This study examines vertical farming (VF) as one of the promising avenues for entrepreneurship among unemployed youth in Oman. The study intends to determine the potential of VF to promote self-sufficiency and economic growth by understanding the role and relationship between awareness, perception, knowledge and career choice as entrepreneurship in VF.
This involves analysing data from 1,259 respondents from institutions of higher learning using Pearson correlation, regression, mediation and pair-plot analysis to determine the relationship between awareness, perception, knowledge and career choices about VF.
The study highlights a positive relationship between awareness and perception variables indicating a strong correlation coefficient, r = 0.5645, p-value < 0.001. The study shows that though awareness brings in career choices, as building perceptions mediates and results in decision-making. It follows then that for the success of promoting VF, moulding positive perceptions about its profitability and sustainability is required. Further, the cost-benefit analysis (CBA) reveals that even as VF has a positive return on investment (ROI), the margins of profit remain low at an ROI of 2.71%, hence making large-scale entrepreneurship potentially unviable without further support.
The study is limited to researchers, academicians and entrepreneurs. The perceptions of the respondents may not comprehensively reflect the potential scope of VF in the regional context. Further, there was limited participation from agri-entrepreneurs from the study area, with very few responses from the conventional farming community.
Amidst these uncertainties, undertaking agriculture through VF technique is a prospective approach to overcome the challenges and meet the national priorities of Oman. Hence, this research study adds insights to the concept of VF as a means of achieving food security, food production demand and self-sufficiency through sustainable management of resources available in Oman and encourages the educated youth to embrace agriculture as prospective ventures for employment.
This study strategically explores the relevance of VF in the socio-economic and climatic environment of Oman, where arable land is scarce and youth unemployment is high. By analysing the dynamics of awareness, perception and career choice and by analysing economic viability in the form of localized CBA, the present study provides original, context-specific contributions that are largely missing from the available literature on sustainable agriculture and entrepreneurship in the Gulf region.
Introduction
Globally agriculture occupies an important and crucial position in economic development and is a critical factor in determining the gross domestic product (GDP) of every nation (Loizou et al., 2019). The sector contributes around 4% of global GDP and accounts for 25% of GDP in least developed nations (World Bank, 2023). In recent decades agriculture has gone through drastic changes in the cultivation mechanism, and innovation in the ways of growing food crops (Pretty, 2018). Vertical farming (VF) is one of the innovative farming advances that have significantly improvised the traditional cultivation methods, particularly in medium to higher-economy nations and to some extent in developing nations (Gurung et al., 2024; van Delden et al., 2021). This innovative farming system adopts different cultivation mechanisms such as hydroponics, aeroponics, aquaponics, etc. (Mohapatra et al., 2023; Buscher et al., 2023), under controlled environment and climatic conditions, optimizing sunlight, water, nutrients and cultivable area (Csordas and Fuzesi, 2023). For that reason, VF has become the preferable choice of farming method in regions that are deprived of arable land, water resources and favourable climatic conditions (Kalantari et al., 2018; Specht et al., 2014). Moreover, with the increasing global population, food demand and shortage of food production, it is important to address the challenges and futuristic food demand (Tamburino et al., 2020; Fu et al., 2020). Contrary, agrarian economies in both developing and developed nations must adopt innovative cultivation techniques to maximize the yield and reduce production losses at every stage to achieve sustainable yields (Boursianis et al., 2020; da Silveira et al., 2021). To effectively confront issues like global hunger, poverty, population growth, climate change, and food production, it is essential to consider factors such as access to agricultural resources, the implementation of sustainable farming practices, technological innovation, and policy interventions at both local and global scales (Paroda and Joshi, 2019; Jonathan et al., 2022).
The global VF market has shown significant growth over recent decades, driven by the rising global population and advancing urbanization Mir et al. (2022), leading to surge in opportunities across many countries (UN, 2019). VF is emerging as a feasible solution due to mounting food insecurity, sustainable practices, technologically advancement (Rajashekar et al., 2024) since growing produce close to consumption centres, utilizing high-tech indoor facilities, has seen successful implementation in numerous locations globally (Jassem and Razzak, 2021; Avgoustaki and Xydis, 2020). The market value has increased from 5.6 billion USD in 2022 and is expected to reach 35 billion USD by the year 2032 (Shahbandeh, 2023). Globally, the USA is leading ahead in the VF sector, while in Asia, countries leading are Japan, Singapore, China, South Korea, Taiwan, and Thailand. In Europe, vertical farms are advancing in France, the UK, Germany, and the Netherlands. In the Middle East region and Gulf Cooperation Council (GCC) Saudi Arabia, and United Arab Emirates (UAE) are leading in VF projects followed by Kuwait, Bahrain, and Oman.
GCC region agro-climatic scenario and vertical farming scope
Traditional agriculture systems in the GCC suffer due to unfavourable climatic conditions and a lack of natural resources to support cultivation except in a few areas (Shahid and Shankiti, 2013). Shortage of sufficient arable land, saline and sandy soils, and low soil nutrient capacity are reasons that persistently affect the region’s food production and agriculture sector. In addition, the growing population and increasing food demand highlight the constraints of food self-sufficiency in the region, hence more than 80% of the food demand is met from imports (Putra et al., 2020; Paudel et al., 2023). In the GCC region, the total arable area is less than 1% which includes only 19.5% of the total land area under agriculture (Hassen and El Bilali, 2019). Moreover, only 4% of the total population of the country is employed in agriculture, contributing around 2% to the country’s GDP. To accommodate the rising food production demand from the growing population and to reduce dependence on food imports, sustainably promoting agriculture is critical to the region (Al Salmi et al., 2020). Studies by Sharma et al. (2023) emphasize that a full analysis of VF in cities allows us to understand it better for sustainable use and Appollani et al. (2024) highlight the need for investigating VF options in emerging economies with all three main types of sustainability frameworks. In addition, studying the business models that bring about better results and sustainability in vertical farms and analysing how public beliefs and intentions influence the wider adoption of VF Marczewska et al. (2025), Shao et al. (2022) are intricate for employment opportunities among youth in the region. These identified gaps and directions justify the need for the present study, aiming to address multi-dimensional aspects of VF including sustainability, economic viability, business strategies, and social acceptance.
