Drivers of Farmers to Adopt Environmental Technologies: Development of an Integrated Model

doi:10.21203/rs.3.rs-4947430/v1

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Drivers of Farmers to Adopt Environmental Technologies: Development of an Integrated Model

https://doi.org/10.21203/rs.3.rs-4947430/v1

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The increasing demand for food owing to population growth, the increase in the number of starving people, the lack of resources, the lowering of the water table and environmental pollution have put great pressure on developing countries to solve this problem by introducing and accepting environmentally friendly technologies. The aim of this study is to determine the effective factors on the intention to adoption of elite farmers in Fars Province, Iran, towards smart farming technologies using an integrated model to create conditions for the adoption of these technologies. The study was conducted using a survey and multistage random sampling in the Fars Province, Iran. The sample included 172 elite farmers in Fars Province. The results show that perceived usefulness, attitude toward behavior, self-efficacy, and personal innovativeness play an important role in shaping the intention to adoption. The study emphasized that perceived ease of use, perceived advantages of smart farming, smart farming knowledge, and controllability of behavior did not directly lead to the intention to adoption. Perceived usefulness also had the greatest influence on attitudes toward behavior. Short- and long-term training, workshops and research farm visits should be conducted to improve the intention to adoption of these technologies.

Integrated model

Intention to adoption

Attitude toward the behavior

Smart farming technologies

In recent years, growing global concerns about the side effects of conventional agriculture on the environment and society have led to the emergence of a special agricultural system known as sustainable agriculture. Sustainable agriculture aims to improve economic efficiency, environmental quality, and social responsibility (Fairweather & Campbell, 2003). Moreover, many studies have shown that conventional agricultural systems destroy the environment and degrade natural resources through indiscriminate use of chemical inputs (Gracia & de Magistris, 2013). Environmentally friendly agriculture is one of the biggest global challenges that has attracted the attention of experts, politicians, and decision-makers. The focus of this type of agriculture is not to neglect nature but to conduct agricultural production in harmony with the environment so that the production process can continue in the future (Rezaei-Moghaddam et al., 2005). This led to a gradual transition from conventional farming to the use of Smart Farming Technology (SFT). Smart farming, which has known as the third green revolution, is a new concept and refers to a set of agricultural management methods that use modern information and communication technologies to improve the quality of agricultural products and services, increase efficiency and reduce resource consumption (Amsini & Rani, 2021; Shahzadi et al., 2016).

In the process of agricultural intelligence, advanced technologies such as the Internet of Things, artificial intelligence, drone technology, sensors, online applications, robots, e-commerce and digital advice are used to improve the accuracy and optimal implementation of agricultural processes (Fabregas et al., 2019; Walter et al., 2017; Ghatrehsamani et al., 2023). Farmers can make smarter decisions regarding irrigation, planting, and crop management by accurately capturing farm data. Smart farming not only helps to improve productivity and save resources but also reduces negative impacts on the environment. This innovation is an important step towards sustainable agriculture, as it improves the accuracy and efficiency of agricultural planning and decision-making (Walter et al., 2017; Amsini & Rani, 2021). Smart farming utilizes data and skills, as well as technological facilities, to manage inputs more easily and efficiently. For example, the Internet of Things have a major impact on improving the quality and reducing the waste of agricultural products. Moisture sensors can be used to accurately track the soil moisture in different parts of the farm. Using these tools, farmers can make smart decisions regarding water allocation to different parts of the farm. Consequently, water consumption will decrease in the long term (Gondchawar & Kawitkar, 2016; Naresh & Munaswamy, 2019; Shahzadi et al., 2016).

SFT encompasses all processes in the agricultural sector, from production to consumption of agricultural products and their transformation into other goods and services. Reducing costs, managing the consumption of limited resources, and protecting the environment are the result of the extensive use of technologies related to intelligence in the agricultural sector (Agussabti et al., 2022; Shahzadi et al., 2016). Smart agriculture is a new approach in the field of agriculture that enables farmers and agricultural experts to optimize agricultural processes through the use of advanced technologies (Agussabti et al., 2022; Saqib et al., 2023). Smart agriculture helps farmers manage natural resources such as water and soil more accurately, choose the most economical time to plant and irrigate crops, and use more precise machinery and methods to manage their farms. However, the successful adoption of smart technologies in developing countries depends on more than just technological features, and is deeply intertwined with many socio-psychological factors (Adnan et al., 2019; Walter et al., 2017; Ghatrehsamani et al., 2023).

