=Paper=
{{Paper
|id=Vol-2413/paper12
|storemode=property
|title=
The Influence of National Information Ecology on e-Commerce Adoption in Developing Countries
|pdfUrl=https://ceur-ws.org/Vol-2413/paper12.pdf
|volume=Vol-2413
|authors=Nixon Muganda Ochara,Armstrong Kadyamatimba,Paul Lehloa,Alexander Sotnikov
}}
==
The Influence of National Information Ecology on e-Commerce Adoption in Developing Countries
==
https://ceur-ws.org/Vol-2413/paper12.pdf
The Influence of National Information Ecology on
e-Commerce Adoption in Developing Countries1
Nixon Muganda Ochara1[0000-0001-5736-7901], Armstrong Kadyamatimba 2[0000-0002-9638-0858],
Paul Lehloa1[0000-0001-8205-3840], and Alexander Sotnikov2[0000-0003-2985-3704]
1
University of Venda, Thohoyandou Limpopo, South Africa
2
Joint Supercomputer Center of the Russian Academy of Sciences
1
nixon.muganda@gmail.com; 2asotnikov@jscc.ru
Abstract. The focus of this study was to identify the impact of government pol-
icy, legal environment, socio-cultural framework, Gross Domestic Product
(GDP), Internet diffusion and technology transfer on the national adoption of e-
Commerce in developing countries. A cross country analysis, using data sets
that were based on a quantitative survey was used in this study. The study con-
cluded that socio-cultural factors have the most influence on e-Commerce adop-
tion. Technology transfer and Internet diffusion provided some level of media-
tion which improved the significance level of other variables. The results also
demonstrate that the information and computer literacy problem can only be
addressed by enhancing various organizing forms in order to improve e-
Commerce diffusion. The mediating role of the organizing forms is critical in
galvanizing local community participation, for instance in capturing and accu-
mulating cultural knowledge as a basis of developing relevant content for en-
hancing community participation in e-Commerce. These findings can advance
policy implementation, particularly in integrated development planning in de-
veloping countries that continue to suffer from a lack of adequate knowledge
regarding the digital commerce environment. Further, as the Big Data & Ana-
lytics finds traction in the e-commerce environment, the trajectory of ecom-
merce adoption is likely to change dramatically in developing countries.
Keywords: e-Commerce Adoption, Technology Transfer, Legal Framework,
Government Policy, Integrated Development Planning, Big Data
1 Introduction
1.1 Background of the Study
Multiple studies of e-Commerce (e-commerce) adoption show that adoption in devel-
oped countries is higher than in developing countries [1]; [2]. The main factors influ-
encing adoption relate to cultural differences, national infrastructure, supportive gov-
ernment policies, education level, economic levels, legal environment and others.
However, these prior studies have focused mostly on individual adoption rather than
1
The study was funded by RFBR and NRF according to the research project № 19-57-60004
Proceedings of the XXII International Conference «Enterprise Engineering and Knowledge
Management" April 25-26, 2019, Moscow, Russia
2
national adoption. It was found that national information ecology factors (e.g., gov-
ernment policy, legal framework and socio-economic infrastructure) together with the
economic level of the country as measured by Gross Domestic Product (GDP) could
play an important role in adoption of e-commerce [1]. Internet diffusion and Technol-
ogy Transfer can be incorporated into the national information ecology framework to
understand factors that could contribute to national adoption of e-commerce in devel-
oping countries. In this study, e-commerce is considered as an electronic means for
conducting business over the Internet [3]. This study aims to answer the following
research question: how does national information ecology influence adoption of e-
commerce in a country? The objective of this study is to test and explain the influence
of national information ecology factors on e-commerce adoption in selected countries
in sub-Saharan Africa.
1.2 Informing Literature and Model Development
According to Kumar, Kumar, Dutta and Fantazy [4], Technology Transfer is an effec-
tive mechanism to advance the flow of technological development in a developing
country’s economy. Technology transfer helps the diffusion process of newer tech-
nologies from developed to developing countries. Strong government support, com-
munication and education of adopters are the most crucial factors in transfer of tech-
nology [4]. Zhu and Thatcher [1] discuss national adoption using a multi-point ap-
proach and institutional theory, focusing on the external environment for e-commerce.
