Abstract
Purpose
This paper aims to discuss Indonesia as a country having a young generation resource crisis in agricultural sectors, and this crisis contributes more than 32.9 per cent higher unemployment than other sectors. Therefore, creative youths are needed to build the sector through entrepreneurship activities.
Design/methodology/approach
The low condition of youth entrepreneurship intention in agricultural sectors leading the Agriculture Ministry of Indonesia launches an Agricultural Young Entrepreneurship Growing Program 2016 (Program Penumbuhan Wirausaha Muda Pertanian, PWMP 2016). Intention in growing phase is critical to avoid the program failure.
Findings
This research was aimed to analyze entrepreneurship characteristics and factors affecting the intentions. The research was conducted in three cities, Bogor, Lampung and Bandung, with 189 respondents who accepted business support from the PWMP in 2016. The accumulated data were then analyzed using descriptive analysis and structural equation model. Respondents have highly average trends on behavior belief, normative belief, motivation to comply, control belief, control belief power and intention. While, only the evaluation of the consequence to give the medium trend is about 50.26 per cent.
Originality/value
Factor affects to the entrepreneurship intention in agricultural sectors is a subjective norm or external factor, and both the attitude toward behavior and perceived behavioral control factors do not affect entrepreneurship intention in the agricultural sector.
Keywords
Citation
Ridha, R.N., , B. and Wahyu, B.P. (2017), "Entrepreneurship intention in agricultural sector of young generation in Indonesia", Asia Pacific Journal of Innovation and Entrepreneurship, Vol. 11 No. 1, pp. 76-89. https://doi.org/10.1108/APJIE-04-2017-022
Publisher
:Emerald Publishing Limited
Copyright © 2017, Rizki Novanda Ridha, Burhanuddin and Budi Priyatna Wahyu
License
Published in the Asia Pacific Journal of Innovation and Entrepreneurship. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Indonesia is a country that has a high unemployment of about 7.24 million of its population and is increasing by 90 thousand compared to the previous year (BPS, 2014). This phenomenon is dominated by youth unemployment which is increasing by years. The unemployment is prominently dominated by labor force in rural areas with averaged growth of about 9.8 per cent (Sakernas, 2012-2014). Moreover, rural areas of Indonesia are mostly dominated by agricultural sector that has an employment level of about 32.9 per cent (BPS, 2016). The sector contributes higher idleness number in Indonesia than other sectors. This higher contribution indicates that agricultural employments are not able to accommodate those amount workforces or moving to other sectors. Youth interest in agricultural sector is decreasing year by year. Furthermore, 35 per cent of those who are working on agricultural sector have a low productivity, and most of them are small-scale farmers and elderly.
The low contribution young generation phenomenon in agricultural sector above is also correlated positively on decreasing national income on the sector. Hence, creative youths are required to develop the sector through entrepreneurship activities. Entrepreneurs are people who are action oriented, having high motivation and daring to take risks and future motivation (Pambudy et al., 2005).
As an agrarian country with tremendous natural resources, agricultural sector of Indonesia is a very potential business area to be developed by youths. Developing agricultural entrepreneurship is needed to drive human resource productivity of the sector. According to Central Bureau of Statistics/BPS (2014), business actors in agricultural commodity are only about 44.20 million people (0.17 per cent) of the Indonesian population. The low number of entrepreneurs is indicated as a gap between human resources and education problems in famer’s level. Colleges play an important role in creating young agricultural entrepreneurs which is a crucial issue in this country. The colleges are responsible on increasing growth of graduated scholar to be unemployment about 14.5 per cent in period 2012-2015. There is low intention of those scholars for being entrepreneurs in agriculture due to financial factors which needs huge financial capital (Boateng et al., 2014; Pande, 2015). Even people from agricultural background are more interested to do business in non-agricultural sector (Parcel, 2003).
