Technology Analysis & Strategic Management
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What drives global B2C E-commerce? An analysis
of the effect of ICT access, human resource
development and regulatory environment
Ibrahim Osman Adam, Muftawu Dzang Alhassan & Yaw Afriyie
To cite this article: Ibrahim Osman Adam, Muftawu Dzang Alhassan & Yaw Afriyie (2020):
What drives global B2C E-commerce? An analysis of the effect of ICT access, human resource
development and regulatory environment, Technology Analysis & Strategic Management, DOI:
10.1080/09537325.2020.1714579
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TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT
https://doi.org/10.1080/09537325.2020.1714579
What drives global B2C E-commerce? An analysis of the effect of
ICT access, human resource development and regulatory
environment
Ibrahim Osman Adama, Muftawu Dzang Alhassanb and Yaw Afriyiea
aDepartment of Accounting, School of Business and Law, University for Development Studies, Wa, Ghana;
bDepartment of Operations and Management Information Systems, University of Ghana, Business School, Accra,
Ghana
ABSTRACT ARTICLE HISTORY
This study explores the drivers of Business to Consumer (B2C) electronic Received 24 September 2019
commerce (e-commerce) adoption at a global level. The study used a Revised 12 December 2019
Technology-Organisation-Environment (TOE) framework to investigate Accepted 31 December 2019
factors that influence B2C e-commerce adoption. The study relied on
archival and cross-sectional data from 135 countries and employed KEYWORDSB2C E-commerce; ICT access;
Partial Least Squares Structural Equation Modelling (PLS-SEM). Our regulatory environment;
findings indicate that B2C E-commerce adoption at the national level is human resource
positively influenced by ICT access, political and regulatory environment development
and human resource development. Our findings show that technological,
organisational, and environmental factors (ICT access, political, human
resource development and political and regulatory environment)
altogether explain 70% of the variance in B2C E-Commerce adoption. Our
findings provide a fresh insight into the adoption of B2C e-commerce
at the national level. Our study provides some implications for research
and practice.
1. Introduction
Electronic commerce (e-commerce) has become the number one priority for many businesses across
the globe (Awiagah, Kang, and Lim 2016). Companies now rely on the Internet to market their pro-
ducts to individuals and to communicate with their shareholders (Rodríguez-Ardura and Meseguer-
Artola 2010). The reliance on the internet by businesses has been triggered by the increasing number
of individuals who patronise online shopping. In 2017, an estimated 11% of online shoppers made
online shopping on a weekly basis through their smartphones (Statista 2019a). E-commerce is
defined as ‘the use of the global internet for purchase and sale of goods and services, including ser-
vices and support after sale’ (Treese and Stewart 1998, 5). E-commerce is changing the way trade is
carried out worldwide. Nevertheless, this transition is rising at different rates in different parts of the
world (Zheng 2016). Asia, the US, and Northern Europe are currently experiencing rapid growth in e-
commerce compared to other parts of the world (Awiagah, Kang, and Lim 2016; Pappas et al. 2017).
There are several e-commerce classifications. E-commerce can be classified based on communicating
entities, i.e. customers, corporations, and governments, most of which are categorised as business-
to-customer (B2C), business-to-business (B2B) and business-to-government (B2G) (Thatcher, Foster,
and Zhu 2006).
CONTACT Muftawu Dzang Alhassan muftydzang@gmail.com University of Ghana, P.O. Box 78, Greater Accra Region,
Legon, Accra, Ghana
© 2020 Informa UK Limited, trading as Taylor & Francis Group
2 I. O. ADAM ET AL.
This study focuses on B2C e-commerce. This category of e-commerce enables firms to adver-
tise, sell products to customers and receive payment for products electronically (Iddris 2012;
Turban et al. 2010). B2C e-commerce is currently experiencing tremendous growth as more and
more individuals favour purchasing goods online (Statista 2019a). In the year 2013, an estimated
1 billion people worldwide purchased goods online. This number increased to 1.8 billion people in
the year 2018 which is nearly one-seventh of the world’s population (Statista 2019a). Furthermore,
in the year 2017, worldwide retail e-commerce sales amounted to $2.3 trillion. It is, however, pro-
jected to increase to $4.88 trillion in the year 2021 (Statista 2019b). Despite the overwhelming
potential of B2C e-commerce, extant studies in this area have largely focused on B2C e-commerce
adoption at the industry or firm-level within a specific country. This includes the study (Rodríguez-
Ardura and Meseguer-Artola 2010) which adopted the framework for technology-organisation-
environment to conduct a study among Spanish companies on the drivers of B2C e-commerce.
Results from their study revealed that technological readiness, increasing scope of the firm’s oper-
ation, environmental factors such as ICT and government regulations influence the adoption of
B2C e-commerce by firms. Similarly, findings from the study of Awiagah, Kang, and Lim (2016) con-
ducted on e-commerce adoption among Small and Medium Enterprises (SME’s) in Ghana indicate
that that government support greatly impacted SME’s intentions to adopt and use e-commerce.
