GSBS6002 Project 2

This project analyzes the effectiveness of a cyber security system for New Castle Bank by examining customer perceptions and identifying the impact of the system on online fraud reduction. It utilizes quantitative research methods, including surveys and statistical analysis, to test hypotheses, consider ethical implications, and provide recommendations for the bank.

GSBS6002 project 2

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INTRODUCTION
Online frauds are been increasing and thus there is requirement of Cyber security system
which assist in reducing this issues. New Castle bank has decided to conduct the research in
order to identify the effectiveness of the Cyber security system for its development. They are
concerned about the customer perception about this system. There are various online fraud which
take place that consist of Phishing, Pharming, Skimming and malware. The Australian bank is
concerned to resolve this issues in order to create value for their customers to provide them high
satisfaction. It will provide analysis of the data on the basis of the survey conducted to gather the
customers views regarding the system. The research will use the Quantitative research to identify
the in - depth information about the effectiveness of the data to develop the cyber security
system. It will use the various test to identify the correlation between the variables. Moreover,
the research will develop the hypothesis test to identify the relation between the variable. It will
provide information about the ethical consideration which is required to take the consent of the
respondents to disclose the information provided by them. Also, It will provide recommendation
to the New Castle Bank on the basis of views and result provided by the statistical analysis on
the basis of the customer data and responses. In this project, it will provide information about
the effectiveness of Cyber security system. It will help in reducing the online frauds and will
provide security to the customer data.
RESEARCH DESIGN
The research design assist in answering the research questions. With the help of research
design the researcher is able to conduct the research in the effective and efficient way (Okada &
et.al., 2018). The research design assist in gathering the in- depth information about the research
to have relevant data in order to identify the effectiveness of the research which will help the
researcher in completing its research.
Data Collection : This method is used to collect the information or data regarding the
research to identify the in - depth information about the topic to have better understanding and
knowledge regarding the research. There are two methods of data collection one is primary and
other is secondary. The primary data uses the first hand information about the research which
include the use of questionnaire, interviews etc. to gather the information through the views of
respondents to identify their opinions and views (Green & Salkind, 2016). Secondary data is the
information which is collected through the use of published sources which consist of internet,
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books, journals etc. In this research. Primary data collection method will be used to gather the
in- depth information about the research.
Sampling : It is using the samples from the large population to gather the information
through the primary sources. There are two sampling methods which consist of probabilistic and
non- probabilistic (Spss, 2016). In this research, simple random sampling method will be used to
gather the data from the sample.
Research design : For conducting the research there are two method which consist of
qualitative and quantitative methods. The qualitative research include the views and opinions of
the respondents. Quantitative research include the number and numerical data to identify the
information and for the conducting the research (Cronk, 2017). The researcher for this research
will use the quantitative data to analyse the data and identify the correlation between the
variables. It will use the statistical data which will be analysed through the use of quantitative
technique.
Research deign assist in identifying the accurate data which is useful for research. Quantitative
research deign is useful in identifying the numerical analysis for the research by collecting the
numerical data the correlation between the variables is easily identified through performing the
various test.
Ethical consideration : It refers to using the ethics and norms for conducting the
research. It includes informed consent, voluntary participation of the respondents, confidentiality
etc. are the consideration which must be considered while collecting the information from the
customers (George & Mallery, 2016). The ethical consideration are required to met by the
research for completing its research in the effective and efficient manner. In the present research,
The researcher has taken the informed consent of the respondents to disclose their information.
Moreover, the survey conducted by the researcher include the voluntary participation of the
respondents.
So, it is identified that the research design include the various methods which are
required for conducting there search and collecting the data for the analysis to derive effective
results.
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HYPOTHESIS DEVELOPMENT
It is related to developing a hypothesis for the variables to identify the significant relation
or not between the variables (George & Mallery, 2016). The Hypothesis test contains the null
and alternative hypothesis which provide information of there is any relation or not between the
various by processing the data.
The Hypothesis development will provide the relation ship between the variables. This
development will be made for the following :
Hypothesis 1 :
H0 = There is no mean significance difference between satisfaction with online security
system and times of experiencing online fraud
H1= There is a mean significance difference between satisfaction with online security
system and times of experiencing online fraud
Hypothesis 2 :
H0 = There is no mean significance difference between satisfaction with online security
system and level of advice received with regards to potential online fraud (Larson-Hall, 2015).
