92 Int. J. Entrepreneurship and Small Business, Vol. 13, No. 1, 2011

Copyright © 2011 Inderscience Enterprises Ltd.











Entrepreneurial intention among secondary students:
findings from Portugal
Arminda do Paço*, João Ferreira,
Mário Raposo, Ricardo G. Rodrigues and
Anabela Dinis
Department of Business and Economics,
University of Beira Interior,
Estrada do Sineiro, 6200-209 Covilhã, Portugal
E-mail: apaco@ubi.pt
E-mail: jjmf@ubi.pt
E-mail: mraposo@ubi.pt
E-mail: rgrodrigues@ubi.pt
E-mail: adinis@ubi.pt
*Corresponding author
Abstract: This paper aims to identify some factors that may be explaining
differences among secondary students in start-up intentions. For that, the study
develops an entrepreneurial intention model sustained by the use of Azjen’s
theory of planned behaviour (TPB). This theory is considered as a relevant tool
to model the development of entrepreneurial intention through pedagogical
processes and learning contexts. Using a sample of students aged between 14
and 15 years old, it was administrated a questionnaire based on the Liñán and
Chen’s entrepreneurial intention questionnaire (EIQ). Data was collected
before the entrepreneurship education programme. The purpose is to test a
model of entrepreneurial intention using structural equations, before the
educational experience.
Keywords: entrepreneurship; education; entrepreneurial intention; EI; theory
of planned behaviour; TPB; Portugal.
Reference to this paper should be made as follows: do Paço, A., Ferreira, J.,
Raposo, M., Rodrigues, R.G. and Dinis, A. (2011) ‘Entrepreneurial intention
among secondary students: findings from Portugal’, Int. J. Entrepreneurship
and Small Business, Vol. 13, No. 1, pp.92-106.
Biographical notes: Arminda do Paço is a Professor at the University of Beira
Interior, Portugal. Her research interests are entrepreneurship education, public
and non-profit marketing and social marketing.
João Ferreira is a Professor at the University of Beira Interior, Portugal. His
research interests are strategy and entrepreneurship.
Mário Raposo is a Full Professor at the University of Beira Interior, Portugal.
He is the Vice-Rector and Director of Regional Studies Center and the Office
of Technology Transference. His research interests are marketing, strategy and
competitiveness and entrepreneurship.

Entrepreneurial intention among secondary students 93














Ricardo G. Rodrigues is a Professor at the University of Beira Interior,
Portugal. His research interests are entrepreneurial marketing, entrepreneurship
education and market orientation.
Anabela Dinis is a Professor at the University of Beira Interior, Portugal. Her
research interests are entrepreneurship and regional/rural development and
social entrepreneurship.

1 Introduction
Entrepreneurship is studied and taught by a very heterogeneous group of academics.
Scholars still do not share a single common paradigm and integrative framework as the
basis of their work (Fayolle et al., 2006; Ferreira et al., 2007; Verduyn et al., 2007). Some
investigations have come to support the idea that the psychological attributes, related to
entrepreneurship can be culturally acquired (Gibb and Ritchie, 1982; Vesper, 1990). To
this extent, it seems pertinent to conduct an analysis concerning the contribution of
education to foment entrepreneurship. Entrepreneurship education based on a theory of
solid learning can contribute to increase the knowledge management and to promote the
psychological attributes associated with entrepreneurs.
The identification and study of students’ entrepreneurial characteristics assumes
special relevance for the development of adequate educational programmes related with
entrepreneurship and business creation. Therefore, investigating what factors determine
the entrepreneurial intention (EI) is a crucial issue in the entrepreneurship research.
For instance, Raposo et al. (2006, 2008b) found that individuals who evidence more
propensity for the start-ups creation seem to possess self-confidence and leadership
capacity. Lee et al. (2006) conclude that the school and the education system play a
pivotal role in predicting and developing entrepreneurial traits.
EI has been described as a conscious state of mind that directs attention (and
therefore, experience and action) toward a specific object (goal) or pathway to achieve it
(means) (Bird, 1989). Researchers typically draw EI to three general factors (Krueger
et al., 2000):
1 person’s attitude towards the behaviour
2 perceived social norms
3 person’s self-efficacy will influence intentions.
In previous research, personal and environment-based determinants of EI, such as
personality traits, attitudes toward entrepreneurship or social environment, have been
extensively discussed (Schwarz et al., 2006; Liñán and Chen, 2007; Ferreira et al., 2007;
Raposo et al., 2008a; Guerrero et al., 2008). EI has proven to be a primary predictor
of future entrepreneurial behaviour (Reynolds, 1995; Krueger et al., 2000; Schwarz et al.,
2006). Nevertheless, there have been just a limited number of studies addressing
influence factors for EI of the secondary students.
A central question that arises is what factors determine EI among secondary students.
Traditional educational methodologies are likely to privilege predictability, well-defined
rules, planning and stability in the classroom. These characteristics may lessen creative