Global and regional studies on vertical farming- research gap
The 4th and 5th industrial revolutions emphasized technological advancement and introduced smart agriculture techniques, methods and innovative farming systems such as VF (Haloui et al., 2024). While VF is gaining advancement in the neighbouring countries UAE and Saudi Arabia, Oman has a good infrastructure for electricity generation, ample land area, and a strategic location on the world map to promote sustainable farming methods (Graamans et al., 2018; Allegaert, 2020). Gurung et al. (2024) and van Delden et al. (2021) reported that in VF, hydroponics, aeroponics and aquaponics are used inside controlled settings to save and optimize resources. Mir et al. (2022) mention that VF will help achieve future food security and provide new opportunities for entrepreneurs locally. Sengodan (2022) examines the interest in VF within land-limited countries such as Malaysia, Singapore and Thailand. In their study, El Dardiry et al. (2022) argue that small indoor farms can help protect both food security and the import costs of food in Bahrain. According to Asem (2024), VF and similar methods will raise local food production in Kuwait. Abdullah et al. (2021) look at how VF systems are doing in Oman’s dry climate and discuss how they provide jobs and keep the community fed. Fatnassi et al. (2022) consider protected agriculture in the UAE and talk about the obstacles and benefits involved. Kalantari et al. (2018) analyse the possible benefits and obstacles faced by VF in terms of sustainability. There is useful information about resource efficiency concerns and challenges in VF in the papers of Kozai et al. (2020) and Benke and Tomkins (2017). Buscher et al. (2023) examine whether VF can be transformative using urban planning theories. Csordás and Füzesi (2023) look at how technophobia affects VF. Banerjee and Adenaeuer (2014) look at how VF works financially. The authors Jassem and Razzak (2021) talk about opportunities for entrepreneurship in urban VF. Jonathan and Magd (2024) underline both economic and policy aspects of VF to help investors in Oman make the best choices. The authors Mir et al. (2022) and Rajashekar et al. (2024) focus on VF as a business chance and a response to the problem of food security. Analogously, Khan et al. (2022) highlighted the importance of understanding how VF can scale up in Oman. Sustainable agriculture and access to food are studied by Pretty (2018), Paroda and Joshi (2019) and Putra et al. (2020). Hassen and El Bilali (2019) discuss food security problems that exist in the GCC countries. According to the World Bank (2023), agriculture impacts the world’s economy and challenges related to food security. Besides, Al-Kodmany (2018), Tooy et al. (2023) and Despommier (2013) emphasize that VF in Oman, given its dry climate, is valuable for social reasons and can help the food sector and the country’s economy. The authors Sharma et al. (2023) point out that VF can be used in urban areas to produce sustainably grown food and support the environment, society and economy. The report from Appollani et al. (2024) examines whether VF investments are possible and sustainable in emerging countries and singles out the Czech Republic and Turkey as areas with great potential for VF development. Marczewska et al. (2025) demonstrate that for VF firms, strong performance is linked to being based in cities, connecting with customers and using B2B sales channels. Moreover, Shao et al. (2022) find that customers are motivated to use VF technologies when they see their usefulness and that demographic details shape their intentions.
Very few studies have been done in Oman that look at VF in its unique setting and not many reports exist about its place in the region. Researchers have mostly worked on the impact of VF in developed and some developing countries, but little is found on its role in Oman. Although studies focus on technology and theory, there is not enough practical training or experience for operating vertical farms and running entrepreneurial businesses. More in-depth consideration is required for the issues of high entry costs, daily running expenses, energy use, how the market responds and how sensitive consumers are to costs. There is not much research on the link between awareness, perception, knowledge and career choice in VF for youths in Oman. Researchers should look at these connections and find ways other GCC countries are handling them to support regional cooperation. Therefore, it is essential to thoroughly analyse the scope of VF in Oman, considering its significance, feasibility, potential, and effectiveness in tackling future challenges related to food production and security.
Study objectives
Against this backdrop, amidst the large unemployed population of youth in Oman, there is a need for entrepreneurial initiatives to adopt innovative agricultural methods, where VF is a significant employment opportunity, in addition also offering solutions to meet the country’s food requirements through entrepreneurship opportunities. In this context, the study intends to achieve the following objectives.
To know the scope of VF in Oman as a means of self-employment (as enterprises) for startups among unemployed youth by critically evaluating the awareness, perception, knowledge and career choice in VF.
To assess the significance of VF promotion in Oman especially to remain self-dependent on food and to reduce imports.
How will VF prove beneficial in terms of economic cost, and profitability to local farmers over traditional farming methods?
Methodology
The study employs an explanatory research design to meet its objectives. This design is selected due to the nature of the required information and its effectiveness in drawing clear conclusions about the research topic.
Data collection
To achieve the objectives of the study, both quantitative and qualitative types of data are used to arrive at possible inferences from this research.
Quantitative methods
The data on the study was collected using a self-administered structured questionnaire, including closed-end questions and a Likert scale (1–5) to gather quantitative information. The questions are grouped into 4 categories covering demographic information, specific questions on awareness, perception and career opportunities. The survey design was adopted to collect responses from a maximum target group of individuals to enable in examining the role of awareness, perception, and career choice toward VF concurring to (Oppenheim, 2000; Likert, 1932; Gati et al., 1996; Creswell, 2014).
Qualitative methods
The study also used qualitative methods such as semi-structured interviews and observations to explore the opinions, views, perceptions, knowledge and experiences of participants in VF as employment through entrepreneurship. Participants were selected based on the theories adopted by Patton (2015), from individuals having familiarity with the study topic. Open-ended and closed-ended questions were used for in-person interviews involving entrepreneurs and technical experts from established private vertical farms.
Study period
For each data type, the survey questionnaire was prepared using Google Forms for distribution online by email. The links for the survey were distributed to students, technical experts, researchers and entrepreneurs through a database available with institution and personal contacts. The data was collected during mid of April 2023 to the End of July 2023 for students and researchers, while for the entrepreneurs and technical experts, the collection period was between January 2024 and Mid-April 2024 considering the methods suggested by Dillman et al. (2014).
Data analysis methods
To achieve the research objectives, statistical and financial analysis methods were applied using SPSS, R (mediation package, ggplot2), AMOS, SmartPLS and Microsoft Excel. To evaluate the connections between awareness, perception, knowledge, residential background and career choice, descriptive statistics and analysis methods such as Pearson correlation and multiple regression were used. To examine how awareness impacts career choice indirectly, mediation analysis showed that perception fully mediated the relationship. Tools such as pair-plot analysis and correlation heatmaps were used to improve our understanding of how variables interact, and path diagrams were prepared using AMOS and SmartPLS to show the structure of relationships. Economic viability was checked by performing a Cost-Benefit Analysis (CBA), Return on Investment (ROI), payback period, Profitability Index (PI) and Sustainability Index (SI). The impact of different yield scenarios on profitability was evaluated through scenario analysis. Combining both the statistical and economic analysis provided a thorough examination of VF, supporting the study’s focus on the subject as a new business opportunity for unemployed youth in Oman.
Results
Oman demography and visionary approach for self-employment among youth
Sultanate of Oman is a country of the Arabian Peninsula of the West Asian region, bordering with Yemen in the south and UAE in the north. The country is divided into 11 governates with almost 87.7% of the population residing in urban areas and the rest in rural areas (O Neill, 2024). The population in 2024 stands at 5.21 million inhabitants and is expected an increase of 17.3% by the year 2029. The age structure shows 70.21% per cent of the population are in the age group 15–64 years, 27.03% in 0–14 years and the rest 2.76% above 65 years. Urbanization trend in the country shows slow steady progress during the last decade with an increase of 13.1% from the year 2012–2022. Oman’s 2040 visionary approach emphasizes education, health, labour, market, civic, social and political participation among youth mainly in the age group 18–29 years in the country. The introduction of the Oman Youth Development Index (OYDI) a mechanism to boost the nation’s youth to future economic development is a tool for the stakeholders, decision decision-makers to frame national policies, and procedures for interest areas. Currently, the OYDI stands at 0.803 according to Youth Development Index (YDI) 2023 reports where 22% of the total national population is between 18–29 years with the highest per cent (21%) residing in the capital and northern governates of Oman. Youth in this age range are very critical for the nation’s economy and growth and form a significant part of job seekers. According to recent statistics, around 5% of the total nation’s youth population is seeking jobs, and a significant percentage are not enrolled in education, work or training. This indicates a potential loss to the national workforce representing lack of skills and knowledge for the right job and inability to acquire decent work, that are critical concerns limiting the progress of the nation’s employment rate among youth. Interestingly, 17% of the total entrepreneurs are in the age group 18–29 years constituting less than a fifth of the total entrepreneurs in the country. These figures indicate there is tremendous scope for youth in the age group to choose entrepreneurship as a career choice for employment by taking advantage of government support through investments. Besides this, it is noteworthy to report that Oman currently holds 11th place in the entrepreneurship index according to the Global Entrepreneurship Monitor 2023/24 global report.