H1 = There is mean significance difference between satisfaction with online security
system and level of advice received with regards to potential online fraud.
Hypothesis 3 :
H0 = There is no mean significance difference between level of assistance and quick
response towards fraud complaints.
H1 = There is mean significance difference between level of assistance and quick
response towards fraud complaints.
Hypothesis 4 :
H0 = There is no mean significance difference between quick resolution and level of
metal and social support victims of fraud.
H1= There is mean significance difference between quick resolution and level of metal
and social support victims of fraud.
Statistical Technique and Justification
The hypothesis will be tested through t statistical techniques which are as follows :
Chi square test : It is the test used for determining the difference between the expected
frequencies with that of actual. It assists in identifying the relationship between the two variables
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T - test : It is the term used in statistics to determine if there is significant relationship
between the mean of the group of variables( Yockey, 2017). It is used in Hypothesis test to
compare the means of two groups.
Paired sample T- test : It is also known as dependent sample T- test. It is used to
determine whether the mean difference between the observations is zero. The set of observation
is measured twice which leads to pair of observation (Miller, 2017). As per this sample T- test, if
the mean of two variables is from related samples than it is paired sample T- test.
Independent sample T - test : It is used to compare the means of two independent group
top identify the significant difference between them (Hayes, 2016). For comparing the mean of
two variables from unrelated sample the independent sample T- test is used.
ANOVA : It is analysis of the variance between two or more means. For identifying the
deviation in the means of the groups this test is performed which provide information about the
variance in the means of group (Brace, Snelgar & Kemp, 2016).
Regression : It is a statistical tool to identify the relationship between two or more
variables. It is used to examine the relationship between the variables where there is dependent
variable and independent variable. Also, the dependent variables is continuous variables (Hinton
& McMurray, 2017).
Correlation : In this technique the variables used are independent that means there is no
dependent variables in the group (Park, 2015). The variables used in the observation are
continuous. It assists in identifying the relationship between the independent variables.
Results, Statistical and Non statistical interpretation
Hypothesis 1
Chi- Square
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Question 2 * Question 4 Crosstabulation
Count
Question 4 Total
1 2 3 4 5 6 7
Question 2
1 12 8 7 8 7 4 6 52
2 24 19 7 7 6 2 4 69
3 19 22 14 6 7 3 4 75
4 7 12 18 16 9 2 5 69
5 4 4 6 24 14 1 3 56
6 0 1 1 7 18 1 1 29
7 0 2 0 0 12 12 5 31
8 0 0 0 0 0 5 10 15
9 0 0 0 0 0 0 4 4
Total 66 68 53 68 73 30 42 400
T-Test
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Question 2 400 3.80 1.998 .100
Question 4 400 3.68 1.906 .095
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Paired T-Test
Paired Samples Test
Paired Differences t df Sig. (2-
tailed)Mean Std.
Deviatio
n
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
Question 2 -
Question 4 .115 1.989 .099 -.081 .311 1.156 399 .248
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Pair
2
Question 4 -
Question 2 -.115 1.989 .099 -.311 .081 -
1.156 399 .248
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Coefficientsa
Model Unstandardized
Coefficients
Standardiz
ed
Coefficient
s
t Sig. 95.0% Confidence
Interval for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constan
t) 1.937 .180 10.783 .000 1.584 2.290
Question
2 .459 .042 .482 10.963 .000 .377 .542
a. Dependent Variable: Question 4
Correlation:
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Interpretation: From the above statistical data it is identified that there is significant
relationship between the variables as identified by processing the different test. As per Chi –
Square test it is determined that the significance level is less than 0.05 which shows that there is
positive relation. Moreover, the relationship shown through the use of test of relationship which
contains the regression and correlation(Pituch & Stevens, 2015 ). The regression analysis shows
that the variables are related to each other by 23%. Also, the correlation analysis shows the
significance of .000 which shows that there is positive relationship (H1).