94 A. do Paço et al.













learning and behaviour. An alternative methodology was needed to raise the students’
entrepreneurial propensity (Oosterbeek et al., 2007; Rodrigues, 2007).
This study follows a cognitive approach through the application of an EI model. The
paper is structured as follows. First, we give some theoretical background and state our
hypothesis. This is followed by a description of our research conceptual model, including
the sample, the measures and the analysis, and also the presentation of our findings is
stated. The paper ends with final remarks referring important implications for researcher,
practitioners and educators.
2 Literature review
A review of recent literature measuring the impact of general education on
entrepreneurship and entrepreneurial activity suggests some possible generalisations.
Evidence suggesting a positive link between education and entrepreneurship is robust.
For example, Ferreira et al. (2007) and Raposo et al. (2008a, 2008b) found that the most
important effect on the propensity to start-up a firm among students was education.
Results point out the importance of entrepreneurship education in the promotion of the
EI. These conclusions have support in others studies (Kennedy et al., 2003; Brice, 2004;
Bhandari, 2006; Li, 2006; Hmieleski and Corbett, 2006; Florin et al., 2007). Florin et al.
(2007) stated that the students need to perceive that the application of the skill is feasible
and that an entrepreneurial approach is desirable and a focus on developing a positive
attitude toward entrepreneurial behaviour appears to be central to entrepreneurship
education.
Furthermore, some works advance the idea that early formal entrepreneurship
education affects the attitudes of students, influencing them in the direction of their future
career and affect their propensity for entrepreneurship when they become adults.
For instance, Kourilsky and Walstad (1998) indicate that the very early stimulus of
entrepreneurial attitudes, even before high school, can encourage entrepreneurship as a
career option, although they have not tested this assertion empirically. Lee et al. (2006)
refer that pedagogical approach should encourage children to make decisions and accept
mistakes as part of the learning process. In this sense, on the education level, active
experimentation should be balanced with abstract conceptualisation, contributing to
infuse in the students a larger propensity to entrepreneurship.
Thus, there has been recently an increased interest from researchers about
entrepreneurship education programmes (Veciana et al., 2005; Chand and Amin-
Choudhury, 2006; Fayolle et al., 2006; Lee et al., 2006; Brodie and Laing, 2007; Frank
et al., 2007; Pihie, 2007; Tang et al., 2007; Verduyn et al., 2007). Fayolle et al. (2006)
refer about the importance to develop a common framework to evaluate, compare and
improve the design of educational programmes of entrepreneurship.
Guerrero et al. (2008) identified the six main models about EI developed in this field,
and they are:
1 entrepreneurial event model (Shapero, 1982) that considers the business creation
as an event that can be explained with the interaction between initiatives, abilities,
management, relative autonomy and risk

