Measuring Commuters' Perception on Service Quality Using SERVQUAL in Delhi Metro

 
Measuring Commuters’ Perception on Service Quality
              Using SERVQUAL in Delhi Metro
                  Anjali Sharma, Research Scholar, University of Rajasthan
             Dr. A.K Mishra, Lecturer, Govt P.G College, University of Rajasthan

                                        Abstract
Delhi Metro is a world-class metro. To ensure reliability and safety in train operations, it
is equipped with the most modern communication and train control system. It has state-
of-art air-conditioned coaches. The present paper focuses measuring the service quality
of Delhi Metro DMRC in NCR India. A sample of 1200 respondents from different
stations of Delhi metro in Delhi NCR was selected through non-probabilistic convenience
sampling. Exploratory factor analysis was conducted and it was found that Reliability,
Assurance, Tangibles, Empathy, and Responsiveness Delhi Metro consumers also expect
Security as a parameter for service quality. Security includes Separate compartment for
women and the metro station should be near to my office. So based on these conclusions
Delhi Metro should concentrated on their security in delivering satisfaction to the Delhi
Metro users.
Key Word: SERVQUAL, Delhi Metro.

Introduction
Delhi Metro is a world-class metro. To ensure reliability and safety in train operations, it
is equipped with the most modern communication and train control system. It has state-
of-art air-conditioned coaches. Ticketing and passenger control are through Automatic
Fare Collection System, which is introduced in the country for the first time. Travelling
in Delhi Metro is a pleasure with trains ultimately available at three minutes frequency.
Entries and exits to metro stations are controlled by flap-doors operated by 'smart-cards'
and contact less tokens. For convenience of commuters, adequate numbers of escalator
are installed at metro stations. Unique feature of Delhi Metro is its integration with other
modes of public transport, enabling the commuters to conveniently interchange from one
mode to another. To increase ridership of Delhi Metro, feeder buses for metro stations are
Operating. In short, Delhi Metro is a trendsetter for such systems in other cities of the
country and in the South Asian region.
The primary objective of this study is to measure the service quality of Delhi Metro
DMRC in NCR India
Literature review- Service Quality
Ji Cheng Zhu at el (2011) compared the results of measuring Service Quality (SQ) using
the SERVQUAL instrument and the analytic hierarchy process (AHP) at a Hewlett-
Packard Authorized Service Centre in Beijing, China in 2006 and found that the
significant differences between the results of the two methods suggested that the
approaches differed in terms of their capabilities in reflecting respondent opinions
accurately. Robert E. Miller (2011) examined a potential issue in measuring service
quality using the SERVQUAL instrument and presented the results of a field study in
which randomized and non-randomized versions of SERVQUAL were administered in
multiple organizations and resulting samples were then used to generate factor structures
which proved to be non-congruent. Elizabeth Vaughan, Helen Woodruffe-Burton, (2011)
found that ARCHSECRET was superior to the modified SERVQUAL in terms of its
overall predictive power and ARCHSECRET was found to be reliable and valid for the
measurement of the disabled student experience in higher education, while acting as a
diagnostic tool for the identification of service quality shortfalls. Godwin J. Udo (2011)
highlighted Assurance, Empathy, Responsiveness and Website Content can impact e-
learning quality and ‘‘Website Content” has the strongest influence on perceived e-
learning quality. Ahmadreza Shekarchizadeh (2011) found that five factors in the form of
professionalism, reliability, hospitality, tangibles, and commitment were uncovered and
the single mean t-tests for the three methods of gap analysis indicated that all the items of
perception were perceived as significantly negative as compared to expectations in
university senior management. Also, the findings from the study would assist in
designing a quality system that involves not just the employees, but also the students.
Rosemary Batt (1999) analyzed the strengths and weaknesses of Total Quality
