger information	.287	.068	4.25	***
Service availability	→	Environmental impact	.282	.069	4.08	***
Service availability	→	Amenities	.325	.065	5.02	***
Service availability	→	Overall service quality	−.017	.045	−0.37	.708
Safety and security	→	Service availability	.337	.052	6.53	***
Safety and security	→	Passenger ease	.374	.067	5.46	***
Safety and security	→	Amenities	.241	.049	4.86	***
Safety and security	→	Seamless connectivity	.654	.038	17.16	***
Safety and security	→	Overall service quality	.125	.037	3.35	***
Passenger ease	→	Service availability	.479	.058	8.28	***
Passenger ease	→	Environmental impact	.220	.071	3.11	**
Passenger ease	→	Passenger information	.334	.063	5.34	***
Passenger ease	→	Overall service quality	.422	.048	8.73	***
Seamless connectivity	→	Passenger ease	.365	.090	4.05	***
Seamless connectivity	→	Environmental impact	.302	.072	4.20	***
Seamless connectivity	→	Overall service quality	.077	.042	1.83	**
environmental impact	→	Passenger information	.208	.066	3.18	**
environmental impact	→	Overall service quality	.289	.044	6.61	***
Passenger information	→	Amenities	.324	.055	5.86	***
Passenger information	→	Overall service quality	.042	.042	0.99	.321
Amenities	→	Overall service quality	.153	.039	3.87	***

***

p-value < 0.001; **p-value < 0.05.

Figure 2 presents the final PLS path model with significant relations. Notably, seven constructs altogether can explain overall service quality with explained variance of 82.6% (R² = 0.826) in the model. Therefore, the obtained PLS path model revealed significant prediction power (with R² > 0.75). In addition, the predictive relevance measure (Q²) values of all endogenous constructs are greater than zero, suggesting the substantial predictive quality of the model. Concerning fitness assessment of the model ( 37 ), approximate fit indices, that is, the standard root mean square residual with an obtained value of 0.072 (<0.08), normed fit index with 0.891 (≥0.9), and root mean squared residual with 0.108 (<0.12), attained acceptable values. Besides, exact fit indices, that is, the unweighted least squares discrepancy (d_ULS) and geodesic discrepancy (d_G) values (d_ULS = 1.223; d_G = 0.284), were below the threshold of the 95% bootstrap discrepancies quantile (HI₉₅ of d_ULS = 1.491; HI₉₅ of d_G = 0.316). Therefore, the assessed fit indices reveal the substantial fitness of the model.

Figure 2.

Partial least squares path model with significant relations.

Discussion

Service Quality Interrelations from E&PD Passengers’ Perspective

The integrated Bayesian networks–PLS path modeling approach has unveiled various distinct and hidden relationships of service quality factors within and with overall service quality. Table 4 depicts specific effects (direct, indirect, and total) of seven service quality factors on overall service quality, along with their importance ranking. Similar to existing studies on service quality models for other demographic groups of transit passengers ( 12 , 13 , 36 ), for E&PD passengers, each service quality factor showed at least some (direct, indirect, or both) influence on overall service quality. Most service quality factors showed a direct effect on overall service quality. Besides, passenger information and service availability have only indirect effects on overall service quality. This is possible when E&PD passengers rely on fellow passengers or metro staff to solicit information with respect to train arrivals/departures, delays, and service frequency. Table 5 provides a more detailed interpretation of the indirect effects of service factors on overall service quality with the help of lived experiences of respondents.

Table 4.

Specific Effects of Service Quality Factors on Overall Service Quality

Service quality factor	Direct effect	Indirect effect	Total effect	Ranking
Service availability	0.000	0.163	0.163	5
Passenger ease	0.422	0.168	0.590	2
Passenger information	0.000	0.050	0.050	7
Amenities	0.153	0.000	0.153	6
Safety and security	0.125	0.559	0.684	1
Seamless connectivity	0.077	0.309	0.386	3
Environmental impact	0.289	0.019	0.308	4

Table 5.