Hypothesis 2
Chi – square
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Question 4 * Question 5 Crosstabulation
Count
Question 5 Total
1 2 3 4 5 6 7 8
Question
4
1 5 35 10 6 4 1 4 1 66
2 2 4 37 5 6 6 5 3 68
3 4 9 2 27 3 4 1 3 53
4 3 4 4 3 43 8 2 1 68
5 1 7 6 11 4 38 6 0 73
6 0 0 2 2 3 2 19 2 30
7 1 1 1 3 6 3 2 25 42
Total 16 60 62 57 69 62 39 35 400
T-Test
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Question 4 400 3.68 1.906 .095
Question 5 400 4.55 1.973 .099
One-Sample Test
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Test Value = 0
t df Sig. (2-tailed) Mean
Difference
95% Confidence Interval of the
Difference
Lower Upper
Question 4 38.621 399 .000 3.680 3.49 3.87
Question 5 46.126 399 .000 4.550 4.36 4.74
Paired T-Test
Paired Samples Correlations
N Correlation Sig.
Pair 1 Question 4 & Question 5 400 .572 .000
Pair 2 Question 5 & Question 4 400 .572 .000
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ANOVA
REGRESSION
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Correlations
Descriptive Statistics
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Mean Std. Deviation N
Question 4 3.68 1.906 400
Question 5 4.55 1.973 400
Interpretation: From the above data it can be interpreted that there is significant
difference between the variables as shown by the various T – test. It is identified that the test
performed for identifying the significant difference between the variables is Chi- square , T- test,
paired sample T – test and ANOVA which has provided information that the variables have
positive difference because the p value is less than 0.05. It has provided with the relationship test
which shows the relationship between the variables (Sivam & et.al., 2018). The regression
analysis provide that there is 32.7% relationship between the variables.
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Hypothesis 3:
T-Test
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Oneway
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Model Summary
Mod
el
R R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .007a .000 -.002 1.974 .000 .022 1 398 .882
a. Predictors: (Constant), Question 6
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1 Regression .085 1 .085 .022 .882b
Residual 1550.092 398 3.895
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Total 1550.178 399
a. Dependent Variable: Question 7
b. Predictors: (Constant), Question 6
Coefficientsa
Model Unstandardized
Coefficients
Standardiz
ed
Coefficient
s
t Sig. 95.0% Confidence
Interval for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constan
t) 3.470 .271 12.813 .000 2.937 4.002
Question
6 -.014 .097 -.007 -.148 .882 -.205 .177
a. Dependent Variable: Question 7
Correlations
Interpretation : From the above Hypothesis test it is interpreted that there is no
significant difference between the two variables as indicated by the test of comparison which has
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included Chi- square test, sample test, paired - sample t test etc. The significance level is 0.882
which shows that the valorises have no significant difference (Sivam & et.al., 2018)). The
regression analysis shows that the variables are not related as there is 0% relationship between
the variables.
Hypothesis 4
Crosstabs
Question 8 * Question 9 Crosstabulation
Count
Question 9 Total
1 2 3 4 5 6 7 8 9
Questio
n 8
2 3 19 19 11 11 11 6 0 3 83
3 5 4 19 13 10 13 3 2 2 71
4 1 7 15 11 18 9 2 1 1 65
5 0 8 9 9 23 17 4 0 2 72
6 1 6 12 13 15 21 4 3 1 76
7 0 2 2 2 5 3 0 0 0 14
8 0 2 5 5 1 6 0 0 0 19
Total 10 48 81 64 83 80 19 6 9 400
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 62.028a 48 .084
Likelihood Ratio 66.123 48 .042
Linear-by-Linear
Association 5.510 1 .019
N of Valid Cases 400
a. 38 cells (60.3%) have expected count less than 5. The minimum
expected count is .21.
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T-Test
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Question 8 400 4.26 1.721 .086
Question 9 400 4.41 1.729 .086
One-Sample Test
Test Value = 0
t df Sig. (2-tailed) Mean
Difference
95% Confidence Interval of the
Difference
Lower Upper
Question 8 49.542 399 .000 4.263 4.09 4.43
Question 9 50.963 399 .000 4.405 4.24 4.57
T – Test
Paired Samples Statistics
Mean N Std. Deviation Std. Error
Mean
Pair 1 Question 8 4.26 400 1.721 .086
Question 9 4.41 400 1.729 .086
Pair 2 Question 9 4.41 400 1.729 .086
Question 8 4.26 400 1.721 .086
Paired Samples Correlations
N Correlation Sig.
Pair 1 Question 8 & Question
9 400 .118 .019
Pair 2 Question 9 & Question
8 400 .118 .019
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Paired Samples Test
Paired Differences t df Sig. (2-
tailed)Mean Std.
