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European Planning Studies

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Entrepreneurial process in peripheral regions: the role of motivation and culture

Francisco J. García-Rodríguez, Esperanza Gil-Soto, Inés Ruiz-Rosa & Desiderio Gutiérrez-Taño

To cite this article: Francisco J. García-Rodríguez, Esperanza Gil-Soto, Inés Ruiz- Rosa & Desiderio Gutiérrez-Taño (2017) Entrepreneurial process in peripheral regions: the role of motivation and culture, European Planning Studies, 25:11, 2037-2056, DOI: 10.1080/09654313.2016.1262827

To link to this article: https://doi.org/10.1080/09654313.2016.1262827

Published online: 14 Dec 2016.

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Entrepreneurial process in peripheral regions: the role of motivation and culture Francisco J. García-Rodrígueza, Esperanza Gil-Sotoa, Inés Ruiz-Rosab and Desiderio Gutiérrez-Tañoa

aDepartment of Business Management and Economic History, Universidad de La Laguna, Tenerife, Spain; bDepartment of Economy, Accountant and Financial, Universidad de La Laguna, Tenerife, Spain

ABSTRACT The entrepreneurial potential of a region is a key factor in linking innovation to the market, thus leading to economic growth. This is especially important in peripheral regions that are characterized by low innovative dynamism. This paper analyses the entrepreneurial process in a European peripheral region, the Canary Islands, Spain. It attempts to determine possible cultural specificities and the role of motivation in the entrepreneurial process. To do this, an analysis of entrepreneurial intention (EI) is framed within the theory of planned behaviour and using motivation, opportunity and ability theory. An empirical study was carried out using a sample of 1457 university students participating in the Global University Entrepreneurial Spirit Students’ Survey project. Results indicate that motivation influences EI directly and indirectly through an individual’s attitude towards entrepreneurial behaviour. The perception of business opportunities is also a significant antecedent of entrepreneurial motivation. Consequently, entrepreneurial education and policies to foster entrepreneurship in peripheral regions should not attempt to transform individuals’ attitudes towards entrepreneurship directly, but rather focus on improving motivation using intensive pedagogical strategies in creativity that go beyond mere informative content. Methodologies and content focused on recognizing opportunities and problem-solving would also be effective elements in educational programmes of entrepreneurship.

ARTICLE HISTORY Received 2 April 2016 Revised 12 July 2016 Accepted 23 October 2016

KEYWORDS Entrepreneurial process; peripheral region; motivation; culture; entrepreneurial intention; GUESSS project

Introduction

The study of innovation’s positive impact on the economic growth of regions and countries is a classic theme in academic literature (Damanpour, 1991; Damanpour & Scheneider, 2006; Drucker, 1986; Hult, Hurley, & Knight, 2004; Schmiedeberg, 2008). Much effort has been invested in trying to understand the dynamics of the innovation process and the policies and incentives that drive or hinder its development (Asheim, 2009; Piñero, Rodríguez-Monroy, & Arbola, 2012; Van Oostrom, 2015).

However, when regional development is analysed, at least at European level, it is clear that ‘no one size fits all’ (Asheim, Moodyson, & Tödtling, 2011; Tödtling & Trippl, 2005).

© 2016 Informa UK Limited, trading as Taylor & Francis Group

CONTACT Francisco J. García-Rodríguez [email protected]

EUROPEAN PLANNING STUDIES, 2017 VOL. 25, NO. 11, 2037–2056 https://doi.org/10.1080/09654313.2016.1262827

It is not possible; therefore, to perform homogeneous analyses, since each region has specific features in terms of its innovative capacity, and the impact investments in inno- vation would generate in terms of economic growth. From this point of view, Asheim et al. (2011) distinguish between metropolitan, industrial and peripheral regions. The latter are characterized by low-level innovative activity due to a lack of dynamic firms and knowl- edge-generating organizations, a ‘thin’ and less specialized structure of knowledge suppli- ers, educational institutions and poorly developed networks of these suppliers.

By contrast, metropolitan regions are innovation centres that benefit from economies of scale and agglomeration as well as a high density of knowledge centres, clusters and support institutions. Industrial regions also have a high level of expertise in certain key industries around which knowledge-generating organizations and educational institutions can focus.

Among the dynamics that determine regional character regarding its innovative poten- tial, cultural specificities have been shown as some of the most important elements (Clifton, Gärtner, & Rehfeld, 2011). However, few studies have systematically analysed this connection, with the exception of the seminal analysis of Saxenian (1994), which com- pared the impact of sociocultural aspects in the success of Silicon Valley as an innovative region with the so-called Route 128 in Boston. The author concludes that the culture of interaction between the actors in the Californian region was the decisive factor for its great innovative dynamism. In this sense, Keeble and Wilkinson (2000) also highlight the impact of cultural influences on learning processes and innovation in European high-tech regions.

Therefore, in explaining the dynamics of innovation, the institutional and organiz- ational contexts in different regions must be considered, as well as the processes of gen- eration and exploitation of knowledge and interactions between different actors (Autio, 1998; Cooke, Heidenreich, & Braczyk, 2004; Doloreux, 2002; Tödtling & Trippl, 2005). The importance of these institutional and cultural specificities is reinforced by the need for modern innovation policies to be based not only on promoting investment in R&D (driven by supply), but also on promoting demand from users (Asheim, 2009).

Among the institutional and cultural aspects that can influence innovation and regional economic development, the degree of entrepreneurial potential in the territory should be highlighted as one that generates innovation and promotes economic growth. There need to be entrepreneurs with the ability to link innovation to the market, generating value, creating demand and the resulting economic growth (Audretsch & Keilbach, 2004; Gon- zález, Peña, & Vendrell, 2012; Guerrero & Peña-Legazkue, 2013).