National Information Ecology deals with policy, legal and socio-cultural environ-
ments. Previous studies have incorporated institutional theory of new institutional
economics and ecosystems in the study of national information ecology in developing
countries [1]; [5]). Rogerson and Rogerson [6] point out that conducive government
policy is necessary for the private sector to operate; that it reduces the cost of doing
business, unleashes economic potential and attracts investment. A study by Molla and
Licker [7] also reveals strong links between government policy and e-commerce
adoption. The readiness of government to promote e-commerce defines the institu-
tional environment within which businesses operate and influences their confidence
and level of e-commerce aspirations. Based on the analysis above, we propose nation-
al information ecology factors (e.g., government policy, legal environment, and socio-
cultural infrastructure) as critical influencers of e-commerce adoption, mediated by
Internet diffusion and Technology Transfer; while GDP acts as a control variable. We
test this model, using data from SADC. The propositions emanating from the research
model are highlighted in Figure 1.
Hypotheses:
.H11: Government policy influences adoption of e-commerce in a country
H10: Government policy does not influence adoption of e-commerce in a country
H21: Legal environment influences adoption of e-commerce in a country
H20: Legal environment does not influence adoption of e-commerce in a country
H31: Socio-cultural factors influence adoption of e-commerce in a country
3
H30: Socio-cultural factors do not influence adoption of e-commerce in a country
H41: Internet diffusion influences adoption of e-commerce in a country
H4: Internet diffusion does not influence adoption of e-commerce in a country
H51 Technology transfer influences adoption of e-commerce in a country
H50: Technology transfer does not influence adoption of e-commerce in a country
Fig. 1. - Research framework (adopted from Zhu and Thatcher, 2010)
2 Research Methodology
2.1 Research Design
A positivist quantitative survey approach relying on datasets was adopted in this
study. The sources of secondary data are e-commerce and IT reports from Economist
Intelligence Unit (EIU), World Bank, World Economic Forum (WEF) and United
Nations Conference on Trade and Development (UNCTAD), Global Information
Technology Report (GITR) that provide current and historical global data on ICT
related developments and the Electoral Institute for Sustainable Democracy in Africa
(EISA) and other miscellaneous sources. This study focused on the SADC region
particularly because its member countries are all under the developing countries’ cat-
egory and most of them share common characteristics.
2.2 Measurement Items
The dependent variable (DV) is e-commerce adoption, while the socio-cultural
framework, legal and policy environments are the Independent Variables (IV), and
Internet Diffusion as well as Technology Transfer are mediating variables (MV) and
GDP is the control variable. Data for DV is gathered from the GITR (2012), while all
other variable are taken from the World Bank online database. The data is a mixture
of ratios (e.g., Literacy Rate, Unemployment and Poverty Ratio) which are all part of
the socio-cultural component as well as Internet Penetration. Government Policy and
4
Legal Environment values are also ratios and E-Commerce Adoption and Technology
Transfer are rank values from a Likert scale of 1 to 7 with 1 being the lowest and 7
being highest as per the GITR description (2012, p171). The measurement items for
the study are found in Table 1.
Table 1. Measurement Items
Measurement Items Variables Sources
Government Policy 1. Government Effectiveness World Governance Indicators
2. Political Stability (World Bank, 2012)
3. Rule of Law
Legal Environment 1. Regulatory Quality World Governance Indicators
(World Bank, 2012)
Social and Cultural 1. Literacy Rate World Bank (Bank Data, 2012)
Infrastructure 2. Unemployment Rate EISA
3. Poverty Rate World Bank (Bank data, 2012)
E-Commerce Adoption 1. Business Usage Source (GITR, 2012)
2. Individual Usage
3. Government Usage
Internet 1. Internet Penetration www.Internetworldstats.com
Diffusion 2. Internet Penetration Internet world statistics (2012)
Technology Transfer 1. Infrastructure and Digital Content Source (GITR, 2012)
GDP EISA
3 DATA ANALYSIS AND RESULTS
3.1 Overview
The overall objective of the analysis in this section is to explain the degree and nature
of the relationship between E-Commerce Adoption (DV) and the independent and
mediating variables relating to Government Policy, Legal Environment, Social and
Cultural Infrastructure, Internet Diffusion, Technology Transfer and GDP.