Furthermore, managerial support and regulatory conditions played a key role in inspiring SME’s
adoption of e-commerce. While important, these studies largely examined the impact or effect
of each TOE construct on the adoption of B2C e-commerce at the firm or industry level. Therefore,
aiming to fill the gap inherent in literature, this study seeks to examine the effect of each TOE con-
struct B2C e-commerce adoption at the global level. In addition, we examine the relationship
between the constructs in the framework and how they subsequently influence e-commerce
adoption at the global level. To carry out this study, we rely on data from 135 countries from
sources including the World Bank World Development Report (World Bank 2006), and World Econ-
omic Forum (WEF) Global Information Technology Report (GITR) (Baller, Dutta, and Lanvin 2016).
The rest of the paper is structured as follows; first, in Section 2, we highlight the theoretical frame-
work and the development of the hypothesis. In Section 3, we outline our methodology. Furthermore,
we analyse data and present findings in Section 4. Finally, we discuss findings and conclude our
research whilst outlining the limitations and implications of this study for future research in Sections
5 and 6 respectively.
2. Theory and hypothesis development
2.1. Technology-organisation-environment framework
To carry out this study, we lean on the foundations of the Technology-organisation-environment
(TOE) framework as proposed by DePietro, Wiarda, and Fleischer (1990). The TOE framework seeks
to explain the process through which an organisation or firm adopt and implement technological
innovations. The framework argues that the process by which organisations adopt and implement
technological innovations is determined or influenced by the technological, organisational and
environmental contexts (DePietro, Wiarda, and Fleischer 1990). First, the technological context is
made up of both internal and external technology technologies which are essential for the organis-
ation. These technologies may include, but not limited to equipment, processes, etc. In addition, the
organisational context relates to the features and resources of the organisation, for example, human
resources, the size of the firm, managerial structure, the number of slack resources, linkage among
employees and degree of centralisation. Lastly, the environmental context consists of the firm’s com-
petitors, the size and structure of the firm, the regulatory environment and the macroeconomic
context (DePietro, Wiarda, and Fleischer 1990). Together, these constructs (i.e. Technology, Organis-
ation and Environment) influence the manner in which organisations view the need for the adoption
of new technology.
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 3
Extant studies in the field of Information Systems (IS) have adopted and applied the TOE frame-
work across several research areas. This includes domains such as electronic data interchange
(Musawa and Wahab 2012), adoption of inter-organisational systems (Al-Hujran et al. 2018; Bhatta-
charya and Wamba 2015; Gangwar, Date, and Ramaswamy 2015; Grover 1993), enterprise systems
(Ramdani, Kawalek, and Lorenzo 2009), open systems (Chau and Tam 1997), e-business (Soto-
Acosta, Popa, and Palacios-Marqués 2015; Srivastava and Teo 2010; Srivastava, Teo, and Teo
2006; Zhu and Kraemer 2005) and a broad range of IS applications (Thong 1999). TOE has similarly
been adopted by IS researchers to study e-commerce adoption. However, considerable studies
have largely focused on e-commerce adoption at the firm or industry level within a single
country (Aljowaidi, Arbia, and Arabia 2015; Awiagah, Kang, and Lim 2016; Ibrahim and Stevens
2014; Mohtaramzadeh, Ramayah, and Jun-Hwa 2018a). A dearth of studies relying on the TOE fra-
mework to undertake studies on e-commerce adoption at the global level exist. This includes the
study of (Jennifer and Kenneth 2004). They adopted the TOE framework to examine the drivers of e-
commerce adoption of 2,139 establishments across ten countries. The findings from their study
revealed that environmental factors, as well as technological readiness, plays a major role in the
firms’ adoption of e-commerce. Although the significance of this study cannot be excluded, a
problem arises when findings from this study are generalised to the global context. Furthermore,
previous studies failed to examine the relationships between constructs in the TOE framework and
how they subsequently influence B2C e-commerce adoption. It is against this backdrop that we
seek to carry out this study to examine the drivers of B2C e-commerce adoption at the global
level. Furthermore, we examine the relationships between constructs in the TOE framework and
how they influence B2C e-commerce adoption. Figure 1 depicts our model based on the TOE
framework.
