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من کارشناس ارشد مدیریت دولتی ،گرایش منابع انسانی هستم و در زمینه بانکداری دارای بیست سال سابقه کاری در بانک ملت می باشم و دروس بانکداری را در دانشگاه های مختلف تدریس می کنم و از سال 1381 پست مدیریتی دارم و مدت دو سال هم ریس حوزه غرب استان مازندران در بانک ملت هستم.
هدف از ایجاد این وبلاگ ارائه تجربیات بانکی و درج مقالات علمی در ضمینه بانکداری به بازدید کنندگان محترم است.

سخنی با كاربران محترم وبلاگ

پیش از هر چیز از كلیه دوستانی كه ما را مورد لطف قرار داده اند سپاسگزاریم.

از كلیه دوستانی كه مایل به درج مقالات خود در این وبلاگ هستند خواهش مندیم تا با مدیر وبلاگ تماس بگیرند.

برای پاسخ به سوالاتی كه مطرح میشود ما طبق برنامه سوالات را با دانشجویان مطرح و از ایشان درخواست مقاله در ضمینه سوال مطرح شده می كنیم . لذا از تاخیری كه در پاسخ سوالت پیش می آید قبلا عذر خواهی می كنیم.

به زودی از منابع ترجمه در ضمینه بانكداری الكترونیك استفاده خواهیم كرد.

از پیشنهادات و انتقادات شما استقبال می كنیم


عطااله فدایی
آرشیو مطالب
نویسندگان
صفحات اضافی
گزارشی از ...6 -سوئیفت چیست ؟
گزارشی از ..5 - سامانه تسویه ناخالص آنی- ساتنا
گزارشی از ..4 - مدیریت وبرنامه ریزی استراتژی
گزارشی از ...3 -مدیریت ریسک
گزارشی از ...2 - راهنمای استفاده از اینترنت بانک برای مشتری
گزارشی از نحوه عملکردسیستم بانکــی ومدیریت وبرنامه ریزی استراتژی
پول شویی 2 /
پول شویی1 /محقق مجتبی ثمر بخش طهرانی
مدیریت دانش 2
مدیریت دانش 1
مراحل گشایش اعتبار اسنادی 2
مراحل گشایش اعتبارات اسنادی /محقق فهیمه پور مهربان
صندوق بین المللی پول3
صندوق بین المللی پول2
صندوق بین المللی پول1
مالیه عمومی و تعیین خط مشی دولت2
مالیه عمومی و تعیین خط مشی دولت1
تورم 5 /احسان شمس
تورم 4 /احسان شمس
تورم 3 /محقق احسان شمس
تورم 2 /احسان شمس
تورم1/محقق :احسان شمس
باتکداری الکترونیکی2 /محمد حسین آقازاده و پیام رضازاده
باتکداری الکترونیکی1/محققین :محمد حسین آقازاده و پیام رضازاده
تورم/محقق امید رمضانپور
بانکداری الکترونیک و سیرتحول آن در ایران 3 /محقق مریم مجدی
بانکداری الکترونیک و سیرتحول آن در ایران 2 /محقق مریم مجدی
بانکداری الکترونیک و سیرتحول آن در ایران 1/محقق مریم مجدی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق9/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق8/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق7/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق6/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق5/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق4/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق3/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق2/عطااله فدایی
شیوه‌های اعتبارسنجی مشتریان ـ مطالبات معوق1 /عطااله فدایی
پول شویی/محقق محمد انصاری جاوید
سیر تحول بانکداری اینترنتی در بانک پارسیان-فصل دوم2/محقق مهدیه اشکوری
سیر تحول بانکداری اینترنتی در بانک پارسیان-فصل دوم/محقق مهدیه اشکوری
سیر تحول بانکداری اینترنتی در بانک پارسیان-فصل اول/محقق مهدیه اشکوری
نقشه راه بانکداری نوین ایران :نظام پرداخت نوین بانکداری الکترونیک (3 )
نقشه راه بانکداری نوین ایران :بانکداری یکپارچه (2)
تعریف بانکداری(1)
تاریخچه بانکداری الکترونیک در جهان
لیست موضوعات پژوهشی در حوزه بانکداری الکترونیک و مجازی
بانک ملت از آغاز تا امروز
آمار و امكانات
آخرین بروزرسانی :
تعداد كل مطالب :
تعداد کل نویسندگان :
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Evaluation of the Survey

In the second part of the study, a survey was conducted to aim at determining
IB users’
perception level of IB services. 630 survey forms were sent to different
cities across
Turkey by post, of which 527 were returned. Of these returned
forms,  466 had been
completed fully, whereas 61 survey forms were not included
 in the  analysis because
there were missing values. The sampling group consists
of bank customers from
different professions.
The demographic profiles of the sampling group were expalined by the help of
frequencies and percentages. Then, the factors affecting IB use of the bank
 costumers
and the effect of demographic features on the IB use of bank
costumers  were examined
by the help of cross tabs and logistical regression
analysis.
 Finally, the extent to which
participants’ IB perception levels affect frequency
and time duration of IB use were
investigated via the multiple regression
 analysis. The findings of the study have been
evaluated below
The Analysis and Evaluation of the Findings
Demographic Profile: Demographic features of the bank costumers were investigated in
this section using frequency and percentage descriptive statistics. Distribution of the
bank costumers by their gender is summarised in Table 4. It is understood from the data
in the table that 40 % of the participants are female and 60 % are male.