Travel Experiences/Comments of Respondents on Service Aspects

Safety and security affects overall service quality through passenger ease, service availability, and amenities.• “I fear for my safety while boarding the train because of the huge crowd on the platform. At some stations, the level of the train and the platform are not the same. I take metro staff assistance whenever I travel in metro.”• “Being in an old age, I have fear of accidents in escalators due to rush. Also, when someone is sick, there should be some medical help during emergencies.”• “I have stopped traveling at night time due to my low vision. It’s too dark outside as soon as you step out from Hauz Khas station. Metro people should place security personnel around the station.”

Passenger ease affects overall service quality through service availability and passenger information.• “I really appreciate the metro services provided to us. But, it's a struggle for me to reach the station. Accessibility to reach the metro station needs to be improved.”• “Lifts are in the corners, you have to search for them. Sometimes, as soon as people get off the train, they rush to get into the lift. We people (elderly) have to wait to use the lifts. Lifts are for old people, disabled, needy people, it should be strictly executed.”• “Seat availability at the platforms is a serious issue.”• “The interchange distance at a few metro stations is huge and no facility is usually available for us. On a daily basis, it is difficult to walk that far. Metro agencies should focus on alternatives for walking too long at interchanges.”• “Face reader or other technology should be there instead of card swiping, as people like me feel difficulty in doing so each time at entries and exits.”• “Metro travel is satisfying, but there is a lack of dedicated washroom facility for disabled people in metro stations. Some washrooms are not disabled-friendly and not accessible.”

Seamless connectivity affects overall service quality through passenger ease.• “Trains on the busy lines are mostly crowded in peak hours. People do not offer seats to old age persons.”• “Underground stations have mobile network problems. Most of the time, I don’t get a signal on my phone.”

Amenities affects overall service quality.• “I am totally satisfied with metro services. The staff are very supportive. Clean and green system!”• “The Delhi government is offering free bus travel to senior citizens. Metro should also offer some concessions to senior citizens.”

Passenger information affects overall service quality through amenities.• “Sometimes, announcements are not audible due to crowd noise. I couldn’t see train details properly and got confused between interchanges.”• “I often ask the supporting staff about the train arrival time. They are always kind to respond”

Owing to visual, auditory, and mobility challenges, E&PD passengers consider safety and security as the most important factor that explains overall service quality. This finding is in line with Yuan et al.’s ( 27 ) finding that disabled passengers are concerned more about safety and security than any other factor. A one-unit increase in the perception of safety and security will induce a 68.4% (total effect = 0.684) increase in their perception of overall service quality. This influence is dominated by indirect effects (0.559) through factors such as service availability, passenger ease, amenities, and seamless connectivity on overall service quality. E&PD passengers with a positive sense of safety against accidents would perceive metro amenities such as handrails/grab handles availability, lighting, and so forth, more positively. As soon as an E&PD passenger feels secure against crime/aggression/theft at a metro station, he/she does not mind waiting on the platform for the train. A positive perception of safety would stimulate E&PD passengers to perceive accessibility to wheelchairs inside the station more positively. Besides, they more often relate a positive sense of safety and security to seamless metro travel. These inferences justify the indirect influence of safety and security on overall service quality through service availability, passenger ease, amenities, and seamless connectivity for E&PD passengers.

Passenger ease is realized to have the second largest influence on overall service quality (total effect = 0.591) for E&PD passengers. Remarkably, its direct effect (0.422) is much more than its indirect effect (0.168) on overall service quality. Moreover, the accessibility aspect is always of significant concern for disabled and elderly people during their travel on public transit (5, 7, 24, 32 ). Its indirect influence on overall service quality is visible through service availability, passenger information, and environmental impact factors. For example, the availability of informative and helpful staff persuades E&PD passengers to have more clarity in the travel-related information, supporting the relation of passenger ease with passenger information. Also, difficulty in accessing transit stations caused by poor quality footpaths, inadequate crossings, and signage dissuades E&PD passengers from positively perceiving the ecological environment outside the station. Besides, seamless connectivity influences overall service quality directly as well as indirectly through passenger ease and environmental impact. For instance, E&PD passengers may feel comfortable inside the metro when he/she gets a reserved seat while traveling, explaining the relation of seamless connectivity with passenger ease.

Service availability has only an indirect effect (0.163) on overall service quality through three factors, that is, passenger information, environmental impact, and amenities. In fact, punctual, regular, effective metro services encourage E&PD passengers to perceive information more positively. The relation of service availability with passenger information is justified. Besides, passenger information affects overall service quality indirectly through amenities. This result aligns with the findings of Mandhani et al. ( 13 ) for routine transit passengers. E&PD passengers, especially those with low vision, relate the travel-related information, such as real-time information, signboards, and route maps with lighting in the system, justifying the relation of passenger information with amenities.

