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

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

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

 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

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

SOCIO-ECONOMIC ANALYSIS

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

ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

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

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

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

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

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

Table 9: R-squared values

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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ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION