View of DETERMINATION OF SERVICE QUALITY OF TRANSPORT FACILITY SYSTEM USING SERVQUAL: A CASE STUDY AT BRTS, INDORE (M.P)

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Vol.04,Special Issue 04, ²^nd Conference (ICIRSTM) April 2019, Available Online: www.ajeee.co.in/index.php/AJEEE

DETERMINATION OF SERVICE QUALITY OF TRANSPORT FACILITY SYSTEM USING SERVQUAL: A CASE STUDY AT BRTS, INDORE (M.P)

Kapil Gour(Author)

Assistant Professor, MDITM, City Indore Raju Raikwar (Assistant Professor)

S.NArya (Instructor)

Abstract—Defining and measuring the quality of service has been a major challenge for

organizations. But, now-a-days, service firms like transport organizations are realizing the significance of customer-centered philosophies and are turning to quality management approaches to help managing their businesses. SERVQUAL as an effective approach has been studied and its role in the analysis of the difference between customer expectations and service providers’ perceptions has been highlighted through a case study conducted at Bus Rapid Transport System, Indore, which is one of the best government organizations giving quality service to passengers. The main objective of this project is to demonstrate the use of SERVQUAL for measuring customers’ perceptions of transportation quality at given system.

The research methodology consists of preparing a detailed questionnaire based on five SERVQUAL attributes, tangibles, reliability, responsiveness, assurance, and empathy.

Opinions of 102 passengers are taken to find out the service quality perceived by them. The data obtained is analyzed using software IBM SPSS 22.0 and analysis has been done to draw conclusions using descriptive statistics, Pearson’s correlation and multiple regression analysis.

Keywords: Service quality, SERVQUAL, transportation, customer satisfaction.

1 INTRODUCTION

India represents one of the largest economies in the world by nominal GDP, and the third largest by purchasing power parity (PPP).

The country has recorded the highest growth rates in the mid-2000s, and is one of the fastest-growing economies in the world. India has recorded a growth of over 200 times in per capita income in a period from 1947 to 2015, which was led primarily due to a huge increase in the size of the middle class consumer, a large labor force, growth in the manufacturing sector due to rising education levels and engineering skills and considerable foreign investments.

Recently the World Bank has placed the country in the list of low-medium income economy. India’s economy primarily depends on agriculture, handicrafts, textile, manufacturing, and a multitude of services.

The two-thirds of the Indian workforce depend directly or indirectly on agriculture.

However, the service sector also plays an important role in India’s economy. India’s major industries include textiles, chemicals, food processing, steel, transportation equipment, cement, mining, petroleum, machinery, and software. India stands out for the size and dynamism of its services sector. The contribution of the services sector to the Indian economy is around 60%

in gross domestic product (GDP).

Central Statistical Organization (CSO) classification of the services sector falls under four broad categories, namely

1. Trade, hotels, and restaurants;

2. Transport, storage, and communication;

3. Financing, insurance, real estate, and business services;

4. Community, social, and personal services.

Out of above mentioned classification, Tourism - and travel-related services, and transport services are considered as major items in India’s service sector. Considering the importance of transportation in Indian economy present research work is based on evaluation of service quality of a transport facility system for Indore city.

2 LITERATURE REVIEW

A Service is any act or performance that one party can offer to another that is essentially intangibles and does not result in the ownership of anything. (Kotler, 2003) A service is defined as a set of singular and perishable benefits, delivered from the accountable service provider, mostly in close coactions with his service suppliers, generated by functions of technical systems and/or by distinct activities of individuals, respectively and/or rendered individually to an authorized service consumer at his/her

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dictated trigger, and, finally, consumed and utilized by the triggering service consumer for executing his/her upcoming business or private activity.

There are some major differences between services and goods. The nature of services is intangible whereas goods are tangible. Since services are intangible, measurement of service quality can be more complicated. Service quality measures how much the service delivered meets the customers’ expectations. In order to measure the quality of intangible services, researchers generally use the term perceived service quality. Perceived service quality is a result of the comparison of perceptions about service delivery process and actual outcome of service (Yarimoglu, 2014).

