Post on 24-Feb-2023
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Author version
E-readiness assessment of non-profit ICT SMEs in a developing country: the case of Iran
Mohammad Fathian , Peyman Akhavan , and Maryam Hoorali
Citation: Fathian, Mohammad, Akhavan, Peyman and Maryam Hoorali (2008), E-readiness assessment of non-profit ICT SMEs in a developing country: The case of Iran, Technovation, Vol. 28, No. 9, pp. 578-590.
Abstract
We are experiencing a new kind of commerce in the recent era, known as e-commerce, which
considers Information and Communication Technology (ICT) as the main enabler of commerce.
Considering Small and Medium Enterprises (SMEs) as micro elements of society and part of
macro economy, ICT becomes crucial for e-commerce companies to attain sustainable
competitiveness.
Towards this, organizations must re-evaluate every aspect of their strategies and quickly adapt
their working models to incorporate e-commerce as an essential factor for their success.
SMEs are critical to the economies of all countries, and specially the developing ones. They
cannot be left behind and many of them are already demonstrating their entrepreneurship
strength by grasping the opportunities offered by ICT.
E-readiness assessment is an evaluation tool that can be used for measuring the diffusion rate of
ICT. However, critical issues for e-readiness assessment of SMEs have not been systematically
investigated for developing countries. Some existing studies have derived their critical factors
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from macro perspectives at country level and have not considered the important factors at micro
level for SMEs in an integrated way This paper aims to bridge this gap.
This research paper first reviews the e-readiness assessment models proposed for countries at
macro scale and then identifies the critical factors for SMEs e-readiness assessment. This is
achieved through factor analysis at the micro perspective of some Iranian non-profit ICT SMEs.
The extracted factors are organizational features, ICT infrastructures, ICT availability and
security and legal environment.
This study is probably the first to provide a perspective of critical issues for e-readiness
assessment in SMEs based on macro models in a developing country. It gives valuable insight
and guidelines which hopefully will help the managers in developing countries to consider the
critical issues for e-readiness assessment of their organization in an effective way.
Key words: Information and Communication Technology, E-readiness Assessment, Small and
Medium Enterprise, Developing country, Iran.
Introduction
E-readiness can mean different things to different people, in different contexts, and for different
purposes (Peters, 2001). Thus, it is important to define e-readiness in the context of this paper.
E-readiness of an SME is defined here as the ability of an SME to successfully adopt, use and
benefit from information technologies such as e-commerce.
Information Technology (IT) is a term that generally covers the harnessing of electronic
technology for the information needs of a business at all levels. It utilizes computer-based
systems as well as telecommunication technologies for the storage, processing and
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communication (Anderson, 1990; Claus and Schwill, 1992). While an information system (IS) is
a group of formal processes that together collect, retrieve, process, store and disseminate
information for the purpose of facilitating, planning, control, coordination and decision-making
in organizations, IT on the other hand provides the technical solutions identified in an IS,
including the networks, hardware and software (Grainger-Smith and Oppenheim, 1994). IT today
is basically electronics based on integrated circuits or silicon chips. Hanson and Narula further
identified two major forms of IT as Telematics (meaning ‘big media’) and Ethnotronics
(meaning ‘small media’). Telematics consist of technologies such as computers, telephone,
satellites, television, radio, video and those that rely on large-scale infrastructures. Ethnotronics
include technologies such as typewriters, audio cassette recorders, fax machines, paper copiers,
calculators, digital watches and other more personal types of technology (Hanson and Narula,
1990).
Morgan et al (2006) identified three main features of ICT initiatives in SMEs, including the
operation and structure, the success from a participant’s perspective and the development of a
model of blended learning to support and continue developing the program.
IT creates many new inter-relationships among businesses, expands the scope of industries in
which a company must compete to achieve competitive advantage. Information systems and
technology allow companies to coordinate their activities in distant geographic locations. Thus,
one could say that IT is also changing the way companies operate (Porter and Miller, 1985).
Grandon and Pearson (2004) conducted a survey between managers and owners of some SMEs
and identified four factors that influence e-commerce adoption including organizational
readiness, external pressure, perceived ease of use and perceived usefulness.
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IT has an essential role in supporting the current and common operations in most of the
contemporary organizations. Nowadays, the time cycle of these operations keep shrinking. The
risk of missing opportunities that negatively impact businesses is very high. In this situation,
because of the increasing rate of changes, the role of IT becomes much more profound.
The potential contribution of ICT to improving the competitiveness of SMEs has long been
recognized (Morgan et al, 2006).
Information and communication technologies are today generally recognized as one of the
central forces in the transition toward a new economic system.
During the height of the techno-enthusiasm that underpinned the dot.com phenomenon, this
transition tended to be identified with e-business which mostly meant the ‘transfer’ of existing
business processes onto an online environment (Maksoud and Aziz Youssef, 2003). A number of
studies have been conducted for assessing a country’s e-readiness, addressing the preparations
for using IT and entering the digital world. Assessments were based on a variety of indicators
such as e-connectivity, human capital, business climate, leadership and others.
Bayo-Moriones and Lera-Lopez (2007) explored ICT adoption by looking at five factors such as
environment, firm structural characteristics, human capital, competitive strategy, and internal
organization. Quantitative and qualitative indices were devised and used to evaluate and rank
countries on the e-readiness scale.
Oyelaran-Oyeyinka and Lal (2006a) found evidence of ICT investment and learning processes in
some selected developing countries such as India, Nigeria, and Uganda. Their study showed that
there is clear evidence of increasing complexity in adapting and using ICT in developing
countries firms. In addition to the above findings Oyelaran-Oyeyinka and Lal (2006b) identified
that technological progress requires skills upgrading through explicit learning of the new
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technologies; and finally, firm performance is highly associated with learning capabilities, levels
of technology, and a host of firm-level knowledge, skills and experience.
Haj Bakry (2003) cited that e-readiness assessments, for various countries, are associated with
the investigation of their state of readiness for digital integration which includes ICT
infrastructure and applications of e-government, e-commerce, e-learning, and other e-
applications.
Tarantolaand Gatelli (2006) also developed a methodology for sensitivity analysis and then
tested that technique on a case study involving the construction of a composite indicator of e-
business readiness of European enterprises.
Although there are many studies looking at e-readiness of countries, few studies have attempted
to evaluate e-readiness from a micro perspective. In particular, a relatively small number of
studies have undertaken assessments of the e-commerce adoption in SMEs in the United States,
Australia, some European and Asian countries (www.bridges.org).