It can be argued that a country or region’s competitiveness is based on its investment in research and technological development (R&D) and its ability to generate and attract skilled human capital. It is also essential to have ‘the existence of a business network that is able to tap into the sources of knowledge and technology at its disposal to produce new products and services that have acceptance in the global marketplace’ (COTEC Foundation, 2015, p. 21). For peripheral regions, this poses additional difficul- ties, in that most studies agree that individuals’ motivation for entrepreneurial activities in non-core regions is mainly need-driven (Baumgartner, Pütz, & Seidl, 2013; Kalantaridis, 2004). Thus, following the terminology of Liñán, Fernández-Serrano, and Romero (2013), it is mainly necessity entrepreneurship rather than opportunity entrepreneurship. There is

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also a tendency for the most talented entrepreneurs from the periphery to migrate to the core regions (Kaufmann & Malul, 2015).

It is therefore important to understand the cultural and institutional specificities of a country or region in which entrepreneurial activities are carried out, since these can be barriers and obstacles to transferring innovative efforts into economic development (Guerrero & Peña-Legazkue, 2013). Overcoming these limitations is essential to access the benefits of a virtuous circle that could increase regions’ economic prosperity through investment in innovation, which in turn can improve potential innovative and entrepreneurial activity (Audretsch & Peña, 2012; González, Martiarena, Navarro, & Peña, 2009).

Empirical studies have demonstrated how the cultural aspects of a specific region can affect entrepreneurial intention (hereinafter EI) even more than economic variables (García-Cabrera & García-Soto, 2008; García-Rodríguez, Gil-Soto, Ruiz-Rosa, & Sene, 2015; Hofstede et al., 2004; Liñán, Urbano, & Guerrero, 2011; Wennekers, Thurik, Van Stel, & Noorderhaven, 2007), in that the former tend to present a more permanent char- acter than the latter. For Mueller and Thomas (2001), the concept of ‘culture’ is associated with the system of fundamental values and principles specific to a particular group or society that, at the same time, give rise to certain personality traits and individual motiv- ations that are not reproduced in other societies. Hofstede (1984) distinguishes four dimensions when analysing cultural differences among countries or regions: power dis- tance, uncertainty avoidance, individualism–collectivism in a country, and masculinity– femininity. Later, Hofstede (1991) added a fifth dimension: individuals’ orientation towards the short/long term.

For a given region, it is clear that having university education contributes to the inno- vative potential and impact of the region’s economic development, not only through classic knowledge transfer, but also by providing leadership for the creation of entrepre- neurial thinking, actions and institutions (Guerrero, Cunningham, & Urbano, 2015; Guer- rero, Urbano, & Fayolle, 2016). Therefore, it is particularly interesting to analyse the dynamics of the entrepreneurial process and the possible existence of institutional obstacles or cultural barriers among university students (Guerrero & Peña-Legazkue, 2013).

Together with cultural aspects, individual motivation is an important explanatory factor in the entrepreneurial process. It is related to the so-called theories of needs, which identify individuals’ internal stimuli (hunger, fear, etc.) that guide their behaviour, and ‘incentive theories’, which suggest that individuals develop one behaviour or another in search of external objectives or prizes (Carsrud & Brännback, 2011; Fayolle, Liñán, & Moriano, 2014).

Taking into account the above aspects, this paper analyses the entrepreneurial process in a European peripheral region, the Canary Islands, Spain. It attempts to determine poss- ible cultural specificities and provides an integrated view of motivation’s role in EI and its antecedents. It adopts the perspective based on the approach of Shapero and Sokol (1982) and the theory of planned behaviour by Ajzen (1991). Attitude towards entrepreneurial behaviour, subjective norms and perceived behavioural control are assumed to be antece- dents of EI. This view is complemented by the so-called motivation, opportunity and ability (MOA) theory, originally adopted by MacInnis and Jaworski (1989), which deter- mines the impact of motivation on EI either directly or indirectly through its antecedents.

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The model is tested on a sample of undergraduates, who participated in the Global Uni- versity Entrepreneurial Spirit Students’ Survey (GUESSS) project.

After having contextualized the importance of potential entrepreneurs to regions, especially peripheral ones and highlighting specific cultural features, the second section of the paper explains entrepreneurial process and motivation from a theoretical perspec- tive and puts forward the working hypotheses. Subsequently, an empirical analysis is carried out among university students in the Canary Islands, Spain, a European peripheral region. Results show the main relational links between motivation and EI. Finally, the most relevant conclusions are drawn, from both academic and applied perspectives.

The role of motivation and cultural aspects in the entrepreneurial process

The entrepreneurial process

The phenomenon of entrepreneurship, as a process that occurs over time (Gartner, Shaver, Gatewood, & Katz, 1994), begins long before the moment when an individual sets up a business. Thus, as with all behaviours, it requires a planning process even to reach the stage of EI. This intention, therefore, is prior to the creation of a business and can be considered one of the best predictors of entrepreneurship (Ajzen, 1987, 1991, 2001, 2002; Krueger & Brazeal, 1994).

This perspective runs parallel to the conviction predominantly supported by the litera- ture since the 1980s, stating that it is not nature but nurture that makes an entrepreneur. Therefore, entrepreneurship is associated with learning processes, maturing and possible changes in individuals’ abilities and personal capacities (Minniti & Bygrave, 2001).

