Agripreneurial Success Among University Students: Perceived Barriers to Agricultural Entrepreneurship in India

Agripreneurial Success Among University Students: Perceived Barriers to Agricultural Entrepreneurship in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Agripreneurial Success Among University Students: Perceived Barriers to Agricultural Entrepreneurship in India Rohit Kumar, Anil Bhat, Ankit Magotra, Kuldeep Singh, Eva Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5437635/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The paper attempts to gauge the success of agripreneurs and seeks to explore the barriers which hinder the agripreneurial success among agricultural university students pursuing agriculture and allied education. This study proposes eight-dimension survey instrument called AgriSuccess scale for measuring the agripreneurial success. Responses from 300 students across various agricultural universities are analysed using SmartPLS. The study confirms the internal consistency and validity of the scales, with Cronbach's alpha values exceeding 0.7, indicating good reliability. The Fornell-Larcker criterion is met, ensuring adequate discriminant validity among constructs. This research adds to the literature on agricultural entrepreneurship by identifying specific barriers faced by aspiring agripreneurs and offering actionable recommendations for policymakers and educators. Agripreneurs agricultural university agripreneurial success students Figures Figure 1 Figure 2 Introduction Agriculture has traditionally been considered as be backbone of the Indian economy, contributing significantly to the national income and employing a substantial portion of the population. Historically, over half of the national income was derived from agriculture, with more than 70% of the population dependent on this sector at the time of India’s independence (Pandey, 2013). Despite this reliance, the agricultural sector has faced numerous challenges, particularly since the economic reforms initiated in the early 1990s, which emphasized liberalization, privatization and globalization (Singh, 2013). While these reforms aimed to enhance growth in rural areas, the expected agricultural performance has not materialized satisfactorily in subsequent years. Factors such as waste land, depleting natural resources and rural youth migration to urban areas have necessitated a paradigm shift in agricultural practices, moving beyond traditional methods of land tilling and crop harvesting (Uplaonkar & Biradar, 2015). In this context, the concept of agripreneurship has emerged as essential component for economic development in rural areas. Agripreneurship refers to the application of entrepreneurial principles in the agricultural sector, leading to increased productivity, job creation and the introduction of innovative products and services (Alex, 2011). The adoption of entrepreneurial practices within agriculture can address several pressing issues, including unemployment and poverty in rural communities and can contribute to overall economic growth (Bairwa et al., 2014a). However, many traditional farmers lack the necessary managerial, technical and innovative skills to cope with modern agricultural challenges such as delayed monsoons, drought and market fluctuations, which can lead to dire consequences, including financial distress and even suicide (Sah, et al 2009). Despite government initiatives aimed at promoting entrepreneurship, such as the Make in India and Start-up India campaigns, significant barriers persist. Funding remains a primary challenge for aspiring agripreneurs, as many face difficulties in convincing funding agencies to support their innovative ideas (Verma et al., 2019). Moreover, while the Indian agricultural sector has the potential for substantial growth-given its diverse climatic conditions, rich biodiversity and vast cultivable land—these opportunities are often underutilized due to inadequate infrastructure, lack of access to technology, and insufficient knowledge about market dynamics (Chand, 2019). To foster agripreneurship effectively, it is essential to identify and address these perceived barriers. The qualities and skills necessary for successful agripreneurs include problem-solving abilities, creativity, market orientation and leadership (Singh, 2013). Additionally, understanding the regional factors that influence the transition from employment to self-employment is crucial for developing effective policies to promote agripreneurship (In, For, & Development, 2016). As India's innovative agripreneurs begin to explore opportunities beyond the domestic market, technology adoption and innovation emerge as key strategies to ensure world-class quality, while also boosting farm productivity and profitability. The shift toward agripreneurship will likely appeal to highly educated youth, especially as it becomes both socially accepted and financially rewarding. Well-designed agripreneurship programs have the potential to steer young people towards either entrepreneurial ventures or roles within the agricultural workforce, contributing to the sector's global advancement (Bairwa et al., 2014b). Literature Review and Hypotheses: Agripreneurship, blending agriculture with entrepreneurship, has become a vital focus area, especially in emerging nations like India, where agriculture plays a major role in the economy. Yet, despite its importance, agripreneurship encounters multiple obstacles, particularly for aspiring agripreneurs like university students. Research has delved into various factors impacting agripreneurial success, from government policies to personal competencies, though the unique challenges faced by students aiming to become agripreneurs are seldom highlighted. Agripreneurship holds the potential to foster sustainable economic growth in India by rejuvenating the agricultural sector, enhancing livelihoods, and supporting environmental sustainability. Success in agripreneurship hinges on aspects like market and economic conditions, knowledge and skill development, cultural and social dynamics, policy and government backing, technological access and the availability of resources such as land and credit (Arumugam & Manida, 2023; Singh, 2013). Therefore, the following hypothesis is proposed: H1: Environmental and technological barriers have a significant negative impact on agripreneurial success. Government policies, along with initiatives from non-governmental organizations and private sector partnerships, are pivotal in fostering an environment conducive to agripreneurship in India. Effective policies can help remove barriers and provide necessary support for agripreneurs to succeed (Arumugam & Manida, 2023). In contrast, policies in some regions have been criticized for inefficiently using public funds and promoting low-growth ventures with minimal innovation (Acs et al., 2016). However, in the Indian context, well-designed government policies play a critical role in agripreneurial development, contributing significantly to the country's economic progress (Obaji & Olugu, 2014). Therefore, the following hypothesis is proposed: H2: Government and policy support positively influence agripreneurial success. For agripreneurs to succeed, they must demonstrate proactiveness, curiosity, determination and possess strong management and organizational skills, which are key to navigating the challenges of agribusiness (Singh, 2013). A comprehensive approach that includes high-quality training in both business and life skills, as well as relevant technical knowledge, is critical for agripreneurial success (Nain et al., 2019). Agripreneurship also requires ongoing training, technical skills and robust market connections, with successful implementation relying on coordinated efforts from diverse stakeholders to foster the growth and long-term success of agribusiness ventures (Arumugam & Manida, 2023). Therefore, the following hypothesis is proposed: H3: Knowledge and skills are positively correlated with agripreneurial success. According to Barau and Adesiji (2018), economic status, gender, area of residence, family lifetime and ethnicity, are some of the socioeconomic factors that impact the level of willingness to become agripreneurs. Within the context of these studies, the views about agricultural activities in general, together with agricultural education at the secondary level and availability of financing assist youth in intending to become agripreneurs (Magagula & Tsvakirai, 2020). Furthermore, to enable agripreneurial activities, there is need for an integrated approach that combines gender sensitivity, entrepreneurial traits and skills and technical and professional business management capabilities. Therefore, the following hypothesis is proposed: H4: Social and cultural barriers negatively affect agripreneurial success. Key characteristics for successful agripreneurial innovation include extensive networks, integration with knowledge providers, direct communication and collaboration with individuals both within and outside the agricultural sector (Lambrecht et al., 2018). Social networking plays a essential role in fostering agripreneurial ventures, serving as a crucial determinant for building connections, sharing knowledge and accessing resources that support agripreneurship (Mortala Boye et al., 2022). Digital access further enhances these networks by providing agripreneurs with broader market reach and more efficient communication channels. Therefore, the following hypothesis is proposed: H5: Networking and digital access facilitate agripreneurial success among university students. Excessive peer-to-peer copying, extensive knowledge sharing without appropriate innovation, and a lack of institutional support for handling emerging technologies are some of the factors that contribute to the difficulties faced by agripreneurs and cause instability in their commercial endeavors (Adobor, 2020). Additionally, agriculture businesses can become even more unstable due to a variety of start-up conditions, uneven pricing policies, different management styles, limited beginning resources, and desires for completely new equipment (Escalante & Turvey, 2006). According to Duchesneau and Gartner (1990), successful agripreneurs typically have more starting experience, business exposure, and entrepreneurial backgrounds, but they also frequently feel that they have less control over their performance than unsuccessful agripreneurs. Therefore, the following hypothesis is proposed: H6: Marketing and economic conditions significantly impact agripreneurial opportunities. Material and Methods In order to assess agribusiness success, the current study looks at the perceived obstacles that students encounter. Students enrolled in various agricultural and related education programs at agricultural universities make up our target population. 300 filled questionnaires were successfully gathered for analysis during our field survey. According to Chisnall (2001), using a questionnaire to collect data is an efficient way to get precise answers. We sought to collect numerical data appropriate for statistical analysis using structured questionnaires so that we could assess the connections between various independent variables and the dependent variable of agribusiness success. We created scales and items pertaining to perceived obstacles to agripreneurship success based on prior research following a thorough literature analysis. Notably, we discovered a deficiency in thorough, standardized tools for assessing the performance of agribusiness. We created a brand-new survey tool, the AgriSuccess Scale, to solve this. Environmental and technological barriers, government and policy support, knowledge and skills, marketing and economic conditions, networking and digital access, resource availability, and social and cultural barriers are the seven elements that make up this scale. We carried out a pilot study and had regular conversations with subject matter experts and agripreneurs to improve the AgriSuccess Questionnaire. They provided essential insights that helped define and develop the questionnaire. A pre-final version with 28 qualities designed to gauge agripreneurs' satisfaction was examined in the pilot study. In the end, we found 23 traits that fit the seven AgriSuccess Scale aspects well, bringing the total down from 34. The completed questionnaire is adaptable and can be used to evaluate perceived obstacles to agribusiness development both in India and internationally. Using SMART PLS analysis, we were able to verify the AgriSuccess instrument's dependability. The Composite dependability (CR) was higher than the required minimum of 0.70, showing high reliability. Demographic details and information on perceived obstacles to agribusiness success were also included in the poll. Because of its clarity, we selected the commonly used five-point Likert scale from among the available scaling options, as recommended by Malhotra and Birks (2003). On this scale, 5 means "very important," 1 means "not important at all," and the intermediate values are "very important," "important," "neutral," "not important," and "not important at