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

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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).

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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 + EI].

H2 PBC positively influences EI [PBC + EI].

H3 SN positively influences EI [SN + EI].

H4 SN positively influences PA [SN + PA].

H5 SN positively influences PBC [SN + PBC].

H6 PA positively influences PBC [PA + 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

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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.

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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

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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 EI 0.636 0.632 0.065 0.065 9.859 0.000

PA PBC 0.517 0.533 0.079 0.079 6.513 0.000

PBC EI 0.195 0.204 0.087 0.087 2.243 0.028

SN PA 0.358 0.361 0.112 0.112 3.186 0.002

Note: 1,000 bootstrap samples

The paths SN PBC and SN 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 EI n.s. 0.264 0.264

PA EI 0.636 0.101 0.737

PBC EI 0.195 - 0.195

SN PA 0.358 - 0.358

SN PBC n.s. 0.185 0.185

PA PBC 0.517 - 0.517

Note: n.s. – non-significant with α = 0.05

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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 + EI – Supported

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H2 PBC + EI – Supported

H3 SN + EI – Partially supported1

H4 SN + PA – Supported

H5 SN + PBC – Partially supported2

H6 PA + 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 EI 0.636 0.632 0.079 0.079 8.040 0.000 PA PBC 0.477 0.494 0.087 0.087 5.460 0.000 PBC EI 0.193 0.200 0.087 0.087 2.211 0.030 SN EI 0.003 0.010 0.093 0.093 0.031 0.975* SN PA 0.357 0.364 0.117 0.117 3.047 0.003 SN 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 EI 0.637 0.634 0.065 0.065 9.877 0.000 PA PBC 0.477 0.493 0.082 0.082 5.806 0.000 PBC EI 0.194 0.203 0.089 0.089 2.165 0.034 SN PA 0.358 0.362 0.119 0.119 3.006 0.004 SN 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 EI 0.636 0.632 0.065 0.065 9.859 0.000 PA PBC 0.517 0.533 0.079 0.079 6.513 0.000 PBC EI 0.195 0.204 0.087 0.087 2.243 0.028 SN PA 0.358 0.361 0.112 0.112 3.186 0.002