In the modern era, information and communication technology (ICT) has a significant contribution in all over the world in productivity, growth and efficiency. ICT is the combination of hardware, software, telecommunication and information techniques that are used to store and transform information
Developing countries are facing the problem of technological and innovation capabilities
The adoption and use of ICT depends on many factors such as performance expectancy, effort expectancy, social influence and facilitating conditions
The process of new technological adoption is very complex because many qualitative and quantitative factors are involved in adoption process. The adoption has three stages such as initiation, adoption and implementation. The first stage deals with assessing the innovation. In 2nd stage decision is taken to adopt new innovation. The last stage is used to check the effect of innovation. ICT adoption is the use of ICTs tools by using internet
ICT adoption in any country occurs when Government invests in knowledge technology in order to support the business activities regarding ICT and people start to use it
ICT adoption has impact on economy of the world, particularly influence on the economic development, environment, employment, human development and national competitiveness
ICT adoption is the main indicator of a country`s economic achievements. In this perspective, developed countries adopt more rapidly and use new technologies if compared economically to developing countries. Moreover, it is argued that ICT adopted speedily in the World in spite of the level of economic development. On the other hand, adoption level of ICT and execution of new technology is determined by economical, environmental and social related factors
In the modern era, ICT is penetrated in all human fields in the world at record rate. ICT is contributed in all sphere of development such as economic, social and human development in developing countries. Globally, through ICT adoption, developing countries established well-developed communication system and infrastructure and also enhance the capacity which is used in policy implementation and regulation
Adoption level of ICT, ICT investment and its Impact on development is highly different in developing countries as compared to high income economies due to many reasons. Developing economies have many deficiencies as limited financial resources, unskilled human capital, lack of skills knowledge & computer literacy rate and low knowledge about the benefits of ICT
ICT adoption factors have been classified into the following categories: economic factors, social factors, legal factors, environmental factors and cognitive factors. These factors have been furthers classified. Economic factors comprises income, cost, financial, trade and wealth related factors
The current study is organized into six sections as follows: First section describes the brief introduction. The next section, briefly explores the review of literature which indicate the determinant if ICT adoption in different economies. Section 3 shows the descriptive analysis of each panel. In the section 4 revealed the econometric methods which are used in the empirical investigation. Section 5 explains the results of CSD, unit root test, coinegration and regression analysis. The last section briefly discusses the conclusion and policy recommendations from the empirical findings.
Many previous studies used the components of ICT like internet, mobile phones and computer penetration as a dependent variable
ICT technologies involved sophisticated and modern infrastructure. The adoption of ICT was the path of development which carries to the less greenhouse gas emissions and against the environment degradation
It is studied that internet adoption in 45 developing countries depend on price regulation, transparency, government effectiveness and personal computers per capital
ICT adoption is determined by social, economic and cognitive factors. In these factors government effectiveness, human capita, international trade, and adoption of predecessor technologies are positively significant to the ICT adoption rate
McKenney & McFarlan in 1982 considered that, identification and investment are the first step of adoption of ICT in any organization
The main objective of the current study is to explore the determinants of ICT adoption in developing countries. It was found that GDP per capita, Foreign Direct Investment (FDI), access to electricity, ICT good imports, urban population, Control of Corruption and Government effectiveness are the main factors of ICT adoption in developing countries
Whereas ICTDI is expressed as ICT development Index (0-100), ATE indicates access to electricity (% of population), GDP shows GDP per capita (current US $), FDI shows foreign direct investment (% of GDP), ICTGI shows ICT good imports (% of total imports), UP shows urban population, CoC shows Control of Corruption (estimate of governance range from -2.5 to 2.5), GE shows Government Effectiveness (estimate of governance range from -2.5 to 2.5), subscript t shows time period from 2000 to 2018 and i denotes cross section.
