Entrepreneurship, innovation and competitiveness

Int. J. Business and Globalisation, Vol. 18, No. 1, 2017

Entrepreneurship, innovation and competitiveness: what is the connection? João J. Ferreira* University of Beira Interior and NECE – Research Unit, Estrada do Sineiro, Polo IV 6200-201 Covilha, Portugal Email: [email protected] *Corresponding author

Cristina I. Fernandes Polytechnic Institute of Castelo Branco and NECE – Research Unit, University of Beira Interior, Portugal Email: [email protected]

Vanessa Ratten Department of Management and Marketing, La Trobe Business School, La Trobe University, Bundoora, Melbourne, 3086, Australia Email: [email protected] Abstract: Entrepreneurship has attracted growing interest about its role in decision-making policies capable of fostering economic and social development. There has also been an increased attention on how entrepreneurs innovate and in so doing consequently contribute to higher levels of international competitiveness. Following recourse to Global Entrepreneurship Monitor (GEM) and the Global Competitive Index (GCI) data, the objective of this study involves analysis of the interlinkages between three constructs: entrepreneurship, innovation and competitiveness across the three phases of development defined by the GEM. To this end, we analyse the influence of the entrepreneur profile not only through both their intrinsic and extrinsic knowledge but also through their innovation and competitiveness across the three GEM development phases. The results convey how the importance attributed to entrepreneurship is dependent on the stage of economic development and consequently may reflect in either a positive or negative impact on this same economic growth strategy. Keywords: entrepreneurship; innovation; competitiveness. Reference to this paper should be made as follows: Ferreira, J.J., Fernandes, C.I. and Ratten, V. (2017) ‘Entrepreneurship, innovation and competitiveness: what is the connection?’, Int. J. Business and Globalisation, Vol. 18, No. 1, pp.73–95.

Copyright © 2017 Inderscience Enterprises Ltd.

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J.J. Ferreira et al. Biographical notes: João J. Ferreira is an Associate Professor at University of Beira Interior (UBI), Portugal. He completed the European Doctoral Programme of Entrepreneurship and Small Business Management at the Autonomous University of Barcelona (UAB), Spain. He is currently the Scientific Coordinator of NECE – Research Unit of Business Sciences, UBI. Since 2013, he has been a member of the A3Es agency evaluator group, responsible for evaluating management, marketing and entrepreneurship degrees in Portugal. He has published various articles in international journals and is editor of some international books. His areas of interest are strategy, competitiveness, and entrepreneurship. Cristina I. Fernandes is an Assistant Professor at Castelo Branco Polytechnic and researcher at NECE – Research Unit in Business Sciences, University of Beira Interior (UBI). She has published papers in a several international journals. Her research interests are: knowledge services, innovation and entrepreneurship, and regional competitiveness. Vanessa Ratten is an Associate Professor at La Trobe University, Australia. She completed her PhD at the UQ Business School, The University of Queensland, Australia, which is rated as Australia’s number 1 Business School and highest ranked in the Asia-Pacific region. She currently is the Program Coordinator of the Entrepreneurship and Innovation degrees at La Trobe Business School and teaches innovation, entrepreneurship, marketing and management courses. She has previously been on the business faculties of Deakin University, The University of Queensland, Queensland University of Technology and Duquesne University (USA). Her research interests are: international entrepreneurship, technology innovation and sport entrepreneurship.

1

Introduction

Schumpeter (1934) maintained that company owners are individuals taking on the function of supervising the production of new combinations of resources with the entrepreneurial function thus consisting of identifying and engaging with new opportunities in the economy. However, it was only in the 1980s that there emerged an interest in the role of entrepreneurship and economic development, which was shaped by the revolution in endogenous growth theory (Low and MacMillan, 1988). This breakthrough triggered a new wave of research that placed the individual capacity to confront and cope with risk at the centre of economic analysis (Groot et al., 2004). However, the risk dealing capacity represented only one, even if precociously studied, of the factors characteristic of entrepreneurship (Kihlstrom and Laffont, 1979; Parker, 1997). Correspondingly, entrepreneurial activities, along with all of the factors underlying their existence, and their respective influence on economic regional development have been subject to study by a diverse range of authors (e.g. Arauzo and Manjón, 2004; Birley, 1985; Kirchoff and Phillips, 1988; Storey, 1994). In addition, the relationship between entrepreneurship and economic growth, diverse authors concluded

