Chika Analysis.docx

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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


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


Variance 0.027

Minimum 4.67

Maximum 5.00

COLLECTING PAYMENTS


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


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


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


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 ***


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


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 ***


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.


H3 There is a positive relationship exists between

customer satisfaction and collecting payments.


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.

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Chika Analysis.docx