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CHAPTER IV
DATA PRESENTATION & ANALYSIS
4.1 Introduction
In Chapter three, researcher had discussed the research design and methodology, origin of the
research, design of the research, variable of the research, population and sample of the research,
tools for data collection, development stage of the CAI package, procedure for data collection,
statistical analysis done in research work.
Data analysis is considered to be important step and heart of the research in research work. After
collection of data with the help of relevant tools and techniques, the next logical step, is to
analyze and interpret data with a view to arriving at empirical solution to the problem. The data
analysis for the present research was done quantitatively with the help of both descriptive
statistics and inferential statistics. The descriptive statistical techniques like mean, standard
deviation and for the inferential statistics analysis of co-variance were used during data analysis.
For the analysis of hypotheses in questionnaire regression analysis was used.
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4.2 Descriptive StatisticsCONFIRMED BOOKINGS (CB)
Table 4.1
This above Table 4.1 suggests that most of the customers are satisfied with the airline booking
process of the organizations. The frequency level over 4.0 is the satisfied level and it shows that
each and every person (100) agreed to this procedure.
Table 4.2
CONFIRMED BOOKINGS
N Valid 100
Missing 0
Mean 4.8767
Median 5.0000
Mode 5.00
Std. Deviation 0.16175
Variance 0.026
Minimum 4.67
Maximum 5.00
CONFIRMED BOOKINGS
Frequency Percent Cumulative
Score
Cumulative
Percent
Valid 4.67 37 37.0 37.0 37.0
5.00 63 63.0 63.0 100.0
Total 100 100.0 100.0
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Figure 4.1
FILTERING APPLICATIONS (FA)
Table 4.3
FILTERING APPLICATIONS
Frequency Percent Cumulative
Score
Cumulative
Percent
Valid 4.6 34 34.0 34.0 34.0
5.0 66 66.0 66.0 100.0
Total 100 100.0 100.0
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COLLECTING PAYMENTS (CP)
Table 4.5
Table 4.5 shows that most of the customers are satisfied with the payments collection process in
the organization. The frequency level over 4.0 is the satisfied level and it shows that all people
(100) are happy with the process.
Table 4.6
COLLECTING PAYMENTS
N Valid 100
Missing 0
Mean 4.8700
Median 5.0000
Mode 5.00
Std. Deviation 0.16340
Variance 0.027
Minimum 4.67
Maximum 5.00
COLLECTING PAYMENTS
Frequency Percent Cumulative
Score
Cumulative
Percent
Valid 4.67 39 39.0 39.0 39.0
5.00 61 61.0 61.0 100.0
Total 100 100.0 100.0
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Figure 4.3
CUSTOMER SATISFACTION (CS)
Table 4.7
CUSTOMER SATISFACTION
Frequency Percent Cumulative
Score
Cumulative
Percent
Valid 4.67 47 47.0 47.0 47.0
5.00 53 53.0 53.0 100.0
Total 100 100.0 100.0
This above Table 4.7 suggests that most of the customers are affected by airline agency
operations process in the organization. The frequency level over 4.0 is the satisfied level and it
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shows that all the people (100) are affected by the airline agency operations process that directly
influences the individual customer satisfaction.
Table 4.8
CUSTOMER SATISFACTION
N Valid 100
Missing 0
Mean 4.8433
Median 5.0000
Mode 5.00
Std. Deviation 0.16720
Variance 0.028
Minimum 4.67
Maximum 5.00
Figure 4.4
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4.3 Inferential Statistics
Correlation Coefficient
Table 4.7
The correlation matrix is revealed below with the values of all the variables.
The correlation variables have been explained under Correlation Matrix. The above table reveals
that CS has a positive correlation (0.814**) with CB indicating that if booking process gets
favorable the customer satisfaction will also be increased. The significance level remains at 0.01
levels. Likewise, the relationship of CS and FA, CP also significantly and positively connected at
0.01 levels.
Testing of Hypothesis
In this study, the researcher introduced 3 hypotheses. The Bivariate correlations of all the
hypotheses at 0.01 levels of significance are shown as follows.
