The impact of ethical commitments on the intention to adopt digital agricultural technologies

Abstract

Sustainable food security is a critical global concern and an urgent priority in developing countries such as Iran. Digital agricultural technologies (DAT) represent significant solutions in this regard, yet their adoptions and development in Iran face limitations. Theoretical studies have underscored the importance of ethical commitments in the adoption process. This study aims to investigate whether ethical commitments regarding food security can influence the intention to adopt digital technologies by farmers. The study employed the Norm Activation Model and integrated two additional components, namely perceived risk and social capital. We applied this framework to examine research works on farmers of Kermanshah Province in the west of Iran, using survey data (sample n = 384). Data analyses were done through structural equation modeling (SEM). Based on the results, the developed Norm Activation Model can be used to predict the adoption intention of DAT by farmers; with the model explaining 65% of total variance. Feeling guilt exhibited the highest direct effect, followed by feeling proud. Furthermore, ethical norms had a direct and indirect impact on intention through the mediating variables of feeling proud and Feeling guilt. The findings of this study contribute to facilitating innovation adoption strategies. It is recommended that, in order to facilitate and stabilize farmers' adoption of innovation, their sense of guilt should first be aroused. After stimulating the farmers' sense of pride toward the adoption; emphasis should be placed on ethical commitments. Ultimately, the introduction of technology and the facilitation of infrastructure should be pursued.

Introduction

Motivation of the research

Food insecurity represents the most significant challenge of the present century globally, which is felt more in developing countries^1,2. The number of people exposed to food insecurity has doubled compared to the past³. Seventy years after the adoption of the Universal Declaration of Human Rights in 1948, the universal access to sufficient and healthy food still is an illusion^4,5. Approximately 80% of the impoverished population resides in rural areas, with almost 20% experiencing severe poverty in developing countries⁶. Predicting a global population growth exceeding 9 billion by 2050, failure to address critical issues in food production could subject 1.5 billion people to food shortages and hunger⁷.

While the global agricultural workforce has consistently dwindled annually, the world's agricultural system faces mounting challenges worldwide as it relies increasingly on non-renewable resources^8,9. These instabilities exacerbate risks, multiply the perils of failure, and ultimately pose a significant barrier to change¹⁰. Therefore, the swift implementation of strategies to enhance production techniques and organize agricultural systems for increased resilience is imperative for sustainable agricultural development^11,12.

Amidst these challenges, digital agricultural technologies (DAT) have appeared as a means to enhance efficiency, promote sustainable agricultural development, and alleviate food insecurity¹³. The "Fourth Agricultural Revolution," termed "Digital Agriculture," represents the pinnacle of scientific agriculture and is considered a hopeful solution to future challenges in agricultural and food systems, offering a promising avenue to enhance sustainable food production and improve resource and environmental management^14–17. The emphasis on digital agriculture stems from its use of precision and data-driven technologies to assist farmers in real-time decision-making in specific locations^18,19. This form of agriculture has the potential to reduce greenhouse gas emissions, increase productivity, and simultaneously ensure food security^15,17. Some researchers have indicated that the digitization of agriculture leads to increased production with fewer inputs and reduced toxins from agricultural chemicals^16,20. This phenomenon, where more food is produced on less land with fewer inputs, has less environmental impacts and can contribute to the renewal of agricultural ecosystems, including agricultural services (insurance, consulting), value chain organization, and agricultural area management¹⁰. Digital agriculture enables us to meet the predicted growing demand for food from nearly 10 billion people by the year 2050²¹. The FAO underscores the development of digital agriculture as it lifts family farmers out of poverty, addresses rural youth unemployment, increases income for rural residents, enhances living standards, protects the environment through efficient use of natural resources, and ultimately contributes globally to achieving food security and sustainable development goals^21,22. Specifically, the digitization of agriculture is a pathway for fundamental and predominantly structural transformations in the food systems^23–27. Given the numerous advantages, the rapid growth of digitization in agriculture has transformed it into a swiftly expanding trend, particularly in countries like the United States and Brazil, which are leading grain producer's globally²⁸, and even underdeveloped African countries. The situation in Iran, however, is different and the goals remain unfulfilled despite expectations and capacities.

The agricultural sector in Iran, contributing 12% to the Gross Domestic Product (GDP) and employing 23% of the workforce, it struggles to ensure the country's food security²⁹. Iran, being one of the largest importers of food products, considers food security as one of the fundamental objectives in its agricultural development plans³⁰. In the Constitution of the Islamic Republic of Iran, principles Three, Twenty-Nine, and Forty-Three, the emphasis is placed on the necessity of meeting basic needs, alleviating poverty, and addressing any form of deprivation in terms of nutrition, individual, and social welfare. The Islamic Republic of Iran has officially declared its political commitment and general resolve several times, especially during Millennium Summit sessions, to reduce hunger, improve nutrition, and achieve sustainable food security^29,31. However, there is a considerable gap to achieve the set goals. According to the Food and Nutrition Monitoring Information System, half of the country's provinces are in an insecure food situation³², and the net number of individuals suffering from malnutrition in Iran has increased in recent years³³. According to FAO statistics in 2018, approximately four million people in Iran experience food insecurity³⁴, and the COVID-19 pandemic has intensified its severity³³.

