THE INNOVATIVE POLICIES ARE THE KEY OF AN INNOVATION- DRIVEN ENTREPRENEURSHIP: EVIDENCE FROM GEM
Author 1: El Zaitouni, Z.
University of La Rioja & University Mohamed V of Souissi, Rabat, Morocco, z.elzaitouni@gmail.com
Author 2: Maaninou. A
University Mohamed V of Souissi, Rabat, Morocco, amalmaaninou@hotmail.fr
Abstract— From Global Entrepreneurship Monitor project, this article highlights the influence of technological innovation on the entrepreneurial activity in early stage and how innovative entrepreneurship can increase the economic growth in developing countries at the same time analyzes the innovation-driven entrepreneur. The literature review of this article concentrates on innovation and entrepreneurship in order to identify the innovative initiatives of entrepreneurship. The methodological approach uses a logit model drawing from a robust Spanish database of General Entrepreneurship Monitor (GEM) in 2018. Our analysis depend on three variables groups: the Total Early-Stage Entrepreneurial Activity (TEA), technology newness and product newness. The results show maximum frequency of entrepreneurial activities among those initiatives that affirm that products and services offered are perceived as novel only by some customer and initiatives that use technologies between 1 and 5 years.
Keywords—Entrepreneurship; innovation; use of technology; variables.
I. INTRODUCTION
Nowadays, innovation and entrepreneurship are considered crucial to the economic growth of a country and its survival in the modern global economy. They are also one of the main drivers of economic development in the current wave of globalization (Abid Bashir and Akhtar, 2016). At present, all sectors of economy and society are involved in entrepreneurship (Ng. Jhony, 2012). Education establishments remember entrepreneurial awareness; universities and higher education centers offer subjects on entrepreneurship; development centers participate in the transmission of technology, companies stimulate entrepreneurship inside and governments establish policies to support and encourage entrepreneurial activity (Henrekson & Stenkula, 2010). Its importance can’t be unnoticed, since entrepreneurship not only contributes to economic growth and innovation but also to the convergence of markets providing economic efficiency, through the creation of products and processes, an important area in the competitiveness of a country (Kritikos, 2014).
Some scholars suggest that new firm creation and entrepreneurship are indispensable for the long- term economic growth of a country and its regions (Rui & Preto, 2011; Almodóvar, Fernández & Díaz, 2020). In recent years, the literature on entrepreneurship has been expanded due to its relevance to employment, productivity and socioeconomic performance (Wiklund, Nikolaev, Shir, Foo & Bradley, 2019; Stoica, Roman
& Rusu, 2020). Entrepreneurship is one of the ways that some countries adopt to face the times of crisis, change and uncertainty that societies undergo (Ratten, 2020). Innovation is the implementation of a new organizational method in business practices, workplaces organization or external relations, manifesting itself as a new marketing method, a significantly improved product good, service or process (Tidd & Bessant, 2014; Dogan, 2017). Likewise, innovation is a policy tool for promoting socioeconomic development, while serving as a driver of economic growth in a region or a country (Maradana, Pradhan, Dash, Gaurav, Jayakumar & Chatterjee, 2017).
Kardos (2012) mention the relationship between innovation and entrepreneurship by reminding us of the Schumpeterian study about it “carrying out innovations is the only function which is fundamental in history.”
Entrepreneurship and innovation has been regarded as linked concept (Mrozewski and Kratzer, 2016).
Innovative entrepreneurship combines elements of entrepreneurship and innovation, resulting in new firms based on innovative ideas (Stefan, Comes, Szabo, and Herman, 2012). Innovative entrepreneurship can give an economy the higher competitive edge and make it more competitive against neighboring economies. The objective of this paper is to analyze the effect of innovation in early entrepreneurship activities using an entrepreneurship dataset provided by the Global Entrepreneurship Monitors (GEM).