Deviatio
n
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
Question 8 -
Question 9 -.143 2.291 .115 -.368 .083 -
1.244 399 .214
Pair
2
Question 9 -
Question 8 .143 2.291 .115 -.083 .368 1.244 399 .214
Regression
Descriptive Statistics
Mean Std. Deviation N
Question 8 4.26 1.721 400
Question 9 4.41 1.729 400
Correlations
Question 8 Question 9
Pearson Correlation Question 8 1.000 .118
Question 9 .118 1.000
Sig. (1-tailed) Question 8 . .009
Question 9 .009 .
N Question 8 400 400
Question 9 400 400
Model Summary
R Change Statistics
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Mod
el
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .118a .014 .011 1.711 .014 5.573 1 398 .019
a. Predictors: (Constant), Question 9
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 16.315 1 16.315 5.573 .019b
Residual 1165.123 398 2.927
Total 1181.438 399
a. Dependent Variable: Question 8
b. Predictors: (Constant), Question 9
Coefficientsa
Model Unstandardized
Coefficients
Standardiz
ed
Coefficient
s
t Sig. 95.0% Confidence
Interval for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constan
t) 3.747 .234 15.984 .000 3.286 4.208
Question
9 .117 .050 .118 2.361 .019 .020 .214
a. Dependent Variable: Question 8
Correlations
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Descriptive Statistics
Mean Std. Deviation N
Question 8 4.26 1.721 400
Question 9 4.41 1.729 400
Correlations
Question 8 Question 9
Question 8
Pearson Correlation 1 .118*
Sig. (2-tailed) .019
N 400 400
Question 9
Pearson Correlation .118* 1
Sig. (2-tailed) .019
N 400 400
*. Correlation is significant at the 0.05 level (2-tailed).
Interpretation : From the above data it has provided understanding about the significant
difference between the variables and the relationship between them. It can be interpreted that
there is no significant difference between the variables (Abu-Bader, 2016). As the value of p
determined shows it is more than the 0.05 which means the variable show that there is no
significant difference between them. Moreover, the regression analysis shows that the variables
are 14% related to each other.
RECOMMENDATION
 On the basis of the finding it is recommended to Newcastle bank that it should provide
customer with better security system to provide confidentiality of their data.
 It is recommended to the bank to increase their security in order to provide customer
satisfaction to enhance their experience.
 The Newcastle bank should develop Cyber security system to protect the customer data.
Analysis and Summary of statistical results
From the above report it is analyses that the Cyber security system which is required to
be developed by New castle bank for reducing the online fraud. The variables for the question 2
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and 4 are having significant difference and they are related to each other which means if there is
any difference in these variables it will affect the other variables.
RECOMMENDATION
 On the basis of the finding it is recommended to Newcastle bank that it should provide
customer with better security system to provide confidentiality of their data.
 It is recommended to the bank to increase their security in order to provide customer
satisfaction to enhance their experience.
 The Newcastle bank should develop Cyber security system to protect the customer data.
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REFERENCES
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Carte. Pearson.
Spss, I. B. M. (2016). Statistics for Windows, Version 24. 0 [Computer Software]. Armonk. NY:
IBM Corp.
Cronk, B. C. (2017). How to use SPSS®: A step-by-step guide to analysis and interpretation.
Routledge.
George, D., & Mallery, P. (2016). IBM SPSS statistics 23 step by step: A simple guide and
reference. Routledge.
Larson-Hall, J. (2015). A guide to doing statistics in second language research using SPSS and
R. Routledge.
Yockey, R. D. (2017). SPSS demystified. Taylor & Francis.
Miller, R. L. (2017). SPSS for social scientists. Macmillan International Higher Education.
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Hinton, P. R., & McMurray, I. (2017). Presenting Your Data with SPSS Explained. Taylor &
Francis.
Park, H. M. (2015). Univariate analysis and normality test using SAS, Stata, and SPSS.
Pituch, K. A., & Stevens, J. P. (2015). Applied multivariate statistics for the social sciences:
Analyses with SAS and IBM’s SPSS. Routledge.
Sivam, S. S. S. and et.al., (2018). Grey Relational Analysis and Anova to Determine the
Optimum Process Parameters for Friction Stir Welding of Ti and Mg Alloys. Periodica
Polytechnica Mechanical Engineering. 62(4). 277-283.
Abu-Bader, S. H. (2016). Advanced and multivariate statistical methods for social science
research with a complete SPSS guide. Oxford University Press.
Okada, T., & et.al., (2018). Science exploration and instrumentation of the OKEANOS mission
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