Descriptive analysis
To assess the objectives of the study, the survey focused on five key variables: awareness, perception, knowledge, residential community and career choice in VF as a potential entrepreneurship opportunity. We received a total of 1,259 responses comprising from higher education institutes in Oman. The summary statistics of the survey are presented in Table 1.
Summary statistics of survey responses for the study
| Awareness | Perception | Knowledge | Residential | Career | |
|---|---|---|---|---|---|
| Count | 1,259 | ||||
| Mean | 3.18 | 3.18 | 2.98 | 0.29 | 3.28 |
| Std. Dev | 0.18 | 0.73 | 1.43 | 0.45 | 0.79 |
| Min | 0.81 | 0.86 | 0.92 | −0.13 | 1.41 |
| Max | 4.76 | 4.61 | 4.86 | 1.1 | 5.14 |
| Awareness | Perception | Knowledge | Residential | Career | |
|---|---|---|---|---|---|
| Count | 1,259 | ||||
| Mean | 3.18 | 3.18 | 2.98 | 0.29 | 3.28 |
| Std. Dev | 0.18 | 0.73 | 1.43 | 0.45 | 0.79 |
| Min | 0.81 | 0.86 | 0.92 | −0.13 | 1.41 |
| Max | 4.76 | 4.61 | 4.86 | 1.1 | 5.14 |
The results show that most of the respondents hold university-level education and are urban inhabitants, only a negligible per cent do not have formal university education. The survey shows, 43% of the participants are male, 53.6% are females and 3.4% have not disclosed their gender. Of the total number of male participants, 91% are urban inhabitants and the rest are from rural communities, whereas in the case of female participants, 93% belong to urban communities and the rest represent rural inhabitants. The survey analysis shows all the respondents are educated to have either a university or non-university degree. The details of the survey results are presented in Table 2.
Analysis showing the demographic information for the study
| Detail | Male | Female | Not disclosed | Total |
|---|---|---|---|---|
| Gender | 553 | 663 | 43 | 1,259 |
| Rural | 51 | 42 | 3 | 96 |
| Urban | 502 | 621 | 40 | 1,163 |
| Education – university | 550 | 656 | 37 | 1,243 |
| Non-university | 3 | 7 | 6 | 16 |
| Detail | Male | Female | Not disclosed | Total |
|---|---|---|---|---|
| Gender | 553 | 663 | 43 | 1,259 |
| Rural | 51 | 42 | 3 | 96 |
| Urban | 502 | 621 | 40 | 1,163 |
| Education – university | 550 | 656 | 37 | 1,243 |
| Non-university | 3 | 7 | 6 | 16 |
Assessing the significance of vertical farming as an entrepreneurship career- statistical analysis
Statistical analysis was conducted between the five variables of the study to uncover the significance of VF as an entrepreneurship career among unemployed youth. To achieve this objective Pearson correlation coefficient analysis was performed to comprehend the relationship between the variable’s awareness, perception, career choice and residential location. Analysis shows a strong positive relationship between awareness and perception levels r = 0.631, p < 0.01, while a weak positive association exists between awareness of youth on VF and career choice as entrepreneurship in VF r = 0.393, p < 0.01. Association between perception on VF and choosing career as entrepreneurship in the VF, shows there is a strong positive correlation r = 0.653, p < 0.01. On the other hand, knowledge and residential community does not have any association with awareness, perception and career choice indicating that these variables do not show any influence. Further, multiple regression analysis was performed to understand the effect of awareness, perception, knowledge and residential location on the career choice of youth towards entrepreneurship in VF. The analysis shows all the variables significantly predicted the career choice F (4, 1,254), = 234.52, p < 0.001, R2 = 0.42 indicating that 42.8% of the variance in career choice is attributed to awareness levels, perception, knowledge and residential community. However, considering the five subjective variables, awareness levels, perception, knowledge and residential location, the perception levels significantly predicted the career choice of youth (β = 0.673, t = 24.44, p < 0.001) whereas the awareness levels (β = −0.38, t = −1.356, p = 0.175), residential community (β = 0.038, t = 1.741, p = 0.082), knowledge (β = −0.001, t = −0.59, p = 0.953) did not significantly predict the career choice of youth towards entrepreneurship in VF (Figure 1).
The correlation heatmap with a 5 by 5 grid showing the relationships between the variables labeled “Awareness,” “Perception,” “Career,” “Residence,” and “Knowledge.” These values are arranged along the horizontal axis from left to right and also along the vertical axis from top to bottom. The color scale on the right ranges from blue at the bottom with a value of negative 1.00 to dark red at the top with a value of 1.00, with an interval of 0.25. The row-wise values are given below. Row 1: 1, 0.63, 0.39, 0.15, negative 0.011. Row 2: 0.63, 1, 0.65, 0.092, negative 0.023. Row 3: 0.39, 0.65, 1, 0.094, negative 0.013. Row 4: 0.15, 0.092, 0.094, 1, negative 0.028. Row 5: negative 0.011, negative 0.0023, negative 0.011, 0.028, 1.Correlation heatmap showing an association between the variables of the study. Source: Figure created by author
The correlation heatmap with a 5 by 5 grid showing the relationships between the variables labeled “Awareness,” “Perception,” “Career,” “Residence,” and “Knowledge.” These values are arranged along the horizontal axis from left to right and also along the vertical axis from top to bottom. The color scale on the right ranges from blue at the bottom with a value of negative 1.00 to dark red at the top with a value of 1.00, with an interval of 0.25. The row-wise values are given below. Row 1: 1, 0.63, 0.39, 0.15, negative 0.011. Row 2: 0.63, 1, 0.65, 0.092, negative 0.023. Row 3: 0.39, 0.65, 1, 0.094, negative 0.013. Row 4: 0.15, 0.092, 0.094, 1, negative 0.028. Row 5: negative 0.011, negative 0.0023, negative 0.011, 0.028, 1.Correlation heatmap showing an association between the variables of the study. Source: Figure created by author
A pair-plot analysis exposes a comprehensive view of the relationships between all variables in our dataset. Each scatter plot shows the relationship between two variables, while the diagonal plots show the distribution of each variable. The key observations from the pair plot and descriptive statistics indicate that most variables have roughly normal distributions with some slight skewness in certain cases. Knowledge and residential community do not seem to have a strong linear relationship with the other variables, implying residing in an urban or rural community is not a deciding factor for the choice of career among unemployed youth. In the study, an interaction analysis was conducted to determine whether the relationship between the independent variables and the dependent variables varies under different conditions. It is observed that awareness, perception, and knowledge all have significant positive effects on a career; there is no strong evidence of moderation effects. The relationship between awareness or perception and career does not significantly change based on the level of knowledge. This suggests that these factors independently contribute to career outcomes, rather than interacting in complex ways (Figure 2).