GEM was originally founded in 1999 by researchers at the London Business School (UK) and Babson College (USA). Its aim is to identify the link between economic growth and entrepreneurial activities (Álvarez, Urbano, and Amorós, 2014; Nieto and González-Álvarez, 2016). Despite increasing collaboration in academic and private research utilizing the GEM project only few systematic reviews of GEM-based research can be
found, according to the Social Sciences Citation Index (SSCI) (Álvarez et al., 2014). There are lacks and imperfections regarding to the innovative entrepreneurship policy research in developing countries (Bakir, 2013; Bakir & Gunduz, 2017; Bakir & Jarvis, 2017, Bakir & Jarvis, 2018). Entrepreneurial policy had not been analyzed or studied clearly in a context of innovation, although it stimulates a set of perceptual and cognitive skills directly related to the process of opportunity identification (Valliere, 2013). Furthermore it has not been employed that much to explain the innovation of entrepreneurial activities (Fuentelsaz et al, 2018).
Bakir & Gunduz (2019) state that policy entrepreneurship research required more theoretical, conceptual and empirical engagement to minimize the conceptual stretching that exist in this field. To fill in these gaps, it is essential to analyze the potential convergence that exist between innovation and entrepreneurship policy, particularly when the policy target is to encourage and foster the start-up of technology-based, innovative and rapidly growing knowledge-based enterprises in developing countries also study innovation-driven entrepreneur in these countries. In this sense we adopted a methodology of logit models regressions depending on multi variables groups based on the GEM database and the Adult Population Survey (APS) in order to fill in and cover those gaps.
There are several common weaknesses of policy entrepreneurship research in developing countries. Some of these are prevalent in policy entrepreneurship research in general (Bakir, 2013; Bakir & Gunduz, 2017; Bakir &
Jarvis, 2017, Bakir & Jarvis, 2018). The structure of this paper is as follows: a literature review that aims to expose the role of innovation-driven entrepreneur and entrepreneurship in section II; data and methodology used to analyze the degree of competitiveness of each region; a discussion of the results of the study; and finally a presentation of the results and conclusions.
II. LITERATURE REVIEW
Throughout history there have been numerous research projects that have been focus on the study of the determinants of entrepreneurship from different disciplines such as economics, sociology, finance or psychology (among others). This has led to a global recognition of the entrepreneurship phenomenon due to large part to the transversality, heterogeneity and subjectivity of the phenomenon itself (Abu et al. 2017;
Ferreira et al. 2017).
The literature highlights many benefits of entrepreneurship to a society’s social and economic life. According to Doran, McCarthy & O’Connor (2018) entrepreneurship becomes the dynamic engine of socio-economic development around the world and plays an important role in today’s employment market and economic growth.
They indicate that it is indispensable for the economic prosperity and future economic development of a country because it injects new dynamism into an economy. Entrepreneurship has proven itself to be an outstanding instrument to reduce welfare drain and unemployment through job creation. As stated by Ferreria et al. (2017)
entrepreneurship is a catalyst for economic growth and competitiveness and is a main contributor to economic development
The academic study of entrepreneurship began with the theory of Knight who is known for having made the important contribution of relating entrepreneurship to the concept of “risk” (Kwabena & S. Kwabena, 2011).
Ever since, risk still considered as one of the main key features of entrepreneurship. Our understanding of entrepreneurship was developed further when the concept of “innovation” was added by Schumpeter in the thirties which incorporate the creation of novel activities such as new products, markets and methods of production (Kwabena & S. Kwabena, 2011). Other authors such as Kirzner and Penrose in the sixties tried to expand the concept of entrepreneurship by making associations with: access to information service and product opportunities; and personal qualities and human capital including communication skills, business experience, and foresight and imagination (Duening, 2010, Kwabena & S. Kwabena, 2011).The diversity of disciplines that have been used to study the complex entrepreneurship phenomena and the different paradigms adopted implies that consensus has not arisen. (Abu et al. 2017)
Historically, the credit for innovations has typically given to exceptional entrepreneurs like Henry Ford, Sam Walton, or Bill Gates; caution should be heeded in order not to overlook the potential that policies could have on bringing about innovation as well. Policies that are drafted with an understanding of the innovative process and that foster the creation of rapidly growing firms are some of the key factors of a theory of entrepreneurship (Stough, 2016).
