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Int. J. Electronic Business, Vol. 1, No. 1, 2003 3
Copyright © 2003 Inderscience Enterprises Ltd.
A framework for measuring national e-readiness
Tung X. Bui*
College of Business and Administration,
University of Hawaii at Manoa, 2404 Maile Way, E303a,
Honolulu, HI 96822
Fax: (808) 956-9889 E-mail: tbui@cba.hawaii.edu
*Corresponding author
Siva Sankaran
College of Business and Economics, California State University,
18111 Nordhoff Street, Northridge, CA 91330-8372, USA
Fax: (818) 677-2456 E-mail: siva.sankaran@csun.edu
Ina M. Sebastian
College of Business and Administration,
University of Hawaii at Manoa, 2404 Maile Way, E303a,
Honolulu, HI 96822
Fax: (808) 956-9889 E-mail: ina@cba.hawaii.edu
Abstract: Technology and societal changes are moving the global market
rapidly towards a new economic order rooted in e-commerce. Hence, assessing
and monitoring the e-readiness of a nation has become an increasing challenge.
This paper proposes a framework to evaluate the e-readiness of a nation based
on eight factors: digital infrastructure, macro economy, ability to invest,
knowledgeable citizens, competitiveness, access to skilled workforce, culture,
and cost of living and pricing. We identify 52 surrogate measures that can be
used to quantify these factors and describe an algorithm to calculate an overall
e-readiness index for a country. Using data published by different world
organisations on these measures for ten East Asian, USA and G7 countries, we
illustrate how the proposed framework can be used in providing e-readiness
assessments and in making national strategic decisions on infrastructure that is
conducive to the new economy.
Keywords: E-commerce; e-readiness; e-strategy; new economy.
Reference to this paper should be made as follows: Bui, T.X., Sankaran, S. and
Sebastian, I.M. (2003) ‘A framework for measuring national e-readiness’, Int.
J. Electronic Business, Vol. 1, No. 1, pp.3-22.
Biographical notes: Tung X. Bui is Matson Navigation Company Chair of
Global Business and Professor of Information Technology Management at the
College of Business Administration, University of Hawaii, Manoa. As PRIISM
director and APEC Study Center co-director, he is interested in the study of the
effective use of information technology in organisations and negotiation
support systems.
4 T. Bui, S. Sankaran and I.M. Sebastian
Siva Sankaran is Professor of Information Systems at the California State
University, Northridge. He previously taught at the Naval Postgraduate School
in Monterey, California. He holds a PhD in information systems from New
York University. His research interests include e-commerce and distance
learning.
Ina M. Sebastian is a research assistant at the Pacific Research Institute for
Information System Management (PRIISM) at the College of Business
Administration, University of Hawaii.
1 Introduction
E-commerce is still in its infancy in many countries around the world. Many economists
argue that this is due to many factors that still confound and challenge the adopters of
e-commerce [1]. Although technology infrastructure is believed to be the key for
e-commerce growth, its relevance, evidence of productivity and return on IT investment
have yet to be proved [2]. Further, social needs and priorities are constantly in flux in
developing economies. With unpredictable outcomes, support for e-commerce has not
been at the top of the agenda of many governments. However, to be able to compete
effectively in the emerging global economy, it is important that all nations continue to
nurture e-commerce development [3–5].
This brings us to the question of how best to prepare a nation to successfully
assimilate e-commerce. Part of the answer lies in identifying specific national factors –
enablers and inhibitors – that help to e-ready a nation. Without exception, each country
has its own unique set of e-readiness characteristics. Yet, national policies regarding
information infrastructure and e-commerce development have often been strikingly
similar. It is important that each member economy find the most opportune pathway for
its e-commerce development strategy as there is no single magic formula. Each economy
must strive to find e-readiness factors that would best align with its business strategies
and national trade priorities [4,6,7].
The purpose of this research is three-fold:
1 to identify the factors that contribute to increased e-readiness of a country
2 to develop a set of measures that can be used to quantitatively score e-readiness
across each of these factors
3 to provide an overall theoretical framework that incorporates these factors toward
developing a composite e-readiness indicator.
This paper is organised as follows. In Section 2, we discuss the characteristics of the new
Internet-based economy and the foundations of e-readiness. In Section 3, we describe a
conceptual framework and a computational algorithm to measure e-readiness. An
example with real world data on East Asian economies is presented. In Section 4, an
assessment of the framework is given, comparing it to the methods currently available
and weighing its benefits and limitations. The managerial implications are presented in
Section 5. In the final section, we conclude by summarising the implications of the
framework relative to formulating national e-strategies.
A framework for measuring national e-readiness 5
2 The new economy and the need for e-readiness
From an e-business perspective, we define the new economy as a global market in which
business transactions are being conducted in real time, around the clock, with digital
goods that can be mass customised to individual customer’s needs, and
delivered instantaneously to the customer regardless of location. Competitiveness in a
real-time economy depends on the ability of a country to achieve the following
commerce dimensions: immediacy, re-intermediation, knowledge and innovation,
integration/internetworking, and virtualisation [8–13].
