Youth perceptions of agriculture: influence of cognitive processes on participation in agripreneurship

ABSTRACT

This article investigates the nature of youth perceptions and their influence on youth’s intentions of engaging in agripreneurship. The study findings reveal that the youth held positive economic perceptions of the agricultural sector. Along with the provision of secondary school agricultural education and a significant amount of financial support, these perceptions positively influenced their intentions to participate in agripreneurship. The findings affirm the need for improving awareness of the economic opportunities available in the agricultural sector. The study recommends that programmes that aim to encourage agripreneurship target both the socio-economic and cognitive limitations of youths.

KEYWORDS:

• Aid – Capacity development
• Civil society – Participation
• Environment (built and natural) – Agriculture
• Gender and diversity – Youth
• Labour and livelihoods – Economics
• Sub-Saharan Africa

Introduction

Several studies have shown that stimulating entrepreneurship in the smallholder farming sector enables the achievement of economic goals at community and national levels (Bruton, Ketchen, and Ireland 2013; Díaz-Pichardo et al. 2012; Sinyolo and Mudhara 2018). Because of this, many governments have facilitated the setting up of programmes which encourage the development of small- to medium-scale businesses, especially those that are headed by youths. Examples of these are the Ghana Youth Social Entrepreneurship Program, The Youth Entrepreneurship and Innovation Multi-Donor Trust Fund (YEI Trust Fund) programme, and the Pan-African Youth Entrepreneur Development (PAYED). The motivation of youth inclusion in business, particularly agribusiness, has partly been attributed to concerns for the industry’s continuity in the face of increasing levels of chronic diseases which threaten aging farmer communities and the high youth unemployment rate (Aphunu and Atoma 2010; Nhamo and Chikoye 2017). Forecasts of a swelling global population have further encouraged the transfer of food production activities to younger, more energetic, and productive members of society (Pindado and Sánchez 2017).

In South Africa, youth participation in agriculture has been a key focus area of important policies such as the National Development Plan (Sinyolo and Mudhara 2018). The government’s New Growth Path also emphasised the expansion and commercialisation of smallholder agriculture by setting a target of establishing 300,000 additional market-oriented smallholder producers by 2020 (DED 2011). In alignment with this goal, The National Youth Development Agency (NYDA) was formed with the purpose of assisting South African youths to start businesses and to finance existing businesses (BER 2016). Further support has been offered by the Department of Science and Technology through the establishment of the Technology and Innovation Agency (TIA), which is geared towards supporting technological innovation, as well as enhancing the global competitiveness of South African businesses (BER 2016). The National Empowerment Fund (NEF) was subsequently founded to offer further financial and non-financial support. The expectation is that the promotion of agricultural entrepreneurship would make farming a more attractive and profitable venture for the youth, and thus make agriculture an important tool for reducing rural unemployment and controlling rural–urban migration.

Unfortunately, the government’s ardent efforts have not been met with enthusiastic buy-in from the youth. The institutional and infrastructural investments that have followed state-led directives have not produced the expected flow of young people into the industry. The low response to government’s incentives suggests that there could be obstinate factors, such as preconceived ideas of the industry or other equally under-investigated influential socio-economic factors, which are influencing career decisions of the youth. Thus, this article embarks on a two-pronged task of first examining the nature and key indicators that inform the perceptions of the youth towards the agricultural industry, and second, investigating the effects of these perceptions, as well as other socio-economic factors impacting on the intentions of youth towards participating in agricultural business ventures (agripreneurship) in Mpumalanga Province. Knowing the reasons for the sluggish or insignificant response to government’s initiatives will enable authorities to successfully formulate campaigns which could ignite and nurture interest in agribusiness development by the youth.