Table 4. Distribution by Gender
Frequency Percentage Valid Percentage Cumulative Percentage
Female 184 39,5 39,5 39,5
Male 282 60,5 60,5 100,0
Total 466 100,0 100,0
When distribution of the participants by their age is examined in Table 5, it is observed
that 67 % are between 20 and 35, 29 % are between 36 and 50 and 4 % are 50 and
above.
Table 5. Distribution By Age
Frequency Percentage Valid Percentage Cumulative Percentage
20-35 311 66,7 66,7 66,7
36-50 137 29,4 29,4 96,1
50 > 18 3,9 3,9 100,0
Total 466 100,0 100,0
Distribution of the bank costumers by their marital status is summarised in Table 6. It is
understood that 37 % of the particiapnts are single and 63 % are married.
Table 6. Distribution by Marital Status
Frequency Percentage Valid Percentage Cumulative Percentage
Single 172 36,9 36,9 36,9
Married 292 62,7 62,7 99,6
Other 2 ,4 ,4 100,0
Total 466 100,0 100,0
It is understood from the data in Table 7 that 54 % of the participants are in the private
sector, 32 % are in the public sector and 14 % are engaged in other professions.
Table 7. Distribution by Profession
Frequency Percentage Valid Percentage Cumulative Percentage
Private Scetor 252 54,1 54,1 54,1
Public Sector 151 32,4 32,4 86,5
Other 63 13,5 13,5 100,0
Total 466 100,0 100,0
Distribution of the bank costumers by their level of education is summarised in Table 8. It
is understood from the data in the table that 7 % of the participants are primary
education graduates, 15 % are high school graduates, 50 % hold bachelor’s degree, 15
% hold master’s and 13 % hold PhD.
Table 8. Distribution by Level of Education
Frequency Percentage Valid Percentage Cumulative Percentage
Primary 31 6,7 6,7 6,7
High School 71 15,2 15,2 21,9
Bachelor’s 234 50,2 50,2 72,1
Master’s 69 14,8 14,8 86,9
PhD 61 13,1 13,1 100,0
Total 466 100,0 100,0
Distribution of the bank customers by their level of income is summarised in Table 9. It is
seen in the Table 7 that 13 % of the participants have an income that is less than 750
TL, 15 % have an income between 750 and 1000 TL, 25 % have an income between
1001 and 1500 TL, and 47 % have an income above 1500 TL.
Table 9. Distribution by Level of Income
Frequency Percentage Valid Percentage Cumulative Percentage
Less than 750 TL 60 12,9 12,9 12,9
750-1000 TL 70 15,0 15,0 27,9
1001-1500 TL 116 24,9 24,9 52,8
More than 1500 TL 219 47,0 47,0 99,8
5,00 1 ,2 ,2 100,0
Total 466 100,0 100,0
Factors That Influence Participants’ IB Use: In this section, factors that influenced the IB
use of the bank customers were investigated. In this framework, the participants’
evaluations on the IB service and the factors concerning the nature of the IB services
were taken as independent variables whereas the customers’ state of IB use was taken
as a dependent variable.
The correlation between the independent variables and the state of IB use, which was
the dependent variable, was investigated in the first stage of the analysis by the help of
cross tabs. In this framework, the profile of the customers who did not use IB was
handled and thus an effort was made to reach an understanding about the effects of
independent variables. Moreover, possible interactions between independent variables
may reduce the effects of some variables or remove it totally. For example, it may lead
one “not to know how to use IB” or to think that “IB is difficult and requires mental effort”.
Therefore, the relationships between the variables were tested using the logistic
regression analysis so as to be able to make more informed comments.
The relationship between the participants’ IB use and the state of banks’ offering IB
service is summarised in Table 10. It is understood that 66 % of the participants made
use of the IB service whereas 34 % were not IB users.
Table 10. The Relationship between IB Use and the State of Banks’ Offering IB Service
IB Use
Total
Uses IB Does not Use IB
Service Offers
Total 307 159 466
% Service 65,9% 34,1% 100,0%
Total
Total 307 159 466
% Service 65,9% 34,1% 100,0%
Tablo 11 summarises the relationship between the participants’ IB use and the state of
their finding the IB service secure. It is seen from the data in the table that 91 % of the
bank customers who did not find the IB service secure and 30 % of the bank customers
who found the IB service secure did not use IB.
Table 11. The Relationship between IB Use and the State of Finding IB Service Secure
IB Use
Total
Uses IB Does not Use IB
Security
Insecure
Total 3 32 35
% Security 8,6% 91,4% 100,0%
Secure
Total 304 127 431
% Security 70,5% 29,5% 100,0%
Total
Total 307 159 466
% Security 65,9% 34,1% 100,0%
The relationship between the participants’ IB use and their finding the benefits of the IB
evident is summarised in Table 12. It is observed that 88 % of the bank customers who
did not find the benefits of IB evident and 33 % of the bank customers who found the
benefits of IB evident did not use IB.

Table 12. The Relationship between IB Use and the State of the Benefits of IB Evident
IB Use
Total
Uses IB Does not Use IB
Benefits
Not Evident
Total 1 7 8
% Benefits 12,5% 87,5% 100,0%
Evident
Total 306 152 458
% Benefits 66,8% 33,2% 100,0%
Total
Total 307 159 466
% Benefits 65,9% 34,1% 100,0%
Table 13 shows the relationship between the IB use of the participants and their finding
the prices of IB services reasonable. It is understood from the table that 50 % of the
bank customers who did not find IB services reasonable and 34 % of the bank
customers who found the IB service prices reasonable did not use IB.