2.2. Technology context
Oliveira and Martins (2008) refer to technology readiness as a firm’s technology infrastructure and
Information Technology (IT) human resources. Thus, the range of technologies available to a firm
(Scupola 2009). This consists of internal and external technologies that are considered essential to
the firm or organisation. Results from the study of Krishnan, Teo, and Lim (2013) indicates that a
nation’s technology infrastructure promotes the adoption of different kinds of ICT systems. Further-
more, findings from the studies of (Aljowaidi, Arbia, and Arabia 2015; Ibrahim and Stevens 2014) on e-
commerce adoption in Ghana and Saudi Arabia respectively, revealed that, a nation’s technological
infrastructure influences the firm’s adoption of e-commerce. Similarly, results from the study of Fer-
guson and Yen (2006) on e-commerce adoption at the global level revealed that a nation’s ICT infra-
structure influences its adoption of e-commerce. We, therefore, extend this to postulate that, ICT
access which represents the technology construct in our study will positively affect B2C e-commerce
adoption and use at the global level. We measure ICT access in this study by fixed telephone subscrip-
tion, household with internet access, households with personal computers, and Internet bandwidth
per user (ITU 2017). We, therefore, hypothesise that;
H1: The level of ICT access in a country is positively associated with its B2C e-commerce adoption
Furthermore, studies conducted on e-commerce adoption have largely examined the effect or
influence of the technological context or construct on e-commerce adoption and use (Awiagah,
Kang, and Lim 2016; Ho, Kauffman, and Liang 2007; Mohtaramzadeh, Ramayah, and Jun-Hwa
2018b; Rodríguez-Ardura and Meseguer-Artola 2010). In addition, the literature reviewed indicates
that public policies and regulations are essential in advancing ICT diffusion (Billon, Marco, and
Lera-Lopez 2009). Although results from these studies are valuable, we extend them to postulate
that, ICT access within a country is positively associated with its political and regulatory environment
(ICT Regulation). We, therefore, argue that once individuals or citizens in a country have ICT access,
4 I. O. ADAM ET AL.
Figure 1. Research model.
the government rolls out certain mechanisms or policies to regulate the use and ensure security and
privacy on e-commerce platforms. As a result, we hypothesise that;
H2: The level of ICT access in a country is positively associated with its political and regulatory environment.
2.3. Organisational context
Organisational context refers to the features and resources of the organisation or firm. This may
include, its human resources, size, managerial structure, degree of centralisation, degree of for-
malisation and linkages among employees (Tornatzky, Eveland, and Fleisher 1990). Findings
from the study of Bogaert, Martens, and Cauwenbergh (1994), revealed that, that employees
should be viewed as the most important resource for organisational development. Therefore,
applying this to the global level, we argue that, education and training of individuals within
a country will positively influence B2C e-commerce adoption. From the study of (Srivastava
and Teo 2010), it was found that education and training was the main driver for e-business
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 5
and e-government use. Similarly, findings from the study of (Ho, Kauffman, and Liang 2007) also
indicate that education and the quality of human resources influence B2C e-commerce adop-
tion and use. We, therefore, extend these results to postulate that, organisation, which is rep-
resented in this study as human resource (HR) development positively influences B2C e-
commerce adoption. We further measure HR development by Adult literacy, Gross enrolment
Ratio, Expected Years of Schooling and Mean Years of Schooling (World Bank Group 2016).
We, therefore, hypothesise that;
H3: The level of HR development in a country is positively associated with its B2C e-commerce adoption
In addition, there exists a lack of studies examining the effect or influence of HR development and
political and regulatory environment. The lack of professional and educated human resources, insti-
tutional structures and processes, would make it difficult for regulatory agencies to act in a credible
manner (Quartey 2005). This lack of credibility poses a negative effect on the total effectiveness by
reducing the relationship between the regulators and the various stakeholders concerned in the
regulated sector. We, therefore, postulate that HR development in a country will positively
influence its political and regulatory environment. That is, we argue that a trained and educated
HR will consciously use ICTs and as such, regulations relating to the use of ICTs would be lenient.
This, therefore, leads to our nest hypothesis that states that;
H4: The level of HR development in a country positively influences its political and regulatory environment.
2.4. Environmental context
According to Tornatzky, Eveland, and Fleisher (1990) and Zhu and Kraemer (2005), the environment
construct comprises of the firm’s regulatory environment, its size and structure, as well as its macro-
economic environment. Findings from the study of (Khan and Krishnan 2019) on ICT laws and its
impact on ICT diffusion within countries revealed that ICT laws within a country positively
influence ICT diffusion among individuals, businesses, and governments within a country. Similarly,
findings from the studies of Awiagah, Kang, and Lim (2016), Mohtaramzadeh, Ramayah, and Jun-
Hwa (2018b), Rodríguez-Ardura and Meseguer-Artola (2010) showed that the environmental con-
struct plays a key role in e-commerce adoption. Relying on this evidence stated above, we postulate
that the environmental construct which is represented by the political and regulatory environment in
this study positively influences B2C e-commerce adoption. We measure this construct by the effec-
tiveness of law-making bodies, laws relating to ICTs, judicial independence, efficiency of the legal
system in settling disputes, intellectual property protection, software piracy rate, the percentage of
installed software, number procedures to enforce a contract and number days to enforce a contract
(in the 2016 Network Readiness Index) drawn from the World Economic Forum (WEF) Global Infor-
mation Technology Report (GITR) (Baller, Dutta, and Lanvin 2016). We, therefore, hypothesise that;
H5: The level of political and regulatory environment within a country positively influences its B2C e-commerce
adoption.
