showing the problem

1. Md. Enamul Islam Shemul
Student of Patuakhali Science and Technology University
Faculty of Business Administration and Management
Session 2013-14

2. Abstract
E-commerce is the modern type of business which involved in online business. But it has been
seen that there lot of barriers involving e-commerce mostly in rural areas.
Introduction
Business is the potential way of development for a country. Majority of the people of a nations
are involved in it. There are lots of ways of having a business in a country today and e-commerce
is one of them. E-commerce is type of way in which business are conducted through technical
process rather than physical process. Technical process means internet, online etc. The number
of Internet users around the world has been steadily growing and this growth has provided the
impetus and the opportunities for global e-commerce. Because there are easy ways of having a
business in online, most of the people are likely to have this type of business. E-commerce is
growing widely in every country. E-commerce has been predicted to be a new driver of
economic growth for developing countries. But in now a day’s it is getting to much diplomatic
because of some barriers. Now a day internet cost is too much, besides this the human capital
cost of installing, operating, maintaining, support and training and, these are beyond the means
of many enterprises in developing country. Consequently, there is still doubt about how e-
commerce will actually lead firms in developing countries to new trading .The obstacles to
reaping the benefits brought about by e-commerce are often underestimated. In rural areas, it
becomes very inappropriate because of the lack of awareness of the people.

3. Objectives
This report is based on critical judgment about the E-commerce barriers in rural areas in
Bangladesh. So therefore some objectives are fulfilled in this report. Those are:
 Analyze the present condition of E-commerce in rural areas in Bangladesh
 To know about the barriers of E-commerce.
 To find the optimal solutions regarding the barriers of E-commerce.
 To know the factor analyses of e-commerce in dumki
Methodology
Data collection
Data for this report has been collected from both primary and secondary source. People give their
opinions about the e-commerce and what type of barriers they are facing.
Primary source
We have made some questionnaires to know about the present condition about E-commerce in
rural areas like dumki. People responded to our forms and told us about their perceptions. The
questionnaire was established about the barriers of e-commerce in their area.
Secondary source
We have used different websites of e-commerce regarding Bangladesh and its rural areas.
Sample size
We have collected samples of one hundred respondents in dumki.
Tools used: we used some tools in these report such as descriptive analyses, KMO and Bartlett's
Test, Communalities, Total Variance Explained, Component Matrix, Component Score
Covariance Matrix etc.

4. Conceptual discussion
E-commerce
E-commerce means electronic commerce. It is a technical form of having a business. Majority of
the people are like to adherer to this. It is now a global form of business. There is no physical
substance of this type of business. It follows the process like people can buy their products via
internet or online. The formalities and prices are available there in the internet. Today, e-
Commerce is transforming the international trade landscape. Bangladesh is a developing country
and recently e-commerce is showing great signs of raising that development.
Barriers to E-commerce
There are lots of barriers in e-commerce. Some of those are discussed below:
Economic barriers
Economic barriers means the barriers that are emerging because of economic situation of a
country. Normally e-commerce needs lots of components by which it can probably grow up. But
most of the times there seem to be lack of developments. If the countrymen do not effort to make
investments in e-commerce than it will decline its power of developments. Lack of investment
leads to no innovations which causing too much problems. If there is no innovations, then there
will be slow internet. Slow Internet diffusion in developing countries can be attributed to market
and infrastructural factors controlling the availability of ICTs. People will leave the way of e-
commerce if they get their results in inappropriate manner. Besides this there is a problem of
ATM cards. Most of the banks do not provide which is useful to use.
Sociopolitical barriers
Sociopolitical barriers are those barriers which are indulge with formal and informal institutions
of a country. It has been seen that it often become more difficult and time consuming to
overcome than technological barriers. Social barriers are related with informal institutions. It has

5. been found that Personal relationships are more important in businesses rather than the
anonymous online relationships. People like to have face-to-face communications and e-mails
for establishing relationships which is a barrier to e-commerce.
Political barriers
This is a very common type of barrier. It implies the abuse of power of the political parties.
There are always some group of people who like to dominate the country and people of that
country. They use the e-commerce system for their own benefit. Some developing countries treat
ICT products as luxury items and impose import duty, surtax, value added tax, sales tax, etc.
Weak formal institutions also lower consumer trust in e-commerce and willingness to buy online.
Because of these type of barriers customers are getting bad and negative influence about the e-
commerce business.
Cognitive barriers
Cognitive means the factors that are related to mental situations of individuals and organizational
decision makers. It has many effects like such as inadequate awareness, knowledge, skills, and
confidence serve as cognitive feedbacks. Sometimes it causes because of the top management‘s a
prior evaluation influences cognitive bias toward e-business. In many countries it has been found
that people are not aware of the technologies. As the time passing the technologies are getting
better and better so countries which are indulge to this has better chance of developing. Besides
this organizations ‘human, business, and technological resources and understanding of potential
opportunities, risk aversion and inertia often lead to a negative cognitive assessment of e-
commerce.