Table 13. The Relationship between the IB Use and the State of Finding IB Service Prices
Reasonable
IB User
Total
Uses IB Does not IB
Price
Not Reasonable
Total 1 1 2
% Price 50,0% 50,0% 100,0%
Reasonable
Total 306 158 464
% Price 65,9% 34,1% 100,0%
Total
Total 307 159 466
% Price 65,9% 34,1% 100,0%
The relationship between the participants’ IB use and their finding the use of IB services
difficult is summarised in Table 4. It is seen from the table that 100 % of the bank
customers who found the use of IB difficult and 34 % of the bank customers who did not
find the IB use difficult did not use IB.
Table 14. The Relationship between IB Use and the State of Finding the Use of IB Services
Difficult
IB User
Total
Uses IB Does not Use IB
Use Difficult
Total 0 4 4
% Use ,0% 100,0% 100,0%
Not Difficult
Total 307 155 462
% Use 66,5% 33,5% 100,0%
Total
Total 307 159 466
%Use 65,9% 34,1% 100,0%
Table 15 summarises the relationship between the participants’ state of IB use and their
knowing how to use IB. It is seen that 100 % of the bank customers who did not know
how to use IB and 27 % of the bank customers who knew how to use IB did not use IB.
Table 15. The Relationship between the IB Use and the State of Knowing How to Use IB
IB User
Total
Uses IB Does not Use IB
Method of Use
Does not Know
Total 0 45 45
% Method of Use ,0% 100,0% 100,0%
Knows
Total 307 114 421
% Method of Use 72,9% 27,1% 100,0%
Total
Total 307 159 466
% Method of Use 65,9% 34,1% 100,0%
The relationship between the participants’ IB use and the state of their preferring face-toface
banking is given in Table 16. It is understood from the data that 99 % of the bank
customers who preferred face-to-face banking and 23 % of the bank customers who did
not specifically prefer face-to-face banking did not use IB.
Table 16. The Relationship between the IB Use and the State of Preferring Face-to-Face
Banking
IB User
Total
Uses IB Does not Use IB
Face-to-Face banking
Prefers
Total 1 67 68
% Face-to-Face 1,5% 98,5% 100,0%
Does not Prefer
Total 306 92 398
% Face-to-Face 76,9% 23,1% 100,0%
Total
Total 307 159 466
% Face-to-Face 65,9% 34,1% 100,0%
The relationship between the IB use of the participants and their having internet
connection is summarised in Table 17. It has been determined that 97 % of the bank
customers who did not have internet connection and 29 % of the bank customers who
had internet connection did not use IB.
Table 17. The Relationship between the IB Use and Having Internet Connection
IB User
Total
Uses IB Does not Use IB
Internet Connection
Yes
Total 1 33 34
% Internet Connection 2,9% 97,1% 100,0%
No
Total 306 126 432
% Internet Connection 70,8% 29,2% 100,0%
Total
Total 307 159 466
% Internet Connection 65,9% 34,1% 100,0%
Logistical regression analysis was performed at this stage of the study in order to
determine the effects arising from the relationships between the independent variables
and make more informed interpretations1. The Nagelkerke R Square value, which is
given in the model summary table, shows the extent to which the research model
conforms to the real data (Howitt and Cramer, 2008). The closer this value is to “1”, the
more the model conforms to the research data. In this framework, it can be argued on
the basis of the value of .907 given in the table that the research data are in conformity
with the proposed model of dependent and independent variables.
Table18. Model Summary
-2 Log likelihood Cox & Snell R Square Nagelkerke R Square
101,579 ,656 ,907
The total percentage value given in the classification table indicates the accuracy rate of
the classification made concerning the state of being IB user (Landau and Everitt, 2004).
According to this, it can be argued that 98 % of the research data were accurately
classified.
Table 19. Classification Table
Observed
Estimated
IB User
Correct Percentage
Uses IB Does not Use IB
IB User Uses IB 304 3 99,0
1 The choice of “I do not find IB secure” is encoded as “10”, IB benefits are not evident as “11”, prices of IB services are
not reasonable as 12”, IB use is difficult and requires mental effort as “13”, I do not know how to use IB as “14”, I prefer
individual and face-to-face banking as “15” and I do not have internet connection as “16”.
Does not Use IB 5 154 96,9
Total Percentage 98,3
The results of the regression analysis are given in Table 20. It is understood that among
the values in the significance column of the table, the relationship between the choices
of “I do not find IB secure”, “the benefits of IB are not evident”, “I prefer individual and
face-to-face banking” and “I do not have internet connection” and “the state of IB use” is
significant at the 1 % level.
On the other hand, when the values in the Exp (B) column are examined, it is observed
that the state of using individual and face-to-face banking is the factor that affects IB use
the most. Having internet connection is the second most important factor that affects IB
use. Not finding IB secure and not finding IB’s benefits evident are factors that affect IB
use relatively less but still on a significant level. The relationship between the choices of
“the prices of IB services are not reasonable”, “IB use is difficult and requires mental
effort”, and “I do not know how to use IB” and the state of IB use is not statistically
significant.
Table 20. Results of Regression Analysis
B S.E. Wald df Sig. Exp(B)
D10 6,159 ,832 54,857 1 ,000 473,106
D11 3,761 1,465 6,592 1 ,010 43,002
D12 -24,003 36011,222 ,000 1 ,999 ,000
D13 23,042 15018,525 ,000 1 ,999 1,016E10
D14 23,980 4727,266 ,000 1 ,996 2,595E10
D15 7,546 1,081 48,714 1 ,000 1892,597
D16 7,057 1,088 42,093 1 ,000 1161,176
Costant -3,761 ,382 96,779 1 ,000 ,023
The Effects of Demographic Features on Bank Customers’ IB Use: This section deals
with the effects of demographic features of the bank customers on IB use. In this
framework, the demographic features of the customers were taken as independent
variables while their state of IB use was taken as the dependent variable.