all." In addition, we applied the Fornell-Larcker Criterion for discriminant validity and computed Cronbach's Alpha, R-square, and F-square to evaluate the validity and reliability of the constructs used in the study. Table 1 Seven dimensions of AgriSuccess Scale and attributes of the study Constructs Codes Items Resource availability RA1 Availability of Land RA2 Availability of Labour RA3 Availability of Capital RA4 Access to Credit and Financing Options Knowledge and Skills KS1 Proper Agriculture Knowledge KS2 Access to Technology KS3 Education and Training KS4 Extension Services Market and Economic Conditions MEC1 Marketing Support MEC2 Supply Chain Issues MEC3 Market Information and Intelligence MEC4 Consumer Preferences and Trends Government and Policy Support GPS1 Govt Policies GPS2 Regulatory Environment GPS3 Risk Management Social and Cultural Barriers SCB1 Fear of Failure SCB2 Not Considered as Good Profession SCB3 Social and Cultural Factors Environmental and Technological Barriers ETB1 Climate Change ETB2 Perishable Produce ETB3 Environmental Sustainability Networking and Digital Access NDA1 Networking and Collaboration NDA2 Digital Literacy and Internet Access Agripreneurial Success AS1 Successful career path AS2 Sustainable income and livelihood AS3 long-term growth and profitability Table 1 outlined the factors influencing agripreneurial success, categorized into key areas. In order to sustain agribusiness endeavors, resource availability identifies critical inputs like land, manpower, capital and financing access. In order to prepare agribusiness owners for success, knowledge and skills highlight the importance of appropriate agricultural education, technical access, and training. The market and economic conditions draw attention to elements that impact agripreneurship, such as consumer preferences, supply chain dynamics, and marketing assistance. With an emphasis on laws and rules that promote growth, the significance of government and policy support is highlighted. Social and Cultural Barriers expose attitudes like fear of failure and disapproval of agriculture that discourage young people from pursuing agripreneurship. Technological and environmental barriers deal with issues that obstruct advancement, such as access to technology and climate change. Digital access and networking highlight the importance of teamwork and digital literacy in enhancing opportunities. Finally, Agripreneurial Success encompasses indicators of achievement, including career paths, sustainable income and long-term growth. Table 2 Demographic Profile of Respondents Demographic Category Responses Percentage (%) Gender Male 180 60% Female 120 40% Education Level B.Sc 120 40% M.Sc 110 36.67% Ph.D 70 23.33% Background Rural 190 63.33% Urban 110 36.67% Want to Pursue Agribusiness Yes 90 70% No 210 30% The descriptive statistics highlighted key demographic insights from the study's participants, university students in agribusiness and related programs, as shown in Table 2 . Males comprised 60% of the respondents, while females accounted for 40%, indicating a gender imbalance. The education levels varied, with 40% holding a Bachelor of Science, 36.67% pursuing a Master of Science, and 23.33% engaged in Ph.D. programs. Notably, a significant 63.33% of participants came from rural backgrounds, suggesting that their direct connection to agriculture could have shaped their views on agripreneurship. However, only 30% expressed interest in pursuing agribusiness, with 70% indicating no interest, highlighting potential barriers or a lack of enthusiasm for agripreneurship. Results Table 3: Results of Structural Equation Modeling (SEM) for Independent Variables Affecting Agripreneurial Success Construct/Factors Outer loadings Path coefficients f-squared Value % of variance explained Environmental & Technological Barriers 0.452 0.150 21.625 ETB1 0.812 ETB2 0.850 ETB3 0.871 Government & Policy Support 0.450 0.200 19.402 GPS1 0.831 GPS2 0.868 GPS3 0.925 Knowledge & Skills 0.300 0.250 16.62 KS1 0.813 KS2 0.854 KS3 0.892 KS4 0.862 Marketing & Economic Condition 0.350 0.200 17.568 MEC1 0.859 MEC2 0.897 MEC3 0.893 MEC4 0.887 Networking and digital Access 0.286 0.175 NDA1 0.926 NDA2 0.921 Resource Availability 0.224 0.180 14.256 RA1 0.875 RA2 0.866 RA3 0.863 RA4 0.800 Social and Cultural Barriers 0.150 0.025 8.251 SCB1 0.810 SCB2 0.798 SCB3 0.788 Table 3 presents a detailed summary of the Structural Equation Modeling (SEM) analysis results, highlighting the independent variables that influence agripreneurial success. Four key metrics—outer loadings, path coefficients, f-squared values and the percentage of variation explained—are used to assess each construct. Each item's outer loading shows how strongly it is related to its relevant construct; values nearer 1 suggest a greater association. Items ETB1, ETB2 and ETB3 have high outer loadings of 0.812, 0.850 and 0.871 in the Environmental & Technological Barriers construct, respectively. This suggests that these metrics provide a substantial contribution to the construct, successfully encapsulating the perceived obstacles that agribusiness owners encounter in the areas of technology and environmental difficulties. Each construct's direct impact on agribusiness success is measured by path coefficients. With the highest path coefficient of 0.250, Knowledge & Skills shines out in this case and suggests a strong positive correlation with agribusiness success. This implies that agribusiness results will probably be significantly improved by gains in knowledge and abilities. With noteworthy path coefficients of 0.200 apiece, Marketing & Economic Conditions and Government & Policy Support both play crucial roles in promoting agribusiness development. On the other hand, Social and Cultural Barriers has a low path coefficient of 0.025, which indicates that it has little direct influence on the success of agribusiness. Each independent variable's impact on the dependent variable, agribusiness performance, is measured by the f-squared values. With the highest f-squared value of 21.625 among the constructs, Environmental & Technological Barriers appears to have a significant impact on agribusiness success. With an f-squared value of 16.62, Knowledge & Skills likewise exhibits a significant effect. Social and Cultural Barriers, on the other hand, have a low f-squared value of 8.251, suggesting a negligible impact. Last but not least, the Percentage of Variance Explained shows the extent to which each independent variable explains the variances in agribusiness success. Constructs like Knowledge & Skills (16.62%) and Marketing & Economic Conditions (17.568%) strongly contribute to the explanation of the variance in agribusiness success, highlighting their significance as important factors. However, social and cultural hurdles only explain 8.251% of the variance, indicating that although these obstacles are recognized, their total effect on agribusiness is comparatively minimal. Table 4: R-Squared and Adjusted R-Squared Values for Agripreneurial Success Dependent Variable R-square R-square adjusted Agripreneurial Success 0.713 Table 4 presented the R-squared and adjusted R-squared values for the dependent variable, Agripreneurial Success. The R-squared value of 0.713 indicated that approximately 71.3% of the variance in agripreneurial success could be explained by the independent variables included in the model. This high value suggested a strong relationship between the predictors and the outcome, affirming that the factors identified in the study were relevant contributors to agripreneurial success. The adjusted R-squared value of 0.699 provided a more conservative estimate by adjusting for the number of predictors in the model. This value indicated that after accounting for the number of independent variables, about 69.9% of the variability in agripreneurial success was explained by the model. The proximity of the adjusted R-squared to the R-squared value reflected the model's robustness, suggesting that the inclusion of independent variables added meaningful explanatory power without introducing excessive complexity. Overall, these statistics underscored the model's effectiveness in capturing the key determinants of agripreneurial success among university students Table 5: Reliability and Validity Measures of Constructs of dependent and independent variables Construct Cronbach's Alpha Composite Reliability (rho_a) Composite Reliability (rho_c) Average Variance Extracted (AVE) Agripreneurial Success 0.836 0.870 0.806 0.586 Environmental & Technological Barriers 0.791 0.800 0.830 0.617 Government & Policy Support 0.774 0.785 0.870 0.689 Knowledge & Skills 0.754 0.780 0.842 0.572 Marketing & Economic Condition 0.789 0.790 0.863 0.613 Networking and Digital Access 0.732 0.740 0.850 0.731 Resource Availability 0.729 0.790 0.890 0.586 Social and Cultural Barriers 0.781 0.800 0.820 0.605 Table 5 summarized the reliability and validity measures for the constructs examined in the study. Cronbach's Alpha and Composite dependability were used to evaluate the constructions' dependability. With a Cronbach's Alpha of 0.836, Agripreneurial Success showed a high degree of internal consistency. With a Composite Reliability of 0.870, this construct was likewise robust. Similarly, with Cronbach's Alphas of 0.791 and 0.774, respectively, Environmental & Technological Barriers and Government & Policy Support demonstrated good reliability scores. Excellent reliability was indicated by Resource Availability's highest Composite Reliability of 0.890. The majority of constructs appeared to account for a significant amount of the variance in their indicators, as indicated by the Average Variance Extracted (AVE) values, which varied from 0.572 for Knowledge & Skills to 0.731 for Networking and Digital Access. Overall, the findings supported the study's conclusions about the perceived obstacles to agripreneurship by confirming the constructs' validity and dependability. Table 6: Fornell-Larcker Criterion Results for Discriminant Validity Assessment Construct AS ETB GPS KS MEC NDA RA SCB Agripreneurial Success (AS) 0.765 Environmental & Technological Barriers (ETB) 0.658 0.786 Government & Policy Support (GPS) 0.738 0.701 0.830 Knowledge & Skills (KS) 0.732 0.701 0.691 0.756 Marketing & Economic Condition (MEC) 0.775 0.688 0.683 0.795 0.783 Networking and Digital Access (NDA) 0.688 0.575 0.560 0.690 0.757 0.855 Resource Availability (RA) 0.640 0.594 0.522 0.621 0.710 0.625 0.586 Social and Cultural Barriers(SCB) 0.676 0.675 0.654 0.665 0.632 0.615 0.528 0.778 Table 6 displayed the Fornell-Larcker Criterion results, which assessed the discriminant validity of the constructs in the research. For every construct, the diagonal values represented the square root of the Average Variance Extracted (AVE). With a grade of 0.765, Agripreneurial Success (AS) showed a high level of individuality. Knowledge & Skills (KS) and Government & Policy Support (GPS) had values of 0.730 and 0.756, respectively, while Environmental & Technological Barriers (ETB) displayed a similarly strong value of 0.786. Further evidence that the constructs were sufficiently different from one another was provided by the off-diagonal values, which showed the correlations between the constructs and stayed below the diagonal values. Overall, the findings on perceived obstacles to agripreneurship were supported by the Fornell-Larcker Criterion results, which successfully validated the discriminant validity of the variables. Table 7: Collinearity Statistics (VIF) for Outer Model Analysis Variable VIF Environmental & Technological Barriers -> Agripreneurial Success 1.524 Government & Policy Support -> Agripreneurial Success 1.865 Knowledge & Skills -> Agripreneurial Success 1.912 Marketing & Economic Condition -> Agripreneurial Success 2.000 Networking and Digital Access -> Agripreneurial Success 1.496 Resource Availability -> Agripreneurial Success 1.435 Social and Cultural Barriers -> Agripreneurial Success 1.611 Table 7 presented the Variance Inflation Factor (VIF) values for the outer model, which assessed the collinearity among the independent variables influencing agripreneurial success. The VIF values indicated the degree of multicollinearity, with values below 5 typically suggesting no significant issues. The Knowledge & Skills construct had the highest VIF value of 1.912, followed closely by Marketing & Economic Condition at 2.000, both of which remained below the critical threshold, indicating acceptable levels of multicollinearity. Other constructs, such as Government & Policy Support and Social and Cultural Barriers, also displayed VIF values of 1.865 and 1.611, respectively. The values for Environmental & Technological Barriers, Networking and Digital Access, and Resource Availability were lower, at 1.524, 1.496 and 1.435. Overall, the results demonstrated that multicollinearity among the variables was not problematic, allowing for reliable interpretation of their relationships with agripreneurial success. Table 8: Model Fit Indices for Saturated and Estimated Models Model Saturated model Estimated model SRMR 0.048 0.048 d_ULS 0.073 0.073 d_G 0.95 0.95 Chi-square 125.237 125.237 