Further, environmental impact seems to have a significant impact on overall service quality (total effect = 0.308). It influences overall service quality directly as well as indirectly through passenger information. E&PD passengers with sensory disabilities could not hear audio announcements because of surrounding noise levels at the station or on-board, resulting in their dissatisfaction with passenger information attributes and eventually with overall service quality. Finally, amenities entail a direct and reasonable effect on overall service quality. Increasing the level of satisfaction with metro amenities, such as smart card availability, handrail/grab handle availability, lighting, and cleanliness, inside the metro system would eventually enhance the perception of E&PD passengers of overall metro services. Improving service factors such as safety and security, passenger ease, seamless connectivity, and environmental impact is essential for Delhi Metro to satisfy E&PD passengers. Transit agencies of metro rail transit in other cities may consider these interrelations (size and effect) among service quality factors while planning barrier-free transit services for E&PD passengers.

Priority Areas for Service Improvements

Transit agencies must consider the existing performance and importance of service quality factors to identify action plans for E&PD-friendly transit services. Importance–performance map analysis provides an analytical illustration of the performance and importance of service quality factors in four quadrants, namely “concentrate here” (higher importance but lower performance), “keep up the good work” (higher performance and higher importance), “possible overkill” (lower importance but higher performance), and “low priority” (lower importance and lower performance). It demonstrates the possible gaps between the expectation of passengers and the reality of transit services. These gaps are essential improvements where transit agencies must focus on achieving barrier-free mobility to E&PD passengers. For this purpose, this study developed an importance–performance map by plotting latent variable scores of service quality factors obtained from PLS path modeling as the performance metrics on the X-axis and total effects of service quality factors on overall service quality as the importance metrics on the Y-axis.

Figure 3 depicts the importance–performance map for service quality factors from E&PD passengers’ perspective. It can be observed that three service quality factors, passenger ease, seamless connectivity, and safety and security, are located in the “concentrate here” quadrant and that improvements are urgently needed. Passenger information is the only service quality factor that lies in the “lower priority” quadrant. Besides, environmental impact, service availability, and amenities are under the “possible overkill” quadrant. These positions of service quality factors in the map indicate some important improvements that meet the satisfaction of E&PD passengers. Firstly, E&PD passengers are more susceptible to locomotor challenges. Therefore, safety and security must be a high priority among all other service quality factors. It is urgent to design safety facilities within the metro station and its premises that allow E&PD passengers to continue usage. Passenger ease, especially seat availability, convenience facilities, and ease of interchanging, should be improved first, given their lower satisfaction of E&PD passengers. Besides, ease of access, purchasing tickets, and customer services need immediate attention.

Figure 3.

Importance–performance map for elderly and physically disabled passengers.

According to E&PD passengers, Delhi Metro is performing well with respect to environmental impact, service availability, and amenities. These factors are possible aspects where transit services can score well. For instance, providing “laminating gardens'' inside metro stations, publicizing regularity, punctuality, and efficiency of metro systems with respect to environment-friendly carbon credits, mitigation of crowd noise, and special enforcement for E&PD passengers could benefit their positive perception of overall service quality. Finally, transit operators may consider passenger information as an advantage and must perform consistently. In summary, transit officials must bear in mind that any kind of improvements to transit service quality strategized in the favor of E&PD people will benefit not just E&PD people, but in fact all passenger groups.

Conclusions and Policy Implications

E&PD passengers are vulnerable transit users, who have received very limited attention in the past. In fact, providing satisfactory transit services to this group is often a critical issue for transit officials to deliver services that are easy and convenient to them. Thus, the scientific literature (in this backdrop) highlighted that exploring interrelations among service quality factors can be a useful instrument in identifying service improvements for E&PD passengers. Existing studies on transit service quality lack insights on E&PD passenger issues because of three technical reasons, namely, inadequate data, lack of in-depth survey instruments, and use of sophistical modeling approaches. Besides, transit officials follow a “one-size-fits-all” approach in the planning, design, and operation of transit services. These unprecedented gaps in planning practices might be the possible reasons for the declining proportion of E&PD passengers in public transit usage ( 34 ).

