The effect of entrepreneurship ecosystem on the entrepreneurial skills of agriculture students: The mediating role of social intelligence and emotional intelligence (The case of Zabol University, Iran).

The present research aimed to analyze the effects of different dimensions of the entrepreneurship ecosystem on the agricultural students' entrepreneurial skills mediated by social and emotional intelligence. The research was conducted among the agricultural students at Zabol University (N = 893) of whom 269 students were sampled by the stratified randomization technique with proportional allocation. The measurement instrument was a questionnaire whose validity was confirmed by a panel of entrepreneurship experts and its reliability was determined by calculating the ordinal coefficient theta (0.81-0.92) in a pilot study. The results showed that the indicators used to measure the research variables were consistent with the factor structure and theoretical framework of the research. The entrepreneurship ecosystem was found to have a positive and significant effect on the students' social intelligence and emotional intelligence. Also, the students' entrepreneurial skills were found to be influenced by the entrepreneurship ecosystem, social intelligence, and emotional intelligence positively and significantly. It can be concluded that higher social and emotional intelligence in students and the improvement of the entrepreneurship ecosystem can provide the conditions for the students to acquire and reinforce entrepreneurial skills.

Introduction

Despite extensive endeavors on the development of entrepreneurship in recent years, entrepreneurship and small and medium-sized enterprises (SMEs) have not developed adequately and sustainably in Iran. Many entrepreneurs in Iran are faced with such barriers as changing policies of the government, the arbitrary adoption of policies, unhealthy business environment, instability of governmental managers and employees, inappropriate and non-supportive regulations, lack of environmental confidence, lack of commercial infrastructure, inappropriate market, the high interest rate of bank loans, and so on, which have created an adverse business environment for them (Fallah Haghighi et al., 2018a; Mohammadi Elyasi & Notash, 2012; Moradi et al., 2012). Consequently, most students and graduates are reluctant to launch their own businesses, or those who engage in entrepreneurial activities stop or abandon them incomplete. On the other hand, to move towards development, society and universities should lay the required ground for the students to be equipped with entrepreneurial knowledge and skills so that the students can use these capabilities to channelize other resources of the society towards value creation and growth and development (Ataei et al., 2020; Fallah Haghighi & Bijani, 2016; Fallah Haghighi et al., 2018b). The World Bank reported in 2015 that the difficulty of business initiation in Iran has been aggravated in recent years. In this respect, it was ranked 120 among 191 countries by the Central Bank of Iran in 2017, downgraded by two ranks versus the preceding year. Given the extensive progress of other nations, Iran’s rank decreased from 167 to 170 in transboundary trade in 2017. The Global Competitiveness Report 2016/2017 published by the Global Economic Forum ranks Iran 124th in ease of access to loans and 110th in venture capital availability among 138 countries (Mansuri et al., 2017). Most countries have concluded that they need to foster a competitive and dynamic entrepreneurial economy, but they have mostly failed to proceed towards such an economy due to the lack of an entrepreneurial ecosystem, unclear policies on entrepreneurship, and lack of knowledge as to how to respond to the emergence of entrepreneurial society (Tsai & Kuo, 2011). Since the sectoral policies have proven to be inefficient in developing entrepreneurship, it seems necessary to have an ecosystem-oriented approach with an intermingled relationship of its elements (Nadgrodkiewicz, 2013). On the other hand, researchers state that the lack of entrepreneurial skills is a major challenge of entrepreneurship development (Hosseininia et al., 2017; Parvin et al., 2012) whereas disproportion of educational content with the needs of the labor market, inattention to entrepreneurship training, and poor entrepreneurial skills of college students are among the key reasons for their unemployment after graduation (Hosseini & Yaqoubi, 2005; Khayri et al., 2011). Also, some scholars argue that the two psychological components of social intelligence and emotional intelligence contribute to the entrepreneurial process (Agha Ahmadi et al., 2012; Bar-On, 1997; Pradhan & Nath, 2012; Tabatabaei et al., 2015). People with higher social intelligence and emotional intelligence are more likely to be an entrepreneur because they can cope with negative and vexing emotions and the stress caused by the work and environment during launching and managing a new business (Kong & Zhao, 2013). These people usually analyze and interpret issues positively, identify opportunities, and are very optimistic about the prospect and viability of the new business (Baron, 2008). Social intelligence is connected with entrepreneurial behavior and activities as it represents the ability to compromise with others, the consciousness of social issues, and the capability of inter-personal communications and cooperation (Moradi-Pordanjani & Sadeghi-Dehcheshmeh, 2014).

