ECommerce Adoption in Developing Countries Model And

www.elsevier.com/locate/dsw

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 eCommerce
adoption 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 show
the 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 (managerial
imperative attribute); and

(3) k
ey organizational components, such as its
resources, 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


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


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]


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:


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 0.5
on 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 of
parsimony.

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


6. C
omponentwise with Varimax raw rotation factor
extraction.

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 –

–


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.


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, the
advantages 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.


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


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


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


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


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


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.



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