A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students Olguín, E. & Caro, F.J.

PAPER ID: 2024

A COMPREHENSIVE ONTOLOGICAL LEARNING MODEL FOR ENTREPRENEURSHIP TRAINING AMONG ENGINEERING STUDENTS Eduardo Olguin1, Francisco J. Caro2 1

Universidad San Sebastián (CHILE) Universidad de Sevilla (SPAIN)

2

Abstract This research describes the effect of the Comprehensive Ontological Learning Model (MOAI) on entrepreneurship. Its impact on the Theory of Planned Behavior (PBC) is also analyzed. A multi group analysis is conducted, based on Partial Least Squares (PLS) to compare model differences before and after entrepreneurial training. Results show PBC`s validity to explain entrepreneurial intention and its stability over time, since no significant model differences were found. Keywords: Engineering education, entrepreneurship, entrepreneurship intention, experiential learning.

1

INTRODUCTION

Entrepreneurship is becoming one of the most attractive ways to create jobs and reinvent careers related to engineering. Entrepreneurship has many benefits: it promotes innovation, creates jobs, develops human potential and addresses new consumer needs [1] [2] [3]. Entrepreneurs are characterized by seizing opportunities to create new companies; to do so they develop technological, organizational or product innovations [4]. They may even create new markets. Promoting entrepreneurship among engineers is essential to deploy business models, which can be adapted to complex and uncertain context situations. Institutional support to entrepreneurship, in the form of mentoring, funding, and programs to simplify bureaucracy, has proved to be insufficient to arise entrepreneurial vocations. Educational systems are a key element in this, given the need to influence personal attitudes that may arouse entrepreneurial intentions. Peterman y Kennedy (2003) [5] found that business management training among students from other fields (such as engineering) had positive results in terms of higher that average entrepreneurial intention. Thus, the need to strengthen entrepreneurship training in engineering programs. Engineering Schools that are aware of this challenge are starting programs to foster entrepreneurial attitudes among their students (quote). However, current trends in entrepreneurial training are proving to be unable to address challenges from companies and society in general. Those based on transmitting techniques and tools have limited impact, since they do not create entrepreneurial individuals but individuals with knowledge about it [6]. Society needs entrepreneurial individuals with the ability to be in charge and become key players. To do so entrepreneurship training strategies must see students in a holistic way.

1 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students PAPER ID: 2024

Olguín, E. & Caro, F.J.

This work proposes and ontological model for a comprehensive teaching and learning process that arouses entrepreneurial vocation as well as addresses technical requirements for business development. Further research is needed to assess the impact of this strategy in entrepreneurial intention among engineering students. We undertook this empirical research using a theoretical framework based on Azjen`s Theory of Planed Behavior (TPB) [7], used by many researchers working on entrepreneurship studies.

2

COMPREHENSIVE ONTOLOGICAL LEARNING MODEL

The so-called Comprehensive Ontological Learning Model (MOAI, according to its Spanish acronym) has many sources of inspiration, but the most important one is the so-called “Santiago School”. This was a movement of creative and innovative thinkers from the late 1960s and early 1970s, initially based in Santiago, Chile. These included Humberto Maturana, Francisco Varela, Francisco Hoffmann, Claudio Naranjo, Sammy Frenck, Fernando Flores and others. This experiential teaching-learning model has been adopted by the Engineering and Technology Faculty at Universidad San Sebastián (FIT USS) to train engineers. MOAI takes care of some actual challenges of engineering education: (1) The accelerated world change (2) the new possibilities, which calls for new education practices and styles, partly linked to technology-driven training and education, (3) A new, highly connected generation of students that need to be stimulated on a permanent basis; (4) Companies’ demand for engineers with social skills, intra-entrepreneurship, teamwork and soft competencies in general. And very important the (5) Economic development calls for design-oriented engineers with entrepreneurial skills and attitudes. MOAI, in the entrepreneurship field, have five concerns: (1) Development of self-reliance, (2) development of linguistic and conversational skills, (3) development of skills and attitudes to identify opportunities and become a key player, (4) teamwork and collaboration, and (5) lean startup tools and methodologies for entrepreneurship. The change management process to implement the model is based in a teaching transformation process and an environment that consider a series of principles and attitudes to address the challenges.

