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START-UP BUSINESSES INTENTIONS AMONG SECONDARY STUDENTS









Anabela Dinis (adinis@ubi.pt)
Arminda do Paço (apaco@ubi.pt)
João Ferreira (jjmf@ubi.pt)
Mário Raposo (mraposo@ubi.pt)
Ricardo Gouveia Rodrigues (rgrodrigues@ubi.pt)
University of Beira Interior, Department of Business and Economics, Research Unit NECE
Estrada do Sineiro, 6200-209 COVILHÃ, PORTUGAL

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Abstract
The identification of students’ entrepreneurial characteristics assumes special relevance
for the development of adequate educational programmes related with entrepreneurship and
business creation. This study aims to test a model of entrepreneurial intention among
secondary students based on their psychological characteristics. A sample of secondary
students was chosen ranging from 14 to 15 years old. Data was collected trough questionnaire
and analysed using structural equations modelling. Results show that propensity to risk
influence negatively entrepreneurial intentions, meanwhile self-confidence and need for
achievement influence positively the construct. The relation between tolerance to ambiguity,
locus of control and innovativeness with entrepreneurial intentions did not present statistical
significance.

1. Introduction
The educational system is a key area where it is possible to intervene and present
entrepreneurship as a viable alternative to dependent employment. The support for this view
comes from a widely literature review of enterprise, entrepreneurship and business creation.
This idea is also present in the European Union’ recommendations (see Action Plan to
Promote Entrepreneurship and Competitiveness – BEST Action Plan and Green Paper on
Entrepreneurship) which refers to the promotion of entrepreneurship through the education
system from primary school to university as a main goal (Frank et al., 2005).
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.
Accordingly to Kuratko (2005:580), “it is becoming clear that entrepreneurship, or certain
facets on it, can be taught”.Most of the empirical studies indicate that entrepreneurship can be
taught, or at least, encouraged by entrepreneurship education (Gorman et al., 1997; Ferreira et
al., 2007; Raposo et al., 2008). Additionally, Collins and Moore (1964) suggest that the
entrepreneurial role can be culturally and experimentally acquired, and therefore influenced
by education and training. Thus the present educational system should encourage the concept
of an enterprise culture.
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. In this sense, Lee et al. (2006)
suggest that pedagogical approach should encourage children to make decisions and accept
mistakes as part of the learning process. Fayolle et al. (2006) underline the importance to
develop a common framework to evaluate, compare and improve the design of educational
programmes of entrepreneurship. Klapper (2004) states that is crucial to create the right
entrepreneurial environment at the education institution. Entrepreneurial activities should be
integrated into the programmes of the institution from an early stage and need to be supported
by school culture. Thus, in entrepreneurship education literature, primary and secondary
school has received growing attention and enterprise education programs in secondary school
were confirmed to be important for later entrepreneurial intentions. It is believed that the ideal
stage to acquire basic knowledge about entrepreneurship and to foster a positive attitude

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towards entrepreneurship is during childhood and adolescence years (Peterman and Kennedy,
2003).
The knowledge of students’ entrepreneurial characteristics that have more impact on
entrepreneurial intentions can be an important contribution for the development of adequate
educational programmes related with entrepreneurship and business creation.
The purpose of this research is to study the effects of psychological dimensions on
perception of start-up in students of secondary school. In order to reach this goal, some
hypotheses of entrepreneurial intention related to psychological characteristics, will be tested.
This paper comprises the following major sections. The first section reviews the
literature about entrepreneurship education, specifically in what concerns to the psychological
approach. The second section presents the research hypotheses and the resulting structural
model. The third section discusses the research methodology and the fourth section presents
the results. Finally, the last section discusses some practical implications and presents some
conclusions.

