ecommerce adoption in developing countries model and
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adoption in developing countries modelTRANSCRIPT
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Information & Management 42 (2005) 877–899
eCommerce adoption in developing countries:
a model and instrument
Alemayehu Mollaa,*, Paul S. Lickerb,1
aIDPM, The University of Manchester, Oxford Road, Manchester M13 9QH, UKbDepartment of Decision and Information Sciences, Oakland University, Rochester, MI 48309, USA
Received 21 September 2003; received in revised form 19 November 2003; accepted 18 September 2004
Available online 6 January 2005
Abstract
Several studies of eCommerce in developing countries have emphasized the influence of contextual impediments related to
economic, technological, legal, and financial infrastructure as major determinants of eCommerce adoption. Despite operating
under such constraints, some organizations in developing countries are pursuing the eCommerce agenda while others are not.
However, our understanding of what drives eCommerce among businesses in developing countries is limited by the absence of
rigorous research that covers issues beyond contextual imperatives. This paper discusses a holistic and theoretically constructed
model that identifies the relevant contextual and organizational factors that might affect eCommerce adoption in developing
countries. It provides a research-ready instrument whose properties were validated in a survey of 150 businesses from South
Africa. The instrument can be used as a decision tool to locate, measure, and manage some of the risk of adopting eCommerce.
Implications of the study are outlined; they indicate a need to consider eCommerce, micro, meso, and macro issues in
understanding the adoption of eCommerce in developing countries.
# 2004 Elsevier B.V. All rights reserved.
Keywords: eCommerce adoption; Developing countries; Perceived eReadiness; PERM
1. Introduction
There is a belief that eCommerce contributes
to the advancement of businesses in developing
* Corresponding author. Tel.: +44 161 275 3233;
fax: +44 161 273 8829.
E-mail addresses: [email protected]
(A. Molla), [email protected] (P.S. Licker).1 Tel.: +1 248 370 2432; fax: +1 248 370 4604.
0378-7206/$ – see front matter # 2004 Elsevier B.V. All rights reserved
doi:10.1016/j.im.2004.09.002
countries [63,73,75]. This belief is driven by the
perceived potential of the Internet in reducing
transaction costs by bypassing some, if not all, of
the intermediary and facilitating linkages to the
global supply chains. In order to take advantages of
these potentials, businesses must adopt eCommerce.
However, the diffusion in developing countries has
fallen far below expectations [74]. Several studies
have attempted to explain the facilitators and/or
inhibitors [14,18,30,50,70]. Predominantly, these
.
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899878
studies identified physical, technological, institu-
tional, and socio-economical eReadiness impedi-
ments that discouraged eCommerce adoption.
We question the emphasis placed only on macro
level constraints and the underlying environmental
imperative view. For instance, the extent of eCom-
merce adoption in other markets, where the environ-
ments were relatively conducive [66,79], lead us to
suspect that environmental constraints should not be
considered as sole determinants. In addition, the
literature from developed countries identified the role
of managerial, organizational, and eCommerce related
considerations in adoption decisions [4,13,23]. Some
emphasized the relevance of organizational readiness,
however defined, in the decision to implement
eCommerce [9,20,28]. Contextual differences (both
organizational and environmental) between these two
socio-economic arenas have not supported the
generalizability of developed countries’ findings in
other markets. However, it is reasonable to expect that
some factors could affect developing countries
businesses’ intentions and decisions to adopt eCom-
merce.
Therefore, it is important to understand how
businesses in developing countries could overcome
the environmental and organizational eReadiness
impediments and benefit from eCommerce. Hence,
the purposes of this study are to:
(1) d
efine the eReadiness concept;(2) s
uggest an underlying model of eCommerceadoption to identify the relevant managerial,
organizational, technological, and environmental
factors that affect decisions to open or develop
eCommerce systems in developing countries;
and
(3) d
evelop sufficiently validated measures to showthe utility of the model.
2. eCommerce in developing countries
Businesses in developing countries face challenges
different from those in developed countries. This calls
for models that are robust enough to capture most, if
not all, of the idiosyncrasies. For instance, businesses
in developed countries have employed a relatively
well-developed, accessible and affordable infrastruc-
ture, while in most of the developing countries,
eCommerce adoption has been constrained by the
quality, availability, and cost of accessing such
infrastructure [27]. The low level of information
and communications technology (ICT) diffusion in an
economy can also limit the level of eCommerce
awareness, a factor taken for granted in the developed
countries. In addition, in most developing countries,
Internet use and eCommerce practices have yet to
reach a critical mass for the network externalities to
take effect and encourage businesses to opt for
eCommerce innovations. The readiness of institutions
to govern and regulate eCommerce is an essential
element, but one lacking in developing countries, for
the trust necessary to conduct e-business [56].
In addition, most businesses in developing coun-
tries are small. Their lack of complexity can facilitate
eCommerce adoption, but this also means lack of
adequate resources to invest in IS and IT and absorb
possible failure [19]. Hence, a firm’s human,
technological, and business resources need to be
considered in making adoption decisions [26]. The
practice of doing business electronically, dealing with
non-cash payments, anonymous and electronic-based
intra and inter-business relations, all of which are
important in eCommerce, are not common for
businesses in developing countries. Thus, success
depends on making changes in the organizational
structure, product characteristics and business culture
of their enterprises to develop such practices [45,53].
In addition, most, if not all, businesses in developing
countries tend to have a highly centralized structure
[76]. This suggests that the perception of the managers
about their organization, innovation, and their
environment is likely to be critical in adopting
eCommerce.
3. Theoretical background
The literature on the adoption of innovation
promotes several dominant perspectives: managerial
imperative [21,36,65]; organizational imperative
[34,49]; technological imperative [64], environmental
imperative [47,52]; and interactionism [54,55].
Technological imperative models, such as diffusion
of innovation (DOI) [64] and technology acceptance
(TAM) [15] consider the complexity, compatibility,
relative advantage, ease of use, usefulness and other
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899 879
attributes as key drivers of adoption. Managerial
imperative models seek to explain innovation adoption
based on the innovativeness attributes of managers,
their commitment to the innovation and IT back-
ground [10,22]. Organizational imperative models
assert that the key determinants of adoption reside
within the internal context of an organization. As a
result, they look at organizational characteristics such
as specialization, functional differentiation, formali-
zation, centralization, readiness, risk taking propen-
sity, and innovativeness as major determinants of
adoption [12]. Environmental imperative models, on
the other hand, tend to focus on external influences
[51]. External pressure from market forces, inter-
organizational relationship, institutional forces, and
the eReadiness of socio-economic forces are some of
the environmental factors likely to affect innovation
adoption, especially those innovations that cut across
firm boundaries [33,39].
