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

 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

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

= 0.644.

Model

Non standardized

coefficients

Standardized

coefficients

t Sig.

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

= 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

= 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

= 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

= 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.

REFERENCES

Bassi, M.C. (2002) "La Catalogazione delle Risorse

Informative in Internet", Prefazione di Riccardo Ridi.

Bauer, Johannes M., Michel Berne, and Carleen F.

Maitland. (2002). “Internet Access in the European

Union and in the United States. “ Telematics and

Informatics 19:117-137.

Beilock, Richard, and Daniela V. Dimitrova. (2003). “An

Exploratory Model of Inter-Country Internet

Diffusion.” Telecommunications Policy 27:237-252.

Castells Manuel. 2001. The Internet Galaxy. New York:

Oxford University Press.

Chinn, M. D. and R.W. Fairlie. (2004). The determinants

of the Global Digital Divide: A Cross-Country

Analysis of Computer And Internet Penetration.

Madison, WI, University of Wisconsis, Madison and

NBER.

Coffman K.G. and Odlyzko A.M. (2001). Internet growth.

AT&T Labs report.

De Arcangelis, Jona-Lasinio C., Manzocchi S. (2002)

“Sectoral Determinants and Dynamics Of ICT

Investment in Italy”- EEA 2002 Annual Meeting,

Venice, 22-24 August 2002.

Diez-Picazo G.F. (1999). An Analysis of International

Internet Diffusion. Ph.D. Thesis, MIT.

Florida R. (2002). “The rise of the creative class”. Basic

Books, New York, NY.

Gilder, George. (2000). Telecosm: How Infinite

Bandwidth will Revolutionize the World. New York:

Free Press.

Gini, C. (1960), Statistics, Edizioni Metron, Roma.

Guillén, Mauro F., and Sandra L. Suárez. (2001).

“Developing the Internet: Entrepreneurship and Public

Policy in Ireland, Singapore, Argentina, and Spain.”

Telecommunications Policy 25:349-371.

Guillèn, Mauro F., and Sandra L. Suaréz. (2004).

“Explaining the global digital divide: Economic,

Political, and Sociological drivers of cross-national

Internet use”. The Wharton School, University Of

Pennsylvania, Philadelphia.

Hansons W. (2000). “Internet marketing” ed. Tecniche

Nuove, Milano.

ICEIS 2006 - SOFTWARE AGENTS AND INTERNET COMPUTING

130

Hargittai, E. (1999) “Weaving the Western Web:

Explaining differences in Internet connectivity among

OECD countries. “ Telecommunication Policy

23:701-718.

Hoffman, D. & Novak, T. (1999). Building contrust

online. Communications of the ACM, 42 (april),80-85.

ITU, International Telecommunication Union. (2003).

World Telecomunications Indicators: Database.

http://www.itu.int/ti/pubblications/world/world.htm.

Kirkman, Geoffrey S., Peter K. Cornelius, Jeffrey D.

Sachs, and Klaus Schwab. eds. (2002). Global

Information Technology Report 2001-2002 . New

York: Oxford University Press.

Kiiski, Sampsa, and Matti Pohjola. (2002). “Cross-

Country diffusion of the Internet.” Information

Economics and Policy 14:297-310.

Naldi M. (1997). Size estimation and growth forecast of

the Internet. Centro Volterra, Tor Vergata.

Negroponte. (1995). “Being Digital” Knopf,k New

York,NJ, USA.

Norris, P. (2001). Digital divide: Civic Engagement,

Information Poverty, and the Internet Worldwide.

New York: Cambridge University Press.

NUA, Network Users Association. (2002). How Many on

Online? Accessed on September, 2002,

http://www.nua.com/surveys/how_many_online/index.

html.

RIPE-NCC, RIPE Network Coordination Centre,

http://www.ripe.net.

Rogers. (2001). “The digital divide”. New York: Free

Press

Tapscott, Don, and Art Caston. (1993). Paradigm Shift:

The New Promise of Information Technology. New

York: McGraw-Hill.

UCLA. (2000). Surveying the Digital Future. Los

Angeles, CA: UCLA Center for Communication

Policy. www.ccp.ucla.edu

UCLA. (2003). Surveying the Digital Future: Year Three.

Los Angeles: UCLA Center for Communication

Policy. www.ccp.ucla.edu

U.S. Department of Commerce. (1999). Falling through

the Net: Defining the Digital Divide. Washington, DC:

U.S. Department of Commerce.

Warschauer M. (2001), What is the digital divide?,

University of California

Zook M.A. (1999). The Web of Consumption: Spatial

Organization of the Internet Industry in the United

States. American Behavioral Scientist (forthcoming).

Zook, M.A. (2000) Internet metrics: using host and

domain counts to map the Internet

Telecommunications Policy 24:6-7.

Zook M. A. (2001). Old hierarchies or new network of

Centrality? - The Global Geography of the Internet

Content Market. American Behavioral Scientists, 44

(Special Issue: Mapping the global Web).

E-PROCUREMENT ADOPTION AMONG ITALIAN FIRMS BY USING DOMAIN NAMES

131