Entrepreneurial intention among secondary students 95














2 theory of planned behaviour (TPB) (Ajzen, 1991) with the premise that any
behaviour requires a certain amount of planning and it can be predicted by the
intention to adopt that behaviour
3 entrepreneurial attitude orientation (Robinson et al., 1991) that explains the attitude
prediction through four different sub scales (achievement, self-esteem, personal
control and innovation) and three types of reactions (affective, cognitive or conative)
4 intentional basic model (Krueger and Carsrud, 1993) that examines the relationship
between attitudes and EIs using a scale to permit greater flexibility in the analysis of
exogenous influences, attitudes and intentions
5 entrepreneurial potential model (Krueger and Brazeal, 1994), based on the previous
models of Shapero and Ajzen supporting their evidence from the corporate venture
and enterprise development perspectives
6 Davidsson model (Davidsson, 1995) intention can be influenced by the conviction
defined by general attitudes, domain attitudes and the current situation.
In the next section, the model about EI used in this paper, based in the TPB, will be
explained.
3 Model of EI
As a result of the literature review, we support our research based on Liñán and
Chen’s (2007) model. This model is sustained by Ajzen’s (1991) approach and some
propositions from institutional economy theory (North, 1990). The Azjen’s (1991) TPB is
considered as a relevant tool to model the development of EI through pedagogical
processes and learning contexts (Fayolle et al., 2006). Ajzen (1991) considers that
intentions toward target behaviour depend on a set of underlying attitudes. Particularly,
intentions to take a certain course of action depend on the perceptions of participants
regarding personal and social desirability of the behaviour and the perceptions of
participants of whether they can successfully perform such action. The TPB is part of
larger family of international models that have been used to try to explain the mergence
of entrepreneurial behaviour and it assumes that human social behaviour is reasoned,
controlled or planned in the sense that it takes into account the likely consequences of the
considered behaviour (Ajzen, 1991).
According to Liñán and Chen (2007), it could be argued that perceptions regarding
general society and environment values do have an influence on motivational factors
determining the EI. Figure 1 presents the model that will be explored in this research and
it describes the attitudinal dimensions as latent variables of EI.
The model above considers a group of variables likely to influence the EI and it is
composed of various constructs, each one being measured by several indicators. At it
can see the constructs personal attitude (PA), subjective norm (SN) and perceived
behavioural control (PBC) are included in the model and, all together, will contribute to
the EI. There is also a connection between the constructs PA and PBC. The construct PA
includes five indicators; the construct SN includes three indicators; the construct PBC
includes six indicators; and the construct EI includes six indicators (see Annex A).

96 A. do Paço et al.













Figure 1 EI model (see online version for colours)

Notes: PA – personal attitude, SN – subjective norm, PBC – perceived behavioural
control and EI – entrepreneurial intention
In order to test the EI model (Figure 1), a set of research hypothesis were formulated, as
we can see below:
H1 PA positively influences EI [PA Æ+ EI].
H2 PBC positively influences EI [PBC Æ+ EI].
H3 SN positively influences EI [SN Æ+ EI].
H4 SN positively influences PA [SN Æ+ PA].
H5 SN positively influences PBC [SN Æ+ PBC].
H6 PA positively influences PBC [PA Æ+ PBC].
The evidences show that the first three hypotheses correspond to the traditional intention
model usually used. In what concerns to H4 and H5, these would explain the internal
antecedents.
4 Methodology
For this study, the model of data collection was a survey by self-administered
questionnaire with several groups of questions related to the demographic characteristics,
the PAs, the SNs, the PBC and the EI. The questionnaire is based on the existing
theoretical and empirical literature about the application of the TPB to entrepreneurship.
Questionnaires were administered in class, with permission from the school director
and with the collaboration of the two secondary student’s classes, aged between 14 and
15 years old, who will participate in an entrepreneurial learning pilot experience.
This educational experience will be based on an extensive network of ‘mini-
companies’ exchanging information, catalogues and products. It will include all stages to
the creation, development and dissemination of a cooperative inside the school, where the
H1
H2
H3
H6
H4
H5