Management and Self-Managed Teams, as compared to mass production approaches to
service delivery, among customer service and sales workers in a large unionized regional
Bell operating company and represented a "strong test" of the efficacy of teams because
theory predicts weak outcomes for self-managed teams among service and sales
employees in establishments where technology and organizational structure limit
opportunities for self-regulation, the nature of work and technology do not require
interdependence, and downsizing creates pervasive job insecurity-conditions found at the
company studied here. Terence A. Oliva, Richard L. Oliver, Ian C. MacMillan(1992)
examined the issue in terms of customer service for practitioners and academicians as
they have noted that simply investing in greater service delivery may not return the cost
of the additional investment and proposed a method for analyzing this complex behavior
in a way that can lead to the development of more accurate service strategies through an
understanding of the relationships among customer-transaction costs, satisfaction, and
purchase loyalty. They used a catastrophe model to describe a service loyalty customer-
response surface. Then, by presenting a "real-world" application with a small service-
quality customer dataset provided by General Electric Supply, they show how one
actually estimates such a model and interprets the results. Bo Edvardsson, BengtOve
Gustavsson (2003) examined in the research on new service development (NSD), the
interest has mainly been on structural aspects of the service offering found that many
requirements are the same in service organizations as in manufacturing companies but
also that there are distinct differences based on the analysis presents a sixth requirement.
Examples of requirements were the ability to control the work situation and to be
involved in the decision-making processes, a safe physical work environment and the
ability to develop social relationships through the work. Mary Jo Bitner, Bernard H.
Booms, Lois A. Mohr (1994) explored those sources in service encounters from the
contact employee's point of view of the hotel, restaurant, and airline industries and their
results generally support the theoretical predictions and also identify an additional source
of customer dissatisfaction-the customer's own misbehavior and the findings have
implications for business practice in managing service encounters, employee
empowerment and training, and managing customers. Research on service quality has
been done from various aspects from a very long time, sufficient research has been
contributed by (Gronroos, 1982; Berry, Zeithaml, & Parasuraman, 1985; Parasuraman,
Zeithaml, & Berry,1985; Zeithaml, Parasuraman, & Berry, 1985; Brady & Cronin, 2001)
in developing the service quality concept. There is a need for conceptual changes to be
built as the present concept of service quality does not fit the multidimensional situations
across nations. (Cronin and Taylor, 1992; Brady and Cronin, 2001) in their study argued
that there is a need to address multidimensional aspect of service quality. The issue of
measuring service quality across several service sectors has been explored by researchers
like (Parasuraman et al, 1985; Parasuraman, Berry, & Zeithaml, 1991; Koelemeijer,
1991; Cronin & Taylor, 1992; Vandamme & Leunis, 1993; Parasuraman, Zeithaml, &
Malhotra, 2005). Though SERVQUAL as a measurement tool used in numerous studies,
it was tailored to fit a particular sector and context, like E-S-QUAL for electronic sector
and SERVPERF for service preference. Hence there is a scope for SERVQUAL to be
further modified for universal standardization (Parasuraman et al, 1991). The issue of
improving service quality where by organization can derive competitive advantage has
been investigated by (Reicheld and Sasser, 1990; Berry, Zeithaml, & Parasuraman, 1990;
Hensel, 1990; Berry, Parasuraman, & Zeithaml, 1994; Berry & Parasuraman, 1997;
Glynn & Brannick, 1998; Johnston & Heineke, 1998; Harvey, 1998). Service quality has
been used as an ingredient in understanding consumer behaviour. A positive consumer
behaviour on service quality will lead to higher returns (Zahorik & Rust 1992; Boulding,
Kalra, Staelin, & Zeithaml, 1993; Zeithaml, Berry, & Parasuraman, 1996; Liu,
Sudharshan, & Hamer, 2000).

