DETERMINANTS OF INFORMATION TECHNOLOGY

ADOPTION IN PORTUGAL

Tiago Oliveira and Maria F. O. Martins

Instituto Superior de Estatística e Gestão Informação, Universidade Nova de Lisboa

Campus de Campolide, 1070-312 Lisboa, Portugal

Keywords: Web site adoption, e-Commerce adoption, Technology-organization-environment, Binary decision.

Abstract: There is an absence of empirical studies on information technology (IT) adoption decision in Portugal. This

paper is based on a representative sample of 2626 Portuguese firms and it analyzes the determinants of web

site and e-commerce adoption decisions using a technology-organization-environment (TOE) framework.

The proposed statistical methodology advocates that the IT adoption decisions are taken sequentially, stage

by stage. Findings also suggest that the relevant drivers of web site and e-commerce adoption are not

necessarily the same. For web site adoption we found 8 drivers. For e-commerce we found 5 drivers.

Explanations and implications are offered.

1 INTRODUCTION

Literature on information technology (IT) adoption

and diffusion at firm level (Hong and Zhu, 2006)

suggests that when analyzing this topic, one should

consider the nature of the IT. For simple

technologies, like the internet or Web site, the

adoption process is expected to be inexpensive and

easy and probably will not bring about fundamental

changes to the firm. However, for advanced

technologies, especially those related to online

transactions, the adoption process may be

complicated and costly. That is perhaps why, in

2006, even though most firms in Portugal are

internet adopters (83%), only 35% owned a web site

and a limited part of them, 7%, have adopted e-

commerce. These national figures are clearly below

the EU-15 mean level, where 94% of firms are

internet users, 66% own a web site and 16% have

adopted e-commerce practices.

The two main purposes of this study are the

following:

 To examine the importance of technology-

organization-environment (TOE) related factors

as fundamental determinants of web site and e-

commerce adoption;

 To analyze the extent to which there are

significant differences in the factors driving these

two types of IT.

To achieve these research objectives, we used a

rich data set of 2626 firms that are representative of

Portuguese firms with more than 10 employees in

2006, (excluding the financial service sector).

In this study, as suggested by Hong and Zhu

(2006), we defined e-commerce as any application

of web technologies that enables revenue generating

business activities over the internet.

2 THEORICAL FRAMEWORK

AND CONCEPTUAL MODEL

The TOE model (Tornatsky and Fleischer, 1990)

identifies three aspects that may possibly influence

web site and e-commerce adoption: technological

context (technology readiness, technology

integration and security applications); organizational

context (firm size, perceived benefits of electronic

correspondence, IT training programs, access to the

IT system of the firm, internet and e-mail norms and

main perceived obstacle); and environmental context

(competitive pressure). In accordance with the TOE

theory, we developed in the next subsection a

conceptual framework for web site and e-commerce

adoption (see Figure 1).

264

Oliveira T. and O. Martins M. (2009).

DETERMINANTS OF INFORMATION TECHNOLOGY ADOPTION IN PORTUGAL.

In Proceedings of the International Conference on e-Business, pages 264-270

DOI: 10.5220/0002261502640270

 SciTePress

Figure 1: Conceptual framework for web site and e-

commerce adoption.

2.1 Technology Context

Technology readiness can be defined as technology

infrastructure and IT human resources. Technology

infrastructure establishes a platform on which

internet technologies can be built; IT human

resources provide the knowledge and skills to

develop web applications (Zhu and Kraemer, 2005).

Theoretical assertions are supported by several

empirical studies (Hong and Zhu, 2006, Iacovou et

al., 1995, Kwon and Zmud, 1987, Zhu et al., 2003,

Zhu and Kraemer, 2005, Zhu et al., 2006, Zhu et al.,

2004)

H1a and H1b. The level of technology readiness

is positively associated with web site and e-

commerce adoption

Evidence from the literature suggests that

integrated technologies help improve firm

performance by reduced cycle time, improved

customer service, and lowered procurement costs

(Barua et al., 2004). As a complex technology, e-

commerce demands close coordination of various

components along the value chain. Correspondingly,

a greater integration of existing applications and the

internet platform represent a greater capacity of

conducting business over the internet (Al-Qirim,

2007, Mirchandani and Motwani, 2001, Premkumar,

2003).

