Assessing the Service Quality Attributes Affecting the Satisfaction of the Northern Railway Passengers: An Empirical Study

Abstract

The present study aims to assess the intangible attributes of the service that have an impact on customer satisfaction. The Intangible attributes attached to the service, are difficult to determine. The Indian Railway network is one of the largest railway networks in the world. It is spread over 115,000 km having 21,617 passenger trains carrying 23 million passengers every day. In terms of revenue generation, it is a major contributor to the Indian economy but even then, the service level is very poor as compared to the other parts of the world. Due to increased competition in the modes of transportation, the Service attributes of Indian Railway acts as a strong influencer on Passenger Satisfaction. Indian Railways has a huge potential in terms of economic benefits if their service quality is improved. Various studies have tried to identify the important attributes regarding the Service Quality of Indian Railway. The SERVQUAL model provided important insights into the service attributes. The study attempts to identify the gap that exists in the service level, that is, service offered by the Indian Railway and expectation of the customers. In the study, only internal aspects like facilities (attributes) which make the journey comfortable and the absence of these attributes makes the passengers’ journey uncomfortable are included. The result indicates that there exists a considerable gap in Reliability and Assurance dimensions of Railway service quality and the most important factors determining satisfaction of passengers are basic facilities, safety and security, cleanliness and employee behaviour towards passengers.

Keywords

Service gap overall satisfaction Indian Railway (Northern)

Introduction

Indian Railways was founded on 16 April 1853, almost 167 years ago. It is one of the cheapest modes of transport among all the mode of transport available. The first train in the country had run in between Roorkee and Piran Kaliyar in the year 1821. As per the Indian Railways, it is one of the world’s largest networks containing 115,000 km of track, covering 65,000 km and carrying 23 million passengers every day. The Northern Railways is an important zone of the Indian Railways, which was established in the year 1952, and remains the largest zone in terms of route kilometres, even after the re-organization of the Indian Railways into 16 zones. Northern Railway now comprises five divisions—Ambala, Delhi, Firozpur, Lucknow and Moradabad. Some of the main services provided by Indian Railway apart from transporting passengers are freight services, parcel carrier, catering, tourism services and other related services. It is one of the most popular forms of transportation but, still, the level of the service is very poor in the trains. With the increased competition in the transportation sector, the customers are becoming more and more demanding concerning quality of services. The customers want the full value for their money spent (Lagerstrom, 2002). Indian Railways owing to their size and quantum of operations should be one of the benchmarks for other nation to copy in terms of services but on the contrary, there is a huge gap in the standard service delivery mechanism and actual service delivered by the Indian Railway. This is the area of concern with which the Indian Railways is grappling to cope up with the cheap and better services as provided by the airlines. The internal facilities or attributes which are one of the most important in creating a positive image in the mind of the passengers while they travel from one station to another is pathetic. Along with the uniqueness of products other factors like physical facility, style, image and quality of service play an important role in the customer delighted as proposed by Lin (2007). The quality of services can be measured in terms of customer perception, customer expectation, customer satisfaction and customer attitude as suggested by Sachdev and Verma (2004). The internal facilities ambience and aesthetics inside the train helps in impacting the perception and attitudes of the customer. Silcock (1981) highlighted some of the important service quality attributes for the public transport industry as the measures of accessibility, reliability, comfort, convenience and safety. The present study is focused on finding out the reactions of the passengers in terms of the service delivered by the Indian Railway.

Literature Review

Various studies have been conducted in recent past related to consumer satisfaction and service quality, the most cited paper is by Parasuraman et al. (1985) in which they identified key determinants of service quality namely reliability, responsiveness, competency, accessibility, credibility and tangibility to formulate a service quality framework. A study by Sathyanarayana et al. (2017) suggested improving the quality dimension of services such as tangibility, reliability, empathy and overall performance of the service quality to retain and create a set of loyal customers. Priyadharshini and Selladurai (2017) in their finding suggested ways to improve the service quality of the Indian Railways by focusing on improving the reservation facilities and infrastructural facilities. They also suggested proper training to all the employees to improve service quality. Kumar and Jatin (2017), in their study, suggested that the number of general compartments should be increased to reduce the crowd along with proper training to the railway staff. The study also suggested providing proper training to all the staff of the railway to improve the satisfaction of the customers. Eboli and Mazzulla (2007) in their study measured customer satisfaction about the public transportation factors like availability of shelter and benches, cleanliness, overcrowding, information system, safety, personnel security, courtesy and infrastructure. Sheeba and Kumuthadevi (2013) found that the factors affecting the satisfaction of the passengers in a specific order are basic facilities, hygiene, safety and security and catering service. Vanniarajan and Stephen (2008) explained the attributes that are used for evaluating the service quality of Indian Railways, which are reliability, assurance, empathy, tangibles and responsiveness. It was found that passengers were moderately satisfied with these dimensions. Waris (2010) identified the factors that have a huge impact on the customers they are punctuality, frequency, speed, space, reliability and comfort, safety and train operations. Khurshid et al. (2012) in their study discussed the current issues of a transport sector that how service quality affects customer satisfaction. They highlighted the importance of customer satisfaction and in case of failure to satisfy customer some factors which were identified are as follows: non-availability of seats for females, less security, mental harassment, time problems, frequency of announcements, fans at platforms, security of self, etc.