Accordingly, the present research aimed to shed light on the impact of the entrepreneurial ecosystem on the entrepreneurship skills of agriculture students mediated by social and emotional intelligence. The objectives were to expand previous research, study what factors have more impact on students' entrepreneurial skills, and uncover unexplored interactions between the entrepreneurship ecosystem and emotional and social intelligence. Moreover, the study aimed to investigate students' perceptions of the entrepreneurship system, and their impact on their self-reported entrepreneurial skills.

Entrepreneurial ecosystem

Entrepreneurship development requires a network of different elements – this network is called the entrepreneurship ecosystem. Entrepreneurship ecosystem refers to elements, individuals, organizations, or institutions that can stimulate individuals or inhibit them from becoming an entrepreneur. This ecosystem encompasses a plethora of components categorized in six primary domains including market, policy, financial capital, culture, supports, and human capital (Isenberg, 2011). The entrepreneurship ecosystem emerged as a basis for designing entrepreneurship policies, especially for technology-oriented start-ups. Research on the entrepreneurship ecosystem focuses on the close relationships of people, government, and relevant agencies and other effective factors for the support of entrepreneurial activities defined in a certain geographical region. This ecosystem may be formed at the regional, national, or even university level (Malecki, 2011; Mason & Brown, 2014; Theodotou, 2012). Isenberg (2011) suggests that an ecosystem plays a critical role in economic development. Accordingly, universities are linked with the entrepreneurship ecosystem at two levels. The first level is their presence as a part of the regional entrepreneurship ecosystem and the second is the entrepreneurship ecosystem inside the university itself. Concerning the entrepreneurship ecosystem inside a university, factors like desirable educational climate, management and leadership style, and appropriate infrastructure including training courses can be effective in fostering potentially entrepreneur students (Miller & Acs, 2017). Entrepreneurship ecosystems are quickly turning into a general instrument in the studies on entrepreneurship geography. Ecosystems are a group of concentrated cultural perspectives, social networks, financial support, universities, and active economic policies that create a supportive environment for innovation-based ventures.

In a study on the content of the policies in Iran and its consistency with the dimension of entrepreneurship ecosystems, Ghambarali et al. (2016) reveal that the national macro policies, e.g. the Constitution, the 1404 Perspective Document and its policies, and the policies in Development Action Plans, are supportive of entrepreneurship. The trend of considering entrepreneurship has been almost ascending in the 2nd and 4th action plans and descending in the 3rd and 5th plans, implying the fading attention to entrepreneurship ecosystem dimensions in the mindset of policymakers. Entezari (2018) shows that the free economy and effective political environment are the key components of an innovative entrepreneurship ecosystem in Iran and they constitute the basis for the formation of the other components of the ecosystem. Based on the studies of Regele and Neck (2012) and Suresh and Ramraj (2012), an entrepreneurship ecosystem is, by nature, the result of the interaction between people, roles, organizations, infrastructure, and events that create an environment apt for the enhancement of entrepreneurial activities. This ecosystem is composed of components like access to capital, entrepreneurship training, the presence of government-oriented entrepreneurship plans, research and development, commercial and legal policies and infrastructure, and easy regulations to enter a job. It plays a role in the creation and development of entrepreneurship through ethical, financial, technological, market, social, network, governmental, and environmental supports.

Entrepreneurial skills

People at all levels need entrepreneurial skills to enable them to improve their ability to face current changes in life and future uncertainties (Sajadi Qeidari et al., 2017; Yaghoubi Farani et al., 2019) because entrepreneurial skills are necessary for developing innovations in production and service and extending solutions for need formation in the market (Imani Jajromi et al., 2012). Mousivand et al. (2017), Tripathy (2019), Reyad et al. (2019), Ismail et al. (2019), and Woodcock et al. (2019) suggest that the success of entrepreneurs strongly depends on their business skills and entrepreneurial educations. In this respect, they assert that universities must develop different plans to promote entrepreneurship among their graduates because the young generation needs to know how to act in complicated environments with changing regulations in the labor market.

According to Khosravi et al. (2014) and Mardanshahi et al. (2015), the status of entrepreneurial skills among the students in Iran is unsatisfactory and lower than the average. Another research on the entrepreneurial skills for the labor market in the higher educational content shows that the present programs in universities teach entrepreneurial skills to the students at an average to a low level (Salehi Emran & Yaghmouri, 2010). The prerequisite for entrepreneurship is to acquire different skills such as the ability of planning, the appropriate exploitation of opportunities, negotiation skills, marketing, motivating, and so on (De Araujo et al., 2020). Safari and Samiazadeh (2012) conclude that students need to acquire such skills as the principle of business, business design, communication, management and organization, legal and commercial regulations, accounting, business establishment, marketing, strategic management, and international business. Zarei Sebhati et al. (2016) and Dizji and Zini (2018) also studied entrepreneurial skills in the context of personal, managerial, and technical skills. Kashani et al. (2017) enumerated the skills of business launching, working with the Internet and computer, familiarity with labor and commercial rules and regulations, team-working, planning and goal-setting, opportunity recognition, and financial affairs. Likewise, Hosseininia et al. (2017) listed six entrepreneurial skills to include the knowledge of the business plan, communication, familiarity with legal and commercial rules and regulations, planning, team-building, and marketing.

Social intelligence

Social intelligence refers to the ability to understand and manage others and display wise behavior in interaction with others (Frankovsky & Birknerová, 2014). Some components of social intelligence include perceptional, analytical-cognitive, and behavioral components (Björkqvist, 2000). This can be perceived as the ability to understand and rein one’s emotions and feelings to help intellectual activities, decision-making, and communication. Based on the findings of Alizade Aghdam et al. (2016), people with higher emotional intelligence know how to control and direct their own emotions and feelings and those of others.

Agha Ahmadi et al. (2012) found a positive and significant relationship between the students’ entrepreneurial skill and their social intelligence so that social intelligence and its components could predict their entrepreneurial skills. In a study on the relationship of social intelligence with entrepreneurial skills and creativity of students, Tabatabaei et al. (2015) concluded that social intelligence and its dimensions were related to the students’ entrepreneurial skills positively and significantly. Moradi Pardanjani et al. (2015), who addressed the effect of social intelligence on social entrepreneurship, concluded that people’s possession of the dimensions and components of social intelligence and social entrepreneurship was higher than the average and there was a significant relationship between all components of social intelligence and social entrepreneurship. Alizade Aghdam et al. (2016) revealed a significant relationship between students’ social intelligence and their entrepreneurial capability.

Emotional intelligence

In addition to social intelligence, an inherent and psychological attribute that affects the entrepreneurship process is emotional intelligence (Bar-On, 1997), which reflects the ability to recognize a person’ own emotions and those of others to stimulate and manage one’s emotions and relations (Bardzil & Slaski, 2003). Extensive studies have revealed a positive relationship between emotional intelligence and entrepreneurial attitude (Pradhan & Nath, 2012; Zampetakis et al., 2012). Emotional intelligence has also been found to influence the entrepreneurship process (Ahmetoglu et al., 2011; FakhrEldin, 2017; Mortan et al., 2014). Zampetakis et al. (2012) found a positive relationship between emotional intelligence and attitude towards entrepreneurship and an indirect relationship between emotional intelligence and entrepreneurial intention of the students. Karimi (2016) and Ahmadi Kafeshani and Nazemi (2014) found that the dimensions of emotional intelligence were related to the attitude, subjective norms, and entrepreneurial intention of the students. Rezaei (2017) reported a significant relationship between emotional intelligence and the entrepreneurial abilities of the students. In a study on the emotional intelligence of entrepreneurs, Rhee and White (2007) revealed that successful entrepreneurs had a high level of such factors as team-working, self-reliance, stress management, and self-confidence, which should be considered in developing emotional intelligence. In a study entitled ‘Emotional intelligence: The secret of successful entrepreneurs’, Boren (2010) concluded that entrepreneurs with higher emotional intelligence would be more successful in entrepreneurship and the development of innovative businesses. In an attempt to find a relationship between emotional intelligence and successful entrepreneurs, McLaughlin (2012) detected higher emotional intelligence in entrepreneurs.

A review of the literature showed that the entrepreneurial skills of people can be enhanced by several factors. It can be said that to reinforce and develop entrepreneurial skills, an entrepreneurship ecosystem should be formed. In other words, the components of an entrepreneurship ecosystem, such as human capital, markets, finance, supports, culture, and policy, should influence people’s entrepreneurial skills. On the other hand, there are some psychological elements that can affect the development of a business and entrepreneurial skills. Previous research has proven that factors like social intelligence and emotional intelligence can affect this process – the former through social awareness, social skills, and social information process and the latter through the components of adaptability, mood, stress management, interpersonal skills, and intra-personal skills. Accordingly, the conceptual framework of the research was developed as depicted in Fig. 1.