NFI 0.950 0.950 The model fit indices in the table 8 assessed the overall adequacy of the saturated and estimated models used in the study. The Standardized Root Mean Square Residual (SRMR) value for both models was 0.048, indicating a good fit since values below 0.08 are generally acceptable. The d_ULS and d_G values, both recorded at 0.073 and 0.95, respectively, suggested that the distance between the empirical and estimated covariance matrices was minimal, reinforcing the models' adequacy. The Chi-square statistic was 125.237 for both models, signifying that the fit was statistically significant. Additionally, the Normed Fit Index (NFI) was 0.950, which exceeded the threshold of 0.90, indicating a strong fit. Overall, the results confirmed that both the saturated and estimated models fit the data well, allowing for confident interpretation of the relationships within the study. Discussion The study tested six hypotheses, each focusing on various barriers or facilitators influencing agripreneurial success among university students. According to the first hypothesis, agribusiness success would be adversely affected by technological and environmental obstacles. This included problems with environmental sustainability, the perishable nature of agricultural products, and climate change. The data unexpectedly contradicted this expectation, and the theory was rejected. As demonstrated by a noteworthy path coefficient of 0.452, these barriers were found to promote a favorable link with agripreneurial outcomes rather than impede success. This demonstrated that students saw these environmental issues as chances for creativity rather than just obstacles. For instance, agribusiness owners have adopted sustainable practices, developed cutting-edge storage technology and experimented with climate-resilient crops in response to the pressing need to address climate change and manage perishable goods. This change demonstrated the increasing importance of sustainability in the agricultural sector, where growth and competitiveness depend on flexibility and the application of cutting-edge technologies. Popescu et al. (2023) expressed a similar viewpoint, highlighting the dynamic interaction between agripreneurship obstacles and innovation. The findings strongly supported the second hypothesis, which looked at how government and policy support affected agribusiness success. This theory concentrated on elements including risk management procedures, regulatory environments, and governmental policies. The results, which had a path coefficient of 0.450, demonstrated how important government assistance is to agribusiness success. In particular, programs that provide financial aid, subsidies, credit availability and risk management techniques enable students to get past early obstacles like obtaining funds and comprehending intricate rules. Furthermore, a supportive regulatory framework that reduces bureaucratic barriers and streamlines compliance creates a more encouraging atmosphere for agripreneurs, allowing them to focus on expanding their businesses. Furthermore, efficient risk management systems offer a buffer against unforeseen agricultural difficulties like crop failures or price swings, enabling agribusiness owners to make well-informed choices and invest in their projects with assurance. These observations highlight the need of carefully thought-out government initiatives in assisting would-be agribusiness owners in successfully overcoming obstacles. Yami et al. (2019) reported similar results, highlighting the need of supportive policies in agripreneurship. With a path coefficient of 0.300, the third hypothesis—which looked at how knowledge and skills affect agribusiness success—received strong support. This outcome confirmed the benefits of having access to technology, continuing education, extension services and appropriate agricultural knowledge. Students agreed that addressing the difficulties faced in agriculture requires having the appropriate skill set, which includes a solid grasp of contemporary agricultural methods and the capacity to use cutting-edge technology. Extension services were especially valued for their ability to bridge the gap between theoretical knowledge and its practical application by offering technical assistance and workable solutions. This research emphasizes the necessity of incorporating thorough agribusiness education into university courses to guarantee that students gain the entrepreneurial and technical skills required to prosper in a fast-evolving agricultural environment. Ahmad & Ahmad (2013) came to similar conclusions, highlighting the importance of knowledge and skills in promoting agribusiness success. The fourth hypothesis looked at how social and cultural barriers affect agribusiness success and proposed that they would be detrimental. The investigation took into account factors including fear of failure and societal views of agriculture as a less prestigious career. Although this hypothesis was confirmed, the route coefficient of 0.150 showed that these obstacles, while important, had less of an impact than other elements like market circumstances and government assistance. Many students stated that they were deterred from pursuing jobs in agribusiness by cultural perceptions of agriculture, namely the idea that it is an unpleasant profession. Another significant impediment was the fear of failing, which is frequently influenced by expectations from family and the society. The comparatively smaller impact of these social and cultural barriers, however, indicated that students were increasingly figuring out how to get around these perceptions, perhaps with the help of outside support networks like mentorship programs and entrepreneurial networks that boost their self-esteem and lessen the stigma attached to pursuing a career in agriculture. Similar results were found in a study by Ephrem et al. (2021), which showed that youth aspirations to pursue agricultural prospects in Eastern DRC were highly influenced by perceived societal norms. The importance of networking and digital access as enablers of agribusiness success was investigated in the fifth hypothesis. With a path coefficient of 0.286, the results provided excellent support for this hypothesis and highlighted the importance of professional networks and digital literacy in assisting students in overcoming the difficulties that come with working in agriculture. Through networking, agribusiness owners can gain access to important resources, possibilities for collaboration, and knowledge exchange, all of which can result in alliances and market expansion. Additionally, students can reach a larger market and use internet resources to increase their operational efficiency thanks to digital connectivity, particularly in remote locations. Their competitiveness is increased by being able to manage supply chains, sell their goods more successfully and interact with customers directly thanks to digital literacy. This research emphasizes the necessity of enhancing digital infrastructure and providing instruction in digital tools, both of which may help agribusiness owners thrive in the fast-paced market of today. Kaur et al. (2022) revealed similar findings, highlighting the significance of these elements in promoting agribusiness expansion. The sixth and last hypothesis examined how marketing and the state of the economy affect agribusiness prospects. With a path coefficient of 0.350, this hypothesis was well supported, demonstrating the crucial impact of elements like supply chain management, marketing support, market intelligence and knowledge of customer preferences. In order to effectively contact consumers and stay competitive in the market, students emphasized the importance of putting strong marketing plans into practice. Furthermore, their ability to handle supply chain difficulties, stay up to date on market developments, and adjust to changing customer preferences greatly improved their ability to position products favourably. These results emphasize how crucial it is for agribusiness owners to concentrate on both production and matching their products to consumer needs in order to achieve long-term success. Garima et al. (2023) came to similar conclusions, reaffirming that agribusiness success is largely dependent on marketing expertise. Overall, the study revealed that while certain barriers, such as resource availability and social perceptions, influence agripreneurial outcomes, other factors—particularly government support, knowledge, market conditions, and networking—are significantly more impactful in determining success. Notably, the findings regarding environmental and technological barriers challenge conventional perspectives, indicating that these challenges can act as powerful catalysts for innovation and resilience. The positive correlation between environmental challenges and agripreneurial success suggests that aspiring agripreneurs must prioritize adaptability and sustainability to thrive in an increasingly unpredictable agricultural landscape. These results are consistent with the research conducted by Khayri et al. (2011), which underscores the importance of embracing challenges as opportunities for growth in the agribusiness sector. The study also revealed several additional findings that contribute to a more comprehensive understanding of agripreneurial success among university students. The R-squared value of 0.713, which shows that the independent variables in the model account for roughly 71.3% of the variance in agribusiness success, is one of the noteworthy findings. This high R-squared value indicates that the elements taken into account—such as market conditions, government backing, knowledge and skills, social and cultural obstacles, networking, and technological and environmental constraints—are very important in forecasting agribusiness success. The model's strength highlights how well the components selected for this study capture the crucial factors affecting agripreneurs' results, highlighting how crucial it is to address these obstacles in educational and policy interventions. The impact of individual barriers on agribusiness success was further shown by the substantial F-squared values. Significantly, restrictions related to technology and the environment that were first thought to have a negative effect showed a big effect size, indicating that these perceived difficulties have a significant impact on agribusiness innovation. This outcome supports the previous discovery that students' adoption of sustainable behaviours and new technologies is motivated by environmental concerns. In order to address the difficulties brought on by climate change and environmental degradation, agribusiness owners are progressively adjusting to shifting environmental conditions and utilizing technological breakthroughs. Future agripreneurs' success may be greatly influenced by their capacity to turn these obstacles into chances for expansion and uniqueness, particularly as environmental uncertainty increasingly impacts the agriculture industry. Cronbach's Alpha and Composite Reliability, which measure the constructs' validity and reliability, showed excellent internal consistency among the variables. Cronbach's Alpha values for all constructs were significantly higher than the 0.7 cutoff, suggesting strong reliability. For example, the Composite Reliability was very strong for constructs such as marketing conditions and government backing, which strengthened the internal coherence of the items in these constructs. This guarantees that the study's variables—such as government policies, marketing assistance and technological access—are accurate and meaningful indicators of the obstacles and facilitators faced by agribusiness owners. The findings are further supported by the high reliability of these constructs, which imply that the measures employed well captured the students' perceptions of the elements affecting their performance in agripreneurship. An additional noteworthy finding pertains to the notions' discriminant validity as evaluated by the Fornell-Larcker Criterion. The research verified that the constructs were different from one another, indicating that each one assessed a different facet of agribusiness success. For example, it was discovered that market conditions—which concentrated on supply chains, market intelligence, and customer preferences—were different from government assistance, which included elements of legislation, the regulatory environment and risk management. This separate division of constructs emphasizes the multifaceted character of agribusiness success, where several enablers and impediments interact but have diverse effects on results. The results pertaining to the impact of personal obstacles, such social and environmental factors, can be safely evaluated without worrying about concept overlap thanks to the great discriminant validity. Additionally, the investigation uncovered information from the collinearity statistics, specifically the values of the Variance Inflation Factor (VIF). Multicollinearity was not an issue in the model, as these values, which varied from 1.435 to 2.000, were all below the critical threshold of 5. This indicates that the independent variables—knowledge and skills, government backing and networking—did not show troublesome intercorrelations, making it possible to assess their distinct contributions to agribusiness performance with greater reliability. A clearer view of how many factors support or impede agribusiness expansion is provided by the absence of multicollinearity, which guarantees that each barrier can be evaluated separately. The model's suitability was further confirmed by the model fit indices. Given that values below 0.08 are often regarded as acceptable in structural equation modeling, the Standardized Root Mean Square Residual (SRMR) value of 0.048 suggested a reasonable fit. The model's strong fit was further supported by the Chi-square statistic and the Normed Fit Index (NFI), which indicated that the relationships between the components were well represented. The data can be used with confidence to make conclusions and offer recommendations because of the high model fit, which validates the hypothesized links between the barriers and agribusiness success. Implications for Agripreneurial Stakeholders The findings of this study highlight crucial implications for various stakeholders in agribusiness, emphasizing a collaborative approach to enhance agripreneurial success. For entrepreneurs, the positive relationship between environmental and technological barriers and success suggests that these challenges can be seen as opportunities for innovation, particularly through sustainable practices and improved networking and digital literacy. Scholars are encouraged to build on these findings by examining the evolving interplay of barriers and facilitators over time, while also considering the impact of emerging technologies and consumer preferences. Policymakers must recognize the importance of effective policies that provide financial assistance and reduce bureaucratic hurdles, particularly in rural areas where access to resources is often limited. Practitioners, including agricultural educators and extension services, should integrate practical skills and technological knowledge into their programs to equip aspiring agripreneurs for success. Lastly, stakeholders such as NGOs and financial institutions can play a pivotal role by offering mentorship, access to credit and networking opportunities, thus helping agripreneurs navigate challenges and strengthen the agricultural sector's growth and sustainability. Future research can expand on this study's findings by exploring several promising avenues. Longitudinal studies could track changes in agripreneurial success over time, while comparative analyses between regions or countries could highlight how contextual factors like cultural attitudes and government support influence outcomes. Investigating the impact of emerging technologies, such as precision agriculture and blockchain, could reveal how these tools mitigate barriers and enhance productivity. Additionally, examining the role of social media and digital marketing in facilitating networking and market access could provide insights into consumer engagement. Research focusing on the mental health and well-being of agripreneurs, evaluating specific government policies and strategies for engaging youth in agriculture could inform support programs and interventions. Finally, adopting interdisciplinary approaches that integrate insights from various fields could lead to holistic models that better capture the interactions influencing agripreneurial success, ultimately fostering innovation and sustainability in this vital sector. Conclusion This study investigated the perceived barriers to agripreneurship among university students in India, focusing on factors influencing agripreneurial success. The analysis revealed significant insights into the relationships between various barriers and success factors. Key findings indicated that environmental and technological barriers, along with government and policy support, were crucial for enhancing agripreneurial success. The results highlighted the importance of knowledge, skills and marketing conditions in facilitating agripreneurship. Furthermore, the study emphasized the limited impact of social and cultural barriers on success, suggesting that addressing more tangible factors could improve agripreneurial outcomes. Overall, this research contributes to the understanding of agripreneurial dynamics, providing valuable recommendations for policymakers and educational institutions to foster a more supportive environment for aspiring agripreneurs. Declarations Acknowledgements The authors wish to express their gratitude to all the students of agricultural universities who participated in this study. Funding: The authors declare that there was no funding received for this research. Competing Interests: The authors declare that they have no competing interests related to this research. Availability of Data and Materials: The datasets analysed during the current study are available from the corresponding author on reasonable request. Ethics statement: The need for formal review was waived by University (Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu). Ethics committee as the study complied with the university’s established norms for non-invasive social research. Informed consent: Informed consent was obtained from all participants prior to their involvement in the study. Participants were fully informed about the purpose of the research, the nature of their participation and their rights, including the option to withdraw at any stage. The confidentiality and anonymity of the participants were safeguarded and all data were used solely for academic purposes. Authors' Contributions: RK and AB conceptualized the study, designed the research methodology and contributed to data analysis and interpretation. AM critically reviewed and revised the manuscript for intellectual content and contributed to data analysis. KS and ES assisted in data analysis. All authors read and approved the final manuscript References Acs, Z., Åstebro, T., Audretsch, D., & Robinson, D. (2016). Public policy to promote entrepreneurship: a call to arms. Small Business Economics , 47, 35–51. https://doi.org/10.2139/ssrn.2728664. Addo, L. (2018). Factors influencing agripreneurship and their role in agripreneurship performance among young graduate agripreneurs. International Journal of Environment, Agriculture and Biotechnology , 3, 2051–2066. https://doi.org/10.22161/IJEAB/3.6.14. Adobor, H. (2020). Entrepreneurial failure in agribusiness: evidence from an emerging economy. Journal of Small Business and Enterprise Development , 27, 237–258. https://doi.org/10.1108/jsbed-04-2019-0131. Ahmad, S., & Ahmad, K. (2013). Market Driven Agribusiness Education in Agricultural Institutions for Sustainability. 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Agricultural Finance Review , 66, 61–75. https://doi.org/10.1108/00214660680001180. Garima., Bhardwaj, K., Sharma, D., & Dhingra, A. (2023). Marketing and Operational Issues Faced by First Generation Agripreneurs in Managing Agribusiness: A View Point of Haryana. International Journal of Professional Business Review . https://doi.org/10.26668/businessreview/2023.v8i10.3880. In, I., For, E., & Development, A. (2016). Innovation in agricultural research and education for agripreneurship development in India. Mini Review Article , 1(3), 72–78. Retrieved from http://www.innovativefarming.in/index.php/Home/article/view/18/17. Khayri, S., Yaghoubi, J., & Yazdanpanah, M. (2011). Investigating barriers to enhance entrepreneurship in agricultural higher education from the perspective of graduate students. Procedia - Social and Behavioral Sciences , 15, 2818-2822. https://doi.org/10.1016/J.SBSPRO.2011.04.195 . Lambrecht, E., Crivits, M., Lauwers, L., & Gellynck, X. (2018). Identifying key network characteristics for agricultural innovation: a multisectoral case study approach. Outlook on Agriculture , 47, 19–26. https://doi.org/10.1177/0030727018760604. Magagula, B., & Tsvakirai, C. (2020). Youth perceptions of agriculture: influence of cognitive processes on participation in agripreneurship. Development in Practice , 30, 234–243. https://doi.org/10.1080/09614524.2019.1670138. N., Singh, R., Mishra, J., & Sharma, J. (2019). Theoretical foundations of agripreneurship development process: a study of best practices, facilitative factors and inhibitive factors of achiever farmers. Journal of Community Mobilization and Sustainable Development , 14, 373–377. Obaji, N., & Olugu, M. (2014). The role of government policy in entrepreneurship development. Science Journal of Business Management , 2, 109. https://doi.org/10.11648/J.SJBM.20140204.12. Pandey, G. (2013). Agripreneurship education and development: need of the day. Asian Resonance , 2(4), 155–157. Parmar, G., & Rathod, R. M. (2022). A study on perceived barriers towards entrepreneurship in agriculture. EPRA International Journal of Multidisciplinary Research, 8 (12), 269–273. Sah, P., Sujan, D. K., & Kashyap, S. K. (2009). Role of agripreneurship in the development of rural area. Paper presentation in ICARD at Banaras Hindu University, Varanasi – 221005. Singh, A. (2013). Factors influencing entrepreneurship among farming community in Uttar Pradesh. Researchers World , 4, 114. Singh, A. P. (2013). Strategies for developing agripreneurship among farming community in Uttar Pradesh, India. Academicia: An International Multidisciplinary Research Journal , 3(11), 1–12. Verma, R. K., Sahoo, A. K., & Rakshit, S. (2019). Opportunities in agripreneurship in India: need, challenges and future prospects. Journal of Global Innovations in Agricultural Sciences ,13. Popescu, G., Popescu, M., Pampana, S., Khondker, M., Umehara, M., Hayashi, H., & Touch, N. (2023). Sustainability as an agroecological strategy towards resilience in agricultural systems. Agronomy Journal . https://doi.org/10.1002/agj2.21483. Yami, M., Feleke, S., Abdoulaye, T., Alene, A., Bamba, Z., & Manyong, V. (2019). African Rural Youth Engagement in Agribusiness: Achievements, Limitations, and Lessons. Sustainability . https://doi.org/10.3390/SU11010185. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5437635","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383039005,"identity":"1f8b821d-8cfe-4a9b-b7cf-f2e766b9c74d","order_by":0,"name":"Rohit 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Research Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5437635/v1/a4056645d0976ec68e672b35.png"},{"id":70194814,"identity":"7318f49f-087d-49f5-997a-41d1aa13ff15","added_by":"auto","created_at":"2024-11-29 11:17:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":202529,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath coefficients and outer loadings for the variables on agripreneurial success\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5437635/v1/28917be877c7b6bb4ccf792c.png"},{"id":70671379,"identity":"e67a1a9a-71c6-434f-993e-3e7760a94b33","added_by":"auto","created_at":"2024-12-05 12:54:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1046873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5437635/v1/23293c07-f405-46b3-bda1-51a12f94952d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Agripreneurial Success Among University Students: Perceived Barriers to Agricultural Entrepreneurship in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAgriculture has traditionally been considered as be backbone of the Indian economy, contributing significantly to the national income and employing a substantial portion of the population. Historically, over half of the national income was derived from agriculture, with more than 70% of the population dependent on this sector at the time of India\u0026rsquo;s independence (Pandey, 2013). Despite this reliance, the agricultural sector has faced numerous challenges, particularly since the economic reforms initiated in the early 1990s, which emphasized liberalization, privatization and globalization (Singh, 2013). While these reforms aimed to enhance growth in rural areas, the expected agricultural performance has not materialized satisfactorily in subsequent years. Factors such as waste land, depleting natural resources and rural youth migration to urban areas have necessitated a paradigm shift in agricultural practices, moving beyond traditional methods of land tilling and crop harvesting (Uplaonkar \u0026amp; Biradar, 2015). In this context, the concept of agripreneurship has emerged as essential component for economic development in rural areas. Agripreneurship refers to the application of entrepreneurial principles in the agricultural sector, leading to increased productivity, job creation and the introduction of innovative products and services (Alex, 2011). The adoption of entrepreneurial practices within agriculture can address several pressing issues, including unemployment and poverty in rural communities and can contribute to overall economic growth (Bairwa et al., 2014a). However, many traditional farmers lack the necessary managerial, technical and innovative skills to cope with modern agricultural challenges such as delayed monsoons, drought and market fluctuations, which can lead to dire consequences, including financial distress and even suicide (Sah, et al 2009). Despite government initiatives aimed at promoting entrepreneurship, such as the Make in India and Start-up India campaigns, significant barriers persist. Funding remains a primary challenge for aspiring agripreneurs, as many face difficulties in convincing funding agencies to support their innovative ideas (Verma et al., 2019). Moreover, while the Indian agricultural sector has the potential for substantial growth-given its diverse climatic conditions, rich biodiversity and vast cultivable land\u0026mdash;these opportunities are often underutilized due to inadequate infrastructure, lack of access to technology, and insufficient knowledge about market dynamics (Chand, 2019). To foster agripreneurship effectively, it is essential to identify and address these perceived barriers. The qualities and skills necessary for successful agripreneurs include problem-solving abilities, creativity, market orientation and leadership (Singh, 2013). Additionally, understanding the regional factors that influence the transition from employment to self-employment is crucial for developing effective policies to promote agripreneurship (In, For, \u0026amp; Development, 2016). As India's innovative agripreneurs begin to explore opportunities beyond the domestic market, technology adoption and innovation emerge as key strategies to ensure world-class quality, while also boosting farm productivity and profitability. The shift toward agripreneurship will likely appeal to highly educated youth, especially as it becomes both socially accepted and financially rewarding. Well-designed agripreneurship programs have the potential to steer young people towards either entrepreneurial ventures or roles within the agricultural workforce, contributing to the sector's global advancement (Bairwa et al., 2014b).