Immediacy refers to a market driven by instantaneous supply and demand reactions
[8,9]. Technology enables enterprises to achieve real-time strategies, such as reducing
production cycle time, increasing delivery speed via digital highways and one-to-one
product customisation. Re-intermediation occurs in e-commerce as a result of the
replacement of traditional middlemen with Internet-related professionals to support
online business transactions such as IT consultants and e-market research specialists
[8,9]. Today, human capital is becoming an ever more important asset. This is because in
the new economy only enterprises with knowledge and innovative capabilities can remain
competitive [13–15].
Integration and internetworking is another important driver of the new digital
economy. Internet technologies allow companies to extend the supply chain beyond its
traditional physical borders. Web-servers, as the corporate electronic windows, could be
used as a platform to integrate conventional management information systems, such as
customer management databases, production and inventory applications, and accounting
systems, to provide a seamless and personalised service to the online customer [5,8,16].
Virtualisation is yet another dimension. A virtual organisation can be defined as a
task-driven, ad-hoc entity set up to accomplish a particular task that disappears once the
task is accomplished [8,9,12]. The benefits of such an organisation include the ability to
be put together quickly to respond to sudden market opportunities, thus avoiding the
typical inertia of large-scale bureaucracy and long-established institutions.
In this study, e-readiness is defined as the aptitude of an economy to use information
and communications technologies to migrate traditional businesses into the new
economy. E-readiness reaches its optimal level when the economy is able to create new
business opportunities that could not be done otherwise [12,17]. The concept of
e-readiness is important because its level can be a strong predictor of how well a country
can perform in the new economy. An e-readiness assessment would provide policy
makers with a detailed scorecard of their economy’s competitiveness relative to its
international counterparts. Further, a breakdown of indicators allows policy analysts to
pinpoint areas of strengths and weaknesses, thus providing a balanced perspective in
guiding a country through the digital transformation.
Table 1 provides a synoptic view of various perspectives of e-readiness. These views
are based on the necessity of having access to Internet technology, an economic, legal
and social climate that is conducive to doing business, and the ability to create new
business values.
6 T. Bui, S. Sankaran and I.M. Sebastian
Table 1 E-Readiness definitions
Focus Definitions Source
Value creation “Ability to pursue value creation opportunities facilitated
by the use of the Internet”.
Center for
EBiz Talk,
MIT [18]
Network access
and appropriate
applications
“An ‘e-ready’ community 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 and online
security; and government policies which are favorable to
promoting connectedness and use of the network”.
CSPP [19]
ICT; internet
applications;
e-government
“An ‘e-ready’ society is 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 organisations 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”.
CID
[20–22]
Promotion of free
trade, regionally
and
internationally
“A country that is ‘ready’ for e-commerce has free trade,
industry self-regulation, ease of exports, and compliance
with international standards and trade agreements”.
APEC [23]
e-society “An ‘e-ready’ country has extensive usage of computers
in schools, businesses, government, and homes;
affordable reliable access in a competitive market; free
trade; skilled workforces and training in schools; a
culture of creativity; government-business partnerships;
transparency and stability in government and an evenly
enforced legal system; secure networks and personal
privacy; and regulations allowing digital signatures and
encryption”.
McConnell
International
[24,25]
Facilitation of
e-commerce
“An ‘e-ready’ country requires consumer trust in e-
commerce security and privacy; better security
technology; more trained workers and lower training
costs; less restrictive public policy; new business
practices adapted to the information age; and lower costs
for e-commerce technology”.
WITSA [26]
Realising the importance of e-readiness measurement and its implications for
economic planning, many governmental and world organisations have created
instruments either in the form of self-assessment tools or surveys. The most prominent of
these institutions are the World Bank, McConnell, EIU, ASEAN, APEC, MOSAIC, and
CSPP [6,19,23-25,27-32]. Even though they set out to measure, presumably, the same
e-readiness element, they share little commonality in terms of the standards they use. For
example, APEC and CID factor only macro-economic factors and digital infrastructure in
estimating e-readiness whereas MOSAIC includes ‘knowledgeable citizens’ as an e-
readiness measure. CSPP has the same number of factors as MOSAIC, namely three, but
A framework for measuring national e-readiness 7
they only share one common factor, ‘macro-economy’, with CSPP using two new factors
called ‘competitiveness and access to skilled workforce’. As shown in Table 8,
McConnell uses six factors in estimating e-readiness, doubling the number used by
ASEAN and CSPP, and tripling those of APEC and CID [20-22]. The World Bank and
EIU propose the largest inventory of factors, namely seven, used as an instrument in
evaluating e-readiness but they are not the same; World Bank uses a factor called
‘culture’ and EIU uses a factor called ‘cost of living and pricing’, each of which is not
shared by the other organisation.
