Analysis the role of Psychological factors on intention to apply environmental and meteorological information by farmers in Dehloran Town (The combined application of social cognition theory and technology acceptance Model)

Introduction
Climate change is a topical subject worldwide and there is evidence that this phenomenon is taking place. Agriculture is one of the sectors most affected by climate change and Due to smallholder farmers heavy reliance on rainfed agriculture, climate change will increase vulnerability of the rural populations due to food and nutrition insecurity. Climate change is expected to affect agriculture in different ways and to a different extent in different parts of the world and in different agro-ecosystems. In particular, Communities in most developing countries have been identified as being the most vulnerable to climate change because of multiple stressors and reduced adaptive capacity. Adaptation is one of the policy options for reducing the negative impact of climate change in agriculture sector. A wide variety of adaptation options has been proposed as having the potential to reduce vulnerability of agricultural systems to risks related to climate change. In this regard, Climate information and forecast can be of value when used in decisions involving risks posed by adverse weather or climate. In fact, Climate information is an important pre-requisite for informed decision-making in risk management and adaptation that would help prevent climate extremes from becoming disasters and threats to livelihoods.
Climate forecast have shown potential for improving adaptation of agriculture to climate shocks, but uncertainty remains about whether farmers would use such information in crop management decisions. Despite tremendous efforts to improve weather and climate predictions and to inform farmers about the use of such weather products, farmers’ intention toward forecast use remain poor and farmer use of forecasts has not increased. Because very little is known about the motivations underlying farmer decisions to use or not to use weather and climate forecasts, we designed and conducted a survey based on the combining social cognitive theory and Technology Acceptance Model to gather such information from farmers in Dehloran, Iran. The Technology Acceptance Model is a frequently used behavioral model for predicting and explaining Information Technology usage. A key purpose of TAM is to provide a basis for tracing the impact of external variables on internal beliefs, attitudes, and intentions. The TAM identifies two two most important factors namely perceived ease of use and perceived usefulness. To date the TAM has been used to address why users accept or reject information technology. This model is an adaptation of the theory of reasoned action proposed by Fishbein and Ajzen to explain and predict the behaviours of people in a specific situation. Social cognitive theory (SCT) also is a theoretical framework for analyzing human motivation. The Social cognitive theory consists of factors influencing behavior intention. We used environment factors, Perception of others’ behavior, outcom expentency and self-efficacy as variables of Social cognitive theory in our integrated model.
Hence, in our integrated model two factor of perceived ease of use and perceived usefulness were considered as independent factors, Perception of others’ behavior, outcome expediency, self-efficacy and environment factors as mediated and intention to use or not to use weather and climate forecasts as dependent factor.

Methodology
In this paper, we address the questions of whether smallholder farmers in Dehloran would intend to use climate forecasts in making crop management decisions and whether such use would lead to benefits. A structural equation model was developed to explore relationships between factors affecting intention to use Climate information and forecast.
The study was designed as a cross-sectional survey. Target population of this study consisted of 3820 wheat growers. Using a multistage stratified random sampling method, 350 farmers were selected for this study. The sample size was determined using the Morgan table. Data were collected based on a questionnaire structured to assess the components of combining model. We use a self-report questionnaire to examine the proposed research model empirically. A self-report method refers to an approach in which observation data are provided by participants instead of raters or coders. The questionnaires data were gathered based on a face-to-face survey of farmers. The respondents were assured about anonymity and confidentiality. They were also given the right to refuse participation and also to refuse to answer any question they deemed to be too sensitive or that they felt uncomfortable about. Those declining participation were replaced by other students. No payment was made to the respondents. Answering time for the questionnaire was about 15-20 min.
The survey was pre-tested and piloted on 30 farmers from outside the study area. Cronbach alpha reliability coefficients were calculated for the pilot study and used to refine the questions for the final questionnaire. All scales indicated good-to-excellent reliability, generally 0.76–0.89.

Results
Regarding demographic variables, the participants were aged from 22 to 85 and had a mean age of 44.59 years (SD = 14.24). in main analysis, Structural equation modeling (SEM) was used to assess the causal relationships that were hypothesized in the proposed model. the results of structural equation modeling obtained for the proposed conceptual model revealed that χ2 /d.f. = 1.98 (p < 0.001), GFI = 0.83, RMSEA = 0.053, NFI = 0.79, RFI = 0.78, and CFI = 0.88. Accordingly, the summary of the overall goodness-of-fit indices indicated good fit of the model and data. (χ2 /d.f. value was less than the recommended threshold value 5, RMSEA value was less than the recommended threshold value 0.08).
The finding indicates that technological Acceptance Model factors (perceived ease of use and perceived usefulness) significantly positively affect the social cognitive theory factors (self-efficacy, outcome expectancy and Perception of others’ behaviour). Path relationships revealed that outcome expectancy (β = 0.58, p < 0.001) and self-efficacy (β = 0.26, p < 0.001) had a positive direct relationship with intention to use meteorological information. In addition Perceived ease of use has direct effect on perceived usefulness (β = 0.74, p < 0.001), self efficacy (β = 0.80, p < 0.001) and outcome expectancy (β = 0.27, p < 0.001). perceived usefulness also has direct effect on Perception of others’ behavior. However, environment factors and Perception of others’ behaviour affected intention nonsignificantly.
Findings suggest that respondents’ self-efficacy help predict whether an individual intends to use weather and climate information. Regarding indirect effect, perceived usefulness and perceived usefulness had a strong indirect effect on intention. The model accounted for 59% of variance in intention to use Climate information and forecast.
Also, The finding indicates that in suggenstion model environment factors (beta= 0.01, p>0.05) and Perception of others’ behaviour (beta= 0.56, p>0.05) were not significant predictors of intention to use climate forecast.

Conclusion
To successfully transfer costly weather and climate products into meaningful information that farmers can use in their decisions, farmers must understand the products and have the intention and motivation to extract the relevant pieces of information and apply them to specific decision contexts. This study integrated two sociopsychological theories, social cognitive theory, with a widely used information system technology acceptance model (i.e., the TAM) to provide a comprehensive behavioral model for understanding elderly farmers intention toward using meteorological information. The framework was extended from the original TAM by considering the relationships among technological factors (perceived ease of use and perceived usefulness), and social cognitive factors (system self-efficacy, environment factors and Perception of others’ behavior, outcom expentency), and behavioral intention to use the meteorological information. regarding the samples, the integrated model fitted considerably well.
The proposed model has been proven to be valuable for evaluating and predicting the behavioral intention of climate information and forecast because it provides an integrative perspective that prompts researchers and practitioners to pay attention to the interdependence of these aspects. This study is a justification for using the constructs of this model in politics and decision making that encourages farmers to use meteorological information. The proposed integrative congnitive-technological model may serve as a theoretical basis for future research and can offer empirical foresight to practitioners and researchers in the agricultural departments and rural communities.