Keywords

Entrepreneurial Intentions, Perceived Opportunity, Perceived Capability, Government Support, GEM.

Introduction

What influences Entrepreneurial Intentions? Intentions have been recognized as a significant factor in the managerial literature (Fini et al., 2012). Previous contributions demonstrate that

“Intentions can be used to foresee both individual behavior (Ajzen, 1991) and organizational results, such as development and improvement (Mitchel, 1981).”

Therefore, the ability to comprehend and to predict intentions becomes a strong point of interest for policymakers, organizational leaders and entrepreneurs themselves.

Intention models are placed under the Bandura’s Social Cognitive Theory (SCT), the focal principle of SCT is that people can impact their own activities (Ratten & Ratten, 2007). It proposes a system for predicting, comprehending and changing human conduct (Davis, 2006). Within this concept, intention models contribute in predicting action. Intentions represent people’s inspiration to follow up on a cognizant plan or choices (Conner & Armitage, 1998). EI is subsequently a person`s inspiration to make a cognizant arrangement to set up a business. Thompson (2009) defines EI as the self-acknowledged conviction by an individual that they intend to set up a business and deliberately plan to do as such sooner or later.

In the entrepreneurship field, numerous researchers have concentrated on intentions (Bird, 1988; Krueger et al., 2000). Intentions have been proven to be the best indicators of individual practices especially when the behaviour is uncommon, difficult to observe, or includes erratic time lags (Krueger & Brazeal, 1994). The foundation of new ventures and the creation of new incentives in existing ones, which have been distinguished by Bird (1988) as the two results of EI, are great examples of such practices/behaviours.

The existing literature distinguishes individual domains, such as motivation, personality and previous experience, as well as relevant factors, such as social setting, markets and financial aspects, as the two measurements in charge of the development of EI (Bird, 1988). Concerning the individual domain (Zhao et al., 2005) demonstrate that

“Psychological qualities (e.g., entrepreneurial self-efficacy and risk-taking affinity), together with created aptitudes and capacities, impact EI.”

Different researchers, investigating the contextual domain, demonstrate that environmental impacts, such as industry openings and market heterogeneity (Morris & Lewis, 1995) and environmental support, such as financial, infrastructural and political support (Luthje & Franke, 2003) affect EI.

It is evident that most of what is considered to be entrepreneurial activity can be categorized as intentions; thus, they are planned behaviours. The emphasis on business plans in almost every practical and/or academic treatment on venturing into a new business is clear evidence. There exist catalysts, such as downsizing or the possibility of retrenchment, within an organization that spur individuals into entrepreneurial acts, in most cases they are signs of the long-time existence of desires and interests to build one’s own business.

With the emergence of new organizations over time, the pre-organizational occurrences, such as the decision to venture into entrepreneurship as a career, are not only important but interesting (Bird, 1988; Katz & Gartner, 1988). Therefore, we can attach intentions to the emergence of organizations; however, the timings of their beginnings may be relatively unplanned, they can be the result of opportunities that arise abruptly. Intention is the best predictor of planned behaviour; we can easily predict planned behaviour through observation of intentions towards that specific behaviour-not just personality, attitudes, demographics, or beliefs (Bagozzi et al., 1989).

In its least difficult frame, intentions can be used to predict behaviour; on the other hand, certain states of mind anticipate aim. Intentions subsequently fill in as a course to better comprehend an act itself (Ajzen, 1985: 1991). In that capacity, intentions fill in as critical interceding factors between the demonstration of beginning a business venture and potential exogenous impacts. Aims toward conduct are completely basic to understanding different forerunners. These incorporate situational convictions, consequent arbitrators, including the apparent accessibility of basic assets and the final outcomes, including the start of a new business venture.

