DETERMINANTS OF YOUTHS’ INTENTION IN AGRIBUSINESS USING THEORY OF PLANNED BEHAVIOR

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INTRODUCTION
Agriculture contributes about 41% to the nation's GDP and it accounts for about 70% labour force being engaged in various agricultural activities (Technology Center for Agriculture and Rural Cooperation [CTA], 2015;Kemi, 2016; National Bureau of Statistics [NBS], 2019; and Food and Agriculture Organization [FAO], 2019). However, Agricultural sector is not very popular among youth although it accounts for roughly one-third of global gross-domestic product which means it has a lot of potentials to be discovered for poverty eradication (World Bank, 2008). A number of factors shaped the opinion of young people over this sector such as having relatively limited career options due to the lack of support from the government and discriminatory policies that prioritize urban development, not to mention the perception and profile of agriculture as dirty laboring work and low-income career (Lohento & Ajilore, 2015).
One obvious, but still elusive, developmental approach is to engage rural youth in productive and profitable agriculture, including crops, livestock, and fisheries. Associated with this view is the assumption that rural young people would be better off if they did not migrate to urban areas, thus avoiding exposure to risky and illegal behavior. Underlying this agenda, however, is the "tension" between the futures that youth, their parents and rural planners imagine for them, and the 'entrepreneurial', agriculture-focused future as proposed is not always well received. Agriculture remains hard work, and risky, and the allure of middle class lives in urban areas remains strong. Education increases more than just skills or immediate employability, it changes who people are and what they expect from life, and for agribusiness to meet their aspirations it must be viewed as a viable livelihood option (Leavy & Smith, 2010).
In rural areas the turn toward entrepreneurship has been combined with renewed interest in the agricultural sector and agricultural value chains as sources of jobs for young people (FAO, CTA and International Fund for Agricultural Development [IFAD], 2014). However, youth unemployment as well as underemployment in Nigeria, one of the developing countries, is on a steady rise. For instance, the NBS (2017) reports that combined unemployment and underemployment rate for the entire youth labour force in Nigeria (15-35 years) are 52.65% or 22.64 million youths.
The study bridged these contradictions on youth's engagement in agribusiness by exploring predictors of youth intention in North western Nigeria. The objectives of this study were to: describe the socio-economic characteristics of the respondents; determine the factors influencing the youths' intention to engage in agribusiness in North western Nigeria using TPB.
The theory of planned behavior (TPB) predicts an individual's intention to engage in a behavior at a specific time and place. The concept was proposed by Icek Ajzen in 1991. The TPB suggests that an individual's belief about performing a behavior influences their behavioral intentions (Ajzen, 2005). It posits that individual behavior is driven by behavioral intentions, where behavior intentions are a function of three determinants: an individual's attitude toward behavior, subjective norms, and perceived behavioral control (Ajzen, 1991). Attitude toward behavior refers to the degree to which a person has positive or negative feelings of the behavior of interest. Subjective norm refers to the belief about whether others think a person will perform the behavior. Perceived behavioral control refers to the individual's perception of the extent to which performance of the behavior is easy or difficult. It is the perceived ability and confidence a person possesses in performing a behavior.

MATERIALS AND METHODS The Study Area
The study was carried out in the north western Nigeria. This area covers Kaduna, Katsina, Kano, Zamfara, Kebbi, Sokoto and Jigawa States. The North West region of Nigeria offers a wide range of Islamic beauty and culture, from the seat of caliphates of Sokoto to the land of equity to the free trade zone in Jigawa. It majorly consists of Hausas and Fulanis and the predominant religion in the region is Islam. The weather is usually dry and the temperature drops at night. The region is located between latitude 9 0 10 1 N and 13 0 50 1 N and longitude 3 0 35 1 E and 9 0 00 1 E and covers 221,437 square kilometers out of the 923,768 square kilometers total land mass of Nigeria. The zone is blessed with population of 35,786,969 million (National Population Commission [NPC], 2006) with an estimated population of 50,998,616 million in 2018 (NBS, 2018).