The study contributes to the emerging agricultural entrepreneurship literature, a field in entrepreneurship that has been overlooked in the past (Alsos, Ljunggren, and Pettersen 2003). As an extension to the few existing previous African studies (such as Aphunu and Atoma 2010; Mibey 2015) that have merely looked at socio-economic factors, it emphasises the value of perceptions in directing an individual’s entrepreneurial decisions. Its findings highlight that the agricultural industry’s societal embeddedness (roles of individuals in society and their intricate links with societal ideologies) plays an equally important guide to people’s intentions for starting businesses as profit maximisation aspirations.

The importance of youth participation the agricultural sector

The demands, opportunities, and challenges of the changing business environment in the agricultural industry have required farmers to become entrepreneurial (Sinyolo and Mudhara 2018). The combined effects of factors such as market liberalisation, climate change, increased dominance and penetration of modern food chains, and waning levels of farmer support, have forced producers to develop new skills and capabilities in order to survive or remain competitive (Díaz-Pichardo et al. 2012; Pindado and Sánchez 2017). Entrepreneurial options that farmers have employed include: implementing selective product specialisation, enterprise diversification, market orientation, production up-scaling, product development, processes innovation, and vertical integration (McElwee and Bosworth 2010). According to Hansson et al. (2013), the definition of agripreneurship also incorporates other income-generating strategies such as off-farm employment and multiple business holdings.

Due to the evolving production and business environment in the twenty-first century, efforts to increase the participation of younger generations in agriculture have been increasing. These have also been driven by concerns that the continuous reliance of agriculture on the aging population, the members of which are prone to chronic diseases, could negatively impact agricultural production. In the USA, the average age of a farmer is reported to be 55 years (BLS 2015), “while that of South Africa is estimated to be 7 years higher” (Sihlobo 2015). Furthermore, the low levels of mechanisation in the small-scale enterprises that dominate South Africa’s industry renders the aging farming community an issue of great concern, as physical strength is a key asset in such enterprises. According to Nnadi and Akwiwu (2008), the availability of able-bodied, young labour has determined the agricultural industry’s output levels in the past, and it is posited that the youths’ unique capabilities (dynamism, adventure, and ambition) will play a greater role in the sector’s continued success in a world where there is rapid technological change.

Despite the central role that the evolving agricultural sector has earmarked for the youth, the younger generations are reluctant to take up vocations in the sector due to various misconceptions associated with the industry, and a lack of information and awareness of opportunities (Aphunu and Atoma 2010). The lack of obvious linkages between the hyperbole that has been created around the fourth industrial revolution and the agricultural sector has led to the youth searching for professional growth opportunities that are focused on non-agricultural sectors. Previous studies have found that some youth have also shied away from the sector due to its uncompetitive wages and high physical demands (Mibey 2015). It is feared that the lower youth participation in agriculture is likely to threaten the long-term future of the agricultural sector, as the youth represent the link between the present and the future (Okeowo, Agunbiade, and Odeyemi 1999).

Conceptual framework: the entrepreneurial cognition approach

This article adopts the cognitive approach to entrepreneurial research in order to explain the link between agriprenueral intentions of the youth, their socio-economic characteristics, and their perceptions. This approach has emerged from the interaction of socio-psychology and organisational management (Díaz-Pichardo et al. 2012). It postulates that human behaviour is influenced by mental processes such as perceptions or attitudes, personality traits, and socio-economic factors. Various theories have been developed using this approach and one that has been used to explain the decision-making process that individuals use when choosing a vocation is the Social Learning Theory of Career Decision-Making (Mitchell and Krumboltz 1990). The theory identifies the interactions of socio-demographic factors (e.g. race, age, marital status, ethnicity, culture, and gender), knowledge (e.g. formal education, technical education, and tacit knowledge) and attitude or perceptions (e.g. individual perceptions, perceptions of economic opportunities, and socio-cultural perceptions) (Esters and Bowen 2005). According to Mitchell and Krumboltz (1990), these factors form entrepreneurial cognitions that are defined as the knowledge structures that people use to make assessments, judgments or decisions involving opportunity evaluation, venture creation and growth. The theory posits that each of these influencing factors plays a part in all career decisions that are made, but different combinations of interactions of the factors produce a multitude of different career choices (Esters and Bowen 2005).