6. Discussion
Figure 1: Gender of the respondents
In the above chart we can easily say that, 36% of the respondents are Male and rests of the 64%
respondents are Female.
Figure 2: Educational Qualification of the respondents
In this figure we can easily say that, 69% of the respondents are from Hons, 20% of the
respondents are from MS/MBA and rest of the respondents is from others.
36%
64%
Gender
Male Female
69%
20%
11%
Educational Qualification
Hons
MS/MBA
Others

7. Figure 3: Age of the respondents
In this chart we can easily say that, 87% of the respondent’s age is situated at the range of 20-30,
12% of the respondents age is situated at the range of 31-40 and rests 1% of the respondents age
is situated at the range of 41-50.
Figure 4: Profession of the respondents
In this figure we can easily say that, 91% of the respondents are student, 2% of the respondents
are teacher and rests 7% of the respondents profession is others.
87%
12%
1% 0
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
20-30 31-40 41-50 51-60
AGE
91%
2% 0
7%
STUDENT TEACHER BUSINESS OTHERS
Profession

8. Analysis
Descriptive Statistics
Mean Std.
Deviation
Analysis
N
VAR000
01
2.78 1.382 100
VAR000
02
3.15 1.201 100
VAR000
03
2.87 1.125 100
VAR000
04
3.68 1.317 100
VAR000
05
3.30 1.322 100
VAR000
06
3.67 1.111 100
VAR000
07
3.24 1.240 100
VAR000
08
3.48 1.176 100
VAR000
09
3.59 1.045 100
VAR000
10
3.73 .908 100
VAR000
11
3.44 .957 100
VAR000
12
3.41 .866 100
VAR000 3.62 .940 100

9. 13
VAR000
14
3.23 .941 100
VAR000
15
3.49 .937 100
VAR000
16
3.50 .937 100
VAR000
17
3.37 1.031 100
VAR000
18
3.47 .937 100
VAR000
19
3.31 .929 100
VAR000
20
3.59 .996 100
VAR000
21
3.44 .957 100
VAR000
22
3.28 .922 100
VAR000
23
3.36 1.124 100
VAR000
24
3.57 1.066 100
Interpretation: The first output from the analysis is a table of descriptive statistics for all the
variables under investigation. Typically, the mean, standard deviation and number of respondents
(N) who participated in the survey are given. Looking at the mean, one can conclude that Q.10 is
the most important variable that influences customers to buy the product. It has the highest mean
of 3.73.

10. KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
.672
Bartlett's Test of
Sphericity
Approx. Chi-Square 889.691
Df 276
Sig. .000
Interpretation: From the same table, we can see that the Bartlett’s Test OfSphericity is
significant (0.672). That is, significance is more than 0.05. In fact, it is actually 0.672 that
indicate the acceptable relationship among variables. Values between 0.7-0.8 acceptable and
values above 0.9 are superb the significance level is small enough to reject the null hypothesis.
This means that correlation matrix is not an identity matrix.
Communalities
Initial Extracti
on
VAR000
01
1.000 .796
VAR000
02
1.000 .833

11. VAR000
03
1.000 .670
VAR000
04
1.000 .504
VAR000
05
1.000 .710
VAR000
06
1.000 .679
VAR000
07
1.000 .723
VAR000
08
1.000 .762
VAR000
09
1.000 .663
VAR000
10
1.000 .725
VAR000
11
1.000 .574
VAR000
12
1.000 .600
VAR000
13
1.000 .712
VAR000
14
1.000 .624
VAR000
15
1.000 .775
VAR000
16
1.000 .577
VAR000
17
1.000 .787

12. VAR000
18
1.000 .781
VAR000
19
1.000 .703
VAR000
20
1.000 .623
VAR000
21
1.000 .718
VAR000
22
1.000 .612
VAR000
23
1.000 .757
VAR000
24
1.000 .588
Extraction Method: Principal
Component Analysis.
Interpretation: The next item from the output is a table of communalities which shows how
much of the variance (i.e. the communality value which should be more than 0.5 to be
considered for further analysis. Else these variables are to be removed from further steps factor
analysis) in the variables has been accounted for by the extracted factors. For instance over 92%
of the variance in Q.19 and Q.22 is accounted for, while 43% and 48% of the variance in Q.6
and Q.10 is not accounted for .
Total Variance Explained
Com
pone
nt
Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Cumulative Total % of Cumulative