3.2 Descriptive Analysis
According to the results presented in Table 2, two variables are missing a score. Mul-
tiple Regression uses only the 13 variables with complete data (e.g., Angola, Botswa-
na, Lesotho, Malawi, Madagascar, Mauritius, Mozambique, Namibia, South Africa,
Swaziland, Tanzania, Zambia and Zimbabwe). Data for Business Usage and Infra-
structure plus Digital Content was missing for the Democratic Republic of Congo and
Seychelles. Population had a high mean and the frequency graph shows that GDP is
skewed to the left showing a large gap in GDP among the sample under review. Data
from the World Bank (2010) in Millions of US Dollars shows that South Africa had a
highest GDP at 276.8, Angola at 84.937, Tanzania at 22.915, Botswana at 14.838,
DRC at 13.144, Zambia at 14.314, Mozambique at 9.735, Mauritius at 8.651, Namib-
ia at 8.563, Malawi at 4.269, Swaziland at 2.618, Lesotho at 2.179, Seychelles at
0.835 and Zimbabwe at 0.641.
5
Table 2. Descriptive Statistics
Mean Std. Deviation Skewness N
Gross Domestic Product (GDP) .0532839 1.06880407 3.402 13
Internet Penetration -.1086203 .73356015 1.434 13
Literacy Rate -.0090683 .98895867 -0.557 13
Unemployment -.0581109 .92019580 1.473 13
Poverty Ratio .0345676 .83252174 -0.373 13
Government Effectiveness .0741100 .91323328 -0.166 13
Political Stability .1174103 .74180768 -1.363 13
Rule of Law .0796999 .95905771 -0.289 13
Regulatory Quality .1240085 .98079701 -0.282 13
Individual Usage .0000000 1.00000000 2.236 13
Business Usage .0000000 1.00000000 0.677 13
Government Usage .0000000 1.00000000 -0.556 13
Technology Transfer .0000000 1.00000000 0.515 13
The skewness above 1.5(+ or -) are considered to abnormally distributed [8]. GDP
exceeded 2.5 and hence was not normal in this case. The skewness of GDP was ex-
pected since the different countries are at different levels of economic development.
GDP could not be excluded since other studies have shown that it plays a major role
[1]. The standard error of skewness is indicative of normal distribution of data, which
is less than 1.5 for all variables, except GDP and individual usage. Multiple Regres-
sion does not allow treatment of multiple DVs thus individual use and government
use are dropped leaving Business Use as a proxy for E-Commerce Adoption because
it has a smaller standard deviation. Rule of Law was also dropped leaving Regulatory
Quality because it has the smaller standard deviation.
3.3 Inferential Analysis
This study followed a four-step analysis process. Step 1 involved a Multiple Regres-
sion Analysis (MANOVA) with all variables excluding mediating variables (i.e., In-
ternet Diffusion, Technology Transfer) in order to test for a direct relationship with E-
Commerce Adoption. Step 2 is using a MANOVA with all variables predicting the
mediation caused by Internet Diffusion and Technology Transfer. Step 3 is using a
MANOVA with Internet Diffusion, then Technology Transfer predicting E-
Commerce Adoption. Step 4 is using a MANOVA with all variables including medi-
ating variables predicting E-Commerce Adoption (Table 3).
3.4 Testing for Mediation with Regression Analysis.
The results indicate that in Step 1, all variables excluding the mediators significantly
predict E-Commerce Adoption at a model fit of 91%. There is no significant relation-
ship in Step 2 for both Technology Transfer and Internet Diffusion. However, there is
a strong relationship between Technology Transfer and E-Commerce Adoption and
6
very little between Internet Diffusion and E-Commerce Adoption. The relationship
between all variables including mediators is significant at a p-value < 0.1 with 72%
explanation for the variance in E-Commerce Adoption. The observation is that some
form of mediation is supported as shown by a significant value in Step 4.