2. Entrepreneurship Education: Psychological Approach
Cunningham and Lischeron (1991) identified six different schools of thought which
view the notion of entrepreneurship from different perspectives: (1) the “great person” school
of entrepreneurship, that views an entrepreneur as a person who is born with intuition,
dynamism, energy, persistence and self-esteem); (2) the psychological characteristics school
of entrepreneurship, that views entrepreneurs as individuals with unique values, attitudes and
needs which drive them and differentiate them from the other individuals; (3) the classical
school of entrepreneurship, that identifies entrepreneurship with innovation, creativity and
discovery; (4) the management school of entrepreneurship, that describes an entrepreneur as
one who organizes, owns, manages and assumes risk; (5) the leadership school of
entrepreneurship, that views an entrepreneur as one who motivates and leads; and (6) the
intrapreneurship school of entrepreneurship, that focuses on skilful managers within complex
enterprises.
This study adopts the psychological characteristics school of entrepreneurship. This
school of thought defends that individuals’ needs, drives, attitudes, beliefs and values are
primary determinants of behaviour. As such, this approach focuses on
personality/psychological factors and characteristics.
Early research on the factors that influence the decision to create a business focused on
trait or personality characteristics of individuals (McClelland, 1961; Brockhaus, 1980). For
example, Mitton (1989) describes entrepreneurs as those who have certain psychological
characteristics such as a commitment to their work, a need for total control and a liking for
uncertainty and challenge. According to Koh (1996) this should be expected, given the
understanding of psychological traits that are unique to entrepreneurs. Park and Ku (2008)
found that psychological traits are predictors of the entrepreneurial orientation.
One of the educational theory referred by Béchard and Grégoire (2005) in their
analytical framework focused on education research concerns, is the personalist theory. Its

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theoretical supports are the humanistic psychology, the personalism, the open education and
the nondeterministic free school. Usually this kind of researches shows a concern for the
development of the individual, for acknowledging individual needs and differences. So
entrepreneurship education should emphasize development of the skills needed to evaluate
venture opportunities.
From the literature review it can be seen that theoretical and empirical research has
associated psychological characteristics with entrepreneurship. For instance, Bygrave (1989)
presented a model that includes need for achievement, internal locus of control, tolerance for
ambiguity and risk-taking propensity as determinants of entrepreneurial intention. Also
Robinson et al. (1991) in their research find that achievement, innovativeness, control and
self-confidence could be predict entrepreneurial attitudes.
In general, the main psychological characteristics associated with entrepreneurship in
the literature are: (i) locus of control, (ii) propensity to take risk, (iii) self-confidence, (iv)
need for achievement, (v) tolerance of ambiguity; and (vi) innovativeness, which will be
explored in the next section. The hypotheses relating to these variables and the resulting
model will be presented in the next section.

3. Hypotheses and Model of Entrepreneurial Intention

Locus of control
Locus of control is the degree in which the individual believes that the reinforcements
are dependent on his behaviour. This individual believes that the accomplishment of a goal or
purpose depends on his own ability and actions rather than luck or other people’ efforts (Kuip
and Verheul, 2003). The empirical evidence shows that small businesses entrepreneurs are
more oriented at the internal level, than population in general (Kets Vries, 1977; Begley and
Boyd, 1987; Beverland and Lockshin, 2001). Brockhaus’ (1980) longitudinal study suggests
the existence of a positive correlation between orientation to locus of control and
entrepreneurial success. In another study Brockhaus and Horwitz (1986) reinforce that locus
of control could distinguish entrepreneurs who are successful from those who are
unsuccessful. Robinson et al. (1991) state that internal control leads to a positive
entrepreneurial attitude and most students who receive entrepreneurial formation may develop
a higher level of control and self-efficiency.
Given the above, the first hypothesis that will be tested in this study is as follows:
H1: Locus of Control positively influences Entrepreneurial Intention [LC Æ+ EI]

Propensity to take risk
This variable refers to risk acceptation when entering an activity, that is, it is related to
the probability of an activity having less than 100% success (Kuip and Verheul, 2003). Even
if risk-taking propensity is often mentioned as a determinant of entrepreneurial intention (e.g.