Entrepreneurship, innovation and competitiveness

75

that the former proves fundamental to the latter and hence the relevance of understanding the role of entrepreneurs given the contribution made in terms of both creating employment and advancing with innovation (Thurik and Wennekers, 2004; Welter and Lasch, 2008; Wennekers and Thurik, 1999). Entrepreneurship has more recently been defined as the creating of new economic activities (Davidsson et al., 2006). While entrepreneurs are subject to individual level analysis, they actually operate at the organisational, economic, social and institutional levels (Veciana and Urbano, 2008). According to Drucker (1985), innovation is a specific instrument to entrepreneurs. This constitutes the act endowing resources with a new capacity for creating wealth. Hence, correspondingly, the innovating companies thus tend to present better economic and financial performances than their non-innovative counterparts (Fernandes et al., 2013; Ferreira et al., 2010). Innovation thus proves fundamental to survival and prevailing in an increasingly globalised world in most sectors of the economy. Innovation aids companies in responding to diversified and constantly evolving demand whilst enabling improvements across the different domains and activities of a society (Cooke, 1998). Hence, innovation is perceived as the motor driving progress of competitiveness and economic development (Johansson et al., 2001; Romer, 1994). We may therefore correspondingly verify the importance attained by innovation, entrepreneurship and competitiveness but there has not been any simultaneous study of these three concepts. To this end, our research seeks to provide a contribution to the study of the effects of entrepreneurship and innovation on competitiveness and, as not all countries share the same level of performance and growth, we made recourse to the GEM database to better reflect these differences at the global level. Thus, this study seeks to contribute towards the literature through the simultaneous analysis of entrepreneurship, innovation and competitiveness. To this end, we analyse the influence of entrepreneur profiles in terms of their intrinsic and extrinsic knowledge in terms of innovation and competitiveness.

2

Literature review

Innovation takes place within a specific social, cultural, economic and political environment and displays systemic characteristics (Cooke, 1998). Innovation is defined as the process through which opportunities get transformed into practical utilities (Tidd et al., 1997). The effective implementation of innovation has come in for increasing recognition as a synonym for the construction of sustainable competitive advantage and therefore strengthening organisational performance levels (Koc and Ceylan, 2007). Within an ever more competitive environment, innovation is a critical factor to companies seeking to obtain a dominant position and as well as boosting their profits (Hu and Hsu, 2008; Kaminski et al., 2008). There are various authors who defend that innovation would seem to be the only way in which companies may adapt to the ever more dynamic environments surrounding them (Doloreux and Melancon, 2008; Hua and Wemmerlov, 2006; Roberts and Amit, 2003).

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J.J. Ferreira et al.

The analysis of the introduction of new processes, products and ideas at the organisational level, helps in evaluating the scope for measuring the innovative capacity of companies and firms (Hurley and Hult, 1998). The innovation derived from the flexibility of companies being able to choose between different options for meeting consumer demands (Banbury and Mitchell, 1995), through a sustained strategy focused on the company’s resources and capacities, enables not only the meeting of those demands in the present but also into the future (Barney, 1991). Nevertheless, as innovation proves a complex process, small and medium sized companies encounter obstacles to innovation and only manage to innovate through cooperating with other companies and optimising the application of their internal knowledge in combining this with the specific competences of their partners (Ferreira et al., 2015; Muller and Zenker, 2001). Furthermore, there are obstacles on the road towards innovation. For example, Kleinknecht (1989) identifies the following as obstacles to innovation: 1

scarcity of financial capital

2

lack of management relevant qualifications

3

difficulties in obtaining the technological information and know-how necessary to innovation.

The growing utilisation of information flows represents an essential dimension to establishing the organisational capacities capable of generating the foundations fundamental to organisational success (Cohendet and Steinmueller, 2000). In addition, Bughin and Jacques (1994) state that the major stumbling block to innovation does not stem from the ‘myopia’ companies suffer from but is rather bound up with the incapacity of firms to implement what they designate the ‘key principles of management’: 1

efficiency in marketing and R&D

2

synergies between marketing and R&D

3

capacity to communicate

4

excellence in organising and managing innovations

5

protecting innovation.

This conveys how internal R&D, for the majority of companies, does not prove sufficient for firms to become able to identify and leverage innovation. New products require new capacities or, at the very least, a new means of combining the already existing competences (Koch and Strotmann, 2008; Ratten, 2015). These new competences, as a pre-condition to the generation of new products and services may be seen as a result of the acquisition, assimilation and dissemination of new knowledge (Cohen and Levinthal, 1989, 1990) and may correspondingly be labelled the innovative capacity. This innovative capacity derives from individual level competences, the pre-acquired knowledge and the specific competences of companies as well as recourse to the diverse means and modes of knowledge (Cohen and Levinthal, 1990). Very commonly, and in particular at small innovative firms, these idiosyncratic internal capacities prove particularly related with the profile of their entrepreneurs and thus interlinked with their experiences, motivations, networks, creativity and strategic orientation as well as the innovation activities ongoing (Lynskey, 2004; Webster, 2004).