Fig. 1

The conceptual framework of the research

Methodology

The present research is a quantitative study of the descriptive-correlational type in terms of data collection methodology and generalizability. It was conducted by the survey technique. The research population was composed of the agriculture students at Zabol University amounting to 893 people. The sample was taken by the stratified randomization technique with proportional allocation. The sample size was determined to be 269 individuals by Krejcie and Morgan’s (1970) table. For this purpose, the students were divided into 13 departments. Since the departments had different numbers of students, their sample sizes were determined in proportion to their size. The data collection instrument was a questionnaire administered and completed in face-to-face interviews. At the final stage of data collection, a total of 269 questionnaires were distributed, out of which 269 were returned (with a return rate of 100%). The interviewer informed the students before starting the survey that their answers would be anonymized. They may, at any time, withdraw their participation, including the withdrawal of any information they have provided. If they completed the interview, however, it would imply that they had consented to participate in this research and agreed to the publication of the overall results of this research with the understanding that anonymity of the interviewees would be taken into account. The dimensions of entrepreneurship ecosystem (human capital, markets, finance, supports, culture, and policy) were assessed by Isenberg’s (2011) standard questionnaire, the dimensions of emotional intelligence (adaptability, mood, stress management, inter-personal skills, and intra-personal skills) were assessed by Bar-On’s (1997) standard questionnaire, the dimensions of social intelligence (social awareness, social skills, and social information processing) were assessed by the Tromsø standard questionnaire (Silvera et al., 2001), and the students’ entrepreneurial skills were assessed by a self-designed questionnaire. The face validity of the questionnaire was confirmed by a panel of entrepreneurial experts. To check its reliability, 30 questionnaires were filled by individuals outside the statistical population in a pilot study, and the ordinal coefficient theta was calculated. The scales used in this study were adapted to students. The scales of Tromsø and Isenberg’s standard questionnaires were adapted based on the study population (students). The scales of Bar-On’s standard questionnaire were administered to similar populations. The reliability (Cronbach’s alpha) of Bar-On, Tromsø, and Isenberg’s questionnaires was calculated to be 0.85–91, 0.79–0.91, and 0.73–0.94 in their research, respectively. Tables 1, 2, 3 and 4 present the results of the ordinal coefficient theta for the research variables. The variables were measured on a five-point Likert scale. Some items of the questionnaire included I use good moods to help myself keep trying in the face of obstacles or when I feel a change in emotions, I tend to come up with new ideas (emotional intelligence scale), I can predict other people ‘s behavior or people often surprise me with the things they do (social intelligence scale), there are many opportunities for students and graduates to develop entrepreneurship or students have easy access to the networks of entrepreneurs and business owners (entrepreneurship ecosystem), I have the ability to write a business plan or I have the ability to gather capable people to form a work team (entrepreneurial skills). Data were analyzed by structural equation modeling in SPSS23 and AMOS22 software packages.

Table 1

Measurement coefficients, significance level of confirmatory factor analysis, and validity and reliability of latent traits for the entrepreneurship ecosystem

Latent variables	Observed variables	Standardized loading	AVE	CR	t-Value	R2
Policy	eco1	0.736	0.541	0.820	Fixed	0.541
	eco2	0.931			10.59	0.866
	eco3	0.589			9.24	0.346
	eco4	0.641			3.74	0.410
	eco5	Dropped			-	-
Markets	eco25	Dropped	0.587	0.808	-	-
	eco26	0.745			10.09	0.555
	eco27	0.869			10.15	0.755
	eco28	0.673			Fixed	0.452
Finance	eco6	0.554	0.433	0.750	Fixed	0.306
	eco7	0.585			7	0.342
	eco8	0.801			8.15	0.641
	eco9	0.667			7.58	0.444
Supports	eco15	0.568	0.479	0.841	3.98	0.322
	eco16	0.620			4.05	0.384
	eco17	0.691			4.13	0.477
	eco18	0.644			4.08	0.414
	eco19	0.556			3.96	0.309
	eco20	0.985			Fixed	0.970
Culture	eco10	0.560	0.300	0.682	Fixed	0.313
	eco11	0.555			6.61	0.308
	eco12	0.574			6.76	0.329
	eco13	0.525			6.36	0.275
	eco14	0.525			6.37	0.275
Human capital	eco21	0.670	0.406	0.729	7.07	0.448
	eco22	0.753			7.36	0.567
	eco23	0.571			6.49	0.326
	eco24	0.534			Fixed	0.285

Table 2

Measurement coefficients, significance level of confirmatory factor analysis, and validity and reliability of latent traits for emotional intelligence