Ruikar et al (2006) developed an e-readiness assessment prototype application for construction
companies. It assessed the e-readiness of construction companies in terms of their management,
people, processes and technology perspective.
Tan et al (2007) extended the e-readiness Model of Molla (Molla et al,2005a; Molla et al, 2005b)
which had been implemented between 150 businesses from South Africa as depicted in Figure 1,
to e-commerce in China in an empirical study of 134 Chinese SMEs. Findings showed that the
dominant inhibiting factors in China are: restricted access to computers; lack of internal trust;
lack of enterprise-wide information sharing; intolerance towards failure and incapability of
dealing with rapid change.
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The main objective of this research is to present a model that assesses the e-readiness of non-
profit SMEs in a developing country, particularly their preparedness for adoption of electronic
commerce.
Figure 1. E-readiness Model of Molla et al, 2005a
E-readiness models and concepts
Over the past last years, a number of models for e-readiness assessment of countries at macro
level have been developed by different organizations. On the surface, each model gauges how
ready a society or economy is to benefit from information technology and e-commerce. On
closer examination, the models use varying definitions for e-readiness and different methods for
measurement. These e-readiness assessment models are mainly grouped in four categories
according to bridges website (www.bridges.org):
1. Ready-to-use tools: There are few ready-to-use tools freely available on the web.
2. Case studies: There are numerous case studies assessing specific countries’ e-readiness,
and many of these could be used as bases for e-readiness tools.
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3. Third party surveys and reports: These reports aim to rank and rate countries based on
various measures held to indicate e-readiness.
4. Other e-readiness assessment models: In addition to the formal tools and surveys
described above, there is a range of other frameworks such as digital divide reports and
position papers that can be similarly used for e-readiness assessment.
Table 1 summarizes some important e-readiness assessment models.
Table1- e-readiness assessment models
Model Name Author Website Focus
APEC
Electronic Commerce Steering Group-The Asian
Pacific Economic Cooperation (APEC)
www.brigge
s.org
E-Commerce Readiness
CSPP Computer Systems Policy Project www.cspp.o
rg
Existing Infrastructure
CID
The Center for International Development at
Harvard and IBM.
www.readin
essguide.org
Society
McConnell
International
McConnell International prepared this report in
collaboration with World Information
Technology and Services Alliance (WITSA)
www.brigge
s.org
Infrastructure, Digital
Economy, Education and
Government
MQ Mosaic Group http://mosai
c.unomaha.e
du/gdi.html
Internet
CIDCM
University of Maryland, Center for International
Development and Conflict Management
http://www.
bsos.umd.ed
u/cidcm/proj
ects/leland.h
tm
Qualitative Assessment based
on past performance and
current internet pervasiveness
EIU The Economist Intelligence Unit http://www. E-Business Readiness
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ebusinessfor
um.com/ind
ex.asp
IDC International Data Corporation (World Times /
IDC's Information Society Index)
www.brigge
s.org
Infrastructure
KAM World Bank, Knowledge Assessment Matrix http://www1
.worldbank.
org/gdln/ka
m.htm
K-Economy
NRI Center for International Development (CID) at
Harvard and the World Economic Forum
http://www.
cid.harvard.e
du/cr/gitrr_0
30202.html
Infrastructure, E-Society,
Policies, Digital Economy,
Education and Government
ITU International Telecommunications Union's
Internet Country Case Studies
http://www.i
tu.int/ITU-
D/ict/cs
Telecommunications
Sida
Swedish International Development Cooperation
Agency (Sida)
http://www.s
ida.se
Mainly SWOT analysis of a
Nation
USAID U.S. Agency for International Development http://www.
usaid.gov/re
gions
Access, Government, People
There are several definitions for e-readiness. The CSPP model defines an ‘e-ready’ community
as one that has: high-speed access in a competitive market; with constant access and application
of ICTs in schools, government offices, businesses, healthcare facilities and homes; user privacy;
online security and government policies which are favorable to promoting connectedness and
using the network. The Asian Pacific Economic Cooperation (APEC) group defines a country as
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e-ready if it demonstrates e-commerce, has free trade, industry self-regulation, ease of exports
and compliance with international standards and trade agreements. McConnell International
defines e-readiness as the capacity of nations to participate in the digital economy. Finally, the
Center for International Development (CID) at Harvard University, the most acclaimed
institution in e-readiness research, defines an ‘e-ready’ society as one that has the necessary
physical infrastructure (high bandwidth, reliability, and affordable prices); integrated current
ICTs throughout businesses (e-commerce, local ICT sector), communities (local content, many
organizations online, ICTs used in everyday life, ICTs taught in schools), and the government (e-
government); strong telecommunications competition; independent regulation with a
commitment to universal access and no limits on trade or foreign investment.
While the above mentioned models focus on assessing readiness of countries, governments and
policies for adopting information technologies, some others such as IQ Net Readiness Scorecard
assess the readiness to adopt other different concepts. IQ Net Readiness Scorecard was
developed by CISCO and is a Web-based application that assesses an organization's ability to
migrate to an Internet Business model. It is based on the book Net Ready (Hartman et al, 2000),
which gauges the readiness of IT service providers.
E-readiness of small and medium enterprises
There are a number of definitions of what constitutes an SME. Some of these definitions are
based on quantitative measures such as staffing levels, turnover or assets, while others employ a
qualitative approach. Some researchers suggest that any description or definition must include a
quantitative component that takes into account staff levels, turnover, assets together with
financial and non-financial measurements, but the description must also include a qualitative
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component that reflects how the business is organized and how it operates (MacGregor and
Vrazalic, 2004). One of the SME definitions is shown in Table 2 (Damaskopoulos and Evgeniou,
2003).
Table2- Definition of SMEs
Enterprise category Headcount Turnover or Balance sheet total
medium-sized < 250 ≤ € 50 million ≤ € 43 million
Small < 50 ≤ € 10 million ≤ € 10 million
micro < 10 ≤ € 2 million ≤ € 2 million
Until the mid-seventies, SMEs had a minor role in economic development due to the dominance
of the mass production paradigm in industry. After this period, this paradigm was increasingly
challenged, leading to large firms’ fragmentation, unemployment growth and creation of new
SMEs (Acs, 1992). Empirical studies show a clear trend towards reduction of size of
manufacturing firms in developed countries. Possible reasons for this are the diffusion of flexible
modes of production and the downsizing of large firms (Acs, 1992; Khan and Khan, 1992).
There are two main findings in the literature that have implications for economic policy
concerning SMEs. The first is that the nature of innovation adoption differs according to the size
of the firm. The second is that clusters of small firms or industrial districts can be important for
regional development (La Rovere, 1998).