The illustration displays a scatterplot matrix of five variables: “Awareness,” “Perception,” “Career,” “Residence,” and “Knowledge.” The matrix is arranged in a 5 by 5 grid format. The horizontal axis for the first column is labeled “Awareness” and ranges from 0 to 4 with an interval of 2. The horizontal axis for the second column is labeled “Perception” and ranges from 2 to 4 with an interval of 2. The horizontal axis for the third column is labeled “Career” and ranges from 2 to 4 with an interval of 2. The horizontal axis for the fourth column is labeled “Residence” and ranges from negative 0.5 to 1.5 with an interval of 0.5. The horizontal axis for the fifth column is labeled “Knowledge” and ranges from 0 to 6 with an interval of 2. The vertical axis for the first row is labeled “Awareness” and ranges from 1 to 4 with an interval of 1. The vertical axis for the second row is labeled “Perception” and ranges from 1 to 4 with an interval of 1. The vertical axis for the third row is labeled “Career” and ranges from 2 to 5 with an interval of 1. The vertical axis for the fourth row is labeled “Residence” and ranges from 0 to 1 with an interval of 0.2. The vertical axis for the fifth row is labeled “Knowledge” and ranges from 1 to 5 with an interval of 1. First row (at the top): The first plot of “Awareness” versus “Awareness” shows a right-skewed distribution. Most of the region is concentrated between 2 and 4 on the horizontal axis and peaks just before the value of 4 for awareness. The second scatterplot of “Awareness” versus “Perception” shows clustered data points extending from the center to the top right, and some of the data points are positioned near the bottom left side. The third scatterplot of “Awareness” versus “Career” shows clustered data points at the center and top center, and some of the data points are positioned near the bottom left side and bottom center side. The fourth scatterplot of “Awareness” versus “Residence” shows clustered data points in two vertical columns at the residence values of 0 and 1. The fifth scatterplot of “Awareness” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5. Second row: The first scatterplot of “Perception” versus “Awareness” shows clustered data points extending from the center to the top right, and some of the data points are positioned near the bottom left side. The second scatterplot of “Perception” versus “Perception” shows a right-skewed distribution. Most of the region is concentrated between 2 and 4 on the horizontal axis and peaks around the value 3 of perception. The third scatterplot of “Perception” versus “Career” shows clustered data points at the center and top center, and some of the data points are positioned near the bottom center side. The fourth scatterplot of “Perception” versus “Residence” shows clustered data points in two vertical columns at the residence values of 0 and 1. The fifth scatterplot of “Perception” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5. Third row: The first scatterplot of “Career” versus “Awareness” shows clustered data points at the center, and some of the data points are positioned near the bottom left side. The second scatterplot of “Career” versus “Perception” shows clustered data points spread diagonally from bottom left to top right, and some of the data points are positioned near the left side. The third scatterplot of “Career” versus “Career” shows a distribution curve with two peaks nearly at the center. Most of the region is concentrated between 2 and 4 on the horizontal axis and peaks around the value 3 of career. The fourth scatterplot of “Career” versus “Residence” shows clustered data points in two vertical columns at the residence values of 0 and 1. The fifth scatterplot of “Career” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5. Fourth row: The first scatterplot of “Residence” versus “Awareness” shows clustered data points at the top and bottom, arranged horizontally. The second scatterplot of “Residence” versus “Perception” shows clustered data points at the top and bottom, arranged horizontally. The third scatterplot of “Residence” versus “Career” shows clustered data points at the top and bottom, arranged horizontally. The fourth scatterplot of “Residence” versus “Residence” shows a distribution curve with two peaks away from the center on both sides. The fifth scatterplot of “Residence” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5, positioned at the top and bottom sides. Fifth row (at the bottom): The first scatterplot of “Knowledge” versus “Awareness” shows clustered data points in five horizontal rows at the knowledge values of 1 to 5. The second scatterplot of “Knowledge” versus “Perception” shows clustered data points in five horizontal rows at the knowledge values of 1 to 5. The third scatterplot of “Knowledge” versus “Career” shows clustered data points in five horizontal rows at the knowledge values of 1 to 5. The fourth scatterplot of “Knowledge” versus “Residence” shows data points in five horizontal rows at the knowledge values of 1 to 5, clustered on the left and right sides, forming two columns. The fifth scatterplot of “Knowledge” versus “Knowledge” shows a distribution curve with five peaks at the center.Pair-plot analysis showing the degree and association among the examined variables. Source: Figure created by author
The illustration displays a scatterplot matrix of five variables: “Awareness,” “Perception,” “Career,” “Residence,” and “Knowledge.” The matrix is arranged in a 5 by 5 grid format. The horizontal axis for the first column is labeled “Awareness” and ranges from 0 to 4 with an interval of 2. The horizontal axis for the second column is labeled “Perception” and ranges from 2 to 4 with an interval of 2. The horizontal axis for the third column is labeled “Career” and ranges from 2 to 4 with an interval of 2. The horizontal axis for the fourth column is labeled “Residence” and ranges from negative 0.5 to 1.5 with an interval of 0.5. The horizontal axis for the fifth column is labeled “Knowledge” and ranges from 0 to 6 with an interval of 2. The vertical axis for the first row is labeled “Awareness” and ranges from 1 to 4 with an interval of 1. The vertical axis for the second row is labeled “Perception” and ranges from 1 to 4 with an interval of 1. The vertical axis for the third row is labeled “Career” and ranges from 2 to 5 with an interval of 1. The vertical axis for the fourth row is labeled “Residence” and ranges from 0 to 1 with an interval of 0.2. The vertical axis for the fifth row is labeled “Knowledge” and ranges from 1 to 5 with an interval of 1. First row (at the top): The first plot of “Awareness” versus “Awareness” shows a right-skewed distribution. Most of the region is concentrated between 2 and 4 on the horizontal axis and peaks just before the value of 4 for awareness. The second scatterplot of “Awareness” versus “Perception” shows clustered data points extending from the center to the top right, and some of the data points are positioned near the bottom left side. The third scatterplot of “Awareness” versus “Career” shows clustered data points at the center and top center, and some of the data points are positioned near the bottom left side and bottom center side. The fourth scatterplot of “Awareness” versus “Residence” shows clustered data points in two vertical columns at the residence values of 0 and 1. The fifth scatterplot of “Awareness” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5. Second row: The first scatterplot of “Perception” versus “Awareness” shows clustered data points extending from the center to the top right, and some of the data points are positioned near the bottom left side. The second scatterplot of “Perception” versus “Perception” shows a right-skewed distribution. Most of the region is concentrated between 2 and 4 on the horizontal axis and peaks around the value 3 of perception. The third scatterplot of “Perception” versus “Career” shows clustered data points at the center and top center, and some of the data points are positioned near the bottom center side. The fourth scatterplot of “Perception” versus “Residence” shows clustered data points in two vertical columns at the residence values of 0 and 1. The fifth scatterplot of “Perception” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5. Third row: The first scatterplot of “Career” versus “Awareness” shows clustered data points at the center, and some of the data points are positioned near the bottom left side. The second scatterplot of “Career” versus “Perception” shows clustered data points spread diagonally from bottom left to top right, and some of the data points are positioned near the left side. The third scatterplot of “Career” versus “Career” shows a distribution curve with two peaks nearly at the center. Most of the region is concentrated between 2 and 4 on the horizontal axis and peaks around the value 3 of career. The fourth scatterplot of “Career” versus “Residence” shows clustered data points in two vertical columns at the residence values of 0 and 1. The fifth scatterplot of “Career” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5. Fourth row: The first scatterplot of “Residence” versus “Awareness” shows clustered data points at the top and bottom, arranged horizontally. The second scatterplot of “Residence” versus “Perception” shows clustered data points at the top and bottom, arranged horizontally. The third scatterplot of “Residence” versus “Career” shows clustered data points at the top and bottom, arranged horizontally. The fourth scatterplot of “Residence” versus “Residence” shows a distribution curve with two peaks away from the center on both sides. The fifth scatterplot of “Residence” versus “Knowledge” shows clustered data points in five vertical columns at the knowledge values of 1 to 5, positioned at the top and bottom sides. Fifth row (at the bottom): The first scatterplot of “Knowledge” versus “Awareness” shows clustered data points in five horizontal rows at the knowledge values of 1 to 5. The second scatterplot of “Knowledge” versus “Perception” shows clustered data points in five horizontal rows at the knowledge values of 1 to 5. The third scatterplot of “Knowledge” versus “Career” shows clustered data points in five horizontal rows at the knowledge values of 1 to 5. The fourth scatterplot of “Knowledge” versus “Residence” shows data points in five horizontal rows at the knowledge values of 1 to 5, clustered on the left and right sides, forming two columns. The fifth scatterplot of “Knowledge” versus “Knowledge” shows a distribution curve with five peaks at the center.Pair-plot analysis showing the degree and association among the examined variables. Source: Figure created by author
Mediation analysis is also performed on the data set to examine the indirect effect of an independent variable (X) on a dependent variable (Y) through a proposed mediator variable (M). The results of the mediation analysis of awareness to perception show a significant positive relationship between Awareness and Perception (coefficient = 0.5645, p < 0.001). The model’s analysis shows that awareness has a significant positive overall effect on youths career choice in entrepreneurship related to VF (coefficient = 0.3827, p < 0.001). When perception is included in the model, the direct effect of awareness on career choice becomes non-significant (coefficient = −0.0307, p = 0.252). However, perception itself shows a strong and significant positive effect on career choice (coefficient = 0.7325, p < 0.001).