According to Kotler (2003), the growth of the service sector is expected to be an effective means of promoting economic restructuring and raising the competitiveness of new private enterprises.

In several transition countries, growing exports of services have been enabled by successful institutional transformations and technological advances. Additional revenues and growth stimulated by exports have, in turn, influenced favorably the potential for internal reforms, facilitating structural adjustments and modernization.

In a broad sense, services represent a diverse group of economic activities which are not directly associated with the primary or secondary sectors i.e. agriculture, mining and manufacture of goods. Production of both goods and services typically involves the provision of human value added in the form of labor, knowledge, and skills. In both cases, it can be based on high technology and advanced knowledge or, alternatively, can engage large quantities of low-skilled labor.

As customer satisfaction determines the performance of an organization on the basis of customer preferences regarding the organization, it becomes very essential to measure it. Following are the contributions of different researcher(s) in the field of customer satisfaction

Table 2.1: Contributions of different researchers in the field of Customer’s

Satisfaction S.No Researcher(s) (Year) Contribution

1. Oliver (1997) Presented a model in which satisfaction is a function of disconfirmation,

which in turn is a function of both expectations and performance.

2.

Caruana et

al.(2000) Their research work

provides the

grounding for the vast majority of satisfaction studies and encompasses four constructs expectations, performance, disconfirmation and satisfaction.

3.

Westbrook (1983)

Views satisfaction as a discrepancy between the observed and the desired.

4.

Oliver (1997) Presented a model in which satisfaction is a function of disconfirmation, which in turn is a function of both expectations and performance.

5.

Kotler (2003) Satisfaction is a person’s feelings of

pleasure or

disappointment resulting from

comparing a

product’s perceived performance (or outcome) in relation to his or her expectations.

6.

Gronroos

(1998) Customer

satisfaction or dissatisfaction is the customer’s response to the evaluation of discrepancy

perceived between previous

expectations with actual performance of the perceived product.

Table 2.2: Differences b/ w Customer Satisfaction and Service Quality

(Yap and Kew, 2007)

S.No Customer Satisfaction Service Quality 1. Customer satisfaction

can result from any dimension, whether or not it is quality belated.

The dimensions underlying quality judgments are rather specific.

2. Customer satisfaction judgments can be formed by a large number of non- quality issues, such as needs, equity, perceptions of fairness.

Expectations for quality are based on ideals or perceptions of excellence.

3. Customer satisfaction is believed to have more conceptual antecedents.

Service quality has less conceptual antecedents.

4. Satisfaction judgments do require experience with the service or provider.

Quality perceptions do not require experience with the service or provider.

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Figure 2.2: Model of Service Quality Gaps (Parasuraman et al., 1985; Curry, 1999) According to Brown and Bond (1995), "the gap model is one of the best received and most heuristically valuable contributions to the services literature". The model identifies seven key discrepancies or gaps relating to managerial perceptions of service quality, and tasks associated with service delivery to customers. The first six gaps (Gap 1, Gap 2, Gap 3, Gap 4, Gap 6 and Gap 7) are identified as functions of the way in which service is delivered, whereas Gap 5 pertains to the customer and as such is considered to be the true measure of service quality. The Gap on which the SERVQUAL methodology has influence is Gap 5. In the following, the SERVQUAL approach is demonstrated (Shahin, 2006).

Service quality is one of the key factors in determining the success or failure of electronic commerce. E-service can be defined as the role of service in cyberspace (Rust and Lemon, 2001). This study proposes a conceptual model of e-service quality (Figure 2.2) with its determinants. It is proposed that e-service quality have incubative (proper design of a web site, how technology is used to provide consumers with easy access, understanding and attractions of a web site) and active dimensions (good support, fast speed, and attentive maintenance that a web site can provide to its customers) for increasing hit rates, stickiness, and customer retention.

Figure 2.2: Model of e-service quality (Santos, 2003)

Following are the limitations of different service quality evaluation models:

Table 2.3: Limitations of Service Quality Models (Jain and Aggrawal, 2015) S.No Year Researcher Limitation 1. 1982 Lehtinen Researchers

have argued the validity of the model given by Lehtinen in the manufacturing industry. The model is also not applicable in this era of technology where internet and self service technologies have

revolutionized the working of retail sector 2. 1982 Gronroos The model only

laid down the components of service quality as technical, image and functional without mentioning about the techniques or

tools to

measure these components.