As Rothwell and Dodgson (1993) suggest, both SMEs and large firms have some advantages in
innovation adoption, but these advantages differ. While large firms have material advantages,
due to their greater capability to support Research and Development (R&D), SMEs have
behavioral advantages that stem from their greater flexibility and ability to adapt changes in the
market.
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SMEs can be classified in four different types, according to their market sector, their location,
their prevalent innovation rate and their organization. SMEs can be organized individually in
competitive markets with low innovation rates. They can also be organized individually in highly
dynamic industries with high innovation rates. On the other hand, SMEs can be organized as
production cooperatives (clusters), or in networks under the dominance of a large firm. Each type
of SME has specific contributions to economic growth, and will accordingly perceive IT in
different ways. The first type perceives IT as a cost reduction tool, while the second perceives IT
as a business opportunity. The third and the fourth use IT in their productive processes and in
their relations with the other firms, thus improving their interface with the market (La Rovere,
1998).
The differences between SMEs and their larger counterparts are highlighted even more when
their approaches to IT are considered. Khan and Khan (1992) suggest that most SMEs avoid
sophisticated software and applications. This view is supported by studies carried out by Chen
(1993), Cragg and King (1993), Holzinger and Hotch (1993) and Delvecchio (1994). In addition,
the SMEs locations are also important. Gillespie et al. (1995) noted that the use of IT
applications can vary in different regions of a country.
A combined study of Danish, Irish and Greek SMEs carried out in the early 1990’s by Neergaard
(1992) concluded that there were four main reasons for the acquisition of IT by SMEs. These
were increased productivity, streamlining work procedures, better client service and better record
keeping. Fink and Tjarka (1994) in a study of Australian executives described their three reasons
for IT acquisition as ‘doing the right thing’, ‘doing things right’ and ‘improving the bottom line’.
Auger and Gallaugher (1997) noted that improvement in customer services and improvement to
internal control of the business were strong criteria for e-commerce acquisition in SMEs. The
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strong desire for control was also noted in studies carried out by Reimenschneider and Mykytyn
(2000), Poon & Joseph (2001) and Domke-Damonte and Levsen (2002). A number of studies
have been found that some SMEs have adopted e-commerce following customer pressure (Power
and Sohal , 2002).
In our view, SMEs’ e-readiness is the ability of an SME to successfully adopt, use and benefit
from information technologies (IT) such as e-commerce. It is related to the level of IT
acquisition or adoption (especially e-commerce) by them. Many other studies have attempted to
describe the factors influencing IT adoption in SMEs. For example, Iacovou et al. (1995) studied
factors influencing the adoption of electronic data interchange (EDI) by seven SMEs in different
industries, they included perceived benefits, organizational readiness, and external pressure. To
measure perceived benefits they used awareness of both direct and indirect benefits. Variables
measuring organizational readiness were the financial and technological resources. In order to
measure external pressure, they considered competitive pressure and its imposition by partners.
The results suggested that a major reason that small firms become EDI-capable is due to external
pressure (trading partners). The adoption of the Internet was also studied by Mehrtens et al.
(2001). In order to develop a model of Internet adoption, they conducted a case study with seven
SMEs. They devised their model using perceived benefits, organizational readiness and external
pressure as determinant factors. All the factors were found to affect Internet adoption by the
small firms. Mirchandani and Motwani (2001) investigated the factors that differentiate adopters
from non-adopters of e-commerce in small businesses. The relevant factors included enthusiasm
of top management, compatibility of e-commerce with the work of the company, relative
advantage perceived from e-commerce, and knowledge of the company’s employees about
computers. The degree of dependence of the company on information, managerial time required
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to plan and implement the e-commerce application, the nature of the company’s competition, as
well as the financial cost of implementing and operating the e-commerce application were not
influencing factors.
However, SMEs also confronted barriers in e-commerce adoption. Hadjimanolis (1999), in a
study of SMEs in Cyprus, considers barriers to e-commerce adoption to be as either external or
internal to the organization. External barriers include difficulties in obtaining finance, difficulties
in obtaining technological information and difficulties of choosing the appropriate hardware and
software. Hadjimanolis (1999) further nominates two other sub-categories of external barriers
which are termed as demand barriers and environmental barriers. Demand barriers include e-
commerce not fitting with products and services offered or not fitting with the way their
customers wished to conduct their business. Environmental barriers included complicated
governmental regulations and security concerns. He subdivided the internal barriers into two
categories as well. They are termed resource barriers (which included lack of management
enthusiasm and lack of technical expertise) and systems barriers (which included e-commerce
not fitting with current business practices).
Lawrence (1997) defined three categories for e-commerce barriers in SMEs, company, personal
and industry barriers. Company barriers, included low level of technology use within the
business, limited financial and technical resources available, organizational resistance to change
and lack of perceived return on investment. Personal barriers included lack of information on e-
commerce, management preferring conventional approaches to business practice and inability to
see the advantages of using e-commerce. Industry barriers included some respondents believing
that the industry, as a whole was not ready for e-commerce technology.
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Other more recent studies have shown that many of the barriers reported in the late 1990’s by
Lawrence and Hadjimanolis, still exist in today’s SMEs. Tambini (1999) and Eid et al (2002)
found that SME managers are still not convinced that e-commerce fits the products or services
that their businesses offer. Studies by Bakos and Brynjolfsson (2000), Sawhney and Zabin
(2002), and Merhtens et al (2001) have found that there is still a reluctance for SME managers to
adjust their businesses to the requirements and demands placed on it by e-commerce
participation. Some of these barriers are summarized in Table 3.
Table 3 - Barriers to e-commerce adoption in SMEs
Barriers References
E-commerce doesn’t fit with products/services Eid et al (2002) , Kendall and Kendall (2001), Tambini
(1999), Hadjimanolis (1999)
E-commerce doesn’t fit with the way we do
business
Sawhney & Zabin (2002),Mehrtens et al (2001), Bakos &
Brynjolfsson (2000), Farhoomand et al (2000), Poon &
Swatman (1997)
E-commerce doesn’t fit the way our customers
work
Bakos & Brynjolfsson (2000), Hadjimanolis (1999)
We don’t see the advantages of using E-
commerce
Lee & Runge (2001), Chau & Hui (2001), Purao &
Campbell (1998),Lawrence (1997), Hadjimanolis (1999)
Lack of technical know how Mirchandani & Motwani (2000), Hadjimanolis (1999),
Farhoomand et al (2000), Purao & Campbell (1998)
Security risks
Oxley & Yeung (2001), Reimenschneider & McKinney
(2001), Purao & Campbell (1998), Hadjimanolis (1999)
Cost too high Reimenschneider & McKinney (2001), Ratnasingam (2000)
, Hadjimanolis (1999), Purao & Campbell (1998),Lawrence
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(2002)
Not sure what hardware/software to choose Farhoomand et al (2000), Hadjimanolis (1999)
E-readiness of SMEs (ESME) is related to the level of IT acquisition or adoption especially
e-commerce by them. SMEs must remove the above mentioned barriers and also pay attention to
the factors influencing IT adoption for achieving a good level of e-readiness.