Panel data of 67selected developing countries for the period of 2000 to 2018 are collected from World Bank and International Telecommunication Union (ITU) websites. The countries were selected on the basis of data availability. Selected developing countries were categories into four panels on the basis of income according to World Bank criteria; such as low income countries, lower middle income countries, upper middle income countries and high income countries. 14 countries were selected from lower income economies, 20 were selected from lower middle income economies, 23 from upper middle income economies and 10 were selected from high income economies. The variables were selected from Economic factors, environmental factors, legal factors and cognitive factors. The selected variables were access to electricity. GDP, FDI, ICT good imports, urban population, control of corruption and government effectiveness while ICT development index is used as dependent variable. The ITU provides complete data regarding telecommunication sectors of selected variables of all selected countries while World Bank provides complete data on economic, social and legal factors.
The following steps have been selected in econometric procedure: (a) cross section dependence test (b) unit root test analysis (c) cointegration test analysis (d) regression analysis.
Panel |
Mean |
Min. |
Max. |
Std.Dev. |
|
||||
Low income Countries |
1.226 |
0.132 |
3.23 |
0.612 |
Lower Middle Income Countries |
2.258 |
0.225 |
6.45 |
1.350 |
Upper Middle Income countries |
3.423 |
0.101 |
7.55 |
1.842 |
High Income Countries |
4.239 |
0.321 |
7.63 |
2.157 |
|
||||
Low income Countries |
25.512 |
3.653 |
79.930 |
16.272 |
Lower Middle Income Countries |
74.445 |
15.328 |
100 |
22.011 |
Upper Middle Income countries |
94.534 |
24.8 |
100 |
12.302 |
High Income Countries |
98.600 |
81.401 |
100 |
3.601 |
|
||||
Low income Countries |
3.592 |
0.746 |
9.176 |
1.655 |
Lower Middle Income Countries |
5.971 |
1.421 |
51.476 |
6.981 |
Upper Middle Income countries |
8.806 |
0.006 |
31.819 |
5.875 |
High Income Countries |
7.450 |
1.019 |
26.068 |
5.786 |
|
||||
Low income Countries |
557.337 |
111.927 |
1674.003 |
264.933 |
Lower Middle Income Countries |
1771.646 |
258.471 |
4366.076 |
1051.955 |
Upper Middle Income countries |
5823.994 |
622.7421 |
16054.49 |
3403.778 |
High Income Countries |
14775.22 |
3624.198 |
47741.91 |
8084.746 |
|
||||
Low income Countries |
4.001 |
-1.811 |
39.456 |
5.261 |
Lower Middle Income Countries |
3.379 |
-37.154 |
43.912 |
4.665 |
Upper Middle Income countries |
4.355 |
-0.750 |
55.075 |
5.273 |
High Income Countries |
4.680 |
-46.123 |
54.222 |
7.906 |
|
||||
Low income Countries |
5683502 |
630758 |
22678295 |
4262680 |
Lower Middle Income Countries |
42937038 |
1369717 |
460000000 |
82316846 |
Upper Middle Income countries |
53285341 |
506439 |
824000000 |
133000000 |
High Income Countries |
7847499 |
237094 |
28255384 |
8505160 |
|
||||
Low income Countries |
-0.695 |
-1.663 |
0.007 |
0.281 |
Lower Middle Income Countries |
-0.712 |
-1.496 |
0.369 |
0.359 |
Upper Middle Income countries |
-0.339 |
-1.467 |
1.216 |
0.558 |
High Income Countries |
-0.329 |
-1.300 |
1.216 |
0.517 |
|
||||
Low income Countries |
-0.052 |
-1.129 |
1.056 |
0.472 |
Lower Middle Income Countries |
-0.483 |
-1.323 |
0.643 |
0.374 |
Upper Middle Income countries |
-0.123 |
-1.581 |
1.056 |
0.497 |
High Income Countries |
-0.147 |
-1.129 |
0.725 |
0.411 |
To check the presence of cross sectional dependence in panel data, the following three different testing procedures has adopted; (a) cross section dependence (CD) test of pesaran (b) CD test of Friedman (c) Frees test. The null hypothesis that “there is no CD in panel” has used
Pesaran (2004) has proposed the following CD test:
Whereas ^ρij shows the residual pairwise correlation sample estimate which was estimated by linear regression equation. The null hypotheses of no cross sectional dependence CD→N (0, 1) for N relatively small and T sufficiently large. The null hypothesis should be accepted if the panel data has no cross sectional dependency.