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Efficient institutions and a culture of support for entrepreneurship may render the capture of perceived opportunities feasible by economic actors (Sautet and Kirzner, 2006). Regions with entrepreneur favourable institutions and a similarly suitable culture may boost their competitive advantage and thereby attract investment in addition to qualified and talented staff (Turok, 2004). The regions with strong business traditions display a high level of competitive advantage (Audretsch and Fritsch, 2002; Mueller, 2006; Parker, 2004). Kirzner (1973) correspondingly defends entrepreneurs as dynamic actors in fostering market equilibrium and beyond acknowledging their activities as essential to sustaining competitiveness. From the outset, competitiveness proves inherent to the entire extent of the entrepreneurial process. Competitiveness would seem a simple concept around which there is little scope for disagreement. According to the Concise Oxford Dictionary, competing is striving for superiority in a particular quality. However, it is only when we actually attempt to measure competitiveness that we begin understanding the difficulties involved in its definition as competitiveness proves just as much a relative concept as it does a general concept (Scott and Lodge, 1985). Other difficulties emerge out of both selecting the appropriate unit of analysis and the perspective of the respective analyst. Politicians are interested in economic competitiveness (national, regional or local), industries and business associations restrict their interests to their own particular industry whilst entrepreneurs and managers focus on the capacities of their own respective companies to compete in specific markets. Scott and Lodge (1985) perceive national competitiveness as the capacity of a country to create, produce and distribute either products or services within the scope of international trade and earning increased returns on the resources applied. They furthermore observe how this capacity increasingly derive from the strategic orientations adopted and correspondingly decreasingly resulting from the natural resources and skills in effect. As Newall (1992) proposes, competitiveness incorporates the production of ever more goods of ever better quality alongside services that are retailed successfully to internal and external consumers. This results in companies getting well rewarded and therefore able to generate the resources necessary to supply an appropriate infrastructure for the provision of public services and support for the socially disadvantaged parts of society. The Organization for Economic Cooperation and Development (OECD), in its report on global competitiveness, defines competitiveness as the level at which a country may, under free and fair market conditions, produce goods and services that meet the demands prevailing in international markets whilst simultaneously maintaining and expanding the real earnings of their people into the long term. However, the literature proves not to have any generally accepted definition for competitiveness. The concept may perhaps be overly broad and too complex to fit into any single universal application. Porter (1990) is one researcher who contributes to the wide variety of perspectives on competitiveness while Krugman (1996) argues that bad politics at the national level have resulted in an obsession over the nature of competitiveness. The problems surrounding international competitiveness have received a range of different definitions. These (and the solutions thereby proposed for dealing with the problems) are not only at partially mutually inconsistent but also entirely confusing to the majority of academics, politicians, political decision makers and business managers. Spence and Hazard (1998) refer to how there are good reasons for such confusion with the collection of issues falling within the complexity of the ‘competitiveness’ framework.

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Misunderstandings are common whether in terms of empirical effects and policy consequences or in terms of actually defining the problem. Recently, Schwab (2010) has defined competitiveness as a set of institutions, policies and factors determining the level of productivity of any given economy and its resulting capacity to generate wealth and returns on investment and thus explaining the potential for economic growth. This structure rests on twelve core pillars: institutions; infrastructures; the prevailing macroeconomic environment; primary level healthcare and education; higher education and training; the efficiencies of the goods markets; the level of employment market efficiency; the level of financial market sophistication; the technological level; market scale; the level of business sophistication; and innovation. From the Porter and Stern (2001) perspective, there is a set of factors transversal to the economy that acts in support of innovation, including the human and financial resources allocated to making scientific and technological progress, the level of technological sophistication, the public policies targeting innovation related activities, which cover the protection of intellectual property, fiscal incentives for innovation, implementation and encouragement for antitrust laws preventing abuse of market powers, encouraging innovation based on market competition and the overall openness of the economy to trade and investment. Innovation has become a decisive challenge within the framework of global competitiveness; in order to obtain success, companies need to know how to accept this challenge and leverage the advantages to their respective location for the creation and commercialisation of new ideas. In advanced economies, producing standard products, through recourse to standard methods, does not enable the attainment of competitive advantage. Companies have to prove able to innovate in global marketplaces, creating and launching a flow of new products, displacing the frontier of the technological state of the art and evolving faster than their rivals (Porter and Stern, 2001). This global innovativeness is characterised by their capacity to, within the scope of free and fair markets, produce goods and services able to meet the needs of the market, maintaining and expanding the flow of earnings to their surrounding population into the long term (Budd and Hirmisf, 2004). In turn, entrepreneurship has taken up a central role in economic and industrial policies spanning the setting up of new businesses, the development of new business opportunities in already existing organisations and defined under the auspices of the Global Entrepreneurship Monitor (GEM) project as any attempt to launch a new business or new initiative, including self-employment, a new business organisation or the expansion of an existing business (GEM, 2007). This has meant that entrepreneurship constitutes a high risk dynamic and a highly binomial approach – rewarding in that the success of an entrepreneur very commonly stems from a mixture of luck, a good idea, possession of the right information combined with actions resulting from competitive decision-making. The relevant information should integrate into a project and a business plan based upon the sources of opportunity and other business and entrepreneurship related research (for example, digital databases as sources of information and local centres of economic development) (Kirkwood, 2010; Ratten, 2014).