In this sense, entrepreneurs’ abilities are not fixed personality traits or characteristics; instead, their abilities can change over time, develop and be learnt through experience (Gibb, 1993, 2000). This is the context in which entrepreneurship can be explained based on the theory of attitudes (Robinson, Stimpson, Huefner, & Hunt, 1991) and studies such as the one by McCline, Bhat, and Baj (2000). The basic idea is that the concept or the attitude of an individual is dynamic and changing. Individuals respond to external incentives, and therefore it is more appropriate to explain entrepreneurship this way rather than in a more static way associated with personality traits.

Among the various theoretical models proposed to describe and explain the entrepre- neurial process, it is worth mentioning Shapero and Sokol’s work (1982) and Ajzen’s (1991) theory of planned behaviour. Both models have been widely tested and validated in numerous scientific studies, particularly noteworthy are the ones by Krueger and Brazeal (1994), Peterman and Kennedy (2003) and Souitaris, Zerbinati, and Al-Laham (2007).

According to this perspective, the decision to initiate a new entrepreneurial activity depends on three elements: the perception of desirability, feasibility and the individual’s willingness to act. Krueger and Brazeal (1994) differentiate between an individual’s entre- preneurial potential, which can remain in a ‘latent’ state and his/her EI, which is a reaction to a relevant event that can cause a change in behaviour. Thus, the perceived desire, per- ceived feasibility and propensity to act are antecedents of EI (Shapero & Sokol, 1982). To these three elements, Azjen’s ‘subjective norms’ can be added, which support the entrepre- neur’s behavioural setting.

2040 F. J. GARCÍA-RODRÍGUEZ ET AL.

To the degree personal and social factors influence entrepreneurial conduct, in contrast to the approach of Shapero and Sokol (1982), the theory of planned behaviour (Ajzen, 1991, 2001) has become consolidated as the most commonly used approach in recent research into EI (Liñán & Fayolle, 2015; Moriano, Gómez, Laguna, & Roznowski, 2008).

To sum up, the theory of planned behaviour holds that EI depends on the influence of three variables: attitude towards entrepreneurship, subjective norms and perceived behav- ioural control (Ajzen, 1991, 2001). In the latter variable, Ajzen (2001) incorporates two dimensions: self-efficacy (belief in one’s own capacity to organize and perform behaviour) and controllability (belief in one’s control of his/her conduct).

This theoretical model has had wide empirical testing with the works of Liñán and Chen (2009), Souitaris et al. (2007), Peterman and Kennedy (2003), Audet (2002, 2004), Krueger, Reilly, and Carsrud (2000), Kolvereid (1996) and Tkachev and Kolvereid (1999) being worthy of particular mention.

Motivation in the entrepreneurial process

However, the research focus on EI within the field of entrepreneurship as the best predic- tor of behaviour has meant that another key element in the entrepreneurial process has been left aside: motivation (Carsrud & Brännback, 2011). The entrepreneurial process may not be linear, but instead could be better understood as behaviour directed towards the search for objectives at distinct levels of deliberation. These objectives serve as sources of external motivation (Lawson, 1997).

There are essentially two perspectives in motivational theories: one based on economics and the other on psychology (Fisher, 1930), which attempt to answer three questions: What activates a person? What makes the individual choose one behaviour over another? Why do different people respond differently to the same motivational stimuli? (Carsrud & Brännback, 2011).

Among the main theories that support the study of motivation in entrepreneurship, there are ‘incentive theories’ based on the idea that individuals develop one kind of behav- iour or another in search of objectives and external prizes created to incentivize. There are also the ‘theories of needs’ that rely on the existence of internal stimuli in individuals that guide their behaviour (Carsrud & Brännback, 2011; Fayolle et al., 2014). Theories of needs applied to the entrepreneurial process hold that individuals have intrinsic needs that gen- erate internal tensions, which motivate them to act. In this way, motivation will influence the antecedents of EI and finally EI itself (Fayolle et al., 2014; Solesvik, 2013).

Motivation can be an important explanatory element both of EI’s antecedents (Solesvik, 2013), as well as influencing the relationship between EI and the decision to be an entre- preneur (Carsrud & Brännback, 2011; Fayolle et al., 2014). Various studies have demon- strated the existence of relationships between several motivational variables and EI (Chen, Greene, & Crick, 1998; Souitaris et al., 2007). More recently, Hui-Chen, Kuen-Hung, and Chen-Yi (2014) have proposed an integrated model of the entrepreneurial process, finding that motivation affects EI through attitudes and perceived behavioural control. From the perspective of ‘incentive theories’, motivation could play a key role in transforming EI into the actual setting up of a business activity by being the missing link between intention and behaviour (Carsrud & Brännback, 2011; Edelman, Brush, Manolova, & Greene, 2010).

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On the other hand, another important concept that has emerged recently in the field of entrepreneurship, related to but different from motivation, is individual values (Fayolle et al., 2014). These can be considered as general principles that show certain stability over a long period and guide the conduct of the individual (Schwartz, 2011).

Formulating hypotheses

Based on the above, and, in particular, on the recent works of Solesvik (2013) and Hui- Chen et al. (2014), motivation would be expected to influence EI positively. This could occur both directly and indirectly through its antecedents and to the extent that the entrepreneurial process is not linear but rather a search for objectives (Carsrud & Brännback, 2011; Lawson, 1997). In this sense, a significant number of studies have demonstrated that motivation is directly linked to entrepreneurs (Caird, 1991; Durand & Shea, 1974; Morris & Fargher, 1974; Robinson et al., 1991). In accordance with Fayolle et al. (2014), it would be possible to relate the theory of motivation based on need with greater EI owing to the internal tension generated, which is then chan- nelled through the antecedents of the intention, making it possible to propose the fol- lowing hypothesis:

H1. Motivation has a positive impact on (a) EI, (b) personal attitude, (c) perceived behav- ioural control and (d) subjective norms.