Latent variables	Observed variables	Standardized loading	AVE	CR	t-Value	R2
Adaptability	emot1	0.803	0.559	0.752	Fixed	0.644
	emot12	0.793			2.98	0.628
	emot16	0.624			2.13	0.389
	emot22	-0.660			-3.73	0.435
	emot31	-0.765			-3.78	0.585
	emot37	-0.704			-2.04	0.495
	emot42	-0.667			-3.76	0.444
	emot46	-0.822			-3.31	0.675
	emot52	0.739			3.52	0.546
	emot57	0.826			2.45	0.682
	emot61	0.801			2.45	0.641
	emot67	0.583			1.99	0.339
	emot72	0.667			2.46	0.444
	emot76	0.627			2.46	0.393
	emot82	0.899			2.45	0.808
	emot87	0.888			3.90	0.788
	emot7	Dropped			-	-
	emot14	Dropped			-	-
Mood	emot2	0.718	0.479	0.9	Fixed	0.515
	emot9	0.460			2.95	0.211
	emot17	0.711			3.12	0.505
	emot24	0.820			3.10	0.672
	emot32	0.621			2.54	0.385
	emot39	0.689			2.04	0.474
	emot47	0.765			2.93	0.585
	emot62	0.658			2.81	0.432
	emot69	0.604			2.19	0.364
	emot77	0.806			2.39	0.649
	emot54	Dropped			-	-
	emot84	Dropped			-	-
Stress management	emot4	0.494	0.504	0.908	Fixed	0.244
	emot11	0.585			7.11	0.342
	emot19	0.715			7.90	0.511
	emot26	0.710			7.88	0.504
	emot49	0.813			6.20	0.660
	emot56	0.751			2.34	0.564
	emot64	0.622			3.91	0.386
	emot71	0.782			2.81	0.611
	emot79	0.766			3.96	0.586
	emot86	0.795			4.08	0.632
	emot34	Dropped			-	-
	emot41	Dropped			-	-
Intra-personal skills	emot8	0.467	0.478	0.934	Fixed	0.218
	emot13	0.765			6.36	0.585
	emot14	0.648			6.13	0.419
	emot23	0.643			6.12	0.413
	emot28	0.555			6.78	0.308
	emot29	0.781			6.14	0.609
	emot38	0.661			5.55	0.436
	emot43	0.746			6.66	0.556
	emot44	0.893			8.30	0.797
	emot53	0.554			4.76	0.306
	emot68	0.672			6.01	0.451
	emot73	0.659			6.83	0.434
	emot74	0.667			5.95	0.444
	emot83	0.702			6.43	0.492
	emot88	0.808			8.12	0.652
	emot89	0.723			7.72	0.522
	emot58	Dropped			-	-
	emot59	Dropped			-	-
Interpersonal skills	emot3	0.523	0.480	0.959	Fixed	0.273
	emot5	0.483			6.4	0.233
	emot6	0.873			4.03	0.762
	emot10	0.344			4.92	0.118
	emot15	0.615			7.51	0.378
	emot18	0.643			7.72	0.413
	emot20	0.739			8.33	0.546
	emot21	0.718			8.21	0.515
	emot25	0.755			8.43	0.570
	emot33	0.895			6.77	0.801
	emot35	0.724			6.37	0.524
	emot36	0.729			6.45	0.531
	emot40	0.744			6.68	0.553
	emot45	0.861			7.94	0.741
	emot48	0.759			7.92	0.576
	emot50	0.737			7.57	0.543
	emot55	0.538			2.12	0.289
	emot60	0.585			2.32	0.342
	emot63	0.773			2.64	0.597
	emot65	0.707			6.66	0.499
	emot66	0.715			6.77	0.511
	emot70	0.881			2.76	0.776
	emot75	0.794			6.45	0.630
	emot78	0.531			2.01	0.281
	emot80	0.494			3.46	0.244
	emot81	0.736			2.10	0.541
	emot90	0.414			2.16	0.171
	emot85	Dropped			-	-
	emot30	Dropped			-	-
	emot51	Dropped			-	-

Table 3

Measurement coefficients, significance level of confirmatory factor analysis, and validity and reliability of latent traits for social intelligence

Latent variables	Observed variables	Standardized loading	AVE	CR	t-Value	R2
Social awareness	socio14	0.508	0.461	0.833	Fixed	0.258
	socio15	0.767			7.89	0.588
	socio16	0.737			7.75	0.543
	socio17	0.824			8.11	0.678
	socio18	0.584			6.85	0.341
	socio19	0.599			6.96	0.358
	socio20	Dropped			-	-
Social skills	socio8	0.558	0.423	0.776	Fixed	0.311
	socio9	0.570			7.81	0.324
	socio10	0.437			6.38	0.190
	socio13	0.887			7.26	0.786
	socio21	0.711			9.06	0.505
	socio11	Dropped			-	-
	socio12	Dropped			-	-
Social information	socio7	0.798	0.463	0.853	Fixed	0.636
	socio6	0.762			13.55	0.580
	socio5	0.853			15.69	0.727
	socio4	0.515			8.53	0.265
	socio3	0.562			9.40	0.315
	socio2	0.572			9.60	0.327
	socio1	0.624			10.61	0.389