3. Research methodology
3.1. Data sources
The data for the study was drawn from several archival sources for the period 2016. Specifically, data
were drawn from the ITU (for ICT access data), the World Bank (World Development Indicators-
Human capital index) and World Economic Forum (for the political and regulatory environment of
the Network Readiness Index). The data were collected from 135 countries globally from these
sources because; first, the researchers were constrained resources and time to be able to collect
primary data on this scale and so the need to depend on these reliable data sources which have
6 I. O. ADAM ET AL.
been used in past research of this nature. Secondly, data was not readily available in a single source
and so collecting and compiling the data from these databases for this research was needful. The ITU
database contained data for 176 countries whilst the data from the WDI contained 193 countries. The
GCI report, on the other hand, contained data for 151 countries. These different secondary data
sources were merged for all the countries which had complete data in relation to the constructs
under study. We took cognisance of the fact that the variables used were taken from these
different sources, we only considered data for the countries that were available in all reports. The
data points which were common across all the reports were then analysed and these led to a list
of 135 countries. The data points for indicators that had missing data of less than 5% were mean-
replaced (Hair et al. 2016).
3.2. Constructs measures
They were four latent variables under consideration. These were ICT access, political and regulatory
environment, human resource development and B2C e-commerce. ICT access concerns communi-
cation technologies that provide access to information. This includes access to the Internet, wireless
networks, cell phones, and other communication technologies. ICT access is measured by indi-
cators such as fixed telephone subscriptions, households with personal computers, households
with internet access, internet bandwidth per user and mobile cellular subscriptions. ICT access is
a subcomponent of the ICT development index, which is drawn from the 2016 International Com-
munications Union (ITU) report (ITU 2017). The political and regulatory environment is a pillar of the
World Economic Forum (WEF) Global Information Technology Report (GITR) (Baller, Dutta, and
Lanvin 2016). It consists of nine variables and these variables assess the extent to which a national
legal framework facilitates ICT penetration and the safe development and engagement in business
activities. Generally, features such as the protection afforded to property rights, the independence
of the judiciary, and the efficiency of the law-making process as well as more ICT-specific dimen-
sions such as the passing of laws relating ICTs and software piracy rates. The human resource devel-
opment constructs in the model are measured by four indicators. This consists of adult literacy,
gross enrolment ratio, expected years of schooling and the mean years of schooling for each
country. The source of this is the Human Capital Index from the 2016 World Development
Report. The B2C e-commerce adoption construct is measured by the use of B2C consumer internet
use, which was drawn from the 2016 GITR. It assesses the overall use of the internet by businesses
to interact with consumers. The WEF’s Global IT Report (Baller, Dutta, and Lanvin 2016) reports the
measure that captures the extent of B2C internet use in different countries. Table 1 provides a
summary of the constructs, the indicators and the sources of the data. The acronyms for each indi-
cator are also provided.
4. Results and analysis
We used Smart PLS to conduct our data analysis in exploring the drivers of B2C e-commerce by asses-
sing the effects of IT access, human resource development and the political and regulatory environ-
ment of a country. The results of the analysis is depicted in Figure 2. The study used partial least
squares structural equation modelling (PLS-SEM) over covariance-based SEM techniques because
we found the use of PLS-SEM in dealing with small sample sizes appropriate, especially when
models used comprise of many constructs (Fornell and Bookstein 1982; Hair et al. 2017; Willaby
et al. 2015). Secondly, the use of distributional assumptions in PLS-SEM (do Valle and Assaker
2016; Hair et al. 2012; Nitzl 2016) was the reason because of the reliance on non-normal data.
Lastly, PLS-SEM was found to be suitable for exploratory research with secondary data, since it pro-
vides some flexibility for the interplay between theory and data (Nitzl 2016).
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 7
Table 1. Constructs and sources of data.
SN Latent Variable Proxy Source Indicator Acronym
1 Business-to- Business-to-consumer Network Readiness Business-to-consumer B2C Int Use
Consumer Internet use Index (NRI) Internet use
E-commerce
2a ICT Access ICT Access ITU Fixed Telephone Fixed Tel Subs
Subscriptions
b Households with internet HH with internet
access acc
c Households with computers HHwith PC
d Internet bandwidth per user Int Bdw per User
e Mobile cellular subscriptions Mob Cel Subs
3a Political and 1st pillar: Political Network Readiness Effectiveness of law-making ELMB
Regulatory and regulatory Index (NRI) bodies
b Environment environment Laws relating to ICTs LR ICTs
c Judicial independence JI
d Efficiency of legal system in ELSSD
settling disputes
e Efficiency of legal system in ELSCR
challenging regs
f Intellectual property IPP
protection
g Software piracy rate, % SPR
software installed
h No. of procedures to enforce PEC
a contract
i No. of days to enforce a DEC
contract
4a Skilled Labour HCI World Development Adult Literacy Adult Lit
b Report Gross Enrolment Ratio Gross Enr Ratio
c Expected years of schooling Exptd yrs of sch
d Mean years of schooling Mean yrs of sch
4.1. Assessment of the measurement model
The model under study was a reflective one and so we tested for reliability and validity of the
measurement model before an assessment of the structural model was conducted. To do this we
assessed for internal consistency for reliability, convergent validity, and discriminant validity (Hair
et al. 2019). As a reflective model, our first was the indicator loadings. Loadings above 0.7 are rec-
ommended (Hair et al. 2019) since they indicate that acceptable item reliability is achieved
because at least the construct explains more than 50% of the indicator’s variance. Not all the indi-
cators loaded significantly on their corresponding constructs (Gefen and Straub 2005). When the
analysis was first run some of the indicators were less than the threshold of 0.7 and so were
dropped. Specifically, and in terms of ICT Access, HH with internet acc was 0.411 and int bdwth
pp 0.573. In terms of the Pol. and Reg Envt, SPR, PEC, and DEC were −0.033, 0.521, and −0.396
respectively and were also deleted. The model was re-run after that. The results were then extracted
for an assessment and evaluation of the measurement and structural model. This meant that not all
the indicators were a good measurement of the latent constructs. Construct reliability and validity
were assessed in different ways. The internal consistency, Cronbach’s alpha and Fornell-Larker’s com-
posite reliability (Fornell and Larcker 1981; Jöreskog 1971) were used. This is summarised in Table 2.