Following Social Learning Theory, the Theory of Planned Behavior (TPB), Entrepreneurial Event Theory (ENT) and Role Model Theory have been developed (Liñán, Santos, and Fernández 2011). TPB incorporates the influence of intentions and motivations as measures of the effort the individual plans (Esters and Bowen 2005), while ENT integrates the perception of the ability to perform (perceived feasibility) (Hormiga, Batista-Canino, and Sánchez-Medina 2011). The value of the latter theories lies in the fact that they introduced dimensions which allowed the identification, definition, and measurement of some important elements of perceptions, such as individual perceptions. Following the achievement of such a seminal milestone, cognitive research has gained much relevance in problem analysis and has become the most popular way for analysing entrepreneurship (Liñán, Santos, and Fernández 2011). Recent developments to the approach have seen its application in the analysis of cognitive biases and heuristics that elicit a person’s tendency to make errors in judgment based on cognitive factors, such as perceptions and motivations (McElwee and Bosworth 2010). Other developments have resulted in the approach including the measurement of farmers’ entrepreneurial capability and skills (Sinyolo and Mudhara 2018).

According to the Entrepreneurial Cognition Approach, socio-economic factors have a significant effect on agripreneurship in smallholder farming due to the social embeddedness of the sector. The identification of new business opportunities and the development of new ventures are “inextricably linked” to family roles and relationships (Liñán, Santos, and Fernández 2011). This implies that the specific characteristics of the sector (strong family links and institutional support) and socio-demographic factors can influence the decision to commence entrepreneurial activity (Arenius and Minniti 2005; Pindado and Sánchez 2017). Arenius and Minniti (2005) showed that a higher level of income earned by a household enhanced an individual’s probability of becoming an entrepreneur, as this provided capital for starting a business. Some studies have shown that the need to supplement formal income or gain additional sources of money has been experienced more frequently in large households than in smaller ones (Pindado and Sánchez 2017). Other factors, such as culture, have also been found to influence entrepreneurship both through social legitimation and through promoting positive attitudes related to firm creation in individuals (Liñán, Santos, and Fernández 2011).

Most studies that use the entrepreneurial cognitive approach have found that individuals with higher levels of education are better able to perceive and exploit opportunities for profitable business (Hormiga, Batista-Canino, and Sánchez-Medina 2011). This finding has been explained by the fact that educated individuals tend to have better financial and problem-solving skills than their counterparts. According to Abdullah and Sulaiman (2013), higher levels of academic knowledge can lead to the acquisition of greater skills by the youth as they prepare to establish their own businesses. Likewise, knowledge acquired in previous entrepreneurial experiences (tacit knowledge) often enhances the probability of becoming an entrepreneur, and furthermore allows entrepreneurs to avoid costly mistakes, thus providing an advantage of their better exploitation of business opportunities (Abdullah and Sulaiman 2013).

Perceptions play an important role in influencing the interests of the youth in agripreneurship. According to Liñán, Santos, and Fernández (2011), individuals decide to start an entrepreneurial activity if it is perceived to be more desirable and feasible than other alternatives. Other studies have found that individuals whose parents are entrepreneurs often become entrepreneurs because of their individual perceptions that will have formed through observing these role models (Arenius and Minniti 2005). Analysts have found that this subjective interpretation of reality (perception) plays a central role because the entrepreneurial environment is often characterised by imperfect markets and incomplete information. Particularly in cases where career guidance is limited or not easily accessible, an individual’s views or perceptions may become the most influential factor in decision making. Studies have shown that individuals continue to rely on their perceptions beyond the career decision-making point, as nascent entrepreneurs significantly rely on subjective and often biased perceptions, rather than on objective expectations of success (Liñán, Santos, and Fernández 2011).