Entrepreneurial intention among secondary students 97














students will have the opportunity to interact with another national or foreign school. So,
this methodology will be based on practical experience where students will have the
opportunity to display a wide array of social, personal and business skills.
After collection, the data was statistically analysed and interpreted using the statistical
software SPSS 16.0 (Statistical Package for Social Sciences). The partial least squares
(PLS) technique was also used to test the model recurring to the Smart PLS software.
This method consists of a statistical modelling-based technique through structural
equations that allows for the simultaneous estimation of a group of equations, by
measuring the concepts (measurement model) and the relationships between them
(structural model) and it has the capacity to address concepts not directly observable.
Table 1 shows the main methodological aspects related to the investigation.
Table 1 Synthesis of methodological aspects
Time basis Cross-section
Sampling unit Secondary students
Sample 74 individuals
Response rate 100%
Research method Self-administered questionnaire
Statistical analysis Bivariate, multivariate – PLS
5 Results
Total sample size was 74 secondary students. None of the questionnaires presented
missing values. 47.3% were female and the average age was 14.3 years.
According to Nunnally (1978), reliability and validity are essential psychometrics to
be reported. The first step was using Cronbach’s alpha and composite reliability to test
reliability of the proposed scales. The usual threshold level is 0.7 for newly developed
measures (Nunnally, 1978). Values range from 0.69 to 0.79 in the case of Cronbach’s
alpha and from 0.66 to 0.78 in the case of composite reliability (Table 2). Therefore,
these scales may be considered as reliable.
Table 2 Reliability measures
Construct Composite reliability Cronbachs alpha
EI 0.78 0.79
PA 0.71 0.70
PBC 0.66 0.69
SN 0.66 0.79
To access discriminant validity, we used correlations among indicators and constructs.
Items should have higher correlation with their own construct than with any other,
signifying that they are perceived by respondents as fitting in that theoretical construct
(Messick, 1988). According to the results presented in Table 3, all indicators correlate
higher with their own construct than with any other.

98 A. do Paço et al.













Table 3 Crossloadings
EI PA PBC SN
EI1 0.551 0.351 0.250 0.029
EI2 0.547 0.414 0.204 0.085
EI3 0.521 0.408 0.379 0.258
EI4 0.629 0.550 0.358 0.177
EI5 0.636 0.421 0.305 0.078
EI6 0.763 0.522 0.394 0.340
PA1 0.326 0.394 0.338 0.261
PA2 0.252 0.438 0.251 0.144
PA3 0.539 0.774 0.301 0.299
PA4 0.557 0.716 0.389 0.235
PA5 0.400 0.517 0.206 0.052
PBC1 0.304 0.226 0.473 0.026
PBC2 0.184 0.242 0.491 0.150
PBC3 0.336 0.266 0.579 0.186
PBC4 0.130 0.071 0.432 –0.012
PBC5 0.308 0.308 0.639 0.198
PBC6 0.217 0.327 0.352 0.204
SN1 0.177 0.150 0.142 0.407
SN2 0.192 0.246 0.185 0.765
SN3 0.181 0.270 0.211 0.693
EI 1.000 0.737 0.524 0.281
PA 0.737 1.000 0.517 0.358
PBC 0.524 0.517 1.000 0.284
SN 0.281 0.358 0.284 1.000
5.1 Structural analysis
The division of a model implies a measurement model and a structural model
(Anderson and Gerbing, 1988). The measurement model refers to the indicators and/or
subconstructs that reflect the relevant constructs, while the structural model addresses the
relationships between constructs.
Due to the fact that EI is a not directly an observable variable, an analysis based
on structural equations is considered adequate. This modelling technique allows
incorporating not directly observable variables (latent variables or constructs) to the
models. The constructs may be measured by indicators or even by subconstructs.
Firstly, the estimation of the model is performed by computing the latent variables
through an iterative procedure that requires the regression of the variables of the outer