Table 3. Test for Mediation
Adjusted
Step IV DV Sig
R2 (%)
GDP, Unemployment, Government Ef-
fectiveness, Rule of Law, Regulatory
E-Commerce Adop-
1 Quality, Literacy Rate, Poverty Ratio, 0.070 91%
tion
Government Usage, Individual Usage,
Political Stability
GDP, Unemployment, Government Ef-
fectiveness, Rule of Law, Regulatory
2 Quality, Literacy Rate, Poverty Ratio, Technology Transfer 0.233 69%
Government Usage, Individual Usage,
Political Stability
Internet Diffusion 0.722 36%
3 Technology Transfer E-Com. Adoption 0.000 72%
Internet Diffusion E-Com. Adoption 0.181 8%
GDP, Unemployment, Government Ef-
fectiveness, Rule of Law, Regulatory
Quality, Literacy Rate, Poverty Ratio, E-Commerce Adop-
4 0.126 72%
Government Usage, Individual Usage, tion
Political Stability, Technology Transfer
And Internet Diffusion
The rest of the analysis will continue based on the results in Step 4. GDP together
with Internet Penetration violated the kurtosis rule. Mean and standard deviation satis-
fied the rules at the mean of 0 and standard deviation of 1 for all variables (Table 4).
The assumptions for using a MANOVA include the following; that the relationship
between each of the predictor variables and the DV is linear and that the error, or
residual, is normally distributed and uncorrelated with the predictors [8]. The simulta-
neous multiple regressions using all the variables at the same time was used given that
the number of predictors was small. After dropping the Individual Use and Govern-
ment Use, leaving Business Use as a proxy for ICT Usage and hence E-Commerce
Adoption plus dropping Rule of Law, leaving Regulatory Quality, the mean and
standard deviation met the rules on 0 and 1 respectively (Table 4).
Table 4. Table of Residuals
Minimum Maximum Mean Std. Deviation N
Predicted Value -1.3605034 2.1515248 .0000000 .98656873 13
Residual -.27351445 .23217899 .00000000 .16334670 13
Std. Predicted Value -1.379 2.181 .000 1.000 13
Std. Residual -.684 .580 .000 .408 13
a. Dependent Variable: Business Usage
7
Linearity Using ANOVA Test.
Since multiple regression models assume linearity, the linear association between
each IV and DV was tested using an ANOVA test of linearity (Table 5). The F statis-
tic is 4.360 and significance value for the nonlinear components was above the critical
value (i.e., p < 0.1). The results showed that all IVs do not have a linear relationship
with the DV of Business Usage. The significance value of .126 indicates that these
variables can be used to explain the DV given that the study is using secondary data,
the population size is small and that there could be some other factors that could play
a role in explaining E-Commerce Adoption but are not included in this model. There-
fore, at the significance level of .126, the model can be accepted, meaning the predic-
tors can marginally explain E-Commerce Adoption.
Table 5. ANOVA Table
Model Sum of Squares df Mean Square F Sig.
Regression 11.148 9 1.239 4.360 .126b
1 Residual .852 3 .284
Total 12.000 12
a. Dependent Variable: Business Usage
b. Predictors: Technology Transfer, Unemployment, GDP, Political Stability, Internet Penetra-
tion , Literacy Rate, Poverty Ratio, Government Effectiveness, Regulatory Quality
Given that:
Business Usage = function (GDP, Internet Penetration, Literacy Rate, Unem-
ployment, Poverty Ratio, Government Effectiveness, Political Stability, Regu-
latory Quality)
Or
Business Usage = β1 + β2 x GDP + β3 x Internet Penetration+ β4 x Literacy
Rate + β5 x Unemployment + β6 x Poverty Ratio + β7 x Government Effec-
tiveness + β8 x Political Stability + β9 x Regulatory Quality + β10 x + c.