MacInnis and Jaworski (1989) were the authors who originally proposed the MOA theory. This theory has had a varied academic path. It has been applied to diverse areas to try to explain behaviours of choice among companies and individuals (Binney, Hall, & Shaw, 2003; Gruen, Osmonbekov, & Czaplewski, 2005, 2006, 2007; Rothschild, 1999; Siemsen, Roth, & Balasubramanian, 2008). Therefore, in this case, as well as in motivation, it will be necessary to consider it as an explanatory element of EI and EI’s antecedents: oppor- tunity and individuals’ ability.

Opportunities will be external factors that favour or hamper the achievement of indi- viduals’ desires (Binney et al., 2003). Therefore, from the perspective of becoming an entrepreneur, if opportunities do not exist, it is unlikely that the desired behaviour will be achieved despite great motivation on the part of an individual. It is also necessary to consider individuals’ abilities, that is, their competencies that would favour the setting up of a business activity, since these will help individuals feel ready to achieve their goals (Binney et al., 2003; Rothschild, 1999). Thus, it is possible to put forward the following hypotheses:

H2. Opportunity has a positive impact on (a) EI, (b) motivation, (e) personal attitudes, (f) perceived control of behaviour and (g) subjective norms.

H3. Abilities have a positive impact on (a) EI, (b) motivation, (c) opportunity, (e) personal attitudes, (f) perceived behavioural control and (g) subjective norms.

In addition, various studies based on Ajzen’s (1991) theory of planned behaviour have shown a positive relationship between EI and personal attitudes and perceived control (Audet, 2002, 2004; Finisterra Do Paço et al., 2011; Kolvereid, 1996; Krueger et al., 2000; Liñán & Chen, 2009; Souitaris et al., 2007; Tkachev & Kolvereid, 1999), thus, making it logical to propose that:

2042 F. J. GARCÍA-RODRÍGUEZ ET AL.

H4. Personal attitudes have a positive impact on (a) EI.

H5. Perceived control of behaviour has a positive impact on (a) EI.

Finally, Liñán and Chen (2009) show that many previous works on the theory of planned behaviour did not include the subjective norms variable in the analysis, so bearing in mind studies that did (Kolvereid, 1996; Tkachev & Kolvereid, 1999), it is reasonable to expect that:

H6. Subjective norms have a positive impact on (a) EI, (e) personal attitudes and (f) perceived control of behaviour.

Empirical work

Regional context, data gathering and sample description

Canary Island is a Spanish fragmented island territory with a surface area of 7447 km2. It is considered a peripheral region by the European Union, geographically located in the African continent, on the eastern edge of the mid-Atlantic, at the southern limit of the temperate zone, with the Sahara Desert to the east. Its mild, warm climate combined with the political and socio-economic stability of the region has meant that its tourism sector has developed to become the leading international tourist destination during the winter season receiving almost 12 million visitors in 2014. However, this polarized econ- omic activity is not enough to create sufficient employment, as current unemployment in the Canary Islands stands at over 30%.

According to the COTEC Foundation (2015), the Canary Islands ranks as the second region with least resources devoted to R&D in Spain in 2013 (0.52% of GDP). Moreover, it should be noted that Spain’s R&D expenditure represented 1.24% of GDP, which remains well below the average for OECD (2005) countries (2.4%) and the EU-28 (1.92%). This is why the Canary Islands constitutes a representative example of a European peripheral region.

The data to carry out this study come from answers to a questionnaire as part of the GUESSS project with students from the University of La Laguna in Canary Islands. GUESSS is an international research project that attempts to analyse EI and its antecedents for students at a global level. In its sixth edition, data were gathered from over 700 univer- sities in 34 different countries, between October 2013 and March 2014, with Spain parti- cipating for the first time in the analysis.

The GUESSS project aims to carry out a comprehensive analysis not only of the psycho- logical characteristics of future entrepreneurs but also specific attitudes, personal antece- dents and situational variables. The main contribution of the project has been to homogenize methodology, so that comparisons can be made between different countries, significantly enhancing the study of EI among students. The sixth edition, corresponding to 2013, contained 12 blocks that have been translated and validated by experts in entre- preneurship, from both academic and applied areas.

At the University La Laguna, the questionnaire was sent to all the students (20,729) by email in November 2013 and 1457 responses were obtained over the two months in which the fieldwork took place. The questionnaire was in electronic format and was self-admi- nistered with assistance using the Qualtrics programme. The sample consisted of 37.5%

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men and 64.3% women; regarding age, 81.5% were between 18 and 24 years, 12.5% between 25 and 30 years and 5.9% over 30 years. There were 80.6% studying undergradu- ate degrees, 18.4% were doing postgraduate studies and 0.8% doctorate students. Out of the total sample, 25.7% were studying business, economics and law, 42.5% science and medicine, and 31.8% social sciences.

Differences in variables such as age, gender and educational level could influence the pre- dictive ability of the model for the sample. In order to assess their impact and determine whether these control variables influence indicators of the MOA model constructs, an analy- sis of variance was carried out for each of the three classification variables. The ANOVA results for the three age groups (18–24, 25–30 and >30) and educational level (degree, master and doctorate) and the analysis of mean difference in gender (men and women) led to the conclusion that there is no association between independent variables or any of the three control variables since no significant statistical F values (p > .05) were obtained.