VISION

TEACHING

CLASSROOM ENVIROMENT

Sustainability

Focused on students

Equality and horizontality

Entrepreneurial attitude

Active learning

Listening to students and their concerns

Collaboration

Focus in the process

Respect

Autonomy

Learning oriented design

Bonding with students

Multiverse

Systemic vision

Formative evaluation

Diversity

Building upon strength

Knowing and validating student’s personal histories

Respect for the Other

Experiential methodology

Teacher as facilitator

Responsibility and conscience

Learning by role-modelling

Adequate layout

Figure 1. Teaching Principles and Attitudes

3

THEORY OF PLANNED BEHAVIOR (TPB)

To assess the impact of MOAI on engineering students we will use a model derived from TPB [7]. TPB’s basic premise is that there exists a strong correlation between the intention to act and actual behavior. In

2 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students Olguín, E. & Caro, F.J.

PAPER ID: 2024

a meta-analysis of 185 studies using TPB, Armitage y Conner (2001) [8] confirmed that theory could explain 27% of behavioral variance and 39% of entrepreneurial intention. Research about entrepreneurial intention (EI) confirms TPB’s relevance since it explains the link between EI background, actual intention and actual behavior [9] [10] The TPB model under analysis can be assessed in graph 1. There are 5 variables; the independent variable is Entrepreneurial Intention (EI), and dependent variables are personal attitudes related to entrepreneurship (PA), perceived behavioral control (PBC), social norms (SN) and social valuation of entrepreneurship (SV) Entrepreneurial Intention (EI) is a key element to understand the entrepreneurial process. “A person forms an intention to produce a certain behavior, this intention is kept as a disposition until, in an appropriate moment, intent is made to translate intention into action” [11] In our case EI represents a construct that connects the process of detecting an opportunity with the act of making it happen [12] [13]. The study of this variable constitutes one of the most viable precursors of entrepreneurial behavior, which brings new businesses into existence [14]. Perceived behavioral control (PBC) is another important variable in EI prediction and in entrepreneurial behavior. This construct reflects the perception of an individual’s capability to create and manage a new company. Researchers use the term PBC in different ways, they frequently use PBC and self-efficiency indistinctly or introduce little nuances that differentiate these terms [8]. Researchers such as Armitage and Conner (2001) [8], and Pruett et al (2009) [18] found a significant correlation between EI and PBC. However, in Engle’s multicultural study [15] only seven out of twelve country studies showed a strong link between PBC and EI. The study of this link in differential contexts is therefore relevant. In any case, according to research, the PBC construct and PA are the strongest EI predictors and are frequently used to explain EI [13]. Personal attitudes towards entrepreneurship (AP) is the model´s third construct. It is a personal factor that signals the individual’s desire to create value by developing an entrepreneurial behavior. Its positive and direct correlation with EI was identified in many studies [15] [6]. Other key constructs to understand entrepreneurial intention’s background drivers are social norms (SN) and social valuations (SV). They help to understand how and individual’s close environment affects his/her entrepreneurial behavior [16]. Research supports that social norms have an impact on entrepreneurial intention since parents; friends and other relevant persons might promote or disapprove an individual’s EI [17] [15]. However Shook y Bratianu (2010) [19] found a negative correlation between context norms and EI, while Krueger et al (2000) [9] and Armitage and Conner (2001) [8] found a weak correlation between both variables. Finally, Ajzen (2005) [11] and Guerrero, Lavín and Álvarez (2009) [20] found and indirect link between social norms and EI valuation with PA and PBC mediation. Norms tend to promote a positive attitude towards entrepreneurship and they may also increase PBC or self-efficiency, having an impact on EI. TPB studies do not always reach the same conclusions. While Gelderen et al (2008) [21] and Gird and Bagraim (2008) [22] found a strong correlation between EI`s three drivers and the intention to start a new business, Liñán y Chen (2009) [23] state that there is no correlation between EI an social norms, confirming previous conclusions from Krueger et al., (2000) [9]. In their study, Engle et al (2010) [15] found that social norms and entrepreneurial intention were relevant EI predictors in half of their 12-country sample. Social norms and perceived behavioral control were relevant for the other half. The implications of this latter study are clear: more specific, TPB-based studies are to be conducted, instead of trying to identify universally applicable models. Research is not homogeneous and do not take contextual factors into account [24] [25]. Besides, research is mainly based on U.S data; with some exceptions, there are few international studies comparing different social and cultural environments [26] [27] [23] [28]. With this article we want to produce new evidence about TBP’s structural model applied to entrepreneurship and answer the question if it varies when studying the same population in two different moments.