To comprehend the outcomes of intentions, especially in terms of activities, we must first comprehend their precursors. Much of entrepreneurship is intentional, thus, the utilization of thoroughly considered and tested intention models ought to give decent methods for analysing the antecedents to new business ventures. Acknowledging that beginning a business is an intentional act holds generous ramifications for research. If reaction models cannot completely display deliberate practices, then we require testable, hypothesis-driven process models of entrepreneurial comprehensions that emphasize aims and their perceptual bases (Bird, 1988; Katz & Gartner, 1988; Shaver & Scott, 1992). At the point when behaviour is uncommon or hard to observe (Ajzen, 1991), intentions offer basic bits of knowledge into hidden procedures, for example, opportunity acknowledgment.

Empirically, behaviour is frequently just feebly anticipated by dispositions alone or by exogenous components that are either situational (for instance, informational cues or employment status) or individual (for instance, personality or demographic characteristics). Thus, anticipating entrepreneurial exercises by modelling/displaying only exogenous figures regularly brings disappointingly little logical power. Keep in mind, exogenous impacts for the most part influence intentions and behaviours indirectly, through mentality changes (Ajzen, 1991). Along these lines, intention models offer a chance to expand our capacity to clarify and clearly predict entrepreneurial activities.

Forces following up on a potential behaviour do so by implications affecting goals through certain key states of mind (attitudes). Exogenous factors impact attitude and may likewise moderate the connections amongst intentions and behaviours. For instance, exogenous variables may serve to hinder one from understanding the intention to venture into entrepreneurship. Intentions and their fundamental attitudes are perception based, which ought to mean they are discernible. Appropriately, they will differ across people and circumstances. On the other hand, exogenous individual or circumstance factors have a more indirect impact and, in this manner, are just feebly prescient of entrepreneurial activity. The predictive force of intentions is much more grounded for more molar behaviour chains, catching long-run inclinations by wiping out variations in circumstances over time. For example, the intent to go to church predicts annual participation much better than such intent predicts participation in any one week, which might be influenced by outside situational components like floods, illnesses, or other unforeseen incidences (Epstein, 1979).

Intentions are likewise unprejudiced indicators of action (Bagozzi et al., 1989), even where time slacks exist. Therefore, a solid expectation to begin a business ought to bring about a possible endeavour, regardless of the possibility that prompts the conditions; for example, marriage, completing school, or getting lucrative or well-compensated work, may influence a long postponement. The moderately molar domain of entrepreneurship ought to be very manageable to the fruitful utilization of intention-based models. Intentions may clarify why it seems less difficult to recognize interminable business visionaries, the individuals who make several new business pursuits in a lifetime.

Generally, quite a bit of human behaviour is arranged (planned); it is hard to imagine beginning a business where the early firm is propelled basically as an adapted reaction to a jolt. It is similarly hard not to view beginning a business as a vocation/career choice. A sensible collection of past research underpins the conflict that profession choices are obviously arranged in nature, not reactions to boosts, in this way reflecting some level of subjective preparing. EI are derived from perceptions of attractiveness, feasibility and the tendency to act upon opportunities (Lee et al., 2011). EI provides a bridge that brings us closer to understanding, explaining and predicting entrepreneurial activities (Krueger et al., 2000).

Further, Krueger, Reilly & Carsrud (2000) assert that

“Any business venture is an intentional act.”

Thus, research on EI is worth delving into since intentions have great predictive power. Previous studies have shown that intentions can be used as unbiased predictors of action. Therefore, with EI, we can easily predict the growth or decline of entrepreneurship and factors that affect it.

Bird, in her work, recognizes that

“EI and entrepreneurship, in general, is affected by two factors: Individual and Contextual factors.”

Individual factors include personal characteristics, abilities and individual’s prior experience, while the contextual factors are social, political and economic (Bird, 1988). EI plays a major role within organizations. Individual’s character and values have been proven to affect organizational strategies (Bird, 1988). Building on this, it can be accepted that citizen’s individual behaviours, character and values can affect the overall EI and other activities, of an entire nation. Thus, studying EI at a national level is viable and a clear picture of the current and future entrepreneurial position of a nation can be clearly depicted through EI.

