SUSTAINABLE DEVELOPMENT AND INVESTMENT IN
INFORMATION TECHNOLOGIES
A Socio-Economic Analysis
Manuel João Pereira
Instituto Nacional de Administração, Palácio dos Marqueses de Pombal, Oeiras, Portugal
Luís Valadares Tavares
Instituto Nacional de Administração, Palácio dos Marqueses de Pombal, Oeiras, Portugal
Raquel Soares
Universidade Católica Portuguesa, Palma de Cima, Lisboa, Portugal
Keywords: Information technology; Sustain
able Development; Economy
Abstract: The output of investments in Information Systems and Technologies (IST) has been a topic of debate among
the IST research community. The “Productivity Paradox of IST Investments” sustains that the investment in
IST does not increase productivity. Some researchers showed that developed countries have been having a
rather stable and sometimes declining economic growth despite their efforts in Research and Development
(R&D). Other researchers argue that there is sound evidence that investments in IST are having impacts on
the productivity and competitiveness of countries. This paper analyses the relationship between IST and
R&D investments and the global development of countries (not only productivity of countries) using
economic, demographic and literacy independent variables that explain global development. The objective
is to research whether R&D and IST investments are critical to the productivity and to global development
of the countries. Working at a country level, the research used sixteen socio-economic variables during a
period of five years (1995-1999). The research methodology included causal forecast, cluster analysis,
factor analysis, discriminant analysis and regression analysis. The conclusion confirms the correlation
between the Gross National Product (GNP) and R&D and IST investments. The variables illiteracy rate, life
expectancy at birth, Software investment as percentage of GNP and number of patents per 1000 inhabitants
can explain the development of a country.
1 INTRODUCTION
Research on the relationship between technology
and economic growth started long ago and has been
studied by several authors. Arrow (1962), on the
other hand, suggested that endogenizing the change
in technology, the long-term economic growth
depends of population growth. Uzawa (1965),
Phelps (1966), Ackoff (1967), Conlisk (1967,1969)
and Shell (1967) developed studies in the area of
technological growth and development of new
technologies. Castells (1997) showed that there is a
relationship between the demographic position and
the development of the country/area. More recently,
Romer (1990), Grossmann (1991), Allen (1997),
Pereira (2004) and Tavares (2002), all share the idea
that persistent investment in new information
technologies conducts to continuous economic
growth.
The debate on the productivity paradox of IST
i
nvestments has several justifications. Jones (1995)
showed that the number of researchers working in
R&D (generally accepted as an indicator of the state
of technology) in developed countries has increased
substantially over the post-war period, while the
economic growth has hardly changed. He tried to
explain the contrast between the state of technology
and the economic growth, holding that the
movement of other variables, different from the state
81
João Pereira M., Valadares Tavares L. and Soares R. (2005).
SUSTAINABLE DEVELOPMENT AND INVESTMENT IN INFORMATION TECHNOLOGIES - A Socio-Economic Analysis.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 81-88
DOI: 10.5220/0002551000810088
Copyright
c
SciTePress
of technology, affected the economic growth
permanently and suggested that continuous policy
measures that probably should have permanent
effects on economic growth do not have.
Another well-known author of the productivity
paradox of IST investments, Paul Strassmann
(1997), indicates that productivity of a country or a
company, must be the result of a good economic and
financial strategy because economic figures are
more important then the technical decisions when it
comes to invest in IST. Only this way, this
investment can be profitable and therefore contribute
to productivity growth. The market pressure for
higher productivity drives decision makers to big
investments in IST, sometimes without an objective,
quantitative knowledge of the markets and strategic
positioning. Other authors used a micro-economic
approach to study this question (Alpar and
Kim,1997).
Several authors and researchers indicate
explanations to these findings:
a) R&D statistics do not show all efforts
attributed to the technological progress
(mainly the efforts from SME´s), Kraemer
and Deadrick (1996);
b) In order to achieve full use of technologies
there has to be both a change in the
organisational structure and the
development of complementary
technologies (David, 1990);
c) Investments in IST have been directed to
product differentiation and less to effective
innovation, increasing costumers welfare
but not economic growth (Soete,
1996;Young, 1998);
d) Changes in the economy induces changes
in the investment of companies and in
consumer preferences (Kurdas,1994): when
the economy grows both companies and
consumers spend a lot on a wide variety of
products and services. In the opposite
scenario, real interest rates rises, consumers
tend to spend on essential products while
companies discard their risky efforts in
R&D and IST and invest in the existing
products. During this period, companies
tend to choose the self-financing option
instead of looking up for funding in the
financial market (Kurdas, 1994).
