E-PROCUREMENT ADOPTION AMONG ITALIAN FIRMS BY
USING DOMAIN NAMES
Maurizio Martinelli, Irma Serrecchia, Michela Serrecchia
Institute for Informatics and Telematics – Italian National Research Council (IIT-CNR)
Via Giuseppe Moruzzi, 1 – 56124 Pisa - Italy
Keywords: Internet Diffusion, Digital Divide, Domain names.
Abstract: The digital divide can occur either as a “local” (within a given country) or “global” (between developing
and industrialized countries) phenomenon. Our study intends to offer an important contribution by
analyzing the digital divide in Italy and the factors contributing to this situation at the territorial level (i.e.,
macroareas: North, Center, South and at the provincial level) To do this, we used the registration of Internet
domains under the “.it” ccTLD as proxy. In particular, we analyzed domain names registered by firms. The
analysis produced interesting results: the distribution of domains registered by firms in Italian provinces is
more concentrated than the distribution according to income and number of firms, suggesting a diffusive
effect. Furthermore, when analyzing the factors that contribute to the presence of a digital divide at the
regional level, regression analysis was performed using demographic, social, economic and infrastructure
indicators. Results show that Italian regions that have good productive efficiency measured by added value
per employee and a high educational level measured by number of firms specialized in the ICT service sale
(provider/maintainer) and by number of employees devoted to research and development are the best
candidates for utilization of the Internet.
1 INTRODUCTION
Internet growth has captured the imagination of
users, policymakers, entrepreneurs, corporate
managers, military strategists, social commentators,
scholars and journalists (Guillèn & Suarèz, 2004).
The Internet is seen by some researchers as a new
technological means that will lead to a “smaller,
more open world” (Tapscott & Caston, 1993).
According to some researchers the Internet
symbolizes “the triumph over time and space” the
rise of the “netizen”, and the crowing of the
“customer as sovereign” (Gilder, 2000).
According to Coffman, Odlyzko (2001) the
Internet is a means of communication that is
expanding very rapidly. Studies carried out by the
Network User Association (NUA Ltd) estimates the
worldwide on-line population in 1999 and in 2002
and shows that in Europe the number of individuals
on-line came to 190.91 million in 2002, compared to
47.15 million in 1999.
Companies as well as individuals also turn to the
Internet to exploit its communication potential.
Today, information infrastructures are reaching out
to the individual consumer, and telematic networks
reduce the cost of communications. This statement
agrees with economics literature (Hoffman &
Novak, 1996), which confirms that the Web is
becoming a dynamic and personal means of
communication.
According to other authors (Bassi, 2002) the
spread of the Internet and the functions of electronic
commerce will permit individual clients to choose
from a wide array of products and reduce costs,
selecting and buying goods directly from the source
and allowing companies to sell while by passing
traditional channels. Scandinavia, at 8.6%, leads the
region with the highest percentage of on-line sales,
usually computers and related products, travel, video
and music, and books.
This situation could prove to be quite worrisome
for traditional businesses, as emerges from a survey
carried out by the Syndicate Agents Union and
representatives of the Italian Commerce in
November 2000.
However, companies must adopt entirely new
forms of commercial activity so that online sales
will be successful.
The advantages for businesses provided by the
Internet are not only linked to the sale of products
124
Martinelli M., Serrecchia I. and Serrecchia M. (2006).
E-PROCUREMENT ADOPTION AMONG ITALIAN FIRMS BY USING DOMAIN NAMES.
In Proceedings of the Eighth International Conference on Enterprise Information Systems - SAIC, pages 124-131
DOI: 10.5220/0002494501240131
Copyright
c
SciTePress
and services (direct advantages) but can also be
indirect (Hansons, 2000). For example, among the
most important of these are reduced costs, image
consolidation, greater customer loyalty, and a wider
diffusion of products offered by the company. They
are referred to as “indirect” since they do not lead
directly to sales and do not generate immediate
profits; however, they are important since they will
probably be the greatest benefits offered to
businesses by the Internet.