\u003c/p\u003e\n\u003ch3\u003eLiterature Review and Hypotheses:\u003c/h3\u003e\n\u003cp\u003eAgripreneurship, blending agriculture with entrepreneurship, has become a vital focus area, especially in emerging nations like India, where agriculture plays a major role in the economy. Yet, despite its importance, agripreneurship encounters multiple obstacles, particularly for aspiring agripreneurs like university students. Research has delved into various factors impacting agripreneurial success, from government policies to personal competencies, though the unique challenges faced by students aiming to become agripreneurs are seldom highlighted. Agripreneurship holds the potential to foster sustainable economic growth in India by rejuvenating the agricultural sector, enhancing livelihoods, and supporting environmental sustainability. Success in agripreneurship hinges on aspects like market and economic conditions, knowledge and skill development, cultural and social dynamics, policy and government backing, technological access and the availability of resources such as land and credit (Arumugam \u0026amp; Manida, 2023; Singh, 2013).\u003c/p\u003e \u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1: Environmental and technological barriers have a significant negative impact on agripreneurial success.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGovernment policies, along with initiatives from non-governmental organizations and private sector partnerships, are pivotal in fostering an environment conducive to agripreneurship in India. Effective policies can help remove barriers and provide necessary support for agripreneurs to succeed (Arumugam \u0026amp; Manida, 2023). In contrast, policies in some regions have been criticized for inefficiently using public funds and promoting low-growth ventures with minimal innovation (Acs et al., 2016). However, in the Indian context, well-designed government policies play a critical role in agripreneurial development, contributing significantly to the country's economic progress (Obaji \u0026amp; Olugu, 2014).\u003c/p\u003e \u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2: Government and policy support positively influence agripreneurial success.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFor agripreneurs to succeed, they must demonstrate proactiveness, curiosity, determination and possess strong management and organizational skills, which are key to navigating the challenges of agribusiness (Singh, 2013). A comprehensive approach that includes high-quality training in both business and life skills, as well as relevant technical knowledge, is critical for agripreneurial success (Nain et al., 2019). Agripreneurship also requires ongoing training, technical skills and robust market connections, with successful implementation relying on coordinated efforts from diverse stakeholders to foster the growth and long-term success of agribusiness ventures (Arumugam \u0026amp; Manida, 2023).\u003c/p\u003e \u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH3: Knowledge and skills are positively correlated with agripreneurial success.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAccording to Barau and Adesiji (2018), economic status, gender, area of residence, family lifetime and ethnicity, are some of the socioeconomic factors that impact the level of willingness to become agripreneurs. Within the context of these studies, the views about agricultural activities in general, together with agricultural education at the secondary level and availability of financing assist youth in intending to become agripreneurs (Magagula \u0026amp; Tsvakirai, 2020). Furthermore, to enable agripreneurial activities, there is need for an integrated approach that combines gender sensitivity, entrepreneurial traits and skills and technical and professional business management capabilities.\u003c/p\u003e \u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH4: Social and cultural barriers negatively affect agripreneurial success.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eKey characteristics for successful agripreneurial innovation include extensive networks, integration with knowledge providers, direct communication and collaboration with individuals both within and outside the agricultural sector (Lambrecht et al., 2018). Social networking plays a essential role in fostering agripreneurial ventures, serving as a crucial determinant for building connections, sharing knowledge and accessing resources that support agripreneurship (Mortala Boye et al., 2022). Digital access further enhances these networks by providing agripreneurs with broader market reach and more efficient communication channels.\u003c/p\u003e \u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH5: Networking and digital access facilitate agripreneurial success among university students.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eExcessive peer-to-peer copying, extensive knowledge sharing without appropriate innovation, and a lack of institutional support for handling emerging technologies are some of the factors that contribute to the difficulties faced by agripreneurs and cause instability in their commercial endeavors (Adobor, 2020). Additionally, agriculture businesses can become even more unstable due to a variety of start-up conditions, uneven pricing policies, different management styles, limited beginning resources, and desires for completely new equipment (Escalante \u0026amp; Turvey, 2006). According to Duchesneau and Gartner (1990), successful agripreneurs typically have more starting experience, business exposure, and entrepreneurial backgrounds, but they also frequently feel that they have less control over their performance than unsuccessful agripreneurs.\u003c/p\u003e \u003cp\u003eTherefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH6: Marketing and economic conditions significantly impact agripreneurial opportunities.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eIn order to assess agribusiness success, the current study looks at the perceived obstacles that students encounter. Students enrolled in various agricultural and related education programs at agricultural universities make up our target population. 300 filled questionnaires were successfully gathered for analysis during our field survey. According to Chisnall (2001), using a questionnaire to collect data is an efficient way to get precise answers. We sought to collect numerical data appropriate for statistical analysis using structured questionnaires so that we could assess the connections between various independent variables and the dependent variable of agribusiness success. We created scales and items pertaining to perceived obstacles to agripreneurship success based on prior research following a thorough literature analysis. Notably, we discovered a deficiency in thorough, standardized tools for assessing the performance of agribusiness. We created a brand-new survey tool, the AgriSuccess Scale, to solve this. Environmental and technological barriers, government and policy support, knowledge and skills, marketing and economic conditions, networking and digital access, resource availability, and social and cultural barriers are the seven elements that make up this scale. We carried out a pilot study and had regular conversations with subject matter experts and agripreneurs to improve the AgriSuccess Questionnaire. They provided essential insights that helped define and develop the questionnaire. A pre-final version with 28 qualities designed to gauge agripreneurs' satisfaction was examined in the pilot study. In the end, we found 23 traits that fit the seven AgriSuccess Scale aspects well, bringing the total down from 34. The completed questionnaire is adaptable and can be used to evaluate perceived obstacles to agribusiness development both in India and internationally. Using SMART PLS analysis, we were able to verify the AgriSuccess instrument's dependability. The Composite dependability (CR) was higher than the required minimum of 0.70, showing high reliability. Demographic details and information on perceived obstacles to agribusiness success were also included in the poll. Because of its clarity, we selected the commonly used five-point Likert scale from among the available scaling options, as recommended by Malhotra and Birks (2003). On this scale, 5 means \"very important,\" 1 means \"not important at all,\" and the intermediate values are \"very important,\" \"important,\" \"neutral,\" \"not important,\" and \"not important at all.\" In addition, we applied the Fornell-Larcker Criterion for discriminant validity and computed Cronbach's Alpha, R-square, and F-square to evaluate the validity and reliability of the constructs used in the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeven dimensions of \u003cem\u003eAgriSuccess\u003c/em\u003e Scale and attributes of the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCodes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of Land\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of Labour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of Capital\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccess to Credit and Financing Options\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge and Skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProper Agriculture Knowledge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccess to Technology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation and Training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtension Services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket and Economic Conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarketing Support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupply Chain Issues\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarket Information and Intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsumer Preferences and Trends\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment and Policy Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGovt Policies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegulatory Environment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial and Cultural Barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFear of Failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Considered as Good Profession\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial and Cultural Factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental and Technological Barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClimate Change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerishable Produce\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental Sustainability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetworking and Digital Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNetworking and Collaboration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital Literacy and Internet Access\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgripreneurial Success\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuccessful career path\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSustainable income and livelihood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elong-term growth and profitability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlined the factors influencing agripreneurial success, categorized into key areas. In order to sustain agribusiness endeavors, resource availability identifies critical inputs like land, manpower, capital and financing access. In order to prepare agribusiness owners for success, knowledge and skills highlight the importance of appropriate agricultural education, technical access, and training. The market and economic conditions draw attention to elements that impact agripreneurship, such as consumer preferences, supply chain dynamics, and marketing assistance. With an emphasis on laws and rules that promote growth, the significance of government and policy support is highlighted. Social and Cultural Barriers expose attitudes like fear of failure and disapproval of agriculture that discourage young people from pursuing agripreneurship. Technological and environmental barriers deal with issues that obstruct advancement, such as access to technology and climate change. Digital access and networking highlight the importance of teamwork and digital literacy in enhancing opportunities. Finally, Agripreneurial Success encompasses indicators of achievement, including career paths, sustainable income and long-term growth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Profile of Respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB.Sc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM.Sc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePh.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWant to Pursue Agribusiness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics highlighted key demographic insights from the study's participants, university students in agribusiness and related programs, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Males comprised 60% of the respondents, while females accounted for 40%, indicating a gender imbalance. The education levels varied, with 40% holding a Bachelor of Science, 36.67% pursuing a Master of Science, and 23.33% engaged in Ph.D. programs. Notably, a significant 63.33% of participants came from rural backgrounds, suggesting that their direct connection to agriculture could have shaped their views on agripreneurship. However, only 30% expressed interest in pursuing agribusiness, with 70% indicating no interest, highlighting potential barriers or a lack of enthusiasm for agripreneurship.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 3: Results of Structural Equation Modeling (SEM) for Independent Variables Affecting Agripreneurial Success\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct/Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOuter loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ef-squared Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% of variance explained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eEnvironmental \u0026amp; Technological Barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e21.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eETB1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eETB2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eETB3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eGovernment \u0026amp; Policy Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e19.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eGPS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eGPS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eGPS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eKnowledge \u0026amp; Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eKS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eKS2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eKS3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eKS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eMarketing \u0026amp; Economic Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e17.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eMEC1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eMEC2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eMEC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eMEC4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eNetworking and digital Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eNDA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eNDA2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eResource Availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e14.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eRA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eRA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eRA3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eRA4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eSocial and Cultural Barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e8.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eSCB1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eSCB2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42.7083%;\"\u003e\n \u003cp\u003eSCB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 presents a detailed summary of the Structural Equation Modeling (SEM) analysis results, highlighting the independent variables that influence agripreneurial success. Four key metrics\u0026mdash;outer loadings, path coefficients, f-squared values and the percentage of variation explained\u0026mdash;are used to assess each construct. Each item\u0026apos;s outer loading shows how strongly it is related to its relevant construct; values nearer 1 suggest a greater association. Items ETB1, ETB2 and ETB3 have high outer loadings of 0.812, 0.850 and 0.871 in the Environmental \u0026amp; Technological Barriers construct, respectively. This suggests that these metrics provide a substantial contribution to the construct, successfully encapsulating the perceived obstacles that agribusiness owners encounter in the areas of technology and environmental difficulties. \u0026nbsp;Each construct\u0026apos;s direct impact on agribusiness success is measured by path coefficients. With the highest path coefficient of 0.250, Knowledge \u0026amp; Skills shines out in this case and suggests a strong positive correlation with agribusiness success. This implies that agribusiness results will probably be significantly improved by gains in knowledge and abilities. With noteworthy path coefficients of 0.200 apiece, Marketing \u0026amp; Economic Conditions and Government \u0026amp; Policy Support both play crucial roles in promoting agribusiness development. On the other hand, Social and Cultural Barriers has a low path coefficient of 0.025, which indicates that it has little direct influence on the success of agribusiness. Each independent variable\u0026apos;s impact on the dependent variable, agribusiness performance, is measured by the f-squared values. \u0026nbsp;With the highest f-squared value of 21.625 among the constructs, Environmental \u0026amp; Technological Barriers appears to have a significant impact on agribusiness success. With an f-squared value of 16.62, Knowledge \u0026amp; Skills likewise exhibits a significant effect. Social and Cultural Barriers, on the other hand, have a low f-squared value of 8.251, suggesting a negligible impact. Last but not least, the Percentage of Variance Explained shows the extent to which each independent variable explains the variances in agribusiness success. Constructs like Knowledge \u0026amp; Skills (16.62%) and Marketing \u0026amp; Economic Conditions (17.568%) strongly contribute to the explanation of the variance in agribusiness success, highlighting their significance as important factors. However, social and cultural hurdles only explain 8.251% of the variance, indicating that although these obstacles are recognized, their total effect on agribusiness is comparatively minimal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: R-Squared and Adjusted R-Squared Values for Agripreneurial Success\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR-square\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR-square adjusted\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgripreneurial Success\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.713\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 presented the R-squared and adjusted R-squared values for the dependent variable, Agripreneurial Success. The R-squared value of 0.713 indicated that approximately 71.3% of the variance in agripreneurial success could be explained by the independent variables included in the model. This high value suggested a strong relationship between the predictors and the outcome, affirming that the factors identified in the study were relevant contributors to agripreneurial success. The adjusted R-squared value of 0.699 provided a more conservative estimate by adjusting for the number of predictors in the model. This value indicated that after accounting for the number of independent variables, about 69.9% of the variability in agripreneurial success was explained by the model. The proximity of the adjusted R-squared to the R-squared value reflected the model\u0026apos;s robustness, suggesting that the inclusion of independent variables added meaningful explanatory power without introducing excessive complexity. Overall, these statistics underscored the model\u0026apos;s effectiveness in capturing the key determinants of agripreneurial success among university students\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Reliability and Validity Measures of Constructs of dependent and independent variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026apos;s Alpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Reliability (rho_a)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite Reliability (rho_c)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariance Extracted (AVE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eAgripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eEnvironmental \u0026amp; Technological Barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eGovernment \u0026amp; Policy Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eKnowledge \u0026amp; Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eMarketing \u0026amp; Economic Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eNetworking and Digital Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eResource Availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.5873%;\"\u003e\n \u003cp\u003eSocial and Cultural Barriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9206%;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4921%;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.5079%;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5 summarized the reliability and validity measures for the constructs examined in the study. Cronbach\u0026apos;s Alpha and Composite dependability were used to evaluate the constructions\u0026apos; dependability. With a Cronbach\u0026apos;s Alpha of 0.836, Agripreneurial Success showed a high degree of internal consistency. With a Composite Reliability of 0.870, this construct was likewise robust. Similarly, with Cronbach\u0026apos;s Alphas of 0.791 and 0.774, respectively, Environmental \u0026amp; Technological Barriers and Government \u0026amp; Policy Support demonstrated good reliability scores. Excellent reliability was indicated by Resource Availability\u0026apos;s highest Composite Reliability of 0.890. The majority of constructs appeared to account for a significant amount of the variance in their indicators, as indicated by the Average Variance Extracted (AVE) values, which varied from 0.572 for Knowledge \u0026amp; Skills to 0.731 for Networking and Digital Access. Overall, the findings supported the study\u0026apos;s conclusions about the perceived obstacles to agripreneurship by confirming the constructs\u0026apos; validity and dependability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Fornell-Larcker Criterion Results for Discriminant Validity Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eETB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eAgripreneurial Success (AS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eEnvironmental \u0026amp; Technological Barriers (ETB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eGovernment \u0026amp; Policy Support (GPS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eKnowledge \u0026amp; Skills (KS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eMarketing \u0026amp; Economic Condition (MEC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eNetworking and Digital Access (NDA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eResource Availability (RA)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.2964%;\"\u003e\n \u003cp\u003eSocial and Cultural Barriers(SCB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.81759%;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.14332%;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.30619%;\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.65472%;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6 displayed the Fornell-Larcker Criterion results, which assessed the discriminant validity of the constructs in the research. For every construct, the diagonal values represented the square root of the Average Variance Extracted (AVE). With a grade of 0.765, Agripreneurial Success (AS) showed a high level of individuality. Knowledge \u0026amp; Skills (KS) and Government \u0026amp; Policy Support (GPS) had values of 0.730 and 0.756, respectively, while Environmental \u0026amp; Technological Barriers (ETB) displayed a similarly strong value of 0.786. Further evidence that the constructs were sufficiently different from one another was provided by the off-diagonal values, which showed the correlations between the constructs and stayed below the diagonal values. Overall, the findings on perceived obstacles to agripreneurship were supported by the Fornell-Larcker Criterion results, which successfully validated the discriminant validity of the variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Collinearity Statistics (VIF) for Outer Model Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003eEnvironmental \u0026amp; Technological Barriers\u0026nbsp;-\u0026gt;\u0026nbsp;Agripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e1.