This piece of work extends the literature of EI by studying and testing variables contained in the GEM datasets hypothesized to be affecting entrepreneurial activities; the study gives a picture of EI globally as well as a comparison of various regions, i.e., Asia, South America and Europe.

GEM data is used to identify the EI patterns of countries across the globe, specifically identifying what affects it. What do individuals respond to before venturing into the entrepreneurship world? Do external factors such as government support & policy influence EI? GEM develops an annual measurement of entrepreneurial activities (EI included) in different countries and the factors that affect them and their link with economic growth (Mok, 2005). GEM data is internationally executed and centrally coordinated. It provides high quality, comprehensive and highly trusted data. It looks at two elements of entrepreneurship: entrepreneurial attitude & behaviour and the national context of entrepreneurship. This paper utilizes elements from both contexts in finding the trend of EI. This work will follow Bird’s suggestion that

“Entrepreneurial activities should and must include both the individual and contextual domain if meaningful results are to be achieved.”

Variables from both contextual and individual levels will be included in the model and, therefore, this piece will go a long way in producing invaluable results, suggestions and considerations for many factors affecting EI.

This paper has been organized into the following sections. After this introduction, the second part presents the literature review: the relevant theories referred to by other researchers and scholars. Third, are the hypotheses and intention model followed by the data and variables? The fifth section will be the presentation of results and, finally, conclusions.

Theoretical Background

Social psychology offers strong models of behavioural intentions with significant demonstrated predictive value for several behaviours. Such models offer sound hypothetical structures that particularly delineate the procedures underlying intentional acts. Metaexaminations (Kim & Hunter, 1993) empirically demonstrate that

“Intentions effectively predict behaviour and attitude (states of mind) effectively predicts intentions.”

Over an extensive variety of studies identifying a wide range of behaviours and intentions to take part in those behaviour’s, attitudes explain more than half of the changes in intentions.

Intentions clarify at least 30% of the changes in behaviour. Clarifying 30% of the difference in behaviour analyses positively to the 10% level and is ordinarily clarified straightforwardly by quality measures or attitudes (Ajzen, 1987). More distal marvels, for example, profession decisions will probably bring about a smaller effect. Still, intentions remain a huge, unbiased indicator of career choice (Lent et al., 1994).

The Theory of Reasoned Action

The theory of reasoned action (TRA) (Fishbein & Ajzen, 1975; Ajzen & Fishbein, 1980) generally asserts that

“The central cause of an action/behaviour is the intention, more specifically behavioural intention, that is, what one anticipates doing or not doing.”

The intention, on the other hand, is dictated by attitude (evaluation of the action/behaviour) and a subjective norm (evaluation of other available options) (Trafimow, 2009).

TRA is comprised of three noteworthy constructs: (1) the behavioural intention that relies upon (2) subjective standards (nor) (3) dispositions (attitudes). The more grounded the inspirational dispositions toward conduct are and the more grounded the social standards toward conduct are, the more grounded the intention is. The higher the intention, the higher the chances of an individual executing a specified behaviour shows in Figure 1.

Behavioural Intentions measures the quality of the intention to execute a predefined activity. Subjective standards depict the weight by associates or companions to conform to standards. If, for instance, entrepreneurship is viewed as excessively unsafe by guardians and companions, an individual will be more averse to entrepreneurial conduct. Attitudes comprise the assumptions about the outcomes of performing a predefined activity.

Behavioural beliefs are assumed to be a principle impact on one’s attitude towards performing a certain action/behaviour; normative beliefs on the other hand impact one’s subjective norm towards performing an action/behaviour (Madden et al., 1992). In summary, according to the theory of reasoned action, the immediate precursor of behaviour is intention which is a function of information and beliefs (Madden et al., 1992).

The Theory of Planned Behaviour (TPB)

The TPB (Ajzen, 1985) extends the limit of unadulterated volitional control indicated by the TRA. This is accomplished by including convictions with respect to the ownership of essential assets and opportunities to proceed with a given conduct. The more assets and opportunities people think they have, the more prominent their apparent behavioural control over their conduct ought to be. As an account of behavioural and standardized convictions, it is additionally conceivable to isolate these convictions and regard them as halfway autonomous determinants of conduct (behaviour) (Madden et al., 1992).