13. Variance % Variance %
1 5.360 22.332 22.332 5.360 22.332 22.332
2 2.629 10.955 33.287 2.629 10.955 33.287
3 1.847 7.697 40.984 1.847 7.697 40.984
4 1.736 7.233 48.217 1.736 7.233 48.217
5 1.430 5.960 54.178 1.430 5.960 54.178
6 1.318 5.493 59.671 1.318 5.493 59.671
7 1.170 4.875 64.546 1.170 4.875 64.546
8 1.003 4.180 68.726 1.003 4.180 68.726
9 .935 3.898 72.624
10 .906 3.776 76.400
11 .752 3.134 79.534
12 .718 2.993 82.527
13 .542 2.260 84.787
14 .516 2.148 86.934
15 .500 2.085 89.019
16 .434 1.809 90.828
17 .407 1.694 92.522
18 .359 1.498 94.020
19 .348 1.449 95.468
20 .321 1.336 96.804
21 .263 1.094 97.898
22 .211 .877 98.775
23 .177 .738 99.513

14. Interpretation: The scree plot is a graph of the eigenvalues against all the factors. The graph is
useful for determining how many factors to retain. The point of interest is where the curve starts
to flatten. It can be seen that the curve begins to flatten between factors 5 and 6. Note also that
factor 5 onwards have an eigenvalue of less than 1, so only three factors have been retained.
24 .117 .487 100.000
Extraction Method: Principal Component
Analysis.

15. Component Matrixa
Component
1 2 3 4 5 6 7 8
VAR000
01
.318 .739 .014 .364 .092 .003 .058 -.060
VAR000
02
.303 .740 .046 .338 .038 .111 .234 .095
VAR000
03
.394 .397 -.257 .326 -.254 .049 .274 -.207
VAR000
04
.355 .012 -.313 -.307 -.078 .385 .164 .067
VAR000
05
.654 .308 .154 -.279 -.052 -.173 .224 -.054
VAR000
06
.381 -.149 .387 -.360 .086 -.394 .062 -.255
VAR000
07
.524 .230 .263 -.003 -.140 .194 -.519 -.012
VAR000
08
.385 .141 .633 -.122 -.248 -.119 -.265 .180
VAR000
09
.527 .133 -.278 -.110 .255 -.263 -.244 .291
VAR000
10
.477 .121 -.357 .354 -.173 -.251 -.366 -.059
VAR000
11
.385 -.155 -.245 -.117 -.339 .108 .117 .434

16. VAR000
12
.343 -.330 -.265 -.199 -.082 .483 -.152 .010
VAR000
13
.500 .135 -.257 -.324 .370 -.146 -.156 .299
VAR000
14
.277 -.534 .198 .328 -.332 -.032 .028 .059
VAR000
15
.558 -.351 .051 .283 -.444 -.198 .108 .101
VAR000
16
.448 -.227 .026 .255 .059 -.117 .232 .434
VAR000
17
.190 -.181 .600 .340 .438 .139 .037 .175
VAR000
18
.693 -.338 .033 .140 .126 .257 -.167 -.236
VAR000
19
.621 -.348 -.158 .192 .032 .016 -.075 -.357
VAR000
20
.278 -.234 -.380 .182 .305 -.441 -.078 -.139
VAR000
21
.588 -.053 .174 .148 .396 .398 .046 -.028
VAR000
22
.570 -.238 -.079 -.123 .289 .016 .348 -.062
VAR000
23
.626 .006 .165 -.412 -.162 -.123 .320 -.157
VAR000
24
.489 .402 .015 -.325 -.162 .113 -.171 -.111
Extraction Method: Principal Component Analysis.
a. 8 components extracted.

18. Component Score Covariance Matrix
Com
pone
nt
1 2 3 4 5 6 7 8
1 1.000 .000 .000 .000 .000 .000 .000 .000
2 .000 1.000 .000 .000 .000 .000 .000 .000
3 .000 .000 1.000 .000 .000 .000 .000 .000
4 .000 .000 .000 1.000 .000 .000 .000 .000
5 .000 .000 .000 .000 1.000 .000 .000 .000
6 .000 .000 .000 .000 .000 1.000 .000 .000
7 .000 .000 .000 .000 .000 .000 1.000 .000
8 .000 .000 .000 .000 .000 .000 .000 1.000
Extraction Method: Principal Component Analysis.
Component Scores.