In this paper, we try to extract critical factors of e-readiness assessment for SMEs with regarding
the e-readiness assessment models at macro level. In this way, the authors try to extract the
critical factors from macro level and validate it through some strong statistical tools such as
factor analysis. Table 4 shows important areas for e-readiness assessment at macro level.
Table 4- Important areas for e-readiness assessment at macro level
Area E-readiness assessment models
Information Infrastructure CID, APEC, CSPP, McConnell,
EIU,ITU,USAID,CIDCM,NRI
Internet Availability and Affordability CID, APEC, CSPP, McConnell,
EIU,USAID,CIDCM,NRI
Network Speed and Quality CID, APEC,CSPP, McConnell, EIU,
ITU,USAID,NRI
Hardware and Software CID, APEC, CSPP, McConnell, EIU,NRI
ICT Services and Support CID, EIU
Skills and Human Resources(Information
Literacy)
CID, APEC, CSPP, McConnell,
EIU,ITU,USAID,NRI
People and Organizations Online (employees
and divisions)
CID, APEC, CSPP, McConnell, NRI
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Locally Relevant Content CID, APEC,EIU,
Investment and Financial Support for ICT
Development
McConnell, EIU,ITU,USAID
Information and Communication
Technologies in the Workplace
CID, APEC,CSPP,EIU
ICT Employment Opportunities CID, EIU
Business-to-Consumer (B2C) Electronic
Commerce( relation to consumers)
CID, APEC,CSPP, EIU
Business-to-Business (B2B) Electronic
Commerce(relation to the other enterprises)
CID, APEC, EIU
E-Government CID, APEC, McConnell, USAID, NRI
Legal Environment and Regulations (such as
copy right, intellectual property...)
CID, APEC, McConnell, EIU,USAID,NRI
ICT Management and Policy CID,CSPP, EIU, ,CIDCM
Revenue on Electronic Services (cost, charge
and tariff)
APEC,ITU
Degree of Innovation EIU,CSPP
Industry Standards(for ICT Development) APEC, EIU
Security and Encryption (such as public key
infrastructure, digital signature, privacy...)
APEC, CSPP, McConnell, EIU, USAID
Research methodology
This research applies a statistical approach based on factor analysis. Factor analysis is a
technique particularly suitable for analyzing the patterns of complex, multidimensional
relationships encountered by researchers. It defines and explains in broad, conceptual terms the
fundamental aspects of factor analytic techniques. Factor analysis can be utilized to examine the
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underlying patterns or relationships for a large number of variables and to determine whether the
information can be condensed or summarized in a smaller set of factors or components. To
further clarify the methodological concepts, basic guidelines for presenting and interpreting the
results of these techniques are also included.
Factor analysis is also known as a generic name given to a class of multivariate statistical
methods whose primary purpose is to define the underlying structure in a data matrix. Broadly
speaking, it addresses the problem of analyzing the structure of the interrelationships
(correlations) among a large number of variables (e.g., test scores, test items, questionnaire
responses) by defining a set of common underlying dimensions, known as factors. With factor
analysis, the researcher can first identify the separate dimensions of the structure and then
determine the extent to which each variable is explained by each dimension. Once these
dimensions and the explanation of each variable are determined, the two primary uses for factor
analysis, summarization and data reduction can be achieved. In summarizing the data, factor
analysis derives underlying dimensions that, when interpreted and understood, describe the data
in a much smaller number of concepts than the original individual variables. Data reduction can
be achieved by calculating scores for each underlying dimension and substituting them for the
original variables (Hair et al, 1998).
In this way, a questionnaire was designed based on the factor analysis principles whilst
incorporating the important areas of e-readiness assessment listed in Table 4, with 20 questions
measuring attitude and the available responses evaluated by Likert Scale (Likert, 1974) are:
strongly disagree, disagree, no opinion, agree, or strongly agree .
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It is important to say that our question will be made according to the mentioned area listed in
Table 4 and the same 20 criteria will be considered as the questions of our questionnaire. For
example our first question will be as follows:
H1: Information infrastructure is positively associated with the e-readiness of SMEs.
Table 5 summarizes the content of the 20 questions. The content of the questions has been
summarized for better readability (all questions have a format such as above H1).
A research framework was designed according to all these features, as shown in Figure 2. In
addition, information related to the basic profile of the interviewees was requested at the end of
the questionnaire.
To evaluate the questionnaire, eleven experts participated in a pilot test. All the pre-test
participants expressed strong agreement with the suitability of the questionnaire. The
questionnaire was considered finalized after some minor modifications.
Data collection
The research targets were members of 45 non-profit SME organizations in Iran, specializing and
working in an ICT domain including hardware and software, ICT consulting and ICT services.
As most of the SMEs in Iran are non-profit especially in the IT area, we implemented our
research through these kinds of SMEs. Furthermore, the selected IT SMEs are more familiar with
“e” activities and are seen as pioneers in this area in Iran.
In order to understand the viewpoints on e-readiness from all sectors of these organizations,
questionnaires were sent to information, finance, research and development, academic and
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human resource departments of these organizations. The main sampling targets were senior
managers, department managers and personnel who were involved in decision making.
Table 5- The 20 criteria of the research questionnaire (based on table 4)
Criteria
H1 Information Infrastructure
H2 Internet Availability and affordability
H3 Network Speed and Quality.
H4 Hardware and Software.
H5 ICT Services and Support.
H6 Information and Communication Technologies in the Workplace
H7 Skills and Human Resources(Information Literacy)
H8 People and Organizations Online(employees and divisions)
H9 ICT Employment Opportunities.
H10 ICT Management and Policy.
H11 Investment and Financial Support for ICT Development
H12 Revenue on Electronic Services(cost, charge and tariff)
H13 Degree of Innovation
H14 E-Government
H15 Security and Encryption (such as public key infrastructure, digital signature, privacy,...)