Marketing researchers, as well as social psychologists, have had significant achievements utilizing intention-based models in pragmatic applications and fundamental research. Such reliable, vigorous and replicable ideal models have been generally applied in practical circumstances, such as career/profession preferences, weight loss and coupon use (Ajzen, 1987; Kim & Hunter, 1993). Various formative intention models and consistent advances in displaying intention predecessors have brought about Ajzen's Theory of Planned Behaviour (TPB), presently the best TBD shows in Figure 2.

TPB distinguishes three attitudinal predecessors of expectation. Two mirror the apparent attractive quality of playing out behaviour: individual attitude toward results of the behaviour and perceived social standards/norms. The third, perceived behavioural control reflects observations that the behaviour is individually controllable. Perceived behavioural control reflects the apparent feasibility of playing out behaviour and is accordingly identified with a view of situational competence (self-efficacy). TPB additionally determines the forerunners of each of these attitudes.

Shapero’s Model

Shapero’s model of the Entrepreneurial Event (SEE) is another important theory in the entrepreneurship intentions world. It has been referred to as an implicit intention model specific to the entrepreneurship domain (Krueger et al., 2000). In this model, the intention to venture into business is said to be derived from the propensity to act upon opportunities, perceptions of its attractiveness and lastly, likelihood of its success (Krueger et al., 2000). The model assumes that “human behaviour is guided by inertia until an outside force interrupts that inertia.” The interruption is most often negative such as an abrupt job termination; however, sometimes the interruption of the inertia can be due to positive events such winning a lottery (Shapero & Sokol, 1982). The interruptions trigger a change in behaviour and the victim is always forced to make decisions that seek the best opportunity available. (Krueger et al., 2000)

According to Shapero, behaviour depends on credibility and propensity to act. Credibility demands behaviour to be both feasible and desirable. Thus, entrepreneurial events require both in order for the desired potentiality, to start a business, to be achieved. In summary, the entrepreneurial event is viewed as a result of cultural, social and personal factors

Further, Shapero defines perceived desirability as the attractiveness of starting a business, i.e., both intra and extra personal impacts and defines perceived feasibility as the level to which one feels capable of venturing into the entrepreneurship world. This is achieved empirically by using his proposed testable, eight-item, inventory questions that aim at various aspects of perceived feasibility and desirability. On propensity to act, Shapero conceptualized shows in Figure 3.

“People act on decisions based on their own personal disposition and thus reflect the aspects of intentions.”

Acting on an opportunity is highly dependent on control perceptions; the urge to gain control as a result of taking action.

Hypothesis Development

The intention models prevail in recent entrepreneurship studies; however, different scholars point out the relevance of adding more variables into the models (Liñán et al., 2011). Bird’s suggestion is that

“Both contextual and personal factors must be used in deriving a causality of the influences of EI.”

Since, when contextual or individual domains are studied individually, they are poor predictors of Intentions (Fini et al., 2009); this paper seeks to identify its variables with this key suggestion put into consideration.

It may be argued that it is difficult to measure some variables at the national level at which this paper is testing EI, however, available GEM data can be easily connected to the domains mentioned by the theories above. In Shapero’s model, perceived desirability was defined as the attractiveness of entrepreneurship. Attractiveness is caused by contextual factors, which according to Bird are social, political and economic (Bird, 1988).

The business environment has also been cited by scholars as an influence on entrepreneurial activities. Government policies and local context (such as the availability financial investors, infrastructure and other externalities) influence EI (Morris & Lewis, 1995). The role of government in entrepreneurship is huge. It intervenes with mechanisms such as tax policies, funding schemes and other support mechanisms, all of which aim to promote entrepreneurship (Lerner, 1999). Furthermore, environmental factors such as Government support and policy, physical assets and other entrepreneurial support programs, including but not limited to training opportunities and competition, have been acknowledged to impact EI and entrepreneurship. Thus:

H1 GSP positively impacts EI.