H2a and H2b. The level of technology

integration is positively associated with web site and

e-commerce adoption.

The lack of security may slow down

technological progress. For example, for Portugal in

2002 this was the greatest barrier to internet use

(Martins and Oliveira, 2005) and in China it is one

of the most important barriers to the adoption of e-

commerce (Tan and Ouyang, 2004).

H3a and H3b. Security applications is positively

associated with web site and e-commerce adoption

2.2 Organization Context

Firm size is one of the most commonly studied

determinants of IT adoption (Lee and Xia, 2006).

Several empirical studies indicate that there is a

positive relationship between the two variables (Pan

and Jang, 2008, Premkumar et al., 1997, Thong,

1999, Zhu et al., 2003)

H4a and H4b. Firm size is positively associated

with web site and e-commerce adoption

Empirical studies consistently found that

perceived benefits have a significant impact in IT

adoption

(Beatty et al., 2001, Gibbs and Kraemer, 2004,

Iacovou et al., 1995).

H5a and H5b. Perceived benefits of electronic

correspondence is positively related with web site

and e-commerce adoption

We used IT training programs as a proxy of

employees’ education level, because in our survey

we do not have this variable. The presence of skilled

labor in a firm increases its ability to absorb and

make use of an IT innovation, and therefore it is an

important determinant of IT diffusion (Caselli and

Coleman, 2001, Hollenstein, 2004, Kiiski and

Pohjola, 2002).

H6a and H6b. IT training programs are

positively associated with web site and e-commerce

adoption

The fact that workers can have access to the IT

DETERMINANTS OF INFORMATION TECHNOLOGY ADOPTION IN PORTUGAL

265

system from outside of the firm reveals that the

organization is prepared to integrate its technologies

(Mirchandani and Motwani, 2001).

H7a and H7b. The level of access to the IT

system from outside of the firm is positively

associated with web site and e-commerce adoption

Regulatory environment has been acknowledged

as a critical factor influencing innovation diffusion

(Zhu et al., 2003, Zhu and Kraemer, 2005, Zhu et al.,

2006, Zhu et al., 2004). Firms often refer inadequate

legal protection for online business activities,

unclear business laws, and security and privacy as

concerns in using web technologies (Kraemer et al.,

2006).

H8a and h8b. The presence of internet and e-

mail norms is positively associated with web site and

e-commerce adoption

Research into IT adoption and implementation

suggests that when the technology is complex, as is

the case for e-commerce, the main perceived

obstacles are particularly relevant because in this

case, the adoption process may be complicated and

costly (Hong and Zhu, 2006).

H9b. Main perceived obstacle is negatively

associated with e-commerce adoption

2.3 Environmental Context

Competitive pressure refers to the degree of pressure

felt by the firm from competitors within the industry.

Porter and Millar (1985) analyzed the strategic

rationale underlying competitive pressure as an

innovation-diffusion driver. They suggested that, by

using a new innovation, firms might be able to alter

the rules of competition, affect the industry

structure, and leverage new ways to outperform

rivals, thus changing the competitive landscape. This

analysis can be extended to IT adoption. Empirical

evidence suggests that competitive pressure is a

powerful driver of IT adoption and diffusion (Al-

Qirim, 2007, Gibbs and Kraemer, 2004, Hollenstein,

2004, Iacovou et al., 1995, Mehrtens et al., 2001,

Zhu et al., 2003).

H10a. The level of web site competitive pressure

is positively associated with web site adoption

H10b. The level of e-commerce competitive

pressure is positively associated with e-commerce

adoption.

3 DATA AND METHODOLOGY

3.1 Data

The data used in this study were provided by

National Institute of Statistics (INE) and result from

the survey on the use of communication and

information technologies in firms (iutice) in 2006.

