Prioritization of various dimensions of service quality in hospitality industry

Prioritization of Various Dimensions of Service Quality In Hospitality Industry, Jagbir Singh Dalal,
Journal Impact Factor (2015): 7.9270 Calculated by GISI (www.jifactor.com)
www.iaeme.com/ijm.asp 12 editor@iaeme.com
1
Research Scholar, Uttarakhand Technical University
Dehradun, Uttarakhand, India
ABSTRACT
This study evaluates the relative importance of service quality (SERVQUAL) dimensions by
applying multiple regression analysis and presents an attractive application of these dimensions in
the hospitality industry. The primary purpose of present research paper is to prioritize the various
dimensions of service quality using multiple regression analysis. Thus, this paper provides a
comprehensive way to rank these dimensions, especially in the context of hospitality industry. It
gives the detailed narrative about the regression analysis. For this, primary data were collected from
the selected industrial estates of Uttarakhand which limits the generalizability of the findings of our
study. The paper ends with conclusion and implications along with directions for future research.
1. INTRODUCTION
Service quality is considered the life of hotel (Min and Min, 1996) and core of service
management (Chen, 2008). Service quality is related with customer satisfaction (Shi and Su, 2007)
and customer satisfaction is associated with customers revisit intention (Han, Back & Barrett, 2009).
If an effective image is portrayed to customers, it will create competitive advantage for hotel (Ryu,
Han & Kim, 2008). As a result of service development process three concept of service is composed
and these three steps are service process, system and Service resources-structure (Edvardsson, 1997).
Service quality means the difference between the customers’ expectation of service and their
perceived service. Thus it is the result of the comparison that customers make between their
expectations about a service and their perception of the way the service has been performed
(Gronroos, 1984; Parasuraman et al., 1985, 1988). A number of experts define service quality
differently. Parasuraman et al. (1985) define it as the differences between customers, expectation of
services and their perceived service. If the expectation is greater than the service performance,
perceived quality is less than satisfactory and hence, customer dissatisfaction occurs. Lewis and
Chambers (1989), Dotchin and Oakland (1994 a, b), and Asubonteng et al. (1996) define service
quality as the extent to which a service meets customers, need and expectation. In this study, the
assessment standards of Zeithaml, Berry & Parasuraman (1988) will be used, which consist of five
dimensions: tangibility, reliability, responsiveness, assurance, and empathy.
Marketing is the main factor that only focused on the Customer satisfaction (Flint, Woodruff
& Gardial, 1997). Customer satisfaction plays an important role in financial performance of hotel
PRIORITIZATION OF VARIOUS DIMENSIONS OF SERVICE
QUALITY IN HOSPITALITY INDUSTRY
1
Jagbir Singh Dalal
Volume 6, Issue 6, June (2015), pp. 12-28
Article ID: 10120150606003
International Journal of Management (IJM)
IAEME: http://www.iaeme.com/IJM.asp
ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
IJM
© I A E M E