Generally, the four imperatives focus either on the
innovation or the organization or the environment or
the mangers. The fifth approach – interactionism –
allows for treatment of all these forces and their
interaction in one dynamic framework. This assumes a
co-influence among the forces of the innovation, the
external environment, and the internal organization
(including managers) such that the external environ-
ment determines the internal organization, which, by
articulating a problem or formulating a solution or
unintentional actions, affects conditions outside the
organization [29]. The interactionism model can
explain marked differences in the performance of
organizations in identical contextual situations [48]. In
addition, it suggests why certain kinds of innovations
are successful in a given organization while other
innovations are not.
A review of the literature reveals the explanatory
power of adoption models that are based on the
interactionism perspective. For instance, Kuan and
Chau [35] have suggested a model of EDI adoption
based on a technology–organization–environment
framework. Other studies [41] have also mixed
innovation, organizational, and environmental
imperatives, hence adopting an interactionism per-
spective to explain differences in eCommerce adop-
tion. However, almost all are based on developed
countries and none is based on the notion of
eReadiness.
On the other hand, recently a number of eReadiness
assessment tools have been developed and many
countries have been assessed for their eReadiness [6].
The environmental imperative idea is the underlying
framework of this literature. The enquiries have not
sufficiently explained eCommerce adoption variation
among organizations operating in the same context. In
addition, studies have not discriminated adopters from
non-adopters or the degree of adoption. They also have
lacked rigor and focus to guide governments and
businesses to exploit specific eCommerce opportu-
nities [8].
Here, we followed interactionism as the theoretical
root of the model. Working from this perspective, we
posited that a multi-perspective audit of the manage-
rial, internal organizational, and external contextual
issues can provide meaningful predictors of eCom-
merce adoption in developing countries. We proposed
the concept of perceived eReadiness to represent
managers’ assessments of the four adoption contexts.
We defined ‘‘perceived eReadiness’’ as an organiza-
tion’s assessment of the eCommerce, managerial,
organizational, and external situations in making
decisions about adopting eCommerce. We refer to the
model as the Perceived eReadiness Model (The
PERM), an earlier version of which was discussed
in [44].
In order to make the model parsimonious, we have
identified two constructs – Perceived Organizational
eReadiness (POER) and Perceived External eReadi-
ness (PEER). POER indicates an audit of:
(1) t
he organization’s perception, comprehension,and projection of eCommerce and its potential
benefits and risks (innovation imperative attri-
butes);
(2) t
he commitment of its mangers (managerialimperative attribute); and
(3) k
ey organizational components, such as itsresources, processes, and business infrastructure
(organizational imperative attributes).
PEER represents an organization’s assessment and
evaluation of relevant external environmental factors
(environmental imperative attributes). Taken together,
PEER and POER are hypothesized to predict eCom-
merce adoption and explain a significant part of
the variance in the level of eCommerce adoption in
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899880
Fig. 1. The PERM general structure.
developing countries. Fig. 1 captures the structure of
the model.
4. Research methods
Before designing the instrument to be used,
existing instruments were considered. While there
are a number of instruments for assessing eReadiness,
none were appropriate for the purposes of our
research, because they focused on the assessment of
a macro environment or they had inherent assumptions
about the level and sophistication of eCommerce
activities on a par with Cisco, Oracle, and Amazon
[24]. In order to ensure the accuracy and validity of the
instrument and to reduce the measurement error, the
instrument development procedure suggested by
Churchill [7] was followed. This involves specifying
the domain of constructs, generating representative
sample of items, purifying the measure through a pilot
study, collecting further data, and assessing the
validity and reliability of the measure.
4.1. Specify domain of construct
Defining a construct’s meaning and domain are
necessary steps in developing an accurate and content
valid instrument. Two approaches were used in
identifying the theoretical constructs of the PERM:
socio-technical systems (STS) and competitive context
approaches. STS is the extensive body or conceptual
framework underlying the introduction of innovations
into organizations [37,72]. The premise is that an
organizational performance hinges on how well the
social and technical systems of the organization are
designed and collectively tuned to provide a means to
interact with the environment. STS is also a useful
framework to understand why results are meaningful
and how the integration of the social and technical
systems leads to improved results [67]. STS has
particular relevance to understanding organizations in
developing countries where resources and social issues
have been identified as some of the chief challenges in
the implementation of information systems [25].
The competitive context analysis [60] focuses on
national circumstances rather than organizational
performance. It provides a comprehensive and
empirically supported framework for analyzing the
role and importance of national factors that define the
environment of its firms. In this, demand conditions,
related and supporting industries, and government are
some of the most important attributes. This helps firms
to understand their national context and the salient
environmental factors that are crucial in affecting their
eCommerce implementation.
The two approaches and the literature review
provide the basic language and analytical framework
for an investigation of the eCommerce, managerial,
organizational and environmental variables that might
affect eCommerce adoption decisions in developing
countries. We also conducted exploratory interviews
and informal discussions with three academicians and
three consultants who had relevant experience in
eCommerce issues. The main purpose was to confirm
the important factors and thus to decide on the initial
items to be included in the instrument.
On the basis of these premises, definitions of the
major constructs were obtained. POER was defined as
managers’ evaluation of the degree to which they
believed that their organization had the awareness (A),
resources (R), commitment (C), and governance (G) to
adopt eCommerce. PEER was defined as the degree to
which mangers believed that the market forces, the
government, and other supporting industries were
ready to aid in the organizations’ eCommerce
implementation.
The dependent variable was eCommerce adoption.
Because it can take various forms and complexities,
for operational reasons and in order to make the
proposed model tractable, it was instructive to
differentiate between entry-level adoption and its
extent. We refer to the first as initial eCommerce
adoption and the second as the institutionalization of
eCommerce. This is consistent with previous research
[80] and it covers both initial adoption and the
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899 881
Fig. 2. Conceptual representation of the PERM.
maturity level of eCommerce. To operationalize these
two dimensions, we used an eCommerce maturity
model. eCommerce researchers appear to accept the
concept that organizations follow certain migration
paths [16,43]. From the literature, a six-phase
eCommerce status indicator, relevant to the eCom-
merce realities of developing countries, was defined:
no eCommerce, connected eCommerce, static eCom-
merce, interactive eCommerce, transactive eCom-
merce, and integrated eCommerce.
Many researchers (e.g. [32,71]) have accepted
interactive eCommerce as the beginning of eCommerce.
Therefore, a business was defined as having adopted
eCommerce if it has attained an interactive eCommerce
status. The second measure of adoption, institutiona-
lization, indicated the extent of eCommerce utilization.
This was operationalized by looking into whether an
organization had attained an interactive, transactive, or
integrated status. Fig. 2 captures the model and Table 1
summarizes the definitions of the variables.
4.2. Initial instrument preparation
The initial instrument was prepared in two phases.