3 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students Olguín, E. & Caro, F.J.

4

PAPER ID: 2024

PROPOSED MODEL AND HYPOTETICAL MODEL

The proposed TPB model is shown in Fig. 2.

Figure 2. Proposed Model In this model certain correlations are proposed between Perceived Behavior Control (PBC), Personal Attitudes (PA) and Entrepreneurial Intention (EI). This basic TPB model is enriched by two drivers, Social Norms (SN) and Social Valuation of Entrepreneurship (SV). The following hypothesis are proposed based on the former theoretical analysis: H1: PBC is positively correlated with EI. H2: PA is positively correlated with EI. H3: SN is positively correlated with PBC. H4: SN is positively correlated with PA H5: SN is positively correlated with EI H6: SV is positively correlated with PBC. H7: SV is positively correlated with PA H8: SV is positively correlated with EI Based on the impact of the TBP model on entrepreneurship training, it was suggested that the weight of construct correlation can change after a specific training period in this subject-matter. The following hypothesis is thus set out. H9: there are statistically meaningful differences in the structural model when measured in two different time-frames after an entrepreneurship-specific training process.

5

RESEARCH MODEL

Empirical research was based on a non-random convenience sampling. Data was collected in Chile using a classroom questionnaire among civil engineering students at Universidad San Sebastián (Chile). Student University is a frequently used context in this kind of studies. [30] [31] The same questionnaire was used twice with the same population. First it was used at the beginning of the MOAI training process (June 2014) and at the end of it (December 2014). Invalid questionnaires due to duplication or empty fields gave a final sample of 95 students for the initial period and 102 for the final

4 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students PAPER ID: 2024

Olguín, E. & Caro, F.J.

one, since many students joined the program later. Gender composition of this sample matches the explored universe, 70% male and 30% female. The items of the questionnaire were adapted from existing and well tested scales offered by the extant literature. The questionnaire used by Sahilis et al (2012) [32] was adapted since it uses the same tools developed by Liñán and Chen (2009) [23] and Guerrero et al. (2009) [20].

6 6.1

ANALYSIS AND RESULTS Research model valuation

To test the research model a Partial Least Squares (PLS) approach for a Structural Equations Model (SEM) was used [32] [33] Reasons for the choice of PLS over other modelling tools using structural equations are [34] [35]: (1) the exploratory nature of this work, (2) PLS does not require large samples compared to other tools (AMOS, EQS, etc.), to produce results; (3) PLS is a non-parametric technique, so it does not need data normalization. The proposed model has been validated for each group, before training (G1) and after training (G2). Multi group PLS analysis [37] was used to compare differences between groups. M3’s SmartPLS 2.0 software was used in this analysis [37]. The structural equation model is described by two models: (1), a measuring model between manifest variables (MVs), and (2) a structural model between endogenous variables and other variables.

6.2

Analysis of the measuring model

Before analyzing the structural model, model reliability and validity of the measuring model were determined. Individual reliability was evaluated through charge evaluation (λ) or simple correlation between measures or indicators with their respective latent variables (LV) (indicators with λ ≥ 0.707 were accepted) (see chart 1). Chart 1. Results of cross loading procedures for Group 1 and 2. G1