Service Quality in Metro Trains
Increasing travel demand and preferences in using private vehicle is causing rapid
motorization in many counties around the world. Most people are now highly dependent
on private motorize travel (Ellaway et al. 2003). This phenomenon was caused because of
attractiveness of car and people love to drive (Beirão & Sarsfield Cabral 2007). An
increased private motorization has resulted in an increased traffic congestion which in
turn result in longer travel times for many people (Beirão & Sarsfield Cabral 2007; Asri
& Hidayat 2005) In addition to congestion, private motorization is also affecting the
safety of vulnerable road users (Kodukula 2009), high consumption of non-renewable
resource (Aßmann & Sieber 2005), and causes serious threat to the quality of human
environments (Goodwin 1996; Greene & Wegener 1997). In order to prevent more
problems caused by this increase in motorization it is highly recommended by many
researchers as well as public decision makers to provide an attractive public transport
service as an alternative transport mode in many cities. Quality is the overall experience
which a customer perceives through interacting with a product and service. Parasuraman
et al. (1988) has captured the definition of quality taken as a whole judgment. Brown et
al. (1992) has referred to organization bearing high service quality as preferable which
facilitates them to charge premium price. While Parasuraman et al. (1988) indicate it as
“competitive weapon”. Public transport should become part of a solution for sustainable
transport in the future. However, in order to keep and attract more passengers, public
transport must to have high service quality to satisfy and fulfill more wide range of
different customer’s needs (Oliver 1980; Anable 2005). It is important to summarize
knowledge about what drives customer satisfaction and dissatisfaction in public transport
area to design an attractive and marketable public transport. Copley (2004: 18-21) states
that organizations should analyze all related information in their social and economic
environment and use it to guide their activities. Lidén (2003:346) also indicates that the
absence of communication can affect customer perceptions of service quality. The service
delivery improvement plan of the Department of Transport in South Africa (National
Department of Transport, 2006) stressed that “communication has a key role to play in
improving service delivery”. The intercity bus transport industry in South Africa, as a
public transport service, is a very important element of South African tourism
development. The improvement of the intercity bus transport service quality should lead
to an increase in the industry’s productivity and customer satisfaction (SA transport gets
money injection from big funder, 2006). SERVQUAL, as a measurement tool to analyze
service quality, will determine which factors in intercity bus transport industry are
influencing the service delivery system. As a 2010 FIFA World Cup host country, South
Africa should also be concerned with good communication skills in the whole social and
economic    environment     (National   Department     of   Transport,   2006).   Effective
communication was able to affect the service quality of the transport industry, even the
image of a country (Friman and Edvardsson, 2003: 22).

Sudin Bag at. el (2012) found that in today’s competitive scenario consumer satisfaction is
the first priority. For this, business is to meet the expectation of its customers. The
organization should aim not only at satisfying the customer but also focus on the delighting
them. Thus it has become essentials for organization to identify the factors that affect
customer satisfaction level and consciously measure them so as to try and bring about the
necessary changes on the basis of customer perception and requirements. Their research used
data collected through a structured questionnaire from a sample of 250 respondents tries to
find the factors related to Kolkata Metro Railway services that have an impact on customer
satisfaction.
Transport plays an important role in the economic development of the country by
creating employment opportunities and sustaining economic activities. Transport is the
channel of social and economic interaction involving the physical movement of people
and goods. The quest for service quality has been an essential strategic component for
service firms like Delhi Metro Rail Corporation attempting to succeed and survive in
today’s competitive environment. The SERVQUAL model focuses on the difficulty in
ensuring a high quality of service for all customers in all situations. SERVQUAL
methodology is an analytical approach for evaluating the difference between customers'
expectations and perceptions of quality.
Objective of the study
The primary objective of this study is to measure the service quality of Delhi Metro
DMRC in NCR India:-
    •    To measure the service quality of DMRC           with the help of      SERVQUAL
        developed by (Parasuraman et al., 1988)