Devi Prasad and Raja Shekhar (2012) found that there is a significant relationship between in train service, train punctuality, ticketing, reservation, safety and security on overall service quality. Gloria and Agyapong (2011) stated that among the various variable that significantly affects customer satisfaction of the passengers are: tangibility, responsiveness and commitment of the railway staff towards better service. Balakrishnan (2012) in his study found the relationship between railway service quality attributes and customer satisfaction based on the perception of the passengers. Some of the important factors for the relationship are seat condition, spacing between the seats, luggage storage facilities, cleanliness inside the train, washrooms, food, security, punctuality and behaviour of the staffs at the station. Mohammed (2008) in his study suggested that the employee should quickly respond to the feedback of the customer and improve upon the mistakes to create a positive image of the company. Another factor which was highlighted in the study was related to the timeliness. Sachdeva and Verma (2014) emphasized that the five service quality dimensions given by Zeithaml et al. (1988) plays a significant role in creating the quality of the service and the all the companies should maintain and improve upon these factors in the service domain to bring the satisfaction in the mind of the customers. The five service quality dimensions are represented diagrammatically in Figure 1.

Figure 1.

Source: Zeithaml et al. (1988).

The Objective of the Research

The objective of this study is to find out the service gap in terms of the expectation of the passengers and service delivered by Indian Railways inside the train. The study aims to identify the determinants of passenger satisfaction on service quality factors the effective implementation of these determinants will lead to customer satisfaction. Customer delighted is commonly accepted as an indicator of best services.

Research Methodology

The survey was conducted on a sample of 100 passengers using convenient sampling technique in the two major stations in Uttarakhand, that is, Dehradun and Haridwar railway stations. The data was collected through a structured questionnaire from the passengers. Five-point Likert scale was used to study the various determinants of customer satisfaction. SERVQUAL model was used for analysing the determinants of service. The literature review helped in identifying various determinants. A descriptive research design was used to gain an insight into consumer’s perception about the services offered by Indian Northern Railways concerning the above-mentioned dimensions. The respondents were asked to provide ratings on the Likert scale for services offered by Northern Railways. Total 10 attributes were selected as mentioned below, which affect the service quality inside the train.

Seat allocations

Luggage storage facility

Cleanliness inside the train

Security inside the train

The behaviour of the railway staff

Spacing among seat inside the train

Condition of fan/light and AC inside the train

Condition of the washrooms inside the train

Pantry service

Conditions of the windows.

Analysis and Interpretation of Data

To identify the key factors affecting the satisfaction of the passengers inside the train factor analysis was used. The primary objective of using the factor analysis is to reduce the data into manageable levels by combining into one factor the variable that is highly correlated with one other. The final data set than gets reduced to factors or components, smaller in number than the original variables that are uncorrelated with each other.

Interpretation: Table 1 exhibit means rating, standard deviation, the minimum and maximum rating given by the 100 respondents regarding 10 attributes affecting the satisfaction of the passengers inside the train while travelling through Indian Railways with special reference to Uttarakhand state. The attributes have been captured through using five-point Likert scale indicating 5 as maximum rating and 1 as minimum rating respectively.

Table 1.

