Measuring Commuters' Perception on Service Quality Using SERVQUAL in Delhi Metro
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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 IndiaLiterature 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 closelyrelated 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 .869It 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 .902the 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 99.70729 .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 -.083It 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 homeIt 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.You can also read