First, an initial pool of 136 items was generated. The
items were reviewed and edited to capture the essence
of the concepts and constructs and a preliminary
instrument containing 88 items resulted. Following the
methods of [11], a panel of 20 experts, including the
six previously involved in generating the domain,
reviewed and pre-tested the instrument. The panelists
were selected on the basis of their experience and
knowledge of eCommerce issues in developing
countries. The experts were asked to judge the degree
of relevance of each of the items in the instrument as
measures of the individual variables on a five-point
Likert-type scale ranging from extremely relevant (5)
to not relevant (1). They were also asked to suggest
additional items that were not covered in the
instrument: responses were obtained from 16 mem-
bers of the panel. To check how evaluators agreed in
their assessment of a variable, inter-observer relia-
bility was evaluated using correlation coefficients
[38]. At p = 0.01, all of the inter-rater (correlation
coefficient between different judges) and corrected
rater-total (correlation coefficient of the individual
rater to the total score, excluding the rater’s score)
correlations were significantly high – thus supporting
the stability and reliability of the experts’ judgment
(see Appendix A).
To discern the relevant items based on the experts’
judgment, the mean relevance score (MRS) was
computed for each of the items in the preliminary
instrument. A total of 17 items, whose MRS was less
than average, 2.5, were excluded from the instrument.
The panel of experts also suggested additional items
and modifications to the wordings of some questions.
After careful examination of the suggestions and
discussion, three of the additional items were
introduced into the instrument and the statements
were further edited to make their wordings as precise
as possible. Overall, the procedures adopted can be
considered to be adequate in satisfying the test for
content validity. The 74-item instrument (Appendix B)
was then ready for piloting.
4.3. The pilot study
A pilot study was made to establish the basic
unassailability of the model before scale purification
as well as to eliminate duplicate items (those sharing
the same underlying concept). It also checked for
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2Table 1
Description of the variables in the PERM
Variables Description References
Perceived organizational eReadiness (POER)
Awareness Represents perception of eCommerce elements in the environment; comprehension of their
meaning through an understanding of eCommerce technologies, business models, requirements,
benefits and threats and projection of the future trends of eCommerce and its impact.
[17,23,28,40,73]
Commitment Reflects enough energy and support for eCommerce from all corners of an organization and
especially from the strategic apex. It refers to having a clear-cut eCommerce vision
and strategy championed by top management, eCommerce leadership and organization wide
support of eCommerce ideas and projects.
[1,10,46,62,76]
Human resources Refers to the availability (accessibility) of employees with adequate experience and exposure
to information and communications technology (ICT) and other skills (such as marketing,
business strategy) that are needed to adequately staff eCommerce initiatives and projects.
[41,61,81,82]
Technological resources Refers to the ICT base of an organization and assesses the extent of computerization, the
flexibility of existing systems and experience with network based applications
[25,31,61,81,82]
Business resources This covers a wide range of capabilities and most of the intangible assets of the organization.
It includes the openness of organizational communication; risk taking behaviour, existing
business relationships, and funding to finance eCommerce projects.
[9,24,29,81,82]
Governance The strategic, tactical and operational model organizations in developing countries put in
place to govern their business activities and eCommerce initiatives.
[24,53,78,79]
Perceived external eReadiness (PEER)
Government eReadiness Organizations’ assessment of the preparation of the nation state and its various institutions
to promote, support, facilitate and regulate eCommerce and its various requirements.
[6,35,42,47,52,56]
Market forces eReadiness The assessment that an organization’s business partners such as customers and suppliers
allow an electronic conduct of business.
[2,28,68]
Supporting industries eReadiness Refers to the assessment of the presence, development, service level and cost structure of
support-giving institutions such as telecommunications, financial, trust enablers and the
IT industry, whose activities might affect the eCommerce initiatives of businesses in
developing countries.
[57,60,70,75]
eCommerce adoption
Initial eCommerce adoption A business is considered to have adopted eCommerce if it has achieved an interactive
eCommerce status.
[44]
Institutionalization of eCommerce Indicates whether or not an organization has attained an interactive, or transactive or
integrated eCommerce status.
[44]
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899 883
questions and instruction clarity. The instrument had a
five-point Likert-type scale ranging from strongly agree
(1) to strongly disagree (5). The instrument was pilot
tested in 60 randomly selected business organizations in
South Africa. After 3 weeks and some follow up efforts,
a total of 12 responses were obtained. The response was
adequate for the purposes of the pilot study [77]. In
addition, telephone discussions were held with four
respondents to establish difficulties experienced in
completing the questionnaire.
To test the soundness of the model, correlation
coefficients were examined for all pairs of items within
the two research constructs: POER and PEER. When the
correlation coefficient was significant at p = 0.001, one
item within the pair was considered for elimination to
provide parsimony [58]. Before deleting any item, the
impact on the domain coverage (content validity of the
construct) was evaluated to ensure that the coverage
would not suffer. In addition, the measure’s corrected-
item-total correlation was checked to assess the
improvement of the reliability of the measure as a
result of dropping an item. The one with the lowest
corrected item-to-total correlation was removed. After
this, four items (A6, C6, R18, GVeR1 of Appendix B)
were dropped from the instrument leaving a total of 70
items with an initial reliability of 0.91 and 0.70 for
POER and PEER, respectively. At the end of the pilot
study, we believed that the instrument had high validity
and a reliability within an acceptable range.
4.4. The full study
The questionnaire was administered in South Africa.
A covering letter explaining the purposes of the study;
assuring anonymity of respondents and their organiza-
tion, and providing instructions on how and who to
complete the questionnaire and a postage-paid, self-
addressed return envelope was sent to the managing
directors of 1000 organizations. The recipients were
selected using a random systematic sampling technique
from a reputable business directory publication in South
Africa that has existed for more than 60 years. Follow-
up efforts to non-respondents were made through phone
calls and email. In addition, a second wave of mailings
ware made to a random sample of non-respondents [3].
One hundred twenty-five questionnaires were returned
because either the businesses had closed or changed
address. Out of 169 total responses, 19 were incomplete,
resulting in 150 usable responses, that is, a 19%
response rate from the 875 deliverable questionnaires.
This sample size is considered adequate for the analysis
and is comparable to response rates in the IS literature
[59].
5. Analysis and results
An analysis was conducted to test the instrument
validity and reliability [5,69]. First the initial
reliability was assessed to remove items that did not
have a common core, but produced additional
dimensions in a factor analysis. Second, to assess
whether the measures chosen were true constructs to
describe an event, the construct validity of each item
was examined. Finally, the predictive validity and final
reliability of the instrument were assessed.
5.1. Initial reliability
To test the initial reliability, coefficient alpha and
item–scale correlation were calculated. The corrected
item–scale correlations were plotted in descending
order and items were eliminated if they had a correlation
below 0.4 or their correlations produced a substantial
drop in the plotted pattern and raise the alpha if deleted
(Appendix C). The cutoff was judgmental and followed
Churchill’s [7] suggestion to eliminate items with item-
scale correlation near zero. However, this cutoff was
comparable to those used by previous researchers. As
the result of this, four items from POER (A1, A8, R3,
and R13) and one from PEER (GVER6) were dropped.