Even when the same factor is used by two evaluation instruments, its measurement
differs using a different set of metrics. An example is how APEC and MOSAIC measure
‘macro-economy’. APEC uses two criteria, namely ‘regulatory framework’ and ‘tariff
and non-tariff barriers’; MOSAIC, on the other hand, uses only a single criterion,
i.e., ‘local competition’ to measure this factor.
There are also situations where many instruments appear to measure the same criteria
for a given factor but the definitions they use to describe the criteria are different. For
instance, the World Bank, McConnell, EIU, ASEAN, CID and CSPP all employ a
criterion called ‘extent of staff training’ toward measuring the factor ‘access to skilled
workforce.’ Yet, the World Bank, McConnell, and CSPP share one definition of the
criterion whereas EIU, ASEAN, and CID share a different definition.
In summary, it can be seen that attempting to measure e-readiness at the national level
involves a rather convoluted framework. The difficulty is that if even if one chooses to
use a wide set of potential economic, political and social factors, it is rather easy to lose
sight of the most influential forces. The challenge is to strike a balance between a
comprehensive model that would embrace all the major driving forces of an economy and
a limited set of drivers that would directly impact on the progress of electronic
commerce.
3 A framework for measuring e-readiness
The purpose of this paper is to propose a comprehensive, yet more balanced, set of
metrics to assess the ability of a country to achieve the five characteristics of the real-
time economy we described earlier in this section.
3.1 Methodology
Economists have for a long time studied various factors that would impact economic
growth. Although there have been no comprehensive studies that would propose a
general growth theory of e-commerce, one could derive the lessons learned from a
number of factors that are required to build a sound national infrastructure that is
favorable to e-commerce. These factors include: government spending [33,34],
entrepreneurship [35], innovation, R&D [36], technology transfer and diffusion [37],
technology [38], invention and imitation [39,40], corruption and country risk [41],
knowledgeable workforce [40,42], the effect of political instability [43], and leapfrogging
[44]. We have adopted these factors in the context of the new economy with some
evidence based on economic measurement of the internet economy [5,8,11,42,45].
8 T. Bui, S. Sankaran and I.M. Sebastian
Based on the above literature, we gathered a master list of potential factors that are
widely recognised as having significant impacts on a nation’s e-readiness. We
investigated all possible criteria for each factor, scales that can be used to measure them
and mathematical models that can synthesise the partial results into a single composite
e-readiness indicator. Part of the methodology we used in attaining this consisted of
starting with the superset of all the factors and criteria measures of the prominent
instruments we discussed in the earlier section. We evaluated them for clarity of concept,
distinctiveness from other measures, their logical relationship to e-readiness and ability to
be measured quantitatively. Internal and external validities were also taken into account
and wherever economic and technological data were available. We evaluated them for
their robustness and practical ease of use through simulations. We also organised two
workshops with international academic experts, policy makers and business interests to
learn from their ideas regarding e-commerce readiness.
Based on the above data collected using the above methodology, we arrived at a list
comprising eight global factors that made up our e-readiness. We also assembled 52
variables to measure these factors. They are shown in Table 2. In validating the
instrument, we used the Analytical Hierarchy Process with eigenvalues for consistency
checking, Spearman’s rank correlation as well as sensitivity analysis for checking rank
stability. We contend that together they form a broad and yet focused collection of
factors/measures in assessing the e-readiness status of any country.
3.2 Computing e-readiness
Having collected data on the 52 variables across the 8 factors, the following formula is
proposed in computing a composite e-readiness index for a given country,
e-readinessi = Σj=1,n wij eij /n
where
e-readiness: the overall e-readiness value
i: country
j: each of the 52 measures
wij: relative weights assigned to the 52 measures (j)
eij : individual score for each measure on a scale of 1 to 5
n: total number of measures (52)
The generalised e-readiness index computing procedure may be summarised using the
following steps:
a select the list of countries whose e-readiness is to be compared
b gather data on the individual 52 measures for each country
c sort the data in step (b) by factor. Since there are 8 factors, this will create 8 groups
of data
d choose one factor in step (c) along with its measures
e examine the first measure of the chosen factor. Identify the smallest and the largest
values; determine the range by subtracting the smaller value from the larger
f create a normalised scale for the measure
A framework for measuring national e-readiness 9
1 Divide the range in step (e) into 4 equal intervals
2 Assign 1 to the smallest number in step (e)
3 Assign 5 to the largest number
4 Assign 2, 3 and 4 corresponding to the interval data created in f(i)
g compare each country’s value for the measure against the normalised scale in step (f)
h assign the closest normalised values for each country
i repeat steps (e) – (h) until all measures for the factor are exhausted
j compute the weighted average of the values in step (h); this gives the e-readiness
value for the given factor
k repeat steps (d) – (j) until all factors are exhausted
l average the values of all factors in step (j); this gives the e-readiness index for each
country.
