Agricultural Economics
Alireza Sani Heidary; Ehram Safari
Abstract
Introduction
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 ...
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Introduction
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 technology has recently become evident in the agricultural sector. Solutions based on it help farmers to produce more with less resources; Secondly, to produce quality and healthy products and finally to market their products in a shorter time. Therefore, considering the importance of using artificial intelligence technology in the comprehensive improvement of the agricultural sector, this research seeks to answer the question, what predictors play an imporetant role in the behavioral intention and behavior of using artificial intelligence technology in the agricultural companies?
Materials and Methods
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.
Results and Discussion
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.
Conclusion
This study highlights the determining 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.
Agricultural Economics
A. Sani Heidary; M. Daneshvar Kakhki; M. Sabouhi Sabouni; H. Mohammadi
Abstract
Introduction
Considering being located in arid and semi-arid regions of the world, Iran is influenced by the most severe impacts of drought. Drought is considered a major threat to the livelihood of rural households. During the recent drought, rural households faced significant losses and hardships, ...
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Introduction
Considering being located in arid and semi-arid regions of the world, Iran is influenced by the most severe impacts of drought. Drought is considered a major threat to the livelihood of rural households. During the recent drought, rural households faced significant losses and hardships, underscoring their lack of preparedness for this natural hazard. Consequently, every society must take proactive measures to manage changes, mitigate threats, and respond effectively. A review of the country's drought management programs reveals that policymakers have consistently prioritized increased production, even amid the critical conditions of recent droughts. This focus on boosting production to meet the basic needs of a growing population has taken precedence over enhancing rural households' livelihoods and resilience. However, improving rural households' resilience in drought conditions hinges on prioritizing their capacity for adaptability and flexibility. Therefore, considering the sensitivity of the issue of resilience as a dominant approach effective on the dimensions of life and livelihood of rural households on the one hand and the lack of a comprehensive study on its underlying factors, on the other hand, this research seeks to answer two questions: First, what is the resilience level of rural households against drought? Second, what factors influence the resilience levels of rural households in drought conditions?
Materials and Methods
The statistical population of this study is 16,817 rural households in Zehak city, located in Sistan and Baluchistan province, which are strongly influenced by different climatic events such as drought, excessive heat, low rainfall and 120-day winds. A stratified random sampling method was used to determine the sample size. According to Cochran's formula, the sample size is estimated to be 376 households. Data were collected by completing multidimensional questionnaires along with semi-structured interviews from households in 2023. To measure the resilience capacity of rural households, the theoretical framework of TANGO based on the estimation of the three capacities of absorption, adaptation and transfer was used through the factor analysis method, in which attitudinal and mental aspects of resilience are also taken into account. Finally, partial proportional odds model has been used to evaluate the influencing factors on the resilience capacity of rural households.
Results and Discussion
The results of the state of resilience capacity of rural households in the region indicated that the average value of their resilience capacity is 26.27, which shows the low level of resilience capacity in the region. Also, the households of the region are in a bad situation based on the absorption, adaptation and transmission capacities, and the households of the region have a stronger transmission capacity than the absorption and adaptation capacity against drought. The results of grouping the resilience capacity of households reveal that 32.45% are in the vulnerable group, 28.19% are in the relative resilience group, 22.61% are in the resilient group and 16.76% are in the high resilience group. The results show that more than 60% of households are at very low levels of resilience. Finally, the partial proportional odds model results demonstrated that the variables of education of the head of the household, skill level in agricultural activities, savings, household income, number of household contacts with agricultural extension, membership of the head of the household in social groups and access to microcredits have a positive effect and variables of the value of the loss of agricultural products and the number of livestock lost have a negative effect on the resilience capacity of rural households against drought.
Conclusion
According to the findings, policy-makers should prioritize strengthening the variables that determine the resilience capacity and its dimensions in the implementation of drought management programs so that households can absorb drought shocks without damaging their basic components. Policy-makers should also target specific categories of risks, dimensions of vulnerability and resilience in different time periods (before, during, and after shock) in order to choose comprehensive strategies to build and increase resilience. For instance, before a shock, better access to early detection of emerging climate risks could help farmers plan their cropping activities accordingly. Access to climate information allows for forward-looking adaptation that reduces the impact of shocks and increases resilience.
Agricultural Economics
H. Mohammadi; A.R. Sani Heidary; A. Shahraki
Abstract
A suitable marketing strategy is essential for increasing sales and profitability at different stages of the product life cycle. The main objective of this study was to assess the factors that affect the choice of marketing strategy at various stages of the product life cycle in the food industry in ...
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A suitable marketing strategy is essential for increasing sales and profitability at different stages of the product life cycle. The main objective of this study was to assess the factors that affect the choice of marketing strategy at various stages of the product life cycle in the food industry in Mashhad, Iran. Data were collected in 2017 through a survey which 88 marketing managers in the food production industry completed the questionnaires. To reach the goal of the study, the multinomial logit model was applied to determine the effects of explanatory variables on the probability of choosing a special marketing strategy at the various stages of the product life cycle. Results showed that the manager’s experience, education, type of product, competitiveness, reputable brand, and market share had a significant effect on the chosen strategy at different stages of the product life cycle. Therefore, a company’s profitability in a market could be improved by the implementation of a marketing strategy based on product type and in relation to the specific stage of the product life cycle.