Initial instrument was developed by generating 21-28 items after a thorough
understanding of conceptualization and operationalization of the service quality construct
in DMRC of NCR India. The SERVQUAL developed by (Parasuraman et al., 1988) was
adopted to prepare the initial instrument. The first part of the questionnaire was left with
four items relating to tangibility factor, second part with five items relating to reliability
factor, third part with four items relating to responsiveness factor, fourth part with four
items relating to assurance factor, fifth part with five items relating to empathy factor and
sixth, the last factor with six items relating to demographic factor. All the closed-ended
questions were designed to generate responses on a five point Likert scale to measure the
perception of service quality indicated as -1 strongly disagree, -2 disagree, 0 neither or
nor, +1 agree and +2 strongly agree. Cui, Lewis, and Park, (2003) in a study measuring
service quality using SERVQUAL with five dimensions have achieved successful results
using likert scale with seven point scale.
The research attempts to measure the service quality of DMRC. To fulfill the objectives
questionnaire was developed on basis of the five SERVQUAL dimensions 21-28 items
was chosen to put in SERVQUAL questionnaire for the DMRC.
The questionnaire was divided into three parts. The first part of the questionnaire
consisted of two demographic questions (Gender and Age). The second part was
designed to measure the respondents’ expectations regarding service quality in the
DMRC in NCR India. The third part of the questionnaire was designed to examine the
respondents’ perceptions of service quality actually provided by DMRC. The five-point
Likert scale is the most widely used form of scaled items where the respondent chooses a
point on a scale that best represents his/her view. Scoring for the scale was follows: (1)
strongly disagree, (2) disagree, (3) neutral, (4) agree and (5) strongly agree.
By comparing each value difference between all 21-28 expectations and perceptions, the
level of quality can be concluded. For example, if the perception value is higher than the
expectation value, it can be concluded that the service is satisfactory or ideal. However, if
the expectation value is lower than the perception value, the service quality level can be
regarded as unsatisfactory or even unacceptable.
Methodology and Data Collection
For the purpose of the study primary data was collected from the different routes of Delhi
Metro with the help of a well-drafted Questionnaire. A sample of 1200 respondents was
selected by dividing NCR into routes of Delhi Metro. Further, within these routes non-
probabilistic convenience sampling was followed, as it is appropriate for exploratory
studies. Further convenience sampling method was used for two reasons firstly
respondents are selected because they happen to be in right place at the right time and
secondly, convenience sampling technique is not recommended for descriptive or casual
research but they can be in exploratory research for generating ideas (Malhotra, 2005).
According to the chosen methodological research approach, the quantitative data was
analyzed by using Factor Analysis by using SPSS Software.
Hypothesis Formulated
For the fulfillment of the study following hypothesis have been formulated:
H1 : In terms of service quality the rating given by the respondents are significantly
different from each other.
H2 : There is significant association between DMRC service usage and demography of
the respondent i.e. age gender income and occupation.
H3 : The generic dimensions of service quality is Reliability
H4 : The generic dimensions of service quality is Assurance
H5 : The generic dimensions of service quality is Tangibles
H6 : The generic dimensions of service quality is Empathy
H7 : The generic dimensions of service quality is Responsiveness
H8   : There are five generic dimensions of service quality: Reliability, Assurance,
Tangibles, Empathy, and Responsiveness.
Analysis of the Data
Before any analysis was conducted on the dimensionality or scales, the data was
examined for potential biases. An ANOVA was conducted by using service quality as
dependent variable against each demographic category shown in table 2*. From the
output table I of one-way ANOVA the significance of F-test is found to be 0.000. This
indicated that at 95% confidence level, F-test proves the model is highly significant. In
other words the rating given by the respondents are significantly different from each
other. So we reject the null hypothesis and accept the alternate hypothesis that In terms of
service quality the rating given by the respondents are significantly different from each
other.
H1 : In terms of service quality the rating given by the respondents are significantly
different from each other. (ACCEPTED)

The Chi-square test revealed the significant association between Delhi Metro usage and
the age, gender, income, qualification and occupation. From the Chi–test out put table II
the significance level of 0.000 (Pearson’s) has been achieved. This means that the value
of Pearson’s Chi-test clearly states that there exists a significant interrelationship between
the dependent variable (Delhi Metro) and other independent variables (demography). So
we accept our hypothesis that there is significant association between Delhi Metro usage
and demography of the respondent i.e. age gender income and occupation.
H2 : There is significant association between Delhi Metro usage and demography of the
respondent i.e. age and gender. (Accepted)

Based on the guidelines offered by Susan Devil and H K Dong to measure the service
quality following, one finds a smaller number of dimensions than initially hypothesized
from the qualitative research. Parasuraman and his colleagues initially suggested 10
dimensions and later found, across service industries, five generic dimensions of service
quality: Reliability, Assurance, Tangibles, Empathy, and Responsiveness. Although some
similar dimensions or grouping of attributes have been found, other research shows that
dimensions clearly are service and company dependent. Tangibles, for example, has not
been found to be a dimension of telephone services such as repairs, installations, business
office inquires, or operator services. Previous studies of rigor have also found the
SERVQUAL tangible dimension to be weak (Cronin and taylor1992, 1994; Kettinger and
Lee 1994, 1997; Parasuraman et al. 1991).