All the remaining correlations with the corrected item–
scale (r � 0.4) were significant at p = 0.05. Thus, the
cutoff values were considered high enough to ensure
that the items retained were adequate measures of the
constructs. In addition, the Cronbach alpha values (0.93
for POER and 0.79 for PEER) satisfied the highest
minimum criterion (0.8) of reliability and provided
evidence of initial reliability.
5.2. Construct validity
Principal component analysis was used to test the
validity of the instrument. In order to extract the
factors, the following factor extraction rules were
implemented:
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899884
1. C
Tab
Sum
Item
A2
A3
A4
A5
A7
A9
A1
R1
R2
R6
R7
R8
R9
R11
R12
R14
R17
R20
R22
R23
C1
C2
C3
C4
C5
C8
G1
G2
G3
G6
G7
G8
G1
MF
MF
GV
GV
GV
GV
SIE
SIE
SIE
SIE
Not
ase wise deletion of missing data.
2. A
minimum eigenvalue of 1 as cutoff value.3. D
ropping items with a factor loading less than 0.5on all factors from subsequent iterations.
le 2
mary of factor analysis of the PERM variables
s Factor 1 Factor 2 Factor 3 Factor 4
0.723
0.707
0.784
0.659
0.660
0.699
0 0.658
0.763
0.819
0.745
0.723
0.676
0.485
0.557
0.603
0.674
0.795
0.703
0.737
0.630
0
ER1
ER2
ER2
ER3
ER4
ER5
R1
R2
R3
R4
e: Figures are factor loadings.
4. D
Fact
0.68
0.59
0.62
0.62
0.74
ropping items with a factor loading greater than 0.5
on two or more factors from subsequent iterations.
5. E
xclusion of single item factors for the sake ofparsimony.
or 5 Factor 6 Factor 7 Factor 8 Factor 9
6
7
7
3
5
0.561
0.787
0.664
0.672
0.713
0.741
0.719
0.650
0.827
0.862
0.864
0.917
0.738
0.591
0.536
0.755
0.751
0.588
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899 885
6. C
omponentwise with Varimax raw rotation factorextraction.
Using the iterative sequence of factor analysis, nine
items (R4, R9, R10, R15, R21, C7, G4, G5, G9) were
eliminated from the POER. After this, the factor an-
alysis resulted in a final instrument of 33 items rep-
resenting 6 distinct variables for POER and 10 items
in 3 factors for PEER. The factor analysis for the
organizational and external eReadiness dimensions
further indicated that, except for one variable (C8),
which was expected to load with the commitment
variable but loaded with the governance variable, the
rest of the items uniquely load with their hypothesized
variables. Thus, for the subsequent analysis, C8 was
included within the governance construct. Table 2
presents the final factor loadings.
5.3. Convergent and discriminant validity
Convergent and discriminant validity are compo-
nents of construct validity and refer to the similarity of
the measure within itself and yet its difference from
other measures. In general, the significant loading of
the items on single factors indicates the unidimen-
sionality of each construct, while the fact that cross-
loading items were eliminated supports the discrimi-
nant validity of the instrument. However, to evaluate
the convergent and discriminant validity of the
instrument further, the correlation matrix approach
was applied.
Evidence about the convergent validity of a
measure is provided on the validity diagonal (items
Table 3
Summary of K-count to test discriminant validity
Category Number of items Num
com
item
Awareness 7 26
Human resources 2 31
Business resources 6 27
Technology resources 5 28
Commitment 5 28
Governance 8 25
Government eReadiness 4 6
Market forces eReadiness 2 8
Supporting industries eReadiness 4 6
Total 43
of the same factor) by observing the extent to which
the correlations are significantly different from zero
and large enough to encourage further test of
discriminant validity (Appendix D). The smallest
within-factor (intra-factor) correlation for each factor
were awareness, 0.32; human resources 0.77; Business
resources 0.30; technology resources 0.45; Commit-
ment 0.48; Governance, 0.37; Market forces eReadi-
ness 0.63; Government eReadiness 0.28; and
Supporting industries eReadiness 0.30. These correla-
tions are significantly higher than zero and large
enough to proceed with a discriminant validity
analysis.
To claim discriminant validity, an item should
correlate more strongly with other items of the same
variable than with items of other variables. For each of
the items, discriminant validity was tested by counting
the number of times (K) that the item correlates higher
with items of other factors than with items of its own
factor. For example, the lowest item–factor correlation
for A4 is 0.47 and this correlation is higher than A4’s
26 correlations with items of all other variables within
the POER dimension, that is, the value of K equals
zero. To provide evidence of the discriminant validity
of a measure, the value of K should be less than one-
half of the potential comparisons. Table 3 summarizes
the values of K from all the comparisons.
An examination of both Table 3 and the correlation
matrix from which the table was extracted
(Appendix D) revealed no violations of the discrimi-
nant validity in a total of 948 comparisons. In fact,
K was zero for 17 of the items; less than 3 for 81%
of them and approached the threshold point in only
ber of
parisons for each
in the scale
Maximum
acceptable K
Instances of
validity violation
13 –
14 –
13 –
14 –
14 –
12 –
6 –
4 –
6 –
–
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899886
Table 5
Instrument reliability
Research variable major
construct
Number
of items
Cronbach
alpha
Awareness 7 0.89
Human resources 2 0.87
Business resources 6 0.81
Technology resources 5 0.85
Commitment 5 0.88
Governance 8 0.91
POER 33 0.93
Market forces eReadiness 2 0.78
Government eReadiness 4 0.77
Supporting industries eReadiness 4 0.75
PEER 10 0.79
eCommerce adoption 6 –
Table 4
Predictive validity statistics summary
Research variables A11 R5 R16 R24 C9 G11 EER
Awareness 0.79 0.44 0.43 0.32 0.44 0.30 0.30
Human resources 0.38 0.61 0.34 0.33 0.38 0.34 0.16
Business resources 0.49 0.49 0.68 0.47 0.47 0.30 0.27
Technology resources 0.46 0.50 0.60 0.80 0.39 0.41 0.24
Commitment 0.46 0.53 0.38 0.43 0.73 0.61 0.27
Governance 0.49 0.55 0.41 0.48 0.64 0.76 0.26
Government eReadiness 0.06 0.24 0.17 0.29 0.06 0.19 0.47Market forces eReadiness 0.21 0.38 0.21 0.35 0.39 0.23 0.55Supporting industries eReadiness 0.13 0.25 0.19 0.26 0.10 �0.02 0.54
one case (R9). Thus, there is sufficient evidence of
both convergent and divergent validity and therefore
the instrument can be considered to generate quality
data.
5.4. Predictive validity
Predictive validity examines whether the instru-
ment distinguishes the different cases such as
those with high-perceived eReadiness from those
without it. Correlations between the developed
scales and the control variables were used to study
the predictive power of each of the constructs. Table 4
provides a summary of the correlation matrix.