At first glance, SFT do not seem to be applicable in developing countries because of poor farmers, subsistence farming, lack of knowledge among farmers, and high costs. However, this approach has enormous potential to improve agricultural production in developing countries. The adoption of SFT in Iran, a developing country, is very low. Considering the important role of elite farmers in the adoption of new technologies by other farmers, the aim of this study is to determine the effective factors on intention to adoption of elite farmers in Fars Province, Iran, as future trustees of agricultural technology diffusion in order to prepare the situations for the adoption of these technologies. In this study, the Theory of Planned Behavior (TPB), Innovation and Diffusion Theory (IDT), and the Technology Acceptance Model (TAM) were combined to measure elite farmers' intention to engage in SFT. SFT knowledge, perceived advantages of the SFT, and self-efficacy (from Roca et al. (2006) & Chang et al. (2017)) were also included in the model.

So far, various theories and models have been presented for the study of attitude and intention. The primary models were simple and believed that knowledge, awareness, and concern influence behavior via linear models (Shanks, 2006). By understanding the relationship between attitude, intention and behavior, different theories have been presented to predict the role of attitude and intention in individual behavior (Burton, 2004). The theories and models used in this study are explained below.

2.1. Theory of Reasoned Action (TRA)

TRA examines the effects of attitudes and goals on behavior (Burton, 2004). TRA assumes that behavioral intention is the main reason for a person's behavior. According to this theory, behavioral attitudes are functions of beliefs. Beliefs associated with attitudes towards a behavior are called behavioral beliefs. Subjective norms are also a function of beliefs, which are different and are called norm beliefs (Fig. 1).

2.2. Theory of Planned Behavior (TPB)

Despite the close relationship between intention and behavior, intention does not always lead to actual behavior. In other words: If people do not have complete control over their behavior, behavioral intention cannot be the main determinant of behavior. Therefore, Ajzen proposed the TPB by adding perceived behavioral control to TRA (Grizzell, 2007). According to this theory, people's behavior is controlled by behavioral beliefs, normative beliefs and control beliefs (Fig. 2).

2.3. Innovation and Diffusion Theory (IDT)

Rogers proposed the Innovation Diffusion Theory in 1962 (Rogers, 2003). Diffusion studies have shown that highly innovative individuals actively seek out information (Fig. 3). They can deal with a high degree of uncertainty and develop positive intentions towards technology adoption. Individual innovativeness is described as a tendency to take risks (Lu et al., 2005). These individuals develop a more positive perception of information technology, and ultimately, individual innovativeness results in the epitomizes the risk-taking trait (Jeong et al., 2009).

2.4. Technology Acceptance Model (TAM)

TAM is presented to predict the motivating criteria for the use of information technologies. This model was proposed through the development and completion of the TRA (Adrian et al., 2005). Davis (1989) introduced the TAM based on the TRA by defining perceived usefulness (PU) and perceived ease of use (PEU) (Fig. 4). In the TAM, behavioral intention is used as both a dependent and an independent variable. Behavioral intention is used as a dependent variable to measure the validity of the PU and PEU, and as an independent variable to predict actual use (Turner et al., 2010, Mooya & Phiri, 2021).

Several researchers emphasize that more external variables should be added to the TAM so that it is able to explain technology adoption behavior. Davis et al. (1989) investigated the effective factors on accepting processing software among students. According to the results, PU influenced using technology while PEU had a less influence on using innovation. Adrian et al. (2005) indicated that PU, farm size and farmers' education had meaningful impact on intention toward adoption of technologies. According to Rezaei-Moghaddam and Salehi (2010) attitude, observability and trialability affected intention to application of technologies and attitude to use had the most effect on intention to use. Results of Ajili et al. (2012) showed that Iranian experts' behavioral attitude and observability were the main determinants of experts' behavioral intention to use of variable rate irrigation technology and observability had most causal relationship with behavioral attitude. Kalayou et al. (2020) showed that PU had a direct influence on attitude and intention to act. Su and Li (2021) Showed that PU and PEU had meaningful effect on the intention to adoption. Almaiah and Alismaiel (2018) by presenting an integrate model, claim that perceived usefulness has strong effects on behavioral intention, and behavioral intention has significant effects on actual use. SFT present a wide range of information and it requires learning new skills for using information system, adapting technologies with available equipment and interpreting maps of GIS. Some studies also reveal that the self-efficacy has an impact on perceived ease of use (Roca et al., 2006; Chang et al., 2017).