The primary factors contributing to food insecurity in Iran include a decline in the efficiency of various production factors, a reduction in the number of farmers, increased urbanization, agricultural production instability, heightened use of pesticides and chemical fertilizers, natural disasters such as earthquakes, floods, and droughts, alongside high unemployment rates due to economic downturns, international sanctions, and rising food prices^29,35,36. The nature of most of these factors suggests that digitalization in agriculture, through enhancing the efficiency of production factors, can significantly overcome the mentioned challenges^{17,28,37–39}. In essence, the digitalization of agriculture, by protecting crops against diseases and pests, better farm management, increased efficiency of production factors, optimal access to inputs, water resource management, desirable financial services, improved pricing of products, enhanced marketing methods, sales, and reduced production costs, can be effective in reducing food insecurity and poverty^{16,17,20,27,40,41}.

Research objective exploration

Digital agricultural technologies have played a crucial role in reducing food insecurity worldwide. However, studies have shown that digital agriculture is still an unknown issue in Iran. The adoption of these technologies is not common among Iranian farmers. In some cases, farmers are even forced to use some digital technologies. This issue is one of the reasons for the emergence of serious concerns in the field of food security in Iran, especially in its rural areas^32,42,43.

Today, ethical characteristics have become one of the factors influencing the adoption of technologies⁴⁴. In various studies, the effect of ethical characteristics on technology acceptance has been confirmed^45–48. People with high adaptability, kindness, and spirit of cooperation are more willing to use new technologies⁴⁹. Considering the effects of ethics on the adoption of technologies, the effect of ethical commitments on the creation and strengthening of altruistic behaviors⁵⁰ and the fact that in various researches the necessity of examining ethical commitments in the process of adopting technologies has been emphasized^12,17,37,40, the effects of ethical commitments on the adoption of digital agriculture were determined by this study.

However, to date, no research has been conducted to investigate and substantiate this topic, not only in developing countries but also in developed ones. Our search in reputable international journals using the phrases "factors in the adoption of DAT "and "the impact of ethical commitments on the adoption of digital agriculture" indicated a lack of attention to this topic globally so that there is a research gap in this field. The search results revealed that studies conducted in this area mainly pertain to the acceptance of digital technologies among smallholder farmers, utilizing planned behavior models, social exchange theory, technology acceptance models, and similar approaches^28,37,51. Currently, there is no research available in Iran on the adoption of digital agriculture. Both scientific and practical perspectives need to pay attention toward digital agriculture in Iran. Lack of comprehensive research and failure to adopt digital technologies by farmers make it essential to investigate the adoption of digital technologies while considering the United Nations' goals to achieve sustainable agriculture and food security by 2030^52,53. Therefore, the aim of this research is to fill this gap in the literature and answer the following question: Can ethical commitments towards food security effectively influence the intention to adopt digital agricultural technologies among farmers? In other words, can this characteristic in farmers somewhat overcome the barriers to technology acceptance (such as high costs, lack of knowledge, lack of compatibility with indigenous knowledge, lengthy learning time, etc.) identified in previous studies and increase farmers' intention to adopt digital agricultural technologies? Based on this, the Norm Activation Model was developed and utilized to achieve answers to these questions.

Theoretical review and research hypothesis

Norm activation model (NAM)

Schwartz first designed this model in 1977 in the context of pro-social behaviors⁵⁴. Numerous studies have been conducted using this model. For example, turning off the tap while brushing teeth⁵⁵, reducing greenhouse gas emissions and preventing methadone consumption⁵⁶, reducing car use and opting for short distances^57,58 environmental conservation⁵⁹, reducing carbon dioxide emissions, and blood donation⁵⁵.

This model focuses on altruistic behaviors and explaining how ethical norms impact environmental behavior⁶⁰. In fact, ethical considerations play an important role in this model⁵⁸. Considering that the use and development of digital agriculture, through protecting crops against diseases and pests, managing production factors such as reduced use of pesticides and chemical fertilizers, and optimizing water consumption, contribute to increased productivity towards food security, it can be perceived as a pro-social and environmentally supportive behavior. In essence, farmers, by adopting digital agricultural technologies, contribute to reducing food insecurity through enhanced agricultural performance and efficiency, reflecting an altruistic behavior.

The Norm Activation Model includes several variables that influence ethical norms. One of these variables is mental norm, which encompasses an individual's beliefs about whether others (such as parents, peers, or acquaintances) think they should engage in a particular behavior or not⁶¹. The relationship between mental norms and ethical norms has been confirmed in other studies^62–64. Awareness of consequences (AC) is another variable in the model, indicating whether an individual is conscious of the consequences of their behavior for the welfare of others⁵⁹. The impact of awareness of consequences on ethical norms has been confirmed in other research works. The third variable in ethical norms is responsibility, implying that an individual should feel a sense of personal responsibility for the outcomes of their behavior^50,56,62. Studies have shown a positive correlation between the sense of responsibility and moral norms. Awareness of need (AN) is the fourth variable in the model, referring to an individual's awareness of the necessity to help, involving the extent to which individuals pay attention to an abstract entity (e.g., the environment) that requires attention⁵⁵. Based on studies, awareness of need has a positive effect on ethical norms^58,64–67. Outcome efficacy (OE) is also emphasized by Schwartz in his model as a variable affecting ethical norms of identifying actions to meet the needs of others⁶⁸. Many social problems, especially environmental issues, are related to collective actions, and studies on the efficacy of outcomes are relevant to these issues⁵⁵. Self-Efficacy (SE) is the last variable in the Norm Activation Model, which refers to the perceived ability to perform a behavior. Self-efficacy refers to an individual's assessment of the factors that can either aid or impede their ability to perform a behavior, along with their perception of how well they can execute that behavior⁶⁹. Studies have shown a positive correlation between self-efficacy and ethical norms^55,57,65,66. The hypotheses in this section of the model include the following:

H1:

Farmers' mental norms (MN) regarding DAT have a positive influence on their ethical norms (EN).

