Agricultural Economics
A. Sani Heidary; E. Safari
Abstract
IntroductionIn 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 ...
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IntroductionIn 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. Materials and MethodsThe 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. Results and DiscussionThe 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. ConclusionThis 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.
H. Aghasafari; A. Karbasi; H. Mohammadi; R. Calisti
Abstract
Introduction: The environmental impacts of pesticides, genetically modified organisms and other chemicals used to increase agricultural production have raised consumers' concerns about the quality and safety of agricultural products. And now, with the increase of environmental awareness, it has criticized ...
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Introduction: The environmental impacts of pesticides, genetically modified organisms and other chemicals used to increase agricultural production have raised consumers' concerns about the quality and safety of agricultural products. And now, with the increase of environmental awareness, it has criticized modern agricultural activities. These factors have encouraged consumers to consume organic agricultural products. Way of producing the organic products can increase the production costs and, finally, increase the total price of these products. So, consumers should pay more for these products than the inorganic ones. In spite of higher prices of these products, consumers are increasingly tending to consume organic products. So that, consumers tend to pay more for better and more organic and safe agricultural products. Several factors influence the consumer payment preferences for organic products. In this study, these factors are classified into four groups and their impacts on consumer preferences are examined.Materials and Methods: This study has used the Structural Equation Modeling (SEM). Structural Equation Modeling (SEM) is a powerful collection of multivariate analysis techniques, which specifies the relationships between variables through the use of two main sets of models: Measurement model and Structural model. Measurement model tests the accuracy of proposed measurements by assessing relationships between latent variables and their respective indicators. The structural model drives the assessment of the hypothesized relationships between the latent variables, which allow testing the statistical hypotheses for the study. Additionally, SEM considers the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, and multiple latent independents that each one is measured by multiple indicators. Unlike conventional analysis, SEM allows the inclusion of latent variables into the analysis and it is not limited to relationships among observed variables and constructs. It allows the study to measure any combination of relationships by examining a series of dependent relationships simultaneously while considering potential errors of measurement among all variables. SEM has several advantages over conventional analysis, including greater flexibility regarding assumptions (particularly allowing interpretation even in the face of multicollinearity). SEM allows the use of confirmatory factor analysis to reduce measurement error by testing multiple indicators per latent variable while offering superior model visualization through its graphical modeling interface. Structural Equation Modeling include six steps (data collection, model specification, identification, estimation, evaluation and modification). In the present study, the variables including marketing factors, awareness and knowledge, demographic characteristics and attitudes towards organic products are considered as latent variables that the relationship of these variables with the payment preferences is investigated. In order to collect required data, a researcher-made questionnaire and simple random sampling method has been used.Results and Discussion: Results indicate that given the significance of factor loadings, the indicators (observed variables) such as packaging, brand, advertising, discounts and shopping incentives, familiarity with different types of products, way to get information, familiarity with organic agricultural product stores have the required accuracy to measure latent variables. Regarding the model fitting indexes and being model values in the acceptable range, we can say that the measurement and structural models fit well with the data. The results of structural model and hypothesis testing show that awareness and knowledge, demographic characteristics, attitude towards organic products have a significant effect (0.27, 0.59 and 0.21, respectively) on consumer payments preferences. In other words, increasing the awareness of consumer about organic products, increasing household size and income, the positive attitude of consumers towards the characteristics of organic products would increase consumer payments preferences. Also, marketing factors have a significant effect (0.68) on the attitude toward organic products. So that, marketing factors including packaging, brand, advertising and discounts and shopping incentives have a positive effect on the attitude toward organic products. Therefore, hypotheses 2 through 5 are supported.Conclusion: The Findings indicate that increasing the awareness of consumer about organic agricultural products, increasing household size and income, the positive attitude of consumers towards the characteristics of organic agricultural products will increase consumer payments preferences. Therefore, it is suggested that the relevant authorities take serious action to inform about the properties and nutritional value of organic agricultural products, the differences in the labels of food products, and the existing stores supplying of organic products. Also, it is recommended that the numbers of organic supply stores are boosted, especially in areas where high-income people live.