According to the literature, the following framework (Fig. 5) was presented. In this model, the independent variables are assumed to be the most important and direct determinants of the intention to adoption (IA) and attitude toward the behavior (ATB) of elite farmers towards SFT. These variables include perceived usefulness, perceived ease of use, attitude toward the behavior, self-efficacy (SE), personal innovativeness (PI), perceived advantages of SFT (PASFT), SFT knowledge (SFTK) and controllability of behavior (CB). Also, the model examined in this paper assumes that these variables affect intention and attitude indirectly by considering the indirect effects of these variables.

The survey was conducted in Fars Province, Iran to collect data. The statistical population was all elite farmers in Fars province, Iran. The highest percentage of experimental implementation of SFT and precision farming and the first research and experimental measures in this field in Iran were carried out in Fars province. Fars province is one of the most important provinces of Iran in the field of agriculture. This province is facing a water crisis and high consumption of chemical fertilizers and pesticides. Therefore, alternative solutions should be used to reduce the use of chemicals and water. For these reasons, this province was selected to study. A multi-stage random sampling was used to collect data from 172 elite farmers. The sample size was estimated according to the studied population and based on Cochran formula. A questionnaire was used for data collection. The face validity was tested by a panel of experts. The pilot study was conducted with 30 randomly selected farmers out of sample. Cronbach's alpha was used to determine reliability, with a value between 0.74 and 0.94 for the variables in this study. The data was coded and analyzed using SPSS and EQS version 6.1. The definition of the variables can be found in Table 1.

Table 1

Definition of the variables
Variables	Definitions
IA	It refers to the strength of the elite farmers' intention to make or support the actual use decision (Phillips et al., 1994). This variable was measured with the items of intention to use of SFT in the future, intention to suggest the use of SFT technologies to other farmers, etc.
ATB	Elite farmers' positive or negative feelings about the use of SFT. This variable was measured by items on the meaningfulness, desirability or positive or negative feelings towards SFT, etc.
PEU	Farmers' belief that using a new innovation or technology will improve their work performance. This variable was measured with items such as improvement in production costs, increase in productivity, better control over farming activities, etc.
PU	A person's expectation that the application of SFT will be done without major effort. This variable was measured with the items about ease in using SFT, no need for mental effort to use such technologies, ease in learning to use tools, etc.
SE	It is considered as an individual's belief in their ability to perform the tasks effectively. This variable was measured with questions about confidence in learning SFT and confidence in using such technologies, etc.
PI	It is defined as an individual's willingness to try a new innovation. This variable was measured using the items proposed by Agarwal and Prasad (Agarwal & Prasad, 1998).
SFTK	It refers to the farmers' level of awareness and familiarity with the characteristics of SFT and the amount of information about this farming system, which was measured by asking questions about the activities of each SFT, etc.
CB	It refers to the farmers' opinion about the performance of SFT that is within their control. This variable was measured with questions about the feasibility of using SFT, considering the economic and educational situation, the current condition of agricultural fields, etc.
PASFT	It refers to the farmers' perceptions of the benefits of SFT and was estimated using items on the benefits of SFT (such as pest control, productivity, product sustainability, etc.) and farmers' perceptions of them.