(Nilssom, Johnson & Gustafsson, 2001). In hotel industry, as service has direct interaction with
customers, that is why customer satisfaction can be a replication of service quality in hotels (Shi &
Su, 2007). There are some factors that have significant role in measuring customer association with
hotel: age, gender, income and culture (Ryu et al., 2008). Hotel performance is directly allied to
service quality improvement. There exists a significant relationship between improvement in service
quality and hotel performance change (Narangajavana and Hu, 2008). High level development tools
are used for the satisfaction of multiple users about service and quality (Hope & Wild, 1994). The
key problem lies with hotel manager is to retain and fascinate customers (Shi & Su, 2007).
Customers revisit intention and emotions are mediated by customer satisfaction (Han et al., 2009).
Customer satisfaction plays a role of mediator in perceived value of hotel and behavioral intention
(Ryu et al., 2008). Both Public and private sectors have reviewed the Service quality and to fulfill
their demand, customer-focused approach was highly practiced (Pyon, Lee & Park, 2009).
We, in the present study, evaluate the relative importance of these dimensions by applying
multiple regression analysis. These dimensions will be prioritized and then final ranking of these
factors will be given on the basis of regression coefficients obtained from the analysis. The
upcoming sections of the paper are organized as follows: background of the study (Section 2) which
includes selection of weighting method (Section 2.1), the dimensions of service quality (Section 2.2),
methodology used in the study (Section 3), data analysis and discussion (Section 4), the conclusion,
implications and future research directions (Section 5).
2. BACKGROUND OF THE STUDY
2.1 Selection of Weighting Method
There are various weighting techniques have been adopted in literature for the prioritization
of the factors of a particular concept. These methods include multiple regression analysis,
discriminant analysis, factor analysis, and analytical hierarchy process (AHP). Adoption of any one
of above stated technique depends on three main criteria: internal consistency, flexibility, and
applicability (Singh, Garg and Deshmukh, 2005).
One of the important and widely applied methods is multiple regression analysis. There are
two basically ways to perform a multiple regression analysis: (i) Enter method: where all the
independent variables are inserted at a time, and their relative importance will be estimated by
calculating regression coefficients, and (ii) Step-wise method: where the most significant
independent variable is inserted first, followed by others. All significant variables will be inserted
one by one, until all insignificant variables remain. Each method has pros and cons. The main
problem with the regression analysis is the interpretation of results. In addition, any specific error in
equation formulation impacts the whole system. Therefore, it is not advisable to use regression
analysis in case of complex problems. But, in our case, since we have only five quality dimensions
with one dependent variable, multiple regression analysis will be most suitable.
Discriminant analysis is based on the idea that a variable of the study pursue the normal
distribution. This assumption does not hold any validation in the context of qualitative factors (Garg
et al., 2012). Moreover, discriminant analysis does not provide proper results in the case of outliers.
Factor analysis is pertinent in the case of highly correlated factor of a particular concept. But the
main problem is that the correlation may not be valid in the real situation. In addition, factor analysis
shows a high level of sensitivity with the changes in data, sample size. Therefore, it is not worth to
use factor analysis in case of nonlinear data (Hair, Anderson and Tatham, 1987).
Similarly, analytical hierarchy process (AHP) is another widely used technique for the
prioritization of the factors. This is a multi-criteria decision making technique. The most important
feature of this technique is that it can handle both qualitative and quantitative information (Saaty,
2008). In this technique, the main problem of the study can be simplified by decomposing the main

problem in various hierarchical levels with the help of existing theories to facilitate the decision
maker in having a better understanding (Singh et al., 2005).
Table 1: Application of different weighing methods
Weighting Methods Factors
Multiple Regression Analysis Dimensions of knowledge management in SME sector.
Discriminant Analysis Success factors for project classification.
Strategic alliances factors in SMEs.
Factor Analysis Factors affecting the performance of the safety program.
Factors affecting the cost performance in Indian
construction companies.
Analytic Hierarchy Process Ranking of critical success factors of EIS.
(AHP) Prioritization of factors of customer experience in banking
sector
As mentioned earlier, for the solving a simple problem, multiple regression analysis is most
widely used, while complex problem of ranking of various factors, AHP is one of the best technique
among all the tools available for the prioritization. In addition, using multiple regression analysis
will also fill the gap in the existing literature which shows the unavailability of any study related to
the prioritization of service quality dimensions in the perspective of hospitality industry. To fill this
gap, this chapter is organized as follows: The next section presents a brief summary of the service
quality dimensions. Next to this, an introduction of multiple regression analysis has been provided.
Further, the relative importance of these dimensions has been presented.
2.2 Dimensions of service quality
The literature review identified five key dimensions (Parasuraman et al., 1985, 1988), as
discussed in chapter 2, those are important for measuring the service quality of any service sector.
2.2.1 Tangible: The tangibility dimension with regard to a hotel includes physical evidence, the
appearance of physical facilities, personnel, communication materials, personality and appearance of
personnel, tools, and equipment used to provide the service. For example, some hotel chains (e.g.
Hilton, Mandarin, Sheraton, and Hyatt) consciously ensure that their properties conform to global
standards of facilities wherever they are located (Nankervis, 1995).
2.2.2 Reliability: The ability involves performing the promised service dependably and accurately.
It includes .Doing it right the first time1, which is one of the most important service components for
customers. Reliability also extends to provide services when promised and maintain error-free
records.
2.2.3 The front office staff is willing to help customers and provide prompt service to customers
such as quick service, professionalism in handling and recovering from mistakes. It has been said
that “Today luxury is time”. Therefore, service providers’ ability to provide services in a timely
manner is a critical component of service quality for many guests.
2.2.4 Assurance: Assurance refers to the knowledge and courtesy of employees and their ability to
convey trust and confidence including competence, courtesy, credibility and security.
2.2.5 Empathy: Empathy refers to the provision of caring and individualized attention to
customers, including access, communication and understanding the customers.