In addition, the majority of the students in the sample (91.7%) were not working reg- ularly and in 30.2% of the cases, one or both of their parents were self-employed. In addition, 6% of the sample intended to become entrepreneurs on finishing their studies and 36.3% indicated EI in the future (five years).

The measurement scales used in this research were not prepared ad hoc but used those proposed in the GUESSS model project, whose theoretical foundations are based on the theory of planned behaviour by Ajzen (1991). All scales have been widely validated in pre- vious studies, for example, Liñán and Chen (2009) for Personal Attitude and Entrepre- neurial Intention scales. This confirms the validity of the content used to fulfil the objectives and the hypotheses of the analysis model (Figure 1). In addition, for the

Figure 1. Constructs for MOA model.

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opportunity variable, the questionnaire items refer to the opportunity to initiate an activity after finishing studying and within five years of finishing.

Data analysis

Structural equations were chosen to analyse the data, using the technique of partial least- squares (PLS) (Fornell & Cha, 1994). This technique is designed to reflect the theoretical and empirical characteristics of social sciences and behaviour, in which theories frequently lack sufficient support and there is little information available (Wold, 1979). Specifically, SmartPLS 2.0 was used (Ringle, Wende, & Will, 2005).

The general recommendations regarding the theoretical support for a model of PLS have been followed; the sifting of data and the analysis of the psychometric properties of all the variables of the model before beginning the analysis; examining relationships and effects; standard errors and the predictive power of the model to guarantee its validity. The PLS method allows hypotheses to be tested at the same time as admitting measure- ments with single and multiple items and the use of formative and reflective indicators (Fornell & Bookstein, 1982).

Following the criteria of Chin (1998) regarding the items used for each of the constructs of theproposedmodel,we findthatall the indicatorsofthelatentvariablesarereflective,since an increase in one of the indicators in one direction means that the rest change in a similar way.

To assure the validity of the PLS technique, two steps are required (Barclay, Higgins, & Thompson, 1995). First, the measurement model is evaluated and then the structural one. The evaluation of the measurement model is performed by ensuring the reliability of each item, the reliability of constructs, the average variance extracted (AVE) and the discrimi- natory validity of the indicators that measure the latent variables. The structural model is validated by confirming up to what point the causal relations are consistent with the avail- able data (Real, Leal, & Roldán, 2006).

In the model proposed in this study, there are seven first-order constructs, as observed in Figure 1.

The analysis begins by evaluating the individual reliability of the constructs examining the factor loading, composite reliability (CR) and AVE (Chin, 1998; Fornell & Larcker, 1981). Table 1 shows these measures as well as each of the items used. The factor loadings are all over .707, indicating that at least 50% of the variance of the construct is reflected in the indicator (Chin, 1998). CR is always greater than .7, which is required in the initial stages of research, and is also higher than the stricter value of .8 required for basic research (Nunnally, 1978).

As for AVE, for the indicators of each construct, these should be greater than .5, which explains 50% or more the indicator’s variance (Fornell & Larcker, 1981). This condition was comfortably met in all cases.

To ensure discriminatory validity, the square roots of AVEs are compared (i.e. the diag- onal values in Table 2) with the correlations between constructs (i.e. the elements that are outside the diagonal in the table).

All the constructs are reflective and mainly relate to their own measures rather than to other constructs (Table 3). Additionally, crossed factor loadings have been analysed and it has been confirmed that they are not significant regarding their relation with factor load- ings (Chin, 1998).

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Table 1. Construct properties of model. Constructs and items Mean STD λ AVE CR α

Ability Please indicate your level of competence in performing the following tasks (1 = very low competence, 7 = very high competence)

Q6.3_1 Identifying new business opportunities 4.49 1.43 .975 .952 .994 .993 Q6.3_2 Creating new products and services 4.34 1.49 .976 Q6.3_3 Applying my personal creativity 5.01 1.49 .969 Q6.3_4 Managing innovation within a firm 4.68 1.50 .983 Q6.3_5 Being a leader and communicator 5.02 1.53 .976 Q6.3_6 Building up a professional network 4.62 1.54 .975 Q6.3_7 Commercializing a new idea or development 4.64 1.55 .977 Q6.3_8 Successfully managing a business 4.84 1.55 .974 EIs Please indicate your level of agreement with the following statements (1 = strongly disagree, 7 = strongly agree) Q6.1a_1 I am ready to do anything to be an entrepreneur 3.77 1.73 .936 .930 .988 .985 Q6.1a_2 My professional goal is to become an entrepreneur 3.99 1.86 .968 Q6.1a_3 I will make every effort to start and run my own firm 3.99 1.89 .979 Q6.1a_4 I am determined to create a firm in the future 4.00 1.93 .980 Q6.1a_5 I have very seriously thought of starting a firm 3.91 2.01 .952 Q6.1a_6 I have the strong intention to start a firm someday 3.97 2.04 .972 Motivation How important are the following factors when you decide on your future career path? (1 = not important at all, 7 = very important)

Q4_1 To have a challenging job 5.28 1.40 .892 .759 .969 .963 Q4_10 To take advantage of your creative needs 5.67 1.40 .921 Q4_2 To have an exciting job 5.75 1.25 .928 Q4_3 Freedom 5.77 1.16 .936 Q4_4 Independence 5.68 1.24 .924 Q4_5 To be your own boss 4.43 1.68 .610 Q4_6 To have power to make decisions 5.51 1.27 .684 Q4_7 To have authority 5.02 1.47 .887 Q4_8 To realize your dream 6.34 1.11 .944 Q4_9 To create something 5.47 1.54 .917 Opportunity Which career path do you intend to pursue right after completion of your studies, and which career path five years after completion of studies? I want to be a entrepreneur working in my own firm