Descriptive Statistics of the Attributes Related to the Study Undertaken

Descriptive Statistics
	N	Minimum	Maximum	Mean
1. Condition of seat for seating of passengers inside the train	100	1	5	2.21
2. Overall space inside the train for passengers	100	1	5	2.36
3. Language facilities inside the train	100	1	5	2.56
4. Condition of the window inside the train	100	1	5	2.48
5. Cleanliness inside the train	100	1	5	3.14
6. Toilet facility inside the train for passengers	100	2	5	3.18
7. Security of passengers inside the train	100	1	4	2.67
8. Pantry service offered inside the train for passengers	100	1	5	2.59
9. The behaviour of the railway employee inside the train with the passengers	100	2	4	2.35
10. Fan and AC function ability inside the train	100	1	5	2.58
11. Valid N (listwise)	100

Source: The authors.

Due to a large number of the attribute, the result appears to be vague and interpretation is difficult. Therefore, these 10 attributes have been transformed into a small number of representative factors through factor analysis as given below.

Factor Analysis: Suitability for the Data

Table 2 shows two tests, which indicate the suitability of our data for factor analysis.

The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy indicates that the proportion of variance in variables which is common variance, that is, means which might be caused by underlying factors. High values (close to 1.0) generally indicate that factor analysis may be useful with the given data.

If the value is less than 0.05, the result of the factor analysis probably will not be very useful. In this case from the aforementioned data, it is clear the value of KMO measure is 0.627 thus confirming the appropriateness for factor analysis.

Table 2.

KMO and Bartlett’s Test

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.627
Bartlett’s Test of Sphericity	Approx. Chi-Square	371.342
	df	45
	Sig.	.000

Source: The authors.

Table 3.

Communalities

Communalities
Factors	Initial	Extraction
1. Condition of seat for seating of passengers inside the train	1	0.63459089
2. Overall space inside the train for passengers	1	0.66048977
3. Language facilities inside the train	1	0.78361092
4. Condition of the window inside the train	1	0.76041088
5. Cleanliness inside the train	1	0.50568522
6. Toilet facility inside the train for passengers	1	0.90801361
7. Security of passengers inside the train	1	0.93770227
8. Pantry service offered inside the train for passengers	1	0.78735857
9. The behaviour of the railway employee inside the train with the passengers	1	0.66843084
10. Fan and AC function ability inside the train	1	0.76039904
11. Valid N (listwise)

Source: The authors.

Factor Analysis: Communalities

Communalities indicate the amount of variance in each variable that is accounted for (Table 3).

Extraction Method: Principal Component Analysis

Initial commonalities are an estimate of the variance in each variable accounted for by all components or factors. For principal analysis, this is always equal to the 1.0 (for correlation analyses) or the variance of the variable (for covariance analyses). Extraction communalities are estimates of the variance in each variable accounted for by the factor (or components) in the factor solution.

Factor Analysis: Total Variance Explained (Table 4) Extraction Method: Principal Component Analysis

Interpretation: The first column gives the value based on initial eigenvalues. For the initial solution, there are as many components or factors as there are variables. The ‘Total’ column gives the amount of variance in the observed variables accounted for by each component or factor. The ‘% of Variance’ column gives the present of variance accounted for by each specific factor or component, relative to the total variance in all the variables. The ‘Cumulative %’ column gives the per cent of variance accounted for by all the factors or components up to and including the current one. In good factor analysis, there are a few factors that explain a lot of the variance and the rest of the factor explain a relatively small amount of variance. In the study, the first four components or factors as eigenvalue for them is more than one (1) and account for a cumulative variance of 74.066

The extraction sums of squared loading group give information regarding the extracted factors or components. For principal components extraction, these values are the same as those reported under initial eigenvalues. Next is ‘Rotation sums of squared loading’ group. This column is displayed when we have requested for rotation of factors. In our case, we have gone Varimax rotation. The variance accounted for by rotated factors or components may be different from those reported for the extraction but the cumulative per cent for the set of factor or components will always be the same.

Table 5 refers to correlation between different variables and the components and the values range from -1 to +1.

Factor Analysis: Rotated Components Matrix

Table 6 displays rotated components matrix and report the factor loading for each variable on the component or factor after rotation. Each number represents the partial correlation between the items and the rotated factor. This correlation can help up formulate an interpretation of the factor or components. This is done by looking for a common thread among the variables that have a large loading for a particular factor or components. Factor analysis rotation methods start with the original axes and apply a mathematical rotation which simplifies the relationship between factors and variables. Through the factor analysis, we can extract four factors out of 10 attributes. In other words, 10 attributes have been transformed into four representative factors.

Table 4.