Entrepreneurial intention among secondary students 99














and inner models, with the parameters of one part of the model being fixed while
estimating those of the other part. After this initial step, the relationships of the outer
and inner models are estimated through OLS non-iterative regression. The quality of the
model is determined by the observation of the R2, or by the Stone-Geisser test, and by the
significance of the structural relationships using the Jackknife and Bootstrap techniques
(Chin, 1998).
The measurement model is composed by 20 indicators which measure four
constructs. Constructs may be measured by reflective indicators and/or formative
indicators (Duarte, 2005; Raposo et al., 2008a; Rodrigues et al., 2008). In our model, all
the indicators are of reflective nature, which mean that they measure the same construct
and represent the construct’s visible part.
To test the weights’ significance, we used the bootstrapping technique, which consists
in generating a large number of subsamples from the original sample through the
systematic deletion of observations. The model is recomputed for each subsample and the
resulting weights are averaged. The resulting mean of weights is compared with the
original weight. In this case, 1,000 valid subsamples were extracted. Results of the final
model are shown in Table 4.
Table 4 Bootstrapping results
Path Original sample
Sample
mean
Standard
deviation
Standard
error t statistics Sig.
PA Æ EI 0.636 0.632 0.065 0.065 9.859 0.000
PA Æ PBC 0.517 0.533 0.079 0.079 6.513 0.000
PBC Æ EI 0.195 0.204 0.087 0.087 2.243 0.028
SN Æ PA 0.358 0.361 0.112 0.112 3.186 0.002
Note: 1,000 bootstrap samples
The paths SN Æ PBC and SN Æ EI were considered non-significant and successively
excluded from the original model (see Annex B).
According to Chin (1998), relationships between constructs with structural
coefficients bigger than 0.2 it should be considered as being robust. It should be noted
that the total effect of an independent variable over a dependent variable is bigger than
the direct effect because of the indirect effects (Raposo et al., 2008a; Rodrigues et al.,
2008). The direct, indirect and total effects on the EI are shown in Table 5.
Table 5 Effects
Path Direct effect Indirect effect Total effect
SN Æ EI n.s. 0.264 0.264
PA Æ EI 0.636 0.101 0.737
PBC Æ EI 0.195 - 0.195
SN Æ PA 0.358 - 0.358
SN Æ PBC n.s. 0.185 0.185
PA Æ PBC 0.517 - 0.517
Note: n.s. – non-significant with α = 0.05

100 A. do Paço et al.













There are three structural coefficients (direct effects) with absolute value bigger than 0.2
– the effect of ‘PA’ on ‘EI’, the effect of ‘SNs’ on ‘PA’ and the effect of ‘PA’ on ‘PBC’.
The analysis of the total effects shows that ‘SNs’ and ‘PA’ have a total effect over
‘EI’ bigger than 0.2. ‘PBC’ has a total effect on ‘EI’ very close to the threshold value of
0.2 and should not be neglected due to the exploratory nature of the study.
PA has the most important effect on EI (0.737), with a very large positive value. SNs,
despite not having a direct effect on EI, have an indirect effect over 0.2. As for perceived
SNs, there is no significant direct effect either, but total effect is very close to the
threshold value of 0.2.
In order to complete the model evaluation, it is necessary to assess its explanatory
capacity, given by the proportion of the total variance of each endogenous variable
explained by the model, the R2 statistic (Table 6).
Table 6 Explained variance
Endogenous constructs R square
EI 0.571
PA 0.128
PBC 0.267
This model explains 57.1% of the variance in EI based on PA and PBC. According to
Liñán and Chen (2007), this result is highly satisfactory, since most previous research
using linear models typically explain less than 40%. The model also explains 12.8% of
the variance in PA and 26.7% of PBC. These results concur with the ones obtained by
Liñán and Chen (2007) using a similar model.
Figure 2 Final structural model (see online version for colours)

The significance of structural coefficients and the magnitude of direct effects allow
testing the research hypotheses. Results are as follow:
H1 PA Æ+ EI – Supported