The low intention of youth entrepreneurs on agricultural sector induced Agricultural Ministry of Indonesia for launching an Agricultural Young Entrepreneurship Growing Program 2016 [Program Penumbuhan Wirausaha Muda Pertanian (PWMP) 2016]. This program is launched with an aim to answer the graduated campus unemployment and lack of human resources in the sector. Most of these programs are targeted to agricultural graduates by distributing capital assistance as long as 3 years to those graduates so they able to overcame the lack of financial capital. The needed initial evaluation of the program is intended to presume how successful the program is in the first year to increase entrepreneurship intention. Intention is a high fellow feeling to carry out entrepreneurship activities in agricultural sector. This intention is closely related to attitude toward the behavior, subjective norm and perceived behavior control. The importance to know intention on the first-year program is to avoid program failures.
This paper reveals Indonesian reseachers’ level of understanding on youth entrepreneurial issues. First, this paper aims to analyze young entrepreneurs’ characteristics. Second, this paper proposes to analyze some factors affecting entrepreneurs’ intention on agricultural sector. Discussion is presented in the third section, while recommendations are in the last section.
2. Review of literatures
Entrepreneur intention is identical with individual competency that refers to a willingness to conduct a certain behavior. Intentions of youth entrepreneurship in Chinai agricultural sector is influenced by the attitude factor. The intention basically is an acceptance on a representative relationship, like has been explained in the theory of planned behavior (TBP), between cognitive and conative behavior of readiness of individuals to perform certain act. It plays as a determinant and disposition of behavior, until the individual has proper opportunity and time to display the act in a real situation (Fisben and Ajzen in Yuliana, 2004).
In the TBP proposed by Ajzen (1991), intention is assumed as a portrait of motivation factor which affects an act. This indicates how hard a person tries to formulate his/her behavior. As of, intention highly correlated with behave (Figure 1). According to the theory above, it is influenced by some factors such as attitude toward the behavior, subjective norm and perceived behavioral control.
Attitude toward the behavior refers to how far an individual assess something favorable and unfavorable.
Subjectif norm is a social pressure that is experienced to do an act or behave.
Perceived behavioral control is a perceiving easiness or trouble that is formed from assuming based on past experience and anticipating obstacles and barriers.
TBP can be used as the basis for the analysis of entrepreneurial intentions. A study by Sommer (2011) prove that the TBP can be used to analyze the intention of entrepreneurship and its impact on past behavior. Van Gelderen et al. (2008) suggested that to investigate in detail about the intentions of entrepreneurs, TPB is required where the intention is regarded as a result of the attitudes, perceived behavior control and subjectif norms. According to Zampetakis et al. (2013) and Devi (2015), this variable attitude toward the behavior is an influential variable in an young entrepreneur’s intention in agricultural sector. The same way with Saheed and Kavoos (2016) suggested that youth entrepreneurship in Africa has a positive attitude that is active and ready to take risks and standalone. Similarly, Ahmad (2014) suggested that attitude is the strongest factor affecting the intention of entrepreneurship among students from Nigeria at Utara Malaysia University. Intentions of youth entrepreneurship in Chinai agricultural sector is influenced by the attitude factor (Devi, 2015).
Second factor is proven by Shiri et al. (2012) who express that subjective norm has significantly affected young student entrepreneurs that have an educational background in agriculture; perceived behavioral control factor is being a factor influencing young entrepreneur’s intention. A similar study conducted in Indonesia by Arisandi (2016) suggested that entrepreneurial intentions Bogor agricultural University (IPB) Graduate students in the agricultural sector are in?uenced by subjective norm factors. However, other factors are attitude toward the behavior and behavior control perceive no effect on entrepreneurial intentions IPB graduate students in the agricultural sector. Entrepreneurial intention was positively correlated with extraversion, openness and support of people nearby who are part of the subjective norm factor (Ismail et al., 2009). Contrary to Robledo et al.’s (2015) research, perceived behavior control and attitudes affect entrepreneurial intentions, while subjective norm does not affect entrepreneurial intentions. In addition, the findings indicate that the moderating effect of gender has a positive effect for women to development entrepreneurial intentions (Yeasmin and Latif, 2015).