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Bygrave, 1989), several empirical studies suggest that small business’ entrepreneurs do not
have positive attitudes towards risk and they do not consider themselves as risk takers
(Davidsson, 1989, Baron, 1998), nor do they seem to differ from other groups, in more
objective tests on risk taking (Brockhaus, 1980). According to McClelland (1961) and Bellu
(1988), entrepreneurs seem slightly less attracted to take risks in situations known as pure
shift games. Entrepreneurs’ risk taking may be specific or momentary (Berverland and
Lockshin, 2001). Davidsson (1989) asserts that if the aspirations are sufficiently
accomplished, the entrepreneurs may simply stop taking higher risks. However, risk taking
and acceptance of uncertainty is something that can be slowly modified if desired
(Carayannis, et al., 2003). Thus, it is still not clear in literature if there is a relationship
between risk-taking propensity and entrepreneurial intention neither the nature of such
relationship.
Thus, the second hypothesis is:
H2: Propensity to take risk influences Entrepreneurial Intention [PR Æ EI]

Self-confidence
The high level of self-confidence has been suggested by many studies as an
entrepreneurs’ standard characteristic. In reality, this characteristic emerges constantly in a
compilation of empirical studies stated by Davidsson (1989). Ho and Koh (1992) refer that
self-confidence is an entrepreneurial characteristic and that it is related to other psychological
characteristics, such as locus of control, propensity to take risk and tolerance of ambiguity.
Robinson et al. (1991) have found entrepreneurs to have a higher degree of self-confidence
relative to non-entrepreneurs.
Therefore, another hypothesis was formulated as it follows:
H3: Self-confidence positively influences Entrepreneurial Intention [SC Æ+ EI]

Need for achievement
McClelland (1961) introduces rather revealing empirical evidence (obtained through
several kinds of methods) to the existence of a connection between the need for achievement
and development. Other authors find some sustenance on the relation between need for
achievement and entrepreneur’s behaviour (Davidsson, 1989), considering that the need for
achievement is a crucial factor (Begley and Boyd, 1987; Bellu, 1988; Beverland and
Lockshin, 2001). However, Davidsson and Wiklund (1999), state that the need for
achievement is not an important cause to entrepreneur behaviour. According to these authors,
the concept of need for achievement suffers from a lack of clarity in its definition, as well as
measuring problems. To Davidsson (1989), the basic idea that individuals and cultures differ
regarding the value attached to achievements (economic) and that these differences affect
entrepreneurs’ efforts is still very little plausible. Promoting an attitude toward high
achievement in students that goes beyond the external motivation for high grades is one of the
most difficult challenges in business education (Florin et al., 2007).

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Based on the previous research findings that entrepreneurs are high achievers, this study
postulates the following hypothesis:
H4: Need for Achievement positively influences Entrepreneurial Intention [NA Æ+ EI]

Tolerance of ambiguity
According to Koh (1996:15) “when there is insufficient information to structure a
situation, an ambiguous situation is said to exist”. The way in which an individual perceives
an ambiguous situation and organizes the information reflects his tolerance of ambiguity. If an
individual has a high tolerance of ambiguity it can be said that he considers ambiguous
situations challenging and strives to overcome unpredictable situations in order to perform
well. Mitton (1989) states that entrepreneurs do not only operate in an uncertain environment,
but they also eagerly undertake the unknown and manage uncertainty. So tolerance of
ambiguity may be considered an entrepreneurial characteristic and those who are more
entrepreneurial are expected to display more tolerance of ambiguity than others.
Therefore, the fifth hypothesis was formulated as it follows:
H5: Tolerance of Ambiguity positively influences Entrepreneurial Intention [TA Æ+ EI]

Innovativeness
According to Robinson et al. (1991) the innovativeness is related to perceiving and
acting on business activities in new and unique ways. This idea is one of the recurring themes
in defining entrepreneurship. For example for Schumpeter (1934), innovativeness is the most
fundamental aspect of entrepreneurship and an essential entrepreneurial characteristic.
Evidences from literature review shows that entrepreneurs are significantly more innovative
than non-entrepreneurs (Robinson et al., 1991).
Given the above, the last formulated hypothesis in this study is:
H6: Innovativeness positively influences Entrepreneurial Intention [IN Æ+ EI]

So it seems to be possible to present and test a model based in these assumptions
(research hypotheses 1 to 6). The Entrepreneurial Intention model used in this study includes
several constructs related to the psychological characteristics mentioned above: locus of
control, propensity to take risk, self-confidence, need for achievement, tolerance of ambiguity
and innovativeness (see Figure 1). Each one of the constructs was depicted by means of
several items used in a questionnaire survey, as it will be explained in the following section.

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Figure 1. Entrepreneurial Intention model



4. Methodology
This empirical research is based in a sample of 74 secondary students of two different
classes1, mainly 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 students will have the opportunity to interact with another national or foreign
school. The learning will be based on practical experience where students will have the
opportunity to display a wide array of social, personal and business skills.
Data collection was made through a survey by self-administered questionnaire,
administered in class, about the psychological characteristics applied to entrepreneurship.
This questionnaire include several groups of questions based on the existing theoretical and
empirical literature, namely questions related to demographic characteristics, need for
achievement, locus of control, propensity to take risk, tolerance of ambiguity, self-confidence,
innovativeness (Koh, 1996) and entrepreneurial intention (Liñan and Chen, 2007).
Data was statistically analysed and interpreted using the statistical software SPSS 16.0
(Statistical Package for Social Sciences). The PLS (Partial Least Squares) technique was also
used to test the model recurring to the Smart PLS software. This method consists of a
                                                            
1 Only one class will be submitted to the educational experience. No statistical differences were found between
the two groups on the seven scales used.
H6
H1
H2
H3
H4
H5

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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
Descriptive analysis
Using 74 responses, we created summated scales for each construct, by computing the
respective indicators’ means to each respondent. None of the questionnaires presented
missing values. 47.3% were female, and the average age was 14.3 years.
The descriptive statistics of the summated scales, as well as the results of one-sample t-
test, are presented in Table 2.
Table 2. Descriptives of summated scales and t-tests
Minimum Maximum Mean Std. Dev. t
a Sig.
Entrepreneurial Intention (EI) 1.17 4.50 2.824 0.614 -2.462 0.016
Propensity to take Risk (PR) 1.33 4.33 2.829 0.517 -2.849 0.006
Self-confidence (SC) 1.67 4.33 2.957 0.449 -0.820 0.415
Tolerance to Ambiguity (TA) 1.00 4.00 3.252 0.450 4.821 0.000
Need for Achievement (NA) 1.17 4.50 3.358 0.467 6.607 0.000
Locus of Control (LC) 2.00 4.29 3.390 0.445 7.537 0.000
Innovativeness (IN) 1.20 4.40 3.546 0.415 11.312 0.000
a t-test with 73 degrees of freedom and test value 3.
The scales used to measure the relevant phenomena were Likert scales (min 1, max 5),
where 3 is the indifference value. Values bellow 3 (the median point of the scale) represent
somewhat negative values in the scale, and above 3 are the positive values.
It should be noticed that Entrepreneurial Intention (EI) has the lower mean of the seven
scales, but also the largest standard deviation, meaning that the group is very heterogeneous in
what respects to EI. This scale presents the biggest maximum value of 4.5 (along with NA),
and the largest range (3.33) along with TA. Innovativeness has the largest mean value;
nevertheless, as will be shown later, this construct is not related to EI in this specific sample.

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The scales EI, PR and SC portray means below 3, showing that these students do not
have entrepreneurial intentions and are not prone to risk. Regarding to Self-confidence, the
mean value is not significantly lower than 3, considering a confidence level of 95%.
The scales TA, NA, LC and IN have scores significantly bigger than 3, although not
very far from that value, being all of them lesser than 4.

PLS Modelling
PLS is a components based structural equations modelling technique, similar to
regression, that simultaneously models the structural paths (relationships among latent
variables) and measurement paths (relationships between a latent variable and its indicators).
Rather than assume equal weights for all indicators of a scale, the PLS algorithm allows each
indicator to vary in how much it contributes to the composite score of the construct (Chin et
al., 1996).
The PLS procedure is used to estimate the latent variables as an exact linear
combination of its indicators with the goal of maximizing the explained variance for the
indicators and constructs. Following a series of analyses, PLS optimally weights the
indicators such that a resulting latent variable estimate can be obtained. The weights provide
an exact linear combination of the indicators for forming construct score. This value is both
maximally correlated with its own set of indicators and with other latent variables according
to the structural/theoretical model (Wold, 1985).
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).
Some indicators presented high cross loadings. The measurement model was purified of these
indicators. In Table 3 it can be seen that there is discriminant validity of the purified
measurement model.
It is also necessary to assess how accurate the path estimates are to the “true” effect
(Chin et al., 1996:33). According to Nunnally (1978) reliability and validity are essential
psychometrics to be reported.
Usually, the estimates of the structural paths tend to be more accurate as the reliability
of the estimated construct score increases. To assess the reliability of the construct estimated
by PLS, composite reliabilities are presented in Table 4. In the Cronbach’s alpha there is the
assumption of parallel measures that represent a lower bound estimate of internal consistency.
Nevertheless, a better estimate can be gained using the composite reliability formula.

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Table 3. Cross loadings
EI IN LC NA PR SC TA
EI1 0.617 0.257 0.199 0.366 0.084 0.373 0.081
EI2 0.558 0.232 0.162 0.282 0.088 0.193 0.036
EI3 0.586 0.313 0.389 0.331 0.285 0.243 0.176
EI4 0.594 0.272 0.303 0.277 0.158 0.239 0.146
EI5 0.537 0.041 0.000 0.014 0.115 0.128 0.072
EI6 0.721 0.117 0.359 0.342 0.310 0.191 0.247
IN2 0.268 0.707 0.333 0.399 0.057 0.273 0.210
IN5 0.140 0.435 0.277 0.348 0.204 0.074 0.194
LC1 0.190 0.028 0.450 0.295 0.096 0.084 0.117
LC2 0.136 0.307 0.362 0.292 0.030 0.080 0.032
LC3 0.320 0.047 0.557 0.285 0.167 0.002 0.103
LC4 0.179 0.368 0.519 0.373 0.163 0.124 0.240
LC5 0.181 0.470 0.555 0.490 0.304 0.201 0.336
LC6 0.339 0.506 0.729 0.485 0.502 0.265 0.256
NA1 0.323 0.231 0.295 0.423 0.105 0.021 0.195
NA2 0.332 0.259 0.356 0.634 0.189 0.221 0.151
NA3 0.091 0.215 0.167 0.248 0.016 0.150 0.065
NA5 0.138 0.434 0.368 0.520 0.252 0.177 0.336
NA6 0.197 0.549 0.491 0.600 0.269 0.104 0.174
PR1 0.173 0.173 0.385 0.326 0.543 0.088 0.087
PR2 0.119 0.048 0.246 0.098 0.631 0.125 0.245
PR3 0.284 0.167 0.277 0.231 0.843 0.112 0.015
SC3 0.271 0.161 0.239 0.209 0.071 0.801 0.323
SC4 0.084 0.069 0.072 0.042 0.140 0.328 0.060
SC5 0.293 0.379 0.174 0.206 0.064 0.632 0.260
TA1 0.166 0.138 0.031 0.090 0.013 0.100 0.605
TA2 0.138 0.307 0.387 0.349 0.054 0.299 0.665
TA5 0.101 0.172 0.237 0.167 0.018 0.269 0.544
TA6 0.079 0.053 0.159 0.085 0.230 0.024 0.320

Reliability was tested by using Cronbach’s alpha and Composite reliability of the
proposed scales. The usual threshold level is 0.7 for newly developed measures (Nunnally,
1978). Table 4 shows these measures.
Table 4. Reliability measures
Construct Composite Reliability Cronbach’s Alpha
EI 0.77 0.79
IN 0.50 0.47
LC 0.70 0.65
NA 0.61 0.56
PR 0.72 0.58
SC 0.51 0.39
TA 0.47 0.31

Some constructs have reliability problems, and in the case of IN, SC and TA the
reliability problem may be considered severe. So, reliability analysis will be performed again
after analysing the structural model.

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To test the weights’ significance of the structural model, we used the bootstrapping
technique, which consists in generating a large number of sub-samples from the original
sample through the systematic deletion of observations. The model is recomputed for each
sub-sample, and the resulting weights are averaged. The resulting mean of weights is
compared with the original weight. In this case 1000 valid sub-samples were extracted.
Results of the initial model are shown in Table 5.
Table 5. Initial bootstrap results
Original Sample
Sample
Mean
Standard
Deviation
Standard
Error t Sig.
IN Æ EI -0.012 -0.015 0.113 0.113 0.108 0.915
LC Æ EI 0.110 0.120 0.144 0.144 0.770 0.444
NA Æ EI 0.304 0.330 0.125 0.125 2.427 0.018
PR Æ EI -0.174 -0.164 0.087 0.087 1.995 0.050
SC Æ EI 0.285 0.272 0.076 0.076 3.733 0.000
TA Æ EI -0.015 0.043 0.118 0.118 0.126 0.900