The relationship between the independent variables and the IB use, which was the
dependent variable, was investigated by the help of cross tabs in the first stage of the
analysis. The composition of the customers who did not use IB was dealth with in this
section and by this way an attempt was made to reach a general conclusion about the
effects of demographic features. However, it should not be ignored that interaction,
which could be cited among the demographic features, may reduce or totally remove the
effects of some variables and therefore the tables should be interpreted with caution.
Accordingly, the relationships between the variables were tested using the logistical
regression analysis in order to obtain better results.
The relationship between the IB use of bank customers and their ages is summarised in
Table 21. It is seen that 30 % of the participants between the ages of 20 and 35, 37 % of
the participants between the ages of 36 and 50 and 78 % of the participants above the
age of 50 do not use IB.
Table 21. Correlation between Participants’ IB Use and Their Age
IB User
Total
Uses IB Does not Use IB
Age
20-35
Total 217 94 311
% Age 69,8% 30,2% 100,0%
36-50
Total 86 51 137
% Age 62,8% 37,2% 100,0%
50 >
Total 4 14 18
% Age 22,2% 77,8% 100,0%
Total
Total 307 159 466
% Age 65,9% 34,1% 100,0%
The relationship between IB use of the bank customers and their gender is given in
Table 2. It is understood from the table that 42 % of the female participants and 29 % of
the male participants do not use IB.
Table 22. Correlation between Participants’ IB Use and Their Gender
IB User
Total
Uses IB Does not Use IB
Gender
Female
Total 106 78 184
% Gender 57,6% 42,4% 100,0%
Male
Total 201 81 282
% Gender 71,3% 28,7% 100,0%
Total
Total 307 159 466
% Gender 65,9% 34,1% 100,0%
The relationship between IB use of the bank customers who participated in the study
and their marital status is summarised in Table 23. It is observed from the data in the
table that 38 % of the single participants and 32 % of the married participants do not use
IB service.
Table 23. Correlation between Participants’ IB Use and Their Marital Status
IB User
Total
Uses IB Does not Use IB
Marital Status
Single
Total 107 65 172
% Marital Status 62,2% 37,8% 100,0%
Married
Total 199 93 292
% Marital Status 68,2% 31,8% 100,0%
Total
Total 307 159 466
% Marital Status 65,9% 34,1% 100,0%
The relationship between IB use of the bank customers and their professions is
summarised in Table 24. It is understood that 24 % of the private sector participants, 30
% of the public sector participants and 86 % of the participants from the other sectors do
not use IB service.
Table 24. Correlation between Participants’ IB Use and Their Profession
IB User
Total
Uses IB Does not Use IB
Profession
Private Sector
Total 192 60 252
% Profession 76,2% 23,8% 100,0%
Public Sector
Total 106 45 151
% Profession 70,2% 29,8% 100,0%
Other
Total 9 54 63
% Profession 14,3% 85,7% 100,0%
Total
Total 307 159 466
%Profession 65,9% 34,1% 100,0%
Table 25 shows the relationship between the IB use of the bank customers and their
educational status. It is seen that 84 % of the primary education graduates, 59 % of the
high school gradutes, 29 % of the bachelor’s degree graduates, 20 % of the master’s
degree graduates and 15 % of the PhD graduates do not use IB service.
Table 25. Correlation Between Participants’ IB Use and Their Educational Level
IB User
Total
Uses IB Does not Use IB
Education Primary Total 5 26 31
% Education 16,1% 83,9% 100,0%
High School
Total 29 42 71
% Education 40,8% 59,2% 100,0%
Graduate
Total 166 68 234
% Education 70,9% 29,1% 100,0%
Master’s
Total 55 14 69
% Education 79,7% 20,3% 100,0%
PhD
Total 52 9 61
% Education 85,2% 14,8% 100,0%
Total
Total 307 159 466
% Education 65,9% 34,1% 100,0%
Table 26 summarises the relationship between the IB use of the bank customers and
their income levels. It is understood from the table that 82 % of the participants with an
income level below 750 TL, 54 % the participants with an income level between 750 TL
and 1000 TL, 23 % of the participants with an income of 1001 TL and 1500 TL and 21 %
of the participants with an income of above 1500 TL do not use IB service.
Table 26. Correlation between Participants’ IB Use and Their Income Level
IB User
Total
Uses IB Does not Use IB
Income
Less than 750
Total 11 49 60
% Income 18,3% 81,7% 100,0%
750-1000 TL
Total 32 38 70
% Income 45,7% 54,3% 100,0%
1001-1500 TL
Total 89 27 116
% Income 76,7% 23,3% 100,0%
More than 1500 TL
Total 174 45 219
%Income 79,5% 20,5% 100,0%
Total
Total 307 159 466
% Income 65,9% 34,1% 100,0%
Logistical regression analysis was performed in order to be able to determine the effects
that arose from the relationships between the demographic features and make more
informed comments2. The Nagelkerke R Square value of .465 in the model summary
2 Gender was shown with the value of “1”, Age with “2”, Marital status with “3”, Profession with “4”, Educational Level with
table indicates a medium level conformity. In this framework, it can be stated that
demographic features can account for the state of IB use to a limited extent and that
factors other than demographic ones are effective on IB use.