Assuming that F is testing the hypothesis that β1= β2= β3= β4= β5= β6= β7= β8=
β9= β10, since F is significant, the use of the IV has assisted us in explaining the DV
(Business Usage). Total Sum of Squares (TSS) is 12.000. This shows the deviations
in the DV. Regression shows the Explained Sum of Squares (ESS) of 11.148, which is
the amount of TSS that could be explained by the model. The Residual Sum of
Squares (RSS) is 0.852, which is the amount that could not be explained by the mod-
el. RSS represents unexplained variation and a smaller RSS means that the model fits
the data well.
Correlation Analysis
The results for correlation analysis (Table 6) show that Literacy Rate (LR) is highly
correlated with Technology Transfer (TT) with a coefficient of 0.701 at a significance
level of .008; which means as people get more educated; they are likely to adopt new
technologies. Poverty Ratio (PR) is highly correlated with Government Effectiveness
(GE) with -0.793 at a significance level of .001, and Political Stability (PS) with -
8
0.576 at a significance level of .039. Poverty is also highly correlated with Regulatory
Quality (RQ) with -0.709 at a significance level of .007. These results show that pov-
erty is more prevalent in countries affected by corruption and lack of proper govern-
ance. There will be less poverty in a more stable government since there will likely be
fewer wars, political strife and other factors that consume resources that could other-
wise be channeled to poverty eradication. Rule of Law can be the foundation for
proper governance, which in turn will lead to overall institutional stability hence a
sound business environment.
Table 6. Correlation Analysis
GDP IPR LR U PR GE PS RQ BU TT
Pearson 1 0,19 0,11 -0,06 -0,48 0,24 -0,06 0,26 0,53 0,35
GDP Correlation
Sig. (2-tailed) 0,54 0,73 0,86 0,1 0,43 0,86 0,38 0,06 0,25
Internet Pearson 0,19 1 0,52 0,15 -.602* 0,43 0,17 0,38 0,4 .701**
Penetration of Correlation
population Sig. (2-tailed) 0,54 0,07 0,62 0,03 0,15 0,59 0,2 0,18 0,01
Pearson 0,11 0,52 1 0,5 -.584 *
0,31 0,14 0,11 0,36 .579*
Literacy rate Correlation
(LR) Sig. (2-tailed) 0,73 0,07 0,08 0,04 0,31 0,64 0,73 0,22 0,04
Pearson -0,06 0,15 0,5 1 -0,01 -0,43 -0,35 -.560* -0,08 -0,03
Unemploy- Correlation
ment (U) Sig. (2-tailed) 0,86 .620 0,08 0,97 0,15 0,25 0,05 0,8 0,93
Pearson * * - * - * -
Poverty Ratio Correlation -0,48 -.602 -.584 -0,01 1 -.576 -.612
.793** .709** .734**
(PR)
Sig. (2-tailed) 0,1 0,03 0,04 0,97 0 0,04 0,01 0,03 0
Government Pearson 0,24 0,43 0,31 -0,43 -.793** 1 .711** .961** .705** .789**
effectiveness Correlation
(GE) Sig. (2-tailed) 0,43 0,15 0,31 0,15 0 0,01 0 0,01 0
Pearson -0,06 0,17 0,14 -0,35 -.576* .711** 1 .705** 0,42 0,43
Political Correlation
Stability Sig. (2-tailed) 0,86 0,59 0,64 0,25 0,04 0,01 0,01 0,15 0,14
(PE)
Sig. (2-tailed) 0,75 0,24 0,4 0,08 0,01 0 0 0 0,01 0
Pearson 0,26 0,38 0,11 -.560* -.709** .961** .705** 1 .675* .724**
Regulatory Correlation
quality (RQ) Sig. (2-tailed) 0,38 0,2 0,73 0,05 0,01 0 0,01 0,01 0,01
Sig. (2-tailed) 0 0,04 0,21 0,95 0 0,02 0,52 0,03 0 0
Pearson 0,53 0,4 0,36 -0,08 -.612* .705** 0,42 .675* 1 .860**
Business Correlation
Usage (BU) Sig. (2-tailed) 0,06 0,18 0,22 0,8 0,03 0,01 0,15 0,01 0
Sig. (2-tailed) 0,33 0,16 0,89 0,43 0,14 0,04 0,1 0,03 0,02 0,06
Pearson
Technology Correlation 0,35 .701** .579* -0,03 -.734** .789** 0,43 .724** .860** 1
transfer (TT) Sig. (2-tailed) 0,25 0,01 0,04 0,93 0 0 0,14 0,01 0
Poverty Ratio is highly correlated with Technology Transfer, with a weighting of -
0.734 at a significance level of .004. Government Effectiveness is highly correlated
with Political Stability (0.711); Regulatory Quality (0.961); Business Usage (-0.612)
and Technology Transfer (-0.734). Political Stability is highly correlated with Regula-
