structural equation modeling

Agriculture is an essential part of every country’s economy and plays a decisive role in income, employment, and food security globally. Agricultural soils are critical for the efficient production of crops and safe food to meet the needs of a growing population. However, improving soil quality is a critical component of sustainable agriculture. Given the socio-economic and political pressure to improve soil fertility and increase agricultural productivity, widespread chemical fertilizer use, beginning in the 1950s and 1960s, led to increased food production at significant environmental cost. Chemical fertilizers and pesticides have improved short-term food production. However, soil degradation, greenhouse gas emission increases, and water pollution risks have emerged through their widespread use. Consequently, excessive use of chemical fertilizers negatively impacts human health throughout the food chain. For example, excessive use of phosphate fertilizers can lead to cadmium pollution, which if ingested can lead to osteoporosis. Excessive use of nitrogen fertilizer leads to the accumulation of nitrites in plants; nitrites combine with amines increasing the risk of cancers of the digestive system, and methemoglobinemia in severe cases. Fertilizer use leads to surface runoff and groundwater pollution contributing to eutrophication and consequently the deterioration of natural ecosystems and reduction of genetic diversity. Rice is one of the main Iranian foods, but the excessive use of chemical inputs in the production of this product has reduced its quality and properties, previous research shows that it is possible to use organic fertilizers in rice cultivation. It can provide nutrients needed by the soil to produce this product. But rice farmers do not have much desire to use it in rice cultivation. In line with this importance, the present study was conducted with the general aim of investigating the factors affecting the use of organic fertilizers by rice farmers during rice cultivation in Dezful county.  

The statistical population of this research in 1401 is made up of 4700 rice farmers of Dezful County, and the sample size was estimated based on the table of Karjesi and Morgan, using the cluster sampling method of 360 samples. Data collection was designed in the form of a questionnaire based on the Likert spectrum scale. The form and content validity of the questionnaire was confirmed by experts' opinion and its reliability was confirmed through Cronbach's alpha and composite reliability.

The results of the structural equation modeling research showed that the variables of self-efficacy (ƛ=0.257; P=0.000), action guide (ƛ=0.222; P=0.000), perceived intensity (ƛ=0.121; P=0.035) and perceived sensitivity (ƛ= 0.100; P= 0.046) and perceived benefits (ƛ= 0.319; P= 0.000) have a positive and significant effect on the use of organic fertilizers among rice farmers in Dezful County. This is despite the fact that the perceived obstacles variable did not have a significant effect in this section. In addition, it can be said that the variables of the health belief framework could explain 67% of the variance of the dependent variable of the research (the use of organic fertilizers in rice cultivation). In general, the results of this research can add new knowledge to the existing knowledge and provide new insights for policy makers in this field to promote and develop organic and safe products. 

This study was conducted with the general aim of determining the factors affecting farmers' willingness to use organic fertilizers in rice cultivation. In this study, the health belief model was used to identify the factors. The results showed that this theory is very efficient in this field because it was able to explain 67% of the variance in farmers' behavior in this field. In general, the results of this study can provide new insights for policymakers in this field in order to produce healthy products.

In the continuity of human life, agriculture as a strategic activity plays a key role in providing food. In addition, the agricultural sector plays an important role in economic development, social welfare and environmental sustainability of all countries. However, this sector is facing many challenges in recent years. Some of its most important challenges include the increasing growth of the world's population, a 40% reduction in water and soil resources, the destruction of a quarter of agricultural land, climate change, a lack of specialized labor, poor access to financial resources, strict laws, and a decrease in the number of farmers due to a decrease in motivation. Therefore, in order to meet the growing demand for food and overcome its challenges, the agricultural sector is forced to look for new solutions such as adopting digital transformation enhanced by artificial intelligence technology. The use of artificial intelligence (AI) technology has recently become increasingly prominent in the agricultural sector. AI-based solutions assist farmers in achieving higher productivity with fewer resources, ensuring the production of high-quality and healthy products, and accelerating the marketing process. Given the significance of AI technology in enhancing the overall efficiency of the agricultural sector, this research aims to identify the key predictors that influence the behavioral intention and adoption of AI technology in agricultural companies.
 