The new technologies are used to create more
flexibility in the internal processes and empower the
workers (Alpar and Kim, 1991; Baily and
Gordon,1998; Laudon, 1974, 1986; Barua et al
1995). The so-called Information and
Communication Technologies cannot be analysed
separately but integrated with the surrounding
environment, including the impact in the business
areas and the relations between those areas (Young,
1998). There are activities within the organisation
that do not create value directly (administration, HR
management, R&D, for instance) but are essential to
the well functioning of the organisation since they
support, complement and empower primary
activities (Porter, 1985).
Technology is traditionally used to transform
existing activities improving the efficiency of the
processes and the time of diffusion is probably not
enough to get the real output generated by IST
investments (David, 1990; Dewan, et al, 1992;
Devarej, and Mohli, 2003; Wilcocks and Lester,
1999).
The type of methodology used to understand the
impact of IST investments on organisations is
sometimes not adequate (Allen, 1997; Brynjolfsson
and Hitt, 1999; Barua et al, 1991; Mckeen et all,
1997; Nissen et al, 1998; Orlikowski, 1996) this
discussion is still an up-to-date research topic
(Bauker and Kauffman, 2004).
The main motivation of this research is to help to
determine an answer to the following question: is the
investment in IST and in R&D a relevant factor for
the sustainable development of the countries? The
next chapter will present the hypotheses and the
methodology to understand this relationship.
2 HYPOTHESIS
The previous debate contributed to the generation of
the following hypothesis of this research:
H1: The investments in R&D and in IST are
correlated with the global level of development of
the countries. The confirmation of this hypothesis
implies that higher investments in R&D and IST
lead to a higher global development of a country.
These hypotheses can help to find the answer to
the following two objectives:
Understand the relationship between
technology, sustainable development and
productivity of the countries.
Study the impact of R&D effort on the global
development and productivity of the countries.
3 METHODOLOGY
The methodology used in this research includes
causal forecast, cluster analysis, factor analysis,
discriminate analysis, regression analysis and
descriptive statistics. The main steps of the
methodology are:
ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION
82
a) data gathering about socio-economic and
technology variables of countries (OECD, 2002).
The following countries are included in this analysis:
Portugal, Greece, Spain, Italy, Korea, Ireland,
Australia, Finland, Denmark, Holland, France,
Belgium, Austria, Canada, Norway, Germany,
Switzerland, Sweden, United Kingdom, Japan and
USA. The period of analysis is five years (1995-
1999).
b) identification of basic relationship between R&D
investment and GDP of countries during a longer
period of analysis (1981-1999). Correlation between
both variables and identification of the time gap
between R&D investment and GDP impact for a set
of countries using causal forecast analysis.
c) cluster analysis of global development of the
countries. The following variables are included in
the analysis for each country (OECD, 2002) and this
choice was based in previous studies (Alpar and
Kim, 1991; Baily and Gordon,1998; Brynjolfsson
and Hitt, 1996; Pereira, 2004): GDP per hour
worked (United States = 100); Life expectancy
(years); GDP per capita (United States = 100);
Personal computers per 1,000 inhabitants; Gross
domestic expenditure in R&D; Software investment
as percentage of GDP; Number of patents per 1000
inhabitants; Share of high-technology investment as
percentage of total venture capital of the
communications sector; Electric power consumption
(kwh per capita); Share of high-technology
investment as percentage of total venture capital of
information technology sector; Information
exportation technology (percentage of manufactured
exportation); Illiteracy rate; Share of high-
technology investment as percentage of total venture
capital of health/biotechnology sector; Internet hosts
per 1000 inhabitants; Internet users per 1000
inhabitants; Telecommunications channels per 1000
inhabitants.
The analysis will identify clusters of countries
with different levels of development based on the
average values in a period of time of all variables in
each cluster. The time frame of analysis is five
years, although for some variables, due to the lack of
data, the period is three years.
d) Discriminant analysis to determine which are the
characteristics that distinguish the members of one
group from the members of the other group.
Knowing the data of a country, we can predict to
which cluster it belongs.
The factorial analysis allows to transform a set of
original correlated variables in a smaller number of
hypothetical variables (Principal Components), not
correlated between each other, without loosing
significant information from the original variables.
Each principal component derives from a linear
combination of all original variables.
e) Using variables that reflect the effort in IST and
R&D, a regression analysis is designed to correlate
them with the GNP. A basic analysis of these three
variables is also performed keeping each cluster
together, in order to determine if there is, in fact, a
relevant difference of investment in IST, in R&D
and of GDP between clusters of countries with
different global development.