3. METHODOLOGY
In the present research, a multiple regression analysis is used to predict the level of quality
satisfaction of the respondents visiting and staying in hotels in industrial estates of Uttarakhand. If
successful, it would provide a better foundation for the marketing efforts of hotels in these targeted
areas. To apply regression analysis, the researcher selected quality satisfaction as the dependent
variable (Y) to be predicted by 5 service quality dimensions (i.e., reliability, responsiveness,
assurance, empathy and tangibles) as the independent variables consisting of 27 scale items
representing service quality perceptions of respondents staying at different locations in industrial
estates of Uttarakhand.
The relationship among these 5 independent variables and quality satisfaction was assumed
to be statistical, not functional, because it involved perceptions of performance and may include
levels of measurement error. In the present research, there are total 300 respondents from various
hotels in industrial estates of Uttarakhand. The first question to be answered concerning sample size
is the level of relationship (R2
) that can be detected reliably with the proposed regression analysis.
The sample size of 300, with 5 potential independent variables, is able to detect relationships with R2
values of approximately 5 percent at a power of .80 with the significance level set at .05 and
approximately 7%, if we move to signify level of.01. The total sample of 300 respondents or
observations to 25 scale items (i.e., in a ratio of 6:1) also meets the minimum criterion of the ratio
between observation and scale items (5:1).
4. DATA ANALYSIS AND DISCUSSION
4.1. Estimating the Regression Model and Assessing Overall Model Fit
To estimate the regression model for this particular case, the stepwise method is employed to
select variables for inclusion in the regression variate. Using SPSS (19.0), we select our data file, and
go to the option of multiple regression analysis. To proceed further, first of all, we obtain a
correlation matrix as shown in Table 2.
Table 2: Correlation Matrix
Pearson Correlation Y X1 X2 X3 X4 X5
Quality Satisfaction (Y) 1.000
Reliability (X1) .424* 1.000
Responsiveness (X2) .064 .038 1.000
Assurance (X3) .617* .073 -.095 1.000
Empathy (X4) .479* .058 -.121* .274 1.000
Tangibles (X5) .676* .091 -.134* .745* .559* 1.000
*Items in Italic are significant at .05 level

i. Stepwise Estimation: Selecting the First Variable (X5)
Table 3.1 displays all the correlations among the 5 independent variables (X1 to X5) and one
dependent variable (Y, Quality satisfaction). Examination of the correlation matrix (looking down
the first column) reveals that technical risk the highest significant bivariate correlation with the
dependent variable (.676*). The first step is to build a regression equation using just this single
independent variable. The results of this first step appear as shown in Table 3.1.
Table 3.1: Results of Step 1 of Multiple Regression Analysis
Step 1: Variable Entered: Tangible (X5)
Multiple Regression .676
Coefficient of Determination (R2
) .457
Adjusted R2
.455
Standard error of the estimate .400
Analysis of Variance
Sum of Degree of Mean F Significance
Squares Freedom Square
Regression 40.062 1 40.062 250.780 .000a
Residual 47.605 298 .160
87.667 299
a. Predictors: (Constant), Tangible
Variables entered into the Regression Model after Step 1:
Regression Statistical Correlations Collinearity
Coefficients Sig. Statistics
Variables B Std. Beta t Sig. Zero- Partial Part Toler VIF
Entered Error order ance
(Constant) -2.331 .251 -9.272.000
.937 .059 15.836.000
.676 .676 .676 1.000 1.000
Tangible .676