We used a sample of 2626 firms with more than 9

employees that is statistically representative of the

whole private business sector in Portugal at January

2006, excluding the financial sector.

3.2 Methodology

In our model we examine the influence of several

TOE factors on the adoption decision at two

adoption stages (see Figure 2).

Figure 2: Stage of adoption by firms.

We estimated the following probit model, for

stage adoption i:

P(y

=1/x

)=Φ(x

) for i=1,2

(1)

Where y

=1 is web site adoption, y

=1 is e-

commerce adoption, x

is the vector of the

explanatory variables, β

the vector of unknown

parameters to be estimated, and Φ(.) is the normal

cumulative distribution.

Within our context, the e-commerce adoption

decision (stage 2) should be modeled jointly with the

decision on web site adoption (stage 1), taking into

account the fact that e-commerce adoption decision

is observed only for those firms who own a web site.

As it is usual in statistical analysis, we use a

bivariate probit model with sample selectivity that

estimates simultaneously the system of two

nonlinear equations, in our case, two probit models

taking into account sample selection. If the

hypothesis of uncorrelated errors (ρ=0) is not

rejected then we can proceed as usual by specifying

two sequential models (Greene, 2008). This means

ICE-B 2009 - International Conference on E-business

266

that we can compute, without the existence of

selectivity bias, one binary model (probit or logit)

for web site adoption with all firms and another

binary model (probit or logit) for e-commerce only

with firms that had adopted web site.

The probit or logit model has been used in the IT

literature to study the following adoptions:

computer-mediated communication technologies

(Premkumar, 2003), internet (Martins and Oliveira,

2007), web site (Oliveira and Martins, 2008) and e-

business (Pan and Jang, 2008, Zhu et al., 2003).

Definition of explanatory variables

A technology readiness

index was built by

aggregating 8 items on technologies used by the firm

(on a yes/no scale): computers, e-mail, intranet,

extranet, own networks that are not the internet (own

exclusive networks), wired local area network

(LAN), wireless LAN, wide area network (WAN),

and one item standing for the existence of IT

specific skills in the firm (on a yes/no scale) (Zhu et

al., 2004). The first 8 items represent the penetration

of traditional information technologies, which

formed the technological infrastructure (Kwon and

Zmud, 1987). The last item represents IT human

resources (Mata et al., 1995). To aggregate these 9

items measured in yes/no scale, we used multiple

correspondence analyses (MCA). The MCA is a

method of “multidimensional exploratory statistic”

that is used to reduce the dimension when the

variables are binary (Johnson and Wichern, 1998).

The first dimension explains 38% of inertia. In the

negative side of the first axis we have variables that

represent firms that do not use IT infrastructures and

do not have workers with IT skills. On the positive

side we have the variables that represent the use of

infrastructures and workers with IT skills. This

resulting variable reflects the technology readiness.

Technology integration (TI)

was measured by the

number of IT systems for managing orders that are

automatically linked with other IT systems of the

firm. The variable ranges from 0 to 5. This variable

reflects how well the IT systems are connected on a

common platform. Security applications (SA)

was

measured by the number of existing security

applications in the firms. The variable ranges from 0

to 6. Firm size

was measured by three binary

variables: small firms (S

) (10 up to 49 employees);

medium-size firms (S

) (50 up to 249 employees);

large firms (S

) (more than 249 employees).

Perceived benefits of electronic correspondence

(PBEC) was measured by the shift from traditional

postal mail to electronic correspondence as the main

standard for business communication, in the last 5

years (on a yes/no scale). IT training (ITTP)

programs is also binary variable (yes/no) related to

the existence of professional training in

computer/informatics, available to workers in the

firm. Access to the IT system of the firm (AITSF)

was measured by the number of places from which

workers access the firms information system. The

variable ranges from 0 to 4. Internet and e-mail

norms (IEN) was measured by whether firms have

defined norms about internet and e-mail (on a yes/no

scale). Main perceived obstacles

was measured by

five dummy variables reflecting the main problems

faced in the implementation of e-commerce solution.