3. 1983 Lehtinen  The study has been conducted in

restaurant industry only thereby limiting its applicabilit y to other industries.

 The model can be applied to

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Vol.04,Special Issue 04, ²^nd Conference (ICIRSTM) April 2019, Available Online: www.ajeee.co.in/index.php/AJEEE only

specific situations.

4. 1985 Parasuraman, Berry and Zeithaml

 Many researcher s do not support the view of PZB to measure service quality as the gap between perceptions and expectation s of the consumers .

 Customers do not use expectation

s to

evaluate services as there is no reasonable tool to measure expectation s.

 SERVQUAL model focuses on the process of service rather than on the outcome of service

 High degree of correlation has been found amongst the five dimensions .

5. 1988 Haywood- Farmer

 The proposed model does not provide any informatio n about the measureme nt scale.

 The model fails to provide a direction to the manageme nt on the method and procedures that could be adopted to identify service

quality problems and then to keep a check on such problems.

6. 1990 Brogowicz et

al. The relative

importance of each of the three factors in affecting expectations needs to be empirically researched.

7. 1992 Cronin and Taylor

 The model does not have a good ranking on goodness of fit index in case of different cultures as people from different cultures would have different expectation

s and

scripts to the service encounters .

 The model needs to be validated in high involvemen t

industries.

 Multiple measures of the constructs have not been examined.

8. 1994 Berkley and Gupta

 The model limits itself to

 only the impact of

IT on

service quality.

 There is no

description

 about the level of IT use for different service settings

 The model does not offer a way to measure and

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Vol.04,Special Issue 04, ²^nd Conference (ICIRSTM) April 2019, Available Online: www.ajeee.co.in/index.php/AJEEE monitor

service quality 9. 1996 Dabholkar

,Thorpe, and Rentz

There is a serious

disagreement regarding the number of items to be used in various retail

10. 2001 Brady and

Cronin The research is based on the perceptions of only three organisational types. The study is based on the data gathered at a particular point of time only.

The sample is skewed.

11. 2004 Long and

McMellon The model

lacks validity as convenience sampling technique has been used.

Only limited dimensions of online service quality have been

considered. The model lacks on reliability scores.

12. 2010 Shahin and Samea

 The model lacks validity.

 The research is silent on the measureme nt of the additional gaps proposed 3 RESEARCH METHODOLOGY

According to Seyed (2008), reliability is the consistency of a set of measurements or of a measuring instrument, often used to describe a test. Validity and reliability are two fundamental elements in the evaluation of a measurement instrument. Validity is concerned with the extent to which an instrument measures what it is intended to measure. Reliability is concerned with the ability of an instrument to measure consistently. The reliability of an instrument is closely associated with its validity.

However, the reliability of an instrument does not depend on its validity. It is possible to objectively measure the reliability of an instrument. Cronbach’s alpha (α) is most

widely used objective measure of reliability (Tavakol and Dennick, 2011).

Alpha was developed by Lee Cronbach in 19511 to provide a measure of the internal consistency of a test or scale; it is expressed as a number between 0 and 1.

Internal consistency describes the extent to which all the items in a test measure the same concept or construct and hence it is connected to the inter-relatedness of the items within the test. Internal consistency should be determined before a test can be employed for research or examination purposes to ensure validity. As the estimate of reliability increases, the fraction of a test score that is attributable to error will decrease (Tavakol and Dennick, 2011).

If the items in a test are correlated to each other, the value of alpha is increased.

However, a high coefficient alpha does not always mean a high degree of internal consistency. This is because alpha is also affected by the length of the test. If the test length is too short, the value of alpha is reduced. Thus, to increase alpha, more related items testing the same concept should be added to the test (Tavakol and Dennick, 2011). Cronbatch’s alpha can be calculated using the following equation:

( ∑

) (3.1)

where,

K is the number of components;

σx2is the variance of the observed total test scores; and

σyi2is the variance of component i for the current sample of persons.