tory Quality (0.705). Political Stability means that the government will be able to set
clear and effective regulations that enable the general business environment. There
will be sound and strong governance structures that will enable a conducive business
environment. Regulatory Quality is highly correlated with Business Usage (0.675)
and Technology Transfer (0.724). Thus, sound regulations will enhance the business
9
environment, protect consumer rights and therefore enable people to conduct business
freely whether online or not. Finally, Business Usage is highly significant to Govern-
ment Usage (0.624) and Technology Transfer (0.860). As individuals increasingly
take up and use technology, businesses will more likely grow due to the increase in
the demand for goods and services. The improvement in the business environment
will enable more research and development in a country; hence the increase in tech-
nology development will drive demand for advanced technologies. As business im-
prove and advance in technologies, so will the government as a partner in business.
Multiple Linear Regression Analysis
The results of the multiple regressions are presented in Table 7. The R2 measures the
proportion of the variation in the DV (Business Usage) that was explained by the
variations in the IV (Poverty Rate, Government Effectiveness, Regulatory Quality,
Political Stability, Technology Transfer, Internet Penetration). Thus, 93% of the var-
iation from the mean in the DV was explained. The adjusted R2 is the measure of the
proportion of the variance in the DV (Business Usage) that was explained by the vari-
ations in the IVs. The standard error of estimate is 0.53, which is far less than 10%
and therefore the dispersion of the DVs around the mean is low.
Table 7. Model Summary
Change Statistics
Adjusted Std. Error of Durbin-
Model R R2
R 2
the Estimate R2 Change F Change df1 df2 Sig. F Watson
Change
1 .964 0.929 0.716 0.533007 0.929 4.36 9 3 0.126 1.913
a. Predictors: (Constant), Technology Transfer, Unemployment, GDP, Political Stability, Internet
Penetration, Literacy Rate, Poverty Ratio, Government Effectiveness, Regulatory Quality
b. Dependent Variable: Business Usage
The model summary table shows that the multiple coefficient (R), using all the
predictors simultaneously, is .964 (R2=.929) and adjusted R2 is .716 meaning that
72% of the variance in the DV (Business Usage or E-Commerce Adoption) can be
predicted and explained by the IVs (Government Effectiveness, Literacy Rate, GDP,
Internet Penetration (Technology Transfer) Unemployment, Regulatory Quality, Pov-
erty Ratio and Political Stability). The higher value may be attributed to the decrease
in the number of IVs. Therefore a conclusion was reached that the model is a good fit.
Coefficients Analysis
The Tables of Coefficients (Table 8) show the standardized beta coefficients, inter-
preted similarly to correlation coefficients. The t value and the sig opposite each IV
indicates whether that variable is significantly contributing to the equation for predict-
ing E-Commerce Adoption from the whole set of predictors. GDP, Literacy Rate,
Unemployment and Poverty Rate are significantly contributing to the equation. How-
ever, all the variables need to be included in this result, since the F value was comput-
10
ed with all the variables in the equation. GDP explains the most variance at 84%
(.9182), followed by at 80%, Unemployment (70%) and Literacy Rate (54%). Politi-
cal Stability (38%), Regulatory Quality (26%) and finally Internet Penetration (14%)
show the lowest amount of unique variance. Therefore, GDP, Literacy Rate, Unem-
ployment, Poverty Ration explain E-Commerce Adoption more than other variables.