The study of Wijerathna (2015) in agriculture faculty of Srilangka State University proved that subjective norms and attitudes are the greatest factors that influence entrepreneurial intentions agricultural students in Sri Lanka. Contrasted with Nabila and Haryani (2015) research that entrepreneurial intentions of students in Malaysia is formed by the control behavior and support relationships of an entrepreneur. These findings indicate that not all factors affect youth entrepreneurial intention. However, the third factor in TPB allowed to jointly affect youth entrepreneurial intentions. In Public Malaysia University, the third factor (attitude, subjective norm, perceived behavioral control) affects entrepreneurial intentions with the strongest factors of subjective norms and attitudes (Soon et al., 2016). This is in line with Masoomi et al.’s (2016) research that proves that subjective norms, perceived behavior control and attitude are highly correlated with entrepreneurial intentions of students in agriculture at the University of Shiraz, Iran. Likewise, the students from Nigeria, on Muhammad et al.’s (2015) study, found that attitude, subjectif norm and control behavior power all together influence the entrepreneurial intention of Nigerian students.
A comparison study with different country (Indonesia, Japan, Norwegia) by Indrianti (2008) shows that entrepreneur’s intention among students and the affecting factors are different among those countries. Educational background to be a main factor influences entrepreneurial intention of among those students. The correlation analysis indicated that youth entrepreneurial intentions are positively correlated to their personality traits (risk taking propensity, innovativeness and tolerance of ambiguity), and social learning (knowledge and experience and family upbringing) (Tateh et al., 2014). Entrepreneurship education program has affected student behavior control and anticipation of positive and negative impacts (Zampetakis et al., 2014). This research shows that educational background in agriculture is highly affecting entrepreneurship intention in agricultural aspect. Movahedi et al. (2013) show a motive of a bachelor candidate to work in agricultural sector. Hence, this entrepreneurial intention tends to be prominently dominated by agriculture graduates. Respondents with agriculture background are more likely to become agri-entrepreneurs compared to the ones with social science backgrounds. (Mohamed et al., 2012). Results of the analysis found students’ perception regarding the prospects of agribusiness enterprises in Ghana to have a statistical significant influence at both 1 and 5 per cent levels of significance on students’ intention to take up agribusiness as a future self-employment avenue (Zakaria et al., 2014). However, it is contrasted with research conducted by Aziz and Naem (2013) on youth interests on agricultural entrepreneurship in Malaysia, which shows that knowledge and education background are not significantly affecting those youths to be entrepreneurs.
Agricultural sector is less favorable for young people. Utsugi (2012) in his research in organic agricultural area of Brattleboro states that there are not many young people in Brattleboro wishing to choose organic agriculture as their carrier of work. Research result expresses that those people consider to work in organic agriculture especially as financial factors. They also need to determine that organic agriculture is able to make a decent living or not. It is no more different with researches conducted by Boateng et al. (2014) and Pande (2015) which state that youths perceive lack of capitals, skills, supports, market opportunities and risks to be main obstacles for intending entrepreneurships. Financial condition tends neither capital lack nor business return causing hampered entrepreneurship of the youths (Ahmed et al., 2010; Azwar, 2013; Herawati and Sambharakreshna, 2015). Agricultural sector assessed has high risk so that investment and return are being a sensitive issue recently. Many young agricultural graduates tend to do business in other sectors (Parcel, 2013). This condition attains to become a research reference where agriculture is less interested because of financial problems. Financial support to some PWMP participants should be a respond for youth entrepreneurial crisis in agricultural sector which is later parallel with entrepreneur’s intention of agriculture.
3. Hypothesis and conceptual modes
Hypothesis in this research was based by previous research and the constructed TPB. The based TPB hypothesis more refers to affecting factors young entrepreneur’s intention on agricultural sector. The TPB in this context, intention is influenced by some factors such as attitude toward the behavior, subjectif norm, and perceived behavioral control:
The attitude toward the behavior factor influences positively entrepreneur’s intention of PWMP participants.
The attitude toward the behavior refers to how far an individual assesses something that favorable and unfavorable. This factor is a crucial for an entrepreneur. Zampetakis et al. (2013) and Devi (2015) found that attitude factor on the behavior is a variable that highly affects entrepreneur’s intention of youth in agricultural sector. In their measurements, there are some indicators of the attitude. The attitude toward the behavior factor has some indicators such as appreciation, creativeness, risk taking, tolerance, independence, proactive-innovativeness, focus and self-confidence, time appreciation, hardworking, integrity, toughness, autonomy, leadership, spiritualism, knowing, achieving targets, mental toughness, future orientation, flexibility and diligence (Mohavedi et al., 2013; Arisandi, 2016; Ulfa and Maftakhatusolikhah, 2015).