The main objective of this research is to determine the key predictors of behavioral intention and behavior of using artificial intelligence technology in agricultural companies through the combination of the developed UTAUT2 model and TOE factors. The statistical population of this research is the total employees of nine cultivation and industry of Razavi Agricultural Company, which are about 465 people. Data were collected by completing multidimensional questionnaires along with semi-structured interviews from households in 2023. In total, 250 questionnaires were completed. Data of 39 respondents were excluded due to missing values. The questionnaire is designed based on the seven-point Likert scale (strongly disagree = 1, strongly agree = 7). The questionnaire used in this research includes 14 constructs in the form of 60 items. Excel 2019 software was used to analyze the raw data of the questionnaire and SmartPLS software was used to test the research hypotheses. In order to guarantee the stability of the data, a complete bootstrap method with 5000 sub-samples was performed.
 

The results revealed that the values of Cronbach's alpha and CR for all constructs were higher than 0.7, which shows acceptable internal consistency of the model and adequate reliability of the research constructs. AVE scores and factor loading values for all constructs are above 0.5, which indicates the correct definition of constructs and high convergence between constructs and its items. The values of rho_A as an important reliability measure for PLS-SEM for all constructs are greater than the acceptable value of 0.7. The results of the Fornell-Larcker criteria and the Heterotrait-Monotrait ratio (HTMT) indicate that the model is confirmed in terms of the constructs' discriminative validity. In addition, the research model was able to explain 89.4 and 51.7 percent of the variance of the variables of behavioral intention and the behavior of people to use artificial intelligence technology in the agricultural sector. According to the results, all research hypotheses are confirmed and the behavioral intention to adopt artificial intelligence technology is positively and significantly influenced by expected performance, social effects, hope for effort, facilitating conditions, pleasure-seeking motivation, price-value, habit, trust in technology, technological aspects, organizational aspects, and environmental aspects. However, the fear of technology variable has a negative and significant impact on people's behavioral intention.
 

This study highlights the determining the role of expected performance constructs, social influences, fear of technology, and organizational and environmental aspects compared to other constructs in predicting people's behavioral intention to adopt artificial intelligence technology in the agricultural sector and provides important information for different stakeholders. According to the results, it is suggested that the government should invest in the development of the necessary infrastructure for this technology and provide a platform for its development by establishing efficient laws and paying low-interest facilities. In addition, Designers should create user-friendly tools tailored to the agricultural conditions of the country.

Rural production cooperatives play an important role in reducing rural poverty by increasing production efficiency and providing services that governments are unable to provide for poor people. This research aimed to investigate the 12 components of the Legatum Index improving the welfare of rural households that are members and non-members of rural production cooperatives in Miandoab Township.

The present research was conducted using a survey and descriptive-correlational methodology. The size of the target population includes 17493 rural households, members and non-members of rural production cooperatives, and 377 households were randomly selected. Questionnaire was the main tool for data collecting. Validity of the questionnaire was confirmed by a panel of agricultural extension and education specialists at Tarbiat Modares University and its reliability was confirmed by calculating Cronbach’s alpha coefficient. To investigate the components of improving the welfare of rural households and checking the goodness of fit index, confirmatory factor analysis and structural equation modeling were used. Data were collected through structured interviews and analyzed using SPSS and LISREL software.

The results showed that there is a significant difference between member and non-member rural households in terms of welfare component status. So in the components of safety and security; individual freedom; governance; social capital; investment environment; entrepreneurial conditions; market access and infrastructure; economic quality; living conditions; sanitation and health; and education; there was a significant difference between member and non-member households, and only in the natural environment component, no significant difference was seen.

Rural production cooperatives played an effective role in improving the welfare of rural households in Miandoab City, and its average was calculated as 3.56; which shows the positive effect of cooperatives in improving the overall welfare of rural households. In this regard, it is suggested to provide entrepreneurship training to increase agricultural production, diversify the rural economy, and create the necessary foundation for increasing wealth.