The present study aims to establish interrelations among service quality factors and identify service improvements for E&PD passengers using satisfaction data from 254 E&PD passengers of Delhi Metro. An integrated Bayesian networks–PLS path modeling has been employed to establish interrelations among service quality factors and their influence on overall service quality. The study methodology involved three stages. Firstly, the satisfaction of E&PD passengers on 41 attributes was segmented into eight service quality factors based on evidence from existing studies. A CFA was conducted to confirm the measurement properties of the set of attributes with their respective factors. Secondly, an exploratory model on service quality factors was developed using the Bayesian networks approach. Thirdly, the explored model was tested and analyzed in PLS path modeling. The obtained empirical model has produced some remarkable and meaningful results. Also, the outcomes from importance–performance map analysis can be useful to transit officials by considering priority areas of service improvements.

The findings from this study highlight safety and security, passenger ease, and seamless connectivity as priority service aspects (please refer to Table 4 and Figure 3) needing attention from transit officials. To provide barrier-free transit services, transit officials must consider the following policy implications under each aspect.

Assure safety and security for E&PD passengers.

Provide sufficient enforcement and illuminate the environment outside and inside metro stations. Such an environment tends to be perceived positively by E&PD passengers toward safety and security and then overall service quality.

Metro staff pay more attention to E&PD passengers during quick closures of shuttle doors, and insufficient space in elevators/escalators.

Provide dedicated seats with handrails and seat belts for E&PD passengers.

Provide passenger ease inside and outside metro stations.

Special care should be taken for E&PD passengers to help step in and step out of escalators during peak hours.

Provide “E&PD-friendly” customer help desks for passengers who face difficulty in carrying their belongings with them.

Provision of waiting rooms/toilets for E&PD passengers should be made mandatory in all metro stations, given its lower satisfaction. Owing to locomotive issues such as chronic fatigue and muscle pain, it is natural that these passengers seek waiting areas.

E&PD passengers value the provision of seats and shelters in the waiting areas for feeder buses outside metro stations. These improvements will increase satisfaction with passenger ease and overall service quality.

Introduce E&PD passenger mobile applications to buy tickets or recharge their smart cards to minimize hassle while boarding/alighting trains.

Improve seamless connectivity and educate fellow passengers.

Although a few seats (11 seats per coach) for E&PD passengers are dedicated in Delhi Metro, a large proportion of E&PD passengers are least satisfied (3.42) with seat availability. This is possibly because most of the fellow passengers occupy reserved seats and rarely offer them to the E&PD passengers. Thus, provision or increasing priority seats for E&PD passengers may not be an effective solution. Transit officials, with the help of the local government, should educate fellow passengers through priority seat campaigns or advertising the culture of offering seats to E&PD passengers inside the metro.

Providing horizontal escalators may reduce the difficulty and strain of walking within interchange stations. Some ideal places for such provisions include Nehru Enclave and Nehru Place, where the two different stations of the Violet line and Pink line of Delhi Metro are separated by a 1.5 km interchange walking corridor. Such provisions will provide a sense of positive perception toward seamless connectivity.

In addition, it is important for transit officials to consider the following improvements in amenities, service availability, and passenger information aspects:

offer selective “free-mobility” smart cards to E&PD passengers;

allocate special hours for E&PD passengers in the morning peak hours;

development of a mobile application for real-time travel monitoring and notifications with regard to the dynamic service information of metro trains;

rewarding frequent E&PD passengers may encourage them to continue usage and attract new ones;

roll out periodic surveys on passenger satisfaction to monitor the level of services for E&PD passengers.

All the above-suggested improvements, if considered, will enhance the travel satisfaction of E&PD passengers who frequently use Delhi Metro. However, the study findings propose barrier-free policy measures for all the existing lines of Delhi Metro based on limited data size. It is reflected as a data limitation of the study. Future research may focus on developing a service quality model, considering perspectives of E&PD passengers with an increased data size. Such an elaborative data size will represent the target population of the region and propose service requirements accordingly. Although the present study investigated the joint perspectives of E&PD passengers toward metro service quality, both elderly and disabled people groups individually need much more careful attention in future studies. Further, the present study took into consideration only the viewpoint of E&PD persons who use the metro services. Future research can investigate the viewpoint and concerns of E&PD persons who have stopped using transit because of specific service issues. Although the study findings are limited to the metro rail transit system of a developing nation, it is suggested to explore and confirm the theory of interrelations for any other transit systems in other contexts with similar perception data. Besides, the proposed methodology is robust and can be transferable to any other context in transit service quality evaluations. In addition, future research may focus on more in-depth exploration on the influence of demographic characteristics in E&PD passengers’ perspectives.