G2

EI

PA

PBC

SN

SV

EI

PA

PBC

SN

SV

ei2

0.902

0.7

0.463

0.091

0.385

0.914

0.794

0.596

0.319

0.472

ei3

0.917

0.751

0.507

0.136

0.439

0.924

0.769

0.517

0.31

0.412

ei4

0.916

0.749

0.469

0.117

0.387

0.929

0.746

0.554

0.383

0.386

ei5

0.938

0.73

0.537

0.146

0.433

0.919

0.788

0.502

0.364

0.42

pa2

0.701

0.87

0.282

0.396

0.468

0.739

0.889

0.506

0.448

0.494

pa3

0.581

0.788

0.227

0.283

0.255

0.722

0.897

0.422

0.356

0.314

pa4

0.651

0.896

0.364

0.299

0.368

0.748

0.907

0.458

0.378

0.42

pa5

0.768

0.855

0.468

0.22

0.457

0.802

0.89

0.526

0.312

0.408

pbc1 0.475

0.331

0.754

0.158

0.227

0.397

0.325

0.712

-0.042

0.187

pbc2 0.455

0.343

0.836

0.087

0.336

0.621

0.513

0.854

0.131

0.316

pbc3 0.412

0.38

0.812

0.244

0.238

0.403

0.339

0.732

0.083

0.113

pbc4 0.229

0.131

0.664

0.123

0.204

0.182

0.201

0.592

-0.063

0.067

pbc5 0.268

0.196

0.637

0.187

0.264

0.362

0.411

0.726

0.224

0.37 5

8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students PAPER ID: 2024

Olguín, E. & Caro, F.J.

pbc6 0.463

0.317

0.719

0.077

0.313

0.442

0.424

0.742

0.307

0.424

sn1

0.054

0.166

0.042

0.614

0.182

0.338

0.304

0.173

0.825

0.4

sn2

0.112

0.348

0.2

0.89

0.289

0.357

0.442

0.17

0.901

0.405

sn3

0.133

0.269

0.159

0.814

0.27

0.107

0.172

0.063

0.631

0.254

sv1

0.299

0.293

0.341

0.157

0.767

0.413

0.324

0.392

0.253

0.801

sv2

0.263

0.244

0.219

0.199

0.796

0.389

0.36

0.264

0.403

0.841

sv3

0.466

0.51

0.3

0.373

0.851

0.272

0.413

0.223

0.445

0.718

LV reliability indicates the degree of accuracy of the observed variables in measuring LV Composed reliability was used as an index of LV reliability (LVs with α> 0.7 were accepted). Convergent LV validity was evaluated using an extracted measure variance test (AVE); see Fornell y Larcker (1981) (AVE > 0.5 were accepted). Chart 2 shows composite reliability and AVE for each LV. Chart 2: Composite reliability and AVE G1

G2

Composite reliability

AVE

Composite reliability

AVE

AE

0.914

0.728

0.942

0.803

CCP

0.878

0.548

0.871

0.533

EI

0.956

0.843

0.958

0.849

SN

0.821

0.611

0.834

0.630

SV

0.847

0.649

0.831

0.621

LV discriminating validity was tested analyzing if AVE’s square root for each LV is higher than the correlation with other LVs (see chart 3). Chart 3. LV Correlations for Group 1 and 2 (diagonal elements are AVE square roots). G1 AE AE

6.3

G2 CCP

IE

SN

SV

0.853

AE

CCP

IE

SN

SV

0.896

CCP 0.4

0.741

IE

0.798

0.538

0.918

SN

0.352

0.192

0.134

0.781

SV

0.464

0.361

0.448

0.323

0.806

0.535

0.73

0.841

0.589

0.922

0.418

0.185

0.373

0.794

0.459

0.378

0.459

0.457

0.788

Structural Model Analysis

After proving the measuring model’s validity and reliability, correlation between constructs were tested. Hypothesis were evaluated using a path coefficient test (β) and their significance levels β> = 0.2). A bootstrapping with 500 sub-samples was conducted to test the statistical significance of each path coefficient. Explained variance (R-square) in endogenous LVs and regression signification coefficient (Ftest) serve as indicators of the model’s explanation capacity. 6 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students PAPER ID: 2024

Olguín, E. & Caro, F.J.