All correlations between the major research constructs
and their respective control variables in the organi-
zational eReadiness and external eReadiness
dimensions were quite high and significant at the
0.05 level, thereby showing evidence of predictive
validity.
5.5. Final reliability
Table 5 shows the reliability of the final instrument
and the alpha coefficients of the individual variables.
Overall the final instrument had 6 items to oper-
ationalize eCommerce adoption; 33 items under
POER and 10 items under PEER. To accept a measure
as reliable, Cronbach alpha values of 0.80 for basic
research and 0.90 for applied research are to be widely
accepted. All the reliability coefficients satisfied the
minimum criteria. In addition, the research variables’
reliabilities were consistently close to their respective
overall reliabilities of 0.93 and 0.79 and there was very
little variation among the individual reliabilities
within each of the two dimensions. This showed that
the instrument was sufficiently reliable and could
consistently capture true score variability among
respondents.
6. The PERM
The final PERM is shown as Fig. 3 with its
instrument in Appendix E. It represents progress
towards identification, measurement, operationaliza-
tion, and validation of organizational and environ-
mental eReadiness variables that affect eCommerce
adoption in developing countries. The model is unique
because it departs from the conventional wisdom of
looking into environmental characteristics only and
also looked into internal organizational capabilities
and characteristics of businesses.
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899 887
Fig. 3. The PERM for assessing eCommerce adoption in developing countries.
6.1. A preliminary test of the model
The model was tested using data collected in South
Africa. We used multiple discriminant function
analysis. Both results for initial adoption (Wilk’s
l = 0.2; x2 = 197; d.f. = 8; F = 51.4, p < 0.0001) and
institutionalization of adoption (Wilk’s l = 0.1; x2
= 178; d.f. = 18; F = 17.1; p < 0.0000) produced
statistically significant functions. This indicates that
the model satisfactorily discriminates adopters from
non-adopters and the different levels of institutiona-
lization of eCommerce. Analysis and interpretation of
the findings have led us to the following conclusions
about eCommerce adoption in developing countries:
(1) o
rganizational factors especially the human,business and technological resources, and aware-
ness are more influential than environmental
factors in the initial adoption of eCommerce and
(2) a
s organizations adopt eCommerce practices, theadvantages from resources become less important
and environmental factors, together with commit-
ment and the governance model that organizations
install affect eCommerce institutionalization.
7. Discussions and implications
Businesses in developing countries are faced with a
number of challenges in their adoption and exploita-
tion of eCommerce. Several of the existing models of
adoption emphasize the relevance of technological,
financial, and legal infrastructure constraints. While
most countries still need to address such problems,
improvements (such as in telecommunications devel-
opment) over the last few years make consideration of
contextual constraints as sole determinants of eCom-
merce adoption untenable. Understanding eCom-
merce in developing countries therefore requires
approaches and models that are flexible enough to
capture change. The notion of eReadiness for auditing
the perceived eCommerce awareness; managerial
commitment, and internal organizational and external
contextual determinants of eCommerce provide
meaningful predictors of eCommerce adoption in
developing countries.
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899888
We constructed and empirically tested a model of
eCommerce adoption: the PERM. The underlying
theoretical perspectives allowed us to identify the
relevant eCommerce, managerial, organizational,
and contextual factors that could explain eCommerce
adoption and subsequent development. Our study
served to highlight contextual limitations that often
are taken for granted in other markets. Some
organizations might choose to accept these limita-
tions and decide to wait and see or move very
cautiously. But others with dynamic capabilities,
committed leaders and business resources might have
a very different assessment of their environment and
can decide to adopt innovative business models that
can work even within such constraints. Thus,
government and non-governmental organizations
could use the instrument to understand and locate
important factors influencing eCommerce issues in
developing countries. Our study also served to
identify business practices of firms in developing
countries that could hamper eCommerce adoption
and expansion. Thus, business mangers could use the
instrument to reflect inwards to assess their internal
organization and outwards to assess the external
environment and create a resource-acquisition
agenda to overcome both internal as well as external
limitations.
Finally, despite the steps undertaken to validate the
model and ensure its reliability, we note some
limitations. First, additional items, such as industry
specific considerations, could be introduced to
improve the coverage and reliability of the perceived
external eReadiness measures. Second, while we have
used principal component analysis, a confirmatory
analysis and a multi-country and cross-cultural
validation using other large samples gathered else-
where are essential. This increases the validation and
generalizability of this model and instrument. Sub-
sequent studies would also allow assessing the test–
retest reliability of the instrument.
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Appendix A. Correlations for inter-observer reliability
All marked correlations are significant at p < 0.01000; N = 100 (case wise deletion of missing data)
Rater1 Rater2 Rater3 Rater4 Rater5 Rater6 Rater7 Rater8 Rater9 Rater10 Rater11 Rater12 Rater13 Rater14 Rater15 Rater16 Average
Rater1 1.00
Rater2 0.53 1.00
Rater3 0.38 0.66 1.00
Rater4 0.53 0.66 0.50 1.00
Rater5 0.49 0.66 0.55 0.57 1.00
Rater6 0.46 0.69 0.74 0.59 0.62 1.00
Rater7 0.32 0.64 0.69 0.50 0.51 0.73 1.00
Rater8 0.65 0.66 0.67 0.58 0.60 0.75 0.62 1.00
Rater9 0.56 0.76 0.66 0.73 0.60 0.64 0.61 0.70 1.00
Rater10 0.36 0.78 0.72 0.52 0.55 0.73 0.70 0.65 0.64 1.00
Rater11 0.52 0.58 0.71 0.61 0.62 0.61 0.45 0.65 0.64 0.51 1.00
Rater12 0.58 0.61 0.43 0.62 0.54 0.47 0.46 0.56 0.63 0.53 0.49 1.00
Rater13 0.42 0.47 0.66 0.39 0.79 0.64 0.51 0.61 0.48 0.53 0.64 0.38 1.00
Rater14 0.39 0.67 0.79 0.49 0.59 0.83 0.72 0.69 0.62 0.72 0.67 0.53 0.66 1.00
Rater15 0.50 0.69 0.60 0.70 0.64 0.61 0.72 0.65 0.70 0.56 0.59 0.59 0.46 0.61 1.00
Rater16 0.57 0.69 0.63 0.58 0.66 0.70 0.49 0.77 0.72 0.61 0.71 0.49 0.58 0.66 0.67 1.00
Average 0.66 0.85 0.82 0.76 0.79 0.85 0.76 0.85 0.84 0.79 0.79 0.70 0.73 0.84 0.81 0.83 1.00
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899890
Appendix B. Initial instrument used in the pilot study
Which one best describes your current eCommerce status
1. Not connected to the Internet, no e-mail.
2. Connected to the Internet with e-mail but no web site.
3. Static eCommerce, that is publishing basic company information on the web without any interactivity.
4. Interactive eCommerce, that is accepting queries, e-mail; and form entry from users.
5. Transactive eCommerce, that is online selling and purchasing of products and services including customer service.
6. Integrated eCommerce, that is the web site is integrated with suppliers, customers and other back office systems
allowing most of the business transactions to be conducted electronically.