Tolerance and VIF tell us if there is multi-collinearity problem. If tolerance is low
(<1-R2), then there is a multi-collinearity problem. In this case, adjusted R2 is .842
and 1-.8422 is .29. Unemployment, Poverty Rate, Government Effectiveness and
Regulatory Quality have tolerance levels lower than (1-R2), hence shows a multi-
collinearity problem. However, as previous stated, these variables may be important
in explaining the questions this research is answering..
Table 8. Coefficients
Unstandardized Stand. Collinearity
Correlations
Model Coefficients Coeff. t Sig. Statistics
B Std. Zero- Toler-
Error Beta order Partial Part ance VIF
(Constant) -0.254 0.153 -1.656 0.24
GDP 2.597 1.112 2.775 2.334 0.145 0.534 0.855 0.27 0.009 105.955
Internet
Penetration 2.302 1.327 1.689 1.735 0.225 0.396 0.775 0.2 0.014 71.041
Literacy Rate 0.319 0.369 0.315 0.864 0.479 0.363 0.521 0.1 0.1 9.981
Unemploy-
ment 2.996 1.393 2.757 2.151 0.164 -0.078 0.836 0.248 0.008 123.191
Poverty Ratio 3.628 1.555 3.02 2.333 0.145 -0.612 0.855 0.269 0.008 125.604
Government
Effectiveness -4.216 2.85 -3.85 -1.479 0.277 0.705 -0.723 -0.171 0.002 507.897
Political
Stability -1.094 0.908 -0.811 -1.204 0.352 0.419 -0.648 -0.139 0.029 34.027
Rule of Law 10.065 5.521 9.653 1.823 0.21 0.661 0.79 0.211 0 2101.529
Regulatory
Quality 0.449 0.832 0.441 0.54 0.643 0.675 0.357 0.062 0.02 49.954
Technology
Transfer -3.323 2.158 -3.323 -1.539 0.264 0.86 -0.736 -0.178 0.003 349.218
a. Dependent Variable: Business Usage
3.5 Analysis of Hypotheses
This section gives the analysis of results based on Table 8.
H11: Government policy influences adoption of e-commerce in a country
H10: Government policy does not influence adoption of e-commerce in a country
Literature supports the view that government stimulates technology adoption (Shin,
2007; Cordeiro & Al-Hawamdeh, 2001). However, despite literature support for gov-
ernment influence on e-commerce adoption, our analysis reveals a weak link. In our
study, government policy was represented by Government Effectiveness and Political
Stability. The p-values are .277 and .352, while the beta values are -3.850 and -.811
for Government Effectiveness and Political Stability respectively. All p-values are >
0.1 which shows that there is no relationship between Government Policy and E-
11
Commerce Adoption in the context of this study. However, there is some evidence
that given some level of Technology Transfer and Internet Diffusion, Government
Policy can influence adoption of e-commerce in a country. Therefore, the null hy-
pothesis H11 is rejected and the alternative hypothesis H10 (government policy does
not influence adoption of e-commerce adoption in a country) is accepted. In in line
with the findings from the literature, it may be argued that the alternative hypothesis
maybe accepted, taking into account sample size limitations. However, the correlation
analysis revealed a high correlation between Government Effectiveness variables and
E-Commerce Adoption at acceptable significance levels.
H21: Legal framework influences adoption of e-commerce in a country
H20: Legal framework does not influence adoption of e-commerce in a country
Regulatory Quality is the proxy for legal framework. The beta coefficient is .304
and the p-value is .797. However, when Technology Transfer and Internet Diffusion
are incorporated, the beta coefficient increases to .904 and the significance level in-
creases to .296. Again, the p-value is greater than .01 but there is an enormous differ-
ence caused by Technology Transfer and Internet Diffusion, hence we reject the null
hypothesis H21 and accept the hypothesis H20, (legal framework does not have an
influence on adoption of e-commerce in a country). Zhu and Thatcher (2010) show
that the legal environment should reduce uncertainty by providing adequate, clear and
efficient frameworks for economic exchange. If a government shows a clear commit-
ment to e-commerce, this becomes apparent in its policy measures, which in turn can
encourage e-commerce utilization (Molla & Licker 2005).