The gradual confirmation of the Internet as a
means of communication also permits companies to
access data and a variety of other information; for
example, it is possible to rapidly obtain information
about the market in which one operates by visiting
websites specialized in economic information or
areas that furnish updates on laws, price changes, the
appearance of any new operators in the field, fairs,
competitive bidding, and other news of interest to
operators. One can also identify the competition and
analyze them by means of information published on
company websites, etc.
Our study analyzes the spread of the Internet
among Italian firms utilizing as metrics the number
of domain names registered under the ccTLD “.it”.
We took into consideration domain names, names
that are associated to IP addresses in the net, because
we believe it to be really important for a firm to have
a domain name, as through this name an Italian firm
can exploit the above mentioned direct and/or
indirect advantages. Moreover, it is helpful for a
firm to register a domain name not only to have its
own web site, but also to benefit from the
advantages related to on-line means of
communications (for example e-mails, FTP and so
on). As a matter of fact, on-line means of
communications unlike traditional ones (for example
call-center services or telemarketing) are more
effective as they allow firms to reach, for example,
several customers at the same time, and more
flexible, as some of them allow customers to solve
problems on their own (for example with the FAQs).
In this way, a twenty-four hours a day access to
resources is granted. On the contrary, traditional
customer care methods require intensive work and a
considerable engagement of resources to ensure
prompt and accessible assistance.
Besides, the analysis of the Internet presence in
various social activities and economic and political
areas indicates a critical issue: the existence of a
“digital divide” between those who possess the
material and cultural conditions to exploit the new
technologies, and those who do not, or those who
lack the crucial ability to adapt to the rapid continual
change that characterizes the Internet today
(Warschauer, 2001; OECD, 2001, Kirkam et al.
2002; Norris, 2001; Rogers, 2001). Therefore, it is
not surprising that the announcement of the Internet
potential as “a liberty, productivity and
communication instrument, goes hand in hand with
the digital divide exposure” caused by the uneven
Internet diffusion (Castells, 2001). The 1999 World
Human Development Report written by the United
Nations organization considers the number of
Internet users one of the most widely used indicators
that show the divide between rich and poor
countries. Statistics compiled by the International
Telecommunication Union indicate that by the end
of 2002 Internet users represent in countries such as
Africa, Central America and South America only
1% of the population while this percentage goes up
of 50-60% in countries such as Iceland, United
States, Scandinavia, Singapore or South Korea (ITU,
2003).
In this paper we are going to analyze the factors
contributing to the existence of the digital divide in
Italy, taking into consideration not only economic
variables, but also educational, cultural,
demographic and in the end, technological variables.
2 METHODS
Several metrics are available for measuring Internet
diffusion. The most convenient are the so-called
endogenous metrics which can be “obtained in an
automatic or semiautomatic way from the Internet
itself” (Diez-Picazo, 1999). These metrics have the
undeniable advantage of accuracy, being based on
automatic data collection and retrieval; in addition
they allow good geographical characterization of the
phenomenon being based on data that allow
differentiation of users on a national, regional and
provincial level. Among the endogenous metrics,
according to literature, the most frequently used
ones to evaluate Internet diffusion analysis are
Internet hosts based on host count procedures (see
studies published by Internet Software Consortium
or by RIPE-NCC) and second-level domain names
(Naldi, 1997; Zook, 1999; Bauer, Berne and
Maitland, 2002). Despite the advantages offered by
endogenous measures, there are also a few
disadvantages, since in some cases they tend to
underestimate and in others to overestimate the
phenomenon being studied (Zook, 1999, 2000,
2001). Overestimation can occur when the number
of hosts is used, often associated with IP addresses,
while if we consider the number of registered
E-PROCUREMENT ADOPTION AMONG ITALIAN FIRMS BY USING DOMAIN NAMES
125
domains, more than one domain may be associated
with the same registrant. Underestimation can occur
because not all Internet users register a domain name
under their own ccTLD, and in many countries the
regulations allow foreign citizens to register under
their own ccTLD (for example, Italy allows
organizations and citizens of European Union
countries to register under the “.it” ccTLD).