H2:

Awareness of the consequences (AC) of using DAT has a positive influence on farmers' ethical norms (EN).

H3:

Farmers' sense of responsibility (Res) positively influences their ethical norms (EN).

H4:

Farmers' awareness of the need (AN) for DAT has a positive influence on their ethical norms (EN).

H5:

Perception of the outcome efficacy (Eff) of using DAT has a positive influence on their ethical norms (EN).

H6:

Farmers' self-efficacy (SE) regarding the use of DAT has a positive influence on their ethical norms (EN).

Clearly, EN is the core of the Norm Activation Model and represents the ethical commitment toward performing or refraining from a specific action^58,59. In other words, EN is defined as an inherent ethical commitment in line with an individual's value system that influences intention^70–72. Intention refers to a mental and probable situation connecting the individual with their action⁷³. Intention can be considered a motivational factor that influences behavior, indicating how much individuals are willing to make an effort to enact a behavior⁷⁴. According to Ajzen and Fishbein⁷⁵, the most direct predictor of behavior is the intention to perform that behavior. Davis⁷⁶ also considered intention a crucial variable that can influence behavior and suggested that understanding a person's intention was essential before examining their behavior. Hence, intention is a significant predictor for an individual's likelihood of performing or not performing a specific behavior. Given that the Iranian government's policy is to promote the use of DAT among farmers, yet the adoption of these technologies in Iran has not developed as expected, it becomes essential to investigate farmers' behavior regarding the use of DAT. However, considering the current state of technology adoption, analyzing farmers' intentions toward adopting DAT can provide insights into their potential future behavior. Therefore, the hypothesis for this part of the research is as follows:

H7:

Ethical norm (EN) positively influences the intention (Int) to adopt DAT by farmers.

In the Norm Activation Model, it is assumed that all variables influence anticipated feelings of guilt and pride through ethical norms⁵⁷. In this context, Harland et al.⁵⁵, Mohammadi et al.⁶⁵, Hamid et al.⁶⁶ showed that the moral norm had a positive effect on the feeling of pride and guilt and through these two variables it affected the intention. Guilt is defined as a painful emotion that arises when an individual experiences a negative event, whereas pride is a positive emotion that arises when an individual has encountered a positive event⁶⁸. In the Norm Activation Model, it is assumed that these emotions influence the intention to use digital technologies by farmers. The hypotheses for this section of the model are as follows:

H8:

Ethical norm (EN) positively influences farmers' feelings of guilt (FG).

H9:

Ethical norm (EN) positively influences farmers' feelings of pride (FP).

H10:

Feelings of pride (FP) positively influence farmers' Intention to use DAT.

H11:

Feelings of guilt (FG) positively influence farmers' intention to use DAT.

In addition to the aspects emphasized in the Norm Activation Model regarding pro-social behaviors and environmental protection, studies have suggested that the acceptance and utilization of innovations are primarily dependent on individuals' level of social capital⁷⁷. Social capital is commonly perceived as trust⁷⁸, fostering collaboration, and coordination among individuals to achieve predefined goals and mutual interests⁷⁹. Social capital and innovation are inherently linked⁸⁰. Numerous studies (e.g., Micheels and Nolan⁷⁷, in Canada; Van Rijn et al.⁸⁰, in the South African desert; Hunecke et al.⁷⁹, in Chile; Lambrecht et al.⁸¹, in Congo; Michelini⁸², in Argentina; Sanginga⁸³, in Uganda; Chirwa⁸⁴, in Southern Malawi; Isham⁸⁵, in Tanzania; Yazdanpanah et al.⁸⁶, in Southwest Iran) have found that social capital acts as a valuable asset and may lead to the acceptance of innovations. Therefore, it is necessary to consider the factors influencing innovation acceptance alongside social capital⁸⁷.

Studies have indicated that social capital can influence individuals' pro-social behaviors related to the environment. For instance, Jones et al.⁸⁸ argued that an increase in social capital levels enhanced environmental management in Greece. Azadkhani et al.⁸⁹ in Iran also found a positive and significant relationship between social capital and the extent of environmental protection. According to Thomas⁹⁰, social capital provides a framework for understanding the environmental behavior of individuals in the United States. Moreover, Gorji karsami et al.⁹¹ in Iran showed a significant relationship between key social capital indicators and the management of environmental behaviors. In this study, the role of farmers' social capital as a new and influential variable in the intention of farmers to use DAT was added to the Norm Activation Model. The hypothesis for this section of the research is as follows:

H12:

Social capital (SC) has a positive impact on ethical norms (EN).

Another variable that appears to indirectly influence the Intention to adopt DAT is Perceived risk. Risk refers to the probability that an action or event may have unintended consequences⁹². Perceived risk involves the mental processing of hazard information and coping mechanisms that people employ when faced with uncertain outcomes⁹³. Perceived risk can be considered the interpretation and amalgamation of sensory effects or information about hazards and consequences⁹⁴. Individuals evaluate risks and understanding of risk according to their own ethical perspectives and criteria. Based on this, the assessment of environmental risks related to public opinions, the evaluation of different individuals' perceptions of technical and environmental risks, people's negative feeling (feeling guilt), responses to environmental risks, and how these risks are presented and communicated in social processes are focused on. Therefore, it can be said that identifying public perceptions of environmental threats is the basis for a successful environmental risk management⁹⁵. This issue may also be a crucial factor in increasing individuals' inclinations toward pro-environmental actions⁹⁶. In other words; perceived risk naturally affects people's environmental behaviors through negative feeling^97,98. In confirmation of this notion⁹⁸ in the United States; Adger et al.⁹⁷ in Venezuela; Ghobadi Aliabadi et al.⁹⁹ in Iran; Zeng et al.⁹⁵ in China; and Maqsoon et al.¹⁰⁰ in Pakistan demonstrated that farmers' Perceived risk regarding the environment leads to pro-environmental behaviors. Therefore, considering the impact of the perceived risk variable on feeling guilt and farmers' pro-environmental inclinations, this variable indirectly through feeling guilt was added to the Norm Activation Model.