Variables not Entered into the Regression Equation / Excluded Variables
Statistical Collinearity Statistics
Significance
Beta (β) t Sig. Partial Tolerance VIF
In Correlation
Reliability .365a
9.773 .000 .493 .992 1.750
Responsiveness .157a
3.721 .000 .211 .982 1.043
Assurance .254a
4.066 .000 .230 .444 1.112
Empathy .147a
2.883 .004 .165 .687 1.289
a. Predictors in the Model: (Constant), Tangible (X5)
From the Table 3.1, the researcher can address issues concerning both overall model fit as
well as the stepwise estimation of the regression model. At this point, multiple R is a correlation
coefficient for the simple regression of tangible (X5) and the dependent variable quality satisfaction
(Y). It doesn’t possess any positive or negative sign, reflecting only the degree of association. In the
first step of this stepwise estimation, the multiple R is the same as the bivariate correlation (.676*)
because the equation contains only one variable. While R2
is the correlation coefficient squared
(.6762
= .457), also referred to as the coefficient of determination. This value indicates that
percentage (45.7%) of the total variance of dependent variable (Y, quality satisfaction) explained by
the regression model consisting tangible (X5) as the first variable entered at this stage.
The standard error of estimate is another measure of the accuracy of our predictions. It is the
squared root of the sum of the squared errors divided by the degrees of freedom, also represented by
the square root of the MS residual (√(47.605/298) = .399). The ANOVA analysis provides the
statistical test for the overall model fit in terms of the F ratio. The total sum of squares (87.667) is the
squared error that would occur if we used only the mean of Y to predict the dependent variable.
Using the value of X2 reduces this error by 45.7% (40.062/87.667).
This reduction is deemed statistically significant with an F ratio of 250.780 and a significant
level of.000.
Thus, in the first step, only one independent variable (tangible X5) has entered and used to
estimate the regression equation for predicting the dependent variable. For each variable in the
equation, several measures need to be defined like the regression coefficient, the standard error of
the coefficient, the t value of variables in the equation, and the collinearity diagnostics (tolerance and
VIF). The value .937 is the regression coefficient (b5) for the independent variable (X5). The
predicted value for the each observation is the intercept (2.331) plus the regression coefficient (.937)
times its value of the independent variable (Y = 2.331 + .937X5).
The standardized regression coefficient (β) value of .676 is the value calculated from
standardized data. With only single independent variable (X5), the squared β coefficient equals the
coefficient of determination. This β value enables us to compare the effect of X5 on Y to the effects
of other independent variables on Y at each step, because this value reduces the regression
coefficient to a comparable unit, the number of standard deviations.

The standard error of the regression coefficient is an estimate of how much the regression
coefficient will vary between samples of the same size taken from the same population. In simple
words, it is the standard deviation of the estimates of b2 across multiple samples. In case, when one
were to take the multiple samples of same size from the same population and use them to calculate
the regression equation, the standard error is an estimate of how much the regression coefficient
would vary from sample to sample. The standard error of b5 is .059, denoting that the 95%
confidence interval for b5 would be .937 ± (1.96 * .059), or ranging from a low of .82 to a high of
1.05. The value of b5 divided by the standard error (.937 ÷ .059 =15.83) is the calculated t value for t
test of hypothesis b5 = 0.
The t value of variables in the equation, as just calculated, measure the significance of the
partial correlation of the variables reflected in the regression coefficient. In such situation, it
indicates whether the researcher can confidently say, with a stated level of error, that the coefficient
is not equal to zero. F values may be given at this stage rather than t values.
They are directly comparable because the t value is approximately the square root of the F
value as in the present case (√250.780= 15.83). In the present research, the t value is 15.83, which is
statistically significant at the .000 level. It gives the researcher, a high level of assurance that the
coefficient is not equal to zero and can be assessed as a predictor of customer satisfaction.
Three different correlations are also given that help in evaluating the estimation process. The zero-
order correlation is the simple bivariate correlation between the independent and dependent variable.
The partial correlation denotes the incremental predictive effect, controlling for other variables in the
regression model on both dependent and independent variables. This measure is used for judging
which variable is next added in sequential search methods. Finally, the part correlation denotes the
unique effect attributable to each independent variable. For the first step in a stepwise solution, all
three correlations are identical (.676) because no other variables are in the equation. As variables are
added, these values will differ, each reflecting their perspective on each independent variable’s
contribution to the regression model.
When only one variable (X5) has entered into regression model, the tolerance is 1.00, indicating
that it is totally unaffected by other independent variable. Also, the VIF is 1.00, both values
indicating a complete lack of multicollinearity. With the inclusion of X5 in the regression equation, 4
other potential independent variables remain for inclusion to improve the prediction of the dependent
variable. For each of these variables, four types of measures are available to assess their potential
contribution to the regression model: partial correlations, collinearity measures, standardized
coefficients (Beta), and t values (See Table 3.1).
ii. Stepwise Estimation: Adding the Second Variable (X1)
Reliability (X1) is the next variable to enter into the regression equation due to high partial
correlation. The results of this step 2 are shown in Table 3.2 given below.
Step 2: Variable Entered: Reliability (X1)
) .589
Adjusted R2
.586