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003eGovernment \u0026amp; Policy Support\u0026nbsp;-\u0026gt;\u0026nbsp;Agripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e1.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003eKnowledge \u0026amp; Skills\u0026nbsp;-\u0026gt;\u0026nbsp;Agripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e1.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003eMarketing \u0026amp; Economic Condition\u0026nbsp;-\u0026gt;\u0026nbsp;Agripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e2.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003eNetworking and Digital Access\u0026nbsp;-\u0026gt;\u0026nbsp;Agripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e1.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003eResource Availability\u0026nbsp;-\u0026gt;\u0026nbsp;Agripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e1.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75.3268%;\"\u003e\n \u003cp\u003eSocial and Cultural Barriers\u0026nbsp;-\u0026gt;\u0026nbsp;Agripreneurial Success\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6732%;\"\u003e\n \u003cp\u003e1.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7 presented the Variance Inflation Factor (VIF) values for the outer model, which assessed the collinearity among the independent variables influencing agripreneurial success. The VIF values indicated the degree of multicollinearity, with values below 5 typically suggesting no significant issues. The Knowledge \u0026amp; Skills construct had the highest VIF value of 1.912, followed closely by Marketing \u0026amp; Economic Condition at 2.000, both of which remained below the critical threshold, indicating acceptable levels of multicollinearity. Other constructs, such as Government \u0026amp; Policy Support and Social and Cultural Barriers, also displayed VIF values of 1.865 and 1.611, respectively. The values for Environmental \u0026amp; Technological Barriers, Networking and Digital Access, and Resource Availability were lower, at 1.524, 1.496 and 1.435. Overall, the results demonstrated that multicollinearity among the variables was not problematic, allowing for reliable interpretation of their relationships with agripreneurial success.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Model Fit Indices for Saturated and Estimated Models\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSaturated model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ed_ULS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ed_G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe model fit indices in the table 8 assessed the overall adequacy of the saturated and estimated models used in the study. The Standardized Root Mean Square Residual (SRMR) value for both models was 0.048, indicating a good fit since values below 0.08 are generally acceptable. The d_ULS and d_G values, both recorded at 0.073 and 0.95, respectively, suggested that the distance between the empirical and estimated covariance matrices was minimal, reinforcing the models\u0026apos; adequacy. The Chi-square statistic was 125.237 for both models, signifying that the fit was statistically significant. Additionally, the Normed Fit Index (NFI) was 0.950, which exceeded the threshold of 0.90, indicating a strong fit. Overall, the results confirmed that both the saturated and estimated models fit the data well, allowing for confident interpretation of the relationships within the study.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study tested six hypotheses, each focusing on various barriers or facilitators influencing agripreneurial success among university students. According to the first hypothesis, agribusiness success would be adversely affected by technological and environmental obstacles. This included problems with environmental sustainability, the perishable nature of agricultural products, and climate change. The data unexpectedly contradicted this expectation, and the theory was rejected. As demonstrated by a noteworthy path coefficient of 0.452, these barriers were found to promote a favorable link with agripreneurial outcomes rather than impede success. This demonstrated that students saw these environmental issues as chances for creativity rather than just obstacles. For instance, agribusiness owners have adopted sustainable practices, developed cutting-edge storage technology and experimented with climate-resilient crops in response to the pressing need to address climate change and manage perishable goods. This change demonstrated the increasing importance of sustainability in the agricultural sector, where growth and competitiveness depend on flexibility and the application of cutting-edge technologies. Popescu et al. (2023) expressed a similar viewpoint, highlighting the dynamic interaction between agripreneurship obstacles and innovation.\u003c/p\u003e \u003cp\u003eThe findings strongly supported the second hypothesis, which looked at how government and policy support affected agribusiness success. This theory concentrated on elements including risk management procedures, regulatory environments, and governmental policies. The results, which had a path coefficient of 0.450, demonstrated how important government assistance is to agribusiness success. In particular, programs that provide financial aid, subsidies, credit availability and risk management techniques enable students to get past early obstacles like obtaining funds and comprehending intricate rules. Furthermore, a supportive regulatory framework that reduces bureaucratic barriers and streamlines compliance creates a more encouraging atmosphere for agripreneurs, allowing them to focus on expanding their businesses. Furthermore, efficient risk management systems offer a buffer against unforeseen agricultural difficulties like crop failures or price swings, enabling agribusiness owners to make well-informed choices and invest in their projects with assurance. These observations highlight the need of carefully thought-out government initiatives in assisting would-be agribusiness owners in successfully overcoming obstacles. Yami et al. (2019) reported similar results, highlighting the need of supportive policies in agripreneurship.\u003c/p\u003e \u003cp\u003eWith a path coefficient of 0.300, the third hypothesis\u0026mdash;which looked at how knowledge and skills affect agribusiness success\u0026mdash;received strong support. This outcome confirmed the benefits of having access to technology, continuing education, extension services and appropriate agricultural knowledge. Students agreed that addressing the difficulties faced in agriculture requires having the appropriate skill set, which includes a solid grasp of contemporary agricultural methods and the capacity to use cutting-edge technology. Extension services were especially valued for their ability to bridge the gap between theoretical knowledge and its practical application by offering technical assistance and workable solutions. This research emphasizes the necessity of incorporating thorough agribusiness education into university courses to guarantee that students gain the entrepreneurial and technical skills required to prosper in a fast-evolving agricultural environment. Ahmad \u0026amp; Ahmad (2013) came to similar conclusions, highlighting the importance of knowledge and skills in promoting agribusiness success.\u003c/p\u003e \u003cp\u003eThe fourth hypothesis looked at how social and cultural barriers affect agribusiness success and proposed that they would be detrimental. The investigation took into account factors including fear of failure and societal views of agriculture as a less prestigious career. Although this hypothesis was confirmed, the route coefficient of 0.150 showed that these obstacles, while important, had less of an impact than other elements like market circumstances and government assistance. Many students stated that they were deterred from pursuing jobs in agribusiness by cultural perceptions of agriculture, namely the idea that it is an unpleasant profession. Another significant impediment was the fear of failing, which is frequently influenced by expectations from family and the society. The comparatively smaller impact of these social and cultural barriers, however, indicated that students were increasingly figuring out how to get around these perceptions, perhaps with the help of outside support networks like mentorship programs and entrepreneurial networks that boost their self-esteem and lessen the stigma attached to pursuing a career in agriculture. Similar results were found in a study by Ephrem et al. (2021), which showed that youth aspirations to pursue agricultural prospects in Eastern DRC were highly influenced by perceived societal norms.\u003c/p\u003e \u003cp\u003eThe importance of networking and digital access as enablers of agribusiness success was investigated in the fifth hypothesis. With a path coefficient of 0.286, the results provided excellent support for this hypothesis and highlighted the importance of professional networks and digital literacy in assisting students in overcoming the difficulties that come with working in agriculture. Through networking, agribusiness owners can gain access to important resources, possibilities for collaboration, and knowledge exchange, all of which can result in alliances and market expansion. Additionally, students can reach a larger market and use internet resources to increase their operational efficiency thanks to digital connectivity, particularly in remote locations. Their competitiveness is increased by being able to manage supply chains, sell their goods more successfully and interact with customers directly thanks to digital literacy. This research emphasizes the necessity of enhancing digital infrastructure and providing instruction in digital tools, both of which may help agribusiness owners thrive in the fast-paced market of today. Kaur et al. (2022) revealed similar findings, highlighting the significance of these elements in promoting agribusiness expansion.\u003c/p\u003e \u003cp\u003eThe sixth and last hypothesis examined how marketing and the state of the economy affect agribusiness prospects. With a path coefficient of 0.350, this hypothesis was well supported, demonstrating the crucial impact of elements like supply chain management, marketing support, market intelligence and knowledge of customer preferences. In order to effectively contact consumers and stay competitive in the market, students emphasized the importance of putting strong marketing plans into practice. Furthermore, their ability to handle supply chain difficulties, stay up to date on market developments, and adjust to changing customer preferences greatly improved their ability to position products favourably. These results emphasize how crucial it is for agribusiness owners to concentrate on both production and matching their products to consumer needs in order to achieve long-term success. Garima et al. (2023) came to similar conclusions, reaffirming that agribusiness success is largely dependent on marketing expertise.