H13:

Perceived risk (PR) positively influences on feelings of guilt (FG).

The final variable in the model, encapsulating all the preceding variables, is behavior, referring to the actual actions of individuals¹⁰¹. Considering that the use of DAT by Iranian farmers is limited, the current research did not investigate the behavior of farmers concerning the use of DAT (Fig. 1).

Fig. 1.

Conceptual framework.

The research hypotheses and their respective theoretical foundations are outlined in Table 1.

Table 1.

Research hypotheses and related theoretical studies.

Hypotheses
H1: Farmers' mental norms (MN) regarding DAT have a positive influence on their ethical norms (EN)62–64
H2: Awareness of the consequences (AC) of using DAT has a positive influence on farmers' ethical norms (EN)59
H3: Farmers' sense of responsibility (Res) positively influences their ethical norms (EN)50,56,62
H4: Farmers' awareness of the need (AN) for DAT has a positive influence on their ethical norms (EN)58,64–67
H5: Perception of the outcome efficacy (Eff) of using DAT has a positive influence on their ethical norms (EN)68
H6: Farmers' self-efficacy (SE) regarding the use of DAT has a positive influence on their ethical norms (EN)55,57,65,66
H7: Ethical norm (EN) positively influences the intention (Int) to adopt DAT by farmers70–72
H8: Ethical norm (EN) positively influences farmers' feelings of guilt (FG)55,65,66
H9: Ethical norm (EN) positively influences farmers' feelings of pride (FP)55,65,66
H10: Feelings of pride (FP) positively influence farmers' Intention to use DAT55,65,66
H11: Feelings of guilt (FG) positively influence farmers' intention to use DAT55,65,66
H12: Social capital (SC) has a positive impact on ethical norms (EN)88–91
H13: Perceived risk (PR) positively influences on feelings of guilt (FG)95,97–100

Methodology

Study population and sampling methodology

The study population of this descriptive-correlational research consisted of farmers in Kermanshah Province (N = 164,000). Using the Krejcie and Morgan Table¹⁰², 384 individuals (n = 384) were selected as the sample through a multi-stage random sampling method. In the first stage, one township was selected from each of the five regions of Kermanshah Province. In the next stage, one district was randomly selected from each township, and finally, one village was chosen from each district. From each village, participants were randomly selected based on the proportion of the agricultural population, and questionnaires were distributed among them. A total of 371 completed questionnaires were collected, resulting in a response rate of 96.61%.

Study area

Kermanshah Province, located in western Iran, is the seventeenth-largest province in terms of area (Fig. 2). Its fertile soil and suitable climate have transformed it into one of Iran's agricultural hubs, producing strategic agricultural products such as wheat, barley, sugar beets, and chickpeas, making it one of the top-producing provinces in the country. Moreover, agriculture is the primary occupation for most rural residents in this province. However, despite these agricultural advantages, Kermanshah province, does not enjoy a favorable position in terms of food security. Studies have indicated that Kermanshah is one of the top three provinces in the country facing food insecurity^103,104.

Fig. 2.

Location of the Study Area. (https://en.wikipedia.org/wiki/Kermanshah_province) was used to generate the figure.

To address food insecurity in the province, researchers have suggested the adoption of DAT^105,106. Given that Kermanshah is one of the influential provinces in the country in the field of agriculture, the adoption of DAT by farmers in this province can significantly impact provincial and national food security. Therefore, Kermanshah province in western Iran was selected as the study area.

Research instruments

A two-part questionnaire was employed for data collection. The first section gathered information on the demographic characteristics of farmers, including gender, age, education level, farming experience, household size, religious knowledge, and participation in training courses related to digital agricultural technologies. The second part of the questionnaire consisted of 58 items to determine the components of the developed Norm Activation Model in 12 sub-sections, including: (1) four items for measuring mental norm; (2) seven items for AC; (3) four items for measuring AN; (4) four items for measuring responsibility; (5) three items for measuring OE; (6) three items for measuring SE; (7) five items for measuring ethical norms; (8) seven items for measuring social capital; (9) four items for measuring perceived risk; (10) five items for measuring Feeling guilt; (11) four items for measuring feeling proud, and (12) eight items for measuring intention (Table 4). The respondents were asked to express their agreement or disagreement with the provided statements on a Likert scale (1-very low to 5-very high). To assess the compatibility of the research instrument with the developed Norm Activation Model, previous studies were consulted (Adapting measurements used in an integrated Norm Activation Model from previous studies). Before data collection, all participants were assured that their information would be kept confidential. To ensure anonymity, the questionnaires were administered without names. Finally, all respondents verbally confirmed their consent to participate. The questionnaires were filled out using the individual interview method.

Table 4.

Descriptive statistics of observed variables.