Several studies have been done with respect to demographics, especially gender and age and their influence on EI. Reynolds et al. (2002) show that men in the US are twice as likely to start a business as women. Further, other research reveals different levels of interest between boys and girls towards entrepreneurial careers (Walstad & Kourilsky, 1998).

Scholars show that “personal traits, such as passion, optimism, tenacity and over confidence, impact EI.” Although Gartner, points out that “these variables have resulted in little explanatory value, they have consistently been included in various models (Fini et al., 2009)”. There are many psychological characteristics that have been anticipated to influence EI. For example, the high need for achievement, risk taking propensity, locus control and self-efficacy. Although all were expected to predict EI, only two, risk-taking propensity and self-efficacy have consistently predicted EI shows in Figure 4.

According to Conner & Sparks (2005), self-efficacy is concerned with individual’s beliefs in their abilities to do a specific task to attain the desired goal. This includes goals, opportunities and perceived impediments. We link two variables from the GEM; Perceived opportunities and Perceived capabilities, which measure self-efficacy. We include both in our model and test them independently. Thus:

H2 PO positively impacts EI.

H3 PC positively impacts EI.

Data and Variables

Data

We utilized GEM data. Owing to information availability the period of study is from 2007 to 2015 and the data is reported annually for all countries. Table 1 depicts the list of countries by grouping. The descriptive statistics of the variables considered in this study can be seen in Table 2.

Table 1 List of Countries by Grouping
Europe	South America	Asia
Belgium	Argentina	China
France	Brazil	South Korea
Croatia	Chile	Japan
Netherlands	Peru	Malaysia
Norway	Columbia	Hong Kong
Sweden	Ecuador
Finland	Uruguay
Ireland
Italy
UK
Greece

Table 2 Descriptive Statistics
Variables	Obs	Mean	Std. Dev.	Min	Max
EI	248	17.04	13.33	2.23	60.49
GDP	270	6.14	1.61	2.76	9.80
P	270	17.04	1.51	14.49	21.04
GSP	221	2.76	0.766	1.33	6.48
PO	248	38.23	16.86	2.85	71.49
PC	248	46.58	14.64	8.65	77.86

The dependent variable, EI, though widely discussed in the literature available, has seldom been studied in a wider scope. We use data from 30 countries from across the globe. Our analysis also compares the dependent variable influences on the different continents. Due to missing values, the total numbers of observations by variables vary. The values of the control variables GDP and Population (P) are the natural logs.

Definition of Variables

EI is described as the rate of the population, ages 18-64, (people involved in any phase of entrepreneurial action excluded) who are inactive business visionaries and who intend to start a business inside three years. Government Support & Policy is the degree to which public policies boost entrepreneurship (entrepreneurship as an important economic issue).

Perceived Opportunity is the rate of the population, ages 18-64, which see great chances to begin firms in the zones where they live and Perceived Capability as the rate of the population, ages 18-64, which trust that they have the required abilities and knowledge to begin a business.

Econometric Methods

This study uses panel data analysis. It takes into account the transversal information and a time period of nine years to check whether the variables of interest have an effect on EI. We run and exhibit the results of both fixed (equation 1) and random effect (equation 2) models. However, having run the Hausman test, we apply the random effect for the general model and Europe and the fixed effect for South America and Asia.

The models:

Where, EI=Entrepreneurship Intentions

GSP=Government Support & Policy

PO=Perceived Opportunity

PC=Perceived Capability

GDP=Gross Domestic Product

P=Population

α_i=Unknown intercept for each entity

u_it=Between entity error

ε_it=Error term and within entity error for equation 1 and 2 respectively

Results

To give a clear picture of the effects of the variables of interests on EI, we begin with our general conceptual model for the group of thirty countries, for which, per the Hausman test, the appropriate method is random effects. As seen in Table 3, all our variables of interest; that is, GSP, PO and PC indicate a significant effect on EI.