3.1Descriptive Statistics

Statistical methods which can be used to summarize or describe a collection of data are called descriptive statistics. In the present research work descriptive statistics used are mean, and standard deviation, the details of which are as follows.

3.1.1 Mean

In mathematics and statistics, the arithmetic mean, often referred to as simply the mean or average when the context is clear, is a method to derive the central tendency of a sample space. Mean, also known as arithmetic average, is the most common measure of central tendency and may be defined as the value which we get by dividing the total of the values of various given items in a series by the total number of items, one can work it out as under:

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( ̅) ∑

(3.2)

where

X = Symbol used for mean (pronounced as X bar);

Xi = Value of the i^th item X, i = 1, 2… n; and n = total number of items.

Mean is the simplest measurement of central tendency and is a widely used measure. Its chief use consists in summarizing the essential features of a series and in enabling data to be compared.

It is amenable to algebraic treatment and is used in further statistical calculations. It is a relatively stable measure of central tendency.

But it suffers from some limitations viz., it is unduly affected by extreme items; it may not coincide with the actual value of an item in a series, and it may lead to wrong impressions, particularly when the item values are not given with the average. However, mean is better than other averages, especially in economic and social studies where direct quantitative measurements are possible (Kothari p. 132).

3.1.2 Standard Deviation

Standard deviation is most widely used measure of dispersion of a series and is commonly denoted by the symbol (pronounced as sigma). Standard deviation is defined as the square-root of the average of squares of deviations, when such deviations for the values of individual items in a series are obtained from the arithmetic average. It is worked out as under:

( ) √∑( ̅) (3.3) When we divide the standard deviation by the arithmetic average of the series, the resulting quantity is known as coefficient of standard deviation which happens to be a relative measure and is often used for comparing with similar measure of other series. When this coefficient of standard deviation is multiplied by 100, the resulting figure is known as coefficient of variation.

Sometimes, we work out the square of standard deviation, known as variance, which is frequently used in the context of analysis of variation.

The standard deviation (along with several related measures like variance, coefficient of variation, etc.) is used mostly in research studies and is regarded as a very satisfactory measure of dispersion in a series. It is amenable to mathematical manipulation because the algebraic signs are not ignored in its calculation (as we ignore in case of mean deviation). It is less affected by fluctuations of sampling. These advantages make standard deviation and its coefficient a very popular measure of the scatteredness of a series.

3.2 Correlation

Correlation is a technique for investigating the relationship between two quantitative, continuous variables. Karl Pearson’s coefficient of correlation (or simple correlation) is the most widely used method of measuring the degree of relationship between two variables. This coefficient assumes the following:

 that there is linear relationship between the two variables;

 that the two variables are casually related which means that one of the variables is independent and the other one is dependent; and

 A large number of independent causes are operating in both variables so as to produce a normal distribution.

Karl Pearson’s coefficient of correlation can be worked out thus,

( ) ∑( )( ̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅)

(3.4 )

Karl Pearson’s coefficient of correlation is also known as the product moment correlation coefficient. The value of ‘r’ lies between ± 1. Positive values of r indicate positive correlation between the two variables (i.e., changes in both variables take place in the statement direction), whereas negative values of ‘r’ indicate negative correlation i.e., changes in the two variables taking place in the opposite directions. A zero value of ‘r’ indicates that there is no association between the two variables. When r = (+) 1, it indicates perfect positive correlation and when it is (–) 1, it indicates perfect negative correlation, meaning thereby that variations in independent variable (X) explain 100% of the variations in the dependent variable (Y). We can also say that for a unit change in independent variable, if

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there happens to be a constant change in the dependent variable in the same direction, then correlation will be termed as perfect positive. But if such change occurs in the opposite direction, the correlation will be termed as perfect negative. The value of ‘r’

nearer to +1 or –1 indicates high degree of correlation between the two variables (Kothari p. 140-142). Details of interpretations of different of correlation coefficients are as follows:

 -1.0 to -0.7 strong negative associations;

 -0.7 to -0.3 weak negative association;

 -0.3 to +0.3 little or no association;

 +0.3 to +0.7 weak positive association;

and

 +0.7 to +1.0 strong positive association.