It is contracted to the research conducted by Azwar (2013) found that indicators of the attitude toward the behavior are autonomy/authority, economic challenge, self-actualization, filling trusted, safety, work load, avoiding responsibility and social carrier. In this study, an element of the attitude toward the behavior variable is to seek a work with challenges and high economic values (economic opportunity). While indicator of the attitude toward the behavior is stated by Adetayo (2006) more towards to socio-economic factors such as experience, education and motivation:
Factor of subjective norm affects positively on entrepreneur’s intention of PWMP participants.
This factor is social pressure that is sensed to do an act or not. In the research conducted by Shiri et al. (2012), subjective norm has significant influence on entrepreneur’s intention of students who has agricultural education background. In latent variables, construction stating that belief toward normative referent of hope and motivating on normative hope are keys for forming indicators. While research was carried out by Arisandi (2016) added that indicator in belief toward normative referent of hope constitutes of believing on organization, parents, family, teacher/lecturer and friends expectations. Latent variable of motivation to be obeying on normative referent of hope has motivation on the above expectations:
Factor of perceived behavioral control predisposes positively on entrepreneur’s intention of PWMP participants.
Perceived behavioral control is easiness or that can be perceived or formed difficulty of behavior which is assumed referring on past experiences and anticipating obstacles. In forming these latent variables, it was stated that belief on easiness level of behavior and a control power on easiness level are main keys for framing indicators. Research conducted by Arisandi (2016) states that indicators in belief on easiness level of behavior consists of easy/difficult belief on accessing to financial institution, overcoming fatigue and boring, resolve entrepreneurship complexities and fulfilling agreements. While, control power latent variable on easiness level has indicators as same as previously mentioned above.
4. Methods
4.1 Research design
This research was conducted on three cities, Bogor, Lampung and Bandung by delivering online questionnaires and direct interview to PWMP participants. Determining research locations was carried out by purposive sampling to consider distributing participants about 59 per cent of all PWMP participants. Data were collected from August to September 2016 with qualitative technique. Primary and secondary data were gathered based on research needs. Primary data were taken directly both through questioners and interviews about PWMP program, and secondary data were compiled from related institutions such as Central Bureau of Statistics Republic of Indonesia (Badan Pusat Statistik, BPS), local labor offices, PWMP data and Ministry of Agriculture. Total respondents were 198 who are bachelors of agriculture and receiving financial support for the PWMP program in 2016 at those locations. Those gathered data are then analyzed using descriptive analysis and structural equation model (SEM) (Figure 2) (Table I and Table II).
5. Results and discussion
5.1 General overview of respondents
5.1.1 Age, ethnic and gender.
Youth is a group of people with productive age between 15 and 35 years. In this research, respondents are in aged ranged from 19 to 35 years. The respondents aged 23 years have higher proportion among others.
Ethnic group of respondents are varied such as Melayu, Batak, Minang, Sundanese, Javanesse, Betawi, Chiness and Bugis. Most respondents are Sundanese and Javanese about 43.9 and 32.8 per cent, respectively. This condition indicates that Sundanese respondents are dominant in research location mostly in West Java. Javanese also is considered dominantly after the Sunda. This is due to Javanese mostly live either in Lampung or West Java.
Gender of respondents is constructed by the following proportion: 66.1 per cent for male and 33.9 per cent for female (Table III). This condition exhibits that man has high interest to follow PWMP program than woman.
5.1.2 Marital status.
Married respondent has a low proportion about 14.3 per cent, and the single respondent has a high proportion about 83.7 per cent (Table IV). This situation indicates that the single respondent is more dominant than the married one. Other reason is that the young people do not have orientation to marriage yet before owning established life.
5.2 Descriptive analysis
A descriptive analysis is a technique used for describing sample characteristics. Characteristics of the research are able to be reflected by such variables X1.1, X1.2, X2.1, X2.2, X3.1, X3.2 dan Y[0].1, Y.2. The used techniques in descriptive statistics are percentage, average and deviation standard (Table V).