The reason for normalising the raw data into a range 1 to 5 is to enable us to compare a
given country’s e-readiness with that of other countries. The normalisation scale is so
chosen that a value of 1 represents a country that is least e-ready whereas a value of 5
indicates one that is most e-ready.
3.3 An example
In order to illustrate the procedure involved in calculating the e-readiness, data for the 11
countries shown in Table 3 is used. We first focus on a factor, ‘access to skilled
workforce.’ According to Table 2, there are six measures for this factor. Data on these
measures were collected from secondary sources published by major research
institutions.
Table 2 The 8 factors and their 52 measures for calculating e-readiness
Knowledgeable Citizens Access to Skilled Workforce
Adult Literacy Rate Public Spending on Education as % of GDP
Secondary Enrolment University Education Meets the Needs of Economy
Tertiary Enrolment Well Educated People Do not Emigrate Abroad
8th Grade Achievement in Science Extent of Staff Training
MGMT Education Available in first-class
Business Schools
Research Collaboration Companies/Universities
Flexibility of People to Adapt to New
Challenges
Number of Technical Papers per Million People
10 T. Bui, S. Sankaran and I.M. Sebastian
Table 2 The 8 factors and their 52 measures for calculating e-readiness (continued)
Macro Economy Digital Infrastructure
Trade as % of GDP Telephone per 1,000 People
Adequate Regulation & Supervision of
Financial Institutions
Mobile Phones per 1,000 People
Protection of Property Rights Computers per 1,000 People
Tariff & Non-tariff Barriers Internet Hosts per 10,000 People
Soundness of Banks International Telecom, Cost of Call to USA
Local Competition Investment in Telecom as % of GDP
Regulatory Framework Computer Processing Power (% Worldwide MIPS)
Government Effectiveness E-Government
Political Stability ICT Expenditure as % of GDP
Press Freedom Freedom on the Internet
Rule of Law
Control of Corruption
Industry Competitiveness Culture
Technology Achievement Index National Culture is Open to Foreign Influence
Gross Tertiary Science & Engineering
Enrolment Ratio
English Language
Admin. Burden for Start-Ups Percentage of Urban Population
Patent Applications Granted by USPTO Percentage of Population 65 Years or Older
Private Sector Spending on R&D
Total Expenditure for R&D as % of GNI
High-Tech Exports
(% of Manufactured Exports)
Ability, Willingness to Invest Cost of Living and Pricing
Composite ICRG Risk Rating International Cost of Living based on US $100
Availability of Venture Capital Inflation Rate-CPI in %
Entrepreneurship among Managers GDP per Capita (PPP) in US$
FDI as % of GDP
A framework for measuring national e-readiness 11
Table 3 Data used in the example for calculating e-readiness
Country
Public
spending
on
education
as % of
GDP,
1999 [24]
University
education
meets the
needs of a
competitive
economy
[11]
Well
educated
people
do not
emigrate
abroad
[11]
Extent of
staff
training
([14])
Research
collaboration
between
companies and
universities
[14]
Number of
technical
papers per
million
people
1997 [24]
China 2.00 3.98 3.78 3.60 4.20 2.11
Singapore 2.40 7.61 5.58 5.70 5.60 5.68
Hong Kong 2.80 5.24 5.38 4.60 4.60 5.70
Korea 4.40 3.52 4.11 4.30 4.60 4.60
Philippines 3.00 5.64 2.92 4.00 3.30 1.15
Indonesia 0.60 4.00 5.00 3.80 3.40 0.47
Malaysia 4.00 4.63 4.38 4.60 3.60 2.65
Thailand 3.30 3.60 6.03 3.70 3.90 1.94
Taiwan NA 6.30 5.09 4.90 5.10 NA
Vietnam 2.80 NA NA 3.20 3.50 NA
East Asia 2.81 4.95 4.70 4.24 4.18 2.92
USA 4.70 6.60 8.55 5.90 5.30 6.40
G7 4.99 5.57 5.95 5.47 4.90 6.15
The first measure shown in Table 3, column 2 is ‘spending on education as a % of GDP’.
The smallest value in the column is 0.6 and the highest is 4.99. As mentioned earlier, we
want the e-readiness to be a number between 1 and 5. Therefore, we assign a score 1 to
the country with the smallest value and 5 to the country with the highest value. We then
divide the 0.6 – 4.99 range into four equal intervals and assign the intermediate scores.
Thus, the scores for the first of six measures under the factor ‘access to skilled
workforce’ will look as shown below.
At this point, we go back to data in column 2 of Table 3 and examine each country
and convert the actual values of the measure into a score between 1 and 5 using Table 4
as a guide. For example, it can be seen from Table 3, the value of the first measure for the
Philippines is 3. Turning to Table 4, the closest value in row 1 is 2.8. Row 2 of Table 4
shows 3 as the matching normalised score. We repeat the process for each of the
measures for each country for the same factor, ‘access to skilled workforce.’ The average
score for the factor is then calculated to evaluate the comparative standing of each
country with regard to e-readiness. For this particular measurement, we assigned wij to be
1 for all measures. The results are shown in Table 5.