In today's era, characterized by fluctuating job markets and dynamic business environments, personal attributes such as career adaptability and proactive personality will increasingly influence the post-graduation career trajectory of students. In this context, the research literature shows that career adaptability and proactive personality play an undeniable role in the formation of students' entrepreneurial intention, which is considered an important prerequisite for starting a business. Theoretical evidence suggests that their adaptability resources influence entrepreneurial intention among students. Moreover, the development of career adaptability is intricately linked to a proactive personality, which enables individuals to discern opportunities and devise apt solutions amid uncertain environments, as posited by the Career Construction Theory. In this context, empirical exploration into the nexus of a proactive personality, career adaptability, and entrepreneurial intention remains scarce. Hence, this study aimed to examine the mediating influence of career adaptability between proactive personality and entrepreneurial intention among agricultural students. By fulfilling this research objective, not only does this study contribute to the depth of literature on entrepreneurial intention, but it also sheds light on the significance of fostering career adaptability among agricultural students.

The research adopted a quantitative approach, employing survey methodology and a cross-sectional time frame for data collection and correlational-causal data processing for analysis. The statistical population comprised fourth-year undergraduate agricultural students enrolled in public universities across Iran. Employing the inverse square root method, a sample of 384 students was selected utilizing a multi-stage sampling technique, wherein two universities were randomly chosen from each of the five regions under the Ministry of Science, Research, and Technology, amounting to a total of ten universities. Data were gathered from students enrolled in the selected universities. The research utilized questionnaires assessing entrepreneurial intention (6 items), proactive personality (3 items), and career adaptability (12 items), designed on a five-point Likert scale within the native Porsline system and distributed online. The validity of the research instruments was ensured through reviews and confirmation by a panel of agricultural education experts. Additionally, reliability was established via a pilot study and calculation of Cronbach's alpha coefficient (α > 0.7). The measurement model's validity was affirmed through factor loading calculations and assessments of convergent validity, discriminant validity, and reliability, utilizing Cronbach's alpha coefficient and composite reliability (α and CR > 0.7; AVE > 0.5; HTMT < 0.85). Structural equation modeling, employing the partial least squares approach, was employed for data analysis. The SmartPLS4 software facilitated the data analysis process.

The research findings empirically validated the mediating role of career adaptability in the relationship between proactive personality and entrepreneurial intention among agricultural students (β = 0.271, t = 5.174, p = 0.001). Notably, the results also indicate a direct influence of proactive personality on the entrepreneurial intention of the surveyed students (β = 0.291, t = 4.077, p = 0.001). Moreover, this positive effect is evident in both pathways: the direct relationship between proactive personality and entrepreneurial intention and the relationship mediated by career adaptability. Consequently, the career adaptability variable assumes a complementary mediating role in the relationship between proactive personality and entrepreneurial intention among agricultural students. This study highlights the significance of proactive personality and career adaptability as pivotal antecedents shaping the entrepreneurial intention of agricultural students. Thus, agricultural students with a proactive disposition can enhance their entrepreneurial intention by fostering their career adaptability. Furthermore, the research findings highlight that both direct and indirect effects exhibit comparable strength, collectively explicating 43.5% of the variance in entrepreneurial intention among agricultural students. Hence, enhancing students' career adaptability is indispensable for fostering entrepreneurial intention within agricultural universities.

The findings demonstrate that the career adaptability of agricultural students, serving as a psychological resource, significantly influences the development of their entrepreneurial intention. Furthermore, a proactive personality trait shapes career adaptability. Thus, this research emphasizes that the formation of entrepreneurial intention among agricultural students relies on personal resources, encompassing both career adaptability and proactive personality. Consequently, agricultural higher education institutions aspiring to cultivate entrepreneurial intentions among their students should prioritize initiatives aimed at enhancing career adaptability. These efforts could include organizing educational workshops and inviting successful entrepreneurs. Similarly, fostering proactive personalities can be achieved through empowerment programs and related activities. In essence, the insights collected from this study can inform the design of targeted programs geared toward instilling entrepreneurial intentions among agricultural students. To foster entrepreneurial intention among agricultural students, agricultural higher education institutions are advised to enhance students' career adaptability by engaging entrepreneurs from the agricultural sector and organizing diverse educational workshops, including those focusing on soft skills development. Moreover, initiatives aimed at cultivating a proactive personality among students, such as psychological empowerment programs, can further facilitate the enhancement of their career adaptability. By implementing these strategies, institutions can effectively prepare agricultural students to embark on entrepreneurial endeavors in the agricultural domain.