H16 Business-to-Consumer (B2C) Electronic Commerce ( relation to consumers)
H17 Business-to-Business (B2B) Electronic Commerce(relation to the other enterprises)
H18 Locally Relevant Content(contents for e-services)
H19 Industry Standards(for ICT Development)
H20 Legal Environment and Regulations (such as copy right, intellectual property...).
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Three hundred and ninety, 390, questionnaires were sent out and 175 returned showing a 44.8%
return rate. Seven of the returned questionnaires were incomplete and thus discarded, which
made the number of valid questionnaires returned 168 or 43% of the total sent out.
Figure 2. Research framework
Statistical analysis
This research used the Kolmogorov-Smirnov test to determine whether the significance levels of
sample data fitted a normal distribution (Hollander and Wolfe, 1973). The Kolmogorov-Smirnov
statistical test shows the maximum difference (absolute value) of an observed and theory-based
distribution function under normal distribution and the absolute value of the statistics close to
zero. According to the test results, the p-value of all the questions was less than 0.05, which
showed that the distribution was not normal. The Cronbach's alpha was equal to 91.6.
Analysis of variables in e-readiness assessment of SMEs
The analytical process included a questionnaire reliability analysis, factor analysis and
identification of the factors.
The Cronbach’s Alpha calculated from the 20 variables of this research was 0.916, which
showed high reliability for designed scale. In order to determine whether the partial correlation
Critical factors affecting e-readiness
Factor analysis
E-readiness assessment model
The 20 criteria of the research
(20 questions)
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of the variables was small, the authors used the Kaiser Meyer Olkin (KMO) measure of sampling
adequacy (Kaiser, 1958) and Bartlett’s Chi-Square test of Sphericity (Bartlett, 1950) before
conducting the factor analysis. The result was a KMO of 0.837 and less than 0.05 for Bartlett
test, which showed a good correlation as depicted in Table 6.
Table 6. KMO and Bartlett test results
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .837
Bartlett's Test of
Sphericity
Approx. Chi-Square 2055.556
df
190
Sig.
.000
Factor analysis
Factor analysis provides direct insight into the interrelationships between variables or
respondents and empirical support for addressing conceptual issues relating to the underlying
structure of the data. It also plays an important complementary role with other multivariate
techniques through both data summarization and data reduction. From the data summarization
perspective, factor analysis provides the researcher with a clear understanding of which variables
may act together and how many variables may actually be expected to have impacts in the
analysis. For example, variables determined to be highly correlated and members of the same
factor would be expected to have similar profiles of differences across groups in multivariate
analysis of variance or in discriminant analysis. Examples that highlight the impact of correlated
variables are the stepwise procedures discriminant analysis. These techniques sequentially enter
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variables based on their additional predictive power over variables in the model. As one variable
from a factor is entered, it becomes less likely that additional variables from that same factor
would also be included because they are highly correlated and potentially have less additional
predictive power than the variables which are not appeared in that factor. This does not mean
that the other variables of the factor are less important or have less impact, but instead that their
effect is already represented by the included variable from the factor. The researcher would
better understand the reasoning behind the entry of variables in this technique with knowledge of
the structure of the variables.
According to Hair et al (1998), the insight provided by data summarization can be directly
incorporated into other multivariate techniques through any of the data reduction techniques.
Factor analysis provides the basis for creating a new set of variables that incorporate the
character and nature of the original variables in a much smaller number of new variables,
whether using representative variables, factor scores, or summated scales. In this manner,
problems associated with large numbers of variables or high inter-correlations among variables
can be substantially reduced by substitution of the new variables. The researcher can benefit
from both the empirical estimation of relationships and the insight into the conceptual foundation
and interpretation of the results.
The Factor analysis method, here applied is “Principle Component Analysis (PCA)” developed
by Hotteling (1935) and the condition for selecting factors was based on the principle proposed
by Kaiser (1958): eigenvalue larger than one, and an absolute value of factor loading greater than
0.5. The 20 variables were grouped into four factors; the results can be seen in Tables 7 and 8.
Four factors had an eigenvalue greater than one and the interpretation variable was 65.4 percent.
23
An important tool in interpreting factors is factor rotation. The term rotation means exactly what
it implies. Specifically, the reference axes of the factors are turned about the origin until some
other position has been reached. The un-rotated factor solutions extract factors in the order of
their importance. The first factor tends to be a general factor with almost every variable loading
significantly, and it accounts for the largest amount of variance. The second and subsequent
factors are then based on the residual amount of variance. Each accounts for successively smaller
portions of variance. The ultimate effect of rotating the factor matrix is to redistribute the
variance from earlier factors to later ones to achieve a simpler, theoretically more meaningful
factor pattern. The simplest case of rotation is an orthogonal rotation, in which the axes are
maintained at 90 degrees Hair et al (1998). The results are shown in Table 7 and Table 8.
Table 7- Results of factor analysis
Component Initial Eigenvalues Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 8.021 40.104 40.104 8.021 40.104 40.104
2 2.280 11.400 51.504 2.280 11.400 51.504
3 1.515 7.577 59.082 1.515 7.577 59.082
4 1.259 6.294 65.376 1.259 6.294 65.376
5 .892 5.027 70.402
6 .751 4.256 74.658
7 .730 3.649 78.307
8 .602 3.009 81.316
9 .568 2.842 84.158
10 .493 2.463 86.621
11 .481 2.404 89.025
12 .436 2.178 91.204
13 .357 1.784 92.988
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14 .292 1.460 94.448
15 .267 1.335 95.783
16 .229 1.146 96.929
17 .200 .998 97.927
18 .159 .797 98.724
19 .136 .680 99.404
20 .119 .596 100.000
Extraction Method: Principal Component Analysis.
Factor loading of each variable on the resulted four factors is depicted in Table 8. Each variable
should have significant factor loading only on one factor (for this research we consider the items
which are greater than 0.6 for more strength). Therefore, factors 1, 2, 3, 4, had 4, 4, 3, 2
variables, respectively; So H7, H10, H11, and H12 are significant for factor 1; H1, H3, H5, H9
are significant for factor 2; H2, H6, and H8 are significant for factor 3; and H15 and H20 are
significant for factor 4.