Friedman (1937) suggested a nonparametric test which is based on Spearman’s rank correlation coefficient. Its correlation coefficient is computed the basis of ranking. { ri,1, . . . , ri,T} to be the ranks of {ui,1…..ui,T } and its average rank is (T + 1/2)
Frees (1995) proposed CSD test to check the cross sectional dependency in the data. His statistics is based on the following equations;
where
Two types of unit root test have developed in panel; (a) first generation unit root test (b) second generation unit root test. The model of a first generation test has analyzed the properties of panel unit root test if the assumption follow that data is independent and identically distributed across the variables
To test the presence of long-run cointegration relationship among the integrated variable, three test such as kao, Pedroni and westerlund are available. Kao is the first author to suggest the test for cointegration in homogeneous panels, The Kao test statistics are calculated by pooling all the residuals of all cross sections in the panel. It is assumed in Kao's test that all the cointegrating vectors in every cross section are identical. Kao test fallow the basic approach of pedroni test. There are main five test under kao cointegaration test namely; (a) Dickey–Fuller, (b) Modified Dickey–Fuller, (c) Augmented Dickey–Fuller, (d) Unadjusted Dickey–Fuller and (e) Unadjusted modified Dickey–Fuller. No cointegration among the variables is null hypothesis of Kao cointegration test. If null hypothesis is rejected then there cointegration exists in panel data. If the probability value of above said five cointegration tes is less than 0.05 value it mean panel data is cointegrated
There are many econometrics techniques are used to investigate the presence of long run relationship among variables. In the current study we used FMOLS econometrics technique to find out relationship between ICT adoption and its determinants in developing countries. Philips and Hansen (1990) was introduced FMOLS method to investigate single cointegration relationship which has combination of I(1). FMOLS method utilizes "Kernal estimators of the Nuisance parameters. It has effect on asymptotic distribution of the OLS estimator
Pedroni (2004)
where βFMi is FMOLS estimator applied to ith country and t-statistic is:
[
CD Test |
Low income Countries |
Lower Middle Income Countries |
Upper Middle Income countries |
High Income Countries |
||||
Test-stat. |
Prob. |
Test-stat. |
Prob. |
Test-stat. |
Prob. |
Test-stat. |
Prob. |
|
Pesaran CD |
10.159 |
0.000 |
17.823 |
0.000 |
27.568 |
0.000 |
10.031 |
0.000 |
Friedman CD |
68.377 |
0.000 |
100.759 |
0.000 |
159.163 |
0.000 |
67.693 |
0.000 |
|
||||||||
CD Test |
Low income Countries |
Lower Middle Income Countries |
Upper Middle Income countries |
High Income Countries |
||||
Frees cross sectional independence =1.140 |
Frees cross sectional independence =2.267 |
Frees cross sectional independence=3.885 |
Frees cross sectional independence=1.238 |
|||||
Frees CD test |
Critical value |
Critical value |
Critical value |
Critical value |
||||
|
0.136* |
0.136* |
0.136* |
0.136* |
||||
0.178** |
0.178** |
0.178** |
0.178** |
|||||
0.260*** |
0.260*** |
0.260*** |
0.260*** |
Variables |
Low income Countries |
Lower Middle Income Countries |
Upper Middle Income countries |
High Income Countries |
|
CIPS |
CIPS |
CIPS |
CIPS |
||
ICT DI |
-3.348a |
-2.893a |
-2.489a |
-2.772a |
|
ATE |
-2.226b |
-3.570a |
-1.693 |
-2.389b |
|
FDI |
-2.800a |
-2.397b |
-2.980a |
-2.916a |
|
ICTGI |
-2.992a |
-2.636a |
-2.842a |
-3.535a |
|
CoC |
-2.483a |
-2.250b |
-1.508 |
-1.743 |
|
GE |
-2.584a |