Factor analysis is a statistical technique for condensing many variables into a few
underlying factors, dimensions or constructs and in this case commenced with a study of
the correlation matrix of all 48 of the original scale variables. Hedderson (1991, p160)
suggests that any variable whose correlations with the other variables are less than 0.4 in
absolute terms should be excluded from the factor analysis.
Reliability Analysis
The objective of the research was to measure service quality in Delhi Metro. In order to
do so, five key online shopping motivations were identified from relevant academic
literature: Tangibles (appearance of physical elements), Reliability (dependable, accurate
performance) Responsiveness (promptness and helpfulness), Assurance (competence;
courtesy, credibility, and security) Empathy easy access, good communications, and
customer understanding. The scale items were analyzed in terms of reliability and the
response data checked for invalidity before analysis of the data was conducted.
Cronbach’s Alpha
Reliability is the extent to which a list of scale items would produce consistent results if
data collection were repeated (Malhotra, 2007) and is assessed by determining the
proportion of systematic variation in a scale. Calculating the Cronbach Alpha coefficient
of a scale is the most commonly practiced indicator of internal consistency (Pallant,
2007), with the ideal Cronbach Alpha co-efficient being over 0.7 (Hair et al. 2010). A
value of below 0.7 is considered to indicate unsatisfactory internal consistency reliability
(Malhotra, 2007). Cronbach’s Alpha is used in this research to assess internal consistency
reliability of the 48 scale items of the questionnaire.
Reliability Statistics
                Reliability Statistics

                    Cronbach's
                    Alpha Based
                          on
  Cronbach's        Standardized
     Alpha             Items          N of Items
             .769              .810           48

The Cronbach Alpha coefficient of the service quality measurement scale of the research,
as displayed in is 0.769. Since this figure is above the necessary 0.7 Cronbach Alpha
ideal, the scale items used have a satisfactory internal consistency and can be deemed
reliable statistically.

Factor Analysis
Factor Analysis is a data reduction statistical technique that allows simplifying the
correlational relationships between numbers of continuous variables. Exploratory factor
analysis is used in order to identify constructs and investigate relationships among key
interval scaled questions.
Exploratory Factor Analysis: Principle Component Analysis
Exploratory Factor Analysis is a general name denoting a class of procedures primarily
used for data reduction and summarization (Malhotra, 2007). Exploratory Factor Analysis
allows researchers to condense a large set of variables or scale items down into a smaller,
more manageable number of factors or components (Pallant, 2007). It does this by
summarising the underlying patterns of correlation and looking for groups of closely
related or not related items (Tabachnick and Fidell, 2007). It identifies how many factors
best represent the scale items in the context of the data collected and which factor each
scale item loads most highly onto (Hair et al. 2010). In this research, Principle
Component Analysis (PCA) is a key method in the Exploratory Factor Analysis process
used to explore the underlying structure of the Indian women shopping motivations and
their correlations in the data obtained. In which the original scale items are transformed
into a smaller set of linear combinations, with all variance in the data being used. The
following data and factor analyses were conducted within the Exploratory Factor
Analysis process:

Examining the dimensionality of the 48- item scale was the next task. The output of the
factor analysis is obtained by requesting the principal Component Analysis and
specifying the rotation. After the standards indicate that data is suitable for factor
analysis, Principal Components Analysis was employed for extracting the data, which
allows determining the factor underlying the relationship between numbers of variables.
The total variable Explained box is suggesting that it extracts one factor accounts for
87.9% of the variance of the relationship between variables.
Loading on factors can be positive or negative. A negative loading indicates that this
variable has an inverse relationship with the rest of the factors. The higher the loading the
more important is the factor. However Comrey (1973: 1346) suggested that anything
above 0.44 could be considered salient, with increased loading becoming more vital in
determining the factor. All the loadings in the research are positive. (Factor Table 1)
Rotation is necessary when extraction technique suggest there are two or more factors.
The rotation of factors is designed to give an idea of how the factors initially extracted
differ from each other and to provide a clear picture of which item load on which factor.
There are six factors, each having Eigen value exceeding 1 for SERQVUAL. The Eigen
values for six factors were 14.17, 7.67, 6.244, 5.542, 2.00, 1.815 respectively. (Factor
Table 2) The percentage of total variance is used as an index to determine how well the
total factor solution accounts for what the variables together represent. The index for
present solution accounts for 87.09% of the total variations for choosing a Delhi Metro. It
is pretty good extraction as it can be economize on the number of factors (from 48 it has
reduced to 6 factors) while we have lost 12.91% information content for factors
SERQUAL dimension. The percentage of variance explained by factor one to six factors
for SERQUAL are 32.96, 17.85, 14.52, 12.84, 14.65, 4.65, 4.22 (Factor Table 2). Factor
Table 1 tells us that after six factors are extracted and retained, the communality is 0.934
for variable 1, 0.928 for variable 2 and so on. It means 87.09% of the variance of variable
1 is being captured by the eight extracted factors together. The proportion of variance in
any one of the original variables, which is being captured by the extracted factor, is
known as communality (Nargundkar, 2002).
Large commonalities indicate that a large number of variance has been accounted for by
the factor solution. Varimax rotated factor analytic results for factor for SERQUAL
Factor Table 4.
Interpretation of Factors
Each factor needs to be assigned a name or label to characterise it and aid its
interpretation (Tabachnick and Fidell, 2007). Each of the factors that have been extracted
via Principle Component Analysis in the Exploratory Factor Analysis process of this
research data are displayed. The names allocated to each factor are a result of the
interpretation of its factor scale items and are discussed in the following sub-sections.