Variables	Mean (of 5)	Sd
Mental Norm (MN)	2.48	0.791
Awareness of Consequence (AC)	2.78	0.377
Responsibility (Res)	2.79	0.680
Awareness of Need (AN)	3.06	0.720
Outcome efficacy (Eff)	3.14	0.484
Self-efficacy (SE)	2.54	0.790
Ethical Norm (EN)	3.14	0.862
Feeling Guilt (FG)	3.01	0.771
Feeling proud (FP)	3.27	0.666
Perceived risk (PR)	2086	0.696
Social Capital (SC)	2.46	0.619
Intention (Int)	2.84	0.570

Statement

All interviewees were informed about data protection issues by the enumerators and gave their consent orally at the beginning of each interview. Informed consent was obtained from all individual participants included in the study. All materials and methods were performed in accordance with the instructions and regulations and this research has been approved by a committee at College of Agriculture & Natural Resources, Razi University, Kermanshah, Iran. All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Validity and reliability of research instruments

After designing the questionnaire, nine experts in the fields of environmental science, rural management, rural sociology and development, and psychology reviewed the questions. Following revisions and adjustments to some items, the final questionnaire was confirmed. To evaluate the validity of the research instrument, the convergent validity technique was used, along with the opinions of experts. To check the convergent validity, the average variance extracted (AVE) was used as a criterion. The AVE value for hidden variables was higher than 0.5, which was acceptable. To check the reliability of the questionnaire, Cronbach's alpha coefficient and composite reliability (CR) were calculated. The composite reliability method calculates the reliability of constructs based on the correlation between them. Both Cronbach's alpha and CR indices were at an acceptable level (above 0.7). Factor loading was also used to examine the strength of the relationship between hidden and obvious variables. The results showed that the factor loading for most selected items was acceptable, and statistically significant at P < 0.01, except for two items in the outcome awareness component. These results affirmed the precision of the research components. (Table 2).

Table 2.

Results of confirmatory factor analysis for the measurement model.

Mental norm55,107–112	AVE = 0.684	CR = 0.894	Cranach's alpha = 0.850	ƛ	t
1. I should use digital agricultural technologies because family members, friends, neighbors, and agricultural experts encourage me to adopt these technologies				0.591	3.539
2. I should embrace digital agricultural technologies as a farmer because society expects me to utilize these technologies in my agricultural practices				0.812	3.898
3. If I utilize digital agricultural technologies, my surroundings will approve of my actions				0.823	7.592
4. Considering that friends, family members, and other farmers are using certain digital agricultural technologies, it is necessary for me to also adopt these technologies				0570	0.356

Awareness of Consequence54,58,68,107,112–114	AVE = 0.608	CR = 0.716	Cranach's alpha = 0.710	ƛ	t
1. I believe that the use of digital agricultural technologies leads to water conservation				0.856	5.108
2. I believe that the use of digital agricultural technologies results in reduced production costs				0.314	–
3. I believe that the use of digital agricultural technologies leads to the consumption of fewer inputs (pesticides and chemical fertilizers)				0.809	3.286
4. I believe that the use of digital agricultural technologies enhances crop productivity				0.565	2.253
5. I believe that the use of digital agricultural technologies improves production and marketing methods				0.387	–
6. I believe that the use of digital agricultural technologies reduces food insecurity				0.861	7.633
7. I believe that the use of digital agricultural technologies contributes to environmental preservation				0.731	2.961

Responsibility57,58,68	AVE = 0.686	CR = 0.893	Cranach's alpha = 0.922	ƛ	t
1. I feel that I should take action to ensure sufficient access to food for the people of my country				0.887	3.404
2. I feel somewhat responsible for the food insecurity of the people in my country				0.874	2.721
3. I feel that I am also somewhat responsible for using digital agricultural technologies to increase production and reduce food insecurity				0.893	3.840
4. I believe that every farmer is responsible for sustainable food security in the agricultural sector				0.854	1.608

Awareness of Need55,67,68,113	AVE = 0.692	CR = 0.898	Cranach's alpha = 0.855	ƛ	t
1. The performance of my farm is so good that digital agricultural technology has no impact on it				0.861	5.738
2. There is sufficient rainfall and groundwater, eliminating the need for the use of digital technologies for their management				0.633	2.110
3. There is ample access to food for all the people in my country, and there is no need for digital agriculture to contribute to increased food production per unit area				0.896	5.923
4. Pesticides and chemical fertilizers pose no threat to human health and the environment, making it unnecessary to manage and reduce those using digital technologies				0.949	9.161

Outcome efficacy55,68	AVE = 0.651	CR = 0.846	Cranach's alpha = 0.724	ƛ	t
1. I believe that educating farmers about the positive effects of digital agricultural technologies on human health and the environment can encourage them to adopt these technologies				0.717	36.313
2. In my view, providing education on the positive impacts of digital agricultural technology on food security, such as increased productivity and performance, can motivate farmers to embrace its use				0.664	3.997
3. I believe that educating farmers about the cost-saving benefits of digital agricultural technology can incentivize them to adopt such technologies				0.865	48.207

Self-Efficacy55,57,113	AVE = 0.779	CR = 0.913	Cranach's alpha = 0.855	ƛ	t
1. For me, using digital agricultural technologies is straightforward				0.844	41.961
2. I am confident that if I need to, I can easily employ digital agricultural technologies in my production unit				0.671	20.493
3. I am sure that if I want to, I can learn how to use many digital agricultural technologies effectively				-0.703	5.450