3

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Methodology

3.1 Data The data subject to analysis incorporates aggregate data at the national level sourced from the GEM) and the Global Competitive Index (GCI) for the years between 2009 and 2013 and representing a non-balanced panel (2009: 49 countries; 2010: 57 countries; 2011: 55 countries; 2012: 64 countries; 2013: 63 countries). Table 1 presents the countries featured in the study alongside the corresponding year(s) and the level of economic development. Table 1

Countries, level of development and year

Country

Stage

2009

Algeria

1

X

Angola

1

Argentina

2

Australia

3

Austria

3

Bangladesh

1

Barbados

2

Belgium

3

Bolivia

1

Bosnia and Herzegovina

2

Botswana

1

Brazil

2

2010

2011

2012

X

X

X X

2013 X

X

X

X

X

X

X

X X X

X

X

X

X

X

X

X

X

X X

X

X

X

X

X

X

X

X

X

Canada

3

Chile

2

X

X

X

X

X

China

2

X

X

X

X

X

Colombia

2

X

X

X

X

X

X

X

X

X

X

X

X

Costa Rica

2

Croatia

2

Czech Republic

3

Denmark

3

X

X

X X

Dominican Republic

2

X

Ecuador

2

X

X

X X X

X

X

X

Egypt

1

El Salvador

2

X

X

Estonia

2

X

Ethiopia

1

X

Finland

3

X

X

X

X

X

France

3

X

X

X

X

X

X

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Table 1

Countries, level of development and year (continued)

Country

Stage

2009

2010

2011

2012

2013

Germany

3

X

X

X

X

X

Ghana

1

X

X

Greece

3

X

X

X

X

X

Guatemala

3

X

X

X

Hong Kong

3

X X

X

Hungary

2

X

X

Iceland

3

X

X

X X

X

India

1

X

Indonesia

2

X

Iran

1

X

X

X

X

Ireland

3

X

X

X

X

Israel

3

X

X

X

X

Italy

3

X

X

X

Jamaica

3

X

X

X

Japan

3

X

X

X

X

X

X

Jordan

2

X

Korea

3

X

X

X

X

X

Latvia

2

X

X

X

X

X

Lebanon

1 X

X

X

X

X

Libya

1

Lithuania

2

Luxembourg

3

Macedonia

2

Malawi

1

Malaysia

2

Mexico Montenegro

X X X

X

X

X

X

X

X

2

X

X

X

X

2

X

Morocco

1

Namibia

2

Netherlands

3

Nigeria

1

X

X X X

Norway

3

Pakistan

1

Panama

2

X

Peru

2

X

Philippines

1

Poland

2

Portugal

3

X

X

X

X

X

X

X

X X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X X

Entrepreneurship, innovation and competitiveness Table 1

81

Countries, level of development and year (continued)

Country

Stage

2009

2010

2011

2012

2013

Puerto Rico

3

X

Romania

2

X

X

X

X

X

Russia

2

X

X

X

X

X

Saudi Arabia

1

X

X

Serbia

2

X

Singapore

3

X

X

X

Slovakia

5

X

X

X

Slovenia

3

X

X

X

X

X

South Africa

2

X

X

X

X

X

Spain

3

X

X

X

X

X

Suriname

2

Sweden

3

Switzerland

3

X

Syria

1

X

Taiwan

3

X X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

Thailand

2

Trinidad and Tobago

1

Tunisia

2

Turkey

2

Uganda

1

X

United Arab Emirates

3

X

UK

3

X

X

X

X

USA

3

X

X

X

X

X

Uruguay

2

X

X

X

X

X

Venezuela

1

X

Vietnam

1

Yemen

0

Zambia

1

X

X X X

X

X

X

X X X

X

3.2 Measures 3.2.1 Dependent variable This analysis made recourse to the GCI (GCI databases) as its dependent variable.

3.2.2 Predictor variables •

TEA: Proportion of persons engaged in business start-up activities (TEA) (GEM databases).