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: J. Mandhani, J.K. Nayak, M. Parida; data collection: J. Mandhani; analysis and interpretation of results: J. Mandhani; draft manuscript preparation: J, Mandhani, J.K. Nayak, M. Parida. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is funded by a PhD scholarship awarded to one of the authors, Jyoti Mandhani by the Ministry of Human Resource Development (MHRD), India.

ORCID iD

Manoranjan Parida

References

Roberts

A. W.

Ogunwole

S. U.

Blakeslee

Rabe

M. A.

The Population 65 Years and Older in the United States: 2016. U.S. Census Bureau, 2018.

UNPF. Caring for Our Elders: Early Responses Caring for Our Elders: Early Responses. UNPF, 2017.

Wong

R. C. P.

Szeto

W. Y.

Yang

Y. C.

Wong

S. C.

Elderly Users’ Level of Satisfaction with Public Transport Services in a High-Density and Transit-Oriented City. Journal of Transport & Health, Vol. 7, 2017, pp. 209–217.

Hunter-Zaworski

Hron

M. L.

Bus Accessibility for People with Sensory Disabilities. Transportation Research Record: Journal of the Transportation Research Board, 1999. 1671: 40–47.

Verbich

El Geneidy

The Pursuit of Satisfaction: Variation in Satisfaction with Bus Transit Service among Riders with Encumbrances and Riders with Disabilities Using a Large-Scale Survey from London, UK. Transport Policy, Vol. 47, 2016, pp. 64–71.

Atkins

Older People: Their Transport Needs and Requirements. London, Great Britain, 2001.

Park

Chowdhury

Investigating the Barriers in a Typical Journey by Public Transport Users with Disabilities. Journal of Transport and Health, Vol. 10, 2018, pp. 361–368.

Zhao

Webb

Shah

Customer Loyalty Differences between Captive and Choice Transit Riders. Transportation Research Record: Journal of the Transportation Research Board, 2014. 2415: 80–88.

Transportation Research Board. A Handbook for Measuring Customer Satisfaction and Service Quality. Transportation Research Board, Washington, D.C., 1999.

10.

Weinstein

Customer Satisfaction among Transit Riders: How Customers Rank the Relative Importance of Various Service Attributes. Transportation Research Record: Journal of the Transportation Research Board, 2000. 1735: 123–132.

11.

Eboli

Mazzulla

Relationships between Rail Passengers’ Satisfaction and Service Quality: A Framework for Identifying Key Service Factors. Public Transport, Vol. 7, No. 2, 2015, pp. 185–201.

12.

de Oña

Eboli

Mazzulla

Perceived Service Quality in Bus Transit Service: A Structural Equation Approach. Transport Policy, Vol. 29, 2013, pp. 219–226.

13.

Mandhani

Nayak

J. K.

Parida

Interrelationships among Service Quality Factors of Metro Rail Transit System: An Integrated Bayesian Networks and PLS-SEM Approach. Transportation Research Part A: Policy and Practice, Vol. 140, 2020, pp. 320–336.

14.

Stuart

K. R.

Mednick

Bockman

Structural Equation Model of Customer Satisfaction for the New York City Subway System. Transportation Research Record: Journal of the Transportation Research Board, 2000. 1735: 133–137.

15.

Dell’Olio

Ibeas

Cecin

The Quality of Service Desired by Public Transport Users. Transport Policy, Vol. 18, No. 1, 2011, pp. 217–227.

16.

Tyrinopoulos

Antoniou

Public Transit User Satisfaction: Variability and Policy Implications. Transport Policy, Vol. 15, No. 4, 2008, pp. 260–272.

17.

Shen

Xiao

Wang

Passenger Satisfaction Evaluation Model for Urban Rail Transit: A Structural Equation Modeling Based on Partial Least Squares. Transport Policy, Vol. 46, 2016, pp. 20–31.