Ethical Norm55,57,68,107	AVE = 0.634	CR = 0.807	Cranach's alpha = 0.816	ƛ	t
1. Ethically, I am committed to utilizing digital agricultural technologies for food security				0.618	3.718
2. Incorporating digital agricultural technologies for the sake of the food security of my country's people is a matter of moral principles for me				0.782	5.384
3. I feel obligated to employ digital agricultural technologies due to their positive effects on food security				0.726	4.327
4. I sense a moral commitment to the proper and efficient use of digital agricultural technologies to enhance food security				0.589	2.587
5. The use of digital agricultural technologies for food security is a duty towards both the people and the environment in my perspective				0.634	4.216

Feeling Guilt55,113	AVE = 0.637	CR = 0.894	Cranach's alpha = 0.857	ƛ	t
1. I feel discomfort about not utilizing digital agricultural technologies for increasing production and food security				0.684	4.526
2. I regret not having used digital agricultural technologies to enhance production and food security until now				0.706	4.881
3. I feel embarrassed about not having used digital agricultural technologies for increasing production and food security until now				0.733	11.701
4. I have a guilty conscience about not having used digital agricultural technologies for increasing production and food security until now				0.909	23.354
5. I blame myself for not having used digital agricultural technologies for increasing production and food security until now				0.617	15.864

Feeling Proud57,62	AVE = 0.756	CR = 0.791	Cranach's alpha = 0.710	ƛ	t
1. I feel positively inclined to employ digital agricultural technologies for the purpose of food security				0.941	15.446
2. I take pride in seeking to use digital agricultural technologies for the advancement of food security				0.787	16.033
3. I derive satisfaction from intending to utilize digital agricultural technologies to enhance food security				0.791	12.371
4. I perceive a sense of value in aspiring to employ digital agricultural technologies for the promotion of food security				0.914	5.712

Intention62,67,110,111,113	AVE = 0.617	CR = 0.926	Cranach's alpha = 0.906	ƛ	t
1. I intend to recommend the use of digital agricultural technologies to farmers				0.821	20.154
2. I would like to learn how to use digital agricultural technologies				0.834	21.565
3. I plan to participate in educational classes and briefing sessions related to digital agricultural technologies				0.643	4.053
4. I will use digital agricultural technologies in the future				0.694	4.467
5. I am willing to use digital agricultural technologies to increase efficiency and reduce food insecurity				0.755	11.660
6. I would like to use digital agricultural technologies in the future				0.760	4.826
7. I plan to use digital agricultural technologies in the upcoming growing season				0.747	5.141
8. Considering that digital agricultural technologies are suitable for food security, I am willing to spend more to purchase them				0.793	15.055

Perceived risk115	AVE = 0.684	CR = 0.894	Cranach's alpha = 0.850	ƛ	t
1. Many people in my country do not have access to an adequate food supply				0.879	19.934
2. Many people in my country do not have access to healthy food (due to the excessive use of pesticides and chemical fertilizers in agriculture)				0.771	5.380
3. Due to the lack of access to sufficient food, many people are weak, ill, and even lose their lives				0.455	18.975
4. The excessive use of pesticides and chemical fertilizers in agriculture has led to the emergence of various diseases, including cancer, in humans				0.751	4.853

Social capital116–118	AVE = 0.890	CR = 0.968	Cranach's alpha = 0.833	ƛ	t
1. To what extent are you willing to lend money to strangers and non-relatives?				0.548	8.434
2. To what extent do you trust government organizations such as the Agricultural Jihad Organization, Rural Cooperative Office, Agricultural Bank, and the Department of Natural Resources?				0.753	9.091
3. To what extent are you willing to do something that does not benefit you for the sake of others?				0503	6.003
4. To what extent are you willing to undertake a task that is very time-consuming for the benefit of others?				0.852	6.044
5. To what extent do you assist other farmers in agricultural and horticultural activities in the village?				0.856	6.955
6. To what extent do you participate in solving village problems by collaborating with other residents?				0.915	6.080
7. To what extent do you have a friendly relationship with agricultural experts and promoters?				0.914	5.059

Data analysis

SPSS23 was used for descriptive analysis, including frequency, percentage, mean, and standard deviation.

Structural equation modeling (SEM) was used along with Smart-Pls4 software to test research hypotheses. Structural Equation Modeling (SEM) was used to test the conceptual model. SEM is a combination of structural and measurement models⁹⁸, and it was preferred due to its advantages in providing a powerful multivariate analysis technique, similar to the family of multiple regressions. Another advantage of SEM is its consideration of measurement errors in the analysis⁹⁹. We conducted consistency analysis, including convergent validity (AVE), composite reliability (CR), and Cronbach's alpha coefficient, using Smart-Pls4 software.

Results

Descriptive statistics

As listed in Table 3, the respondents' average age was 43.56 years. The majority had elementary education, constituting 32.61%, while only 12.93% had college/university degrees. The mean household size and farming experience were four individuals and 34.28 years, respectively. The mean monthly household income was $140.32. About 89.48% of respondents stated that they had never participated in any training courses related to digital agriculture. Additionally, 83.53% of respondents considered their religious knowledge to be average or above average.

Table 3.

Demographic characteristics of farmers.

Variable	Category	Frequency (371)	Percent	Mode
Age	Lower than 30	75	20.21
	30–50	197	53.09	*
	More than 50	99	26.68
Education	Illiterate	50	13.47
	Elementary	121	32.61	*
	Secondary	85	22.91
	High school	67	18.05
	College education	48	12.93
Number of household (person)	Lower than 3	66	17.78
	3–4	179	48.24	*
	More than 4	126	33.96
Annual income (dollars)	Lower than 1000	61	16.44
	1000–2000	136	36.65	*
	2000–3000	93	25.06
	More than3000	81	21.83
Work experience	Lower than 20	111	29.91
	20–40	229	61.72	*
	More than 40	31	8.35
Presence in relevant training courses	yes	332	89.48	*
	NO	39	10.51
Knowing yourself religious	Very low	14	3.77
	Low	47	12.66
	Middle	199	53.63	*
	high	66	17.78
	Very high	45	12.12

* Indicates the mode.