Research methodology

Data

This study was conducted in the Nkomazi municipality which is situated in the Mpumalanga province of South Africa. The province is located in the north-east region of the country and is bordered in the east by Mozambique, in the south by Swaziland, and in the north by the Kruger National Park. Nkomazi municipality covers an area of 3,250 km² and is ranked among the South African regions with the highest agricultural potential due to its fertile soil and subtropical climate (Nkomazi Municipality 2015). According to the Nkomazi Municipality Integrated Development Plan (2015), there are about 123,628 youths in the municipality. The municipality was chosen due to the large proportion of the youth in its population (59%), its high levels of unemployment (41%), and the prominence of smallholder farming in the area. In addition to the large number of government programmes which encourage youth participation in agriculture, these conditions would ideally promote the development of agripreneurship among the youth.

The study targeted the youth in the Nkomazi municipal area. According to South Africa’s census of 2017 (Stats SA 2012), the youth comprise individuals aged between 15 and 39 years. The youth are further classified into five categories (15–19, 20–24, 25–29, 30–34 and 35–39 years) in the Nkomazi Municipality Integrated Development Plan (2015). These age bands formed the basis for stratifying the population, with the number of individuals apportioned within each stratum of the 120-member study sample being equal to that found in the population. Data for the study were collected between September and October 2017 through a structured questionnaire that was divided into three sections. Section A captured the socio-economic profiles of the individuals. Section B focused on the knowledge factors that influence the youth to participate in agricultural entrepreneurship. For the last section, the youths were asked to rate the extent to which they agreed with statements that reflected individual, economic and socio-cultural perceptions. Individual perceptions refer to role model perceptions and self-efficacy, economic perceptions relate to views on possible economic opportunities, and socio-cultural perceptions describe a set of values that are upheld by societal groups that the youths identified with (Liñán, Santos, and Fernández 2011). The collection of this information was done by using a five-point Likert scale, ranging from 4 (“strongly agree”) to 0 (“strongly disagree”). The Likert scale was used because it gives reasonably robust correlation coefficients by limiting distortions in data scaling caused by ordinal data (Conradie and Piesse 2016).

Analytical procedure

The intentions of the youth to participate in agripreneurship was measured by using a binary variable that took up a value of 1 when an individual was intending to start an agricultural business in future, and 0 otherwise. Given the dichotomous nature of the dependent variable, its relationship with the various explanatory variables was estimated using a binary logistic regression model. The general formula for binary logistic model is given in Equation (1).(1) $Y_{ij}={\alpha }_j+{\text{β}}_j\sum _{jx1}^n{X}_{ij}+e_i$(1) where: $Y_{ij}$ is the dependent variable (intention to participate in agripreneurship), ${\sum }_{jx1}^n{X}_{ij}$ represents the sum of explanatory variables (perceptions, socio-economic and knowledge variables) for jth of the respondent, while ${\alpha }_j\dots {\text{β}}_j$ are the estimated coefficient, and $e_i$ the error term.

Examples of the socio-economic variables considered in this study are age, income, level of educational, and number of dependents. Tacit, implicit, and explicit knowledge variables were used to measure agricultural knowledge. Perceptions were classified into three categories stated in the Entrepreneurial Cognition Approach (see Liñán, Santos, and Fernández 2011). Each type of the three perceptions (economic, individual and socio-cultural) was measured using a set of indicators. Principal component analysis (PCA) was used to extract composite measures that capture the indicators that reflect the dominant indicators of perceptions.

The fundamental idea of PCA is to lessen the width of a set of data that is comprised of a large number of variables that are interrelated, while retaining the distinction existing in the data (Mabuza, Ortmann, and Wale 2015). This is accomplished by changing the data set into new variables, known as the principal components (PCs), which are not correlated, and to ensure that a few PCs retain most of the distinctions existing among all the original variables (Conradie and Piesse 2016). The PCs were estimated as a linear function, as shown in Equation (2).(2) $P{C}_i=a_{i1}{X}_1+a_{i2}{X}_2+\dots +a_{in}{X}_n$(2) where i is the number of principal components ranging from 1 … n; $a_{i1}\dots a_{in}$ = the component loadings; and $X_1\dots X_n$ are perception indicators.