•

Entrepreneur profile: Existing knowledge and exposure to external knowledge.

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•

Existing knowledge: The variables applied prior experience in setting up businesses, that is, the proportion of persons currently owner-managers running businesses (Ownmge), and perceptions as to respondent capacities to launch a new business, especially the proportion of persons who have the required knowledge/skills to start businesses (Suskil) (GEM database).

•

Exposure to external knowledge: The evaluation of informal networks adopted the proportion of persons who know someone who started a business in the past two years (Knoent) and the proportion of persons who was an informal investor in the last three years (Busangvl) (GEM database).

•

Innovation: innovation was evaluated in terms of the capacity for innovation (Innov1), the quality of scientific research institutions (Innov2), company spending on R&D (Innov3), university-industry R&D collaboration (Innov4), government procurement of advanced technological products (Innov5) and the availability of scientists and engineers (Innov6) (GCI databases).

Table 2 presents a summary of the variables and indicators applied in the empirical study. Table 2

Applied analytical variables

Variable/indicator

Measure

Database

GCI

1–7 (best)

GCI

Persons engaged in business start-up activities

%

GEM

Persons currently owner-managers running business

%

GEM

Persons who have required knowledge/skills to start business

%

GEM

Persons who know someone who started a business in the past two years

%

GEM

Persons who were informal investors in the last three years

%

GEM

Capacity for innovation

1–7 (best)

GCI

Quality of scientific research institutions

1–7 (best)

GCI

Company spending on R&D

1–7 (best)

GCI

University-industry collaboration in R&D

1–7 (best)

GCI

Government procurement of advanced tech. products

1–7 (best)

GCI

Availability of scientists and engineers

1–7 (best)

GCI

3.3 Data analysis Throughout all the analytical procedures, we applied the pooled data estimation method. We first evaluated the validity of the constructs and correspondingly analysed the reliability, the factorial validity, the convergent validity and the discriminant validity. This research evaluated the construct validity through the means of: 1

composite reliability (CR), (CR > 0.70), as this tool is not susceptible to influence from the number of items existing in each construct in contrast to Cronbach’s alpha that deploys the loads of items extracted from the estimated model

2

factorial validity (factorial loads greater than 0.5 and ideally greater than 0.7)


83

3

convergent validity, through average variance extracted (AVE) assuming that there is convergent validity whenever (AVE > 0.50)

4

discriminant validity, with the squared root of the AVE returned by the two constructs due to be greater than the correlation between these two factors (Barroso et al., 2010; Hair et al., 2010; Hulland, 1999).

Following the validation of the instrument and with the objective of validating the hypotheses included within the scope of the conceptual model, we made recourse to structural equation modelling (SEM), estimated through the partial least squares (PLS) method. The application of PLS-SEM as an alternative to covariance-based SEM (CB-SEM) stems from the inclusion of constructs containing but a single item, the items making up the constructs were gathered according to different units of measurement and alongside the existence of non-normal data and the assumptions as regards data distribution in CB-SEM (Hair et al., 2010, 2011, 2012, 2014). The model subject to estimation features in Figure 1. Figure 1

Model under calculation

84


Given that there are no measures for the goodness of the overall fit appropriate to models estimated by PLS as, in the methodologies for covariance-based structural equations, the structural models estimated by PLS get evaluated through the analysis of the values of the coefficient determined by R2 for the endogenous constructs or alternatively the value of the standardised root mean residual (SRMR) (Hair et al., 2011; Hulland, 1999). In order to evaluate constructs potentially generating multicollinearity, we evaluated the variance inflation factors (VIF). In the structural model estimations, in order to determine the T statistics and their respective statistical significance, we carried out 1,000 replicas of the sample. Finally, we proceeded with analysis of the difference in parameters relating to the three stages of the respective countries (Stage 1: factor driven; Stage 2: efficiency driven; and Stage 3: innovation driven). To this end, we implemented multi-group analysis given that the difference might derive from unobserved heterogeneity and hence not susceptible to attribution to one or more of the pre-specified variables (Sarstedt et al., 2011). In order to determine the statistically significant differences between the path coefficients of the three models, we deployed Henseler’s approach (Sarstedt et al., 2011). Throughout all of these estimations, we applied the SmartPLS version 3.2.1 software (Ringle et al., 2014).