Table 3 Entrepreneurial Intention Model
	(1) OLS	(2) Random Effect	(3) Fixed Effect
GDP	-6.25*** (0.63)	-3.44*** (1.28)	4.38** (1.98)
P	6.48*** (0.59)	3.82*** (1.44)	-3.27 (12.10)
GSP	2.12*** (0.68)	1.31** (0.53)	1.27** (0.53)
PO	0.17*** (0.03)	0.18*** (0.04)	0.14*** (0.05)
PC	0.44*** (0.0431)	0.21*** (0.0640)	0.08 (0.07)
CONSTANT	-88.01*** (7.72)	-46.79** (19.07)	34.15 (201.40)
Observations	219	219	219
R-squared	0.690		0.165
Number of countries	30	30	30

The results of the study re-affirmed assertions by previous researchers such as (Morris & Lewis, 1995) that government participation in the promotion of entrepreneurial activities remain as significant in recent times as it was in the era of Schumpeter (1912). Through the enactment of congenial policy prescriptions and initiatives, such incubator schemes, concessionary funding regimes and the institutionalization of start-up/small business support authorities among others, governments have positioned themselves to hugely impel entrepreneurship behaviour’s. GSP is a strong influence on EI across all our tests, i.e., our overall sample and that of various continents.

Both PO and PC indicate a significant influence on EI. PO and PC (efficacy) fit suitably within the framework of the TPB proposed by Ajzen (1988) which postulates perceived behavioural control as a determinant of intention behaviour’s. In the same vein, the findings agree with Conner and Sparks (2005) who found self-efficacy as a variable that significantly stimulates intentions.

Table 4 depicts the results for South America, as indicated above; we pay attention to the fixed effect results. GSP has a stronger and highly significant influence on EI with coefficients for GSP and PO both significant at 1% level, while PC is significant at 5%. The results for Europe, as shown in Table 5, give similar results with the only difference being the coefficient of GSP. Asia, as depicted in Table 6, shows a similar trend to that of South America. Both GSP and PO are positive and significant at the 1% level and PC at the 5 % level.

Table 4 Entrepreneurial Intention Model-South America
	(1) OLS	(2) Random Effect	(3) Fixed Effect
GDP	-0.02* (0.01)	0.01 (0.01)	0.01 (0.01)
P	1.78e-07** (7.02e-08)	5.34e-09 (6.99e-08)	-9.52e-09 (3.41e-07)
GSP	12.32*** (2.44)	6.96*** (1.46)	6.67*** (1.50)
PO	0.31*** (0.11)	0.40*** (0.11)	0.40*** (0.11)
PC	0.247 (0.19)	0.353** (0.15)	0.353** (0.16)
Constant	-29.32** (12.46)	-26.43** (11.11)	-25.81 (20.52)
Observations	58	58	58
R-squared	0.581		0.655
Number of countries		7	7

Table 5 Entrepreneurial Intention Model-Europe
	(1) OLS	(2) Random Effect	(3) Fixed Effect
GDP	-3.92*** (0.99)	-3.01** (1.41)	-1.63 (2.59)
P	4.25*** (1.00)	3.99** (1.60)	-3.92 (8.26)
GSP	0.69* (0.38)	1.02** (0.40)	1.09** (0.43)
PO	0.05*** (0.02)	0.14*** (0.03)	0.17*** (0.04)
PC	0.05 (0.06)	0.10** (0.05)	0.08* (0.05)
Constant	-41.96*** (9.34)	-49.72*** (19.16)	69.92 (136.4)
Observations	82	82	82
R-squared	0.372		0.500
Number of countries		11	11