Total Variance Explained

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
Factors	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance
Condition of seat for seating of passengers inside the train	3.4877679	34.878	34.877679	3.48776795	34.8777	34.877679	2.41494	24.14939
Overall space inside the train for passengers	1.727344139			1.72734414	17.2734	52.151121	1.97353	19.73533
Language facilities inside the train	1.141843	11.418	63.569551	1.14184304	11.4184	63.569551	1.83696	18.36963
Condition of the Window inside the train	1.0497369	10.497	74.06692	1.04973687	10.4974	74.06692	1.18126	11.81257
Cleanliness inside the train	0.806525507
Toilet facility inside the train for passengers	0.6025195	6.0252	88.15737
Security of passengers inside the train	0.4547249	4.5472	92.70462
Pantry service offered inside the train for passengers	0.3027062	3.0271	95.731682
The behaviour of the railway employee inside the train with the passengers	0.2789469	2.7895	98.521151
Fan and AC function ability inside the train	0.1478849	1.4788	100
Valid N (listwise)

Source: The authors.

Figure 2.

Scree Plot

Table 5.

Components Matrix

Component Matrix
Factors	Component
Factors	1	2	3	4
Condition of seat for seating of passengers inside the train	0.699792	–0.3235	0.0737241	–0.18657174
Overall space inside the train for passengers	0.474458	–0.3988	0.1566246	–0.50180484
Language facilities inside the train	0.4272276	–0.615	0.0564169	0.46865653
Condition of the window inside the train	0.5750696	0.597	–0.256995	–0.08508656
Cleanliness inside the train	0.5034028	0.4374	–0.04765	–0.24225704
Toilet facility inside the train for passengers	0.6448069	0.6176	0.1788403	0.2807964
Security of passengers inside the train	0.2019301	0.2067	0.9114708	0.15303519
Pantry service offered inside the train for passengers	0.7067728	–0.246	–0.135962	0.4569744
The behaviour of the railway employee inside the train with the passengers	0.6557261	–0.2302	0.1613612	–0.39925161
Fan and AC function ability inside the train	0.7875592	–0.0004	–0.364816	0.08401652
Valid N (listwise)

Source: The authors.

Table 6.

Rotated Component Matrix

Rotated Component Matrix	Extraction Method: Principal Component Analysis a. Components Extracted Rotation Method: Varimax with Kaiser Normalization b. Rotation Converged in Four Iterations
Factors	1	2	3	4
Condition of seat for seating of passengers inside the train	0.1957321	0.6804	0.3642048	0.02726894
Overall space inside the train for passengers	–0.012982	0.8113	0.0461721	–0.00180606
Language facilities inside the train	–0.20731	0.2229	0.8278079	0.07529508
Condition of the window inside the train	0.8654016	0.0597	–0.003721	–0.08894117
Cleanliness inside the train	0.6610236	0.2368	–0.10697	0.03499454
Toilet facility inside the train for passengers	0.822221	–0.0647	0.2304061	0.41796886
Security of passengers inside the train	0.082733	0.1009	0.0056623	0.95949909
Pantry service offered inside the train for passengers	0.2853241	0.1832	0.8199298	–0.00924271
The behaviour of the railway employee inside the train with the passengers	0.2235236	0.7708	0.135863	0.07684819
Fan and AC Function ability inside the train	0.5773603	0.3179	0.5114696	–0.25372929

Source: The authors.

Note: The bold figures are just representing the highest figures.

Factor Analysis: Factor Transformation Matrix

The factor transformation matrix describes that specific rotation applied to the factor solution. The matrix is used to compute the rotated factor matrix from the original unrotated) factor matrix. If the off-diagonal elements are close to zero. If the off-diagonal elements are close to zero, the rotation was relatively small. If the off-diagonal elements are large (>±0.5) a large rotation was applied.

From Table 7, it is evident that most of the diagonal values are small or close to zero indicating that the rotation required in the current was very small.

Factor Analysis: Naming the Extracted Factors

All the variables that have correlated with a particular factor need to give a name based on the underlying common dimension that is shared by these variables. For our study, all the variables have correlated with a four-factor and these four extracted factors have been named and displayed in Table 8.

Table 7.

Component Transformation Matrix

Component Transformation Matrix
Component	1	2	3	4
1	0.637	0.566	0.515	0.093
2	0.740	–0.453	–0.454	0.202
3	–0.214	0.199	–0.125	0.948
4	–0.026	–0.659	0.716	0.227

Source: The authors.

Extraction method: Principal Component Analysis.

Rotation method: Quartimax with Kaiser Normalization.

Table 8.