Web site competitive pressure (WEBP)

and e-

commerce competitive pressure (ECOMP) are

computed as the percentage of firms in each of the 9

industries that had already adopted a web site/e-

commerce two years before the time of the survey,

i.e. in 2004. As in Zhu et al. (Zhu et al., 2003) the

rationale underlying our model is that an observation

of the firm on the adoption behavior of its

competitors influences its own adoption decision. To

control for type of industry we used a binary

variable (yes/no), representing the service sector

(SER).

We made an analysis of reliability for variables

that were obtained by multi-item indicators. We

used the standardized Kuder-Richardson Formula 20

(KR-20) estimated, which is a special form of

coefficient alpha that is applicable when items are

dichotomous (Kuder and Richardson, 1937). The

KR-20 obtained are: for technology readiness (KR-

20 = 0.78), technology integration (KR-20 = 0.92),

security applications (KR-20 = 0.71) and access to

the IT system of the firm (KR-20 = 0.73). All of KR-

20 are higher than the generally accepted level of

adequacy of 0.60 (Nunnally and Bernstein, 1994).

These results suggest that all of the factors are

considered to be satisfactory for the reliability of

multi-item scale.

4 ESTIMATION RESULTS

Initially we estimated a bivariate probit model with

sample selectivity. No support was found to the

existence of selectivity in our sample, at the usual

5% significance level (p-value=0.12). Since the two

adoption decisions are uncorrelated, we can estimate

our model with two single probit models. Table 1

reports the estimation results. We also estimated two

logit models. As expected, the results are analogous.

DETERMINANTS OF INFORMATION TECHNOLOGY ADOPTION IN PORTUGAL

267

Table 1: Estimated results.

Explanatory

variables

Probit (sequential equations)

Web site (y

) E-commerce (y

)

Coef. Coef.

Technology context

TR 0.699*** -0.055

TI 0.008 0.087***

SA 0.087*** 0.040

Organization context

Firm size (S

is reference variable):

-S

0.064 -0.056

-S

0.263*** 0.158

PBEC 0.166** 0.168**

ITTP 0.274*** 0.112

AITSF 0.170*** 0.099**

IEN 0.152** 0.024

Main perceived obstacle

-Goods and services

are not susceptible of

being sold through the

internet

nc -0.707***

Competitive pressure

WEBP 0.021*** nc

ECOMP nc 0.029***

Controls

SER -0.122* 0.299***

Constant -1.367*** -1.792***

Sample size n

=2626 n

=1773

Note: nc means that the variable is not considerated; * p-

value<0.10; ** p-value<0.05; *** p-value<0.01;

the other

main perceived obstacles are not statistically significant in the

model.

Goodness-of-fit is assessed in three ways. First,

to analyze the joint statistical significance of the

explanatory variables we computed the likelihood

ratio test. Secondly, we use the Hosmer-Lemeshow

test (Hosmer and Lemeshow, 2000), which

compares the fitted expected values of the model to

the actual values. For web site adoption and for e-

commerce adoption, there is no support to reject

both models. Finally, the discrimination power of

the model is evaluated using the area under the ROC

curve, which is equal to 83% and 75% for web site

and e-commerce adoption, respectively. This reveals

an excellent discrimination for both models (Hosmer

and Lemeshow, 2000). The three statistical

procedures reveal a substantive model fit, a

satisfactory discriminating power and there is

evidence to accept an overall significance of the

model.