The six factors shown in Factor Table 4 have been discussed below:-
Factor 1: Reliability
It is the most vital factor, which explains 32.96% of the variation. Reliability factors such
as It should saves my time (0.818), Token should be easily available(0.810), The seats
should be reserved for handicapped (0.791), It should have the feeder bus service (0.823),
It should have connectivity to the airports (0.825), It should have well maintained stations
(0.885), The metro station should be near to my home (0.819) emerge with good positive
correlations. This yields a great influence on choosing Delhi Metro.
H3 : The generic dimensions of service quality is Reliability. (Accepted)

Factor 2: Responsiveness
There are four loads to this factor. The factor “Responsiveness” is the second important
factor of SERVQUAL, which accounts for nearly 17.85% of the variations. The factors It
should have parking facility (0.836), It should saves my time (0.870), It should have the
route map is displayed in the trains and on the stations (0.847), It should be economical
(0.716) signifies that consumers show how responsive the Delhi Metro is.
H7 : The generic dimensions of service quality is Responsiveness (Accepted)

Factor 3: Tangibility
This factor is the seven significant factors, which has 14.52% of the variation, and this
comprises of seven loadings depicting the Tangibility aspect of services as per
SERVQUAL. The factor loading of 0.811,0.858, 0.877,0.844, and 0.880 representing It
should have the feeder bus service, It should have connectivity to the railway station, It
should have connectivity to the airports, Token should be easily available and Smart card
facility should be available respectively show tangibility of a service.
H5 : The generic dimensions of service quality is Tangibles (Accepted)

Factor 4: Empathy
The next important factor, which carry a loading of 12.89% of the variation comprises of
four loadings It should be economical, It should avoid traffic congestion on roads, It
should have an effective AC, It should have comfortable seats rotated value of 0.855,
0.895, 0.876, and 0.760 respectively signifies whether the Delhi Metro understand the
needs of their customers.
H6 : The generic dimensions of service quality is Empathy (Accepted)

Factor 5: Assurance
Assurance is the next factor, which next dimension and has 4.65% of the variation. This
factor has three loading namely The metro station should be near to my home (0.601), It
should have elevators (0.820) and The metro station should be near to my office (0.522)
shows assurance.

H4 : The generic dimensions of service quality is Assurance (Accepted)

Factor 6: Security
There are two loads to this factor. The factor “understand customers” is the next
important factor, which accounts for nearly 4.22% of the variations. The metro station
should be near to my office (0.554), It should have separate ladies compartment (0.576)
signifies that Delhi Metro should understand their customers that they choose Metro for
more safety and security.
H8   : There are five generic dimensions of service quality: Reliability, Assurance,

Tangibles, Empathy, and Responsiveness. (Partially accepted).

Conclusion
The results showed that there are five generic dimensions of service quality: Reliability,
Assurance, Tangibles, Empathy, and Responsiveness. But a new dimension should also
be understood by Delhi Metro that Security to provider services which means firms
should possess the skill and knowledge to perform a service so that maximum satisfaction
can be provided.
The study added to the understanding and applicability of SERVQUAL model in Delhi
Metro. Unlike, PZB’s dimensions of service quality viz., Reliability, Assurance,
Tangibles, Empathy, and Responsiveness Delhi Metro consumers also expect Security as
a parameter for service quality. Security includes Separate compartment for women and
the metro station should be near to my office. So based on these conclusions Delhi Metro
should concentrated on their security in delivering satisfaction to the Delhi Metro users.