-1.972 |
-2.395a |
-2.657a |
|
GDPPC |
-2.727a |
-1.331 |
-1.410 |
-1.740 |
|
UP |
|
-2.573a |
-3.095a |
-2.850a |
|
Critical Values |
1% |
-2.47 |
-2.40 |
-2.32 |
-2.60 |
5% |
-2.26 |
-2.21 |
-2.15 |
-2.34 |
|
10% |
-2.14 |
-2.10 |
-2.07 |
-2.21 |
|
|||||
---|---|---|---|---|---|
Variable |
Low income Countries |
Lower Middle Income Countries |
Upper Middle Income countries |
High Income Countries |
|
CIPS |
CIPS |
CIPS |
CIPS |
||
ICT DI |
-3.541a |
-3.165a |
-2.849a |
-4.247a |
|
ATE |
-3.641a |
-4.193a |
-2.053 |
-2.643 |
|
FDI |
-3.069a |
-2.462 |
-3.465a |
-2.742c |
|
ICTGI |
-3.767a |
-2.990a |
-3.444a |
-3.978a |
|
CoC |
-2.770c |
-2.730b |
-2.636c |
-2.984b |
|
GE |
-2.991b |
-2.337 |
-2.645c |
-3.158a |
|
GDPPC |
-2.783b |
-2.127 |
-2.091 |
-1.958 |
|
UP |
-1.634 |
-1.446 |
-2.104 |
-2.816c |
|
Critical value |
1% |
-3.01 |
-2.92 |
-2.83 |
-3.15 |
5% |
-2.78 |
-2.73 |
-2.67 |
-2.88 |
|
10% |
-2.67 |
-2.63 |
-2.58 |
-2.74 |
|
|||||
---|---|---|---|---|---|
Variable |
Low income Countries |
Lower Middle Income Countries |
Upper Middle Income countries |
High Income Countries |
|
CIPS |
CIPS |
CIPS |
CIPS |
||
ICT DI |
-5.776 |
-4.913 |
-4.800 |
-5.102 |
|
ATE |
-5.232 |
-5.672 |
-2.549a |
-3.670 |
|
FDI |
-4.617 |
-4.079 |
-4.871 |
-4.405 |
|
ICTGI |
-5.419 |
-4.521 |
-4.783 |
-5.274 |
|
CoC |
-4.810 |
-4.506 |
-3.883 |
-4.306 |
|
GE |
-4.422 |
-4.151a |
-4.129 |
-4.735 |
|
GDPPC |
-4.024 |
-2.721a |
-2.966a |
-2.378b |
|
UP |
-2.458b |
-2.493 |
-2.654 |
-2.407 |
|
Critical Values |
1% |
-2.47a |
-2.40a |
-2.32a |
-2.60a |
5% |
-2.26b |
-2.21b |
-2.15b |
-2.34b |
|
10% |
-2.14c |
-2.10c |
-2.07c |
-2.21c |
Panel |
Statistic |
P-Value |
|
||
Modified Dicky-Fuller t |
-4.665 |
0.000 |
Dicky-Fuller t |
-6.109 |
0.000 |
Augmented Dicky-Fuller t |
-0.845 |
0.198 |
Unadjusted Modified Dicky-Fuller t |
-12.033 |
0.000 |
Unadjusted Dicky-Fuller t |
-8.515 |
0.000 |
|
||
Modified Dicky-Fuller t |
-2.085 |
0.018 |
Dicky-Fuller t |
-2.206 |
0.013 |
Augmented Dicky-Fuller t |
-1.258 |
0.104 |
Unadjusted Modified Dicky-Fuller t |
-4.328 |
0.000 |
Unadjusted Dicky-Fuller t |
-3.272 |
0.005 |
|
||
Modified Dicky-Fuller t |
-4.495 |
0.000 |
Dicky-Fuller t |
-4.380 |
0.000 |
Augmented Dicky-Fuller t |
-2.781 |
0.002 |
Unadjusted Modified Dicky-Fuller t |
-6.887 |
0.000 |
Unadjusted Dicky-Fuller t |
-5.250 |
0.000 |
|
||
Modified Dicky-Fuller t |
-3.746 |
0.001 |
Dicky-Fuller t |
-3.537 |
0.002 |
Augmented Dicky-Fuller t |
-2.270 |
0.011 |
Unadjusted Modified Dicky-Fuller t |
-4.957 |
0.000 |
Unadjusted Dicky-Fuller t |
-3.922 |
0.000 |
[
FMOLS results reveal that access to electricity (ATE) is significant and have positive effect on ICT adoption in low income, lower middle income and upper middle income countries but insignificant in high income countries. The regression results show that 1 % increase in ATE 0.3 % increase in the ICT adoption in lower income countries, 0.2 % in lower middle income countries and 0.5 % in upper middle income countries while no effect on high income countries. [
ICT
FDI
Analysis of results explore that GDP per capita has strong impact on ICT adoption in selected developing countries. It shows that GDP per capita has statistically significant in four panels and has positive impact on ICT adoption. The results explain that one unit change in GDP per capita increased 0.008 units ICT adoption level in lower income, 0.001units in lower middle, 0.004units in upper middle income and 0.002 units in high income countries. The literature also reveal that GDP per capita was strongly to ICT adoption in different economies