The entrepreneur plays a crucial role in the introduction of innovations in the market (Brem, 2011; Fuentelsaz et al. 2018). The ‘‘entrepreneur as innovator’’ was coined in the theory of Schumpeter as an important figure to the improvement of the national economy. The entrepreneur’s innovative activity leads to a creative
‘‘destruction process’’ (Dekkers, Talbot, Thomson & Whittham, 2014) by creating opportunities for economic services meanwhile continuously disturbing the equilibrium of an economic system. Innovation cannot happen without a building off point. Other authors have observed that “innovation requires three basic components: the infrastructure; the capital; and the entrepreneurial capacity needed to make the first two work” (Courvisanos &
Mackenzie, 2014; Stam & van de Ven, 2019). Moreover, innovation has to address market needs, and requires entrepreneurship if it is to achieve commercial success (Brem, 2011; Fuentelsaz & Montero, 2018).
Many studies support the entrepreneurship and innovation theories together, since they conceive the term entrepreneur as a person who identifies and explores business opportunities, due to the intentional accumulation of technology and knowledge, coming from innovation (Braunerhjelm, 2010; Van Stel et al., 2014). Sarasvathy (2014) clarifies that entrepreneurship responds to the following economic question: How, by whom and with what effects are opportunities discovered evaluated and taken advantages of creating goods and services in the future?
More entrepreneurs enter the economic system as a result of other innovations being amplified over time.
In this matter, Schumpeter’s theory predicts an increase in economic growth that depends largely on the increasing number of entrepreneurs. This approach has been influential to the field of entrepreneurship albeit difficult to formalize econometrically and the existing literature has found that the degree of innovation within the new venture varies considerably (Brem, 2011; Fuentelsaz et al. 2018).
As has been previously mentioned, innovative entrepreneurship is lacking empirical models in analyzing innovation, competitiveness and economic growth of a country and its regions. Proceeding from Schumpeter’s original approach and as a consequence, empirical economic literature has arisen based on the idea of innovation as a source of economic growth. A noticeable body of empirical work and evidence now exists across many different countries on the subject (Duening, 2010, Kwabena & S. Kwabena, 2011; Brem, 2011;
Fuentelsaz et al. 2018).
There are a few ways of measuring innovation. The number of patents, (Xiao and North, 2018; Stephens et al, 2013), R&D expenditures (Stephens et al, 2013), the number of national science and technology project grants (Xiao and North, 2018) the number of approved intellectual property rights (Xiao and North, 2018) are some tried methods.
Entrepreneurship takes on a different nature depending on the contextual factors, that is, the social, economic, and business environments. Stephens and colleagues (2013) have noted that there is significant research suggesting a link between entrepreneurship and innovation, knowledge spillovers, and economic growth. Most of the previous research has focused on the advantages that urban areas have related to innovative industries and clusters over rural ones. In areas lacking in innovation, human capital can be brought to that area (Braunerhjelm, 2010) and there is evidence that local innovation enhances growth (Bauer et al., 2012). Gonzalez-Pernía, Peña- Lagazkue, and Vendrell- Herrero (2012), focus on the effect of regional productivity, and find that the significant and positive effect of innovation and entrepreneurship involves a synchronous process, and innovation does not have that much influence if we separate it from entrepreneurial activities.
More recently, Block, Thurik, and Zhou (2013) mention that a high rate of entrepreneurship increments the possibility that knowledge will alter “new-to-the-market innovation”.
As a matter of fact, their findings show that the process of the commercialization of knowledge depends largely on Schumpeterian entrepreneurship. And using the total factor productivity and total entrepreneurship activity approximation, Erken, Donselaar, and Thurik (2016) analyze the role of entrepreneurship and its impact per capita income as determinants of economic competitiveness and performance in different regions. Also entrepreneurship, R&D and human capital are all together or individually are significant and influential for the innovative development of the region.