Tablo 27. Model Summary
-2 Log likelihood Cox & Snell R Square Nagelkerke R Square
407,283 ,336 ,465
According to the total percentage value in the classification table, it can be argued that
the research data were classified accurately at a level of 79 %.
Table 28. Classification Table
Observation
Expectation
IB User
Correct Percentage
Uses IB Does not Use IB
IB User
Uses IB 277 30 90,2
Does not Use IB 66 93 58,5
Total Percentage 79,4
Results of the regression analysis are given in Table 29. As can be seen from the values
in the significance column of the table, the relationship between gender, age, profession,
educational level and income level and the state of IB use is significant at the level of 5
%. On the other hand, the relationship between the marital status and the state of IB use
is not statistically significant.
Again, it can be concluded from the values in the significance column that the
relationship between income and IB use arises only from the first category. In other
words, the state of IB use of the group with the lowest income is considerably lower than
the others whereas no remarkable difference exists among other groups in terms of IB
use.
When the values in the Exp (B) column are examined, it is observed that education is
the factor that most affects IB use. On the other hand, the factors of gender, age,
profession and income affect IB use on a marginal but significant level.
Table 29. Results of Regression Analysis
B S.E. Wald Df Sig. Exp(B)
D1(1) ,655 ,273 5,758 1 ,016 1,926
D2 8,969 2 ,011
D2(1) -2,095 ,769 7,421 1 ,006 ,123
D2(2) -1,541 ,759 4,118 1 ,042 ,214
“5”, Monthly income level with “6”.
D3 ,712 2 ,701
D3(1) -,262 1,555 ,028 1 ,866 ,769
D3(2) -,502 1,552 ,105 1 ,746 ,605
D4 34,851 2 ,000
D4(1) -2,140 ,496 18,638 1 ,000 ,118
D4(2) -,560 ,539 1,080 1 ,299 ,571
D5 35,730 4 ,000
D5(1) 3,889 ,757 26,386 1 ,000 48,838
D5(2) 2,712 ,573 22,378 1 ,000 15,056
D5(3) 1,409 ,471 8,950 1 ,003 4,091
D5(4) ,690 ,506 1,859 1 ,173 1,995
D6 14,041 4 ,007
D6(1) 22,176 40194,375 ,000 1 1,000 4,273E9
D6(2) 21,258 40194,375 ,000 1 1,000 1,707E9
D6(3) 20,401 40194,375 ,000 1 1,000 7,245E8
D6(4) 20,509 40194,375 ,000 1 1,000 8,072E8
Constant -19,732 40194,375 ,000 1 1,000 ,000
The Effect of the Participants’ Levels of Perceiving IB on Frequency and Duration of IB
Use: This section deals with the effects of the IB perception levels of bank customers on
the frequency and duration of IB use via the multiple regression analysis. In this
framework, firstly, the reliability and factor structure of the questions used in the study
were tested. Cranbach’s Alpha coefficient was used to test the reliability of the
questions. “Alpha Coefficient of the Scale If the Item Deleted” was calculated in order to
determine to what extent and in what direction the questions influenced the alpha
coefficient. The values in question indicate the internal consistency of the remaining
variables if any one of the variables is deleted. According to the Table 30, a high
reliability value of α = 0,928 was obtained.
Table 30. Reliability Value
Cronbach’s Alpha N
,928 24
The effects of the items constituting the scale on the level of reliability are shown in
Table 31. When the Cronbach’s Alpha values are examined, it is observed that removal
of a variable from the survey does not increase security. In this framework, the survey
structure consisting of 24 questions was maintained.
Table 31. Effects of Variables that Form the Scale on Reliability
Mean of Scale When
the Variable is
Deleted
Variance of the Scale
When the Variable is
Deleted
Total Correlation of
Corrected Variable
Cronbach’s Alpha
When the Variable is
Deleted
D35 97,6361 142,186 ,602 ,925
D36 97,8426 140,061 ,583 ,925
D37 97,8885 137,909 ,644 ,924
D38 97,5803 141,883 ,576 ,925
D39 97,7213 139,998 ,573 ,925
D40 97,7410 140,285 ,662 ,924
D41 97,5967 142,531 ,619 ,925
D42 97,6131 141,442 ,671 ,924
D43 97,9344 137,009 ,657 ,924
D44 97,7902 139,561 ,622 ,924
D45 98,7311 132,763 ,563 ,927
D46 98,4262 135,081 ,542 ,927
D47 97,7803 138,731 ,610 ,924
D48 98,1836 141,150 ,482 ,926
D49 98,2852 138,869 ,550 ,925
D50 98,3574 139,776 ,483 ,927
D51 97,7311 140,052 ,607 ,925
D52 97,6984 141,521 ,589 ,925
D53 98,3213 138,087 ,474 ,927
D54 97,7410 140,883 ,609 ,925
D55 97,7246 140,891 ,602 ,925
D56 98,0721 137,449 ,522 ,926
D57 97,7672 139,778 ,667 ,924
D58 97,9082 136,669 ,653 ,924
The principal components analysis was used in determining the factor structure of the IB
perception scale. It is seen from the data in the table that this scale consists of 5 factors.
Moreover, when the variance values are examined, it is observed that the 5 factors that
constitute the scale account for 65 % of the total variance.