The paths IN Æ EI, LC Æ EI and TA Æ EI were considered non significant (α=0.05)
and successively excluded from the original model. After these exclusions the remaining
paths were considered significant (α=0.05), as shown in Table 6.
Table 6. Final bootstrap results
Original Sample
Sample
Mean
Standard
Deviation
Standard
Error t Sig.
NA Æ EI 0.356 0.388 0.081 0.081 4.376 0.000
PR Æ EI -0.197 -0.192 0.078 0.078 2.535 0.013
SC Æ EI 0.290 0.294 0.069 0.069 4.180 0.000

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, 2008; Rodrigues et al., 2008). In this model there are no
indirect effects, so total effects are the same as direct effects (Table 7).
Table 7. Direct/Total Effects
EI
NA 0.356
PR -0.196
SC 0.290

All these effects (in absolute value) are close to or above the threshold value of 0.2.
Need for Achievement (NA) has the most important effect on EI (0.356). It is interesting to
note that Propensity to take Risk (PR) has a negative effect on Entrepreneurial Intention (EI).
Model evaluation is only complete after the assessment of its explanatory capacity,
given by the proportion of the total variance of each endogenous variable explained by the
model, the R2 statistic (Table 8).

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Table 8. Explained variance and reliability

Composite
Reliability R
2 Cronbach’s Alpha
EI 0.78 0.35 0.79
NA 0.61 - 0.56
PR 0.72 - 0.58
SC 0.63 - 0.43

This model explains 35.0% of the variance in entrepreneurial intention based on NA
and PR and SC. According to Liñán and Chen (2007), this result is convergent with most
previous research using linear models, where models typically explain less than 40%. The
exclusion of three original constructs contributes to this low value. Some constructs in the
final model present a poor reliability, especially NA and SC, with composite reliabilities
slightly over 0.6.
The significance of structural coefficients and the magnitude of direct effects allow
testing the research hypotheses. Results are as follow.
H1: [LC Æ+ EI] – Not Supported
H2: [PR Æ EI] – Supported2
H3: [SC Æ+ EI] – Supported
H4: [NA Æ+ EI] – Supported
H5: [TA Æ+ EI] – Not Supported
H6: [IN Æ+ EI] – Not Supported

Figure 2 presents the final model, with the direct effects and explained variances of the
endogenous construct. Three paths were excluded from the initial model (Figure 1, p.7).

                                                            
2 The effect presents a negative sign.

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Figure 2. Final structural model


6. Conclusions
This study aims to test a model of entrepreneurial intention among secondary students
based on their psychological characteristics. Specifically, the paper investigates if
entrepreneurial intention is significantly associated with locus of control, self-confidence,
need for achievement, propensity to take risk, tolerance of ambiguity and innovativeness.
The group under analysis is very heterogeneous in what respects to entrepreneurial
intention, but it can be said that, in general, these students do not have entrepreneurial
intentions and they are not prone to risk. On the contrary, they present a high score on
innovativeness, and a positive level of locus of control, need for achievement and tolerance to
ambiguity.
Concerning the relationship between psychological characteristics and entrepreneurial
intention, results indicate that:
1) Propensity to take risk influence negatively entrepreneurial intentions.
This result converges with several authors’ conclusions that frequently entrepreneurs do
not have positive attitudes towards risk and they do not consider themselves as risk takers (e.g
Davidsson, 1989, Baron, 1998, McClelland, 1961, Bellu ,1988), nor do they seem to differ
from other groups, in more objective tests on risk taking (Brockhaus, 1980). In the
particularly case of this study, there are other several explanations for this result. One possible
explanation can be that, because students do not have a real knowledge of what is to be an
entrepreneur they are not aware of the risk that this activity involved, assuming it as an always
successful activity. On the other hand, according to Berverland and Lockshin, (2001),
entrepreneurs’ risk taking may be specific or momentary. Since this research is based in “if”
questions, the perception of risk can be considerable low. This does not mean that in a real
situation, the some individual would not consider a higher level of risk. If is so, this results