Table 4

Measurement coefficients, significance level of confirmatory factor analysis, and validity and reliability of latent traits for entrepreneurial skills

Latent variables	Observed variables	Standardized loading	AVE	CR	t-Value	R2
Knowledge of business plan	skill1	0.643	0.443	0.798	Fixed	0.413
	skill2	0.730			9.63	0.532
	skill3	0.649			8.80	0.421
	skill4	0.693			9.26	0.480
	skill5	0.609			8.36	0.370
Communication	skill6	0.637	0.354	0.729	Fixed	0.405
	skill7	0.474			6.63	0.224
	skill8	0.627			8.39	0.393
	skill9	0.674			8.86	0.454
	skill10	0.542			7.45	0.293
Familiarity with legal and commercial rules and regulations	skill11	0.737	0.633	0.838	Fixed	0.543
	skill12	0.834			12.57	0.695
	skill13	0.814			12.36	0.662
Planning	skill17	0.708	0.525	0.815	Fixed	0.501
	skill16	0.752			10.74	0.565
	skill15	0.748			10.70	0.559
	skill14	0.690			9.98	0.476
Team-building	skill22	0.699	0.553	0.860	Fixed	0.488
	skill21	0.799			11.63	0.638
	skill20	0.767			11.23	0.588
	skill19	0.732			10.77	0.535
	skill18	0.719			10.61	0.516
Marketing	skill27	0.799	0.513	0.839	Fixed	0.638
	skill26	0.750			12.49	0.562
	skill25	0.711			11.77	0.505
	skill24	0.717			11.88	0.514
	skill23	0.588			9.49	0.345

Results

Demographic characteristics of the students

The results for the demographic characteristics showed that 44.2% (119 individuals) of the respondents were male and 55.8% (150 individuals) were female. The studied students were, on average, 25.86 years old (SD = 5.14). Also, 58.7% (158 individuals) were undergraduate students, and the remaining were postgraduate students of whom 33.5% (90 individuals) were studying at the master’s level and 7.8% (21 individuals) at the Ph.D. level. In addition, their grade point average (GPA) was, on average, 15.89 of 20.00 (SD = 1.48).

Structural equation modeling (SEM)

The effects of the entrepreneurship ecosystem on entrepreneurial skills were analyzed by SEM. Accordingly, first, the measurement section of the model (to check the validity and reliability of the measurements used) and then, its structural section (to support the theoretical relationships of the variables of the theoretical framework) was assessed. In addition, the overall fit of the model was checked by different indices to ensure the conformity and overall agreement of the model with empirical data.

Estimation of the measurement model

The reliability and validity of the questionnaire were measured by composite reliability (CR) and the discriminant validity was estimated by average variance extracted (AVE). Constructs whose CR is greater than 0.6 are reliable enough. The closer the CR is to 1, the higher the reliability is (Raykov, 1998). Also, constructs whose AVE is greater than 0.5 are valid enough (Iglesias, 2004).

To check the reliability of the model, it is also necessary to check the degree and level of significance of the paths between each latent variable and its related indicators. So, confirmatory factor analysis was employed to test the hypothesis as to whether indicators considered for a construct or latent variable really express it and how accurately the selected indicators can describe the latent variable or how fit they are to the latent variable. Since parameters with values of greater than 1.96 are statistically significant (Bentler & Yuan, 1999), the results revealed that the indicators used to measure the latent traits studied here were in good agreement with the factor structure and the theoretical framework of the research. The reliability of the indicators can be measured by the square of multiple correlations (R2), which shows the amount of variance in each indicator accounted for by the related latent variable (Tables 1, 2, 3 and 4).

Structural model estimation

After the measurement model was estimated, the second step in the model estimation was to test the significance of coefficients for the paths assumed in the research model. Before estimating the path coefficients, the model’s fit indices were tested. To understand the extent to which the model was consistent with the data, the overall fit of the model was assessed using the relevant indices of fitness. They are listed in Table 5, which shows that most reported indices were in the acceptable ranges for the overall fit of the model. So, it can be said that the model was consistent with the data used.