Analysis of the result descriptively shows that most respondents have high significant average on behavior beliefs, normative beliefs, motivation to comply, control belief, control belief power and intension. These evidence that:
respondents surely believe that such entrepreneur’s behaviors, such as appreciating time, taking risks, integrity, self-confidence, innovative, autonomy, leadership, diligent and discipline, have to be owned by an agricultural entrepreneur;
respondents are very confident that expectation coming from parent, family, friends and supervisor are verily motivated to conduct business;
respondents express that entrepreneurship motivation based on the expectation coming from parent, family, friend and supervisor;
respondents are sure that getting easy to, having access for financial institutions, overcoming tired condition, subduing entrepreneurship difficulty easily and fulfilling agreements increase intention in entrepreneur activities;
respondents have an ability to access financial institution, overcome tired, subdue business’s difficulty and meet agreement (negotiation); and
respondents of PWMP have a high entrepreneur’s intention on agricultural sector.
Furthermore, there is only the evaluation of that consequences which gives medium tendency about 50.26 per cent. This is pointed that respondents do not maximize yet to adjust entrepreneur’s behavior such as appreciating time, taking risks, having integrity, self-confidence, creative, innovative, autonomy, leadership, diligent and discipline.
5.3 Structural analysis of equation model
5.3.1 Overall model fit.
Based on the table above, overall model result criteria which fulfill the requirement for goodness of fit means that the resulted model is good fit. The root mean square error of approximation (RMSEA) criteria results in ranged values of 0.055 ≤ 0.08 meaning that the resulted model is good fit (Table VI). The use of other goodness of fit criteria such as goodness of fit (GFI), comparative fit index (CFI), normed fit index (NFI), non-normed fit index (NNFI), incremental fit index (IFI) and relative fit index (RFI), results value >0.90 means the resulted model is good fit. So does another criterion of root mean square residual (RMR) resulting value about ≤0.1 which means the resulted model is good fitted. Because of all criteria infer the model is good of fit, testing the hypothetic theory can be done. This represents that resulted questionnaire data are able to answer the built theory.
5.3.2. Measurement model fit.
Model fit theory of measurement was conducted based on validating indicator variables on its latent variables. An indicator variable is able be told valid when it has standardized loading factor more than its tolerated loading factors about ≥0.50 (Igbaria et al., 1997). Figure 3 shows that loading factors is higher than 0.5 pointing that all variables have met valid requirement.
5.3.3 Analysis of factors affecting intention.
According to the resulting analysis of SEM, it is gained that path coefficients for X1, X2 and X3 are −0.39, 0.58 and 0.22, respectively. While, the resulted t-value for each X1, X2 and X3 are 1.32, 2.28 and 0.62, respectively (Figure 4) (Table VII).
The resulted SEM data processing on attitude toward to behavior factors shows the result is insignificant meaning that H0 is accepted and H1 is refused. This infers that the attitude toward the behavior factor has no effect on youth entrepreneur’s intention in agricultural sector. Entrepreneurship attitude cannot determine how big intentions for entrepreneurs in agricultural sector. Hence, the created entrepreneur’s behavior is not based by attitude toward the behavior. In line with a research conducted by Arisandi (2016) expressing that attitude toward the behavior does not give any positive influence on entrepreneur’s intention in agribusiness sector.
The subjective form shows a significant result on young entrepreneur’s intention in agricultural sector meaning that H0 is refused and H2 is accepted. This factor shows how far social pressure from some sides can increase entrepreneur’s intention. Supports coming from parent, family, friends, consultants and business team affect the high intention’s value. This condition is called as external factors of respondents, and it collaborates positively with entrepreneur’s intention. Confirming research conducted by Shiri et al. (2012), it is expressed that someone who has agricultural education background is highly influenced by the subjective norm.