After the PCA was conducted, Kaiser-Meyer-Olkin and Bartlett’s tests were carried out in order to ensure there was sufficient correlation between the perception indicators to justify the factor reduction procedure. A Cronbach’s alpha analysis was used to test the level of internal consistency between the indicators included in measuring the perception variable. The Kaiser criterion was applied for retaining PCs. The number of indicators showing sufficient association between the original scores and the PCs was determined using the Koutsoyiannis method.

Results and discussion

Descriptive statistics

Table 1 presents the summary of the socio-economic and knowledge variables included in the binary logistic regression model. The survey results showed that the majority (62%) of respondents were female and were not married. Most of the individuals interviewed (89%) had received at least some secondary education that had included an agricultural course. Fifty-four percent of respondents either lived on or grew up on a farm. On average, each respondent had one dependent (child or elderly person whom they took care of). The majority of the interviewed youths reported that they were pursing their studies and received an income of below R5001. However, when participants were asked about their knowledge of agricultural business promotions, most (53%) had no knowledge of government initiatives that promoted agriculture or agripreneurship. The majority of respondents (73%) said that they would only start an agribusiness if they received seed money of over 50% of the starting capital.

Table 1. Description of variables used in the binary logistic model.

Variable code	Variable name and description	Mean	Mode category
Socio-economic variables
AGE	Respondent’s age (15–24 = 1)	0.54	18–22
GENDER	Respondent’s gender (female = 1)	0.62	Female
EDU	Respondent’s educational level (1 = secondary and/or tertiary)	0.89	Secondary education
MARRIED	Respondent’s marital status (1 = Married)	0.13	White/traditional/court marriage
INCOME	Respondent’s income band (1 = R0-R5000)	0.79	R0–R5000
DEPEN	Number of dependents	0.92	1 dependent
EMPL	Employment status (1 = employed)	0.31	Studying
P.SUPPORT	Level of perceived financial parental support (1 = more than 75%)	0.37	50–75%
Knowledge variables
RURAL	Living in rural area (1 = yes)	0.84	Rural abode
KNOWL	Knowledge of government initiatives (1 = yes)	0.47	No
AG.STUDIES	Enrolment in agricultural studies (1 = secondary and/or tertiary)	0.44	Secondary education

Source: Field survey 2017.

Table 2 presents a summary of the statistics on the perceptions of the youth regarding the agricultural sector that were considered in the PCA. As indicated in Table 2, the interviewed youths had mixed perceptions regarding the agricultural sector, which ranged from positive to indifferent. This result contradicts previous studies, such as Mibey (2015), which state that the youth generally have negative perceptions of the agricultural sector. The average mean score of the economic perception indicators was 1.36. This score is closer to 1 than 2, meaning that respondents were more inclined to perceive, rather than to be unknowledgeable of, the agricultural sector’s ability to provide opportunities for them. This implies that the survey respondents perceived that the agricultural sector had the ability to allow them to achieve economic goals. On the other hand, an average mean score of individual perception indicators of 1.78 was acquired. This is closer to 2 than to 1, indicating a lack of individual conviction that their personal abilities, ambitions and qualities would match the characteristics required in the agricultural sector. The acquired average mean score of 2.01 on the socio-cultural indicators shows that the youth were resolutely unpersuaded of the congruence and alignment between the agricultural sector’s activities and the values held in their social circles.

Table 2. Perception indicators included in the principal component analysis.