In the case of hosts, underestimation may be due
to the growing presence of firewalls and private
networks (Intranet) and the use of dynamic IP
addresses, increasingly accompanied by new tools to
access the Net (for example, mobile phones). In
spite of these disadvantages, the numbers of hosts
and Internet domains are the principal means utilized
for analyzing Internet diffusion.
To measure Internet diffusion in Italy among
firms, we used the endogenous measure of second-
level domain names registered under the “.it”
ccTLD, managed by the Institute of Informatics and
Telematics of CNR, Pisa, using data that were
extracted from the registrations databases, using
automatic and semi-automatic procedures. We
created a new database for analyzing Internet
diffusion by initially consulting the WHOIS
database (the latter contains information regarding
the domain names registered under the “.it” ccTLD,
applicants who have signed a contract with IIT-CNR
and technical and administrative contacts) using an
automatic procedure; for example in order to
determine the type of applicant, the automatic
procedure verified whether an ORG field
(organization name) and a DESCR field (description
of the organization registering the domain name)
were present and if they were, depending on the
values of these fields, classified it as a firm. If the
ORG or DESCR fields were wrong, the LAR (is a
Letter of Assumption of Responsibility through
which the applicant assumes full civil and penal
responsibility for the use of the domain name
requested) database (semi-automatic procedure) was
consulted. Finally, where LAR information was not
accurate enough, the Italian Chamber of Commerce
database was consulted.
Approximately 900,000 domain names were
analyzed and grouped into several categories
(individuals, firms, universities, associations, public
groups and other registrants). In this paper particular
attention, as mentioned above, was paid to the
registration of domain names by firms. To be able to
register a domain name under the “it.” ccTLD, firms
must send to the Italian Registry a LAR. The five
LARs currently available differ according to the type
of applicant (individual ,association/foundation,
public administration, professionals, companies).
From this research performed as on December
31, 2004, it was established that the number of
domains registered by firms came to 411,339 of
which 407,030 were registered by Italian firms and
4,309 by foreign firms. Furthermore, 1,944 domains
registered by Italian firms were not classified since it
was impossible to discover the province of origin.
3 RESULTS
To measure the digital divide among Italian regions
(Italy is divided into 20 regions) we utilized as
metrics the number of domain names registered by
firms under the ccTLD “.it”, the penetration rate
calculated every 100 firms, the index calculated by
Zook and the Gini index (Gini, 1960).
The Zook index “Domain name Specialization
Ratio” is “a useful technique for comparing regions
which indicates the extent to which a region is
specialized in domain names compared to the United
States as a whole” (Zook, 1999).
That index has been used by Mattew Zook to
define the digital divide in the United States
utilizing as metrics the number of domains
registered by the firms under the ccTLD .”com
(Zook, 1999, 2000, 2001) and it is calculated in the
following way:
Domain name Specialization Ratio =
(Number of .it domains in a region / Number of
firms in that region ) divided by
(Number of .it domains in a country/ Number of
firms in a country)
An index value greater than one indicates a higher
specialization than the national average and an index
value less than one indicates a lack of specialization.
The penetration rate formula is as follows:
Penetration rate = (Number of .it domains in a
region* 100)/Number of firms in that region
Our research shows and as literature suggests, even
if some regions have a high specialization rate
compared to the national average (for example
Lombardy, Trentino Alto Adige, Tuscany, Latium)
the variance among the analyzed regions could be
extreme (Zook, 1999).
As mentioned before, an additional measure that
was adopted in order to verify the existence of
digital divide in Italy is the Gini concentration index
(Gini, 1960). The Gini index assumes values equal
to 1 and 0. Value 0 indicates equidistribution and 1
signifies the maximum concentration. The aim of the
so-called “statistical theory of concentration” is to
provide tools and techniques for measuring the
concentration in concrete situations or for comparing
ICEIS 2006 - SOFTWARE AGENTS AND INTERNET COMPUTING
126
the degree of concentration among heterogeneous
situations.
The Gini index, calculated on the number of
registered domains (that number should not be
confused with the above mentioned penetration rate)
confirms the above mentioned results. Only firms
with head offices in some provinces of Italy register
a high number of domains while firms with head
offices in other provinces (especially in the South of
Italy) shows scarcely significant percentages. The
first ten provinces over 103 register nearly half of
the domains compared to the national totality
(43.74%).