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highlight the need of promoting the acceptance of uncertainty and the level of risk taking
among students as they improve their understanding/knowledge of the business world. This,
according to Davidsson (1989) is something that can be slowly modified.
2) Self-confidence and need for achievement influence positively entrepreneurial intentions.
These results are according with previous works. In fact, the high level of self-
confidence has been suggested by many studies (e.g Davidsson, 1989; Robinson et al., 1991)
as an entrepreneurs’ standard characteristic. Also the need for achievement is considerer by
many as a crucial factor for entrepreneurship (Begley and Boyd, 1987; Bellu, 1988; Beverland
and Lockshin, 2001). In the view of these results, we agree with Florin et al. (2007) that
promoting an attitude toward high achievement in students that goes beyond the external
motivation for high grades, but also the development of self-confidence that enables action, is
one of the most important but also difficult challenges in business education.
3) The relationship between entrepreneurial intentions and tolerance to ambiguity, locus of
control and innovativeness did not present statistical significance.
Many authors consider tolerance of ambiguity as an entrepreneurial characteristic (e.g
Mitton, 1989, Koh, 1996) and that those who are more entrepreneurial are expected to display
more tolerance of ambiguity than others. Also, many studies suggest the existence of a
positive correlation between orientation to locus of control and entrepreneurship (Kets Vries,
1977; Begley and Boyd, 1987; Beverland and Lockshin, 2001, Brockhaus, 1980, Brockhaus
and Horwitz, 1986, Robinson et al., 1991). In the same way, evidences from literature review
show that entrepreneurs are significantly more innovative than non-entrepreneurs (Robinson
et al., 1991). Results do not confirm these relationships, in spite of the fact that students under
analysis present a high degree of tolerance to ambiguity, locus of control and innovativeness.
However these characteristics can be related to the age of the students (teenagers phase) but
not necessarily directed to business creation. For instance, even if students present
innovativeness, this characteristic may not be related to perceiving and acting on business
activities in new and unique ways. In fact, the younger the students are the more
innovativeness they may display, since they are not so conditioned by institutionalised frames
of thought. These are issues for further research.
In interpreting the results of the study, we can point some limitations, concerning
methodological aspects. First, the study employs a self-report questionnaire, thus there is a
chance of response bias. Second, the sample only includes students from one school.
Additionally the sample size is low. These two aspects impose some precautions in the
generalisation of the results. Furthermore, it should also be noted that the some of the scales
used had reliability problems; for this reason in future studies they should be retested.
Discriminant validity was not obvious with all the indicators, with some of them presenting
high cross loadings in other constructs. If reliability and validity are increased, it can happen
that the excluded constructs may hold in the model, increasing the level of explained variance
(R2), which is spite of being similar to other studies, is not completely satisfactory.
There are also some limitations associated with the explicative variables of
entrepreneurial intentions. In fact, several authors in behavioural line of research (e.g Gartner,
1989) will argue that more important that psychological characteristics are behavioural
characteristics, since entrepreneurship is more related with actions resulting from behaviours,
and behaviours are easier to change than personalities. Other authors consider that there is, in

15
 
fact, a relationship between behaviours and entrepreneurship but also between psychological
characteristics and behaviours. This relationship is not considered in this work.
Given the above, some possible directions for future research can be highlighted.
Considering the methodological aspects, this study must be replicated to include more schools
and more students in order to allow a more reliable generalisation of the results. Future
research must also consider the other methodological limitations mentioned above in order to
improve reliability and validity of the results.
In respect to content aspects, the model should be developed through the incorporation
of other kind of variables, namely those related with behavioural characteristics. This will
allow investigating the relationship between psychological characteristics, behavioural
characteristics and entrepreneurship intention. The research framework could also be
expanded to include other factors such as family, demographic variables and environmental
support.
Finally, it must be stressed that this work represents a first phase in a larger project
which consist in the implementation of an entrepreneurial learning pilot experience during
which students will have the opportunity to display a wide array of social, personal and
business skills. This first phase of the research allowed understanding the point of departure
of the students, concerning their entrepreneurial intentions. In future research it will be
investigated if, with the entrepreneurial learning experience, entrepreneurial intentions
increased in the group and if that increase is related with the development of some or all of
the variables present in the model.

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