OP1 Opportunity on finishing studies 1.71 1.37 .970 .918 .957 .912 OP2 Opportunity five years after 3.11 2.74 .946 Personal attitude Please indicate your level of agreement with the following statements (1 = strongly disagree, 7 = strongly agree) Q6.1b_1 Being an entrepreneur implies more advantages than

disadvantages to me 4.11 1.65 .955 .948 .989 .986

Q6.1b_2 A career as entrepreneur is attractive for me 4.28 1.79 .977 Q6.1b_3 If I had the opportunity and resources, I would become an

entrepreneur 5.07 1.79 .979

Q6.1b_4 Being an entrepreneur would entail great satisfaction for me 4.76 1.83 .982 Q6.1b_5 Among various options, I would rather become an

entrepreneur 4.26 1.87 .976

Perceived behavioural control Please indicate your level of agreement with the following statements (1 = strongly disagree, 7 = strongly agree) Q6.1c_1 I am usually able to protect my personal interests 5.36 1.22 .968 .916 .987 .985 Q6.1c_2 When I make plans, I am almost certain to make them work 5.38 1.21 .971 Q6.1c_3 I can pretty much determine what will happen in my life 3.91 1.56 .945 Q6.1c_4 For me, being an entrepreneur would be very easy 3.60 1.61 .951 Q6.1c_5 If I wanted to, I could easily pursue a career as entrepreneur 3.33 1.59 .939 Q6.1c_6 As entrepreneur, I would have complete control over the

situation 5.04 1.59 .957

Q6.1c_7 If I become an entrepreneur, the chances of success would be very high

4.34 1.43 .967

Subjective norms If you would pursue a career as an entrepreneur, how would people in your environment react? (1 = very negatively, 7 = very positively)

Q6.2_1 Your close family 5.54 1.51 .982 .969 .990 .984 Q6.2_2 Your friends 5.67 1.31 .992 Q6.2_3 Your fellow students 5.40 1.41 .980

Note: CR = composite reliability; AVE = average variance extracted; α = Cronbach’s alpha.

2046 F. J. GARCÍA-RODRÍGUEZ ET AL.

Table 2. AVE and correlations between model constructs.

Ability EIs Motivation Opportunity Personal attitude

Perceived behavioural control

Subjective norms

Ability .976 EIs .708 .965 Motivation .389 .499 .871 Opportunity .790 .712 .660 .958 Personal attitude .768 .903 .480 .737 .974 Perceived behavioural control

.855 .750 .435 .758 .828 .957

Subjective norms .858 .720 .416 .751 .808 .919 .985

Notes: All correlations are significant to the level of p < .01. The diagonal shows the square root of AVE.

Table 3. Table of crossed correlations.

Ability EIs Motivation Opportunity Personal attitude

Perceived behavioural control

Subjective norms

OP1 .269 .336 .680 .970 .334 .324 .318 OP2 .187 .180 .565 .946 .208 .241 .243 Q4_1 .354 .431 .892 .594 .420 .390 .361 Q4_10 .377 .472 .921 .610 .450 .404 .387 Q4_2 .352 .449 .928 .630 .436 .403 .387 Q4_3 .345 .446 .936 .638 .434 .404 .393 Q4_4 .331 .446 .924 .613 .428 .388 .380 Q4_5 .261 .351 .610 .328 .330 .279 .266 Q4_6 .255 .324 .684 .407 .312 .273 .250 Q4_7 .346 .457 .887 .564 .440 .388 .371 Q4_8 .366 .460 .944 .661 .446 .418 .404 Q4_9 .377 .481 .917 .597 .461 .406 .388 Q6.1a_1 .679 .936 .491 .305 .858 .738 .705 Q6.1a_2 .683 .968 .490 .283 .887 .726 .701 Q6.1a_3 .692 .979 .487 .272 .882 .728 .698 Q6.1a_4 .693 .980 .480 .262 .879 .728 .697 Q6.1a_5 .679 .952 .467 .252 .851 .709 .676 Q6.1a_6 .682 .972 .469 .250 .866 .713 .689 Q6.1b_1 .725 .857 .465 .292 .955 .788 .774 Q6.1b_2 .753 .891 .466 .278 .977 .809 .784 Q6.1b_3 .762 .873 .477 .301 .979 .820 .801 Q6.1b_4 .750 .888 .472 .286 .982 .812 .789 Q6.1b_5 .756 .887 .458 .264 .976 .803 .785 Q6.1c_1 .831 .719 .432 .309 .798 .968 .913 Q6.1c_2 .835 .717 .432 .310 .800 .971 .914 Q6.1c_3 .791 .690 .404 .287 .762 .945 .854 Q6.1c_4 .810 .716 .403 .269 .785 .951 .852 Q6.1c_5 .794 .707 .390 .265 .770 .939 .833 Q6.1c_6 .824 .729 .423 .287 .808 .957 .892 Q6.1c_7 .843 .746 .426 .282 .822 .967 .896 Q6.2_1 .848 .718 .406 .289 .799 .905 .982 Q6.2_2 .855 .715 .417 .297 .806 .914 .992 Q6.2_3 .834 .694 .406 .294 .781 .895 .980 Q6.3_1 .975 .704 .377 .236 .760 .834 .841 Q6.3_2 .976 .698 .379 .237 .755 .830 .837 Q6.3_3 .969 .682 .386 .245 .744 .838 .841 Q6.3_4 .983 .701 .382 .234 .759 .839 .839 Q6.3_5 .976 .686 .382 .242 .748 .840 .841 Q6.3_6 .975 .684 .379 .237 .743 .832 .832 Q6.3_7 .977 .693 .376 .235 .746 .828 .837 Q6.3_8 .974 .691 .373 .238 .751 .836 .837