18.

Mandhani

Nayak

J. K.

Parida

Establishing Service Quality Interrelations for Metro Rail Transit: Does Gender Really Matter?

Transportation Research Part D, Vol. 97, 2021, p. 102888.

19.

Carrel

Lau

P. S. C.

Mishalani

R. G.

Sengupta

Walker

J. L.

Quantifying Transit Travel Experiences from the Users’ Perspective with High-Resolution Smartphone and Vehicle Location Data: Methodologies, Validation, and Example Analyses. Transportation Research Part C: Emerging Technologies, Vol. 58, 2015, pp. 224–239.

20.

Iseki

Smart

M. J.

How Do People Perceive Service Attributes at Transit Facilities?

Transportation Research Record: Journal of the Transportation Research Board, 2012. 2274: 164–174.

21.

de Oña

Eboli

Mazzulla

Heterogeneity in Perceptions of Service Quality among Groups of Railway Passengers. International Journal of Sustainable Transportation, Vol. 9, No. 8, 2015, pp. 612–626.

22.

Yaya

L. H. P.

Fortià

M. F.

Canals

C. S.

Marimon

Service Quality Assessment of Public Transport and the Implication Role of Demographic Characteristics. Public Transport, Vol. 7, No. 3, 2015, pp. 409–428.

23.

Barajas

J. M.

Chatman

D. G.

Agrawal

A. W.

Immigration, Income, and Public Transit Perceptions: Findings from an Intercept Survey. Journal of Public Transportation, Vol. 21, No. 2, 2018, pp. 1–18.

24.

Burkhardt

J. E.

Critical Measures of Transit Service Quality in the Eyes of Older Travelers. Transportation Research Record: Journal of the Transportation Research Board, 2003. 1835: 84–92.

25.

Kerschner

Aizenberg

Supplemental Transportation Programs for Seniors. Beverly Foundation, 2001.

26.

Ritter

Straight

Evans

Understanding Senior Transportation: Report and Analysis of a Survey of Consumers Age 50+. AARP, 2002, p. 85.

27.

Yuan

Yang

Rasouli

Lei

Assessing Bus Transit Service from the Perspective of Elderly Passengers in Harbin, China. International Journal of Sustainable Transportation, Vol. 13, No. 10, 2019, pp. 761–776.

28.

O’Neill

O’Mahony

Travel Behavior and Transportation Needs of People with Disabilities: Case Study of Some Categories of Disability in Dublin, Ireland. Transportation Research Record: Journal of the Transportation Research Board, 2005. 1924: 1–8.

29.

Lubin

Deka

Role of Public Transportation as Job Access Mode. Transportation Research Record: Journal of the Transportation Research Board, 2012. 2277: 90–97.

30.

Soltani

S. H. K.

Sham

Awang

Yaman

Accessibility for Disabled in Public Transportation Terminal. Procedia - Social and Behavioral Sciences, Vol. 35, 2012, pp. 89–96.

31.

Sze

N. N.

Christensen

K. M.

Access to Urban Transportation System for Individuals with Disabilities. IATSS Research, Vol. 41, No. 2, 2017, pp. 66–73.

32.

Yang

Rodriguez

D. A.

Michael

Zhang

Active Travel, Public Transportation Use, and Daily Transport among Older Adults: The Association of Built Environment. Journal of Transport and Health, Vol. 9, 2018, pp. 288–298.

33.

Hair

J. F.

Jr , Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool in Business Research. European Business Review, Vol. 26, No. 2, 2014, pp. 106–121.

34.

DMRC. Delhi Metro Rail Transit (DMRC) Annual Report 2018-2019. 2019. https://backend.delhimetrorail.com/documents/20/DMRC-REPORT-2018-2019-English-Web.pdf

35.

Child

The Essentials of Factor Analysis. Continuum International Publication Group, London, 2006.

36.

Díez-Mesa

de Oña

De Oña

Bayesian Networks and Structural Equation Modelling to Develop Service Quality Models: Metro of Seville Case Study. Transportation Research Part A: Policy and Practice, Vol. 118, 2018, pp. 1–13.

37.

Hair

J. F.

Ringle

C. M.

Sarstedt

PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, Vol. 19, No. 2, 2011, pp. 139–152.