Table 8- Rotated component matrix
Component
1 2 3 4
H1 .491 .615 .403 -.176
H2 .240 .418 .777 .148
H3 .272 .721 .103 .036
H4 -.013 .335 .238 .149
H5 .489 .657 .078 .121
H6 .291 .504 .740 .424
H7 .872 .195 .052 .465
H8 .418 .138 .631 .106
25
H9 .387 .768 -.078 .250
H10 .749 .411 .076 .029
H11 .749 .345 .271 .083
H12 .706 .521 .284 .281
H13 .420 .361 -.059 .142
H14 .408 -.110 .123 .336
H15 .399 .237 .024 .655
H16 .092 .078 .226 .043
H17 .485 .149 .197 .081
H18 .312 .317 .182 -.029
H19 -.010 .082 .251 .0403
H20 .412 .111 .470 .820
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Discussion
Before analyzing the results, an overview of IT plans in Iran is provided to increase familiarity
with this developing country. A comprehensive program has been defined and confirmed for all
executives' offices, government departments and ministries in Iran as the priorities of ICT
operational planning in 2002.
This program and the related strategies are known in Iran as TAKFA1 plan which was defined
five years ago. This plan includes seven objectives as follows:
1. e-government plan (system, virtual network, security and laws)
2. deployment of ICT in training and development of digital skills of human resources in
Iran
3. deployment of ICT in higher educations, hygiene, treatment, and doctoral training
4. the plan of ICT deployment in social services
1 TACFA is a Persian abbreviation and stands for "The plan of ICT development"
26
5. development of ICT in economy and commerce
6. ICT deployment in cultural and art area, and reinforcement of Persian language in IT
environment
7. support and development of SMEs in ICT area through incubators and technology parks
As mentioned above, one of the main aims in TACFA plan, is the supporting of IT development
through Iranian SMEs. Therefore, e-readiness assessment of these kinds of enterprises is of great
importance in Iran. It is necessary to say that after confirmation of TAKFA planning, IT
development was accelerated in Iran, and as a result, the e-readiness of the most corporations and
SMEs was increased. On the other hand, the five years economic and social development
planning of Iran is based on the knowledge economy and emphasizes IT deployment in all parts
of society.
It is important to say that because of the dynamic management of private non-profit companies
in contrast with governmental agencies, positive changes through these private companies are
more tangible in ICT area. This explains the phenomenon that e-readiness of these companies is
much more advanced than the corresponding one in governmental corporations.
Generally, we can conclude that there are not many problems in e-readiness of non-profit Iranian
SMEs. Problems such as software, hardware or brainware (skilled human resources) are not
evident however issues such as the lack of a suitable legal environment, the lack of strong
electronic communication platform and some weak management perspectives are prevalent.
The current research aimed to extract critical issues of e-readiness assessment for Iranian SMEs.
The research design was based on a questionnaire of 20 questions, 13 of which were confirmed
through the factor analysis results and the rest of them (7 questions) were rejected.
27
The authors attempted to identify and name the confirmed factors based on the principle of being
concise without losing clarity of meaning. When a factor solution has been obtained in which all
variables have a significant loading, the researcher attempts to assign some meaning to the
pattern of factor loading. Variables with higher loadings are considered more important and have
greater influence on the name or label selected to present a factor. In this way, the researcher will
attempt to assign a name or label to a factor that accurately reflects the variable loading on the
factor [68]. In this way, the names and content of the final four factors are as follows:
• Factor 1 consists of H7, H10, H11, and H12 which are “Skills and Human Resources”,
“ICT Management and Policy”, “Investment and Financial Support for ICT
Development”, and “Revenue on electronic services”. This factor is named
“organizational features” ,
• Factor 2 consists of H1, H3, H5, H9, which are “Information Infrastructure”, “Network
Speed and Quality”, “ICT Services and Support”, and “ICT Employment Opportunities”.
This factor is named as “Information and communication technology infrastructures”,
• Factor 3, that includes H2, H6, and H8, which are “Internet Availability and
affordability”, “Information and Communication Technologies in the Workplace”, and
“People and Organizations Online”. This factor is named “IT availability”, and finally
• Factor 4, which inludes H15 and H20; “Security and Encryption”, and “Legal
Environment and Regulations”. This factor is named “Security and legal environment”.
A model can be conceptualized according to the findings of the research, which shows the
critical issues of e-readiness assessment (Figure 3). This new conceptual model is based on data
analysis of this research and provides a micro model for e-readiness assessment developed for
SMEs through the factors of e-readiness at macro level.
28
According to the study findings, the critical issues for assessment of ESME in Iran were
extracted, based on selected e-readiness assessment models in national level such as APEC,
CSPP, NRI, etc and have been confirmed for micro level (SMEs).
The conceptualized model can also be used for presenting a general view for the assessment and
comparison of e-readiness of the considered SMEs.
Figure3. Conceptual model for assessment of ESME
(Based on research results)
Organizational features
Security and legal environment
ICT availability
Skills and Human Resources
ICT Management and Policy
Investment and Financial Support
Revenue on electronic services
People and organization online
Internet Availability and affordability
ICT in the workplace
ICT infrastructures
Information Infrastructure
ICT Employment Opportunities
ICT Services and Support
Network Speed and Quality
Security and Encryption Legal Environment and Regulations
ESME
29
This research findings have several policy implications. Firstly, SMEs need robust, available
ICT infrastructures with high network speed and quality, ICT services and support, ICT
employment opportunities, Internet availability and affordability in the workplace.
Secondly, the organizational features should be noticed carefully. It includes skills and human
resources, ICT management and policy, investment and financial support for ICT development,
and revenue on electronic services and finally, the regulations and legal operations should not be
forgotten. A legal environment can facilitate the e-adoption in organizations.
In addition to the above, the study results suggest that SMEs in Iran need much greater
infrastructural support in order to reap the benefits of ICT and to develop the capabilities to
contribute to economic development. Associated policies and programs aimed at providing
required infrastructure need to be initiated in developing countries in order to make SMEs more
competitive in the domestic and international markets.
Furthermore, ICT management and policy and the effective support of management are a key to
competitive advantage and further growth.
Nevertheless, our explorative study of Iranian SMEs indicates that innovation activities within
Iranian SMEs seems to be at an early stage as the innovation criteria didn’t appear in the final
results. Given the potential benefits and the pressure to further innovation, we expect a growing
need for Iranian SMEs to increase their know-how concerning creativity factors in order to
improve their innovation processes and hence their competitiveness.
An interesting point in this research is that “industry standards” criteria could not be traced in our
research results. This may be because of the low level of importance towards standards between
the surveyed SMEs as the industries in Iran have recently started new disciplines on quality and
standards.
30
Although the military and some important industries are forced to apply standards and are
considered as pioneers in Iran, average industrial companies have not embarked on
standardisation. It is the authors view that in order to achieve competitiveness in the world
markets, all SMEs in Iran should move towards standards and new quality disciplines.
B2B and B2C models didn’t appear as critical issues in the research results as these models are
not known enough for business leaders.
