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 Statistics

CONFIRMED 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

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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 100 Missing 0 Mean 4.864 Median 5.000 Mode 5.0 Std. Deviation 0.1904 Variance 0.036 Minimum 4.6 Maximum 5.0 Figure 4.2

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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

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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 .000 N 100 100 100 100 FILTERING APPLICATIONS Pearson Correlation 0.893** 1 0.854** 0.720** Sig. (2-tailed) .000 .000 .000 N 100 100 100 100 COLLECTING PAYMENTS Pearson Correlation 0.958** 0.854** 1 0.767** Sig. (2-tailed) .000 .000 .000 N 100 100 100 100 CUSTOMER SATISFACTION Pearson Correlation 0.814** 0.720** 0.767** 1 Sig. (2-tailed) .000 .000 .000 N 100 100 100 100

**. 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

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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.

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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 ***

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

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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 ***

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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 between customer 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.