The perceived behavioral control is an easiness or difficulty are perceived sense that is built from behavior assuming based on past experiences and anticipating obstacles. The research result shows that the perceived behavioral control is not significant, where H3 is rejected by 5 per cent of confidence. Moreover, the perceived behavioral control has no influence on entrepreneur’s intention in agricultural sector. Without any behavior controls, respondents surely believe that entrepreneurship’s intention can be appeared from their self. This refers to Arisandi (2016) which expresses that the perceived behavioral control has no influence on entrepreneur’s intention in agribusiness sector.
6. Conclusion and recommendation
Most respondents have high averaged tends descriptively on behavior beliefs, normative beliefs, motivation to comply, control belief, control belief power and intension. Only the evaluation of that consequences gives a medium tend of about 50.26 per cent. Factor affecting entrepreneur’s intention in agricultural sector is subjective norm or external factor. Nevertheless, the attitude toward the behavior and perceived behavioral control have no effect on entrepreneur’s intention of the sector.
Recommendation of this research is formulated based on likely causes of high entrepreneur’s intention in agricultural sector. Main target of entrepreneur’s intention growth is external factor of the young. Hence, the role of business team, family, parent, friend and business consultant (supervisor) are crucial to determine the intention. Further, the government runs the PWMP program which should pay attention on those excellent factors that there is a strengthening on facilitating external factor for growing more youth entrepreneur’s intention in agricultural sector. There are three development planning of external factor for young entrepreneurs through PWMP program as describing below.
Shaping and directing young entrepreneurs of PWMP program in team work system. Supporting from the team work can strengthen intention’s growth. Furthermore, introducing some activities that nourish the togetherness either in the team work or among teams are important to bridge communication of PWMP participants closely.
Conducting socialization toward parent and community about the importance of growing entrepreneurship in agricultural sector to recover the nation.
Escalating workshops for consultants and controlling on consultants to give supports for the program participants. There is a need for an incentive enhancement for business consultants due to their work hours getting higher in line with the increasing of entrepreneurship’s intention in agricultural sector.
Figures
Explanation for the SEM model
| Latent variables | Manifested variable | Questions |
|---|---|---|
| X1. Attitude toward the behavior | X1.1 Behavior beliefs | Z1 Consequence belief on appreciating time Z2 Consequence belief on bearing to take risks Z3 Consequence belief on integrity Z4 Consequence belief on confidence Z5 Consequence belief on creativeness Z6 Consequence belief on innovativeness Z7 Consequence belief on autonomy Z8 Consequence belief on leadership Z9 Consequence belief on diligence Z10 Consequence belief on discipline |
| X1.2 Evaluation of that consequences | Z11 Evaluating consequence of appreciating time Z12 Evaluating consequence of bearing to take risks Z13 Evaluating consequence of integrity Z14 Evaluating consequence of confidence Z15 Evaluating consequence of creativeness Z16 Evaluating consequence of innovativeness Z17 Evaluating consequence of autonomy Z18 Evaluating consequence of leadership Z19 Evaluating consequence of diligence Z20 Evaluating consequence of discipline |
|
| X2. Subjective norm | X2.1 Normative beliefs | Z21 Belief on organization’s expectation Z22 Belief on parents’ hope Z23 Belief on family’s expectation Z24 Belief on teacher/lecturer’s expectation Z25 Belief on friend’s expectation |
| X2.2 Motivation to comply | Z26 Motivation to comply on organization’s expectation Z27 Motivation to comply on parents’ hope Z28 Motivation to comply on family’s expectation Z29 Motivation to comply on teacher/lecturer’s expectation Z30 Motivation to comply on friend’s expectation |