The study also compares the number of domains
registered by firms with head offices in provinces
with an income perceived by the single province and
the number of firms of a single province in order to
verify if the distribution of the registered domains is
similar to the number of the existing firms and
income distribution. In other words we wanted to
verify if the Italian areas that are the richest and the
most industrialized are also the most inclined to use
the Internet.
Table 1 shows that the Gini index, calculated on
the number of the registered domains is higher than
the index calculated according to income and
number of firms; this to signify that in Italy the most
industrialized and richest provinces not always come
first in the registration of domain names.
Table 1: Gini concentration ratio.
Gini index
No. of registered domains 0.543
Number of firms 0.468
Total income provinces 0.466
A first conclusion that comes from the
observation of the above mentioned results, is that
the Internet cannot be considered as a spreading
phenomenon capable of closing the gap among
Italian regions and provinces: domain names
distribution proves to be more concentrated than
income level and the number of firms, this to signify
that the Internet is far from being an equalizer, it
rather intensifies the differences among Italian areas.
3.1 Factors That Cause the Digital
Divide
To identify the key factors contributing to the
existence of the digital divide at a regional level (the
survey has been conducted at a regional level and
not at a provincial level as many variables were
available only at a regional level ) we identified five
models:
Model 1: stepwise regression taking as dependent
variable the penetration rate calculated every 100
firms and as independent variables economic
indicators;
Model 2: stepwise regression taking as independent
variables indicators that express the cultural
liveliness of a given region;
Model 3: stepwise regression taking as independent
variables indicators that express the educational
attainment of a given region;
Model 4: stepwise regression that takes into
consideration demographic indicators;
Model 5: stepwise regression that takes into
consideration as independent variables indicators
connected to the ICT.
In the stepwise regression the independent variables
are inserted in the equation if the F probability is of
<= 0.050 while they are removed from the equation
if the F probability is of >= 0.100.
Nevertheless the models 1, 2, 3 ,4, 5 show the
multicollinearity problem: the variables studied in
each model could be correlated to the independent
variables examined in the other models generating
an evaluation distortion. For example the
independent variable number of registered patents
of model 1 could be correlated in a positive or
negative way to the independent variable number of
employees devoted to research and development of
model 3.
3.1.1 Model 1
Model 1’s purpose is to verify if the disadvantaged
areas in terms of economic development are also
disadvantaged in terms of Internet diffusion.
In this model the only significant variable that
expresses the variance for the 64.4% of Internet
diffusion among Italian regions is the added value
per employee (see table 2). The rest of the variables
analyzed in the model (see table 3) prove to be
scantly significant as they do not reflect the
literature (Chinn and Fairlie, 2004; Hargittai, 1999;
Guillèn & Suarèz, 2001, Maitland & Bauer, 2001,
Norris, 2001). Besides, although the above
mentioned variables prove to be little significant in
expressing variance at a regional level, the economic
indicators such as total income, per capita income,
number of registered patents every 100 firms and the
percentage of big firms are positively correlated to
the penetration rate. Table 3 indicates the above
described trend.
E-PROCUREMENT ADOPTION AMONG ITALIAN FIRMS BY USING DOMAIN NAMES
127
Table 2: Coefficients (a) - F = 32.62 SIG. = 0.000 R
2
= 0.644.
Model
Non standardized
coefficients
Standardized
coefficients
t Sig.
B
Standard
Error Beta
1 (Constant) -
13.383
3.909 -3.423 .003
added
value per
employee.
.000 .000 .803 5.711 .000
a dependent variable: Penetration rate
Table 3: Pearson’s correlation matrix.
Table 3. Pearson’s correlation matrix
Penetration added value
per employee
.
Percentage big
firms
Total income Per capita
income
Registered
patents every
100 firms
Penetration 1.000
added value per
employee
.
