Chika Analysis.docx

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.

4.2 Descriptive Statistics CONFIRMED BOOKINGS (CB) Table 4.1 CONFIRMED BOOKINGS Frequency

Valid

Percent

Cumulative

Cumulative

Score

Percent

4.67

37

37.0

37.0

37.0

5.00

63

63.0

63.0

100.0

Total

100

100.0

100.0

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 Missing

100 0

Mean

4.8767

Median

5.0000

Mode Std. Deviation

5.00 0.16175

Variance

0.026

Minimum

4.67

Maximum

5.00

Figure 4.1

FILTERING APPLICATIONS (FA) Table 4.3

FILTERING APPLICATIONS Frequency

Valid

Percent

Cumulative

Cumulative

Score

Percent

4.6

34

34.0

34.0

34.0

5.0

66

66.0

66.0

100.0

Total

100

100.0

100.0

This above Table 4.3 implies that all the customers are satisfied with the airline application filtering process of the organizations. The rate of recurrence level over 4.0 is the satisfied level and it shows that each and every person agreed or strongly agreed to this procedure.

Table 4.4 FILTERING APPLICATIONS N

Valid Missing

0

Mean

4.864

Median

5.000

Mode Std. Deviation Variance

Figure 4.2

100

5.0 0.1904 0.036

Minimum

4.6

Maximum

5.0

COLLECTING PAYMENTS (CP)

Table 4.5

COLLECTING PAYMENTS Frequency

Valid

Percent

Cumulative

Cumulative

Score

Percent

4.67

39

39.0

39.0

39.0

5.00

61

61.0

61.0

100.0

Total

100

100.0

100.0

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 Missing

100 0

Mean

4.8700

Median

5.0000

Mode Std. Deviation

5.00 0.16340

Variance

0.027

Minimum

4.67

Maximum

5.00

Figure 4.3

CUSTOMER SATISFACTION (CS)

Table 4.7 CUSTOMER SATISFACTION Frequency

Valid

Percent

Cumulative

Cumulative

Score

Percent

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

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 Missing

0

Mean

4.8433

Median

5.0000

Mode Std. Deviation

Figure 4.4

100

5.00 0.16720

Variance

0.028

Minimum

4.67

Maximum

5.00

4.3 Inferential Statistics

Correlation Coefficient Table 4.7 The correlation matrix is revealed below with the values of all the variables. Correlation Matrix CB CB

FA

CP

CS

0.893**

0.958**

0.814**

0.854**

0.720**

FA

0.893**

CP

0.958**

0.854**

CS

0.814**

0.720**

0.767** 0.767**

N = 100

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.

Table 4.8

Bivariate Correlations

CONFIRMED

Pearson

BOOKINGS

Correlation

CONFIRMED

FILTERING

COLLECTING

CUSTOMER

BOOKINGS

APPLICATIONS

PAYMENTS

SATISFACTION

1

Sig. (2-tailed) N FILTERING

Pearson

APPLICATIONS

Correlation

100 0.893

0.893**

0.958**

0.814**

.000

.000

.000

100

100

100

1

**

0.720**

.000

.000

**

0.854

Sig. (2-tailed)

.000

N

100

100

100

100

**

**

1

0.767**

COLLECTING

Pearson

PAYMENTS

Correlation

0.958

0.854

Sig. (2-tailed)

.000

.000

N

100

100

100

100

**

**

**

1

CUSTOMER

Pearson

SATISFACTION

Correlation

0.814

0.720

.000 0.767

Sig. (2-tailed)

.000

.000

.000

N

100

100

100

**. 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.

100

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

Model Summaryb Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

0.814a

1

0.662

0.659

0.097

a. Predictors: (Constant), CONFIRMED BOOKINGS b. Dependent Variable: CUSTOMER SATISFACTION

Table 4.10 Coefficientsa Model

1

Unstandardized

Standardized

Coefficients

Coefficients

B

Std. Error

(Constant)

0.741

0.296

CONFIRMED BOOKINGS

0.841

0.061

a. Dependent Variable: CUSTOMER SATISFACTION

t

Sig.

Beta 2.502 0.814

13.863

0.0124

**

1.06e-043 ***

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

Table 4.11 Model Summaryb Model

1

R

R Square

0.720a

Adjusted R

Std. Error of the

Square

Estimate

0.513

0.116

0.518

a. Predictors: (Constant), FILTERING APPLICATIONS b. Dependent Variable: CUSTOMER SATISFACTION

Table 4.12 Coefficientsa Model

1

Unstandardized

Standardized

Coefficients

Coefficients

B

Std. Error

(Constant)

1.769

0.300

FILTERING

0.632

0.062

t

Sig.

5.903

3.56e-09 ***

10.267

9.90e-025 ***

Beta 0.720

APPLICATIONS 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.

Figure 4.5

Table 4.13

Model Summaryb Model

1

R

0.767a

R Square

Adjusted R

Std. Error of the

Square

Estimate

0.588

a. Predictors: (Constant), COLLECTINGPAYMENTS b. Dependent Variable: CUSTOMERSATISFACTION

0.584

0.107

Table 4.14 Coefficientsa Model

1

Unstandardized

Standardized

Coefficients

Coefficients

B

Std. Error

(Constant)

1.021

0.323

COLLECTING

0.785

0.066

PAYMENTS a. Dependent Variable: CUSTOMER SATISFACTION

Figure 4.5

t

Sig.

Beta 0.767

3.160

0.0016

***

11.831

2.69e-032 ***

Table 4.15 The summary of the hypothesis testing is as follows.

H1

Hypothesis There is a positive relationship exists between

P Value 1.06e-043 ***

Notes Accepted

9.90e-025 ***

Accepted

2.69e-032 ***

Accepted

customer satisfaction and confirmed bookings 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.