EUROPEAN PLANNING STUDIES 2047

Results

Figure 2 shows the results of the analysis of the structural model, including the explained variance of the constructs (R2) and standardized coefficients (β). The PLS method makes no distribution assumptions about parameters’ estimation, which is why it is more appro- priate than traditional parameter techniques to ensure significance and evaluate the model (Chin, 1998). Another difference between analytical approaches using covariance struc- ture or PLS is that in the latter, there is no single measure to guarantee the goodness of fit (GoF) of the model (Hulland, 1999). Thus, in PLS, the structural model is examined using the values for R2, the Q2 test for predictive relevance and the size of the path coeffi- cients. Finally, the stability of estimations is examined using t-statistics that are obtained by bootstrapping with 500 samples.

Table 4 shows the hypotheses that were proposed, coefficient paths and observed t values with their significance from the bootstrap test. Additionally, the direct effects and the proportion of explained variance are shown, as well as Q2 constructs.

Regarding the explained variance (R2) of the EI latent variable (Table 4), the structural model shows an adequate predictive power, since 82% of the variance is explained. As well as examining R2, the model is evaluated by observing the predictive relevance Q2 of the models’ constructs (Geisser, 1974). This test measures to what degree observed values are reproduced by the model and their estimated parameters (Chin, 1998). A Q2

greater than 0 implies that the model has predictive relevance, whereas if the value is below 0, it indicates that the model lacks predictive relevance. The results shown in

Figure 2. Estimated causal relations in structural model. Significant relation = continuous line; non-sig- nificant relation = dashed line.

2048 F. J. GARCÍA-RODRÍGUEZ ET AL.

Table 4 confirm that the measurement model is appropriate, and that the structural model has predictive relevance.

Finally, to guarantee the quality of the model, the PLS approach has recently developed the GoF test. This is defined as the ‘geometric mean’ of the average communality and the average R2 for endogenous constructs (Tenenhaus, Vinzi, Chatelin, & Lauro, 2005; Wetzels, Odekerken-Schröder, & Van Oppen, 2009), with average communality being measured using the AVE in PLS. In our case, for the complete model (Table 5), we obtained a GoF value of .746. This comfortably exceeds the value of .36 proposed by Wetzels et al. (2009) that considers the most unfavourable situation for this test: a sample with large effects.

Therefore, in relation to the proposed hypotheses in the model, the following con- clusions can be drawn:

. H1a and H1e are confirmed. These establish, respectively, that motivation has a positive impact on EI (β = .113, p < .05) and personal attitude (β = .203, p < .01).

. By contrast, H1f and H1g are not confirmed, so the existence of relations between motivation and perceived behavioural control cannot be claimed (β = .053, n.s.) and neither between motivation and subjective norms (β = .057, n.s.).

. H2b is confirmed. This establishes that opportunity has a positive impact on motivation (β = .598, p < .001).

Table 4. Direct, indirect and total effects, variance explained and test Q2.

Hypothesis Relacion Direct effect Sig. T statistic

Correlation coefficients

Variance explained

Total effect Q2

EIs .823 .751 H3a AB → EI .077 ns 0.903 .710 .055 .714 H1a MO → EI .113 * 1.726 .498 .056 .314 H2a OP → EI −.042 ns 1.112 .281 −.012 .115 H4a PA → EI .858 *** 11.222 .903 .775 .858 H5a PBC → EI .031 ns 0.261 .750 .023 .041 H6a SN → EI −.102 ns 0.888 .720 −.074 .347 Motivation .488 .365 H3b AB → MO .243 *** 3.443 .389 .094 .391 H2b OP → MO .598 *** 5.814 .657 .393 .594 Opportunity .060 .052 H3c AB → OP .244 *** 3.700 .244 .060 .246 Personal attitude .282 .653 H3e AB → PA .262 * 2.011 .769 .202 .859 H1e MO → PA .203 ** 2.666 .480 .097 .236 H2e OP → PA −.059 ns 1.219 .292 −.017 .112 H6e SN → PA .516 *** 3.903 .808 .417 .503 Perceived behavioural control

.864 .770

H3f AB → PBC .245 * 1.993 .855 .210 .859 H1f MO →

PBC .053 ns 1.217 .435 .023 .090

H2f OP → PBC .001 ns 0.030 .300 .000 .097 H6f SN → PBC .686 *** 5.677 .919 .631 .670 Subjective norms .748 .721 H3g AB → SN .822 *** 14.441 .859 .707 .860 H1g MO → SN .057 ns 0.891 .416 .024 .056 H2g OP → SN .060 ns 1.343 .298 .018 .094

Note: Level of significance: ***p < .001; **p < .01; *p < .05; ns, non-significant.

EUROPEAN PLANNING STUDIES 2049

. However, H2a, H2e, H2f and H2g are not confirmed. Therefore, there are no relations between opportunity and EI (β = −.042, n.s.), personal attitude (β = −.059, n.s.), per- ceived control (β = .001, ns) and subjective norms (β = .060, n.s.).

. H3b, H3c, H3e–H3g are confirmed. They establish, respectively, that ability has a posi- tive impact on motivation (β = .243, p < .001), opportunity (β = .244, p < .001), personal attitude (β = .262, p < .05), perceived behavioural control (β = .245, p < .05) and subjec- tive norms (β = .822, p < .001).