4 ANALYSIS
The following subchapters describe the application
of the methodology presented in chapter 3.
4.1 R&D and GDP: Causal Forecast
The first step is to understand the relationship
between R&D and GDP across different countries.
Correlation between investment in R&D and
GDP in the same year for US, Japan, EU and OECD
countries is strong and positive (0,97 for US, 0.99
for Japan, 0.95 for the EU and 0.98 for the OECD
countries as we can see in tables 1, 2, 3 and 4,
leading us to believe that the investment in R&D
depends on the immediate resources generated by
the economy.
The causal forecast analysis of GDP using
investment in R&D as dependable variable, allowed
us to understand that not only R&D is highly
influenced by the GDP of each year, but GDP itself
is influenced by the investment in R&D in previous
years, with different time gaps depending on the
research efficiency and capacity of the economy to
absorb innovation.
In Japan the effects of R&D in the GDP appear 7
years after the investments. In US, the return of the
R&D investment happens after 13 years, in UE after
10 years and in OECD after 11 years. In summary,
Japan has a faster return on R&D then the EU, the
OECD countries and US, in this order.
4.2 Cluster Analysis
The cluster analysis is the second step. A five-year
average of the following statistics are used for this
analyses: Electric power consumption, kwh per
capita (A), GDP per capita, United States = 100 (B),
Information exportation technology (percentage of
manufactured exportation) (C), Illiteracy rate (D),
Internet users per 1000 inhabitants (E), Life
expectancy (F), Personal computers (per 1,000
inhabitants) (G), Share of high-technology
SUSTAINABLE DEVELOPMENT AND INVESTMENT IN INFORMATION TECHNOLOGIES: A
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83
Table 1: Casual Forecast – R&D; GDP – US Table 2: Casual Forecast – R&D; RDP - Japan
R&D Billions
Dollars GDP
Provisional
GDP
Correlation
R&D Billions
Dollars GDP
Provisional
GDP
Correlation
1981 116 4902 0 0,9748 1981 33 1537 0 0,9933
1982 121 4796 1 0,9677 1982 35 1584 1 0,9527
1983 130 4993 2 0,9448 1983 38 1619 2 0,8934
1984 142 5358 3 0,9094 1984 41 1682 3 0,8569
1985 154 5557 4 0,8751 1985 45 1758 4 0,8669
1986 159 5745 5 0,8493 1986 46 1809 5 0,8971
1987 162 5948 6 0,8631 1987 49 1882 6 0,9397
1988 166 6185 7 0,8880 1988 53 1996 1962 7 0,9462
1989 169 6412 8 0,9059 1989 58 2095 2039 8 0,9129
1990 173 6518 9 0,9294 1990 63 2206 2131 9 0,8673
1991 177 6494 10 0,9519 1991 64 2286 2222 10 0,8365
1992 177 6679 11 0,9627 1992 64 2309 2366 11 0,8840
1993 173 6865 12 0,9777 1993 62 2316 2391 12 0,8783
1994 173 7147 7161 13 0,9948 1994 61 2335 2493 13 0,7977
1995 184 7348 7323 14 0,9901 1995 65 2362 2614 14 0,5949
1996 193 7608 7590 15 0,9779 1996 85 2986 2772
1997 204 7946 7973 1997 88 3038 2927
1998 215 8282 8365 EQM 2610 1998 90 2961 2978
1999 226 8577 8494 Intercept 51 1999 90 2961 2955 Intercept
2000 8594 3510,12 2000 2901 912,42
2001 8719 Slop 2001 2881 Slop
2002 8830 31,43 2002 3009 32,05
2003 8939 2003 3621
2004 9062 2004 3735
2005 9072 2005 3797
2006 8947 2006 3797
2007 8946
2008 9283
2009 9583
2010 9928
2011 10278
2012 10626
Data Source: OECD, Analysis by the authors
Table 3: Casual Forecast, R&D, GDP – EU Table 4: Casual Forecast, R&D, GDP - OCDE
R&D Billions
dollars GDP
Provisional
GDP
Correlation
R&D Billions
Dollars GDP
Provisional
GDP
Correlation
1983 97 5558 0 0,9539 1981 261 13236 0 0,9825
1984 101 5693 1 0,9301 1982 273 13239 1 0,9806
1985 109 5851 2 0,8967 1983 287 13621 2 0,9751
1986 114 5991 3 0,8604 1984 309 14240 3 0,9697
1987 119 6184 4 0,8250 1985 336 14751 4 0,9670
1988 124 6433 5 0,8032 1986 347 15223 5 0,9672
1989 129 6675 6 0,8117 1987 360 15785 6 0,9801
1990 133 6788 7 0,8598 1988 374 16468 7 0,9879
1991 130 6839 8 0,9170 1989 390 17087 8 0,9859
1992 130 6885 9 0,9788 1990 403 17531 9 0,9842
1993 129 6888 6875 10 0,9903 1991 417 18623 10 0,9915