Supplying fresh water is one of the issues of humanity today and among the concerns of the United Nations and the FAO. Due to sudden changes in freshwater supply and demand patterns in the current world, its supply management is followed as a challenge. The purpose of this research is to design a conceptual model and structuring of fresh water supply chain using the combined approach of soft systems methodology and structural equation modeling. By conducting an open interview and posing a single and comprehensive question to 20 active experts and experts in a snowball manner, and then the role of researchers in system placement, the model will be presented. Qualitative model extraction was done with the SSM method and then in the quantitative part, the factors involved and identified by this method were measured in the form of hypothesis generation by researchers and their testing in PLS-SMART software, the findings showed that if the fresh water supply chain is considered as a system, The input and supply of this high-quality water system, rain and flood; disruption in the processing and analysis of this stakeholder tapping system; control for culturalization as an influencing and modulating factor of this system; Multiple weather, policy instability, natural disasters as environmental and political factors; The policy of order and quantity consumption and quality control to shape the cultural control; disturbing demand and output in this logistics distribution system are water wastage and prediction of wrong consumption and tax evasion and finally the optimal achievements of the water footprint identification model, analysis of water sustainability issues and water shortage control.

The following research aims to provide a structural model for the development of essential soft skills in agricultural students. In this regard, a mixed approach (qualitative-quantitative) was used. In the qualitative part of the research, the data was collected through semi-structured interviews with 19 Participants (faculty members of agricultural majors) who are expert in the subject of research. Participants were selected using purposeful and snowball sampling methods. In qualitative phase, "grounded theory strategy" was used to design the initial model of the research. In the quantitative part of the research, after confirming the reliability and validity of the instrument, data were collected from 384 of the senior students of agricultural majors. In order to evaluate the research model, Structural equation modeling with partial least squares approach was used. The results of the research confirmed the research model. Based on this model, the factors affecting on essential soft skills and essential soft skills consequences for agricultural students were clarified.

Paying attention to the needs and wants of consumers is the first step to develop the market of organic products, because recognizing consumer behavior and examining the factors affecting it has a very important role in the success of any economic system. Therefore, this study aimed at examining the intention to buy organic food in the critical situation of the existence of the corona virus and risk perception posed by it as one of the factors affecting the consumer's intention to buy using the theory of planned behavior. For this purpose, the required data and information were collected in 2020 through field surveys and completing a questionnaire. The statistical population of the study included all the organic food consumers in Torbat-e Heydarieh county of Iran. Based on Cochran's formula, the sample size consisted of 210 people. Stratified random sampling method was used for sampling and selecting the individuals in the studid community and structural equation modeling was used for data analysis. The results showed that the correlation coefficients of the organic food consumers' risk perception of corona virus with their attitudes, subjective norm, perceived behavioral control and anticipated fear were 0.222, 0.65, 0.625 and 0.592, respectively and the consumer's attitude (0.452), risk perception (0.268) and anticipated fear (0.101) had the greatest impact on the intention to buy organic food. Therefore, by introducing the benefits of organic products through mass media, people's attitudes towards organic products can be changed; also, by informing the people of the society about the risk of the corona virus, it is possible to create the intention of placing organic products in the consumption basket of the household.