Another interesting finding is that e-services are not distinguished. E-services are very weak in
Iran and few companies started to present their services in “e” way. It seems to be at an early
stage and people also hardly believe that they can reach their requirements through electronic
infrastructures.
Conclusion
Recent empirical evidence across countries shows a substantial and increasing return to IT
investment. New technologies open up opportunities for small firms to expand their markets
beyond national borders.
Nevertheless, most SMEs in Iran are still traditional and their school of thought belongs to the
past few decades. Today’s global changes dictate a new model of thinking as a basic
requirement. The SMEs in Iran have to restructure their way of thinking which has a deeply roots
in their culture. The difference between culture in east and west is under the influence of school
of thought they have been growing up under. In this way, to succeed when introducing a change
in SMEs, the culture and beliefs need to be altered first. Following that processes, approaches,
techniques, methodologies, etc. could be re-engineered on the base of change management
program.
31
The way the SMEs in west are behaving in the case of change, is under the influence of the
techniques and thoughts commonly used in that environment. The preliminary instruments used
in daily activities in SMEs in west, is a big project in the SMEs in east. For example, the topic of
e-commerce commonly used in SMEs in the west as a daily routine process, if is traced in
eastern SMEs, little trace can be monitored.
As e-commerce is in it’s infancy in Iran, it necessary to be start the introduction of ICTs in
micro and macro levels to bring and ease of related change in Iranian SMEs. The first step is
assumed to be an e-readiness assessment.
E-readiness of an SME is defined here as the ability of a company to successfully adopt, use and
benefit from Information Technologies such as e-commerce. In this paper, we extract the critical
issues for e-readiness assessment of non-profit ICT SMEs in Iran. We reviewed the important
areas of different countries e-readiness assessment according to some established models (macro
level). Afterwards, we designed a research protocol for SMEs, including 20 questions based on
important areas of e-readiness assessment for macro level. In other words, we designed a
questionnaire that asked the experts about e-readiness assessment factors in SMEs with regarding
the important items of e-readiness assessment for macro level. The analysis results showed the
experts believed that some of those items can also be considered for SMEs.
The authors applied factor analysis technique in this research. Four important factors were
extracted which show the critical issues for ESME assessment. The first factor includes “Skills
and Human Resources”, “ICT Management and Policy”, “Investment and Financial Support for
ICT Development”, and “Revenue on electronic services”, that was named as "Organizational
features".
Factor 2 consists of “Information Infrastructure”, “Network Speed and Quality”, “ICT Services
32
and Support”, and “ICT Employment Opportunities” and was named as "Information and
communication technology infrastructures".
The third factor includes “Internet Availability and affordability”, “Information and
Communication Technologies in the Workplace”, and “People and Organizations Online”. This
factor was named as "IT availability"; and finally factor 4 including "Security and Encryption”,
and “Legal Environment and Regulations” was named "Security and legal environment".
This study is probably the first to provide a perspective of critical issues for e-readiness
assessment in SMEs based on macro models in a developing country. It gives valuable
information and guidelines which hopefully will help the leaders in developing countries to
consider the critical issues for e-readiness assessment through their organization in an effective
way.
It is a future proposition that these recommendations be verified by a larger sample of SMEs and
innovation software producers to confirm the preliminary state of knowledge. While the current
survey cannot be said to be complete, it provides a relatively good overview of the SMEs’ status
in Iran.
Since the study only covers non-profit ICT SMEs, it is suggested that the further studies be
expanded to all kinds of SMEs for more comprehensive follow-up studies.
For achieving more exact results, the research findings must be further examined especially for
different types of SMEs. Future research can compare the results between different types of
SMEs.
References Acs, Z.J., 1992. Small business economics: a global perspective, Challenge, November-December.
33
Anderson, R.G., 1990. Data processing. Information Systems and Technology. Pitman Publishing, London. Auger, P., Gallaugher, J.M., 1997. Factors affecting adoption of an internet-based sales presence for small businesses. The Information Society 13 (1), 55 – 74. Bakos, Y., Brynjolfsson, E., 2000. Bundling and competition on the internet. Marketing Science 19 (1), 63 – 82. Bartlett, M. S., 1950. Test of significance in factor analysis. British journal of psychology 3, 77-85. Bayo-Moriones, A., Lera-Lopez, F., 2007. A firm-level analysis of determinants of ICT adoption in Spain. Technovation 27, 352–366. Chau, P.Y.K., Hui, K. L., 2001. Determinants of small business EDI adoption: an empirical investigation. Journal of Organizational Computing and Electronic Commerce 11 (4), 229 – 252. Chen, J.C., 1993. The impact of microcomputers on small businesses: England 10 years later. Journal of Small Business Management 31 (3), 96 – 102. Claus, V., Schwill A., 1992. Encyclopedia of information technology. Ellis Horwood Limited, England. Cragg, P.B., King, M., 1993. Small firm computing: motivators and inhibitors. MIS Quarterly 17 (1), 47 – 60. DelVecchio, M., 1994. Retooling the staff along with the system. Bests Review 94 (11), 82 -83. Damaskopoulos, P., Evgeniou, T., 2003. Adoption of new economy practices by SMEs in Eastern Europe. European Management Journal 21 (2). Domke-Damonte, D., Levsen, V.B., 2002. The effect of internet usage on cooperation and performance in small hotels. SAM Advanced Management Journal, summer, 31 – 38. Eid, R., Trueman, M. and A.M. Ahmed, 2002. A cross-industry review of B2B critical success factors. Internet Research: Electronic Networking Applications and Policy 12 (2), 110 – 123. Farhoomand, A.F., Tuunainen, V. K., and L.W. Yee, 2000. Barriers to global electronic commerce: a cross-country study of Hong Kong and Finland. Journal of Organizational Computing and Electronic Commerce 10 (1), 23 – 48. Fink, D., Tjarka, F., 1994. Information systems contribution to business performance: a study of information systems executives’ attitudes. Australian Journal of Information Systems 2 (1), 29- 38.