Chart 4. PLS result analysis for group 1 and group 2 (S, strong correlation; W, weak correlation) (*** very meaningful, ** meaningful, nm non-meaningful) Path Coefficients G1

r

Path Coefficients G2

r

t-Values G1

tValues G2

ppValues Values G1 Sig G2 Sig

AE -> IE

0.727

S

0.709

S

11,675*** 9.903***

0.000

***

0.000

***

CCP -> IE

0.255

S

0.185

W

3,597***

3.042***

0.000

***

0.002

***

SN -> AE

0.225

S

0.263

S

2,053**

2.450**

0.041

**

0.015

**

SN -> CCP

0.084

W

0.016

W

0.877nm

0.179ns

0.380

nm

0.858

nm

SN -> IE

-0.197

W

0.017

w

2,798***

0.237ns

0.005

***

0.812

nm

SV -> AE

0.391

W

0.339

S

3,348***

3.784***

0.001

***

0.000

***

SV -> CCP

0.334

W

0.371

S

3,210***

4.194***

0.001

***

0.000

***

SV -> IE

0.082

W

0.056

W

1,409nm

0.965ns

0.159

nm

0.335

nm

Chart 5. Explained variance (R-square) in endogenous LVs Endogenous variable

G1

G2

AE

0.261

0.266

CCP

0.136

0.143

EI

0.728

0.737

Based on these results, hypothesis H1, H2, H4, H6 and H7 are supported for both groups. H5 is not supported for group 2, nor are H3 and H8 supported for neither of them. To compare structural models for both groups, a multiple group PLS analysis was conducted [38] [39]. Last column in chart 6 allows to dismiss hypothesis H9. There are not significant differences in model relations before and after training. Chart 6. Comparison between groups of path coefficients (β), t-value y p-value (Sig. * p =0.05 ** p=0.01 *** p=0.001).

Camino

Path Coefficients (G1 -G2)

t-Value (G1 - p-Value (G1 G2) -G2)

(Sig.)

AE -> EI

0.018

0.192

0.848

n.m.

CCP -> EI

0.070

0.757

0.450

n.m.

SN -> AE

0.038

0.247

0.805

n.m.

SN -> CCP

0.068

0.519

0.604

n.m.

SN -> EI

0.214

2.134

0.034

n.m.

SV -> AE

0.052

0.357

0.721

n.m.

SV -> CCP

0.037

0.273

0.785

n.m.

SV -> EI

0.026

0.324

0.747

n.m. 7

8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students Olguín, E. & Caro, F.J.

7

PAPER ID: 2024

CONCLUSIONS

We want to highlight four contributions of this exploratory study. First of all, TPB has been successfully used to explain entrepreneurial intention among college students in Chile. Second, according the literature [6] [16] there is a strong and meaningful correlation between AP and EI. This has important practical implications, since the most influential construct upon entrepreneurial intention is the attraction that a student might feel towards entrepreneurship. Third, we must highlight how weak is the impact of social norms and social valuations on entrepreneurial intention [8] [9]. Respecting PBC, its impact on entrepreneurial intention is low, although it is a significant one (in group 1 is weak). This implies that acting on motivation by increasing student attraction towards entrepreneurship, is more interesting than reducing uncertainty by increasing the perception of control over the creative process. Fourth and last, this study shows that there are no statistically meaningful differences in the PBC model considering students before and after MOAIe training activities. Correlations and weights remain constant after a comprehensive training process such as MOAIe. Nevertheless, this research is not without some limitations. Firstly, it has been carried out on a sample of students from a single university in Chile. Since conclusions are meant to be generalized, the study should be replicated with different student samples. Secondly, to validate results, a larger simple of individuals is needed.