0.803** 1.000
Percentage big firms 0.539* 0.637** 1.000
Total income 0.480* 0.510* 0.921** 1.000
Per capita income 0.737** 0.828** 0.314 0.166 1.000
Registered patents
every 100 firms
0.702* 0.717** 0.701** 0.611** 0.478* 1.000
** the correlation is significant at the 0.01 level; * the correlation is significant at the 0.05 level
3.1.2 Model 2
Model 2 seems to confirm to a slight extent the
combination between technological indicators and
cultural indicators ( Florida, 2002).
The results are shown in table 4.
Table 4: Coefficients (a) R
2
= 0.34 F = 9.442.
Although the model is rather plain, it expresses only
the 34% of the ICT diffusion variance among Italian
regions, the independent variable have a statistically
significant positive effect in the ICT diffusion.
The H5 hypothesis is confirmed: the Internet is
diffused in Italy among regions with a higher
spending in theatres and music (Beta is equal to
0.587 ).
3.1.3 Model 3
Table 5 shows that the educational attainment plays
an important role in the ICT diffusion among firms,
the model expresses the 93.4% of the Internet
variance diffusion among Italian regions: regions
with a number of employees devoted to research and
development and with a higher number of
Providers/Maintainers (the Providers/Maintainers
are the companies registering a domain name for
somebody else, offering connection to the Internet
services, managing electronic mail and so on - in
practice they are the companies specialized in the
ICT services) are more inclined to utilize the new
technology.
Table 5: Coefficients (a) - R
2
= 0.938 F = 56.58 Sig. =
0.000.
Besides, a result that according to us is worth
mentioning, is that the number of graduate people,
unlike the other variables expressing the educational
attainment level at a regional level, cannot be
considered as a factor that affects Internet diffusion
among firms, the beta tanking into consideration the
number of graduates every 1000 inhabitants proves
to be negative and significantly different from zero
(the beta is equal to -0.232 at a significance level
0.01) (see table 5). This means that regions with a
high level of educational attainment calculated in
terms of graduates, register a lower penetration rate.
This trend is explained by the fact that in less
industrialized areas and where job opportunities are
scanty, 19 years old youngsters continue their
studies and tend to graduate with the hope of finding
a job more easily (usually they find jobs in the North
or in the Centre of Italy anyway) while in the
northern and central regions that are more
industrialized and where there are wider job
opportunities young individuals tend not to continue
ICEIS 2006 - SOFTWARE AGENTS AND INTERNET COMPUTING
128
their studies and start working usually right after
secondary-school diploma.
3.1.4 Model 4
Model 4 shows that there exist a linear relation
between the demographic indicator and the
registered penetration rate (that model expresses the
variance of the 68% of the Internet diffusion).
Regions with a high jobless rate are less inclined to
utilize the new technology, the correlation between
the penetration rate and the jobless rate proves to be
negative and significantly different from zero, the
beta is equal to -0754 (see table 6).
Table 6: Coefficients (a) – R
2
= 0.680 F = 18.079 Sig.
0.000.
3.1.5 Model 5
As it could be expected even model 5’s hypothesis
are confirmed: the infrastructure supply is a good
magnitude to measure the existence of the digital
divide: the technological indicator has also a
statistically significant positive effect on the ICT
diffusion (see table 7), in addition the correlation
between the penetration rate and the indicator that
expresses infrastructures in ICT proves to be
positive and significant to a 0.001 level, (the beta is
equal to 0.673) this means that some regions with a
high investment in IT register also a high penetration
rate.
Table 7: Coefficients (a) – R
2
= 0.45 F= 14.878 Sig. =
0.001
.
Besides, even the above mentioned model expresses
a variance of only 45% of ICT diffusion among
Italian regions.
A first conclusion is that even if in Italy, as
literature suggest (Guillén and Suaréz, 2001; Kiiski
and Pohjola, 2002; Chinn and Fairlie, 2004),
infrastructures play an important role in determining
the digital divide; economic indicators and
indicators related to the educational attainment are
also important to explain the differences about
Internet use among Italian regions.