This analysis suggests full mediation, as the direct effect becomes non-significant when including the mediator. The indirect effect (0.4135) is larger than the total effect (0.3827), resulting in a proportion mediated greater than 100%. This indicates that perception not only fully mediates but also enhances the relationship between awareness and career. The strong mediation effect suggests that awareness primarily influences career outcomes through its impact on perception. The negative direct effect, although non-significant, might indicate a potential suppression effect, but this would require further investigation to confirm (Figure 3).
The path diagram shows that several factors are represented by rectangular boxes, and the causal relationships between them are indicated by arrows. At the top-left, there is a box labeled “R s d,” which is connected by an arrow to a box in the center labeled “P r c” with a coefficient of “0.03” showing a relationship. “R s d” also points to a box at the bottom-left labeled “K n w,” with a dashed bidirectional arrow marked “0.05.” The box “R s d” is also connected to the box “C r r” at the bottom right with a solid arrow marked 0.05 and to the box “A w r” to the right of the box “K n w” with a solid arrow marked 0.21. This “R s d” is also self-looped by a dashed bi-directional curved arrow, which is marked 0.21. The box “P r c” is connected to the box “C r r” by a solid arrow with a value of 0.75. This “P r c” is also self-looped by a solid bi-directional curved arrow, which is marked 0.32. The box “C r r” is self-looped by a solid bi-directional curved arrow, which is marked 0.36. The box “K n w” is connected to boxes “P r c” and “A w r” by solid arrows marked with values 0. The box “K n w” is also connected to the box “C r r.” This “K n w” is also self-looped by a dashed bi-directional curved arrow, which is marked 1. The box “A w r” is connected to boxes “P r c” and “C r r” by solid arrows marked with values 0.59 and negative 0.06, respectively. This “A w r” is also self-looped by a solid bi-directional curved arrow, which is marked 0.65.Mediation path plot diagram showing the interaction and effect among the variables. Source: Figure created by author
The path diagram shows that several factors are represented by rectangular boxes, and the causal relationships between them are indicated by arrows. At the top-left, there is a box labeled “R s d,” which is connected by an arrow to a box in the center labeled “P r c” with a coefficient of “0.03” showing a relationship. “R s d” also points to a box at the bottom-left labeled “K n w,” with a dashed bidirectional arrow marked “0.05.” The box “R s d” is also connected to the box “C r r” at the bottom right with a solid arrow marked 0.05 and to the box “A w r” to the right of the box “K n w” with a solid arrow marked 0.21. This “R s d” is also self-looped by a dashed bi-directional curved arrow, which is marked 0.21. The box “P r c” is connected to the box “C r r” by a solid arrow with a value of 0.75. This “P r c” is also self-looped by a solid bi-directional curved arrow, which is marked 0.32. The box “C r r” is self-looped by a solid bi-directional curved arrow, which is marked 0.36. The box “K n w” is connected to boxes “P r c” and “A w r” by solid arrows marked with values 0. The box “K n w” is also connected to the box “C r r.” This “K n w” is also self-looped by a dashed bi-directional curved arrow, which is marked 1. The box “A w r” is connected to boxes “P r c” and “C r r” by solid arrows marked with values 0.59 and negative 0.06, respectively. This “A w r” is also self-looped by a solid bi-directional curved arrow, which is marked 0.65.Mediation path plot diagram showing the interaction and effect among the variables. Source: Figure created by author
This path diagram provides a clear visual representation of the mediation effect as observed in the analysis. A brief explanation of the diagram shows:
The nodes represent our three key variables: Awareness, Perception, and Career.
The arrows indicate the direction of the relationships between these variables.
The numbers on the arrows represent the coefficients (weights) of these relationships.
Key observations from the diagram:
There’s a strong positive relationship (0.590) between Awareness to Perception.
The direct effect of Awareness on Career is very small and negative (−0.058), which aligns with our finding that it became non-significant when controlling for Perception.
There’s a strong positive relationship (0.746) from Perception to Career.
This visualization reinforces our earlier conclusion about full mediation. It clearly shows that the effect of Awareness on Career is primarily channelled through Perception, with the direct path from Awareness to Career being negligible. The diagram effectively illustrates the concept of mediation, where Perception acts as a mediator in the relationship between Awareness and Career outcomes. This visual representation can be particularly helpful in explaining complex relationships to stakeholders or in presentations.
The indirect path through Perception accounts for more than 100% of the total effect of Awareness on Career, effectively counteracting the small negative direct effect. This analysis further supports our conclusion that Perception fully mediates the relationship between Awareness and Career outcomes. The strong positive indirect effect (0.4135) demonstrates that Awareness primarily influences Career through its impact on Perception, rather than directly. This summary highlights the significant relationships and effects in the mediation analysis, emphasizing the role of perception as a mediator between awareness and career outcomes.
Evaluating the cost-benefit analysis, profitability and sustainability in vertical farming startups
VF is a modern agricultural technique gaining global recognition for its benefits in indoor food production. Grand View Research, Inc. predicts that the global VF market will have the fastest Compound Annual Growth Rate of 12.9% from 2023 to 2030 compared to other indoor farming models. Transitioning to VF offers farmers the chance to reduce reliance on traditional agriculture and improve crop quality. However, implementing profitable VF technologies poses challenges due to the initial investment and ongoing expenses, leading to higher product prices that may deter local consumers. The financial success of a vertical farm hinges on accurate local market information confirming the observations made by Kozai and Niu (2020), Benke and Tomkins (2017) and Beacham et al. (2019) that expensive energy and operations make it difficult for vertical farms to remain profitable and sustainable. Analysing retail presence in the area and evaluating their operations is crucial for determining the right product range and developing an effective sales strategy. For instance, in the UAE and Oman, retail giants like (Carrefour then, now HyperMax), Lulu Hypermarket, and Union Co-Operative dominate the market. Understanding consumer preferences is vital, as seen in the MENA region where processed greens, particularly cut forms, are preferred over potted plants.
Produce from vertical farms differs significantly from field or greenhouse-grown greens, being fresh, healthy, flavourful, and available year-round. It is essential to assess the demand for fresh greens and whether domestic production meets it. Yield plays a key role in a vertical farm’s profitability. Choosing crops based on market demand, pricing, suitable growing systems, and growing cycles is critical. Spicy herbs, microgreens (like basil, cilantro, mint, oregano, rosemary), and leafy greens (such as chards, lettuces, kales, spinach) are popular choices for hydroponic VF, being considered as the most profitable. The importance of choosing suitable crops like spicy herbs, microgreens, and leafy greens for hydroponic VF to maximize yield and profitability is supported by studies such as Mir et al. (2022) and Banerjee and Adenaeuer (2014) who emphasize crop selection-based on market demand and profitability.