Table 4 Normalising actual measures to 1-5 range
Actual value of measure 0.6 1.7 2.8 3.9 4.99
Normalised score 1 2 3 4 5
12 T. Bui, S. Sankaran and I.M. Sebastian
Table 5 Country-wide average scores for the factor, ‘access to skilled workforce’
Country Edu.
spending
Univ.
Edn
Emigra-
tion
Staff
training
Research Tech.
papers
Total Average
China 2 2 1 3 4 2 14 2.33
Singapore 2 5 3 5 5 4 24 4.00
Hong Kong 3 4 3 4 4 4 22 3.67
Korea 5 2 2 4 4 3 20 3.33
Philippines 3 4 1 3 3 1 15 2.50
Indonesia 1 2 3 3 3 1 13 2.17
Malaysia 4 3 2 4 3 2 18 3.00
Thailand 3 2 4 3 4 1 17 2.83
Taiwan 3 5 3 4 5 4 24 4.00
Vietnam 3 1 3 2 3 1 13 2.17
East Asia 3 3 2 4 4 2 18 3.00
USA 5 5 5 5 5 5 30 5.00
G7 5 4 4 5 5 5 28 4.67
We repeat the above process for each of the other seven factors in Table 2. A
benchmarking graph can be drawn as shown in Figure 1.
Figure 1 Benchmarking e-readiness using a 5-point scale
3
2
4
1
5
G7
East
A
sia
CULTURE
COST OF
LIVING
AND
PRICING
KNOWLEDGEABLE
CITIZENS
INFRASTRUCTURE
MACRO
ECONOMY
INDUSTRY
COMPETITIVENESS
INVESTMENT
ACCESS TO
SKILLED
WORKFORCE
A framework for measuring national e-readiness 13
The final step in the computation process is to use the formula Σj=1,n wij eij /n and calculate
the e-readiness value for each country. The weight wij assigned to each measured value eij
reflects the analyst’s view of how important or influential that criterion is relative to the
entire set of measures (n=52) for a particular country based on its overall economy. By
the very nature of the methodology, assigning weights is a subjective process. One
expects that national experts or policy makers would be the best qualified people for this
task.
For purposes of completeness, average values of the eight factors and the computed
e-readiness values for the various countries are presented in Table 6. The measures were
weighted equally. The data came from both published governmental and reputable
international organisations [3,32,46-53]. The details of the calculation are beyond the
scope of this paper.
3.4 Interpretation
The rationale for the selection of the countries shown in Table 6 is that e-commerce in
economies in East Asia – with the exception of Singapore – is in its infancy. Their
aspiration is to raise their e-readiness to the levels of the USA and the G7 countries. The
e-readiness index values in Table 6 show which countries lag behind and need attention
from policy makers and investors to improve their e-commerce climate. Further, by
comparing a factor score of a country with the scores of other countries for the same
factor, the table can help identify the specific areas of a country’s strengths and
weaknesses with regard to e-readiness.
Table 6 Average factor values and computed e-readiness index by country
Factors*
Country KC ME CMP ATI ASW DI CLP CLT
e-readiness
China 2.33 1.75 2.14 3.00 2.33 2.00 3.00 2.00 2.32
Singapore 4.17 4.58 4.29 4.50 4.00 4.00 3.67 4.50 4.21
Hong Kong 3.67 4.33 3.29 3.50 3.67 4.00 3.67 4.00 3.76
Korea 4.50 2.50 4.29 3.25 3.33 3.44 3.67 3.25 3.53
Philippines 3.50 2.33 2.29 2.00 2.50 2.22 2.67 4.25 2.72
Indonesia 2.50 1.83 1.57 1.75 2.17 1.56 3.00 3.00 2.17
Malaysia 3.00 2.67 2.57 3.00 3.00 3.00 3.33 3.50 3.01
Thailand 3.50 2.58 2.43 2.50 2.83 2.00 3.00 3.00 2.73
Taiwan 4.00 3.17 3.86 3.50 4.00 4.22 3.00 3.25 3.62
Vietnam 2.50 1.83 1.86 3.00 2.17 1.78 2.67 2.50 2.29
East Asia 3.17 2.75 3.00 3.25 3.00 2.56 3.00 3.25 2.99
USA 4.67 4.42 4.71 3.75 5.00 4.56 4.50 3.25 4.36
G7 4.50 4.50 4.29 3.25 4.67 4.00 3.33 2.75 3.91
*KC=Knowledgeable citizens ME=Macro economy CMP=Competitiveness
ATI=Attitude to invest ASW=Access to Skilled Workforce CLT=Culture
CLP=Cost of Living & Pricing DI=Digital infrastructure
14 T. Bui, S. Sankaran and I.M. Sebastian
For example, in terms of the ‘knowledgeable citizens’ factor, it can be seen from Table 6
that South Korea has achieved a respectable score of 4.5, which is equivalent to that of
the G7 and almost on a par with that of the USA. On the other hand, the table also reveals
China, Indonesia and Vietnam with their low scores as countries that need to plan
additional investments in education. With regard to the factor ‘macro-economy’, Table 6
shows Singapore has the highest factor score of 4.58, even better than that of the G7 and
the USA in providing a macro-economic environment that is conducive to e-business.