The threshold for Cronbach’s Alpha is 0.7 (Nunnally 1978) and all were from 0.749 to 1.000 in excess of
the minimum The Cronbach’s alpha is said to produce lower values than composite reliability and is a
less precise measure of reliability because the items are unweighted. Therefore, rho A is usually used
as an alternatively approximate measure of construct reliability (Dijkstra and Henseler 2015). Rho A
also exceeded a minimum of 0.7. The composite reliabilities also exceeded the minimum threshold
of 0.7 and this was considered adequate (Fornell and Larcker 1981). Whilst this was considered ade-
quate and proof of internal consistency, a third step assessed was employed to test for the
8 I. O. ADAM ET AL.
Figure 2. Results of PLS analysis.
convergent validity of each construct measure. Convergent validity is used to measure the extent to
which the construct meets to explain the variance of its items. The measure of a construct’s conver-
gent validity is the average variance extracted (AVE). This is used to measure all items on each con-
struct and the threshold is ≥0.50 or higher to indicate that the construct explains at least 50% of the
variance of its items. In our study, all the Average Variances Extracted (AVEs) were higher than the 0.5
threshold (Fornell and Larcker 1981; Hair et al. 2019).
Furthermore, we assessed for discriminant validity. Discriminant validity refers to the degree to
which a construct is empirically different and distinct from other constructs in the structural
model. The cross-loadings were used to assess this. As depicted in Table 3, the loadings for each
Table 2. Construct reliability and validity.
Cronbach’s alpha rho_A Composite reliability Average variance extracted (AVE)
B2C E-Commerce 1.000 1.000 1.000 1.000
HR Devt 0.937 0.945 0.955 0.841
ICT Access 0.749 0.749 0.845 0.581
Pol. and Reg Envt 0.970 0.992 0.975 0.866
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 9
construct exceed its corresponding cross-loadings. For example, Fixed Tel Subs loads high on its cor-
responding construct ICT Access (0.839) but lower on constructs HR Devt (0.715) Pol. and Reg Envt
(0.511) and B2C E-Commerce Adoption (0.659). This means that from the cross-loadings discriminant
validity has been established.
In addition, we assessed for discriminant validity by using the Fornell-Larcker criterion (Fornell and
Larcker 1981). This criterion holds that the AVE of each construct needs to be compared to the
squared inter-construct correlation (to determine a measure of shared variance) of that same con-
struct as well as other constructs which are reflective in the structural model. For all of the model’s
constructs, the shared variance is not supposed to be larger than their AVEs (Fornell and Larcker
1981; Hair et al. 2019). From Table 4 below, the diagonals are the square root of the AVE of the
latent variables. These are highest in any column or row and this shows adequate discriminant
validity.
More recently, Henseler, Ringle, and Sarstedt (2015) propose that the Fornell-Larcker criterion can
be complemented with the Heterotrait-Monotrait (HTMT) ratio of the correlations (Voorhees et al.
2016). The HTMT shown in Table 5 explains the mean value of the item correlations across constructs
Table 3. Indicator item cross loadings.
HR Devt Pol. and Reg Envt ICT Access B2C E-Commerce
Adult Lit 0.870 0.365 0.628 0.627
Exptd yrs of sch 0.951 0.565 0.673 0.685
Gross Enr Ratio 0.937 0.538 0.629 0.661
Mean yrs of sch 0.909 0.545 0.711 0.753
ELMB 0.368 0.917 0.442 0.515
ELSCR 0.402 0.952 0.465 0.577
ELSSD 0.347 0.933 0.412 0.521
IPP 0.612 0.963 0.618 0.735
JI 0.516 0.937 0.548 0.616
LR ICTs 0.695 0.880 0.692 0.847
Fixed Tel Subs 0.715 0.511 0.839 0.659
HHwith PC 0.845 0.639 0.860 0.765
Int Bdw per User 0.130 0.260 0.614 0.233
Mob Cel Subs 0.490 0.378 0.709 0.477
B2C Int Use 0.746 0.710 0.706 1.000
Table 4. Discriminant validity: fornell-larcker criterion.