Sum of Degree of Mean Square F Significance
Squares Freedom
Regression 51.645 2 25.822 212.906 .000b
Residual 36.022 297 .121
Total 87.667 299
b. Predictors: (Constant), Tangible, Reliability
Variables
B Std. Beta T Sig. Zero- Partial Part Toler VIF
Entered
Error order ance
(Constant) -3.092 .232 -13.301 .000
Tangible .891 .052 .643 17.207 .000 .676 .707 .640 .959 1.043
Reliability .255 .026 .365 9.773 .000 .424 .493 .363 .959 1.043
Variables not in the Model/ Excluded Variables
Significance
Beta In t Sig. Partial Tolerance VIF
Correlation
Responsiveness .138b
3.766 .000 .214 .980 1.817
Assurance .250b
4.623 .000 .260 .444 1.112
Empathy .143b
3.231 .001 .185 .687 1.525
b. Predictors in the Model: (Constant), Tangible (X5), Reliability (X1)
As shown in Table 3.2, Reliability (X1) is the next variable to be added to the regression
equation in this stepwise procedure. The multiple R and R2
values have both increased with the
addition of X1. The R2
increased by 17.7% (calculated as: change in R2
= .4932
* 54.3 = .131, i.e.
13.2% of unexplained variance), and therefore the total value of R2
will become 45.7 + 13.2 = 58.9%
as also given in Table 3.2. The adjusted R2
also increased from .455 to .586 and the standard error of

estimate decreased from .400 to .348. Both of these measures also demonstrate the improvement in
the overall model fit.
The regression coefficient for X1 is .255 and the β weight is .365. Although not as large as the
β weight for X5 (.676), X3 still has a substantial impact in the overall regression model. The
coefficient is statistically significant and multicollinearity is minimal with X5 as described in an
earlier section. This tolerance is quite acceptable with a value of.959 indicating that only 1.1 % of
either variable is explained by another. The lack of multicollinearity results in little change for either
the value of b1 (.891) or the β of X1 (.643) in step 1. It further indicates that the variables X5 and X1
are relatively independent (the simple correlation between these two is .091). If the effect of X1 on Y
were totally independent of the effect of X5, the b1 coefficient would not change at all. The t values
indicate that both X5 and X1 are statistically significant predictors of Y. The t value for X5 is now
17.207, whereas it was 15.836 in step 1. The t value of X1 relates to the contribution of this variable
given that X5 is already in the equation. It can be noted that the t value of X1 (9.773) is the same
value shown for X1 in step 1 under the heading “Variables not Entered into the Regression Equation
/ Excluded Variables” (see Table 3.2). Since X5 and X1 both make significant contributions, neither
will be dropped in the stepwise estimation procedure. To identify the other variables to enter in the
regression equation, we can look at Table 3.2 under the section heading “Variables not entered into
the Regression Equation / Excluded Variables”. Looking at the partial correlations for the variables
not entered into the regression equation till now, we see that X3 has the highest partial correlation
(.260), which is also statistically significant at the .000 level. This variable would explain 6.76% of
the unexplained variance (.2602
= .0676), or 2.78% of the total variance (.4582
* 41.1).
iii. Stepwise Estimation: Adding the Third Variable (X3)
X3 has the highest partial correlation (.260), and therefore the capability to enter into the
regression equation. The results of this third step of entering X3 into the regression equation are
given in Table 3.3:
Step 3: Variable Entered: Assurance (X3)
) .617
Adjusted R2
.613
Regression 54.071 3 18.024 158.800 .000c
Residual 33.596 296 .113
Total 87.667 299
c. Predictors: (Constant), Tangible (X5), Reliability (X1), Assurance (X3)

Variables B Std. Beta TSig. Zero- Partia Part Toler VIF
Entered Error order l ance
(Constant) -3.170 .226 -14.056.000 .676 .441 .304 .571 1.752
Tangible .633 .075 .457 8.448.000
Reliability .254 .025 .364 10.065.000 .424 .505 .362 .924 1.082
Assurance .288 .062 .250 4.066 .000 .617 .260 .166 .550 1.817
Variables not Entered into the Regression Equation / Excluded Variables
Significance
Beta In T Sig. Partial Tolerance VIF
Correlation
Responsiveness .137c
3.870 .000 .220 .980 1.162
Empathy .209c
4.817 .000 .270 .641 1.768
c. Predictors in the Model: (Constant), Tangible (X5), Reliability (X1), Assurance (X3)
Entering X3 into the regression equation results that the value of R2
increases by 10.8% (.617
– .589 = .028). Moreover, the adjusted R2
also increases to .613 from .586 and the standard error of
the estimate decreases to .337 from .348 (in step 2). This shows that the new variable X3 makes a
substantial contribution to overall model fit. The entering of X3 brought a third statistically
significant predictor of overall satisfaction (Y) into the regression equation. The regression weight of
.288 is complemented by the β weight of .250, second highest among the three variables in the model
(see Table 3.3).
Once again, the lack of multicollinearity results in little change for the value for b1 from .255
(in step 2) to .254 now or also the β value slightly changes from .365 (in step 2) to .364. It further
indicates that the variables X1 and X3 are relatively independent (the simple correlation between
these two is .073). If the effect of X3 on Y were totally independent of the effect of X5 and X1, the
b3 coefficient would not change at all. The t values indicate that all three X5, X1 and X3 are
statistically significant predictors of Y. The t value for X5 is now 8.448, whereas it was 15.836 in
step 1. The t value of X3 relates to the contribution of this variable given that X5 and X1 are already
in the equation. It can be again noted that the t value of X3 (4.066) is the same value shown for X3 in
step 1 under the heading “Variables not Entered into the Regression Equation / Excluded Variables”.
At this stage of analysis, remaining two variables has the statistically significant partial correlations
for inclusion into the equation (see the last portion of Table 3.3). Here, one interesting thing can be