On the scale of 1 (Strongly Agree) to 5 (Strongly Disagree), indicate your level of agreement with the following
statements
Item ID Description
A1 Our business considers that eCommerce is a North American trend not yet applicable to
our environment
A2 eCommerce applications are becoming common with our partner organizations
A3 Businesses with whom our organization is competing are implementing eCommerce
and e-business
A4 Our business recognizes the opportunities and threats enabled by eCommerce
A5 Our organization has a good understanding of eCommerce business models that are applicable
to our business
A6 We have a good understanding of eCommerce application solutions that are applicable
to our business
A7 We have a clear understanding of the potential benefits of eCommerce to our business
A8 Our organization believes that the gain from eCommerce outweighs its cost
A9 We consider that eCommerce has a tremendous impact on the way business is to be conducted
in our industry
A10 We believe that businesses in our industry that are not adopting eCommerce and e-business
will be at a competitive disadvantage
A11 In general our business has adequate awareness about eCommerce
R1 Most of our employees are computer literate
R2 Most of our employees have unrestricted access to computers
R3 Most of our employees have unrestricted Internet access
R4 We have created clearly defined, eCommerce career paths within our organization
R5 Our business has the necessary technical, managerial and other skills to implement eCommerce
R6 Our people are open and trusting with one another
R7 Communication is very open in our organization
R8 Our organization exhibits a culture of enterprise wide information sharing
R9 We have a policy that encourages grass roots eCommerce initiatives
R10 We are aggressive in experimenting with new technologies
R11 Failure can be tolerated in our organization
R12 Our organization is capable of dealing with rapid changes
R13 We have strong relationships with our suppliers and customers
R14 We have sufficient experience with network based applications
R15 We sufficiently invest in our eCommerce projects
R16 We have sufficient business resources to implement eCommerce
R17 Our organization is well computerized with LAN and WAN
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899 891
R18 Our eCommerce solutions are interactive and allow two way communication
R19 Our existing systems allow us to make changes for eCommerce applications
R20 We have high bandwidth connectivity to the Internet
R21 We have an established enterprise-wide IT infrastructure
R22 Our existing systems are flexible
R23 Our existing are customizable to our customers’ needs
R24 We have adequate technological capability for eCommerce implementations
C1 Our business has a clear vision on eCommerce
C2 Our vision of eCommerce activities is widely communicated and understood throughout our company
C3 Our eCommerce implementations are strategy-led
C4 All our eCommerce initiatives have champions
C5 Senior management champions our eCommerce initiatives and implementations
C6 We have staffed our eCommerce projects with the proper resources to achieve their goals
C7 We have an eCommerce mind-set throughout all levels of management
C8 Our employees at all levels support our eCommerce initiatives
C9 Our business demonstrates adequate level of commitment in eCommerce implementations
G1 Roles, responsibilities and accountability are clearly defined within each eCommerce initiative
G2 eCommerce accountability is extracted via on-going responsibility
G3 Decision-making authority has been clearly assigned for all eCommerce initiatives
G4 Our eCommerce managers are granted the authority to make decisions and take actions as
opportunities arise
G5 Our managers demonstrate readiness for change
G6 We thoroughly analyze the possible changes to be caused in our organization, suppliers, partners,
and customers as a result of each eCommerce implementation
G7 We follow a systematic process for managing change issues as a result of eCommerce
implementations
G8 We define a business case for each eCommerce implementation or initiative
G9 There is smooth relationship between the business and internal IT organization
G10 We have clearly defined metrics for assessing the impact of our eCommerce initiatives
G11 We believe that we have an effective governance model in our eCommerce implementations
MFeR1 We believe that our customers are ready to do business on the Internet
MFeR2 We believe that our business partners are ready to conduct business on the Internet
GVeR1 Our business considers Internet as a safe environment for conducting business
GVeR2 We believe that there are effective laws to protect consumer privacy
GVeR3 We believe that there are effective laws to combat cyber crime
GVeR4 We believe that the legal environment is conducive to conduct business on the Internet
GVeR5 The government demonstrates strong commitment to promote eCommerce
GVeR6 Government regulations allow electronic settlement of eCommerce transactions
SIeR1 Secure electronic transaction (SET) and/or secure electronic commerce environment (SCCE)
services are easily available and affordable
SIeR2 The telecommunication infrastructure is reliable and efficient
SIeR3 The technology infrastructure of commercial and financial institutions is capable of supporting
eCommerce transactions
SIeR4 We feel that there is efficient and affordable support from the local IT industry to support our move
on the Internet
EER In general we consider the local environment is ready for eCommerce
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899892
Appendix C. Item analysis: corrected item–total correlation plotted in descending order
Mean if deleted Variance if deleted S.D. if deleted Item-total correlated Alpha if deleted
Organizational eReadinessa
R10 118.7 448.1 21.2 0.64 0.93
A5 119.0 450.0 21.2 0.62 0.93
C7 118.1 459.8 21.4 0.61 0.93
C1 118.9 459.5 21.4 0.59 0.93
R14 119.1 450.8 21.2 0.58 0.93
C3 119.2 460.1 21.4 0.58 0.93
R9 118.0 449.3 21.2 0.58 0.93
A9 119.5 454.0 21.3 0.58 0.93
R8 118.7 452.9 21.3 0.57 0.93
R4 117.7 450.9 21.2 0.57 1.63
C2 118.5 457.9 21.4 0.56 0.93
R23 118.8 452.0 21.3 0.56 0.93
G7 118.8 460.9 21.5 0.55 0.93
G3 118.8 461.0 21.5 0.55 0.93
C5 119.3 461.0 21.5 0.54 0.93
A4 119.3 457.9 21.4 0.54 0.93
G1 118.7 462.5 21.5 0.53 0.93
R1 119.0 449.1 21.2 0.53 0.93
C4 119.0 461.3 21.5 0.51 0.93
G5 118.8 462.4 21.5 0.51 0.93
C8 118.2 462.6 21.5 0.50 0.93
G2 118.7 464.7 21.6 0.50 0.93
R7 119.0 457.8 21.4 0.50 0.93
R15 118.7 462.9 21.5 0.49 0.93
G6 118.7 462.6 21.5 0.49 0.93
R2 119.1 449.3 21.2 0.49 0.93
G8 119.1 460.6 21.5 0.49 0.93
R6 118.9 457.9 21.4 0.48 0.93
R17 119.6 455.5 21.3 0.47 0.93
R12 118.9 461.1 21.5 0.45 0.93
R22 119.3 453.9 21.3 0.45 0.93
G10 118.3 463.8 21.5 0.45 0.93
A10 119.3 458.7 21.4 0.43 0.93
G9 119.0 466.8 21.6 0.43 0.93
A7 119.3 462.0 21.5 0.41 0.93
R21 118.9 467.9 21.6 0.41 0.93
G4 118.8 465.9 21.6 0.40 0.93
A2 119.2 462.3 21.5 0.39 0.93
R20 118.8 456.8 21.4 0.39 0.93
A3 119.1 461.8 21.5 0.38 0.93
R11 118.6 459.7 21.4 0.38 0.93
R19 118.7 459.1 21.4 0.37 0.93
A8 118.7 462.4 21.5 0.33 0.93
R3 118.2 456.5 21.4 0.33 0.93
A1 119.6 463.8 21.5 0.31 0.93
R13 119.5 471.5 21.7 0.19 0.93
External eReadinessb
GVER3 30.8 30.5 5.5 0.56 0.76
GVER4 31.2 31.0 5.6 0.50 0.77
SIER1 31.5 31.2 5.6 0.48 0.77
GVER2 31.2 30.6 5.5 0.47 0.77
SIER4 32.1 30.7 5.5 0.47 0.77
SIER2 31.4 29.9 5.5 0.46 0.77
SIER3 32.3 31.4 5.6 0.41 0.77
MFER1 31.3 31.4 5.6 0.41 0.77
GVER5 31.3 31.9 5.6 0.41 0.77
MFER2 31.6 31.3 5.6 0.39 0.78
GVER6 31.7 32.2 5.7 0.34 0.78
a Summary for scale: mean = 121.51, S.D. = 21.96, valid N = 150, Cronbach alpha: 0.93, standardized alpha: 0.94.b Summary for scale: mean = 34.63, S.D. = 6.09, valid N = 150, Cronbach alpha: 0.78, standardized alpha: 0.79.