Singapore has been able to accomplish this because it has tuned its regulatory
environment and liberalised the market from early on. It offers a range of business
incentives and periodically adapts laws and regulations conduce to an e-economy.
Table 6 once again shows China, Indonesia and Vietnam as countries that need to work
on improving their macro-economic environment to enhance their business climate.
Singapore and South Korea lead the ranking in ‘competitiveness’ among the East
Asian countries with a factor score of 4.29. The indicator attempts to capture a country’s
capability for innovations and internal improvement. Investment in Research &
Development is a critical component, strengthened by sufficient human resources
focusing on technology and innovation. Reducing the administrative burden for start-ups
is also critical for competitiveness allowing them to dedicate their full potential to
innovation, and to eliminate inefficiencies arising from lack of competition.
Except for Singapore, Hong Kong and Taiwan, the East Asian countries in our study
need to enhance their performance in the ‘attitude to invest’, ‘access to skilled workforce’
and ‘digital infrastructure’ factors. Policy makers in these countries should take particular
note of their status with regard to these factors. They must find ways and resources to
overcome this shortcoming.
Table 6 shows that East Asia handily outperforms the G7 and USA in the two factors,
‘cost of living & pricing’ and ‘culture’. It continues to enjoy a comparative advantage in
the area of labour cost. Having a culture that is diverse and familiar with foreign
influence facilitates international trade using e-technology. A young urban population
with fluency in English also provides a culture that is sufficiently international to deal
with cross-border electronic commerce. It is important to note that many economies in
the survey group have recently enforced the teaching of English as a second language in
their education curricula.
3.5 Validation
The World Times Society (WTS) published a report in 2001 in which a standard measure
called the Information Society Index (ISI) was used to compare the progress of countries
with regard to the various aspects of the information age [15]. The measure was claimed
to represent a country’s “ability to access and absorb information and information
technology in the future” [54]. The ISI index is based largely on a subjective proprietary
evaluation process that professes to capture the true status of an information society
based only on the status of a country’s infrastructure. WTS advocates that governments
use the measure to develop national programs that will stimulate information technology
development.
To gain insight into the validity of our approach, we compared the PRIISM e-
readiness indices with the published ISI indices for the East Asian countries in our study
[15]. The countries’ rankings according to both indices were also compared. Table 7
A framework for measuring national e-readiness 15
summarises the results. With the exception of Vietnam and Indonesia, this corroborates
the results and adds an independent validation of our method.
Table 7 Comparison of our results with ISI for validation
Country PRIISM
Ranking
PRIISM
Index
EIU
Ranking
EIU
Index ISI Ranking ISI
Index
Singapore 1 4.21 1 8.17 1 5,269
Hong Kong 2 3.76 2 8.13 2 4,745
Taiwan 3 3.62 3 7.26 3 4,296
Korea 4 3.53 4 7.11 4 4,283
Malaysia 5 3.01 5 5.50 5 2,220
Thailand 6 2.73 6 3.86 6 1,563
Philippines 7 2.72 7 3.72 7 1,553
China 8 2.32 8 3.64 8 1,198
Vietnam 9 2.29 10 2.96 NA NA
Indonesia 10 2.17 9 3.29 9 1,172
Legend: PRIISM (Pacific Research Institute of Information Systems Management)
EIU (The Economist Intelligence Unit)
ISI (Information Society Index)
4 Assessment of the proposed framework
We acknowledge some of the methodological limitations related to the computation of a
composite measure. These are:
• collected data tend to be outdated, due to the rapid pace of change
• data are more available on the supply side (e.g., number of installed internet servers)
but less on the application side (e.g., what business applications were put on these
servers)
• the interaction between different measurement factors
• interpretation of the measures (e.g., what does it really mean to economic
development if the number of computers per 100 people is 25?)
• evaluation of performance variables is averaged out at the national level, thus
obscuring possible spots of excellence in e-commerce.
Also, the commonly used additive functions might not reflect the composite effect of the
factors. Other non-linear techniques for aggregation could be used. However, the additive
function is easy to understand and seems adequate for the purpose of this paper.
We compared the measures used in our framework with those used in other
methodologies some of which were discussed earlier under literature review. Table 8
shows the comparisons for the measures used in the eight factors. The framework
presented in this paper is indicated by the heading PRIISM (University of Hawaii’s
16 T. Bui, S. Sankaran and I.M. Sebastian
Pacific Research Institute for Information System Management) in the second column of
each table.