Names of the Extracted Factor Along with Their Respective Variables

1	2	3	4
Physical Evidence Factor	Core Service Factor	Value-added Service Factor	Security Factor
Cleanliness inside the toilet	Proper space inside the train	Proper space for the luggage inside the train	Overall security inside the train from passengers
Condition of the window inside the train for passengers	The proper condition of the seat inside the train	Good pantry service inside the train
Cleanliness inside the train compartment	Healthy behaviour of the railway employee inside the train
Proper functioning of fan/AC inside the train

Source: The authors.

Four representative factors have been named as:

Physical evidence service factor

Core service factor

Value-added service factor

Security factor

Regression Analysis: Identifying the Most Important Factors Affecting the Satisfaction of the Customer Inside the Train While Travelling

After reducing the 10 variable that influences the satisfaction of the passengers inside the train while travelling from one place to another place, four representative uncorrelated factors have extracted from the 10 variables taken for the study. After this, regression analysis has been performed to identify those factors that affect the satisfaction of the passengers inside the train. For regression analysis satisfaction variable has been taken as the dependent variable and the extracted 4 factors as independent variables.

Regression Analysis: Descriptive Statistics

Table 9 displays descriptive statistics for each factor that have been extracted after factor analysis. Though the mean rating has been highest for the first factor but with the largest standard deviation making it difficult to decide that it is the most important factor that influences the brand loyalty while they buy the particular brand of detergent powder and cake. Therefore, we move to regression analysis for having a clearer picture to identify the most important factors that affect the satisfaction of the passengers inside the train.

Table 9.

Descriptive Statistics of the Extracted Factors

Descriptive Statistics
Factors	Mean	Std. Deviation	N
Physical evidence service factor	2.859	0.73	100
Core service factor	2.306667	0.62	100
Value-added service factor	2.575	0.94	100
Security factor	2.67	0.85	100

Source: The authors.

Results and Discussion

Service quality can be measured in terms of customer perception, customer expectation, customer satisfaction and customer attitude. Therefore, four main determinants of passenger satisfaction on service quality have been taken into account for the measurement of service quality. From the factor analysis, four extracted factors were identified which are physical evidence service factor, core value service factor, value-added service factor and security factors, which affect the satisfaction of the passengers inside the train while they travel through Indian Railways. Each factor is associated with a certainly linked variable that combines to form the particular variable as displayed in Table 8. While finding the most important factor that affects the satisfaction of the passengers inside the train while they travel through Indian Railways, the physical evidence service factor is the most important factor among the four extracted factors, which gives satisfaction to the passengers in terms of service. The variable which comes under this factor is cleanliness inside the toilet, condition of the window inside the train for passengers, cleanliness inside the train compartment and proper functioning of fan/AC inside the train. This is then followed by core value service factor which includes proper space inside the train, proper condition of the seat inside the train and cordial behaviour of the railway employee inside the train. The least important factor which affects the satisfaction of the passengers inside the train is the security factor. Passengers are mentally prepared to protect their luggage. All the variables have a direct or indirect relation with the satisfaction of the passengers. The major gap has been found in reliability and assurance involving employee service efficiency, inquiries, punctuality, trust, safety, polite and safety of luggage, etc. Therefore, the present study concludes that there is a need to improve upon the factors, which have a direct bearing on the satisfaction of the customers. The factors like proper time management of trains and training of railway staff to be more responsive towards passenger’s requirement and need are the need of the hour. Safety measures need to be improved so that passengers may feel safe while travelling. In other words, the human touch is required which is missing in Northern Railway passenger services. The improvement of these service determinants will help in improving the service quality gaps and ultimately will improve the competitiveness of Indian Railways with regards to the other modes of transportation.

Scope for Future Research

These types of studies play an important role in understanding the expectation of the passengers, which can help the railways in improving their services. The feedback of the passengers helps in improving the deviation, which the passengers experience during the journey. The gap which exists between the expectation and service performed by the Indian Railways in real-time should be identified so that the concerned department can take timely corrective action to improve the level of service. The timely study should be conducted by the railway department in the various zones. Based on the feedback and improvement suggested by the passengers, appropriate steps should be taken to improve the level of service. This will help the Indian Railways in eliminating the gap between the expectation of the passengers and service offered by the Indian Railways. The present study was limited to Uttarakhand state only and it may vary for other zones; hence there is a scope of conducting such type of research for other zones. The study can be conducted using the SERVQUAL model.

Footnotes

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 authors received no financial support for the research, authorship and/or publication of this article.

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