González-Pernía and Peña-Legazkue (2015) also agree that the quality of new firm formation in entrepreneurship can affect positively the economic growth. On the other hands, Pathak, Xavier-Oliveira, and Laplume (2013) examined the determinants of entrepreneurship in developing countries, while González-
Pernía, Jung, and Peña (2015) explored the links between entrepreneurship and knowledge spillovers in highly developed countries. According to their findings, the actors of entrepreneurship and regional economic outcomes change across different stages of development, which demonstrate that developed countries, have good business climate and more suitable environments from which innovative entrepreneurs can evolve, emerge and influence on local and regional economy.
Despite the multiple theories that claimed that entrepreneurship was an activity linked to innovation, there were authors who had different points of view. Mrozewski and Kratzer (2016) did not agree with this theory, since they argued that entrepreneurship and innovation could be considered independent phenomena. Some authors even demonstrated that the countries with the highest entrepreneurship rates were those with the lowest levels of innovation (Braunerhjelm, 2010; Anokhin & Wincent, 2012). Anokhin and Wincent (2012) argued that entrepreneurship was only linked to innovation in the richest countries. The explanation for this was based on the fact that these countries, characterized by having highly developed and competitive economies, contributed to the entrepreneur innovating, since innovation is the most appropriate strategy to escape from the forces of competition (Mrozewski and Kratzer, 2016).
In summary, the influence of the innovative entrepreneurship role and new business formation is progressively being studied, highlighted, analyzed and linked to economic development at both regional and national levels.
However, scholars such as Dejardin and Fritsch (2011) reveal that the effects of different types of new businesses and innovations combined on regional growth are fundamental for further research. In addition, although institutions and researchers made noticeable progress in these kinds of studies, Doran, McCarthy &
O’Connor (2018) point out that the connection between entrepreneurship and growth is yet unclear, besides, public policies are still giving less attention to innovative entrepreneurship. These make it clear that regional and national governments in different countries can encourage and promote all types of new business creation rather than just knowledge- based entrepreneurship or innovation activities, in order to stimulate local economic growth. It is still useful trying to analyze both the effects and occurrence of specific types of entrepreneurship in more deep details, especially its link to innovation and government policies in emerging countries and examine the role of the innovation-driven entrepreneur, which are the aim of this paper.
III. METHODOLOGY
The main objective of this paper is to analyze the effect of different levels of innovation on entrepreneurship in developing countries. To achieve this objective, we used the Global Entrepreneurship Monitor data base on 2018 also we gathered individual- and country-level data from 45 developing economies for the 2013–2018 period. This data base is a well-known international survey of entrepreneurial activities (Abu et al. 2017). The Global Entrepreneurship Monitor project uses an Adult Population Survey (APS) to measure entrepreneurial activities in these developping countries (Bosma, 2012). The entire respondents sample was 30,611 individuals who take in part of the International Global Entrepreneurship Monitor project. Developing economies usually depend on either factor endowments, such as unskilled labor and natural resources, or transformation activities
that require the implementation of efficient production processes (Schwab 2013).
A. Dependent variables:
• Total Entrepreneurship Activity (TEA) (1)
The dependent variable in this study is Total Entrepreneurship Activity (TEA), a dummy variable which 1 indicates involved in total entrepreneurial activity (early phase) and 0 in other case. The Global Entrepreneurship Monitor identifies TEA as individuals aged 18-64 who are active in the phase or either startup or managing a new business fewer than 42 months old (Bosma, 2012).
• Innovation driven entrepreneurship (IDE) (2)
To determine whether an adult individual is an innovation-driven entrepreneur, we use two questions included in the GEM’s APS to assess the innovativeness of the businesses of those who qualify as entrepreneurs. The first question is related to the level of newness of the product or service offered by the entrepreneur’s business, while the second is related to the number of business competitors offering the same product or service in the market in which the entrepreneur’s business operates. Based on these questions, individuals becoming entrepreneurs may be classified according to the innovative orientation of their businesses. Based on the extent to which the product or service is new or unfamiliar to more customers and not offered by other competitors in the market, the entrepreneurs may be considered innovative. More specifically, we operationalize a measure of IDE in such a way that an observation is coded 1 (one) if the individual qualifies as a nascent or new entrepreneur who offers a new product or service to all or some customers in a market where there are few or no competitors offering the same product or service, or 0 (zero) otherwise.