1994 130 7084 7021 11 0,9827 1992 419 19042 18900 11 0,9959
1995 131 7242 7278 12 0,9853 1993 415 19291 19376 12 0,9910
1996 133 7362 7418 13 0,9924 1994 418 19900 19960 13 0,9782
1997 136 7534 7593 14 0,9898 1995 442 20947 20819 14 0,9783
1998 140 7754 7746 1996 462 21593 21905
1999 148 7984 7915 1997 482 22332 22333
2000 8028 Intercept 1998 500 22921 22843 Intercept
2001 7929 3806,43 1999 519 23506 23397 8530,64
2002 7935 Slop 2000 24023 Slop
2003 7915 31,73 2001 24565 39,77
2004 7920 2002 25120
2005 7966 2003 25190
2006 8034 2004 25025
2007 8109 2005 25150
2008 8259 2006 26107
2009 8493 2007 26907
2008 27713
2009 28401
2010 29189
Data Source: OECD, Analysis by the authors
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84
Figure 1: Cluster analysis – Tree Diagram
Data Source: OECD, Analysis by the authors
investment as percentage of total venture capital of
the communications sector (H), Share of high-
technology investment as percentage of total venture
capital of information technology sector (I), Share of
high-technology investment as percentage of total
venture capital for health/biotechnology sector(J),
Internet hosts per 1000 inhabitants (K),
Telecommunication channels per 1000 inhabitants
(L), GDP per hour worked, United States = 100 (M),
Number of patents per 1000 inhabitants (N),
Software investment as percentage of GDP (O),
Gross domestic expenditure in R&D (P).
According to the cluster analysis using the
countries mentioned in chapter 3, two clusters of
countries emerged. Cluster 1 formed by Greece,
Ireland, Korea, Portugal, Spain, with lower average
levels on all indicators. Cluster 2 formed by
Australia, Austria, Belgium, Canada, Denmark,
Finland, France, Germany, Italy, Japan Netherlands,
Norway, Sweden, Switzerland, United Kingdom and
US with higher average levels in all indicators.
4.3 Discriminant Analysis
The objective of the discriminant analysis is to
determine which are the characteristics that
distinguish the members of one group from the
members of the other. One or more classification
functions (multivariable functions) are determined
for each cluster, in order to maximise the difference
between the groups. After the calculation of the
discriminant functions we have to select the ones
that are relevant (F value >4 and a p value <5%).
The solution of the discriminant analysis (table
5) showed that variables Illiteracy rate (D), Life
expectancy (F), Software investment as
percentage of GDP (O) and Number of patents
per 1000 inhabitants (N) are enough to classify
countries as belonging to cluster 1 or to cluster 2.
Figure 2: Cluster analysis – Plot of Means
4.4 Factorial Analysis
To determine which principal components are
designed a combination of three conditions should
be verified: a) to retain the first order factors until
the eigenvalue has a abrupt fall b) to hold the
components that explain a significant percentage of
the total variance, usually above 70% c) and finally,
to exclude the components that have an eigenvalue
under one. The rotation of the principal components
turns it easier to understand the dimension that each
component represents. Four dimensions
(components) were identified as table 7 shows.
The analyses of the factor loadings (varimax
normalized) showed the following (table 8):
a) the first dimension, explaining 43,98% of the
development includes the variables:
A) GDP per capita
E) Internet users per 1000 inhabitants
N) Software investment
b) the second dimension, explaining extra
14,27% of the development includes the
variables investment in IT venture capital
(H,I).
c) the third dimension, explaining extra
10,18% of the development includes the
variables:
F) Personal computers per 1000
inhabitants
H) Number of patents registed
These variables explain 68,43% of the
development of the countries analysed. As we can
see, the IST variables are relevant to the
development of the countries.