. By contrast, H3a is not confirmed. Therefore, no relation can be claimed between ability and EI (β = .077, ns).

. H4a is confirmed. This hypothesis states that personal attitude has a positive impact on EI (β = .858, p < .001).

. However, H5a cannot be confirmed. So, no relation between perceived behavioural control and EI can be claimed (β = .031, n.s.).

. H6e and H6f, respectively, establish that subjective norms have a positive impact on personal attitudes (β = .516, p < .001) and on perceived behavioural control (β = .686, p < .001).

. To the contrary, H6a was not confirmed and, therefore, no relationship can be claimed to exist between subjective norms and EI (β = −.102, n.s.).

Discussion of results

This paper has analysed, from an integrated perspective, the entrepreneurial process in undergraduates in the Canary Islands, Spain, a European peripheral region, and the role of motivation and cultural specificities in forming EI. The results partially confirm those from previous studies, demonstrating the existence of relations between diverse motivational variables and EI (Chen et al., 1998; Souitaris et al., 2007).

Additionally, the application of motivational and needs theories to the entrepreneurial process can be partially accepted. This is particularly true, in the sense that EI is positively affected by individuals’ attempts to reduce certain internal tensions, through greater EI, which is channelled through some of their antecedents (Fayolle et al., 2014; Solesvik, 2013).

As mentioned above, this is only partial insomuch as motivation indirectly affects EI through attitude though not through perceived behavioural control and subjective norms. These results differ from those obtained by Solesvik (2013) and Hui-Chen et al. (2014), who found an indirect effect through all these three variables.

This difference in results could be due to possible cultural differences in regions like those analysed here (García-Rodríguez et al., 2015; Liñán & Chen, 2009), which means

Table 5. GoF test. AVE R2 GoF

EIs .930 .823 Motivation .759 .488 Opportunity .918 .060 Personal attitude .948 .698 Perceived behavioural control .916 .864 Subjective norms .969 .748

.907 .613 .746

2050 F. J. GARCÍA-RODRÍGUEZ ET AL.

that further testing would be necessary in different sociocultural contexts. Thus, it may be that in peripheral regions such as the one analysed, the subjective norms are so strongly rooted in young people that they are difficult to change through motivation.

In addition, it is worth highlighting that motivation also directly affects EI positively, which coincides with the results obtained by Hui-Chen et al. (2014) in their integrated model of the entrepreneurial process. However, a direct and significant relationship between abilities and personal attitude occurs which can be observed in this study, unlike previous works like Hui-Chen et al. (2014). This seems to indicate that in the context analysed, the acquisition of entrepreneurial abilities eventually has an impact, albeit indirectly, in EI.

Conclusions and limitations

The results obtained have important consequences for entrepreneurial education and pol- icies in peripheral regions and, specifically, in the attention that should be paid to motiv- ation. Thus, it seems that by improving young people’s motivation to be entrepreneurs, their EI would also increase, on the one hand, through direct influence and on the other, thanks to the impact of an improvement in personal attitude. This confirms the importance of motivational–inspirational content in actions to promote entrepreneurship, in line with the approaches of Souitaris et al. (2007), and demonstrates the limited effec- tiveness of approaches only aimed at changing individuals’ attitudes.

It seems that rather than attempting to directly transform individuals’ attitudes towards entrepreneurship, it would be more efficient to focus on improving their motivation using intensive pedagogical strategies in creativity that go beyond mere informative content. Education could be particularly important in peripheral regions, for example, inspiring stories of successful entrepreneurs. These stories can convey to young people a vision of entrepreneurial activity in the region itself that is viable, desirable and attractive and thus help break entrepreneurship brain drain from the region (Kaufmann & Malul, 2015).

In connection with the above, it is worth noting the role of opportunity in motivation. Thus, the perception of opportunities for entrepreneurship would indirectly influence motivation in a positive sense. Consequently, it would seem that including methodologies and content focused on recognizing opportunities and problem-solving in educational programmes of entrepreneurship could also be an efficient element.

It also appears that investments in entrepreneurship training (abilities) for young people in peripheral regions could have a high impact on EI, to the extent that it has the power to transform their attitudes. Therefore, policies that invest in human capital in the field of entrepreneurship would be highly recommended in this type of peripheral contexts.

Looking to the future, it would be useful to repeat this study in other regions, peripheral as well as in metropolitan and industrial ones to carry out comparative studies. This would help determine to what extent the characteristics of the entrepreneurial process found here are linked to the peripheral nature of the region and can, therefore, be extrapolated to other similar ones or follow other patterns. In this sense, there is empirical evidence on the relevance of the socio-demographic context in motivation and individuals’ personal- ities (Liñán & Chen, 2009). Second, as in the majority of EI studies, it would be interesting to incorporate the time factor (Audet, 2002), particularly since cross-sectional studies

EUROPEAN PLANNING STUDIES 2051

cannot make strong conclusions about causality because they do not allow causal relations to be tested, and common method variance is also likely to be a problem. Finally, despite the theory of planned behaviour by Ajzen (1991) still being a valid point of reference for the study of EI (Liñán & Fayolle, 2015), researchers should not only study variables that make up an entrepreneur’s psychological profile but should also determine the relative importance of other cognitive and contextual variables. These could influence directly or indirectly antecedents of EI and individuals’ ‘posteriori’ behaviour and could provide potential advances in this field.

Disclosure statement

No potential conflict of interest was reported by the authors.

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