Table 5

The fit indices of the structural model

Test	Recommended value	Proposed model
Likelihood ratio Chi-square (× 2)	Insignificant × 2 (p > 0.05)	0.000
Normed chi-square (× 2/df)	× 2/df < 5	1.58
Root Mean Square Residual	RMR < 0.05	0.06
Root Mean Squared Error	RMSEA < 0.08	0.04
Goodness-of-Fit Index	GFI > 0.90	0.92
Incremental Fit Index	IFI = Values close to 1	0.96
Comparative Fit Index	CFI > 0.90	0.95

Based on the structural model, the entrepreneurship ecosystem had a direct impact on social intelligence and emotional intelligence and a direct and indirect impact on entrepreneurial skills. It was found that the effect of entrepreneurship ecosystem was positive and significant on both social intelligence (β = 0.16; P < 0.05) and emotional intelligence (β = 0.359; P < 0.000). The results showed that social intelligence had a positive and significant impact on the students’ entrepreneurial skills (β = 0.205; P < 0.03). On the other hand, as is evident in Table 6, entrepreneurial skills were positively and significantly influenced by emotional intelligence (β = 0.367; P < 0.000) and entrepreneurship ecosystem (β = 0.18; P < 0.05).

Table 6

The estimation of the structural model’s paths

Independent variable	Dependent variable	Standardized coefficient	t-Value	Sig
Entrepreneurship ecosystem	Social intelligence	0.16	1.97	0.05
	Emotional intelligence	0.359	3.79	0.000
	Entrepreneurial skills	0.180	1.99	0.05
Social intelligence	Entrepreneurial skills	0.205	2.15	0.031
Emotional intelligence		0.367	4.92	0.000

The results, also, revealed that R2 was 0.59 for the students’ entrepreneurial skills, implying that 59% of the variance in the variable of entrepreneurial skills among the students of the agriculture faculty was related to the three variables of entrepreneurship ecosystem, social intelligence, and emotional intelligence. In other words, it can be claimed that the elements of the research’s conceptual framework could capture 59% of the variance in entrepreneurial skills (Fig. 2).

Fig. 2

The structural model of the research

Discussion

The results showed that the entrepreneurship ecosystem had a significant impact on the students’ social intelligence. This means that a network of different elements (e.g. human capital, markets, finance, supports, culture, and policy) is formed and affects the students’ social intelligence. The entrepreneurship ecosystem acts as a motive and reinforces social information processing, social awareness, and social skills. Other researchers (Aliabadi et al., 2019; Hosseininia et al., 2019) have concluded that if the infrastructure for launching entrepreneurial businesses is provided in the context of an ecosystem, the cognitive features of the individuals can be activated and reinforced, thereby supplying the requirements for the development of entrepreneurship. On the other hand, the entrepreneurship ecosystem affected the students’ emotional intelligence positively and significantly. This implies that the adoption of an entrepreneurship ecosystem approach in universities, which means the creation of a supportive climate, can improve the students’ capability in identifying their emotions and those of others to stimulate and manage their emotions and relations. This finding is in agreement with the results of Mousivand et al. (2017), Aliabadi et al. (2016), Izadi et al. (2020), and Ataei et al. (2020). They have concluded that by creating an entrepreneurial environment, people’s psychological attributes can be stimulated.

The results revealed the significant effect of the entrepreneurship ecosystem on the students’ entrepreneurial skills. In an entrepreneurship ecosystem, students not only accumulate human capital and knowledge but also acquire entrepreneurial skills and use them to produce, distribute, assimilate, and commercialize new knowledge. It can be argued that by providing market conditions, finance, supports, human capital, culture, and policy, the students can be encouraged towards acquiring different entrepreneurial skills to launch innovative businesses. It can be argued that by providing an entrepreneurship ecosystem’s infrastructure and components, the psychological characteristics of students can be strengthened. In other words, the entrepreneurship ecosystem can prepare the students’ mental conditions for acquiring entrepreneurial skills and launching innovative businesses. This corroborates the findings reported by Lyons et al. (2020), Guerrero et al. (2020), Ben Hassen (2020), and Hsieh and Kelley (2020) according to whom a dynamic entrepreneurial ecosystem can enhance entrepreneurial skills among people. Based on the results, it was specified that social intelligence can affect students’ entrepreneurial skills. In other words, if the students have the capability of understanding and controlling their emotions and use them to help their intellectual, decision-making, and communicational activities, they can acquire the skills required for launching a business. The same point has been stated by Alizade Aghdam et al. (2016) and Jin (2020), who found a mutual relationship between entrepreneurial skills and social intelligence of people. Besides, the effect of social intelligence was found to be positive and significant on the students’ entrepreneurial skills. People with higher emotional intelligence are more likely to establish an entrepreneurial business because they can cope with their negative emotions caused by work and the environment during the establishment and administration of a new business. This finding supports the results of Karimi (2016), Rezaei (2017), Yitshaki (2012), Rhee and White (2007), and Azma and Kannadas (2020). They have reported that emotional intelligence can prepare people to enhance their entrepreneurial skills and increase their motivation to cope with market skills. In other words, a set of psychological traits (e.g. social and emotional intelligence) under the umbrella of the entrepreneurship ecosystem influence students’ entrepreneurial skills. The deeper the gap between the components of an entrepreneurship ecosystem is, the more unsuccessful the students will be in using their social and emotional intelligence to acquire entrepreneurial skills.