REFERENCES [1] Fernández-Serrano, J., & Romero, I. (2013). Entrepreneurial quality and regional development: Characterizing SME sectors in low income areas. Papers in Regional Science, 92(3), 495-513. doi: 10.1111/j.1435-5957.2012.00421.x [2] Fritsch, M., & Mueller, P. (2004). Effects of new business formation on regional development over time. Regional Studies, 38(8), 961-975. [3] Van Stel, A., & Storey, D. J. (2004). The Link Between Firm Births and Job Creation: Is there a Upas Tree Effect? Regional Studies, 38(8), 893-909. [4] Thornton, P. H., Ribeiro-Soriano, D., & Urbano, D. (2011). Socio-cultural factors and entrepreneurial activity. International Small Business Journal, 29(2), 105-118. doi: 10.1177/0266242610391930 [5] Peterman, N.E. y Kennedy, J. (2003). Enterprise education: Influencing students' perceptions of entrepreneurship. Entrepreneurship: Theory & Practice, 28(2), 129-144. [6] Liñán, F., Rodríguez-Cohard, J. C., & Rueda-Cantuche, J. M. (2011). Factors affecting entrepreneurial intention levels: A role for education. International Entrepreneurship and Management Journal, 7(2), 195–218. doi:10.1007/s11365-010-0154-z [7] Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. [8] Armitage, C.J. y Conner, M. (1999). The theory of planned behaviour: Assessment of predictive validity and 'perceived control. British Journal of Social Psychology, 38(1), 35-54. [9] Krueger, N.F. (2000). The cognitive infrastructure of opportunity emergence. Entrepreneurship: Theory & Practice, 24(3), 9-27. [10] Guerrero, M., Rialp, J. y Urbano, D. (2008). The impact of desirability and feasibility on entrepreneurial intentions: A structural equation model. International Entrepreneurship and Management Journal, 4(1), 35-50. [11] Ajzen, I. (2005). Attitudes, personality, and behavior (2nd ed.). Maidenhead, Berkshire, England; New York: Open University Press.

8 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students Olguín, E. & Caro, F.J.

PAPER ID: 2024

[12] Dimov, D. (2007b). From opportunity insight to opportunity intention: The importance of personsituation learning match. Entrepreneurship: Theory and Practice, 31(4), 561- 583. [13] Carsrud, A. y Brännback, M. (2011). Entrepreneurial motivations: What do we still need to know? Journal of Small Business Management, 49(1), 9-9-26. [14] Prodan, I. y Drnovsek, M. (2010). Conceptualizing academic-entrepreneurial intentions: An empirical test. Technovation, 30(5-6), 332-347. [15] Engle, R., Dimitriadi, N., Gavidia, J., Schlaegel, C., Delanoe, S., Alvarado, I., He, X., Baume, S. and Wolff, B. (2010). Entrepreneurial intent: a 12-country evaluation of Ajzen’s model of planned behavior. International Journal of Entrepreneurial Behaviour and Research, Vol. 16 No. 1, pp. 35-57. [16] White, K.M., Smith, J.R., Terry, D.J., Greenslade, J.H. y McKimmie, B.M. (2009). Social influence in the theory of planned behaviour: The role of descriptive, injunctive, and in-group norms. British Journal of Social Psychology, 48(1), 135-158. [17] Gallurt Plá, P. (2010). Creación de Spin-Offs en las Universidades Españolas: Un modelo de Intenciones. Tesis Doctoral. Universidad Pablo de Olavide. [18] Pruett, M., Shinnar, R., Toney, B., Llopis, F. and Fox, J. (2009). Explaining entrepreneurial intentions of university students: a cross-cultural study. International Journal of Entrepreneurial Behaviour & Research, Vol. 15 No. 6, pp. 571–594. [19] Shook, C., and Bratianu, C. (2010). Entrepreneurial intent in a transitional economy: an application of the theory of planned behavior to Romanian students. International Entrepreneurship and Management Journal, Vol. 6 No. 3, 231-247. [20] Guerrero, M., Lavin, J. and M. Alvarez (2009). The role of education on start-up intentions: A structural equation model of Mexican university students. Available through the internet: sbaer.uca.edu/research/ASBE/2009/p06.pdf [accessed: 14/07/2012]. [21] Van Gelderen, M., Brand, M., van Praag, M., Bodewes, W., Poutsma, E. and van Gils, A. (2008). Explaining entrepreneurial intentions by means of the theory of planned behavior. Career Development International, Vol. 13 No. 6, pp. 538–559. [22] Gird, A. and Bagraim, J.J. (2008). The theory of planned behaviour as predictor of entrepreneurial intent amongst final-year university students. South African Journal of Psychology, Vol. 38 No. 4, pp. 711-724. [23] Liñán, F. and Chen, Y.W. (2009). Development and cross-cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship Theory and Practice, Vol. 33 No. 3, pp. 593617. [24] Elfving, J. (2008). Conceptualizing entrepreneurial intentions: A multiple case study on [25] Kickul, J., Gundry, L.K., Barbosa, S.D. y Whitcanack, L. (2009). Intuition versus analysis? testing differential models of cognitive style on entrepreneurial self-efficacy and the new venture creation process. Entrepreneurship: Theory and Practice, 33(2), 439-453. [26] Barbosa, S.D., Gerhardt, M.W. y Kickul, J.R. (2007). The role of cognitive style and risk preference on entrepreneurial self-efficacy and entrepreneurial intentions. Journal of Leadership & Organizational Studies, 13(4), 86-104. [27] Fernández, J., Liñán, F. y Santos, F.J. (2009). Cognitive aspects of potential entrepreneurs in southern an northern europe: An analysis using gem-data. Revista De Economía Mundial, (23), 151178. [28] Jaén, I. (2010). Una revisión teórica de los valores en el estudio de la intención emprendedora. Trabajo de investigación. Sevilla: Universidad de Sevilla. Peterman, N. E., & [29] Lüthje, C. y Franke, N. (2003). The ‘making’ of an entrepreneur: Testing a model of entrepreneurial intent among engineering students at MIT. R&D Management, 33(2), 135.