B2C E-Commerce HR Devt ICT Access Pol. and Reg Envt
B2C E-Commerce 1.000
HR Devt 0.746 0.917
ICT Access 0.706 0.722 0.762
Pol. and Reg Envt 0.710 0.556 0.592 0.931
Table 5. Heterotrait-monotrait ratio (HTMT).
B2C E-Commerce HR Devt ICT Access Pol. and Reg Envt
B2C E-Commerce
HR Devt 0.768
ICT Access 0.816 0.859
Pol. and Reg Envt 0.692 0.545 0.667
Original sample (O) Sample mean (M) Bias 2.50% 97.50%
HR Devt -> B2C E-Commerce 0.768 0.767 −0.001 0.697 0.824
ICT Access -> B2C E-Commerce 0.816 0.813 −0.003 0.732 0.898
ICT Access -> HR Devt 0.859 0.861 0.002 0.738 0.975
Pol. and Reg Envt -> B2C E-Commerce 0.692 0.69 −0.002 0.593 0.767
Pol. and Reg Envt -> HR Devt 0.545 0.543 −0.002 0.402 0.662
Pol. and Reg Envt -> ICT Access 0.667 0.663 −0.004 0.494 0.811
10 I. O. ADAM ET AL.
relative to the (geometric) mean of the average correlations for the items measuring the same con-
struct. The HTMT has a proposed threshold of 0.90. What this portrays is that with an HTMT value of
0.90, it means that that discriminant validity is not present. Further testing using the HTMT can be
done using bootstrapping to test whether the HTMT value is significantly different from 1.00 (Hense-
ler, Ringle, and Sarstedt 2015). A bootstrapping procedure was run in order to examine if the upper
bound of the 95% confidence interval of HTMT is lower than 0.90.
4.2. Assessment of the structural model
Finally, we assessed for multicollinearity as shown in Table 6. This is done by examining the variance
inflation factor (VIF) taking into cognisance a minimum threshold of 5 or lower. This is to avoid a col-
linearity problem (Hair, Ringle, and Sarstedt 2011). If the VIF value is lower than 5 it indicates that the
variable under consideration is almost a perfect linear combination of independent variables in the
equation (Hair, Ringle, and Sarstedt 2011; Hair et al. 2016; Mansfield and Helms 1982). In our study, all
of the VIF values were less than 5 and this confirms that multicollinearity was not a problem in the
study.
In order to test the hypothesis for significance, we performed a bootstrapping procedure using a
two-tailed t-distribution. The bootstrapping was run using 5000 iterations. The results are presented
in Tables 7 and 8 and supported by Figure 3.
Table 6. Multicollinearity statistics: inner VIF values.
B2C E-Commerce HR Devt ICT Access Pol. and Reg Envt
B2C E-Commerce
HR Devt 2.203 2.086
ICT Access 2.344 2.086
Pol. and Reg Envt 1.625
Table 7. Structural model hypothesis testing for direct effects.
Hypothesis Relationship Std Beta Std Error [t-values] Decision 95% CI LL 95% CI UP
H1 ICT Access -> B2C E-Commerce 0.201 0.068 2.979 Supported 0.084 0.304
H2 ICT Access -> Pol. and Reg Envt 0.419 0.134 2.976 Supported 0.214 0.643
H3 Pol. and Reg Envt -> B2C E-Commerce 0.373 0.052 7.145 Supported 0.285 0.457
H5 HR Devt -> B2C E-Commerce 0.393 0.059 6.646 Supported 0.296 0.489
H4 HR Devt -> Pol. and Reg Envt 0.246 0.136 1.971 Supported 0.009 0.449
Table 8. T-statistics of outer loadings.
T Statistics (|O/STDEV|)
ICT Access Pol. and Reg Envt HR Devt
Fixed Tel Subs <- ICT Access 44.085
HHwith PC <- ICT Access 33.503
Int Bdw per User <- ICT Access 10.051
Mob Cel Subs <- ICT Access 14.879
ELMB <- Pol. and Reg Envt 53.267
ELSCR <- Pol. and Reg Envt 93.725
ELSSD <- Pol. and Reg Envt 58.636
IPP <- Pol. and Reg Envt 173.234
JI <- Pol. and Reg Envt 82.125
LR ICTs <- Pol. and Reg Envt 62.536
Adult Lit <- HR Devt 43.488
Exptd yrs of sch <- HR Devt 134.468
Gross Enr Ratio <- HR Devt 101.898
Mean yrs of sch <- HR Devt 56.706
B2C Int Use <- B2C E-Commerce Single item Construct
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 11
Figure 3. Model hypothesis testing for direct effects.
Table 7 presents a breakdown of the hypothesis results. All five hypotheses proposed in this study
were supported. The analysis was done through bootstrapping with bias-corrected 95% confidence
intervals. The findings related to the individual hypotheses are discussed in the following section
below.