Entrepreneurial intention among secondary students 101














H2 PBC Æ+ EI – Supported
H3 SN Æ+ EI – Partially supported1
H4 SN Æ+ PA – Supported
H5 SN Æ+ PBC – Partially supported2
H6 PA Æ+ PBC – Supported.
Figure 2 presents the final model, with the direct effects and explained variances of
endogenous constructs. Two paths were excluded from the initial model (Figure 1).
6 Conclusions
In this study, we seek the answer to the research question related to what factors
determine EI among secondary students. In order to obtain some explanations for that,
an EI model based on the Azjen’s TPB was applied. This theory was considered an
appropriate tool to model the development of EI through pedagogical processes and
learning contexts.
Having in mind that Lündstrom and Stevenson (2001) states that the entrepreneurial
process begins before the start-up process, it seems important that educational policies
should be directed to create new attitudes among young students and to the creation
of pedagogical materials related with entrepreneurship education. The results of this
research confirmed that supposition because PAs are very important to explain the EI.
So, the education and training should centre itself much more in changing PAs than
in knowledge because the effects could be more significant to the process of business
creation and to overcome the perceived barriers to entrepreneurship.
More, it is desirable that an entrepreneurship educational programme could contribute
to the development of competences related to entrepreneurship; social and civic skills;
communication in a foreign language; mathematical and accounting capacities; digital
competences; creative and artistic skills; and cultural awareness.
As it was possible to observe the extracted variance is less than 0.5 in the two
exogenous constructs. This can be considered a limitation of the study, probably
associated to some problem of the measure model. In this sense, it is necessary to apply
this methodology to different samples. So, we recommend the test of the model here
presented in other populations, as well as the development of new indicators in order to
fully understand how EI help determine start-up decisions. This is the subject of ongoing
research by the authors.
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Notes
1 Structural coefficient non-significant, but total effect of 0.264.
2 Structural coefficient non-significant, but total effect of 0.185.
Annex A
Variables
Personal attitude
Indicate your level of agreement with the following sentences from 1 (total disagreement)
to 7 (total agreement).
a Being an entrepreneur implies more advantages than disadvantages to me.
b A career as entrepreneur is attractive for me.
c If I had the opportunity and resources, I’d like to start a firm.
d Being an entrepreneur would entail great satisfactions for me.
e Among various options, I would rather be an entrepreneur.
Subjective norm
If you decided to create a firm, would people in your close environment approve of that
decision? Indicate from 1 (total disapproval) to 7 (total approval).
a Your close family
b Your friends
c Your colleagues.
Perceived behavioural control
To what extent do you agree with the following statements regarding your entrepreneurial
capacity? Value them from 1 (total disagreement) to 7 (total agreement).
a To start a firm and keep it working would be easy for me.
b I’ am prepared to start a viable firm.
c I can control the creation process of a new firm.

Entrepreneurial intention among secondary students 105














d I know the necessary practical details to start a firm.
e I know how to develop an entrepreneurial project.
f If I tried to start a firm, I would have a high probability of succeeding.
Entrepreneurial intention
Indicate your level of agreement with the following statements from 1 (total
disagreement) to 7 (total agreement).
a I’ am ready to do anything to be an entrepreneur.
b My professional goal is to become an entrepreneur.
c I will make every effort to start and run my own firm.
d I’ am determined to create a firm in the future.
e I have very seriously thought of starting a firm.
f I have the firm intention to start a firm some day.
Annex B
Bootstrapping results
Model A
Path Original sample
Sample
mean
Standard
deviation
Standard
error T statistics Sig.
PA Æ EI 0.636 0.632 0.079 0.079 8.040 0.000
PA Æ PBC 0.477 0.494 0.087 0.087 5.460 0.000
PBC Æ EI 0.193 0.200 0.087 0.087 2.211 0.030
SN Æ EI 0.003 0.010 0.093 0.093 0.031 0.975*
SN Æ PA 0.357 0.364 0.117 0.117 3.047 0.003
SN Æ PBC 0.119 0.110 0.111 0.111 1.069 0.288
Note: *Excluded path
Model B
Path Original sample
Sample
mean
Standard
deviation
Standard
error T statistics Sig.
PA Æ EI 0.637 0.634 0.065 0.065 9.877 0.000
PA Æ PBC 0.477 0.493 0.082 0.082 5.806 0.000
PBC Æ EI 0.194 0.203 0.089 0.089 2.165 0.034
SN Æ PA 0.358 0.362 0.119 0.119 3.006 0.004
SN Æ PBC 0.118 0.111 0.111 0.111 1.061 0.292*
Note: *Excluded path

106 A. do Paço et al.













Model C
Path Original sample
Sample
mean
Standard
deviation
Standard
error T statistics Sig.
PA Æ EI 0.636 0.632 0.065 0.065 9.859 0.000
PA Æ PBC 0.517 0.533 0.079 0.079 6.513 0.000
PBC Æ EI 0.195 0.204 0.087 0.087 2.243 0.028
SN Æ PA 0.358 0.361 0.112 0.112 3.186 0.002