B. Independent variables
• Product newness for potential customer (CUSTGLOB)
Respondents were asked to provide information about how many potential customers consider product new or unfamiliar. The categories used are codified in three levels: many, few or none.
• Technology newness used (TECHGLOB)
Respondents were asked to provide information about how long the technologies or procedure used for his product or service been available. The categories used are codified in three levels: less than a year, between 1 to 5 years, or longer than 5 years.
Once the variables were defined we use SPSS 24.0 software to develop both a descriptive statistic analysis and a binary logistic regression model.
C. Country-level predictor variables
• Investment in R&D
Refers to total expenditure on research and development as a percentage of GDP (mean-centred). This variable is frequently used in the empirical literature as an acceptable approximation that captures the endowment of knowledge within an economy, and therefore the potential for spillovers leading to the introduction of new products by entrepreneurs (Acs et al. 2009).
• Foreign direct investment (FDI)
Denotes the net inflows of investment from foreign investors, measured as a percentage of GDP (mean-centred).
Net inflows of FDI are the sum of new investment inflows less disinvestments (e.g. repatriation of capital). By using net inflows, we specifically capture the variation in the stock of inward FDI as an approximation of how prevalent foreign firms are within an economy.
We expect that the impact of our predictor variables on IDE will not be immediate, but may instead take some time and be conjoined with investments made over several years. As a result, we chose the previous five-year average for investment in R&D and FDI (i.e. the average over years t − 1 to t − 5) in order to capture these variables as structural characteristics of an economy and measure their medium-/long-term impact.
Data for both variables come from the World Development Indicators database provided by the World Bank.
• Individual-level and country-level control variables
A set of variables, at both the individual level and the country level, was also included to control for some drivers of entrepreneurship that have been found to be important in the extant literature. At the individual level, we control for whether the individual is male (Male), for the age of the individual measured in number of mean- centred years (Age), and for whether the individual has a university education (University degree) and other variables mentioned in table V below.
RESULTS AND CONCLUSIONS
In the following table we show the descriptive statistical results of the variables analyzed in the sample. It show how the non-use of new technologies (69.9%) and non-innovation in products (56.1%) predominate, given that for the majority of the sample none of the potential clients considers the product or service as new and use technologies with more than five years old to ensure the goodness of fit of the model, we first use the Omnibus test
TABLE I. DESCRIPTIVE STATISTIC
(P-value <0.00) we first use the Omnibus test (p-value <0.00) which allows us to reject the null hypothesis H0
that the coefficients of the variables added in the model are zero. Next, three measures are used to assess the global fit of the model. -2ll, pseudo R-squared statistic and the Nagelkerke R square. The statistic test shows that the model is significant. Finally, to complement the validity analysis of the model, we use the Hosmer and Lemeshow test. Through this test, observed values are compared with expected values. As the following table show, the significance level is 0.971 indicating that the model fits perfectly.
TABLE II. GOODNESS OF FIT STATISTICS
Once the goodness of fit is assured, the results show us equation variables, regression coefficients with their corresponding standard errors (ET), value of the Wald statistic, associated statistical significance, and value of the odds ratio (Exp (β)).
TABLE III. LOGISTIC REGRESSIONS
The Wald test shows how all the variables introduced in the model are significant (p <0.001). Exp (β) indicates the strength of the relationship, that is, the use of technologies between 1 and 5 years is the greatest effect on initial entrepreneurial initiatives. Values closer to zero would explain a minor effect on the dependent variable.
From the logistic regression coefficients (B), a general formula is reached that can be used for future research.
The results show how odss ratio of the initial entrepreneurial initiatives (TEA), once adjusted the comparison
by the other factor (CUSTGLOB), is 2.8 times higher with the use of very latest technology (newer than one year) and 2.2 times higher with the use of technologies between one to five years.
Likewise, the results show how odss ratio of the initial entrepreneurial initiatives (TEA), once adjusted the comparison by the other factor (TECHCLOB), is 1.50 times higher with the perception for all customer consider product new or unfamiliar and 5.1 times consider higher with the perception for some customer product new or unfamiliar.