These plants have short growing cycles, allowing for high crop density and variety, providing a market advantage. Selecting the right facility type and size is crucial for financial success. Recommendations include a ceiling height of 4.5–6 metres, appropriate building materials for the climate, reliable water supply, an efficient sewer system, and proximity to the city with good transportation access. Utilizing SaaS solutions like iFarm Grow tune can reduce costs and increase efficiency, yield and payback time are influenced by the chosen technological solution, supported by Graamans et al. (2018). For example, typically leafy greens technology yields 48 kg of fresh produce per square metre annually, while Stack Grow technology provides 72 kg of yield. Automation is increasingly in demand, but the choice between fully automated systems and semi-automated rack systems depends on labour costs in the region. The VF units in Oman mostly grow leafy greens and occasionally other veggies and are marketed in large city supermarkets. A comparative analysis of vertical farm-grown veggies over conventional or imported food found from the local supermarket reveals that the unit price of these leafy greens and other veggies is marginally more (average 15–20%) than the unit price of leafy greens and veggies on the imported food. This will result in the overall cost of purchasing vertical farm-grown produce from the local units, which may account for up to 20–22% of expenditure. However, most consumers would not be comfortable spending extra on buying produce available at a higher price regardless of it being grown from VF units. The study by Fatnassi et al. (2022) and Abdullah et al. (2021) agrees with what is observed in the MENA region that many consumers like fresh and processed greens, while many are sensitive to their prices.
Analysis of cost-benefit analysis, return on investment, and scenario analysis
The financial feasibility of the project was assessed using a CBA, which involved a comprehensive evaluation of both the costs incurred and the revenue generated. The total investment for the project amounted to $891,166. The primary cost components included the cost of raw materials ($12,655), electricity ($5,026), farm maintenance ($2,555), and logistics ($2,988). Collectively, these costs totalled $23,224.
The project generated a total revenue of $47,368, resulting in a net profit of $24,144. This positive net profit demonstrates that the revenue exceeds the total costs, indicating that the project is financially viable. The analysis suggests that the benefits derived from the project outweigh the costs, reinforcing the project’s economic justification. CBA based on the given set of parameter inputs given in Table 3.
Return on investment (ROI)
To evaluate the project’s financial outcomes, the ROI was determined. ROI is a crucial performance indicator that assesses the profitability of an investment about its initial expenditure. This project’s ROI was computed using the standard formula.
From the net profit of $24,144 and an initial investment of $891,166, the ROI from the VF unit would be approximately 2.71%. This positive ROI implies that the project has the potential to generate a return on the capital invested. Nevertheless, the ROI is still relatively low, which means the project is yet to reach its full profit potential and indicates that there is room for improvement in operational efficiency or revenue generation (Figure 4).
The mind map is titled “Hashtag Vertical Farming in Oman: A Study Overview.” The central part of the mind map contains the title on the left. From this central point, six main branches extend rightward, each representing a different aspect of the study. The first branch, labeled “Purpose of Study,” extends to two subtopics: “Explore vertical farming as a solution for unemployment and food security” and “Assess economic feasibility and profitability,” which are positioned at the top. The second branch, in blue, is labeled “Benefits” and leads to three subtopics: “Self-sufficiency in food production,” “Entrepreneurship opportunities for youth,” and “Sustainable agriculture practices,” which are positioned just below the first branch. The third branch, in green, is labeled “Challenges” and contains four subtopics: “High initial costs,” “Limited profitability (R O I negative 2.71 percent),” “Energy and resource-intensive,” and “Market acceptance,” which are placed just below the second branch. The fourth branch, in orange, is labeled “Statistical Insights” and connects to three subtopics: “Awareness strongly linked to perception (r equals 0.631),” “Perception influences career choices (r equals 0.653),” and “Knowledge and location less significant factors,” located below the third branch. The fifth branch, in yellow, is labeled “Recommendations” and includes four subtopics: “Government incentives and subsidies,” “Training programs and workshops,” “Integration of renewable energy,” and “Focus on resource efficiency,” which is placed just below the fourth branch. The final branch, in red, is labeled “Future Directions” and extends to three subtopics: “Expand vertical farming initiatives,” “Target urban and rural youth for entrepreneurship,” and “Strengthen public-private partnerships,” positioned at the bottom. Curved lines connect all the branches, indicating the flow of information.Flowchart outlining the overview of the study outcomes on vertical farming’s potential scope for entrepreneurship. Source: Figure created by author
The mind map is titled “Hashtag Vertical Farming in Oman: A Study Overview.” The central part of the mind map contains the title on the left. From this central point, six main branches extend rightward, each representing a different aspect of the study. The first branch, labeled “Purpose of Study,” extends to two subtopics: “Explore vertical farming as a solution for unemployment and food security” and “Assess economic feasibility and profitability,” which are positioned at the top. The second branch, in blue, is labeled “Benefits” and leads to three subtopics: “Self-sufficiency in food production,” “Entrepreneurship opportunities for youth,” and “Sustainable agriculture practices,” which are positioned just below the first branch. The third branch, in green, is labeled “Challenges” and contains four subtopics: “High initial costs,” “Limited profitability (R O I negative 2.71 percent),” “Energy and resource-intensive,” and “Market acceptance,” which are placed just below the second branch. The fourth branch, in orange, is labeled “Statistical Insights” and connects to three subtopics: “Awareness strongly linked to perception (r equals 0.631),” “Perception influences career choices (r equals 0.653),” and “Knowledge and location less significant factors,” located below the third branch. The fifth branch, in yellow, is labeled “Recommendations” and includes four subtopics: “Government incentives and subsidies,” “Training programs and workshops,” “Integration of renewable energy,” and “Focus on resource efficiency,” which is placed just below the fourth branch. The final branch, in red, is labeled “Future Directions” and extends to three subtopics: “Expand vertical farming initiatives,” “Target urban and rural youth for entrepreneurship,” and “Strengthen public-private partnerships,” positioned at the bottom. Curved lines connect all the branches, indicating the flow of information.Flowchart outlining the overview of the study outcomes on vertical farming’s potential scope for entrepreneurship. Source: Figure created by author
Payback period
The Payback Period equals 3.4 years. In this case, the investment of $891,166 resides in the project for exactly 3.4 years until it is paid back from project revenues. This is a reasonably time for recovery considering the size of the amount. According to the analysis, it will take about 3.4 years before the initial investment is recovered and the return on this investment capital will be approximately 2.71%. Investing in a VF startup project, the profitability assessment suggests that the project implementation is possible with a positive net present value (NPV) and a definite period to cover the costs. However, the relatively low ROI suggests that while the project generates profit, there may be room for optimizing costs or increasing revenue to improve returns. A typical startup vertical farm project would generate a profit of $24,144. Given the total investment of $891,166, this shows that the project is profitable but has a relatively low-profit margin compared to the investment with the parameters and infrastructure arrangement in Table 3.