Furthermore, we used the coefficient of determination (R2) to evaluate the structural model. This
was to determine the model’s predictive power. The R2 represents the exogenous latent variables’
combined effects on the endogenous latent variable (Hair et al. 2014). From Table 9 above, the R2
is 0.700 for B2C E-Commerce. This means that the three latent variables (ICT Access, Pol. and Reg
Envt and HR Devt) explain 70% of the variance in B2C E-Commerce. Also, ICT Access and HR Devt
explain 38.5% of the variance of Pol and Reg Envt. The R2 values are between 0 and 1 with higher
values indicating higher levels of predictive accuracy.
Table 9. R Square.
R Square R Square Adjusted
B2C E-Commerce 0.700 0.693
Pol. and Reg Envt 0.385 0.375
12 I. O. ADAM ET AL.
5. Discussion of findings
From our analysis of the results, several findings were revealed. This further indicates the acceptance
of the stated hypotheses in relation to the findings of the research. First, H1 which sought to draw a
positive association between the level of ICT access in a country and its B2C E-commerce adoption
was supported. Although the Internet is a prerequisite for e-commerce, access to the internet does
not indicate that people will actually exploit it for trade purposes. However, whilst the number of
Internet users across the globe has been on the ascendancy, this has provided some opportunities
for global e-commerce (Lawrence and Tar 2010). These opportunities provide an impetus for
people to engage in e-commerce. For instance, considering the proliferation of mobile devices,
mobile commerce (m-commerce) which is an aspect of e-commerce has seen substantial growth
(Khalifa and Shen 2008; Liang and Wei 2004). M-commerce involves the use of wireless and
mobile B2B and B2C exchanges of operational and financial data at different levels of a business
cycle (Elliott and Phillips 2004). Examples of B2C m-commerce include mobile financial services
and mobile shopping. This notwithstanding, globally, a country’s technological infrastructure sup-
ports the adoption of different varieties of systems and this may also aid the growth of e-commerce
(Krishnan, Teo, and Lim 2013). Specifically, our finding is consistent with the findings of Aljowaidi,
Arbia, and Arabia (2015), Ibrahim and Stevens (2014) on e-commerce adoption in Ghana and
Saudi Arabia who respectively reveal a direct association of a country’s technological infrastructure
and firm’s adoption of e-commerce. However, these studies were conducted in a single country
context and findings, therefore, cannot be generalised to the global context. Our findings extend
to the global level and therefore fill the gap in the existing literature. Our findings indicate that
when access to ICTs is provided, it can enable and boost B2C e-commerce at the global level.
Evidence from the literature indicates that public policies and regulations are critical in advancing
or curbing ICT diffusion (Billon, Marco, and Lera-Lopez 2009). Through telecommunication policy, ICT
diffusion can be enhanced through infrastructure development and this can influence the market
structure and local competition and ultimately lead to reduced ICT costs. This can be achieved by
varying the number of ICT providers and thereby affecting access and service costs positively. For
instance, African and Latin American countries that ushered in free competition in the telecommu-
nication sector enjoyed a sharper decrease in costs and sectorial growth than countries that post-
poned market competition (World Bank 2006). The literature also provides evidence that the rule
of law and cultural variables can explain the differences in Internet diffusion (Zhao et al. 2007) sup-
porting Chinn and Fairlie (2007) position that the quality of regulatory influence affects the divide in
Internet use between developed and developing countries. In middle-income countries, however, an
independent ICT regulation authority could be helpful in supporting ICT diffusion (Billon, Marco, and
Lera-Lopez 2009). On one hand, whilst regulation can support ICT access and diffusion among indi-
viduals and firms, a positive regulatory environment can also lead to advancement in B2C e-com-
merce through ICT diffusion. This finding, therefore adds to the body of knowledge as extant
literature has largely ignored the influence of ICT access on political and regulatory environment.
Our finding indicates that, once individuals or citizens in a country have access to ICTs, the govern-
ment can roll out certain policies to regulate the use and ensure security and privacy on e-commerce
platforms.
Furthermore, from the analysis of the results, it was revealed that the hypothesis that the level of
HR development is positively associated with B2C e-commerce was supported. This finding indicates
that the education and training of individuals within a country will positively influence B2C e-com-
merce adoption. Similarly, a skilled labour force within an entity will greatly influence how its tech-
nological resources are used to facilitate B2C e-commerce. This is consistent with the findings of
Srivastava and Teo (2010) and Ho, Kauffman, and Liang (2007) who found that education and training
are critical to e-business adoption and use.
In addition, the hypothesis on HR development being positively associated with political and regu-
latory environment was supported. HR development in terms of the literacy and the ICT skills can
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 13
affect how a population would respond to regulations on ICT use. For instance, people who have
digital skills and can adapt technology to address their needs will be more persistent in their use
of technology for their individual tasks (Yu, Lin, and Liao 2017). As a result, such individuals will be
ready to comply with regulatory policies that will enhance their use of technology. Similarly, a
more educated population will understand regulatory issues more and hence the difficulty in com-
pliance may be low. Therefore, a more a professional and educated HR will enable regulatory
agencies to experience ease in implementing policies because they are more likely to use ICTs in a
law-abiding manner.