Sampling Procedure and Sample Size
A multi-stage sampling procedure was employed for the study. The first stage involves the random selection of four (4) Table 1, only 250 questionnaires were properly filled and usable.

Method of Data Collection
Primary data were used for this study. This was achieved using a well-structured questionnaire and in-depth interview.

Analytical Techniques
The study used descriptive statistics to achieve objective (i) and objective (ii) was analyzed using multiple regression analysis on a 5-point likert type scale of strongly agree = 5, agree = 4, neutral = 3, disagree = 2 and strongly disagree = 1.

RESULTS AND DISCUSSION Reliability Test
The reliability values for the variables are reported in Table 2. The reliability of the constructs in the study was tested through internal consistency, using Cronbach's Alpha coefficients. Agribusiness entrepreneurial intention, attitude towards behavior, subjective norms, perceived behavioral control were deemed reliable and acceptable based on the rule of thumb by George and Mallery (2003) as well as guidelines of Hair et al. (2017). The value of 0.6 -0.79 deemed acceptable, 0.8 -0.9 Good, and 0.9 and higher Excellent. de Moraes et al. (2018) used Cronbach's Alpha to test the reliability of some constructs. Such constructs include self-efficacy (4 items), risk taking (4 items), innovation (3 items), leadership (3 items), planning (4 items), sociability (4 items) and entrepreneurial intention (5 items) with Cronbach's Alpha of 0.819, 0.796, 0.667, 0.726, 0.736, 0.707 and 0.854, respectively.  Table 3 (socio-economic characteristics of the respondents) show that majority (71.2%) of the respondents are male while 28.8% are female. This shows the disparity among males and females in enrollment into agriculture in tertiary education in the study area. This is in agreement with the study by Barau and Adesiji (2018) which revealed that 68.7% of the respondents were male while 31.3% were female. The result also shows that majority (64.8%) of the respondent were between the age of 23-27. The mean of the distribution was 24.40. This implies that the students are within the age where they are young, energetic and productive and can make decision to be independent and self-reliant after graduation. This is in line with the study by Precious and Abubakar (2019) which showed that most of the students fell between age brackets of 24-29 years. The findings also revealed that 46% of the parents of the respondents were civil servants, 20% were businessmen and 18.4% were farmers while 15.6 were into other occupations. This will affect the student's agribusiness entrepreneurial intention. It is known that parents act as role models to their children and the children want to follow their footsteps. This is in agreement with the study by Ojebiyi et al. (2015) which reported that majority of the parents were civil servants

Multiple Regression Analysis
The Model Summary Table 4 indicates that 39.1% of the variation in the dependent variable may be explained by the variation in the independent variables (Attitude, subjective norm and perceived behavioural control) included in the model.  Table 5 shows that attitude towards behavior and subjective norms were positive and significant as predictors of entrepreneurial intention. Attitude towards behavior was significant at 1% with a coefficient of 0.550 while subjective norm was significant at 5% with a coefficient of 0.107. Perceived behavioral control was not significant. This means that an increase in attitude towards behavior and subjective norm will lead to an increase in youth's intention to engage in agribusiness. This is in confirmation with findings of Esther (2015) which shows that attitude towards behavior contributed positively to entrepreneurial intention with a coefficient of 0.627. It is also in line with Tiraieyari and Krauss (2018) and Okun and Sloane (2002) indicate that subjective norm influences the intention to engage in entrepreneurial activities.

CONCLUSION AND RECOMMENDATIONS
The study showed that attitude towards behavior and subjective norms are factors that influence students' intention to engage in agribusiness. This shows that the students have intention to engage in agribusiness but do not have the capability to get through with it. It was recommended that seminars, workshops and classes should be held to encourage students to go into agricultural activities; government should also consider giving out incentives, loans, and subsidies to encourage young farmers.