Hypotheses H1a-H10a and H1b-H10b were

tested analyzing the sign and the statistical

significance of the coefficients of the two adoption

decision models. As can be seen from Table 1, for

the web site adoption decision model all the

coefficients have the expected signs and the only

explanatory variable that is not statistically

significant is technology integration. We can

identify eight relevant drivers of web site adoption;

technology readiness and security application

reflecting the technological context; firm size,

perceived benefits of electronic correspondence, IT

training programs, access to the IT system of the

firm and internet and e-mail norms, representing the

organization context; web site competitive pressure

characterizing the environmental context. We can

conclude that hypothesis H1a, H3a, H4a, H5a, H6a,

H7a, H8a and H10a are confirmed and no support

was found for H2a. For the e-commerce adoption

model, the estimated coefficients also have the

anticipated signs: technology integration has a

positive effect on the e-commerce adoption

probability; perceived benefits of electronic

correspondence, access to the IT system of the firm

and e-commerce competitive pressure are also

important drivers of e-commerce adoption.

Moreover, our results indicate that goods and/or

services provided by the company that are not

susceptible of being sold through the internet is the

most important obstacle of e-commerce adoption. As

a whole, the results substantiate all hypotheses

formulated for the e-commerce adoption model

except H1b, H3b, H4b, H6b and H8b.

5 CONCLUSIONS

In this study we have proposed a conceptual model

based on TOE theoretical framework to analyze the

determinants of two different adoption decisions. At

the basic level, we considered the adoption of a

simple information technology, the web site and at

the more advanced level, a complex technology is

contemplated: e-commerce. While IT adoption

models have been widely discussed and studied in

theory and practice, few empirical publications exist

for southern European countries like Portugal.

We examined 2626 firms representative of the

Portuguese private economic sectors (except the

financial one) and the major findings are the

following: (1) from our empirical results, by

statistical tests, we conclude that the two adoption

decisions are taken at different stages; (2) the

relevant facilitators and inhibitors of web site and e-

commerce adoption decision found in our study for

Portuguese firms are, in general, similar to those

obtained in other IT adoption studies (Al-Qirim,

2007, Hong and Zhu, 2006, Zhu et al., 2006); (3) in

particular, our results suggest that organizational

factors like perceived benefits and access to firms IT

system contribute to both adoption decision process.

Similarly, competitive pressure, an environmental

ICE-B 2009 - International Conference on E-business

268

factor, significantly influences both adoption

decisions, meaning that competitive pressure is an

important innovation-diffusion driver in these two

stages of adoption; (4) other variables have limited

influence: technology readiness as a component of

technological factors, firm size, IT training programs

and internet and e-mails norms as organizational

factors, had a significant effect on web site adoption

decision but had no effect on e-commerce adoption.

This indicates that once a firm decides to own a web

site, these variables become less important for e-

commerce purpose. On the other hand, technology

integration has a relevant impact on e-commerce

adoption decision but is not important within the

web site adoption model, meaning that for e-

commerce adoption technologies that help improve

firm performance by reduced cycle time, improved

customer service, and lowered procurement costs are

needed (Barua et al., 2004).

In terms of policy implications, the above

findings suggest that a key factor is the improvement

of IT skills at the basic and higher levels. This can

be achieved by lowering, through different types of

policy instruments, the IT training cost, and by

promoting a closer relationship between firms,

associations and education institutions. With the cost

of infrastructure technology decreasing, the lack of

qualified IT human resources is probably one of the

major constraints for Portuguese firms’ technology

readiness improvement.

Our study also has important implications for

managers who are involved in processes of

introducing simple and complex IT innovations into

their organizations. First, managers should be aware

that technology readiness constitutes both physical

infrastructure and intangible knowledge such as IT

skills. This urges top leaders to foster managerial

skills and human resources that possess knowledge

of these new information technologies. Secondly,

our study sought to help firms become more

effective in moving from a traditional channel to the

internet by identifying the profile of early web site

and e-commerce adopters. For non-adopters, it

provides a mechanism for self-evaluation. For firms

that are already web site adopters, in the

development of strategies for e-commerce adoption,

it is fundamental to recognize that e-commerce

requires enhanced technology integration between

the existing applications and the internet platform.

The cross-sectional nature of this study does not

allow knowing how this relationship will change

over time. To solve this limitation the future

research should involve panel data.

ACKNOWLEDGEMENTS

We would like to acknowledge the National Institute

of Statistics (INE) for providing us with the data.

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