Lastly, the hypothesis on political and regulatory environment being positively associated with e-
commerce adoption was supported. This finding is consistent with the results of other studies such as
Khan and Krishnan (2019) who found that ICT laws within a country positively influence ICT diffusion
among individuals, businesses, and governments. Similarly, results from the studies of Awiagah,
Kang, and Lim (2016), Mohtaramzadeh, Ramayah, and Jun-Hwa (2018b), Rodríguez-Ardura and Mese-
guer-Artola (2010) revealed that the environmental construct (i.e. government support, enabling con-
ditions, etc.) plays a key role in e-commerce adoption within a country.
6. Conclusion, limitations and future research
Our study set out to understand the drivers of global B2C e-commerce. We considered the role of ICT
access, political and regulatory environment and human resource development at the country level.
Relying on secondary data for 135 countries globally, our study makes some important contributions.
Firstly, existing studies indicate that the lack of adequate technological infrastructure, socio-economic
development as well as lack of national ICT strategies can impede the adoption and growth of e-com-
merce especially, in developing countries (Lawrence and Tar 2010). Our study adds to the body of
knowledge by arguing that the role of ICT access and human resource development in the country,
as well as the political and regulatory environment, is critical in driving B2C e-commerce adoption at
the global level. Secondly, this study is one of the very few studies that examine how human resource
development and ICT access of countries affect the global adoption of B2C e-commerce.
For practice and policy, the implication of our study is that it makes it possible for practitioners and
policymakers to realise the impact of the human resource development through skilled labour force
achieved through education and training one hand. On the other hand, policymakers need to consider
infrastructural development that can bridge the digital divide among citizens to enable more access to
ICTs since these can translate into broader e-commerce adoption and economic development.
Despite this, our study is limited by our dependence on secondary data drawn from varying
sources. As a result of this, we had to rely on metrics and indices as formulated by the reporting
agencies. We could not rely on primary data to ensure better control over the definition of the vari-
ables because this was not feasible. However, these indices were found to be reliable because they
have been formulated by reputable and authorised organisations through the use of robust statistical
procedures. Second, we could not include all countries in the world because the data was not com-
monly available in all the sources. Future studies may consider extending this study by introducing
more variables into the model and including more countries.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Ibrahim Osman Adam holds a PhD in Information Systems from the University of Ghana Business School. Ibrahim holds a
first-class bachelor’s degree in Business Administration (Accounting Option) from the University of Ghana, an MSc in
Development Management from the London School of Economics and Political Science (LSE) (UK), and another MSc
14 I. O. ADAM ET AL.
in Applied Informatics from the Henley Business School, University of Reading (UK). Ibrahim Osman is a Chartered
Accountant and a member of the Institute of Chartered Accountants (Ghana). He teaches management information
systems, accounting information systems and computer applications in management both at the undergraduate and
postgraduate levels at the School of Business and Law, University for Development Studies, Wa Ghana. His research inter-
est are information systems in higher education, cloud computing and ICT4D.
Muftawu Dzang Alhassan holds a Bachelor’s degree in Commerce with a major in Accounting from the University for
Development Studies, Wa Campus, Ghana. Muftawu is a Graduate Assistant at the University of Ghana Business School,
Department of Operations and Management Information Systems where he is also undertaking an MPhil in Management
Information Systems. His research interest includes; Financial technologies, E-commerce and the Sharing economy.
Mr Yaw Afriyie has BSc, in Computer Science and a Master of Science in Computational Mathematics all from the University
for Development Studies. He is currently an Assistant Lecturer in the Department of Accounting at the same University. His
research interests include Parallel Computing, Residue Number Systems, Business Information Systems and Deep Learning.
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Appendix 1. List of countries
Albania, Algeria, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belgium, Benin, Bhutan, Bolivia,
Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Chad, Chile,
China, Colombia, Costa Rica, Côte d’Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Dominican Republic, Ecuador,
Egypt, El Salvador, Estonia, Ethiopia, Finland, France, Gabon, Gambia, The Georgia, Germany, Ghana, Greece, Guatemala,
Guinea, Guyana, Haiti, Honduras, Hong Kong SAR, Hungary, Iceland, India, Indonesia, Iran, Islamic Rep., Ireland, Israel, Italy,
Jamaica, Japan, Jordan, Kazakhstan, Kenya, Korea, Rep. Kuwait, Kyrgyz Republic, Lao PDR, Latvia, Lebanon, Lesotho,
Liberia, Lithuania, Luxembourg, Macedonia, FYR Madagascar, Malawi, Malaysia, Mali, Malta, Mauritania, Mauritius,
Mexico, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New
Zealand, Nicaragua, Nigeria, Norway, Oman, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Qatar,
Romania, Russian Federation, Rwanda, Saudi Arabia, Senegal, Singapore, Slovak Republic, Slovenia, South Africa,
Spain, Sri Lanka, Swaziland, Sweden, Switzerland, Tanzania, Thailand, Trinidad and Tobago, Tunisia, Turkey, Uganda,
Ukraine, United Arab Emirates, United Kingdom, United State, Uruguay, Venezuela, Vietnam, Zambia, Zimbabwe.