Scope for further Research
As the survey conducted was only confined to NCR region results may vary if research is
in conducted in other parts of India and other public transports are considered for
measuring service quality. If the survey is conducted to measure the service quality in
whole India result may substantial different. If other modes of transports are taken into
consideration the results may not be the same. The researcher has taken PZB’s
SERVQUAL model for measuring the service quality if other models are taken into
consideration the results may substantially vary. Again total quality management is
understood as measuring the service quality measurement but other dimensions of total
quality management have not been considered. If other parameters of TQM are taken into
consideration the results may substantially vary.

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Factor Analysis
Factor table 1
                          Communalities

                                   Initial    Extraction
It should saves my time               1.000         .895
The frequency of the trains
                                     1.000         .809
should be high
It should be economical              1.000         .855
It should avoid traffic
congestion on roads                  1.000         .880
It should have an effective
AC                                   1.000         .866
It should have comfortable
seats                                1.000         .838
It should have separate
ladies compartment                   1.000         .934
It should have the route
map is displayed in the              1.000         .911
trains and on the stations
Token should be easily
available                            1.000         .929
Smart card facility should
be available                         1.000         .886

The seats should be
reserved for handicapped             1.000         .831
Proper queue should
made for before entering             1.000         .912
the train
Staff should be friendly and
informative staff                    1.000         .734
It should have the feeder
bus service                          1.000         .869
It should have connectivity
to the railway station        1.000   .908

It should have connectivity
to the airports               1.000   .886

The metro station should
be near to my office          1.000   .873

The metro station should
be near to my home            1.000   .904
It should have elevators
                              1.000   .830
It should be used by my
friends                       1.000   .799

It should have Scanning
machines at the checking      1.000   .705
points
It should have well
maintained stations           1.000   .820
Announcements should be
made in Hindi and English     1.000   .871
It should have parking
facility                      1.000   .893
It should saves my time
                              1.000   .876
The frequency of the trains
should be high                1.000   .877

It should be economical
                              1.000   .900
It should avoid traffic
congestion on roads           1.000   .899

It should have an effective
AC                            1.000   .915
It should have comfortable
seats                         1.000   .934
It should have separate
ladies compartment            1.000   .928

It should have the route
map is displayed in the       1.000   .853
trains and on the stations
Token should be easily
available                     1.000   .890
Smart card facility should
be available                  1.000   .949
The seats should be
reserved for handicapped      1.000   .904
Proper queue should
made for before entering      1.000   .902
the train

 Staff should be friendly and
 informative staff                 1.000           .796
 It should have the feeder
 bus service                       1.000           .856

 It should have connectivity
 to the railway station            1.000           .831
 It should have connectivity
 to the airports                   1.000           .913
 The metro station should
 be near to my office              1.000           .884

 The metro station should
                                   1.000           .842
 be near to my home
 It should have elevators
                                   1.000           .867

Extraction Method: Principal Component Analysis.
Factor table 2
Total Variance Explained

                            Initial Eigenvalues               Extraction Sums of Squared Loadings       Rotation Sums of Squared Loadings
 Component       Total      % of Variance     Cumulative %   Total     % of Variance   Cumulative %    Total     % of Variance   Cumulative %
 1                21.764           50.614           50.614    21.764          50.614         50.614     14.173          32.960         32.960
 2                  5.861           13.631          64.245     5.861          13.631          64.245     7.677         17.853          50.813
 3                  3.800            8.837          73.083     3.800           8.837          73.083     6.244         14.520          65.334
 4                  3.187            7.412          80.495     3.187           7.412          80.495     5.542         12.890          78.223
 5                  1.590            3.698          84.193     1.590           3.698          84.193     2.000          4.652          82.875
 6                  1.249            2.904          87.097     1.249           2.904          87.097     1.815          4.222          87.097
 7                   .836            1.944          89.041
 8                   .773            1.798          90.839
 9                   .590            1.372          92.211
 10                  .530            1.233          93.445
 11                  .363             .845          94.289
 12                  .316             .735          95.025
 13                  .286             .665          95.690
 14                  .254             .590          96.280
 15                  .245             .569          96.849
 16                  .192             .446          97.294
 17                  .166             .385          97.680
 18                  .148             .345          98.024
 19                  .132             .308          98.332
 20                  .105             .245          98.577
 21                  .099             .231          98.808
 22                  .082             .191          98.999
 23                  .080             .187          99.185
 24                  .062             .145          99.330
 25                  .048             .111          99.441
 26                  .045             .104          99.545
 27                  .041             .096          99.641
 28                  .028             .065          99.707
29                   .026             .060         99.766
 30                   .023             .053         99.820
 31                   .018             .043         99.862
 32                   .016             .038         99.900
 33                   .015             .034         99.935
 34                   .009             .022         99.956
 35                   .007             .017         99.974
 36                   .006             .014         99.988
 37                   .002             .004         99.992
 38                   .002             .004         99.995
 39                   .001             .002         99.998
 40                   .001             .002         99.999
 41                   .000             .000        100.000
 42             8.16E-005              .000        100.000
 43              7.52E-016       1.75E-015         100.000
Extraction Method: Principal Component Analysis.
Factor table 4
Rotated Component Matrix(a)