Table 32. Total Variance
Components Initial Values Total of Loadings
Total Variance % Total % Total Variance % Total %
1 9,807 40,860 40,860 4,830 20,125 20,125
2 2,046 8,526 49,386 3,162 13,175 33,300
3 1,335 5,561 54,947 3,055 12,731 46,030
4 1,253 5,221 60,168 2,571 10,713 56,743
5 1,054 4,390 64,558 1,876 7,815 64,558
6 ,898 3,742 68,300
7 ,773 3,222 71,522
8 ,744 3,099 74,621
9 ,720 3,001 77,622
10 ,579 2,413 80,035
11 ,568 2,367 82,402
12 ,538 2,244 84,646
13 ,517 2,153 86,800
14 ,453 1,887 88,686
15 ,415 1,730 90,416
16 ,353 1,469 91,885
17 ,307 1,280 93,165
18 ,299 1,245 94,410
19 ,291 1,213 95,622
20 ,271 1,128 96,751
21 ,231 ,961 97,712
22 ,207 ,861 98,573
23 ,175 ,729 99,303
24 ,167 ,697 100,000
Although the scale is composed of 5 factors from the components matrix obtained as a
result of the principal components analysis, a clear factor structure could not be
obtained. Therefore, the obtained result consisting of 5 factors was subjected to rotate in
order to allow for interfactorial comparison by seeing loadings of each factor more
clearly. Varimax rotation method was used for this purpose.
Table 33. Principal Components Matrix
Components
1 2 3 4 5
D35 ,673 -,203 ,289 -,179 ,177
D36 ,632 -,012 ,263 -,281 ,093
D37 ,710 -,182 ,178 -,254 -,094
D38 ,657 -,341 ,114 -,271 -,153
D39 ,629 -,147 -,110 -,142 -,340
D40 ,732 -,272 ,004 -,017 -,023
D41 ,702 -,329 ,154 -,088 ,108
D42 ,751 -,303 ,059 ,034 -,031
D43 ,696 ,017 -,273 -,140 ,008
D44 ,673 -,086 -,224 -,248 -,144
D45 ,564 ,389 -,385 -,120 ,115
D46 ,547 ,372 -,367 -,165 ,101
D47 ,642 ,094 -,293 -,271 ,042
D48 ,490 ,584 ,420 -,034 -,107
D49 ,546 ,626 ,337 ,054 -,046
D50 ,491 ,560 ,319 -,108 -,093
D51 ,652 ,012 -,022 ,160 ,561
D52 ,651 -,137 ,007 ,105 ,532
D53 ,489 ,333 -,184 ,294 ,126
D54 ,683 -,216 ,219 ,463 -,053
D55 ,659 -,138 ,074 ,491 -,085
D56 ,565 ,033 -,246 ,295 -,186
D57 ,713 -,023 -,058 ,253 -,301
D58 ,683 ,092 -,245 ,183 -,187
The factorial structure obtained as a result of the Varimax rotation method is shown in
Table 34. It is observed from the data in the table that the choice “53. I use IB services
as a source of information” is below 0.5 and has a high loading value on more than one
factor. Therefore, this choice was removed from the scale.
Table 34. Matrix of Rotated Principal Components
Components
1 2 3 4 5
D35 ,670 ,107 ,057 ,216 ,362
D36 ,600 ,020 ,157 ,344 ,230
D37 ,727 ,179 ,172 ,209 ,095
D38 ,775 ,189 ,138 ,043 ,026
D39 ,558 ,347 ,324 ,065 -,157
D40 ,613 ,376 ,215 ,032 ,216
D41 ,682 ,248 ,094 ,056 ,327
D42 ,631 ,426 ,155 ,045 ,235
D43 ,421 ,245 ,558 ,081 ,151
D44 ,548 ,216 ,494 ,045 -,009
D45 ,090 ,124 ,729 ,242 ,175
D46 ,118 ,086 ,714 ,238 ,149
D47 ,402 ,098 ,619 ,112 ,124
D48 ,150 ,130 ,126 ,844 ,044
D49 ,089 ,205 ,207 ,837 ,128
D50 ,171 ,081 ,215 ,771 ,029
D51 ,240 ,222 ,292 ,139 ,745
D52 ,356 ,205 ,225 ,049 ,718
D53 -,079 ,391 ,406 ,273 ,296
D54 ,378 ,707 -,085 ,165 ,319
D55 ,274 ,736 ,035 ,134 ,268
D56 ,150 ,590 ,353 ,068 ,051
D57 ,359 ,659 ,259 ,193 ,004
D58 ,249 ,552 ,458 ,154 ,052
The relationships among each other of the factors, namely ease of use, usefulness, web
security, purpose of use and personal views, which were prepared to determine the
participants’ IB perception levels, were investigated at this stage of the analysis. It is
understood from the values in the significance column of Table 35 that the relationships
among the participants’ IB perceptions are significant at the level of 1 %. In particular,
there is an extremely strong correlation between the factors of ease of use and
usefulness and personal views. On the other hand, the relationship of web security with
the other factors is relatively weak.
Table 35. Results of Correlation Analysis
Ease of
Use Usefulness Web
Security
Purpose of
Use
Personal
Views
Ease of Use Pearson
Correlation 1 ,624** ,394** ,580** ,680**
Significance ,000 ,000 ,000 ,000
N 305 305 305 305 305
Usefulness
Pearson
Correlation ,624** 1 ,468** ,521** ,593**
Significance ,000 ,000 ,000 ,000
N 305 305 305 305 305
Web Security
Pearson
Correlation ,394** ,468** 1 ,335** ,414**
Significance ,000 ,000 ,000 ,000
N 305 305 305 305 305
Purpose of
Use
Pearson
Correlation ,580** ,521** ,335** 1 ,553**
Significance ,000 ,000 ,000 ,000
N 305 305 305 305 305
Personal
Views
Pearson
Correlation ,680** ,593** ,414** ,553** 1
Significance ,000 ,000 ,000 ,000
N 305 305 305 305 305
The relationship between the bank customers’ ease of use, usefulness, web security,
purpose of use and personal views and the frequency of IB use was investigated using
the regression analysis and the results were given in Table 36. It is understood from the
value in the significance column of the table (p = .000) that the relationship among the
related variables is statistically significant.