H31: Socio-cultural factors influence adoption of e-commerce in a country
H30: Socio-cultural factors do not influence adoption of e-commerce in a country
From the coefficients table, Socio-Cultural Factors as represented by Literacy Rate,
Unemployment and Poverty Ratio have beta values of .382, .710 and 1.396, with p-
values of .221, .040 and .022 respectively. Unemployment and Poverty Ratios show a
strong positive relationship with E-Commerce Adoption. Introducing Technology
Transfer and Internet Diffusion, beta values are 113, .503 and 1.075 for Literacy Rate,
Unemployment and Poverty Ratio respectively, with significant values of .822, .347
and .351 respectively. This resulted in a significant decrease in the beta and p-values.
This means that as the Unemployment and Poverty Ratio decrease in a country, the
level of E-Commerce Adoption increases but bringing in Technology Transfer and
Internet Diffusion suppresses the spread and use of e-commerce possibly due to the
anticipated costs brought by new infrastructure that supports new technologies.
The results show that all the Socio-Cultural Factors may have an impact on E-
Commerce Adoption. Therefore, we reject the null hypothesis, H30 and accept H31
(socio-cultural factors contribute positively to the adoption of e-commerce in a coun-
try). The level of influence is quite high looking at the p-value of the significant vari-
ables. Several socio-cultural factors have been identified as barriers to e-commerce
adoption in developing countries.
H41: Internet diffusion influences adoption of e-commerce in a country
H40: Internet diffusion does not influence adoption of e-commerce in a country
The results in the regression analysis show that Internet Diffusion has a signifi-
cance level of .181 on e-commerce with the adjusted R2 of .080 and the beta value of
12
.396. Using the significance level of .10, H41 is rejected thereby inferring that at the
conceptual level, there is no relationship between Internet Diffusion and E-Commerce
Adoption. Given the small sample size and data issues, this result can be questioned.
Literature shows that there is a positive relationship between Internet Diffusion and
E-Commerce Adoption as an artefact of technology [9].
H51: Technology transfer influences adoption of e-commerce adoption in a country
H50: Technology transfer does not influence adoption of e-commerce adoption in a country
Technology Transfer, tested in isolation from the other variables, was found to
have the most significance on E-Commerce Adoption. This was shown by the p-value
of .000 and the beta value of 5.581. However, when combined with all other predictor
variables, the p-value decreases to .126. Based on the literature, Technology Transfer
is believed to have an influence on E-Commerce Adoption in a country [4]. There-
fore, the null hypothesis H50 is rejected and we find (H51) Technology Transfer does
have an impact on E-Commerce Adoption in a country.
4 Conclusions
A model for e-commerce adoption incorporating Internet diffusion and technology
transfer as mediating factors was developed. All variables were tested together to
determine their impact on e-commerce adoption. The study found that GDP, socio-
cultural factors, technology transfer had a significant impact on e-commerce adoption.
This study revealed that government policy, legal environment, socio-economic
framework, Internet diffusion, technology transfer could affect the adoption of e-
commerce in a developing country. Given the context of Sub-Saharan Africa, particu-
larly the SADC region which has for many years been affected by political instability,
poverty, corruption, riots, poor infrastructure, unemployment and high illiteracy rates,
the findings reveal that all variables could contribute to e-commerce adoption to some
extent as shown by a strong contribution by socio-cultural factors and GDP. This can
be validated by the fact that more studies revealed the opposite in developed coun-
tries, which are affected less by the conditions prevailing in the developing world.
The proposed model has identified the link between national ecology factors (par-
ticularly the social-cultural construct), Internet diffusion and technology transfer with
e-commerce adoption. It has shown that socio-cultural issues contribute more than
any other factors to the adoption of e-commerce. A somewhat small effect was real-
ised from technology transfer and Internet diffusion. More research is needed to iden-
tify other factors that could have an impact on e-commerce adoption. The analysis
confirmed that the socio-cultural infrastructure is a critical factor in enabling e-
commerce adoption, when technology transfer influence is taken into account. The
socio-cultural context emphasizes the need to build the Social Capital or Social Re-
sources required for stimulating e-commerce adoption.
13
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