4

Results

4.1 Variable reliability and validity Table 3 presents the results stemming from the descriptive statistics [average and standard deviation (SD)], AVE, CR, Cronbach’s alpha, Pearson’s correlation between the constructs and the square root of AVE in order to evaluate the validity and reliability and VIF within the scope of ascertaining any potential origins of multicollinearity in the SEM calculations. All the constructs subject to analysis returned high levels of reliability, with 0.819 proving the lowest CR returned. As regards the factorial validity, the standardised factorial weightings were greater or equal to 0.771 and thereby attaining factorial validity. The AVE results were equal to or greater than 0.694 and the respective squared root was always greater than the correlation between the particular construct and the remainder and hence confirming the convergent and discriminant validities. These results serve to demonstrate the validity and reliability of the variables subject to analysis by this study.

4.2 Structural equation modelling The VIF results were equal to or lower than 1.78 reporting the absence of multicollinearity in the estimations. The SEM-based modelling returned an acceptable adjustment index given that the SMRM = 0.078 and the R2 were 0.767 and 0.212 and 0.897 for the endogenous constructs for being engaged in business start-up activities, innovation and the GCI, respectively.

Existing knowledge base

Exposure to external knowledge

Being engaged in business start-up activity

Innovation

2

3

4

5

4.49

3.99

11.85

20.78

29.94

Note: The diagonal in bold contains the squared root of AVE.

GCI

1

0.78

7.85

8.09

11.08

0.62

SD

1.27

1.27

1.78

1.78

VIF

0.814

1.000

0.694

0.775

1.000

AVE

0.963

1.000

0.819

0.873

1.000

CR

0.953

1.000

0.588

0.712

1.000

Alpha

0.940

–0.532

–0.426

–0.591

1.000

1

–0.502

0.870

0.662

0.881

2

–0.355

0.649

0.833

3

–0.461

1.000

4

0.902

5

Table 3

Mean

Entrepreneurship, innovation and competitiveness 85

Average, SD, AVE, CR, VIF and Pearson’s correlations for the relationships between the constructs

→ → → →


Being engaged in a business start-up activity


Innovation

Note: *p < 0.05.

→



GCI

GCI

Innovation


0.883

–0.125

–0.461

0.130

0.784

0.883

–0.125

–0.461

0.142

0.775

Bootstrapping

0.017

0.028

0.032

0.049

0.037

SE

51.92

4.41

14.20

2.65

21.05

t

0.000*

0.000*

0.000*

0.008*

0.000*

p

Table 4

Beta

86 J.J. Ferreira et al.

Standardised path coefficients of estimated SEM, standard error (SE), T statistics and p-value of bootstrapping estimation

Entrepreneurship, innovation and competitiveness Figure 2

87

Estimated standardised path coefficients

Table 4 and Figure 2 detail the results returned from the estimated structural model. As regards the being engaged in a business start-up activity construct, our results demonstrate that the existing knowledge base (β = 0.784; p < 0.01) and exposure to external knowledge (β = 0.130; p < 0.01) constructs generate a statistically significant impact on the being engaged in a business start-up activity construct. The higher the score for the existing knowledge base and exposure to external knowledge construct, the higher the score for the entrepreneurial intention construct. Hence, we thus verify how the entrepreneur profile proves critical to growth and the triggering of entrepreneurial activities. As defended by various authors, the intrinsic capacities of individuals to take on exposure to risk and to recognise the opportunities present both foster entrepreneurial activities (Kihlstrom and Laffont, 1979; Parker, 1997). As regards the innovation construct, the being engaged in a business start-up activity (β = –0.461; p < 0.01) construct reports a statistically negative impact as the higher the score returned by the being engaged in a business start-up activity construct, the lower the scores resulting from the innovation construct. Finally, in terms of the GCI, the being engaged in a business start-up activity (β = –0.125; p < 0.01) and innovation (β = 0.883; p < 0.01) constructs hold a statistically significant impact on the GCI given that the

88


higher the score for the being engaged in a business start-up activity, the lower the GCI result whilst the higher the innovation construct result, the higher the level of the GCI. We may therefore state that entrepreneurship and innovation are central facets to the process of economic creativity as well as fostering knowledge, boosting both productivity and employment. Therefore, competitiveness emerges out of a dynamic process with the level of development shaped by the interaction between the ongoing market conditions and the returns on investment in innovation. These R&D investments help in changing company lines of growth due to the fact that new products, new processes and new organisational methods may alter the composition of markets (Audretsch and Fritsch, 2002; Parker, 2004).