\u003c/p\u003e \u003cp\u003eOverall, the study revealed that while certain barriers, such as resource availability and social perceptions, influence agripreneurial outcomes, other factors\u0026mdash;particularly government support, knowledge, market conditions, and networking\u0026mdash;are significantly more impactful in determining success. Notably, the findings regarding environmental and technological barriers challenge conventional perspectives, indicating that these challenges can act as powerful catalysts for innovation and resilience. The positive correlation between environmental challenges and agripreneurial success suggests that aspiring agripreneurs must prioritize adaptability and sustainability to thrive in an increasingly unpredictable agricultural landscape. These results are consistent with the research conducted by Khayri et al. (2011), which underscores the importance of embracing challenges as opportunities for growth in the agribusiness sector. The study also revealed several additional findings that contribute to a more comprehensive understanding of agripreneurial success among university students. The R-squared value of 0.713, which shows that the independent variables in the model account for roughly 71.3% of the variance in agribusiness success, is one of the noteworthy findings. This high R-squared value indicates that the elements taken into account\u0026mdash;such as market conditions, government backing, knowledge and skills, social and cultural obstacles, networking, and technological and environmental constraints\u0026mdash;are very important in forecasting agribusiness success. The model's strength highlights how well the components selected for this study capture the crucial factors affecting agripreneurs' results, highlighting how crucial it is to address these obstacles in educational and policy interventions.\u003c/p\u003e \u003cp\u003eThe impact of individual barriers on agribusiness success was further shown by the substantial F-squared values. Significantly, restrictions related to technology and the environment that were first thought to have a negative effect showed a big effect size, indicating that these perceived difficulties have a significant impact on agribusiness innovation. This outcome supports the previous discovery that students' adoption of sustainable behaviours and new technologies is motivated by environmental concerns. In order to address the difficulties brought on by climate change and environmental degradation, agribusiness owners are progressively adjusting to shifting environmental conditions and utilizing technological breakthroughs. Future agripreneurs' success may be greatly influenced by their capacity to turn these obstacles into chances for expansion and uniqueness, particularly as environmental uncertainty increasingly impacts the agriculture industry.\u003c/p\u003e \u003cp\u003eCronbach's Alpha and Composite Reliability, which measure the constructs' validity and reliability, showed excellent internal consistency among the variables. Cronbach's Alpha values for all constructs were significantly higher than the 0.7 cutoff, suggesting strong reliability. For example, the Composite Reliability was very strong for constructs such as marketing conditions and government backing, which strengthened the internal coherence of the items in these constructs. This guarantees that the study's variables\u0026mdash;such as government policies, marketing assistance and technological access\u0026mdash;are accurate and meaningful indicators of the obstacles and facilitators faced by agribusiness owners.\u003c/p\u003e \u003cp\u003eThe findings are further supported by the high reliability of these constructs, which imply that the measures employed well captured the students' perceptions of the elements affecting their performance in agripreneurship. An additional noteworthy finding pertains to the notions' discriminant validity as evaluated by the Fornell-Larcker Criterion. The research verified that the constructs were different from one another, indicating that each one assessed a different facet of agribusiness success. For example, it was discovered that market conditions\u0026mdash;which concentrated on supply chains, market intelligence, and customer preferences\u0026mdash;were different from government assistance, which included elements of legislation, the regulatory environment and risk management. This separate division of constructs emphasizes the multifaceted character of agribusiness success, where several enablers and impediments interact but have diverse effects on results. The results pertaining to the impact of personal obstacles, such social and environmental factors, can be safely evaluated without worrying about concept overlap thanks to the great discriminant validity.\u003c/p\u003e \u003cp\u003eAdditionally, the investigation uncovered information from the collinearity statistics, specifically the values of the Variance Inflation Factor (VIF). Multicollinearity was not an issue in the model, as these values, which varied from 1.435 to 2.000, were all below the critical threshold of 5. This indicates that the independent variables\u0026mdash;knowledge and skills, government backing and networking\u0026mdash;did not show troublesome intercorrelations, making it possible to assess their distinct contributions to agribusiness performance with greater reliability. A clearer view of how many factors support or impede agribusiness expansion is provided by the absence of multicollinearity, which guarantees that each barrier can be evaluated separately. The model's suitability was further confirmed by the model fit indices. Given that values below 0.08 are often regarded as acceptable in structural equation modeling, the Standardized Root Mean Square Residual (SRMR) value of 0.048 suggested a reasonable fit. The model's strong fit was further supported by the Chi-square statistic and the Normed Fit Index (NFI), which indicated that the relationships between the components were well represented. The data can be used with confidence to make conclusions and offer recommendations because of the high model fit, which validates the hypothesized links between the barriers and agribusiness success.\u003c/p\u003e\n\u003ch3\u003eImplications for Agripreneurial Stakeholders\u003c/h3\u003e\n\u003cp\u003eThe findings of this study highlight crucial implications for various stakeholders in agribusiness, emphasizing a collaborative approach to enhance agripreneurial success. For entrepreneurs, the positive relationship between environmental and technological barriers and success suggests that these challenges can be seen as opportunities for innovation, particularly through sustainable practices and improved networking and digital literacy. Scholars are encouraged to build on these findings by examining the evolving interplay of barriers and facilitators over time, while also considering the impact of emerging technologies and consumer preferences. Policymakers must recognize the importance of effective policies that provide financial assistance and reduce bureaucratic hurdles, particularly in rural areas where access to resources is often limited. Practitioners, including agricultural educators and extension services, should integrate practical skills and technological knowledge into their programs to equip aspiring agripreneurs for success. Lastly, stakeholders such as NGOs and financial institutions can play a pivotal role by offering mentorship, access to credit and networking opportunities, thus helping agripreneurs navigate challenges and strengthen the agricultural sector's growth and sustainability. Future research can expand on this study's findings by exploring several promising avenues. Longitudinal studies could track changes in agripreneurial success over time, while comparative analyses between regions or countries could highlight how contextual factors like cultural attitudes and government support influence outcomes. Investigating the impact of emerging technologies, such as precision agriculture and blockchain, could reveal how these tools mitigate barriers and enhance productivity. Additionally, examining the role of social media and digital marketing in facilitating networking and market access could provide insights into consumer engagement. Research focusing on the mental health and well-being of agripreneurs, evaluating specific government policies and strategies for engaging youth in agriculture could inform support programs and interventions. Finally, adopting interdisciplinary approaches that integrate insights from various fields could lead to holistic models that better capture the interactions influencing agripreneurial success, ultimately fostering innovation and sustainability in this vital sector.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated the perceived barriers to agripreneurship among university students in India, focusing on factors influencing agripreneurial success. The analysis revealed significant insights into the relationships between various barriers and success factors. Key findings indicated that environmental and technological barriers, along with government and policy support, were crucial for enhancing agripreneurial success. The results highlighted the importance of knowledge, skills and marketing conditions in facilitating agripreneurship. Furthermore, the study emphasized the limited impact of social and cultural barriers on success, suggesting that addressing more tangible factors could improve agripreneurial outcomes. Overall, this research contributes to the understanding of agripreneurial dynamics, providing valuable recommendations for policymakers and educational institutions to foster a more supportive environment for aspiring agripreneurs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors wish to express their gratitude to all the students of agricultural universities who participated in this study.\u003c/p\u003e\n\u003cp\u003eFunding: The authors declare that there was no funding received for this research.\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors declare that they have no competing interests related to this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials: The datasets analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics statement: The need for formal review was waived by University (Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu). Ethics committee as the study complied with the university\u0026rsquo;s established norms for non-invasive social research.\u003c/p\u003e\n\u003cp\u003eInformed consent: Informed consent was obtained from all participants prior to their involvement in the study. Participants were fully informed about the purpose of the research, the nature of their participation and their rights, including the option to withdraw at any stage. The confidentiality and anonymity of the participants were safeguarded and all data were used solely for academic purposes.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; Contributions: RK and AB conceptualized the study, designed the research methodology and contributed to data analysis and interpretation. AM critically reviewed and revised the manuscript for intellectual content and contributed to data analysis. KS and ES assisted in data analysis. \u0026nbsp;All authors read and approved the final manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAcs, Z., \u0026Aring;stebro, T., Audretsch, D., \u0026amp; Robinson, D. (2016). Public policy to promote entrepreneurship: a call to arms. \u003cem\u003eSmall Business Economics\u003c/em\u003e, 47, 35\u0026ndash;51. https://doi.org/10.2139/ssrn.2728664.\u003c/li\u003e\n \u003cli\u003eAddo, L. (2018). Factors influencing agripreneurship and their role in agripreneurship performance among young graduate agripreneurs. \u003cem\u003eInternational Journal of Environment, Agriculture and Biotechnology\u003c/em\u003e, 3, 2051\u0026ndash;2066. https://doi.org/10.22161/IJEAB/3.6.14.\u003c/li\u003e\n \u003cli\u003eAdobor, H. (2020). 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African Rural Youth Engagement in Agribusiness: Achievements, Limitations, and Lessons. \u003cem\u003eSustainability\u003c/em\u003e. https://doi.org/10.3390/SU11010185.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agripreneurs, agricultural university, agripreneurial success, students","lastPublishedDoi":"10.21203/rs.3.rs-5437635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5437635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe paper attempts to gauge the success of agripreneurs and seeks to explore the barriers which hinder the agripreneurial success among agricultural university students pursuing agriculture and allied education. This study proposes eight-dimension survey instrument called \u003cem\u003eAgriSuccess \u003c/em\u003escale for measuring the agripreneurial success. Responses from 300 students across various agricultural universities are analysed using SmartPLS. The study confirms the internal consistency and validity of the scales, with Cronbach's alpha values exceeding 0.7, indicating good reliability. The Fornell-Larcker criterion is met, ensuring adequate discriminant validity among constructs. This research adds to the literature on agricultural entrepreneurship by identifying specific barriers faced by aspiring agripreneurs and offering actionable recommendations for policymakers and educators.\u003c/p\u003e","manuscriptTitle":"Agripreneurial Success Among University Students: Perceived Barriers to Agricultural Entrepreneurship in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 11:01:28","doi":"10.21203/rs.3.rs-5437635/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"169a7ec6-66da-421a-8194-2237fd8f07cc","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-05T12:54:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-29 11:01:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5437635","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5437635","identity":"rs-5437635","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}