4 CONCLUSIONS
In our paper we wanted to analyze the factors that
cause the existence of the digital divide in Italy. The
econometric analysis shows that the indicators
related to education, in particular the number of
firms specialized in the ICT services sale,
substantially contributes to the existence of the
digital divide among firms that have their head
offices in a given region and, as economic literature
suggests (De Arcangelis et al., 2002), also the
number of employees devoted to research and
development becomes a crucial element.
Another key factor that causes the existence of the
digital divide in Italy, according to the results
obtained by other researches (Kiiski and Pohjola,
2002) is determined by economic indicators.
Especially in Italy the added value per employee is a
variable that significantly expresses Internet
diffusion among Italian firms variance.
Although the technological indicator, calculated
according to investments in IT among Italian
regions, is an important factor contributing to the
existence of the digital divide in Italy, it does not
express significantly the variance of Internet
diffusion at a regional level. This result disagree
with some researchers. For example Chen, Boase e
Wellman 2002, and UCLA, 2000, 2003 finds that, in
addition to income, access costs are strong
predictors of Internet use.
Finally, according to the results obtained we want to
highlight that in Italy in disagreement with other
researchers (U.S. Department of Commerce, 1999
and Chinn and Fairlie, 2004) even if the variable
showing the educational attainment at a regional
level (i.e. the number of graduates calculated every
1000 individuals), has also a statistically significant
effect in the ICT diffusion, the correlation between
this variable and the penetration rate registered by
firms in a given region prove to be highly negative.
This means that the regions with a high number of
graduates, in proportion to the residing population,
are the less inclined regions to utilize the new
technology.
However, the results obtained in this chapter
illustrates the factors contributing to the existence of
the digital divide at a regional level, utilizing as
E-PROCUREMENT ADOPTION AMONG ITALIAN FIRMS BY USING DOMAIN NAMES
129
metrics the number of domains registered by firms.
It is obvious that economic indicators and other
types of indicators related to education compared to
the number of graduates in Italy, such as the number
of employees devoted to research and development
or the providers/maintainers number are the best
elements contributing to the existence of the digital
divide among firms.
On this matter, in a future research, it would be
desirable to analyze Internet diffusion in Italy among
individuals and to compare the results obtained with
the analysis carried out in this chapter.
In conclusion, the digital divide in Italy depends on
the educational attainment level, on regions that are
productively efficient (that efficiency is calculated in
terms of added value per employee) and with a low
unempolyment rate.
Besides, in this chapter not only we identified the
factors contributing to the existence of the digital
divide, but also, analyzing data, we observed the
presence of a serious issue: Italian regions with a
low economic development and regions with a wide
jobless rate appear to be underdeveloped even from
a technological point of view. The difference
between those who use the Internet and those who
do not is another factor that contributes to the
widening of the gap that makes geographical areas
uneven (Northern and Central areas of Italy not only
are more industrialized, richer with a high
productive efficiency and in the forefront compared
to Southern ones, but are also the areas that have
higher penetration rates). In the first instance the
Internet could be a pervasive phenomenon justified
by the decentralized, non-hierarchical, immaterial
nature of the Internet technology (Negroponte,
1995), which in principle should not have strong
barriers to overcome as it happens in manufacturing
(for example if a new manufacturing company
decides to enter a highly competitive sector of the
market, barriers could be represented by big
companies with strong contractual powers or by high
investments costs required to enter the market). This
to mean that everyone in Italy could use the Internet
to exploit its potentials, considering its low access
costs. Besides Internet is a resource that if used by
an individual, this does not reduce the possibilities
of being used by someone else (immaterial nature),
but on the contrary it brings benefit not only to that
individual but also to all the users (net externality,
Metcalfe law) (Hansons, 2000). Data show that this
effect does not take place at a provincial level at all.
Domains are even more concentrated than the
number of firms and income. A ranking of provinces
by penetration rate, shows that the distribution of
Internet follows large differences in the level of
income: even if some provinces have a high number
of firms and high income, not always they are also
the first in terms of registered penetration rate.
Before drawing conclusions, these data should be
compared to those on the use of domains by
individuals, and this comparison is currently in
progress. Our preliminary conclusion is that, far
from being an “equalizer”, Internet technology
follows and possibly sharpens existing differences in
economic opportunities within industrialized
countries like Italy.
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