The maximum frequency of entrepreneurial activities is among those initiatives that affirm that the products and services they offer are perceived as novel only by some customer with an odss ratio of 1.509 and those initiatives that use technologies between 1 and 5 years old (odds ratio 2.801).
• Descriptive statistics and correlation matrix
Table IV lists the countries included in the sample, classified by geographical region. Table V shows the descriptive statistics and correlation matrix for the entire sample for the 2013-2018 periods. The correlation matrix reveals that most explanatory variables are not highly correlated. However, the coefficient between GDP per capita and DTF in starting a business is slightly below 0.50. To check for possible multicollinearity problems, we computed variance inflation factor (VIF) scores for all variables included in the study. None of the VIF scores exceeded 2.5, providing evidence of no multicollinearity among explanatory variables (Bowerman and O’Connell 1990).
• Effects of R&D and FDI on IDE
The results shown in Table VI represent the impact of R&D investment and FDI on IDE. Model 1 is a null model in which no explanatory variables were included in order to test whether there is (or is not) significant variation in the propensity of an adult individual to be a nascent or new innovation-driven entrepreneur. The random intercept shows a significant variance, 0.692, and the resulting intra-class correlation (ICC) indicates that 17.38% of the total variance within the data is between countries.5
Model 2 is the full model with no dummies and Model 3 represents a non-linear relationship for R&D and FDI with respect to IDE. Both models show a significant variance (0.445 and 0.428, respectively), reducing the resulting ICC to 11.91% in Model 2 and 11.51% in Model 3. Model 4 introduces an interaction to test the moderation effect of R&D on the relationship between FDI and IDE, with dummies for both year and geographical region, in order to capture the effect of unobserved variables (i.e. short-term variations in economic activity or idiosyncratic regional characteristics such as culture and institutions). The results in Model 4 remain consistent, with a significant variance of 0.288 and a corresponding ICC of 8.05%.
We discover that investment in R&D demonstrates a significant and negative effect on IDE, confirming that these variables are not positively associated in developing economies, as they seem to be in advanced countries. As discussed in Section 2.1, companies’ emphasis on the development side of R&D investment over the research side, the presence of market failures and institutional barriers embedded in these economies seem to deter exploration and exploitation of knowledge-based entrepreneurial opportunities.
We also found a positive and significant non-linear relationship between FDI and IDE, confirming the
existence of a U-shaped relationship between these variables in developing countries. Beyond a given FDI level, new entrants are needed in developing economies in order to transform unexploited FDI-driven entrepreneurial opportunities into new products and services.
The extent to which new entrants may take advantage of these FDI-driven entrepreneurial opportunities depends upon the ability of local firms and individuals to absorb and transform knowledge to create value.
Several studies suggest the need for a strong R&D infrastructure to successfully benefit from knowledge spillovers from foreign firms. Our results show that in the case of developing economies, the effect of FDI on IDE is positively and significantly moderated by investment in R&D.
This finding confirms that R&D investment efforts enhance the positive association between FDI and IDE in developing economies.
TABLE IV: Countries in the sample grouped by geographical region.