noticed that, both variables are significant enough at the level of significance of .000. But one
variable ‘responsiveness’ (X2) had insignificant bivariate correlation (.064) with Y, and also has less
partial correlation (.220) than the other variable ‘empathy’ (X4). Empathy (X4) has significant
bivariate correlation (.479) and partial correlation (.270), so it will be the next candidate for inclusion
in the regression equation.
iv. Stepwise Estimation: Adding the Fourth Variable (X4)
X4 has the highest partial correlation (.270), and therefore the capability to enter into the
regression equation. The results of this fourth step of entering X4 into the regression equation are
given in Table 3.4:
Step 4: Variable Entered: Empathy (X4)
) .645
Adjusted R2
.640
Regression 56.521 4 14.130 133.833 .000d
Residual 31.146 295 .106
Total 87.667 299
d. Predictors: (Constant), Tangible (X5), Reliability (X1), Assurance (X3), Empathy (X4)
Variables B Std. Beta T Sig. Zero- Partia Part Toler VIF
Entered Error order l ance
(Constant) -
.233 -15.330 .000
3.575
.290 .676 .260 .161 .558 1.792
Tangible .402 .087 4.633 .000
Reliability .253 .024 .362 10.380 .000 .424 .517 .360 .810 1.235
Assurance . 366 .062 .317 5.878 .000 .617 .324 .204 .475 2.105
Empathy .255 .053 .209 2.883 .000 .479 .270 .167 .566 1.768

Variables not in the Model/ Excluded Variables
Significance
Beta In T Sig. Partial Tolerance VIF
Correlation
Responsiveness .148d
4.326 .000 .245 .976 1.170
d. Predictors in the Model: (Constant), Tangible (X5), Reliability (X1), Assurance (X3),
Empathy (X4)
e. Dependent Variable: Quality Satisfaction
Entering X4 into the regression equation results increases the value of R2
by 2.8% (.645 -
.617 = .028). Moreover, the adjusted R2
also increases to .640 from .613 and the standard error of the
estimate decreases to .325 from .337 (in step 3). All these variations denote that the new variable X4
makes a substantial contribution to overall model fit. The entering of X4 brought a fourth statistically
significant predictor of overall satisfaction (Y) into the regression equation. The regression weight
(b) of .255 is complemented by the beta (β) weight or beta (β) coefficient of .209, lowest among all
the four variables in the model (see Table 3.4).
The lack of multicollinearity results in little change in the values of regression coefficients
(b) and beta (β) values of variables already in the equation. The t values indicate that all four X5, X1,
X3 and X4 are statistically significant predictors of Y. The t value of X4 relates to the contribution
of this variable given that X5, X1 and X3 are already in the equation. It can be again noted that the t
value of X4 (2.883) is the same value shown for X4 in step 1 under the heading “Variables not
Entered into the Regression Equation / Excluded Variables” (see Table 3.2).
After following the same procedure, we can find that the last variable, ‘responsiveness’ (X2)
is also significant enough at the level of significance of .000 (see the last portion of Table 3.4). It has
also an increase in partial correlation from .220 to .245 and a good enough tolerance value (.976).
Thus, we select this variable for the final inclusion in the regression equation.
v. Stepwise Estimation: Adding the Fifth and Final Variable (X2)
As discussed in the last section, X2 is the final variable for inclusion in the regression
equation with statistically significant partial correlation. The results of this fifth and final step of
entering variable X4 into the regression equation are given in Table 3.5:
Step 5: Variable Entered: Responsiveness (X2)
) .666
Adjusted R2
.660
Regression 58.385 5 11.677 117.241 .000c
Residual 29.282 294 .100
Total 87.667 299
c. Predictors: (Constant), Tangible (X5), Reliability (X1), Assurance (X3), Empathy (X4),
Responsiveness (X2). Dependent Variable: Quality Satisfaction