Descriptive statistics of observed variables

The mean score of awareness of needs, outcome efficacy, ethical norms, guilt feelings, and pride were higher than the theoretical mean (3 theoretical median) (Table 4).

Inferential statistics

The proposed conceptual model was evaluated in two parts: measurement model evaluation and structural model evaluation, using the Partial Least Squares (PLS) approach.

Measurement model

Confirmatory Factor Analysis (CFA) was employed to assess the validity and reliability of the research components. The indices listed in Table 5 indicate that the model has an adequate fit.

Table 5.

Summary of goodness of fit indices for the measurement model.

Fit index	SRMR	D-G1	D-G2	NFI	RMS-theta
Suggested value	< 0.1	> 0.05	> 0.05	> 0.90	≤ 0.12
Estimated value	0.08	0.347	0.448	0.94	0.07

Discriminant validity

As depicted in Table 6, the Average Variance Extracted (AVE) of the research constructs (0.78 < (AVE < 1.000) exceeds the inter-construct correlation coefficients among them (0.17 < r < 0.76), confirming the discriminant validity of the components of the proposed model.

Table 6.

Correlations with Square Roots of the AVE.

Constructs	1	2	3	4	5	6	7	8	9	10	11	12
1.AC	0.78a
2.AN	0.51**	0.83a
3. Eff	0.62**	0.63**	0.80a
4.MN	0.62**	0.63**	0.54**	1.000a
5.FG	0.56**	0.56**	0.54**	0.71**	0.79a
6.FP	0.62**	0.42**	0.59**	0.75**	0.50**	0.86a
7.Int	0.64**	0.53**	0.61**	075**	0.62**	0.72**	0.78a
8.EN	0.31**	0.17**	0.31**	0.30**	0.17**	0.46**	0.55**	0.82a
9.Res	0.47**	0.39**	0.14**	0.51**	0.45**	0.60**	0.54**	0.52**	0.82a
10.SE	0.27**	0.38**	0.30**	0.41**	0.42**	0.54**	0.61**	0.55**	0.59**	0.88a
11.SC	0.54**	0.47**	0.33**	0.76**	0.63**	0.72**	0.19**	0.29**	0.45**	0.34**	0.94a
12. PR	0.66**	0.48**	0.43**	0.67**	0.59**	0.66**	0.40**	0.39**	0.51**	0.45**	0.73 **	0.83a

**Correlation is significant at the < 0.01 level. At the square roots of AVE estimate.

After validating the measurement models using Confirmatory Factor Analysis (CFA), the path analysis method (structural model assessment) was employed to test hypotheses within the proposed conceptual model framework. The research path model is presented by showing the standardized and significant factor loads in Figs. 3 and 4.

Fig. 3.

Path model with standardized factor loadings.

Fig. 4.

Path model with t-values.

Research hypothesis testing

The results about the influence of variables on farmers' intention to adopt DAT are presented in Table 7. Bootstrapping method was employed for testing the research hypotheses. The results indicated that all research hypotheses were confirmed, except for the influence of responsibility and mental norm on ethical norms. Furthermore, the research variables were able to elucidate 65% of farmers' intentions to adopt DAT.

Table 7.

Results of research structural models.

Hypothesis	ƛ	t	Result	R2
H1: MN ➞ EN	0.011-	1.420	Reject	EN:0.94
H2: AC ➞EN	0.251	6.566	Confirm
H3: Res ➞ EN	0.014	0.958	Reject
H4: AN ➞ EN	0.407	3.552	Confirm
H5: Eff ➞ EN	0.124	5.028	Confirm
H6: SE ➞ EN	0.151	4.684	Confirm
H7: EN ➞ Int	0.193	8.414	Confirm	Int: 0.65
H10: FP ➞ Int	0.262	2.532	Confirm
H11: FG ➞ Int	0.613	17.314	Confirm
H12: SC ➞ Int	0.149	3.653	Confirm
H13: PR ➞ FG	0.676	15.349	Confirm
H8: EN ➞ FP	0.583	7.794	Confirm	FP: 0.33
H9: EN ➞ FG	0.269	2.443	Confirm	FG:0.57

Discussions

Digital agricultural technologies (DAT) can help increase productivity, promote sustainable development, and reduce food insecurity¹¹⁹. Success in reducing global food insecurity has been limited in many developing countries despite efforts^13,120. Digital agriculture is a rapidly growing trend due to its many benefits¹⁰⁰.

There is a vast area of agricultural land and a considerable number of farmers in Iran. However, the adoption of digital agricultural technologies among Iranian farmers is very limited. This issue is one of the reasons for the emergence of serious concerns in the field of food security in Iran^32,42,43. Studies have highlighted the importance of ethical commitments in the adoption process of digital technologies. However, there is no research available to confirm this issue specifically in Iran. To bridge this gap, the present study employed the norm activation model, along with risk perception and social capital constructs, to investigate the impact of ethical commitments on the intention of farmers to adopt digital technologies.

The results indicated that the developed Norm Activation Model served as a predictor of farmers' intentions to adopt DAT in the studied region. Four components including awareness of the need, outcome efficacy, awareness of the consequences, and self-efficacy significantly and positively influenced ethical norms among farmers.