Perception indicators	Mean	Standard deviation
Individual perceptions
My role models have successful agricultural businesses	1.85	0.31
I can meet the laborious requirements of agripreneurship	1.74	0.45
I am able to develop a success agricultural business	1.82	0.76
Agriculture is an acceptable way of life to me	1.71	0.41
Average mean score	1.78
Economic perceptions
Agricultural remuneration is attractive	1.78	0.26
Agriculture is a profitable business	1.35	0.29
There are opportunities for promotion in agriculture	1.55	0.31
Farming is a business	1.04	0.17
Agriculture creates employment	1.09	0.25
Average mean score	1.36
Socio-cultural perceptions
Farming is not a clean job	1.99	0.4
Farming is suitable for old people	2.01	0.5
Agricultural professions are admirable	2	0.67
Average mean score	2.01

Notes: 0 = strongly agree; 1 = agree; 2 = indecisive; 3 = disagree; 4 = strongly disagree.

Source: Field survey 2017.

Empirical results

Principal component analysis

Three PCs were extracted from the perceptions indicator data by using the Kaiser criterion. These PCs aggregately accounted for 54% of the total variation in the original variables, and were retained and later used in the binary logistic model as explanatory variables. Observations with significant PC loadings are highlighted in Table 3 in bold.

Table 3. Principal component analysis estimated using a covariance matrix.

Component matrix
	Components
	PC1 Economic perceptions	PC2 Economic and individual perceptions	PC3 Economic and individual perceptions
Farming is business	.730	.131	.051
There are opportunities for promotion for agriculture workers	.636	−.174	.547
Agricultural remuneration is attractive	.627	−.249	−.120
Agriculture creates employment	.508	−.153	−.460
Agriculture is an acceptable way of life to me	−.076	.645	.505
Employment opportunities are not scarce in agriculture	−.073	−.633	.535
Agriculture is a profitable business	.417	.606	.002
Summary indicators
Eigen values	1.774	1.318	1.070
Proportion of variance explained	25.347	18.822	15.281
Cumulative proportion of variance explained	25.347	44.169	59.450
Test for robustness
Kaiser-Meyer-Olkin:	0.86
Bartlett’s test:	0.04
Cronbach alpha:	0.86

Source: Field survey 2017.

The first PC (PC1) captured the highest number of perception indicators. This PC accounted for 25% of the total variation in the original indicators. The dominant indicators for PC1 were “farming is a business”; “there are opportunities for promotion for agricultural workers”; “agricultural remuneration is attractive”; “agriculture creates employment”; and “agriculture is a profitable business”. These results show that PC1’s dominant factors were found to represent the economic perceptions of the youth. The perception indicators captured in PC2 were “agriculture is a profitable business” and “agriculture is an acceptable way of life to me”. PC2’s dominant factors were found to represent a combination of economic and individual perceptions. Similarly, PC3 represents a combination of individual and economic perceptions as it captured the three perception statements: “employment opportunities are not scarce in agriculture”; “there are opportunities for promotion for agricultural workers”; and “agriculture is an acceptable way of life to me”.

The robustness of the analysis was tested by using the Kaiser-Meyer-Olkin, Cronbach’s alpha, and Bartlett’s tests. As shown in Table 3, the p-value of 0.04 acquired for the Bartlett’s test and the 0.86 p-value acquired for the Kaiser-Meyer-Olkin show a sufficient measure of sampling adequacy and correlation in the matrix of perception indicators to justify the use of a data reduction procedure using PCA. A Cronbach’s alpha value of 0.8 indicates a high level of internal consistency, implying that all the indicators reliably measured the same latent perception variable.

Regression analysis results

Table 4 shows the results of the binary logistic regression analysis, which was performed using the Statistical Package for Social Sciences.

Table 4. Variables in the equation.

	B	S.E.	Wald	Df	Sig	EXP(B)
Marital status	−1.362	0.705	3.734	1	0.053*	0.256
Parental financial support	1.421	0.771	3.396	1	0.065*	4.139
Agricultural studies	0.779	0.471	2.737	1	0.098*	2.179
Age	0.245	0.520	0.222	1	0.637	1.278
Monthly income	1.045	0.649	2.589	1	0.108	2.843
Principal component 1	0.577	0.269	4.608	1	0.032**	1.780
Principal component 2	−0.157	0.239	0.430	1	0.512	0.855
Constant	−0.014	0.383	0.001	1	0.971	0.986
Pseudo R-squared	0.151
Observations	120
Correct classification	78%

Notes: Variables significant at 1% (***), 5% (**), and 10% (*).