Parameter and infrastructure value inputs considered for establishing vertical farming startup unit
| Parameters | Room size M2 | Available room height (M) | Price per kilogram lettuce (USD) | Price per kilogram herbs (USD) | Selling price of greens (USD) | Cost of 1kwh of electricity (USD) |
|---|---|---|---|---|---|---|
| Value | 500 | 4 | 10 | 10 | 0.6 | 0.6 |
| Land growing area M2 | 730 | |||||
| Required power (kW) | 183 | |||||
| Average daily electricity consumption (kWh) | 2,792 | |||||
| Average daily water consumption M3 | 5 | |||||
| Investment amount (USD) | 891,166 | |||||
| Yield (pots) | 82,188 | |||||
| Yield (kg) | 3,984 | |||||
| Revenue (USD) | 47,368 | |||||
| Cost of raw materials (USD) | 12,655 | |||||
| Electricity cost (USD) | 5,026 | |||||
| Farm maintenance (USD | 2,555 | |||||
| Logistics (USD) | 2,988 | |||||
| Payback period (years) | 3.4 | |||||
| Parameters | Room size M2 | Available room height (M) | Price per kilogram lettuce (USD) | Price per kilogram herbs (USD) | Selling price of greens (USD) | Cost of 1kwh of electricity (USD) |
|---|---|---|---|---|---|---|
| Value | 500 | 4 | 10 | 10 | 0.6 | 0.6 |
| Land growing area M2 | 730 | |||||
| Required power (kW) | 183 | |||||
| Average daily electricity consumption (kWh) | 2,792 | |||||
| Average daily water consumption M3 | 5 | |||||
| Investment amount (USD) | 891,166 | |||||
| Yield (pots) | 82,188 | |||||
| Yield (kg) | 3,984 | |||||
| Revenue (USD) | 47,368 | |||||
| Cost of raw materials (USD) | 12,655 | |||||
| Electricity cost (USD) | 5,026 | |||||
| Farm maintenance (USD | 2,555 | |||||
| Logistics (USD) | 2,988 | |||||
| Payback period (years) | 3.4 | |||||
Scenario analysis
Scenario Analysis was conducted to assess the potential impact of variations in key operational parameters—specifically, yielding the project’s financial outcomes. Three scenarios were considered.
Baseline scenario
Under the baseline scenario, the project yield was 3,984 kg, which resulted in an ROI of 2.71%.
Scenario 1 – yield decrease (−10%)
In this scenario, a 10% decrease in yield led to a proportional reduction in revenue, resulting in a lower ROI of approximately 2.18%. This scenario underscores the sensitivity of the project’s profitability to yield fluctuations, indicating potential financial vulnerability in the face of decreased production.
Scenario 2 – yield increase (+10%)
Conversely, a 10% increase in yield improved the revenue and subsequently increased the ROI to approximately 4.98%. This scenario highlights the potential for enhanced profitability with improved yield management.
The Scenario Analysis demonstrates that the project’s financial performance is highly sensitive to changes in yield. An increase in yield significantly boosts profitability and ROI, while a decrease in yield adversely affects financial outcomes. These findings emphasize the importance of optimizing yield to maximize the project’s financial returns and mitigate risks associated with lower production levels (Figure 4).
Profitability and sustainability of vertical farming entrepreneurship startups
VF entrepreneurship startups hold immense potential for profitability and sustainability. While the initial investment and operational costs are high, the benefits of higher yield efficiency, resource conservation, and meeting the growing demand for local, fresh produce make it a promising venture. Addressing energy consumption challenges and optimizing distribution networks are critical for the long-term success of these startups. As technology advances and market dynamics evolve, VF is poised to become a cornerstone of sustainable urban agriculture. The profitability of VF entrepreneurship startups is influenced by a combination of financial, operational, technological, environmental, and social factors. However, establishing VF units as entrepreneurship startups is resource and capital-intensive; local factors such as infrastructure cost, funding and financing from the government in the form of grants and other investment sources are very critical to assure the unemployed youth to venture into such innovative farming practices. In addition, the potential of VF units to attain profitability also needs support on operational costs, yield and production efficiency, local market demand and affordable pricing, economic scaling and competitive landscape. To promote VF through business startups as a potential entrepreneurship opportunity, high initial capital investment should be provided for setting up vertical farms including costs for advanced technology, climate control systems, and hydroponic or aeroponic systems to remain competitive with other VF units in the region (Figure 4).
There are around 4–5 private large to medium-scale vertical farms in operation in Oman, marketing the produce in local and regional markets. To be successful, the new startup vertical farms must focus on the profitability of its produce, able to find market demand for the produce over the competitors. Further to promote Agri-preneurship in VF, new startups must incorporate sustainability concepts in farming practices which are on par with the existing VF units. Since VF is resource intensive, optimizing their usage to yield maximum outputs is the key to sustaining the vertical farms. Hence it must remain resource efficient in water and land usage, energy consumption by using light-emitting diode lighting systems, and integrating renewable energy sources into the operation and production process. Potential areas to include are reduction in environmental impact and waste management, cost-effectiveness, community engagement by creating job opportunities for local youth. Besides innovative technology and farming techniques imbibing sustainable practices and meeting local regulatory compliance on food safety measures are some of the critical areas to be considered for achieving the profitability and sustainability of the VF startups. Effective management of these factors, along with continuous innovation and market adaptation, can drive the success and longevity of VF ventures.
The Profitability Index, or PI, was calculated to understand the financial viability of this project. The PI is the ratio of net profit generated by the project to the initial investment and measures how much return is generated per dollar of investment. A PI may be calculated using the following formula:
This research found the profits incurred to be $24,144 and the starting funds were placed in the amount of $891,166. The PI as a result was found to be 0.0271. This implies that nearly 2.71 cents in return are generated per dollar invested within the bounds of this project. As the value of the PI computed is less than 1 this implies that although the project does generate some earnings, there will be inadequate returns considering the cost of investment to make on the project. Pines calls for an improvement in operational efficiency or an increase in sales revenue to make the project financially promising.
The SI was computed to assess the project’s long-term viability. This index measures the project’s capability to maintain its operations over time by comparing net profit to total costs incurred. The formula used for calculating the SI is as follows.
In this project, the net profit was $24,144 and the total cost was $23,224. The calculated SI was 1.04, which means this project generates a little more profit compared to the total cost. An SI value greater than 1 indicates that the project will be viable for the long term since it will cover the costs and still yield a surplus. However, the modest SI value suggests that the margin for sustainability may not be very great, and again emphasizes the need for very careful expense management and perhaps considering ways to increase profitability.
Discussion
Impact of awareness and perception on career choice
The analysis has found a significant positive relationship; r = 0.631 and p < 0.01, suggesting that with increased awareness about VF, people develop more positive perceptions about its potential. Similarly, the strong positive association between perception and career choice depicted in r = 0.653 and p < 0.01, explains that with more positive perceptions about VF, the youth will have a greater likelihood of becoming interested in it for a career. This becomes fundamental because it underpins the effect of not only educating young people about VF but also framing it as a promising and viable occupation (Van Gerrewey et al., 2022).
However, the low correlation coefficient between awareness and career choice, r = 0.393 with p < 0.01, indicates that awareness by itself may not be sufficient to cover action. It reveals a wide information-intention gap regarding VF as a career. The reasons for this could be doubt over its profitability, lack of direct training, or financial constraints. Though this is challenging, directed campaigns will not only raise awareness but also spotlight the success stories and practical benefits of VF ventures launched in Oman and other similar regions. In addition, opportunities for practical learning such as workshops or apprenticeships may provide bridges between awareness and action, affording far more precise ideas of what rewards can be earned and what challenges are likely to be faced.
Limited influence of knowledge and residential location
These findings presented that knowledge about VF and residential location did not influence career choices. It is interesting to note that knowledge does not have an influential effect, because it means that technical understanding does not instill the required confidence or interest in choosing a career path in VF. It might be because most of the knowledge shared is either too theoretical or not in tune with the practical setup and running of a vertical farm. This points toward a need for practical knowledge, and demonstrations that will help the entrepreneur visualize the operational aspects and profitability of VF. Besides, the entrepreneurial skills required for farming, like financial management, marketing, and business planning, could also be inculcated in more effective ways within the training programs.
Another important dynamic is revealed by the absence of an impact caused by a place of residence. It could be assumed that rural youth, who are faced more with traditional agricultural challenges, would be more interested in innovative farming methods like VF. The truth is exactly the opposite. The rural youth do not see VF as a solution to their problems, due to excessive costs or a lack of infrastructure. This suggests that even as an investment opportunity, VF is still perceived to be an urban-centric practice dependent on a lot of advanced technology and infrastructure, which is beyond the reach of rural populations. Initiatives like focusing on building rural infrastructure and financing are likely to make VF more accessible to rural communities.