34
Gillespie, A., Richardson, R. and J. Cornford, 1995. Information infrastructures and territorial development, paper prepared for the OECD joint ICCP-TDS Workshop on Information Technologies and Territorial Development, Paris, November. Grainger-Smith, N., Oppenheim C., 1994. The role of information system and technology in investment banks. Journal of Information Science 5 (20). Grandon, E., Pearson, M. J., 2004. Electronic commerce adoption: an empirical study of small and medium US businesses. Information & Management 42, 197–216. Hadjimanolis, A. 1999, Barriers to innovation for SMEs in a small less developed country (Cyprus). Technovation 19 (9), 561 – 570. Hair, J., Anderson, R., Ththam, R. and W. Black, 1998. Multivariate data analysis, Prentice Hall. Haj Bakry, S., 2003. Toward the development of a standard e-readiness assessment policy. International journal of network management 13, 129–137. Hollander, M. and D. A. Wolfe, 1973. Nonparametric Statistical Methods. John Wiley&Sons, New York, 21-132. Holzinger, A.G., Hotch, R., 1993. Small firms usage patterns. Nations Business 81 (8), 39 -42. Hanson J., Narula, U., 1990. New communication technologies in developing countries. Lawrence Erlbaum Association Inc., New Jersey. Hartman, A., Sifonis, J. and J. Kador, 2000. Net ready: strategies for success in the economy. McGraw-Hill, NY, USA. Hotteling, H., 1935. The most predictable criterion. Journal of Educational Psychology 26, 139-142. Iacovou, A. L., Benbasat, I. and A.Dexter, 1995. Electronic data interchange and small organizations: adoption and impact of technology. MIS Quarterly, December, 465–485. Kaiser, H. F., 1958, The varimax criterion for analytic rotation in factor analysis. Psychometrika 23 (3), 187-200. Kendall, J. E., Kendall, K. E., 2001. A paradoxically peaceful coexistence between commerce and ecommerce. Journal of Information Technology, Theory and Application 3 (4), 1 – 6. Khan, E.H., Khan, G.M., 1992. Microcomputers and small businesses in Bahrain, Industrial management and data systems 92 (6), 24 – 28. La Rovere, R. L., 1998. Small and medium-sized enterprises and IT diffusion policies in Europe. Small Business Economics 11 (1), 1–9.
35
Lawrence, K.L., 1997. Factors inhibiting the utilization of electronic commerce facilities in Tasmanian small-to-medium sized enterprises, 8th Australasian Conference on Information Systems, 587 – 597. Lee, J., Runge, J., 2001. Adoption of information technology in small business: testing drivers of adoption for entrepreneurs. Journal of Computer Information Systems 42 (1), 44 – 57. Likert, R., 1974, “The method of constructing an attitude scale”, in Maranell, G.M. (Ed.), Scaling: A Sourcebook for Behavioral Scientists, Aldine Publishing Company, Chicago, IL, 21-43. MacGregor, R., Vrazalic, L., 2004. Electronic commerce adoption in Small to Medium Enterprises (SMEs), A Comparative study of SMEs in Wollongong (Australia) and Karlstad (Sweden), University of Wollongong. Maksoud, A., Aziz Youssef, S. A., 2003. Information and communication technology for SMEs in Egypt, SME Development Unit, Ministry of Foreign Trade. http://www.sme.gov.eg/webpubeng/ICT.pdf. Mehrtens, J., Cragg, P.B. and A.M. Mills, 2001. A model of internet adoption by SMEs. Information and Management 39,165–176. Mirchandani, A. A., Motwani, J., 2001. Understanding small business electronic commerce adoption: an empirical analysis. Journal of Computer Information Systems, Spring, 70–73. Molla, A., Licker, P. S., 2005a. eCommerce adoption in developing countries: a model and instrument. Information & Management 42, 877–899. Molla, A., Licker, P. S., 2005b. Perceived E-Readiness factors in e-Commerce adoption: an empirical investigation in a developing country. International Journal of Electronic Commerce 10 (1), 83–110. Morgan, A., Colebourne, D., and T. Brychan, 2006. The development of ICT advisors for SME businesses: An innovative approach. Technovation 26, 980–987 Neergaard, P., 1992. Microcomputers in small and medium-size companies: benefits achieved and problems encountered, Proceedings of the Third Australian Conference on Information Systems, Wollongong, 579 – 604. Oyelaran-Oyeyinka, B., Lal. and K., 2006a. Learning new technologies by small and medium enterprises in developing countries, Technovation 26, 220–231. Oyelaran-Oyeyinka, B., Lal. and K., 2006b. SMEs and New Technologies: Learning E-Business and Development , Palgrave Macmillan.
36
Oxley, J.E., Yeung, B. 2001. E-Commerce readiness: institutional environment and international competitiveness. Journal of International Business Studies 32 (4), 705 – 723. Peters, T., 2001. Comparison of readiness assessment models, http://www.bridges.org/ereadiness/report.html, 2001. Poon, S., Joseph, M., 2001. A preliminary study of product nature and electronic commerce. Marketing Intelligence & Planning 19 (7), 493 – 499. Poon, S., Swatman, P., 1997. The internet for small businesses: an enabling infrastructure. Fifth Internet Society Conference, 221 – 231. Porter, M.E., Miller, M.E., 1985. How information gives you competitive advantage. Harvard Business Review (July/August),149–160. Power, D.J., Sohal, A.S., 2002. Implementation and usage of electronic commerce in managing the supply chain: a comparative study of ten Australian Companies. Benchmarking: An International Journal 9 (2), 190 – 208. Purao, S., Campbell, B., 1998. Critical concerns for small business electronic commerce: some reflections based on interviews of small business owners. Proceedings of the Association for Information Systems Americas Conference Baltimore, MD, August, 325 – 327. Ratnasingam, P., 2000. The influence of power on trading partners in electronic commerce. Internet Research10 (1), 56 – 62. Reimenschneider, C.K., Mykytyn, P.P. , 2000. What small business executives have learned about managing information technology. Information & Management 37, 257 – 267. Reimenschneider, C. K., McKinney, V. R., 2001, Assessing beliefs in small business adopters and non-Adopters of web-based e-commerce. Journal of Computer Information Systems 42 (2), 101 – 107. Rothwell, R., Dodgson, M., 1993. Technology-based SMEs: their role in industrial and economic change, Inderscience Enterprises. Ruikar, K., Anumba, C. G., and P.M. Carrillo, 2006. VERDICT- An e-readiness assessment application for construction companies. Automation in Construction 15 (1), 98-110. Sawhney, M., Zabib, J., 2002. Managing and measuring relational equity in the network economy. Journal of the Academy of Marketing Science 30 (4), 313 – 332. Tan, J., Tyler, K., and A. Manica, 2007. Business-to-business adoption of eCommerce in China. Information & Management 44, 332–351
37
Tambini, A.M., 1999. E-Shoppers demand e-Service. Discount Store News 11 (38). Tarantola, S., Gatelli, D., 2006. A new estimator for sensitivity analysis of model output: An application to the e-business readiness composite indicator. Reliability Engineering and System Safety 91 (10/11), 1135-1141.