                                                         Component
                               1          2          3               4          5          6
It should saves my time            .818       .044       .188            .426       .073       .038
The frequency of the trains
                                   .576       .045       .166            .666       .060       .020
should be high
It should be economical            .268       .149       .172            .855       .017       .019
It should avoid traffic
congestion on roads            -.135          .172       .151            .895       .091   -.013
It should have an effective
AC                             -.115          .231       .126            .876       .110       .069
It should have comfortable
seats                              .276       .148       .203            .760       .339   -.082
It should have separate
ladies compartment                 .467       .674       .293            .366       .196   -.055
It should have the route
map is displayed in the            .664       .633       .184            .185   -.019          .032
trains and on the stations
Token should be easily
available                          .858       .380       .150            .128   -.085          .053
Smart card facility should
be available                       .810       .436       .167            .093   -.049          .015

The seats should be
reserved for handicapped           .791       .299       .173            .227       .179   -.039
Proper queue should
made for before entering           .283       .867       .221            .111       .004   -.136
the train
Staff should be friendly and
informative staff              -.065          .618       .161            .490       .065   -.277
It should have the feeder
bus service                        .823       .388       .171            .047       .053   -.080
It should have connectivity
to the railway station             .800       .120       .032            .001       .237       .443

It should have connectivity
to the airports                    .825       .344       .157        -.021          .203       .149

The metro station should
be near to my office               .532   -.060      -.043               .072       .522       .554

The metro station should
be near to my home                 .377       .585       .201            .095       .601       .091
It should have elevators
                               -.042          .010       .065            .388       .820       .035
It should be used by my
friends                            .753   -.045      -.092               .187       .424   -.083
It should have Scanning
machines at the checking       .762    .242    .008    -.182   .179    -.004
points
It should have well
maintained stations            .885    -.138   -.103   .020    .070    .050
Announcements should be
made in Hindi and English      .697    .442    .135    .406    -.059   -.050
It should have parking
facility                       .246    .836    .314    .181    -.001   -.042
It should saves my time
                               -.075   .870    .291    .163    -.031   .048
The frequency of the trains
should be high                 .241    .155    .098    .758    .026    .460

It should be economical
                               .479    .716    .202    .172    .120    .273
It should avoid traffic
congestion on roads            .605    .623    .195    .219    .002    .242

It should have an effective
AC                             .823    .248    -.001   .137    -.089   .387
It should have comfortable
seats                          .637    .623    .106    .054    .000    .355
It should have separate
ladies compartment             .606    .370    -.037   .300    .023    .576

It should have the route
map is displayed in the        .195    .847    .173    .052    .021    .253
trains and on the stations
Token should be easily
available                      -.068   .329    .844    .211    .082    -.113
Smart card facility should
be available                   .243    .315    .880    .092    .059    .068
The seats should be
reserved for handicapped       .839    .063    .423    .105    .012    .078
Proper queue should
made for before entering       .667    .243    .606    .130    .013    .115
the train
Staff should be friendly and
informative staff              .495    -.083   .647    .344    -.088   -.001
It should have the feeder
bus service                    .235    .308    .811    .151    .155    -.014

It should have connectivity
to the railway station         -.104   .279    .858    .064    .023    -.039
It should have connectivity
to the airports                .231    .108    .877    .277    -.019   .037
The metro station should
be near to my office           .693    .192    .575    .043    -.075   .169

The metro station should
                               .819    .018    .346    .030    -.168   .150
be near to my home
It should have elevators
                                     .676         .212   .526   -.111   -.209   .181

Extraction Method: Principal Component Analysis.
 Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 7 iterations.
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