Correlation Matrix
CB FA CP CS
CB 0.893** 0.958** 0.814**
FA 0.893** 0.854** 0.720**
CP 0.958** 0.854** 0.767**
CS 0.814** 0.720** 0.767**
N = 100
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Table 4.8
Bivariate Correlations
CONFIRMED
BOOKINGS
FILTERING
APPLICATIONS
COLLECTING
PAYMENTS
CUSTOMER
SATISFACTION
CONFIRMED
BOOKINGS
Pearson
Correlation
1 0.893** 0.958** 0.814
Sig. (2-tailed) .000 .000 .00
N 100 100 100 10
FILTERING
APPLICATIONS
Pearson
Correlation
0.893** 1 0.854** 0.720
Sig. (2-tailed) .000 .000 .00
N 100 100 100 10
COLLECTING
PAYMENTS
Pearson
Correlation
0.958** 0.854** 1 0.767
Sig. (2-tailed) .000 .000 .00
N 100 100 100 10
CUSTOMER
SATISFACTION
Pearson
Correlation
0.814** 0.720** 0.767**
Sig. (2-tailed) .000 .000 .000
N 100 100 100 10
**. Correlation is significant at the 0.01 level (2-tailed).
The total hypothesis and the null hypothesis are as follows.
H1: There is a positive relationship exists between customer satisfaction and confirmed
bookings.
H01 There is a negative relationship exists between customer satisfaction and confirmed
bookings.
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H2: There is a positive relationship exists between customer satisfaction and filtering
applicants.
H02: There is a negative relationship exists between customer satisfaction and filtering
applicants.
H3: There is a positive relationship exists between customer satisfaction and collecting
payments.
H03: There is a negative relationship exists between customer satisfaction and collecting
payments.
Regression Analysis
Table 4.9
Table 4.10
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 0.741 0.296 2.502 0.0124 **
CONFIRMED BOOKINGS 0.841 0.061 0.814 13.863 1.06e-043 ***
a. Dependent Variable: CUSTOMER SATISFACTION
Model Summaryb
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 0.814a 0.662 0.659 0.097
a. Predictors: (Constant), CONFIRMED BOOKINGS
b. Dependent Variable: CUSTOMER SATISFACTION
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Regression Equation
CS = 0.741 + 0.841CB
As per the equation above, it takes a positive value to say that when booking process is favorable
inside the organization, the customer satisfaction gets increased. The P value of the same is
1.06e-043 *** and that is below the rejection level of 0.01. Therefore, H1 is accepted and H01 is
rejected with 0.01 level of significance. Therefore, it can be assumed that there is a positive
correlation exists between Customer Satisfaction and Confirmed Bookings.
Figure 4.4
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Table 4.11
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 0.720a 0.518 0.513 0.116
a. Predictors: (Constant), FILTERING APPLICATIONS
b. Dependent Variable: CUSTOMER SATISFACTION
Table 4.12
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1.769 0.300 5.903 3.56e-09 ***
FILTERING
APPLICATIONS
0.632 0.062 0.720 10.267 9.90e-025 ***
a. Dependent Variable: CUSTOMER SATISFACTION
Regression Equation
CS = 1.769 + 0.632FA
As per the equation above, it takes a negative value to say that when an application filtering
becomes more consistent inside the organization, the customer satisfaction gets decreased. The
P value of the same is 9.90e-025 *** and that is below the rejection level of 0.01. Therefore, H2
is accepted and H02 is rejected with 0.01 level of significance. Therefore, it can be assumed that
there is a positive correlation exists between filtering applications process and customer
satisfaction.
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Figure 4.5
Table 4.13
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 0.767a 0.588 0.584 0.107
a. Predictors: (Constant), COLLECTINGPAYMENTS
b. Dependent Variable: CUSTOMERSATISFACTION
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Table 4.14
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1.021 0.323 3.160 0.0016 ***
COLLECTING
PAYMENTS
0.785 0.066 0.767 11.831 2.69e-032 ***
a. Dependent Variable: CUSTOMER SATISFACTION
Figure 4.5
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Table 4.15
The summary of the hypothesis testing is as follows.
Hypothesis P Value Notes
H1 There is a positive relationship exists betweencustomer satisfaction and confirmed bookings
1.06e-043 *** Accepted
H2 There is a positive relationship exists between
customer satisfaction and filtering applicants.
9.90e-025 *** Accepted
H3 There is a positive relationship exists between
customer satisfaction and collecting payments.
2.69e-032 *** Accepted
4.4 Chapter Summary
This chapter was originated by examining the samples which were under consideration. The
demographics of the data samples, distribution of questionnaire and the final response were
presented in a table and a graphical format. The responses obtained for each variable was then
illustrated. The composition of the respondents was then briefly introduced. A detailed design of
replies according to the different variables was given, with supporting statistical analysis.
The descriptive analysis of all independent and dependant variables was elaborated through a
frequency tables and a histograms. Then by using SPSS V-21 as a statistical tool the analysis of
variables was done by using factor, cluster and co-efficient covariance methods. The above three
methods were broadly described by using tables and charts having comparisons with each factors
and clusters.
Finally all findings were presented in a summarized format and the hypothesis testing also has
been carried out in a structural way.