3.3 Multiple Correlation and Regression When there are two or more than two independent variables, the analysis concerning relationship is known as multiple correlations and the equation describing such relationship as the multiple regression equation. One can explain multiple correlation and regression taking only two independent variables and one dependent variable (Convenient computer programs exist for dealing with a great number of variables). Multiple regression is a statistical technique that allows us to predict someone’s score on one variable on the basis of their scores on several other variables. Its governing equation assumes the form,

Y = a + b1X1 + b2X2 (3.5) where X1 and X2 are two independent variables and Y being the dependent variable, and the constants. a, b1 and b2 can be solved by solving the following three normal equations:

ΣYi = na + b1 ΣX1i + b2 ΣX2i (3.6) ΣX1iYi = aΣX1i + b1ΣX1i + b ΣX 1i X2 i (3.7) ΣX²ⁱYⁱ = aΣX²ⁱ + b1ΣX¹ⁱ X²ⁱ + b2Σ X²²ⁱ (3.8) (It may be noted that the number of normal equations would depend upon the number of independent variables. If there are 2 independent variables, then 3 equations, if there are 3 independent variables then 4 equations and so on, are used.) In multiple regression analysis, the regression coefficients (viz., b1, b2) become less reliable as the degree of correlation between the independent variables (viz., X1, X2) increases.

3.4 Reliability Analysis

In order to perform reliability analysis of received data, Cronbatch’s alpha values for different dimensions were calculated.

Following are the details obtained.

Table 4.2: Cronbatch’s alpha values for different SERVQUAL parameters

S.No Dimension No. of Attributes

Cronbatc h’s alpha value

1. Tangibles 5 0.606

2. Reliability 4 0.531

3. Responsiveness 5 0.712

4. Assurance 5 0.639

5. Empathy 4 0.603

From Table 4.2, one can analyze that as the calculated values of cronbatch’s alpha for different SERVQUAL dimensions are greater than 0.5, the received responses for different dimensions are acceptable. Table 4.3 shows the overall cronbatch’s alpha.

Table 4.3: Overall Cronbatch’s alpha value S.No Cronbach's Alpha No. of Items

1 0.744 23 (separated items) 2 0.582 5 (Averaged items)

From Table 4.3, one can analyze that as the value of overall Cronbatch’s alpha is much greater than permissible value (0.5), responses for different dimensions can be accepted collectively for analysis.

4 DESCRIPTIVE STATISTICS

In next stage of research work, mean and standard deviation for the parameters were obtained. Following are details of results obtained.

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Table 4.4: Descriptive Statistics for SERVQUAL Dimensions S

. N o SERVQUAL

Dimension N Mini

mum Maxi

mum Mean Std. Deviation 1. Tangibles (A) 102 1.00 5.00 3.7667 0.87838 2. Reliability (B) 102 1.50 5.00 4.0539 0.83594 3. Responsiveness

(C) 102 1.00 5.00 3.5431 0.63345

4. Assurance (D) 102 1.00 5.00 3.0353 0.97307 5. Empathy (E) 102 2.00 5.00 3.9706 0.77099

Average 3.

7 0.7

Correlation is significant at the 0.01 level (2-tailed).

4.1 Correlation Analysis

In next step, correlation analysis of the received responses was carried out.

Following are the results of the analysis:

Table 4.5: Details of Correlation Analysis Tangi

bles Reliabi

lity Responsiv eness Assura

nce Empa thy

Tangib les

Pearson Correlatio

n 1 .120 .519^** -.055 .078

Sig. (2-

tailed) .230 .000 .585 .435

N 102 102 102 102 102

Reliabi lity

Pearson Correlatio

n .120 1 .175 -.041 .749^**

Sig. (2-

tailed) .230 .078 .685 .000

N 102 102 102 102 102

Responsiveness Pearson Correlatio

n .519^** .175 1 .118 .166 Sig. (2-

tailed) .000 .078 .238 .096

N 102 102 102 102 102

Assura nce

Pearson Correlatio

n -.055 -.041 .118 1 .484^**

Sig. (2-

tailed) .585 .685 .238 .000

N 102 102 102 102 102

Empat hy

Pearson Correlatio

n .078 .749^** .166 .484^** 1 Sig. (2-

tailed) .435 .000 .096 .000

N 102 102 102 102 102

4.2 Multiple Regression Analysis

In order to identify extent of correlation among different SERVQUAL dimensions, multiple regression analysis on the responses was performed. For this purposes, according to literature survey, dimensions

reliability and responsiveness were chosen as dependent variables. Following are the details of results obtained by choosing reliability as dependent variable.