Regression Statistical Sig. Correlations Collinearity
Coefficients Statistics
Variables
B Std. Beta T Sig. Zero- Parti Part Tolera VIF
Entered
Error β order al nce
(Constant) -4.411 .298 -14.878 .000
Tangible .420 .084 .303 4.979 .000 .676 .279 .168 .551 1.81
Reliability .247 .024 .354 10.448 .000 .424 .520 .352 .805 1.24
Assurance .368 .060 .319 6.091 .000 .617 .335 .205 .462 2.16
Empathy .268 .052 .219 5.198 .000 .479 .290 .175 .562 1.77
Responsiv
.188 .043 .148 4.326 .000 .064 .245 .146 .855 1.17
eness
The final regression model with five independent variables (X5, X1, X3, X4 and X2)
explains almost 66.6% of the variance of overall satisfaction (Y). The entry of X4 in the regression
equation results increases the value of R2
by 2.1% (.666 - .645 = .021). Moreover, the adjusted R2
also increases to .660 from .640 and the standard error of the estimate decreases to .316 from .325 (in
step 4). All these variations once again, denote that the new variable X2 makes a substantial
contribution to overall model fit. The adjusted R2
of .660 indicates no over fitting of the model and
that the result should be generalizable from the perspective of the ratio of observations to variables in
the equation (20:1 for the final model). Also, the standard error of the estimate has been reduced to
.316 which means that at the 95% confidence level (± 1.96 * standard error of the estimate), the
margin of error for any predicted value of Y can be calculated at ± 1.1. With the entry of variable
X2, all five regression coefficients and the constant have become statistically significant predictors
of quality satisfaction (Y) at .05 and even .01 significant levels. The impact of multicollinearity,
even among these five variables is substantial. Of the five variables in the equation, three variables
(X5, X3 and X4) of them have tolerance values nearly .50 indicating that approximately one-half of
their variance is accounted for by the other variables in the regression equation.
If we examine the zero-order (bivariate) and partial correlation, we can see more directly the
effects of multicollinearity. For instance, X5 had the highest bivariate correlation (.676) with Y
among the all 5 variables, yet multicollinearity (tolerance of .551) reduces it to a partial correlation
of .279 which is less than three other variables (X1, X3 and X4) entered after X5 and making it a
marginal contributor in the equation. On the other hand, X2 had low and insignificant bivariate
correlation of .064 only, but the multicollinearity (.562) increases it to partial correlation of value
.290 which is even more than X5, and making it to one of the
contributors in the equation.
vi. Model Summary
As noted earlier, the regression model at this stage consists of the five independent variables
with the addition of X2. All these variables in the model remain statistically significant, avoiding

the need to remove a variable in the stepwise process. Thus, this model is finalized and contains all
variables as predictors of the dependent variable (Y), i.e., quality satisfaction.
In a stepwise estimation procedure, however, the regression model can be markedly affected
by issues such a multicollinearity. In the following section, provides an overview of the estimation
of the regression model from the perspective of overall model fit. The model summary is given in
Table 3.6 which provides a step-by-step summary detailing the measures of the overall fit of the
regression model developed in the present research to predict overall satisfaction of respondents in
terms of different risks. Each of the first three variables added to the regression equation made
substantial contributions to the overall model fit, with a substantive increase in the R2
and adjusted
R2
while also decreasing the standard error of estimate. With only the first three variables, 61.3% of
the variance in overall satisfaction is explained with a confidence interval of 95%. Two additional
statistically significant variables are added to arrive at the final model, but they make smaller
contributions.
Table 3.6: Model Summary of Stepwise Multiple Regression Model
Change Statistics
R Adjusted R Std. Error of R Square F Sig. F
Model R Square Square the Estimate Change Change df1 df2 Change
1 .676a
.457 .455 .400 .457 250.780 1 298 .000
2 .768b
.589 .586 .348 .132 95.503 1 297 .000
3 .785c
.617 .613 .337 .028 21.376 1 296 .000
4 .803d
.645 .640 .325 .028 23.201 1 295 .000
5 .816e
.666 .660 .316 .021 18.718 1 294 .000
a. Predictors: (Constant), Tangibles
b. Predictors: (Constant), Tangibles, Reliability
c. Predictors: (Constant), Tangibles, Reliability, Assurance
d. Predictors: (Constant), Tangibles, Reliability, Assurance, Empathy
e. Predictors: (Constant), Tangibles, Reliability, Assurance, Empathy, Responsiveness
f. Dependent Variable: Quality Satisfaction
4.2. Interpreting the Regression Variate
With the model estimation completed, the regression variate specified, and the diagnostic
tests that confirm the appropriateness of the results administered, we can now examine our
predictive equation based on five independent variables.
Interpretation of the regression coefficients
The first task is to evaluate the regression coefficients for the estimated signs, focusing on
those of unexpected direction. From the last section of the results of step 4 in Table 3.5 headed
“Variables Entered into the Regression Model” yields the prediction equation of the column labeled
“Regression Coefficient: B.” From the column, we read the constant term, (-4.411) and the
coefficients (.420, .247, .368, .268, and .188) for X5, X1, X3, X4, and X2 respectively.
Therefore, the predictive equation is:
Y = -4.411 + .420 X5 + .247 X1 + .368 X3 + .268 X4 + .188 X2
With this regression equation, the overall customer satisfaction level for any customer could
be easily calculated if the customers’ evaluations of all types of risks are known. For example, if any
customer rated these all risks with an average value of 4 for each of these risks. The overall customer
satisfaction level would be:

Overall Satisfaction Level = -4.411 + .420 * 4 + .247 * 4 + .368 * 4 + .268 * 4 + .188 * 4
=-4.411 + 1.68 + .988 + 1.472 + 1.072 + .752
=-4.411 + 5.964 = 1.553
Now, first start with the interpretation of the constant. It is significant (significance = .000),
thus making a substantive contribution to the prediction. However, in the present research, it is
highly unlikely that any respondents would have zero ratings on all the risk variables and their
variables, the constant merely play a part in the prediction process and provides no insight for
interpretation.
When we review the regression coefficients, the sign is an indication of the relationship
between the independent and dependent variables. All of the variables have positive coefficients,
which denote that, an increase in perceptions on these variables has a positive impact on overall
satisfaction. In other words, if the respondents score more on these items and variables, it increases
the overall satisfaction of respondents.
4.3. Assessing Variable Importance
In addition, to provide a basis for predicting overall customer satisfaction, the regression
coefficients also provide a means of assessing the relative importance of the individual variables in
the overall prediction of customer satisfaction. When all the variables are expressed on a
standardized scale, then the regression coefficients represent relative importance. However, in other
instances, the beta weights are the preferred measure of relative importance. In our case of present
research, all the variables are expressed on the same scale, but we will use the beta coefficients for
comparison between the independent variables. In Table 3.5, beta coefficients are listed in the
column headed “Regression Coefficients: β.” Here, we can make direct comparisons among the
variables to determine their relative importance in the regression variate. In the present case, X5
(Tangible) was the most important dimension, followed by X3 (Assurance), X4 (Empathy), X1
(Reliability) and finally X2 (Responsiveness). With a steady decline in the β coefficients across the
variables, it is difficult to categorize variables as high, low, or otherwise. However, viewing the
relative magnitudes does include that, for example, X5 (Tangible) shows a more marked effect (more
than two times) than X2 (Responsiveness).
5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
The present study has contributed to knowledge about the service quality construct in the
hospitality industry in Uttarakhand, India by prioritizing the various dimensions of service quality.
The findings suggest that tangibility is one of the most crucial dimensions, followed by assurance,
empathy, reliability and finally responsiveness. This shows customers in industrial estates of
Uttarakhand expect more with tangible items while getting hospitality services from service
providers. They are more attracted towards service assurance rather than the responsiveness of the
employees. Additionally, these findings have demonstrated that the HOLSERV instrument is
suitable for use by managers in the hospitality industry and hotel owners/managers can confidently
design service strategies that meet guests’ expectations on the basis of this prioritization of service
quality dimensions.
Certainly, at the same time, further studies are needed to prioritize the same dimensions by
different weighting methods such as AHP techniques. Future researchers may apply this AHP
techniques based on the perceptions of experts. While there are certain limitations also with AHP or
any other techniques. Beside this, we followed well-established procedures throughout our study.
For instance, the service quality dimensions identified in this study show similarities to other service
quality measures. This suggests that there may be some potentially universal facets of service quality

and that perhaps we may not need to develop specific measures from scratch for each context.
Instead, existing knowledge base may provide a useful starting point for adaptations to new contexts.
Future research can shed further light on these issues.
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