The main purpose of this study was to explain the factors affecting the adoption and development of durum wheat cultivation in rural areas of five provinces of Fars, Khuzestan, Kerman, Ilam and Tehran in Iran using structural equation model. The required data were collrcted through a questionnaire, the reliability of which was determined by Cronbach's alpha coefficient and composite reliability index (CR); and the extracted mean variance index (AVE) was used to determine the convergent validity of the components. The statistical population of the study included all wheat farmers in the mentioned provinces. Statistical sample size was determined as 274 people using Krejcie and Morgan sample size table and statistical samples were selected using multi-stage sampling method with proportional assignment. The results indicated that the studid indices were desirable. Data analysis was performed in a structural equation modeling method using LISREL8.5 and SPSS22. The study results confirmed the positive and significant effect between the explanatory components affecting the willingness to adopt durum wheat cultivation; in addition, the structural equation model predicted, in total, 47 percent of the variable variations in the tendency to adopt durum wheat cultivation. Finally, the results of this study are expected to be useful in developing strategies for extension and development of durum wheat cultivation. In addition, this research can be useful for breeders to breed varieties appropriate to the wheat farmers` conditions and needs.

Present study aimed at designing a model for the technical development of greenhouses and analyzing its influencing factors through the survey of 1300 greenhouse owners in Pakdasht county. Based on the Cochran's formula, the sample size was estimated to be 150. To increase the accuracy of access to respondents, stratified random sampling technique with proportional assignment was employed. The face validity of researcher-developed questionnaire was confirmed by a panel of experts. Cronbach's alpha coefficient values for the variables ranged from 0.70 to 0.93, indicating acceptable reliability for the instrument. Partial least squares structural equation modeling using SmartPLS2 was used to validate the designed model. The results showed that the two factors of human capital and access to technical infrastructure were the most effective factors in the technical development process of greenhouses. The three variables market access, investment-credit, and cost-benefit in light of government support have been able to affect the level of technical development of greenhouses by improving the level of access to technical infrastructure. Educational-extensional programs and government support have also led to an increase in the level of human capital and, indirectly resulted in the technical development of greenhouses. Generally, the independent variables could account for 42 percent of the variance of greenhouses' technical development. Finally, it can be concluded that the development of technical infrastructure, human capital, market access, investment, extension programs, and appropriate government policies can lead to the technical development of the greenhouse.

Creating employment and improving entrepreneurial space processes in rural areas are one of the essentials of rural development program. This study aimed at providing a model for improving rural entrepreneurship development in Shazand County of Markazi province of Iran. The statistical population of the study included 900 rural entrepreneurs in the county. Random sampling method was used to select subjects for the study. According to the Krejcie-Morgan table, a sample of 265 rural entrepreneurs was selected for the survey. The required data were collected through a structured questionnaire, the validity of which was confirmed by university professors and entrepreneurship experts and its reliability was performed by pre-test. Cronbach's alpha coefficient for the variables and research structures ranged from 0.71 to 0.918. The relationships between the items and structures were analyzed using SPSS22 and Smart PLS3 statistical software. Then, the relations between educational, economic and institutional factors and the rural entrepreneurship development were investigated in a structural equation method. The results showed that the GOF criterion was 0.389, indicating that the model for improvement of rural entrepreneurship development was suitable. According to the model, respectively, the institutional factor over the economic factor and the educational factor over the institutional factor had the highest factor loadings; in other words, for the development of rural entrepreneurship, the paths indirectly affecting the entrepreneurship development should be considered. The results also showed that all three educational, economic and institutional factors affected the entrepreneurship development, but the economic factor could be considered as one with the most direct impact on the rural entrepreneurship development.

Urban agriculture is a method for reducing vulnerability of the world's urban population in the face of ecological changes. The most important functions of urban agriculture are including energy management and reduction of product transfer, urban waste management, improvement in the quality of soil and water and cities air, improvement in the quality of food and food security, managing unexpected situations and dilemmas, and finally improving the status of citizenship and appearing desirable behaviors of citizenship. The aim of this study was to determine the capacities of urban agriculture from the perspective of agricultural students of Tabriz University. This study was applied and descriptive research in terms of data collection. The population of the study consisted 845 students. The sample (n=148) was selected using systematic random sampling method by Cochran's formula. For examining the reliability of the questionnaire a pre-test was conducted. The values of Cronbach's alpha coefficient for different concepts of the questionnaire varied between 0.81 – 0.92. The data was analyzed using SPSS 22 and Smart PLS software. Results showed that the greatest loading factor (0.898) and T statistics (39.441) are related to the urban agriculture capacity in using surface water resources to feed underground water reservoir.