Source: Field survey 2017.

The results show that the marital status of respondents had a negative effect and significantly influenced the intention of the youth to participate in agripreneurship (P < 0.05). Married individuals are less likely to participate in agripreneurship. This may be because married individuals have responsibilities that may reduce the resources (financial or time) that can be made available for venturing into additional activities such as agribusiness. This result is in line with previous research by Angba and Adesope (2007) and Arenius and Minniti (2005), which showed that the unavailability of resources, or the perception of the lack thereof, can retard the entrepreneurial spirit in individuals.

Table 4 shows that parental financial support levels had a statistically significant influence on the intentions of the youth to participate in agripreneurship (P < 0.01). This means that if the youth were provided with more than 50% of their start-up capital from their parents, they would be more likely to participate in agripreneurship. The provision of high levels of financial support also implies that the youth will be cushioned from risks associated with entrepreneurship. The results further show that an individual was four times more likely to have plans of becoming an agripreneur if they were provided with 50% of the business’ initial financial requirements, than their counterparts who were certain of less financial assistance. This result indicates that youths in the study have a very low appetite for risk and may indicate a mentality of over-reliance on external support. This is result is in line with Adenkule et al. (2009), who showed that youth, similar to those interviewed in this study, were risk averse.

The findings show that the agricultural studies variable was statistically significant (P < 0.01) and a positive influence on the intention to participate in agripreneurship. This implies that when youth are exposed to agricultural studies at secondary and tertiary levels of education, they are more likely to have plans to participatie in agripreneurship. This is an expected result as agricultural education provides the basic and advanced skills that are required in agripreneurship (Hormiga, Batista-Canino, and Sánchez-Medina 2011). Abdullah and Sulaiman (2013) also found that individuals often feel more equipped to enter this area of business when they have an advanced knowledge of agriculture. The results show that youths who had studied agriculture at secondary level or higher were twice as likely to venture into agripreneurship than their counterparts who had not.

The findings also demonstrate that the perceptions of the youth (PC1) were a statistically significant (P < 0.05) impact on the intentions of the youth for participation in agripreneurship. When the composition of PC1 is taken into account, it is revealed that, specifically, their positive economic perceptions regarding the agricultural sector increase the likelihood of their participation in agripreneurship. This finding is in line with entrepreneurial literature (Arenius and Minniti 2005; Hansson et al. 2013) that posits that an individual’s decision to become an entrepreneur is often driven by a perception of monetary gains. This decision-making process is a result of a maximisation process in which the individual often compares the returns from alternative income-producing activities and selects the employment opportunity with the highest expected return (Hansson et al. 2013). The results show that youths who hold a positive economic perception of agriculture are twice as likely to venture into agribusiness than their counterparts who do not.

Conclusion and recommendations

The societal embeddedness of the agricultural industry is an equally important guide to people’s intention to start businesses, in addition to the profit maximisation aspirations. In light of this study’s results, we recommend that young people should be encouraged to take up agriculture in their educational curriculum, as this will increase the chances that they develop ambitions of venturing into agripreneurship. Where possible, initiatives that aim to increase youth participation should be designed to provide some seed finances, as well as provide training on how to curb risks, as this study shows that the provision of such services can encourage the younger generation to start agricultural businesses. As economic perceptions were found to strongly influence the intention of the youth to participate in agripreneurship, we recommend that efforts should be increased to raise the awareness of the lucrative business opportunities and remunerative employment that the agricultural sector offers in order to draw more members of the youth into agripreneurship.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Buyisile Magagula is a Student of the Department of Agricultural Economics and Extension, North West University, South Africa.

Chiedza Z. Tsvakirai is a Lecturer at the University of Mpumalanga, South Africa.

ORCID

Chiedza Z. Tsvakirai http://orcid.org/0000-0002-6691-7642