Table 8 Methodology comparison
Knowledge Citizen 1 2 3 4 5 6 7 8 9
Adult Literacy Rate X X X X (X)
Secondary Enrollment X X X X
Tertiary Enrollment X X X
8th Grade Achievement in Science X X
MGMT Education Locally Available in first-class
Business Schools
X X
Flexibility of People to Adapt to New Challenges X X
Macro Economy
Trade as % of GDP X (X)
Adequate Regulations & Supervision of Financial
Institutions
X X (X)
Protection of Property Rights X X (X) X (X)
Tariff & Non-tariff Barriers X X X (X) (X)
Soundness of Banks X X X
Local Competition X X X (X)
Regulatory Framework X X X X (X) (X) (X) (X)
Government Effectiveness X X X X (X)
Political Stability X X X X
Press freedom X X
Rule of Law X X X X
Control of Corruption X X
Competitiveness
Technology Assessment Index X X
Tertiary Science & Engineering Enrollment X X X
Admin. Burden for Start-Ups X X X X
Patent Applications Granted by USPTO 2000 X X X
Private Sector Spending on R&D X X X
Total Expenditure for R&D as % of GNI, 1987-1997 X X X
High-Technology Exports X X X
Ability, willingness to Invest
Composite ICRG Risk Rating 2000 X X
Availability of Venture Capital X X
Entrepreneurship among Managers X X X X
FDI as % of GDP 1990-1999 X X (X)
1: PRIISM 2: World Bank 3: McConnell 4: EIU 5: ASEAN 6: APEC 7: MOSAIC 8: CID 9. CSPP
A framework for measuring national e-readiness 17
Table 8 Methodology comparison (continued)
Access to Skilled Workforce 1 2 3 4 5 6 7 8 9
Public Spending on Education X X X
University Education Meets the Needs of Competitive
Economy
X X X (X)
Well Educated People Do not Emigrate Abroad X X
Extent of Staff Training X X X (X) (X) (X) X
Research Collaboration Companies/Universities X X X X
Technical Papers per Million Population X X X
Digital Infrastructure
Telephone per 1,000 People X X X X X X (X)
Mobile Phones per 1,000 People X X X X X X (X)
Computers per 1,000 People X X X X X X X (X) X
Internet Hosts per 10,000 People X X X X X (X) X
International Telecommunications, cost of call to the
USA
X X (X)
Investment in Telecom X X X
Computer Processing Power
(% of total worldwide MIPS)
X X (X)
E-Government (2001 WEF) X X X X X (X) X X
ICT Expenditure as % of GDP X X (X) X
Freedom on the Internet X X
Cost of Living and Pricing
International Cost of Living Indices based on $100 US X
Inflation Rate-CPI in % X
GDP per capita (PPP) in US$ X X
Culture
National Culture is Open to Foreign Influence X X
English Language X
Percentage of Urban Population X
Percent of Population 65 Years or Older X
1: PRIISM 2: World Bank 3: McConnell 4: EIU 5: ASEAN 6: APEC 7: MOSAIC 8: CID 9. CSPP
An (X) indicates agency quantifies the same measure differently
It should be noted, however, that attempting to make a comparison between the different
methodologies is not without difficulties. Often indicators by different institutions serve a
similar purpose, but are measured differently [44]. Further, several methodologies are not
specified beyond broader categories and indicators are not listed explicitly. Therefore, it
may not be possible to provide a comprehensive evaluation because information about
the factors is only partially available. Another problem to be considered is that some
measures are only partially comparable, since one approach may differ in focus from the
other. This is the case with the measures ‘protection of property rights’ and ‘protection of
18 T. Bui, S. Sankaran and I.M. Sebastian
intellectual property rights.’ In these cases, the mark is in brackets (x) in Table 8 to
indicate that measurements are not exactly the same.
It can be seen from the tables, however, that our framework covers more measures
affecting e-readiness compared to the currently available other methodologies. We
believe this makes our approach more comprehensive. Further, the other methodologies
place emphasis on the continued improvements of national information infrastructure and
internet technologies [55]. We, on the other hand, include measures that gauge the ability
and potentiality of an economy to engage in e-commerce as well as to innovate and
compete effectively in the new economy.
More specifically and with regard to East Asia, the Information Society Index (ISI)
focuses more on digital infrastructure to include the use of technology in the education
sector. As the infrastructure requirements are met, we contend that the influence of other
factors is bound to become more important [36,56]. Our set of variables is closer to that
of EIU in that both the EIU methodology and ours give more importance to the ability of
a country to capitalise on its current information and communication technologies to
create business value. Our methodology supports the EIU approach in that more
advanced and bigger infrastructure might not always be better. Recognising that
technology and economic structures are evolving, we expand the e-readiness framework
to capture the business culture (i.e., management skills, local competitiveness, ability to
take risks, cultural factors open to global business).
Hence, even though our ordinal ranking is similar to both those of ISI and EIU, there
are reasons to speculate that our e-readiness indices (in cardinal terms) are closer to the
true e-readiness in the general context of the new economy.