Perception as the key determinant of career choice
The regression analysis reveals that perception is the most critical predictor of whether youth consider VF as a career choice with a β = 0.673 (p < 0.001); while awareness, knowledge, and residential location are not important predictors of the job. The results therefore underline the need to shape positive perceptions about VF. It indicated that even though people are aware of VF, and have technical knowledge, they would not engage in the same unless it shows up as viable, profitable, and continued career options.
The study offers a useful contribution to the further development of policy and educational systems targeted at the promotion of VF, specifically by demonstrating its economic efficiency and sustainability over the long term. It is recommended that such efforts include the promotion of successful VF entrepreneurs and the financial and social benefits they have managed to achieve. Thirdly, the development of partnerships between local universities and businesses would offer practical exposure to young people in VF and, in the way vertical farms operate, and how they can overcome some challenges at the onset of such businesses.
Cost-benefit analysis and profitability concerns
The CBA revealed that though VF is profitable, the profit margin is rather low, with an ROI of 2.71% and a payback period of 3.4 years. Even though encouraging, the positive return is quite low, indicating that VF might not offer enough short-term financial incentives to attract sizable numbers of unemployed youth. The initial investment costs being high at $891,166, deters several potential entrepreneurs from doing VF as compared those with no access to financial resources or credit.
Scenario analysis shows that the profitability is sensitive to yield variation, where a 10% decrease in yield provides an ROI of 2.18%, while a 10% increase propels it upward to 3.24%. This has huge implications for profitability within vertical farm ventures since consistency in production means everything when it comes to success. Moreover, it implies that VF in Oman requires special consideration of resource management, such as energy, water, and labour. High energy and infrastructure costs, combined with fluctuating market conditions, may have long-term effects on viability in concurrence to reports (Kozai and Nui, 2020; Benke and Tomkins, 2017; Beacham et al., 2019). The low ROI would suggest that VF is likely to be more attractive for young entrepreneurs if the government incentivizes the activity, in the form of subsidies, low-interest loans, or tax breaks for startups in that field. Public-private partnerships could reduce costs by providing technology, sharing infrastructure, or training programs.
Mediation analysis: the role of perception
The mediation analysis shows that perception completely mediates the relationship between awareness and choice of career. Controlling perception, the direct effect of awareness on career choice is insignificant. The finding underpins the hypothesis that it is the perceptions that are the critical drivers in converting awareness into action. While awareness creation on VF is quite relevant, the perception of the individual on the feasibility, profitability, and benefits that could be accrued will determine whether a person pursues it as a career. Again, this points toward perception intervention. An intervention could be showing stories of economic success, showing innovative technologies to make the concept of VF more efficient, or simply emphasizing sustainability and food security. Also, including real-case applications and providing mentorship opportunities with already successful entrepreneurs in VF would reshape the perceptions and create more defined pathways for youth to enter the VF industry.
Challenges to profitability and sustainability
The study also denotes various challenges to profitability and sustainability in VF. The very high upfront and ongoing operational costs of energy use may render the attainment of high profit margins hard for new entrants consistent with the findings reported by Arcasi et al. (2024). Further, the market for produce grown vertically may be yet to mature, with obstacles in the form of consumer preference and price sensitivity. This gets further complicated by the fact that the product through VF could be as much as 20% higher compared to imported goods, as market acceptance may be difficult for such a region depending largely on food imports. The problem that vertical farms have is with high costs of production where they can compete in terms of costs by branding their produce as being superior produce that is fresh, grown locally, naturally without chemicals (Banerjee and Adenaeuer, 2014). However, in some cases, crops produced in vertical farms can also go through the organic marketing classification from the consensus of studies (Butturini and Marcelis, 2020; Allegaert, 2020; Benke and Tomkins, 2017).
The key drivers to enhance profitability and sustainability for VF startups in Oman include a resource-efficient approach, mainly about water and energy consumption (Goldstein et al., 2016; Stein, 2021; Kozai, 2013). Integration of renewable energy sources-solar power being prominent along with advanced technologies of climate control, could help bring down the costs of operation (Gerretsen, 2020; Kozai et al., 2020). Secondly, collaboration with local supermarkets and markets may enable direct-to-consumer model with reduced distribution costs and increased profit margins.
Conclusion
The study points that young people’s choices about embarking on entrepreneurship in VF in Oman depends on their awareness, perception, and knowledge. In all, perception was the leading factor, totally controlling the relationship between awareness and career choice. So, while VF awakens interest, it is successful and profitable in encouraging young entrepreneurs to start a business. There is a strong relationship between awareness, perception, and career choice, while the connection between perception and career choice is even stronger (both with r > 0.6, p < 0.01). The use of regression analysis showed how a person perceives a career is a significant predictor of one’s chosen job (β = 0.673, p < 0.001), but awareness, knowledge and residential location are not significant predictors and has no effect. Furthermore, mediation analysis demonstrates that understanding awareness of career choice depends on perception.
Although VF is shown to be economically viable, it offers only a small profit, with an ROI of 2.71% and a payback period of 3.4 years. Results from the scenario analysis showed that a slight change in yield can lead to a substantial difference in profitability. With a SI of 1.04, the company’s future success depends on the improvement of resource use and efficiency. Yet, VF could provide work for youth and new ideas in Oman’s agriculture ably supported by government intervention through financial assistance and land subsidies.
Limitations of the study
While this study gives ideas about VF in Oman, it is limited in some ways. The study did not include many individuals from rural origin less educated. It is possible therefore that the findings do not apply to diverse populations and the scope might be confined to only the local context. There are fewer ideas and perspectives because most traditional farmers and many agri-entrepreneurs have not taken part in the study. In addition, the reported data depends on people’s opinions, therefore there is a possibility that the ways individuals think and hope to act are not the same as what they end up doing. The results regarding whether an investment is economically sound depends on the given environment and could not be applied to areas with different markets, access to basic services or policies. In addition, for these ventures to thrive, governments must offer useful support, since high-quality technology and thorough urban development may be scarce in some places.
Practical, academic and social implications of the study
This research study gains much importance considering the current and futuristic goals that Oman must achieve concerning ensuring food security, and production demand through sustainable ways of agriculture. By the end of 2050, the population of Oman will reach 4.5 to 5.0 million (Kabir and Rahman, 2012) which will also require an increase in food production to meet the growing demand of consumption which is expected to grow at 4.6% annually according to existing studies. The factors that are restricting the expansion of the agriculture sector in the Sultanate is due to inadequate groundwater resources, dry weather conditions, prevalence of dust storms, high temperatrues, poor soil quality, etc. Amidst these uncertainties, undertaking agriculture through VF technique is a prospective approach to overcome the challenges and meet the national priorities of Oman. Hence this research study adds insights to the concept of VF as a means of achieving food security, food production demand and self-sufficiency through sustainable management of resources available in Oman and encourages the educated youth to embrace agriculture as prospective ventures for employment.
Future research study
Future studies should focus on acquiring an in-depth and complete understanding of perceptions toward VF, targeting rural populations, traditional farmers, and young people with little formal education. Studies tracking youth employment and entrepreneurship over time are important to see how VF helps young people. Examining technology, finance and infrastructure challenges can direct appropriate policies and help programs. Research aimed at combining renewable energy and smart technologies might help businesses become more environmentally friendly and profitable. Furthermore, initiating comparisons with other GCC countries may reveal successful practices and encourage local cooperation in adopting VF.