4.3 Multigroup analysis Finally, multigroup analysis was applied to test for the existence of statistically significant differences between the three phases of economic development in relation to the standardised path coefficients. Table 5 displays the results returned by the structural models estimated for each of the three economic stages. As regards economies in the first stage, we may observe how the existing knowledge base (β = 0.579; p < 0.01) and exposure to external knowledge (β = 0.362; p < 0.01) constructs generate a positive and statistically significant impact on the being engaged in a business start-up activity construct. In turn, this construct (β = –0.429; p < 0.01) returns a statistically significant negative impact on the GCI all the while innovation (β = 0.615; p < 0.01) generates a statistically significant positive impact on the GCI. In the case of second stage economies, we observe how the existing knowledge base (β = 0.767; p < 0.01) and exposure to external knowledge (β = 0.174; p < 0.01) constructs provide a statistically significant positive impact on the likelihood of being engaged in a business start-up activity construct. Furthermore, the innovation construct (β = 0.883; p < 0.01) reports a statistically significant positive impact on the GCI. Finally, in third stage economies, we report that the existing knowledge base (β = 0.722; p < 0.01) construct generates a positive and statistically significant impact on the being engaged in a business start-up activity construct and the innovation (β = 0.928; p < 0.01) construct also returns a statistically positive impact on the GCI. These results once again affirm the conclusions of GEM (2014) who stated that individuals operating in factor-driven economies tend to show more positive attitudes towards business environments fostering perceived opportunities to start a business and perceived skills to start a business in comparison with individuals in efficiency-driven and innovation-driven economies. In addition, despite the diverse economies reporting a higher level of TEA, this does not correspondingly reflect in terms of the level of innovation and of competitiveness and due precisely to the capacity of these individuals to be able to prosper in the businesses they set up and run (GEM, 2014). Table 6 summarises the results of the comparisons between the groups of pairs (pairwise group comparisons) of the estimated path coefficients (Stage 1 vs Stage 2; Stage 1 vs Stage 3, Stage 2 vs Stage 3). Not one of these comparisons displays any statistically significant differences impacting on the innovation construct in the GCI.

Stage 1

Stage 2

→ Engaged in a business start-up activity → Innovation → GCI → GCI


Engaged in a business start-up activity

Innovation


→ GCI → Engaged in a business start-up activity

Innovation


→ GCI


Being engaged in business start-up activity

→ Engaged in a business start-up activity → Innovation


→ GCI

→ GCI

Innovation → Engaged in a business start-up activity

→ Innovation



→ Engaged in a business start-up activity



→ Engaged in a business start-up activity


Note: *p < 0.05

Stage 3

0.015

0.928

–0.049

–0.019

0.722

0.047

0.883

–0.075

0.174

0.767

–0.429

0.615

–0.092

0.362

0.579

0.016

0.928

–0.033

0.001

0.719

0.048

0.883

–0.074

0.182

0.763

–0.416

0.615

–0.105

0.342

0.594

Bootstrapping

0.048

0.027

0.098

0.057

0.041

0.037

0.019

0.129

0.072

0.061

0.090

0.094

0.169

0.091

0.082

SE

0.41

48.00

0.38

0.26

11.91

0.99

32.94

0.77

3.03

18.85

4.76

6.55

0.54

3.99

7.10

t

0.682

0.000*

0.706

0.795

0.000*

0.323

0.000*

0.441

0.003*

0.000*

0.000*

0.000*

0.588

0.000*

0.000*

p

Table 5

Beta

Entrepreneurship, innovation and competitiveness 89

Standardised path coefficients of estimated SEM and p-value of bootstrapping estimation, by country

