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Considering various reasons it is interesting to ask why e-readiness
rankings are so popular in both private and public
sectors across the world. Having an easily quantifiable set of indicators provides an overview of a nation's
situation, and can easily form a basis for comparison and future planning. This
benefit arises, because of the greater ability of e-readiness measure to abridge a
broad set of promising attributes of a given nation
(Pradhan et al., 2009). The nation can be
benefited from Information and Communication Technology (ICT)
(WITSA, 2001) if it is facilitated with the
perceptible levels of infrastructure, education and supportive
government policies. E-commerce is not possible
unless consumers buy online. Similarly, e-government is not possible if citizens of
a nation do not change their attitude to access the
state-of-the-art technology. In recognition of the significance of e-readiness tool and its
realistic implications for economical escalation of a country, most of the
governments and world-wide organizations have developed their own gadgets either in
the form of self-assessment tools or surveys. The most
top-notch institutions, which have created their own tools are
Association of SouthEast Asian Nations (ASEAN,
2002), Computer Systems Policy Projects (CSPP,
1998), Asia Pacific Economic Cooperation (APEC,
2000), McConnell (WITSA, 2000 and 2001), MOSAIC
(2000), the World Bank (2001) and Economist Intelligence Unit
(EIU) (2009).
There are many tools to measure e-readiness with different
sets of factors and indicators, but understanding the crucial factors and indicators
for measuring e-readiness of a nation by the proposed method gives us
immense pleasure. The impact of e-readiness in most of the nations is carried out
by EIU annually. Therefore, these data have been chosen to evaluate the
method used in this study. In general, Figure-of-Merit (FOM) is acting as a basis
for e-readiness metric (Bui et al., 2003; and Pradhan et al., 2009). Normally, FOM computation attributes a chain of factors with equivalent weights, i.e.,
factors-weights. The factors depend on indicators which are grouped under its
umbrella, and the indicators have different weights too (i.e., indicator-weights).
The assignment of weight to each of the indicators or factors is a planning
issue. Therefore, it is the task of policy makers to assign weights properly.
However, two intrinsic problems are always attached with FOM: (1) Vagueness, and
(2) Heterogeneity of the data.
Davidrajuh (2008) has proposed a tool based on FL for
e-readiness measurement. However, there is also an inherent problem associated with the
FL-based tool. The problem is large number of fuzzy rules (Ghosh et al., 2004; and Ross, 2004). Let there be n number of indicators and each having  number of linguistic values. Hence,
the maximum number of fuzzy rules needed in a
Fuzzy Inference System (FIS) is equal to  , which is an intractable problem. Therefore, to
choose an optimal and comprehensible number of fuzzy
rules, genetic algorithms are used (Dev, 2001; and Ghosh et al., 2008). The beauty of GA is, it can explore large search space with multiple modals in a
reasonable amount of time (i.e., it can avoid user frustrations).
This paper discusses the fuzzy inference system and some of the popular
fuzzy membership functions. Then E-readiness using genetic fuzzy inference system
is described and the experimental setup and results are analyzed. Finally,
this paper summarizes with a conclusion. |