|
| X3. Perceived behavioral control | X3.1 Control belief | Z31 Easy/difficult belief on accessing financial institution Z32 Easy/difficult belief in overcoming tired/boring Z33 Easy/difficult belief in surmounting entrepreneurship difficulty Z34 Easy/difficult belief in satisfying agreements |
| X3.2 Control belief power | Z35 Control power on accessing financial institution Z36 Control power on overcoming tired and boring Z37 Control power on surmounting entrepreneurship difficulty Z38 Control power on satisfying agreements |
|
| Y. Intention | Y39 High intention of entrepreneurship Y40 High intention of entrepreneurship in agricultural sector |
Operational definition
| Variable | Operational definition |
|---|---|
| Attitude toward the behavior | How far individual assesses something can be profitable |
| Behavior beliefs | One’s perceived beliefs on behavior, and it will push to be an attitude |
| Evaluation of that consequences | Individual’s positive of negative evaluation on certain behavior based on his/her beliefs |
| Subjectif norm | Perceived close social pressure for doing an act or not |
| Normative beliefs | Perceptive representation of important people on one’s and affects how his/her behavior should be |
| Motivation to comply | One should show his/her certain behavior and motivation for obeying to referenced hopes/expectations |
| Perceived behavioral control | Formed easiness or difficulty of behavior that is assumed based on past experiences and anticipating obstacles |
| Control belief | Belief on opportunity and resources that are owned by an individual to conduct a behavior |
| Control belief power | Power or capability that is owned by one to control perceived factors for facilitating or obstructing a behavior |
| Intention | A sense on something or business activity in agricultural sector among fields such as upstream, on-farm, downstream, and supporting fields |
Age, ethnic and gender of respondents
| Age (year) | |||||||||||||||||
| 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | |
| Total (people) | 1 | 0 | 10 | 34 | 49 | 24 | 21 | 14 | 12 | 6 | 5 | 2 | 0 | 0 | 4 | 2 | 3 |
| (%) | 0.5 | 0.0 | 5.3 | 18.0 | 25.9 | 12.7 | 11.1 | 7.4 | 3.2 | 2.6 | 2.6 | 1.1 | 0.0 | 0.0 | 2.1 | 1.1 | 1.6 |
| Ethnic | |||||||||||||||||
| Melayu | Batak | Minang | Sundanese | Javanese | Betawi | Chines | Bugis | ||||||||||
| Total (people) | 16 | 15 | 3 | 83 | 62 | 3 | 3 | 4 | |||||||||
| (%) | 8.5 | 7.9 | 1.6 | 43.9 | 32.8 | 1.6 | 1.6 | 2.1 | |||||||||
| Gender | |||||||||||||||||
| Woman | Man | ||||||||||||||||
| Total (people) | 64 | 125 | |||||||||||||||
| (%) | 33.9 | 66.1 | |||||||||||||||
Marital status of respondents
| Marital status | Total (People) | (%) |
|---|---|---|
| Married | 27 | 14.3 |
| Single | 162 | 85.7 |
| Total | 189 | 100.0 |
Descriptive data
| Variable | Mean | SD | % inclination | ||
|---|---|---|---|---|---|
| High | Medium | Low | |||
| X1.1 | 43,80423 | 4,393998 | 90.48 | 8.99 | 0.53 |
| X1.2 | 39,67196 | 5,032692 | 43.92 | 50.26 | 5.82 |
| X2.1 | 21,95238 | 2,674318 | 86.77 | 12.70 | 0.53 |
| X2.2 | 21,56085 | 2,735261 | 81.48 | 17.99 | 0.53 |
| X3.1 | 18,14815 | 2,113598 | 92.59 | 6.88 | 0.53 |
| X3.2 | 14,51852 | 2,838925 | 50 | 47 | 3 |
| Y | 9,296296 | 1,160996 | 94 | 5 | 1 |
Measurement model fit indices
| Goodness of fit | Cut-off-value | Result | Annotation |
|---|---|---|---|
| RMR | ≤ 0.05 atau ≤ 0.1 | 0.046 | Good fit |
| RMSEA | ≤ 0.08 | 0.055 | Good fit |
| GFI | ≥ 0.90 | 0.94 | Good fit |
| CFI | ≥ 0.90 | 0.96 | Good fit |
| NFI | ≥ 0.90 | 0.98 | Good fit |
| NNFI | ≥ 0.90 | 0.99 | Good fit |
| IFI | ≥ 0.90 | 0.99 | Good fit |
| RFI | ≥ 0.90 | 0.97 | Good fit |
Structural resulted model
| Relationship among variables | Path coefficients | |t-value| | Conclusion |
|---|---|---|---|
| X1 → Y | −0.39 | 1.32 | Insignificant |
| X2 → Y | 0.58 | 2.28 | Significant |
| X3 → Y | 0.22 | 0.62 | Insignificant |
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