→ GCI → GCI


Innovation


Notes: *p < 0.05; St 1 – Stage 1; St 2 – Stage 2; St 3 – Stage 3

→ Being engaged in a business start-up activity → Innovation


→ Being engaged in a business start-up activity

0.444

–0.268

–0.017

0.381

–0.188

| St 1 – St 2 |

p

0.000*

0.000*

0.556

0.001*

0.013*

–0.045

0.477

0.043

0.045

0.188

| St 1 – St 3 |

P

0.075

0.000*

0.600

0.730

0.040*

0.032

–0.313

0.027

0.193

–0.143

| St 2 – St 3 |

p

0.703

0.000*

0.564

0.018*

0.071

Table 6


90 J.J. Ferreira et al.

Standardised path coefficient differences, Henseler’s multigroup analysis


91

Comparing the results from stage 1 economies with those in stage 2, we observe how the existing knowledge base (βdiff = –0.188; p < 0.01) construct generates an impact of significantly greater magnitude on the being engaged in a business start-up activity construct in stage 2 economies than in their stage 1 counterparts all the while the exposure to external knowledge (βdiff = 0.188; p < 0.01) construct returns an impact of a significantly greater magnitude on the being engaged in a business start-up activity construct in stage 1 economies than in their stage 2 peers. Furthermore, in stage 2 economies, the impact of innovation (βdiff = –0.268; p < 0.01) on the GCI proves significantly higher than in stage 1 economies whilst in the latter the impact of the being engaged in a business start-up activity (βdiff = 0.477; p < 0.01) construct on the GCI is significantly higher than in stage 2 economies. When comparing stage 1 economies with stage 3 economies, we find that the exposure to external knowledge (βdiff = 0.381; p < 0.01) construct results in an impact of a significantly greater magnitude on the being engaged in a business start-up activity construct in stage 1 economies than those in stage 3. In the stage 3 economies, the impact of innovation (βdiff = –0.313; p < 0.01) on the GCI proves significantly higher than in stage 1 economies even while in the latter the impact of the being engaged in a business start-up activity (βdiff = 0.444; p < 0.01) construct on the GCI attains a significantly higher level than in stage 3 economies. Comparing the stage 2 economies with those in stage 3 finds only that the exposure to external knowledge (βdiff = 0.193; p < 0.01) construct generates an impact of a significantly greater magnitude on the being engaged in a business start-up activity construct in stage 2 economies than it does in their stage 3 peers.

5

Final considerations

The late 1980s saw a rebirth of interest in the role of entrepreneurship in the economy. This interest was undoubtedly based on the re-discovery of the seminal work by Schumpter (1934) approaching the role played by innovative entrepreneurs within the overall macroeconomic performance. Schumpeter maintains that through the introduction of new combinations of products and markets, entrepreneurs generate new means of more efficient production whilst simultaneously driving poorly performing companies out of the market: a process of ‘creative destruction’. Whilst various studies have highlighted the importance of entrepreneurship as a field of study undergoing rapid development, Reynolds et al. (1994) verify that there are no internationally comparable data on entrepreneurship and the founding of companies and hence the current importance placed on making these comparisons at the international level. Thus, we may here characterise more precisely that which the GEM terms the Entrepreneurial Framework Conditions (EFCS). Right from its outset, the GEM project proposed that entrepreneurial activities are shaped by a distinctive group of factors, the aforementioned EFCS and these constitute the oxygen necessary to the flourishing of resources, incentives, markets and institutions acting in support of the growth and development of new companies (Bosma et al., 2008). Hence, different countries and their different regions may be expected to display different EFCS or different ‘rules of the game’ and that these impact on the churn taking place across business sector activities.

92


As our study demonstrates, different stages of the economy imply different characteristics in terms of entrepreneurial activities. Furthermore, we also verify how there is a need to look beyond the TEA as otherwise it would not prove possible to grasp how lesser developed countries may report a TEA greater than more developed countries. We would thus then be in contradiction of the premise that entrepreneurship proves the motor of economic development and acts as a driver of competitiveness. In order to answer these questions, we also applied the GCI and thereby conveying how perceptions as to the conditions favourable to entrepreneurship, their effectiveness and maturity reflect on there still being a long road ahead. We find that in relation to first stage economies, the entrepreneurial profile generates a positive impact on the launching of companies even while this generates a contrary effect in terms of the GCI given that innovation, when implemented, returns a statistically significant positive impact on the GCI. In the case of second stage economies, we report how the entrepreneurial profile returns a positive impact on the founding of new companies. Along with the innovation construct, this effect carries over to the GCI. Finally, the third stage economies demonstrate a positive experience for each of the three constructs and thus the entrepreneur profile has a positive effect on innovation and this has a positive impact on the GCI. We may thus state we believe that entrepreneurial thinking contributes to economic development as entrepreneurs set up new companies and consequently generate new employment. In addition, this helps internationalisation of firms as there is more likelihood of guaranteeing product and service variety, intensifying competition and to the extent of potentially boosting productivity through technological changes.

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