Latin America &
Caribbean
Middle East & North Africa
Asia Pacific &
South Asia
Argentina Algeria China
Bolivia Egypt India
Brazil Iran Indonesia
Chile Jordan Malaysia
Colombia Morocco Pakistan
Costa Rica Saudi Arabia Philippines
Ecuador Tunisia Thailand
Guatemala
Jamaica Eastern Europe & Central Asia
Mexico Bosnia and Herzegovina
Panama Croatia
Peru Hungary
Trinidad & Tobago Kazakhstan
Uruguay Macedonia
Lithuania Sub-Saharan Africa
Montenegro
Ghana Poland
Nigeria Romania
South Africa Russia
Uganda Serbia
Zambia Turkey
TABLE V: Descriptive statistics and correlation matrix
Variables Mean s.d. Min. Max. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
Dependent variable
(1) Innovation-driven 0.04 0.2
1 0.00 1.00 1.00 entrepreneurship
(Yes = 1) Individual level
predictors, N = 225, 273
(2) Male (Yes = 1) 0.49 0.50 0.00 1.00 0.03*** 1.00 37.7 12.8 18.00 64.00 −0.03*** −0.02*** 1.00
(3) Age (Years)
(4) University degree
(Yes = 1) 0.07 0.26 0.00 1.00 0.02*** 0.01*** 0.01*** 1.00
(5) Entrepreneurial exposure (Yes = 1)
0.46 0.50 0.00 1.00 0.09*** 0.11*** −0.11*** 0.06*** 1.00
(6) Investor experience
(Yes = 1) 0.07 0.26 0.00 1.00 0.08*** 0.06*** −0.01*** 0.03*** 0.15*** 1.00 (7) Recent experience
(Yes = 1) 0.07 0.25 0.00 1.00 0.05*** 0.02*** 0.00 0.00 0.09*** 0.13*** 1.00
(8) Entrepreneurial skills (Yes = 1)
0.60 0.49 0.00 1.00 0.12*** 0.12*** −0.04*** 0.04*** 0.24*** 0.12*** 0.12*** 1.00
(9) Fear of failure (Yes
= 1) 0.34 0.4 0.00 1.00 −0.05*** −0.07*** 0.05*** −0.01*** -0.05*** −0.04*** −0.01*** −0.16*** 1.00 (10) Opportunity
perception (Yes = 1)
0.43 0.50 0.00 1.00 0.1*** 0.06*** −0.08*** −0.02*** 0.19*** 0.09*** 0.06*** 0.24*** −0.09*** 1.00
Country-level predictors,
N = 45 (11) GDP per capita
($US PPP) 9.89 4.90 1.00 25.56 -0.01*** 0.00 0.08*** 0.06*** -0.10*** -0.05*** −0.1*** -0.10*** 0.05*** -0.11*** 1.00 (12) Δ GDP per capita
(% of annual change)
3.88 2.09 0.05 10.80 0.02*** 0.00* -0.01*** 0.10*** 0.04*** 0.00 0.03*** -0.03*** -0.01*** -0.02*** -0.23*** 1.00
(13) DTF in getting credit
(0–100)
55.35 15.51 18.75 100.00 0.02*** 0.01*** 0.05*** -0.06*** -0.05*** -0.03*** -0.04*** -0.03*** 0.01*** 0.01*** 0.25*** -0.24*** 1.00
(14) DTF in starting a
business (0–100) 68.6 12.5 35.72 90.27 0.00 -0.01*** 0.06*** 0.00 -0.09*** -0.03*** -0.06*** -0.08*** 0.01*** -0.07*** 0.47*** -0.28*** 0.34*** 1.00
(15) Investment in R&D
(% of GDP) 0.48 0.3 0.02 1.53 0.00† 0.01** 0.01*** 0.03*** 0.02*** 0.02*** 0.01*** 0.03*** -0.02*** -0.02*** 0.14*** -0.02*** 0.05*** 0.02*** 1.00
(16) Foreign Direct 4.78 3.9 0.32 26.88 −0.07*** 0.01*** 0.03*** 0.05*** 0.01*** -0.05*** -0.04*** -0.18*** 0.05*** -0.18*** 0.24*** 0.22*** -0.19*** -0.06*** 0.03***
Investment (% of GDP)
Level of statistical significance: ***p ≤ 0.001; **p ≤ 0.01; *p ≤ 0.05; †p ≤ 0.10.