5 Managerial implications
Policy makers, particularly in developing countries, face a chronic shortage of resources.
They need to be able to clearly identify and prioritise sectors in the economy that require
urgent attention. They also want to know which areas will fetch the maximum return
from minimal resource allocations and, thus, move the country closer to national goals.
The methodology proposed in this paper will be very useful in meeting these
requirements as it relates to improving a country’s e-readiness.
There are several reasons why a policy maker should adopt the proposed method.
Firstly, the method has determined that there are eight major factors that have proved to
have an impact on economic growth and the adoption of e-business. Given particular e-
business applications, policymakers could readily identify which factors need to be
improved compared to others. To date, most policy decisions have been based only on
national information infrastructure development. Our model reminds them that there are
other factors of equal importance. For example, based on Table 6, a policy maker in the
Philippines would conclude that the lowest score of 2.0 was for the factor ‘attitude to
invest’ and hence would be persuaded to allocate more than proportionate resources in
tackling this factor alone.
Secondly, the component measures of each factor are clearly defined in the model
(Table 2). This adds clarity by being able to spell out how to budget for each of the
specific measures. For example, by examining the e-readiness values in Table 6, a policy
maker in Vietnam would recognise that the country is doing poorly on factors, ‘access to
skilled workforce’, ‘competitiveness’, ‘macro-economy’ and ‘digital infrastructure’.
A framework for measuring national e-readiness 19
While deciding how much to allocate to improve ‘access to skilled force’ as a factor,
he/she can look through the component measures in Table 2 such as ‘public spending as a
% of GDP’ and ‘educated population emigrating abroad’ and decide on how much
resources should be specifically allocated towards each measure. This procedure helps to
build a budget from bottom-up and adds a degree of efficiency and effectiveness in
national resource allocation decisions.
There are other reasons as well why a policy maker should use the proposed
approach. Since real data from either primary or secondary sources are used for each of
the 52 criteria measures, the approach is objective. As data is normalised using the scale
of 1 to 5, ranking provides an indication of interdependence among competitive nations.
Further, the weights assigned to each measure/factor can be varied to reflect the priorities
of the decision modeller. The computation algorithm is programmable and the process of
calculating the e-readiness can be easily automated. Finally, the final computed
e-readiness indexes for all countries are also comparable.
The methodology is not without some disadvantages. Apart from some of the cons
discussed in Section 4, the additive functions and averaging used in the e-readiness index
may not reflect the composite effect of the factors. The determination of factor weights is
subjective. Also, use of the 1 to 5 scale for normalising the criteria values may not be fine
enough.
6 Concluding remarks
This paper presented a framework that conceptually relates the e-readiness of a country to
52 independent measures across eight factors. This is the broadest selection of indicators
of all the instruments currently available that expands the current measurement
framework that primarily concentrates on technology deployment. We introduce in our
method additional dimensions to capture the business culture, the role of the government
and the ability and potential to compete globally.
One would normally expect that the higher the e-readiness score, the higher the
ability of a country to compete in the new economy. What is actually more appropriate
for an economy is to find e-readiness factors that would best align with its business
strategies and national trade priorities.
We used real-world data on selected East Asian, USA and G7 countries to illustrate
our methodology. The East Asian countries in the sample study need to build a critical
mass of ‘new economy’ opportunities (i.e., new businesses, new dot-com workers, new
business partnerships, new cooperation schemes) to foster an ambiance of innovation and
entrepreneurship. Building e-ready human resources is also undoubtedly another
important challenge in the e-commerce capacity building effort. After all, it is the ability
to innovate that matters most in the knowledge-driven economy, once the information
infrastructure is sufficiently functional.
Perhaps the most important contribution of the proposed methodology is to remind
policy makers that e-business is part of a complex and general economic structure, and its
success depends on that structure. The proposed algorithm provides a structured and
methodical approach rather than relying on intuition and risky conjecture in national
e-strategy decisions. We hope the detailed metrics can be used to help legislators,
business communities and researchers analyse their nation’s unique needs and develop
20 T. Bui, S. Sankaran and I.M. Sebastian
customised action plans to improve e-readiness through an optimum allocation of
resources [7,16]. Despite its simplicity and limitations, the proposed assessment
framework would correct the areas omitted by existing tools and lay a more
comprehensive foundation for measuring and monitoring world e-competitiveness. Once
actual data on e-commerce outputs and productivity are available, it is our intention to
further our study with statistical validation of the e-readiness formula.
Acknowledgement
This research was partially funded by an APEC grant (APEC TEL Working Group
Project TEL01/2001T-Electronic Commerce Capability Building) and by the support of
the Matson Navigation Company Chair of Global Business. The authors would like to
thank the participants of the APEC-TEL workshop in Bangkok, Thailand (June 2002),
and the Asian Productivity Organization (APO) meeting in Honolulu (May 2001) for
input on the status of e-readiness in their respective economies.
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