4.1. Correlation coefficients between variables

The relationship among the variables was tested. The results showed a positive relationship between attitude toward the behavior and intention to adoption (r = 0.55, P < 0.01), perceived usefulness (r = 0.55, P < 0.01), self-efficacy (r = 0.28, P < 0.01), personal innovativeness (r = 0.40, P < 0.01), SFT knowledge (r = 0.28, P < 0.01) and perceived advantages of SFT (r = 0.36, P < 0.01). The results showed a positive and significant correlation between PEU and self-efficacy (r = 0.27, P < 0.01), personal innovativeness (r = 0.18, P < 0.05) and perceived advantages of SFT (r = 0.16, P < 0.05). The analysis of correlation coefficient showed that there were significant relationships between intention to adoption and perceived usefulness (r = 0.70, P < 0.01), self-efficacy (r = 0.61, P < 0.01), personal innovativeness (r = 0.59, P < 0.01), SFT knowledge (r = 0.35, P < 0.01), controllability of behavior (r=-0.18, P < 0.05) and perceived advantages of SFT (r = 0.45, P < 0.01). Pearson correlation analysis indicated significant relationships between the perceived usefulness and the self-efficacy (r = 0.56), personal innovativeness (r = 0.57), SFT knowledge (r = 0.36), and perceived advantages of SFT (r = 0.49). Also, there were positive and significant correlation between self-efficacy and personal innovativeness (r = 0.54, P < 0.01), SFT knowledge (r = 0.30, P < 0.01) and the perceived advantages of SFT (r = 0.45, P < 0.01). Correlation analysis revealed that there were significant relationships between the personal innovativeness with the SFT knowledge (r = 0.43, P < 0.01), the perceived advantages of SFT (r = 0.38, P < 0.01) and controllability of behavior (r=-0.21, P < 0.01). The results show that there is a positive relationship between the SFT knowledge and perceived advantages of SFT (r = 0.42, P < 0.01).

Table 2

The correlation matrix of research variables
Variables	ATB	PEU	IA	PU	SE	PI	SFTK	CB	PA SFT
ATB	1
PEU	0.05	1
IA	^**0.55	0.12	1
PU	^**0.55	0.10	^**0.70	1
SE	^**0.28	^**0.27	^**0.61	^**56	1
PI	^**0.40	^*0.18	^**0.59	^**0.57	^**0.54	1
SFTK	0.28^**	0.04	^0.35^	0.36**	^**0.30	^**0.43	1
CB	-0.08	-0.02	-0.18^*	-0.14	-0.10	-0.21^**	-0.02	1
PASFT	^**0.36	^*0.16	^**0.45	^**0.49	^**0.45	^**0.38	^**0.42	-0.12	1
*Significant level at 0.05
**Significant level at 0.01

4.2. Model’s goodness-of-fit

The results indicate that the goodness-of-fit indices in this survey are acceptable. The chi-square statistic divide to degree of freedom should be less than 3, which is 0.84 in this study. Another indication of the goodness of fit of the model is the p-value. It should be greater than 0.05, which corresponds to 0.66. The normed fit index, the non-normed fit index, the comparative fit index, the goodness of fit and adjust goodness of fit are higher than 0.90, as recommended by Gefen et al. (2000). The root mean square residual and the root mean square error of approximation should be less than 0.05 and 0.10, respectively. The results of this study show that the RMSEA is 0.0001 and the RMSR is 0.03.

4.3. Structural model

The causal relationships between the variables were calculated (Fig. 6). The results indicated that there is a causal relationship between the personal innovativeness and PU. This relationship is positive and significant at the 0.01% (P < 1%, λ = 0.33). The influence of perceived advantages of SFT on the PU was direct and positive and the coefficient value of this relationship is equal to 0.18 and it is significant at the 0.01 (P < 1%, λ = 0.18). Self-efficacy is another variable that had a direct effect on the PU, the path coefficient of this variable with perceived usefulness is 0.30 and it is meaningful at the level of 0.01. Also, PEU had an indirect influence on the PU through the self-efficacy.