Table 36. Results of Multiple Regression Analysis
Model Total Squares df Mean Squares F Sig.
1
Regression 79,080 5 15,816 5,773 ,000
Difference 819,202 299 2,740
Total 898,282 304
The model summary that was formed to demonstrate the extent to which the bank
customers’ ease of use, usefulness, web security, purpose of use and personal views
affect the frequency of IB use is given in Table 37. It is understood from the value in the
R-Square column (R Square = .088) that the effect of perception on the frequency of use
is limited at the level of 9 %.
Table 37. Model Summary
Model R R Square Corrected R Square Std. Error
1 ,297 ,088 ,073 1,65524
On the other hand, the coefficients indicating the effects of each of the factors of ease of
use, usefulness, web security, purpose of use and personal views on the frequency of IB
use are given in Table 38. The data in the table reveal that although the integrated effect
of the factors is significant, individual effects of the factors are not statistically significant.
Table 38. Coefficients
Model
Non-Standard Coefficients Standard Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 7,454 ,877 8,497 ,000
Ease of Use -,223 ,280 -,067 -,797 ,426
Usefulness -,164 ,178 -,072 -,922 ,357
Web Security -,223 ,142 -,100 -1,573 ,117
Purpose of Use -,170 ,191 -,064 -,887 ,376
Personal Views -,218 ,225 -,079 -,971 ,332
The relationship between the bank customers’ ease of use, usefulness, web security,
purpose of use and personal views and the duration of IB use was investigated by the
help of regression analysis and the results were given in Table 39. It is understood from
the table that the relationship between the related variables is statistically significant.
Table 39. Results of the Multiple Regression Analysis
Model Total Squares df Mean Squares F Sig.
1
Regression 32,299 5 6,460 6,606 ,000
Difference 292,390 299 ,978
Total 324,689 304
The model summary is given in Table 40. It is understood from the value in the RSquare
column of the table (R Square = .099) that the effect of perception on the
duration of use is limited to 10 %.
Table 40. Model Summary
Model R R Square Corrected R Square Std. Error
1 ,315 ,099 ,084 ,98888
The coefficients demonstrating the effects of each of the factors, namely ease of use,
usefulness, web security, purpose of use and personal views, on the duration of IB use
are given in Table 41. The data in the table indicate that there is a positive and linear
relationship between the factors of ease of use and purpose of use and the duration of
use. It was concluded on the basis of these findings that the customers who found IB
use easy and thought that this service served their purpose had been using IB for a
longer period. On the other hand, usefulness, web security and personal views do not
have a significant effect on the duration of IB use.
Table 41. Coefficients
Model
Non-Standard Coefficients Standard
Coefficients t Sig.
B Std. Error Beta
1
(Constant) 1,040 ,524 1,985 ,048
Ease of Use ,406 ,167 ,203 2,431 ,016
Usefulness -,023 ,106 -,017 -,218 ,827
Web Security -,005 ,085 -,003 -,054 ,957
Purpose of Use ,256 ,114 ,159 2,236 ,026
Personal Views ,011 ,134 ,007 ,083 ,934
CONCLUSION
Thanks to the rapid developments in information technologies, the internet, which is
intensively used in all fields, is also used extensively in the field of banking. Customers
are able to access their accounts through IB wherever they are and make banking
transactions. Enabling customers to make their transactions on a 24-hour-basis and
keep their portfolios under control at all times, IB also reduces costs of transactions of
banks and lightens their workload. The cost of transactions made via the internet is
about 1 % of the cost of the transactions made via the branches of banks.
In Turkey, especially banks have assumed the leading role in the use of internet.
Transactions are conducted via the internet in all of the 22 banks that operate in Turkey
and were included in the scope of the present study. The websites of the banks were
investigated under three headings, namely financial transactions, non-financial
transactions and evaluation of websites in terms of speed, security and continuity of
services. The five headings that were investigated in the financial transactions section
were money transfers, payments, investment transactions, credit card transactions and
finally account management. Non-financial transactions, on the other hand, were
investigated under four headings, namely applications for credit cards, applications for
laon, regular payment order and information search and change. Based on the
information gleaned from the websites of the banks, the following inferences are made:
We found many banks offering transaction services at the advanced level. Most products
and services are offered via the Internet. Concerning the security component, we can
notice that we practically find the same security elements for all banks. All banks use for
identification user code, password, smart cards and digipass. All sites are multilingual in
order to reach the largest number of clients. It is generally observed that some banks are
better at using IB in banking transactions. However, all of the banks are increasing the
number of transactions that are conducted via the internet. It should be noted here that
expanding the information technology infrastructure in Turkey and employment of
experienced personnel in the field of information technologies will contribute to the
development of IB.
The results of the survey that was conducted to determine the users’ perception levels of
IB services can be summarised as follows: More than half of the bank customers who
participated in the research are male, below the age of 50, married, employed in the
private sector, received master’s level education and earn an income above 1500 TL. 66
% of the participants are IB users whereas 34 % do not make use of the IB service. All of
the participants who are not IB users find IB use difficult or do not know how to use IB.