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Appendix D. Correlation matrix
MFER1 MFER2 GVER2 GVER3 GVER4 GVER5 SIER1 SIER2 SIER3 SIER4
MFER1 1.00MFER2 0.64 1.00GVER2 0.21 0.26 1.00GVER3 0.24 0.19 0.79 1.00GVER4 0.04 0.10 0.45 0.59 1.00GVER5 0.15 0.08 0.35 0.28 0.41 1.00SIER1 0.21 0.28 0.22 0.24 0.19 0.27 1.00SIER2 0.19 0.09 0.15 0.24 0.42 0.08 0.34 1.00SIER3 0.12 0.17 0.09 0.11 0.16 0.24 0.34 0.44 1.00SIER4 0.38 0.41 0.18 0.23 0.20 0.14 0.32 0.30 0.45 1.00
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4Appendix D. (Continued )
A2 A3 A4 A5 A7 A9 A10 R1 R2 R6 R7 R8 R9 R1 R1R2 R14 R17 R20 R22 R23 C1 C2 C3 C4 C5 C8 G1 G2 G3 G6 G7 G8 G10
A2 1.00
A3 0.68 1.00
A4 0.52 0.50 1.00
A5 0.36 0.39 0.67 1.00
A7 0.32 0.34 0.54 0.59 1.00
A9 0.46 0.42 0.62 0.54 0.50 1.00
A10 0.40 0.37 0.47 0.47 0.43 0.73 1.00
R1 0.25 0.21 0.33 0.31 0.24 0.31 0.21 1.00
R2 0.13 0.130 0.25 0.27 0.23 0.28 0.18 0.77 1.00
R6 0.16 0.06 0.21 0.280 0.18 0.28 0.12 0.40 0.37 1.00
R7 0.20 0.07 0.16 0.22 0.16 0.32 0.19 0.36 0.35 0.79 1.00
R8 0.21 0.19 0.210 0.26 0.16 0.35 0.28 0.49 0.50 0.62 0.65 1.00
R9 0.23 0.25 0.37 0.49 0.42 0.36 0.35 0.29 0.24 0.35 0.35 0.49 1.00
R11 0.19 0.16 0.20 0.18 0.05 0.12 0.05 0.17 0.23 0.32 0.34 0.37 0.36 1.00
R12 0.09 �0.01 0.27 0.26 0.22 0.18 0.09 0.31 0.27 0.36 1.00 0.43 0.360 0.34 1.00
R14 0.20 0.17 0.37 039 0.26 0.39 0.24 0.37 0.32 0.34 0.18 0.35 0.38 0.26 0.31 1.00
R17 0.20 0.30 0.38 0.36 0.29 0.28 0.19 0.35 0.34 0.27 0.13 0.23 0.28 0.14 0.11 0.65 1.00
R20 0.21 0.17 0.26 0.30 0.27 0.24 0.18 0.15 0.14 0.27 0.19 0.33 0.42 0.16 0.11 0.44 0.40 1.00
R22 0.22 0.27 0.27 0.21 0.14 0.27 0.26 0.19 0.22 0.16 0.11 0.22 0.28 0.12 0.13 0.46 0.61 0.49 1,00
R23 0.17 0.22 0.34 0.36 0.22 0.28 0.15 0.25 0.24 0.27 0.21 0.24 0.33 0.26 0.27 0.45 0.51 0.45 0.55 1.00
C1 0.22 0.23 0.42 0.60 0.31 0.52 0.42 0.22 0.37 0.30 0.40 0.38 0.46 0.27 0.33 0.27 0.13 0.18 0.26 0.47 1.00
C2 0.34 0.32 0.34 0.50 0.19 0.51 0.37 0.31 0.33 0.30 0.44 0.42 0.33 0.18 0.25 0.28 0.21 0.15 0.43 0.36 0.72 1.00
C3 0.23 0.20 0.38 0.48 0.36 0.42 0.39 0.33 0.33 0.33 0.43 0.45 0.44 0.20 0.24 0.34 0.23 0.27 0.23 0.47 0.73 0.56 1.00
C4 0.21 0.22 0.21 0.31 0.18 0.32 0.31 0.21 0.30 0.15 0.29 0.32 0.36 0.22 0.27 0.32 0.26 0.26 0.43 0.26 0.48 0.50 0.50 1.00
C5 0.06 0.11 0.14 0.33 0.14 0.39 0.29 0.23 0.29 0.24 0.36 0.41 0.37 0.16 0.28 0.34 0.13 0.29 0.32 0.30 0.59 0.54 0.56 0.71 1.00
C8 0.33 0.26 0.23 0.43 0.17 0.32 0.19 0.42 0.28 0.31 0.37 0.34 0.28 0.19 0.38 0.22 0.09 0.13 0.21 0.24 0.46 0.51 0.37 0.36 0.45 1.00
G1 0.22 0.27 0.22 0.36 0.09 0.31 0.24 0.29 0.23 0.09 0.25 0.20 0.31 0.18 0.43 0.31 0.19 0.04 0.30 0.31 0.51 0.49 0.41 0.55 0.49 0.52 1.00
G2 0.09 0.18 0.21 0.40 0.12 0.28 0.15 0.25 0.24 0.18 0.31 0.30 0.22 0.17 0.46 0.29 0.14 0.18 0.21 0.44 0.41 0.41 0.40 0.40 0.51 0.41 0.71 1.00
G3 0.17 0.26 0.17 0.35 0.09 0.27 0.23 0.36 0.26 0.21 0.31 0.29 0.11 0.18 0.25 0.37 0.30 0.10 0.39 0.34 0.43 0.56 0.42 0.55 0.62 0.45 0.64 0.58 1.00
G6 0.16 0.31 0.28 0.37 0.05 0.37 0.17 0.35 0.04 0.30 0.32 0.32 0.27 0.24 0.41 0.47 0.26 0.09 0.21 0.31 0.33 0.25 0.37 0.25 0.32 0.43 0.49 0.45 0.50 1.00
G7 0.24 0.22 0.33 0.38 0.18 0.33 0.25 0.28 0.15 0.29 0.37 0.26 0.29 0.18 0.34 0.41 0.24 0.13 0.39 0.33 0.46 0.49 0.39 0.51 0.45 0.58 0.64 0.51 0.58 0.58 1.00
G8 0.13 0.2 0.21 0.26 0.01 0.29 0.11 0.30 0.21 0.22 0.25 0.31 0.26 0.11 0.31 0.38 0.26 0.20 0.38 0.36 0.43 0.43 0.46 0.50 0.50 0.43 0.60 0.54 0.53 0.58 0.63 1.00
G10 0.21 0.18 0.36 0.24 0.01 0.32 0.10 0.27 0.30 0.20 0.25 0.18 0.25 0.17 0.23 0.28 0.25 �0.12 0.34 0.32 0.44 0.50 0.39 0.41 0.37 0.41 0.55 0.42 0.47 0.37 0.55 0.57 1.00
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899 895
Appendix E. The final instrument
I. Awareness
A1. Our organization is aware of eCommerce implementations of our partner organizations
A2. Our organization is aware of our competitors’ eCommerce and e-business implementations
A3. Our business recognizes the opportunities and threats enabled by eCommerce
A4. Our organization understands eCommerce business models that can be applicable to
our business
A5. We understand the potential benefits of eCommerce to our business
A6. Our organization has thought about whether or not eCommerce has impacts on the way
business is to be conducted in our industry
A7. Our organization has considered whether or not businesses in our industry that fail to adopt
eCommerce and e-business would be at a competitive disadvantage
II. Human resources
HR1. Most of our employees are computer literate
HR2. Most of our employees have unrestricted access to computers
III. Business resources
BR1. Our people are open and trusting with one another
BR2. Communication is very open in our organization
BR3. Our organization exhibits a culture of enterprise wide information sharing
BR4. We have a policy that encourages grass roots eCommerce initiatives
BR5. Failure can be tolerated in our organization
BR6. Our organization is capable of dealing with rapid changes