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Vol.04,Special Issue 04, ²^nd Conference (ICIRSTM) April 2019, Available Online: www.ajeee.co.in/index.php/AJEEE Table 4.6: Model Summary

Model R R Square Adjusted R

Square Std. Error of the Estimate

1 .883^a .780 .771 .39998

a. Predictors: (Constant), Empathy, Tangibles, Assurance, Responsiveness

Table 4.7: ANOVA^a

Model Sum of

Squares df Mean

Square F Sig.

1 Regressio

n 55.060 4 13.765 86.039 .000^b

Residual 15.519 97 .160 Total 70.578 101

a. Dependent Variable: Reliability

b. Predictors: (Constant), Empathy, Tangibles, Assurance, Responsiveness

Table 4.8: Coefficients

Model

Unstandardized Coefficients

Standardi zed Coefficie

nts T Sig.

B Std.

Error Beta

1

(Constant) 1.024 .265 3.866 .000 Tangibles -.033 .054 -.035 -.621 .536 Responsivene

ss .074 .046 .092 1.618 .109

Assurance -.460 .047 -.535 -9.718 .000 Empathy 1.080 .060 .996 18.127 .000 a. Dependent Variable: Reliability

Following are the details of results obtained by choosing responsiveness as dependent variable.

Table 4.9: Model Summary Model R R

Square Adjusted R

Square Std. Error of the Estimate

1 .543^a .294 .273 .88130

a. Predictors: (Constant), Empathy, Tangibles, Assurance

Table 4.10: ANOVA^a Model

Sum of Squares Df

Mean

Square F Sig.

1 Regressi

on 31.755 3 10.585 13.629 .000^b Residual 76.115 98 .777

Total 107.870 101

a. Dependent Variable: Responsiveness

b. Predictors: (Constant), Empathy, Tangibles, Assurance

Table 4.11: Coefficients^a

Model

Unstandardized Coefficients

Standardiz ed Coefficient s

t S i g . B Std. Error Beta

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Vol.04,Special Issue 04, ²^nd Conference (ICIRSTM) April 2019, Available Online: www.ajeee.co.in/index.php/AJEEE 1 (Constant)

.503 .582 .866

. 3 8 9 Tangibles

.611 .101 .519 6.068

. 0 0 0 Assurance

.119 .104 .112 1.148

. 2 5 4 Empathy

.095 .131 .071 .726

. 4 7 0 a. Dependent Variable: Responsiveness

5 RESULTS AND DISCUSSION

On choosing responsiveness as dependent variable and tangibles, assurance and empathy as independent variables, value or r² obtained as 0.294, which means 29.4% of variation in responsiveness is explained by different independent variables. Table 4.11 shows that all the independent variables are positively related to responsiveness. Results also show strong influence of assurance and empathy, and non-influence of tangibles on responsiveness. Table 5.3 shows the summary of discussions.

Table 5.3: Summary of Multiple Regression Analysis

S.No

Depende nt variabl

e

Independ ent variables

with positive relation

Independ ent variables

with strong influenc

e

1. Reliabilit y

Responsiv eness,

and Empathy

Tangibles, and Responsi

veness 2. Responsi

veness

Tangibles, Assuranc

e, and Empathy

Assurance , and Empathy 6 CONCLUSION

Following are the conclusions of present research work:

1. Factors dictating customers’ satisfaction are reliability and tangibles of the system;

2. Factors reliability and empathy are highly correlated, which means if organization focuses on reliability of the system, empathy of the customers towards system shall be enhanced;and 3. For the system, reliability is strongly

influenced by tangibles and responsiveness, and responsiveness is

strongly influenced by assurance, and empathy.

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