9 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.

A Comprehensive Ontological Learning Model For Entrepreneurship Training Among Engineering Students Olguín, E. & Caro, F.J.

PAPER ID: 2024

[30] Souitaris, V., Zerbinati, S. y Al-Laham, A. (2007). Do entrepreneurship programmes raise entrepreneurial intention of science and engineering students? the effect of learning, inspiration and resources. Journal of Business Venturing, 22(4), 566-591. [31] Sahinidis, A. G., Giovanis, A. N., & Sdrolias, L. A.. (2012). The Role of Gender on Entrepreneurial Intention Among Students: An Empirical test of the Theory of Planned Behaviour in a Greek University. International Journal on Integrated Information Management, 1(1), 61-79. [32] Chin, W. W. (1998). The partial least squares approach for structural equation modeling. En: George A. Marcoulides (Ed.), Modern Methods for Business Research, Lawrence Erlbaum Associates. [33] Tenenhaus, M., Vinzi, V.E., Chatelin, Y.-M., Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48, 159-205. [34] Diamantopoulos, A. (1999). Viewpoint-Export performance measurement: Reflective versus formative indicators. International Marketing Review, 16(6), 444-457. [35] Diamantopoulos, A. and Winklhofer, H.M. (2001), “Index construction with formative indicators: an alternative to scale development”, Journal of Marketing Research, Vol. 38, pp. 269 77. [36] Ramírez-Correa, P., Rondán-Cataluña, F. J., & Arenas-Gaitán, J. (2010). Influencia del género en la percepción y adopción de e-learning: Estudio exploratorio en una universidad chilena. Journal of technology management & innovation, 5(3), 129-141. [37] Ringle, C. M., Wende, S., Will, A. (2005). SmartPLS 2.0 (M3) beta, Hamburg: http://www.smartpls.de. ROCA, J.C, Gagne, M. (2008). Understanding e-learning continuance intention in the workplace: A selfdetermination theory perspective. Computers in Human Behavior, 24(4), 1585-1604. [38] Chin, W.W. (2000). Frequently Asked Questions - Partial Least Squares & PLS-Graph. http://discnt. cba.uh.edu/chin/plsfaq.htm [Accesado el 15 de Noviembre de 2009]. [39] Keil, M., Tan, B. C. Y., Wei, K. K., Saarinen, T., Tuunainen, V., Wassenaar, A. (2000). A CrossCultural Study on Escalation of Commitment Behavior in Software Projects. MIS Quarterly, 24(2), 299325.

10 8th annual International Conference of Education, Research and Innovation Seville (Spain). 16th - 18th of November, 2015.