IV. Technological resources
TR1. We have sufficient experience with network based applications
TR2. We have sufficient business resources to implement eCommerce
TR3. Our organization is well computerized with LAN and WAN
TR4. We have high bandwidth connectivity to the Internet
TR5. Our existing systems are flexible
TR6. Our existing systems are customizable to our customers’ needs
V. Commitment
C1. Our business has a clear vision on eCommerce
C2. Our vision of eCommerce activities is widely communicated and understood throughout
our company
C3. Our eCommerce implementations are strategy-led
C4. All our eCommerce initiatives have champions
C5. Senior management champions our eCommerce initiatives and implementations
VI. Governance
G1. Roles, responsibilities and accountability are clearly defined within each eCommerce initiative
G2. eCommerce accountability is extracted via on-going responsibility
G3. Decision-making authority has been clearly assigned for all eCommerce initiatives
G4. We thoroughly analyze the possible changes to be caused in our organization, suppliers, partners,
and customers as a result of each eCommerce implementation
G5. We follow a systematic process for managing change issues as a result of eCommerce
implementations
A. Molla, P.S. Licker / Information & Management 42 (2005) 877–899896
Appendix E. (Continued )
G6. We define a business case for each eCommerce implementation or initiative
G7. We have clearly defined metrics for assessing the impact of our eCommerce initiatives
G8. Our employees at all levels support our eCommerce initiatives
VII. Market forces eReadiness
MFeR1. We believe that our customers are ready to do business on the Internet
MFeR2. We believe that our business partners are ready to conduct business on the Internet
VIII. Government eReadiness
GVeR1. We believe that there are effective laws to protect consumer privacy
GVeR2. We believe that there are effective laws to combat cyber crime
GVeR3. We believe that the legal environment is conducive to conduct business on the Internet
GVeR4. The government demonstrates strong commitment to promote eCommerce
IX. Supporting industries eReadiness
SIeR1. The telecommunication infrastructure is reliable and efficient to support eCommerce
and eBusiness
SIeR2. The technology infrastructure of commercial and financial institutions is capable
of supporting eCommerce transactions
SIeR3. We feel that there is efficient and affordable support from the local IT industry to
support our move on the Internet
SIeR4. Secure electronic transaction (SET) and/or secure electronic commerce environment
(SCCE) services are easily available and affordable
X. eCommerce adoption
Which one of the following best describes your current eCommerce status? Please choose
only one option
EAD1. Not connected to the Internet, no e-mail
EAD2. Connected to the Internet with e-mail but no web site
EAD3. Static Web, that is publishing basic company information on the web without any interactivity
EAD4. Interactive web presence, that is accepting queries, e-mail; and form entry from users
EAD5. Transactive web, that is online selling and purchasing of products and services including
customer service
EAD6. Integrated web, that is the web site is integrated with suppliers, customers and other back
office systems allowing most of the business transactions to be conducted electronically
Scale: 1, strongly agree; 2, agree; 3, neutral; 4, disagree; 5, strongly disagree.
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Alemayehu Molla is a lecturer in Infor-
mation Systems at the Institute for Devel-
opment Policy and Management, the
University of Manchester. He received
his PhD in Information systems from
the University of Cape Town, and MSc
in information science; BA in Business
Management and diploma in Computer
Science form the Addis Ababa Univer-
sity. His research interests include
eCommerce in developing countries, e-trading, IT adoption and
implementations, diffusion, use and impact of the Internet in Africa
and the Middle East. His research has been published in the
Electronic Commerce Research Journal, Journal of Information
Systems Management, Information Technologies and International
Development, and Journal of IT for Development.
Paul S. Licker is Professor and Chair of
the Department of Decision and Informa-
tion Sciences, School of Business Admin-
istration, Oakland University, Rochester,
MI, USA. He received his PhD, MSEE,
and BA from the University of Pennsyl-
vania. His research interests include eco-
nomic effects of information technology
adoption, IT for competitive advantage,
and employment of IT professionals. He
is the author of two textbooks, several trade books, over fifty
published research articles and a similar number of delivered and
invited research papers. He is senior editor of the Journal of
Information Technology Management.