SUSTAINABLE DEVELOPMENT AND INVESTMENT IN INFORMATION TECHNOLOGIES: A
SOCIO-ECONOMIC ANALYSIS
85
Table 7: Eigenvalues of Principal Components
Eigenvalue % Total Variance Cumulative Eigenvalue Cumulative %
1 7,0365 43,9782 7,0365 43,9782
2 2,2828 14,2675 9,3193 58,2457
3 1,6289 10,1809 10,9483
68,4267
4 1,3282 8,3012 12,2765 76,7279
5 1,0077 6,2981 13,2842 83,0259
Table 8: Factor Loadings (Varimax normalized)
Principal components (Marked loadings are > 0,700000)
Factor 1 Factor 2 Factor 3 Factor 4
A
0,776441
0,313813 0,226774 0,217372
B 0,069716 0,365983 0,676391 0,562182
C -0,06284 0,591424 -0,07712 0,283421
D -0,08924 -0,18102 -0,44036 -0,63511
E
0,907184
-0,06792 0,097383 0,083256
F 0,26646 -0,09935
0,830615
0,016621
G 0,420495 0,380441 0,401267 0,644132
H 0,212596
0,790361
0,240486 -0,24583
I 0,169086
0,880694
-0,05586 0,315324
J 0,118514 0,211158 -0,10157
0,836123
K 0,587552 0,387134 0,208871 0,46764
L 0,409986 0,031861 0,286064
0,776305
M 0,027451 0,152493
0,797236
0,277228
N
0,808623
-0,13354 0,022294 0,237529
O 0,277521 0,082643 0,410776
0,779332
P -0,27323 0,614356 0,280418 0,20938
Expl.Var 3,09787 2,79531 2,6494 3,733877
Prp.Totl 0,193617 0,174707 0,165587 0,233367
4.5 Regression Analysis
Analysing the GDP per 100 inhabitants, the
investment in R&D per 100 inhabitants and the
investment in software per 100 inhabitants in 1999,
keeping each cluster together, conclusions of the
cluster analysis are reinforced.
The GDP per 100 inhabitants is higher among
countries of cluster 1 then among countries of
cluster 2. The same conclusion follows the analysis
of the investment in R&D and in Software per 100
inhabitants.
The correlation between these three variables is
high, as displayed in table 9 and shown in figure 6.
The Investment in R&D, investment in Software,
and the GDP are variables correlated.
Figures 3, 4 and 5 :GDP per 100 inhabitants, Investment in R&D per 100 inhabitants and investment in software per 100
inhabitants in US dollars, indexed to 1995. Data Source: OECD, Analysis by the authors
ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION
86
Table 9: R-squared values
R
2
R&D investment Software investment
GDP 0,756 0,7742
Software Inv. per 100 inhabitants
R&D Inv. per 100 inhabitants
Figure 6: GDP, Investment in software and in R&D per 100 inhabitants in US dollars, 1999, indexed to 1995 (GDP per 100
inhabitants presented in circles)
5 CONCLUSIONS AND FUTURE
RESEARCH
The conclusion about the hypothesis formulated is
the following:
A stronger effort of investment in IST creates a
higher sustained development of a country – is
confirmed.
The temporal series analysis shows that there is a
causal relationship between investment in R&D and
productivity. The cluster analysis (figure2) shows
that more developed countries have higher levels of
investment in R&D and higher levels of
productivity. The discriminant analysis shows that
four variables are enough to classify countries
according to their maturity of sustained
development. From these four variables, one is an
indicator of IST (investment in software) and the
other a R&D indicator (number of patents).
GDP is positively and strongly correlated with
the level of investment in R&D and the level of
investment in Software. More developed countries
also show better figures of these three variably.
Finally, the return, of the financial effort in R&D is
not the same for all countries, showing the research
the Japan is the country that profits faster its
investments (7 years).
However, several developments can improve
their work. Future research should increase the time
dimension of the analysis. The methodology should
be applied to a different set of time periods of seven
years, ten and twenty years. The type of variables
can also be argued. An important difficulty, already
mentioned by previous researchers (Byrd and
Marschall, 1997; Gurbaxani and Whang, 1991; Im et
all 2001; Devaraj and Kohli, 2003; Pereira, 2004),
was to select the significant socio-economic and
technological variables. The use of variables
describing in a even more robust way the sustainable
development of a country, the productivity of a
country and the state of the IST and R&D of a
country, can complement future analysis.
The contribution of this paper to the field is to
confirm the importance of IST investments in the
sustainable development of the countries.
To summarise, this research concludes that IST
and R&D variables should not be neglected by
decision makers to achieve a sustainable
development of a country.
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