Table 6 Entrepreneurial Intention Model-Asia
	(1) OLS	(2) Random Effect	(3) Fixed Effect
GDP	-1.94 (3.39)	-1.94 (3.07)	-8.77*** (2.42)
P	5.01* (2.772)	5.01** (2.476)	4.91*** (17.17)
GSP	5.91*** (1.92)	5.91*** (1.64)	4.19*** (0.95)
PO	-0.01 (0.110)	-0.01 (0.11)	0.45*** (0.10)
PC	0.27* (0.14)	-0.005 (0.11)	0.27** (0.08)
CONSTANT	-91.43*** (23.84)	-91.43*** (20.37)	-965.50*** (307.5)
Observations	23	23	23
R-squared	0.835		0.842
Number of countries		4	4
Country Control			YES
Year Control			YES

In summary, the coefficients associated with the variables of interest have positive signs and are statistically significant. The GSP has shown pronounced significance (1%) in the Asian and South American regions relative to Europe (5%). In the same vein, the coefficients for both South America and Asia are higher compared to that of Europe. This disparity could be attributed to the differences in economic progression, which defines the huge, influential roles of governments in different regions.

The coefficients of both PO and PC are more or less the same across the three continents. However, PO for South America and Europe is significantly stronger (1%) compared to that of Asia (5%), while PC for South America and Europe is at the same significance level (5%) with Asia stronger at (1%).

Looking at the control variables considered in this study, GDP indicates a significant negative influence on EI, while Population indicates a significant positive effect in all our analyses except for South America, which shows no significant results for either of the variables. A closer look at the Population figures shows a steady growth in most of countries used in this study. According to the existing literature, an increasing population provides chances for new economic activities since new and greater customer markets rise as a result of the increasing population; the demand for entrepreneurship increases (Wennekers et al., 2005). Thus, our results affirm the push factor effect of an increasing population on EI, which it has been argued, leads to the actual entrepreneurial act. Our results for GDP are consistent with the findings of Griffiths, et al., (2009). They found out that as GDP per capita rises it lowers EI. They argue that as societies economic wellbeing gets better, more career options are created.

Conclusion

In this study, drawing from the existing EI theories and literature, we have proposed and tried a dimensional model of EI and its forerunners. We demonstrate that

“EI is primarily described and clarified by PO (characterized as psychological), PC (individual skills and GSP (environmental influence.”

The positive impact of both psychological attributes and individual skills on EI has been featured in previous studies (Bird, 1988). Additionally, environmental influences impact people's control over entrepreneurial conduct. This bolsters previous studies findings. The study focused on exploring the determinants of EI by employing individual and contextual domain variables. The study found government support to be a strong direct determinant of entrepreneurial behaviour’s, i.e., they influence EI to a large extent. Similarly, on the personal domain, beliefs in the existence of opportunities and personal capabilities were also seen to influence intentions.

These findings define new responsibility and commitment frontiers in the widely known concept of government-industry-academia linkages aimed at building congenial pathways for widespread and sustained entrepreneurial growth. Based on our results, we can easily conclude that

“The influences of EI are the same in Europe, Asia and South America”.

However, our research is limited to the general effects of government support; this opens ways for studies on specific government policies such as taxes and specific government support for entrepreneurship. Further, Acs (2006) asserts that “policies and conditions ideal to EI or entrepreneurship itself in one nation (or region) may not be positive or successful in another.” Strategy creators benefit from understanding that administration activities will influence business development just by being seen in a way that impacts mentality or intentions. Government authorities, legislators and financiers should likewise observe entrepreneurial action as desirable and achievable (Shapero & Sokol, 1982). At long last, this ought to all be finished with an eye toward empowering individuals and new organizations.

In summary, intention is key concepts utilized in psychological sciences to model and study the drivers of human behaviour. When connected to entrepreneurship and, given that a robust model can be developed around the related intentions, this construct gives an extremely helpful tool to policymakers to enhance their approaches and activities towards empowering or disheartening that activity. This not only applies to public policies but also to the administration of any kind of organization. The intention-based approach concentrates on factors that influence potential entrepreneurs to transform into real entrepreneurs, with the positive effect of producing more important data for policy makers.

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