The analysis results show that UP has different economy on ICT adoption in selected developing countries. It is strongly significant in lower middle income and high income countries while insignificant in low income and upper middle income countries. UP has positive impact on ICT adoption in lower middle and high income countries. The results explore that one unit change in urban population increased 7.610 units ICT adoption in lower middle and 9.6 units in high income countries. The descriptive analysis [
The
Variable |
Low income Countries |
Low Middle income Countries |
Upper Middle income Countries |
High income Countries |
||||
Coeff. |
Prob. |
Coeff. |
Prob. |
Coeff. |
Prob. |
Coeff. |
Prob. |
|
ATE |
0.034a |
0.000 |
0.025a |
0.000 |
0.050a |
0.007 |
0.011 |
0.901 |
ICTGI |
0.049b |
0.025 |
-0.013 |
0.382 |
0.055b |
0.053 |
0.396a |
0.004 |
FDI |
0.006 |
0.206 |
-0.032a |
0.003 |
-0.033 |
0.066 |
0.006 |
0.718 |
GDPPC |
0.008a |
0.000 |
0.001a |
0.000 |
0.004a |
0.000 |
0.002a |
0.000 |
UP |
1.230 |
0.547 |
7.610b |
0.053 |
-2.060 |
0.486 |
9.600a |
0.009 |
CoC |
-0.163 |
0.242 |
-0.023 |
0.940 |
-0.67 |
0.145 |
-1.519 |
0.321 |
GE |
0.511a |
0.001 |
-0.476 |
0.156 |
2.406a |
0.000 |
4.720a |
0.001 |
The current research demonstrated that there are long run cointegrations among ICT adoption, access to electricity, GDP per capita, FDI, ICT good imports, urban population, control of corruption, government effectiveness in 67 developing countries. The regression results show that selected ICT adoption determinants have different results in each panel on the base of income. The access of electricity is more effective in low income and lower middle income countries. The analysis results explore the access to electricity is less in low income (25.512 %) and lower middle income (74.445%) countries. It is guideline for the policy-maker should focus on electricity supply in above said both panels because it has great impact on ICT adoption in lower and lower middle income developing countries. Access to electricity is important factor of ICT adoption. ICT good imports are also an important factor for ICT adoption in selected developing countries. It is more effective in three panels of developing countries, lower income, upper middle and high income countries. The results show that it is important determinant for ICT adoption in developing economies but average ICT good imports ratio is low in lower (4.001 %), upper (4.355%) and high income (4.680%) economies. The Government should increase imports of ICT related goods. FDI is no more effective on ICT adoption in developing countries. GDP per capita is an important determinant of ICT adoption in each panel of selected developing countries. GDP per capita increase one unit it increased 0.008 units in low, 0.001 units in lower middle, 0.004 units in upper middle and 0.002 units in high income countries. Increase in GDP per capita increased ICT adoption in developing countries. Urban population has mixed results in selected developing countries. It is not effective in two panels such as low income and upper middle income countries but it is an important factor in lower middle and high income economies. Urban population is more in high income countries as compared to other panels. Control of corruption is not important factor for ICT adoption in developing countries. Government effectiveness is an important determinant of ICT adoption. Government should make effective rules and regulation which enhance ICT adoption in developing countries. Policy maker should focus on GE determinant and provide guideline to the government which is more effective for ICT adoption in developing countries.