TABLE VI: Multilevel logistic regression predicting innovation-driven entrepreneurship, 2013–2018
Model 1 Model 2 Model 3 Model 4
Male 0.152***
(0.021)
0.152***
(0.021)
0.154***
(0.021)
Age −0.008***
(0.001) -0.008***
(0.001) −0.008***
(0.001)
Sq. age −0.001***
(0.000) −0.001***
(0.000) −0.001***
(0.000)
University degree 0.400***
(0.038) 0.401***
(0.038) 0.405***
(0.038)
Entrepreneurial exposure 0.586***
(0.022) 0.586***
(0.022) 0.587***
(0.022)
Investor experience 0.383***
(0.029) 0.383***
(0.029) 0.383***
(0.029)
Recent experience 0.314***
(0.032) 0.315***
(0.032) 0.314***
(0.032)
Entrepreneurial skills 1.067***
(0.030) 1.067***
(0.030) 1.066***
(0.030)
Fear of failure −0.319***
(0.024) −0.319***
(0.024) −0.319***
(0.024)
Opportunity perception 0.474***
(0.022) 0.474***
(0.022) 0.472***
(0.022)
GDP per capital
Sq. GDP per capital
Δ GDP per capital
0.000 (0.016)
−0.003† (0.002) 0.059† (0.031)
0.006 (0.016)
−0.003† (0.002) 0.080*
(0.033)
0.016 (0.018)
−0.004*
(0.002)
0.089**
(0.030)
DTF in getting credit 0.006
(0.004) 0.005
(0.004) −0.005 (0.004)
DTF in starting a business Investment in R&D
Foreign Direct Investment
0.001 (0.005)
−0.680***
(0.180)
−0.005 (0.015)
0.004 (0.006)
−0.981*
(0.228)
−0.061 (0.029)
0.006 (0.005)
−0.578**
(0.181)
−0.035† (0.020)
Sq. Investment in R&D 0.483
(0.460) Sq. Foreign Direct Investment
Investment in R&D× foreign direct
0.004*
(0.002) 0.144***
(0.039)
investment
Intercept −3.500***
(0.07) −5.311***
(0.470) −5.750***
(0.498)
−4.767***
(0.443) Random effects
Variance of intercept 0.692***
(0.089) 0.445***
(0.059) 0.428***
(0.057) 0.288***
(0.039)
ICC 17.38% 11.91% 11.51% 8.05%
Dummies of year No No No Yes
Dummies of geographic
region No No No Yes
Model fit statistics
Number of observations 248,824 248,824 248,824 248,824 Number of countries
[country-year
groups] 45 [139] 45 [139] 45 [139] 45 [139]
Deviance 84,310.14 78,421.28 78,415.61 78,365.59 Deviance difference [Diff. in
degrees of freedom] 5888.86 [17] 5.67 [2] 50.02 [8]
LR test of deviance diff.
*** ***
Standard errors are in parentheses.
Level of statistical significance: ***p ≤ 0.001; **p ≤ 0.01; *p ≤ 0.005; †p ≤ 0.10.
• Conclusions
In conclusion, public authorities are required to promote innovative new ventures, by encouraging entrepreneurship especially among individuals with higher levels of formal education and greater previous experience. Furthermore it should identify explicitly what exactly those specific instruments and initiatives are and how public policy will be used to foster innovative entrepreneurship. However, future researches are needed to analyze the transparency and security to operate, the freedom to run the business effectively, supporting funding for risky activities and access to relevant information in order to achieve economic freedom when talk about innovative entrepreneurship initiatives (Autio et al., 2014; Baumol, 2010).
By providing necessary resources in favorable context like this, innovative entrepreneurship would increases positively.
Although innovative entrepreneurs account for a small portion of the entire population of business founders, they have an extraordinary economic impact, as they develop new technologies, create new jobs and enhance the revitalization capacity of territories.
This research makes a modest contribution to the entrepreneurship, innovation and development literature in at least two ways. Firstly, it deals empirically with a little examined issue, namely the innovation-driven behavior of entrepreneurs and innovative entrepreneurship in developing economies. The majority of studies on innovation have considered the behavior of both large and small companies in advanced economies.
Also future research must focus on more objectives measures that can be complemented to use country level variable and other ones such as R&D investment. On the other hands, using more qualitative interviews with entrepreneurs or elaborating measures of human capital to get an adequate and right profile that would increase innovation (Fuentelsaz et al, 2018).
Finally, to initiate a good innovative entrepreneurship policy, it is wisely to differentiate between the needs of new entrepreneurial firms and other small-medium enterprises (SMEs) because not all entrepreneurial entities keep the same size. Therefore, innovative entrepreneurship could achieve economic growth based on three main policy options: increasing entrepreneurship in general, increasing the frequency of high-growth firms, increasing innovation and R&D in SMEs) (Dahlstrand & Stevenson, 2010).
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