Conclusion

Innovative and entrepreneurial businesses are formed and developed within the framework of an ecosystem. The presence of a proper entrepreneurship ecosystem in universities largely assures the sustainability of knowledge-intensive businesses. So, the present study aimed to study the effect of the entrepreneurship ecosystem on the agriculture students’ entrepreneurial skills mediated by social and emotional intelligence. An entrepreneurial ecosystem encompasses diverse stakeholders, e.g. universities, entrepreneurs, policymakers, public and private sector organizations, knowledge-intensive enterprises, and so on. For the students and graduates to launch innovative businesses, these stakeholders should interact in a free and open economy, a stable and efficient political environment, a high-quality monitoring environment, a simple and flexible business environment, and a knowledge-intensive cultural climate.

The current study explored students' perceptions of the entrepreneurship ecosystem and their self-reported entrepreneurial skills. The results indicated a mismatch between students' perceptions and the policies and efforts of the university to create a supportive climate. In other words, students believed that the components of the university entrepreneurship ecosystem (human capital, markets, finance, supports, culture, and policy) were not strong supporters to improve their entrepreneurial skills. However, in an entrepreneurial ecosystem, the students’ entrepreneurial skills and psychological attributes play a key role so that the formation and development of an innovative business depend on the entrepreneurship ecosystem, entrepreneurial skills, and psychological attributes of the students. The results suggest that the entrepreneurship ecosystem has an essential effect on the students’ entrepreneurial skills, social intelligence, and emotional intelligence. It can be concluded that the interaction and coordination of an entrepreneurship ecosystem’s components provide the students with the appropriate conditions to use their social and emotional intelligence to acquire entrepreneurial skills. On the other hand, the variables of emotional intelligence and social intelligence were positively and significantly influential on the students’ entrepreneurial skills. It can be drawn that students with higher social and emotional intelligence and the improvement of entrepreneurship ecosystem components will lay the ground for the students to acquire and strengthen their entrepreneurial skills.

Among different actors, the role that lawmakers, policymakers, and relevant officials should play in providing an optimal ecosystem is crucial. So, to fill the gaps, which are mainly related to the performance of law-making institutions and executive sectors, lawmakers and executives should consider the different aspects of entrepreneurship and the creation of an optimal business environment. Thereby, proper conditions are provided to students and graduates who intend to launch an entrepreneurial business with the participation of experienced people because they mostly have adequate knowledge, as well as extensive communications, to develop a business and this can increase the chance of their success and the development of the business. On the other hand, to encourage students and develop innovative businesses among graduates, attempts should be made to facilitate the issue of licenses for the establishment of new businesses by the relevant institutions. Also, good financing will pave the way to motivate the students for starting their own businesses. Furthermore, since emotional intelligence and social intelligence are to be acquired, materials can be included in academic programs to enhance them in students. Educational planners and employment policymakers should also perceive the strategic value of emotional intelligence and social intelligence. Then, they can motivate students to acquire entrepreneurial skills and create a competitive advantage in the marketplace and businesses.

This study revealed some important findings, but they are still required to be extended at a greater depth. It is recommended to undertake research with a broader sample of various universities. A further study could investigate how universities can apply the model to their entrepreneurship curriculum and evaluate students' entrepreneurship skills. How the model holds up in a longer-term study and how it applies to other universities in different fields and social contexts could be another interesting avenue for a future investigation.

Like all studies, this study was subject to some limitations. The responses depended on the respondents’ willingness to honestly and reliably recall and report their experiences. Special attention should also be paid to the students’ responses to external shakeouts (COVID-19 pandemic, compactness of exams). It also requires the use of different scales to evaluate the students' entrepreneurship skills instead of their self-reported entrepreneurial skills. However, given the limitations of our data set, we did not test the effectiveness and the success of entrepreneurship education programs.