Moreover, most of them do not find IB service secure and its benefits evident and
therefore prefer face-to-face banking.
When the relationship between the participants’ demographic profiles and their IB use is
investigated, it is observed that education is the most influential factor on IB use.
Likewise, while the IB use of the group with the lowest income level is considerably
lower than the others, no significant difference is observed among the other groups in
terms of the use of IB.
It is observed that the factors of ease of use, usefulness, web security, purpose of use
and personal ideas, which were prepared to determine the IB perception levels of the
bank customers do not have an effect on the frequency of IB use on an individual level.
On the other hand, a positive and linear relationship was observed between the factors
of ease of use and purpose of use and the duration of use. It was concluded on the
basis of this finding that the customers who found IB use easy and thought this service
served their purpose had been using IB for longer period. However, usefulness, web
security and personal views do not have a significant effect on the duration of IB use.
REFERENCES
Achour H., Bensedrine N. (2005), An evaluation of IB and online brokerage in Tunisia,
First International Conference on E-Business and E-Learning (EBEL), Jordan.
Ainin S., Lim C. H., Wee A. (2005), Prospects and Challenges of e-banking in Malaysia,
The Electronic Journal of Information Systems in Developing Countries 22 (1),
pp.1-11.
Al-Sabbagh I., Molla A. (2004), Adoption and Use of IB in the Sultanate of Oman: An
Exploratory Study, Journal of IB and Commerce, Available at http://www.
doaj.org.
Awamleh R., Evans J., Mahate A. (2003), IB in emergency markets the case of Jordon -
A note, Journal of IB and Commerce 8 (1), Available at http://www. doaj.org.
Awamleh R., Fernandes C. (2005), IB: An empirical investigation into the extent of
adoption by banks and the determinants of customer satisfaction in the United
Arab Emirates, Journal of IB and Commerce, Available at http://www. doaj.org
Ayadi A. (2006), Technological and organizational preconditions to IB implementation:
Case of a Tunisian bank, Journal of IB and Commerce 11 (1), Available at
http://www.arraydev.com/commerce/jibc/.
Cheng T. C. E, Lam D. Y.C., Yeung A. C. L. (2006), Adoption of internet banking: An
empirical study in Hong Kong, Decision Support Systems 42, pp. 1558–1572.
Chiemeke S. C., Evwiekpaefe A. E., Chete F. O. (2006), The Adoption of IB in Nigeria:
An Empirical Investigation, Journal of IB and Commerce 11 (3), Available at
http://www.arraydev.com/commerce/jibc/.
Chung W., Paynter J. (2002), An evaluation of IB in New Zealand, Proceedings of the
35th Hawaii international conference in system sciences, IEEE Hawaii, pp 1-9.
Diniz, E. (1998), Web banking in USA, Journal of IB and Commerce 3 (2), Available at
http://www. doaj.org.
Diniz, E., Porto R.M., Adachi T. (2005), IB in Brazil: Evaluation of functionality, security
and usability, The Electronic Journal of Information Systems Evaluation 8 (1), pp.
41-50, Available at http://www.ejise.com.
Eriksson K., Kerem K., Nilsson D. (2004), Customer acceptance of internet banking in
Estonia, International Journal of Bank Marketing 23 (2), pp. 200-216.
Guru, B., Shanmugam, B., Alam, N., Perera, C. (2003), An evaluation of IB sites in
Islamic countries, Journal of IB and Commerce 8 (2), Available at http://www.
doaj.org.
Howitt D. and Cramer D. (2008), Introduction to SPSS in Psychology, Prentice Hall, New
York, p. 311.
Jaruwachirathanakul B. and Fink D. (2005), Internet banking adoption strategies for a
developing country: The case of Thailand, Internet Research 15 (3), pp. 295-311.
Jasimuddin S. M. (2001), Saudi Arabian banks on the web, Journal of IB and Commerce
6 (1), Available at http://www.arraydev.com/commerce/jibc/articles.htm.
Landau S. and Everitt B. S. (2004), Handbook of Statistical Analysis Using SPSS,
Chapman & Hall/CRC, Florida, p. 233.
Mols N. P. (2000), The Internet and services marketing-the case of Danish retail
banking, Electronic Networking Applications and Policy 10 (1), pp. 7-18.
Ndubisi N. O. and Sinti Q. (2006), Consumer attitudes, system’s characteristics and
internet
banking adoption in Malaysia, Management Research News 29 (1/2), pp. 16-27.
Polatoglu V., Ekin S. (2001), An empirical investigation of the Turkish consumers’
acceptance of IB services, The International Journal of Bank Marketing 19 (4),
pp. 156-165.
Sathye, M. (1999), Adoption of IB by Australian consumers: An empirical investigation,
International Journal of Bank Marketing 17 (7), pp. 324-334
Sayar C. and Wolfe S. (2007), Internet banking market performance: Turkey versus the
UK, International Journal of Bank Marketing 25 (3), pp. 122-141.
Singh B., Malhotra P. (2004), Adoption of IB: An Empirical Investigation of Indian
Banking Sector, Journal of IB and Commerce, Available at http://www. doaj.org.
BAT Internet Banking Statistics Report, June 2009.
Wu C.-S. , Cheng F.-F., Lin H.-H. (2004), Web site Usability Evaluation of IB in Taiwan,
Journal of IB and Commerce 9 (1), Available at http://www. doaj.org.
Vijayan, P., Shanmugam, B. (2003), Service quality evaluation of IB in Malaysia, Journal
of IB and Commerce 8 (1), Available at http://www. doaj.org.


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