Perceived ease of use had a direct and positive influence on the self-efficacy (p < 5%, λ = 0.13). Roca et al. (2006); Chang et al. (2017) show that self-efficacy has an impact on perceived ease of use. However, the results of the present study show the opposite. Personal innovativeness was one of the external variables that had a positive and meaningful path coefficient with self-efficacy (p < 1%, λ = 0.43). The perceived advantages of SFT was the third variable that directly affects the self-efficacy (p < 1%, λ = 0.20). Also, perceived advantages of SFT had a direct influence on the attitude toward the behavior (p < 5%, λ = 0.12). The perceived usefulness had a positive and meaningful causal relationship with the attitude toward the behavior (p < 1%, β = 0.36). Kalayou et al. (2020) indicated that perceived usefulness had a significant influence on attitude toward the behavior. Also, perceived advantages of SFT had an indirect impact on the attitude toward the behavior through the perceived usefulness.

The results of causal effect of variables with intention to adoption, showed that perceived usefulness had the most direct influence on the intention to adoption (p < 1%, β = 0.47). Almaiah and Alismaiel (2018) by presenting an integrate model, claim that perceived usefulness has strong effects on behavioral intention. After perceived usefulness, attitude toward the behavior has the greatest effect on intention to adoption (p < 1%, β = 0.33). Kalayou et al. (2020); Ajili et al. (2012); Tohidyan Far and Rezaei-Moghaddam, (2017) showed that attitude toward the behavior had a significant influence on intention to adoption. Self-efficacy (p < 1%, β = 0.24) and personal innovativeness (p < 1%, λ = 0.24) have a direct, positive and meaningful influence on the intention to adoption. Jeong et al. (2009) in their study showed that people with higher individual innovativeness are expected to develop more positive behavioral intention to adoption of new information technology. The results revealed that the perceived advantages of SFT had an indirect influence on the intention to adoption through the perceived usefulness and self-efficacy. The perceived ease of use also had an indirect effect on the intention to adoption through the self-efficacy.

The negative impacts of conventional agriculture have led to an emphasis on the urgent need to develop agricultural technologies that are environmentally, economically, productively and socially sustainable. In this respect, SFT is presented as one of the most important alternative farming systems for food security and the sustainability of agricultural development. The aim of this paper is to determine the effective factors on intention to adoption of elite farmers of Fars Province, Iran, who are future trustees for the diffusion of agricultural technologies in order to provide situations for the adoption of these technologies. The results showed that perceived usefulness had the most direct influence on intention to adoption. It is suggested that short- and long-term trainings, workshops and research farm visits be conducted to establish the usefulness of these technologies in order to greater participation of farmers in these programs. Another variable influencing intention to adoption was attitude toward the behavior. Perceived usefulness had the greatest influence on attitude toward the behavior, showing the great importance of this variable. Personal innovativeness was the most important variable influencing perceived usefulness. The use of mass media is proposed to improve personal innovativeness. Mass media plays an effective role in raising awareness and creating new values in people's thinking and behavior.

Considering that the change of knowledge and awareness is a prerequisite for the development of attitude towards utilization, the results showed that knowledge had no effect on attitude toward the behavior. One of the reasons for this could be the farmers' lack of knowledge about these technologies. It is proposed to develop a practical training program based on the method demonstration and the result demonstration to increase the knowledge and awareness of the farmers. This study could further develop the TAM by adding self-efficacy, personal innovativeness, SFT knowledge, controllability of behavior, and perceived advantages of SFT. It is suggested that future studies take steps to improve this model by examining the effect of other variables. Finally, policy and decision makers in the agricultural and environmental sectors can strategically plan for future decisions and activities based on this study's model.

Author Contribution

S.TF. and K.R conceived and designed the experiments; contributed reagents, materials, analysis tools or data; wrote the paper. S.T.F and S.SHK. analyzed the data. All authors read and approved the final manuscript.

Declarations conflict of interest

There is no conflict of interest to declare.

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No competing interests reported.

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Drivers of Farmers to Adopt Environmental Technologies: Development of an Integrated Model

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Abstract

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1. Introduction

2. Theoretical Background

2.1. Theory of Reasoned Action (TRA)

2.2. Theory of Planned Behavior (TPB)

2.3. Innovation and Diffusion Theory (IDT)

2.4. Technology Acceptance Model (TAM)

3. Research method

4. Results and Discussion

4.1. Correlation coefficients between variables

4.2. Model’s goodness-of-fit

4.3. Structural model

5. Conclusion

Declarations

Author Contribution

References

Additional Declarations

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