Awareness of the need, which refers to a person's awareness of the necessity of help⁵⁴, through ethical commitments can be effective on farmers' intention to use technologies. However, the mean awareness of farmers regarding digital agriculture was at a moderate level, suggesting a considerable gap up to high and very high levels. This finding showed that farmers have not yet felt the need to use digital technologies to increase production performance and reduce food insecurity. This issue can be caused by their lack of belief in the efficiency of technologies due to their lack of knowledge in this field. Therefore, it is imperative to enhance farmers' awareness of the positive impacts of digital technologies, and increase their perceived needs. This is possible by providing trainings about the efficiency of technologies or performing results demonstrations.

Based on the results, effectiveness had a positive effect on ethical norms. In addition, through the ethical norm, it affected the intention to adopt digital technologies by farmers, which was in line with Mohammadi et al.⁶⁵ and Hamid et al.⁶⁶. According to Harland et al.⁵⁵, solving many social problems is related to collective actions. Farmers who believe that digital agricultural technologies are effective in improving food security tend to have a higher intention to adopt them. Therefore, by increasing farmers' awareness of the food insecurity crisis and the need for everyone's efforts to deal with it, it is possible to strengthen the ethical commitments to use technologies. Considering that the farmers perceived training programs effective in this context, it is recommended to organize educational courses to encourage farmers to embrace digital technologies. Furthermore, demonstration farms, engaging in discussions with farm owners, and witnessing the tangible results of digital technology applications could contribute to enhancing farmers' ethical commitments towards adopting these technologies.

Awareness of the consequences was another crucial component influencing ethical norms. Consistent with this finding, Savari et al.⁶⁴ confirmed that farmers' awareness of the positive effects of environmental behaviors was effective in the intention to adopt conservation behaviors. The descriptive results indicated farmers' low awareness of the positive effects of digital technologies on food security. In other words, farmers are not informed that the use of digital technologies saves water, reduces input consumption, improves production operations and preserve the environment. Given that acquiring awareness of the positive effects of pro-social behaviors is the first step towards achieving sustainability and a fundamental prerequisite for the future survival of humanity⁶⁴, emphasizing the intensity of positive effects of digital technologies creates a sense of ethical duty and positive intrinsic feelings among farmers, thereby elevating their ethical commitments towards adopting these technologies. Farmers, with higher awareness, will have a stronger sense of creating food security. As long as farmers lack awareness of a particular subject, expecting appropriate responses from them and meaningful responsibility towards their activities is futile. Therefore, in order to improve the attitude of farmers towards digital technologies, it is necessary to raise awareness about the negative consequences of not using technology in their lives, the lives of others, and the environment.

The last variable with confirmed impact on ethical norms was self-efficacy. The findings revealed that an increase in farmers' understanding of their capabilities to utilize digital technologies significantly enhanced their ethical commitments towards adopting such technologies. These findings align with the Bandura's social cognitive theory, which asserts that human behavior in reciprocal interaction depends on individual behaviors, internal factors (such as thoughts and beliefs), and environmental factors¹²¹. Moreover, the results support Bandura's arguments regarding the influence of self-efficacy on decision-making, action methods, effort levels, perseverance, and flexibility in individuals¹¹². However, descriptive findings showed that the level of self-efficacy of farmers was low, and it was not easy for them to work with technologies. Considering the level of literacy of farmers, this finding was expectable.

Contrary to previous studies^109,110,112, the mental norm of farmers did not influence their ethical norms, which ws an unexpected result. In other words, farmers believed that the expectations of relatives, friends, and neighbors did not affect their willingness to use technologies. This can be interpreted through two interpretable realities: firstly, lack of trust in governmental, non-governmental organizations, and even rural associations. This makes unwilling to follow their recommendations. Consistent with this claim, Ashouri et al.¹²² demonstrated that the performance of Agricultural Education and Extension Institute in Iran was not considered acceptable by 80.8% of farmers. Additionally, Azami et al.¹²³ showed that nearly 80% of rural residents were dissatisfied with the performance of rural cooperatives, and Rahmani Fazli and Kavyani¹²⁴ revealed a gap between the current status and the desirable status of the Agricultural Bank in providing services to farmers. Another interpretation in this regard can be attributed to the low social capital of farmers, as identified in this study, a result consistent with Mohammadi and Yaghobi⁴⁵ and Doustmohammadi and Alavian¹²⁵. This issue, in addition to reducing cohesion and solidarity among farmers, can diminish their trust in each other and even in the words and behavior of other farmers. According to the theory of Golman (1985), trust and solidarity are included in the definition of social capital, and these two dimensions can also influence prosocial behaviors. Considering that social capital has a positive effect on intention through moral norms, it is suggested that governmental organizations prioritize transparency in responsiveness, and fighting corruption to enhancing social capital, and consequently, farmers' ethical commitments. These organizations should strive to uphold the principles of integrity, justice, and regulation, and implement necessary supportive policies for the agricultural sector, including ensuring the fair supply and distribution of agricultural inputs, providing long-term and low-interest banking facilities, paying farmers' damages fairly and on time, and guaranteeing the purchase of agricultural products. Given that the government also oversees the non-governmental sectors, active monitoring and evaluation of their activities can ensure the provision of quality services in non-governmental sectors such as rural cooperatives, and agricultural unions. Empowering farmers by creating agricultural associations is another solution to strengthen the social capital.

Surprisingly, the results indicated that farmers' sense of responsibility toward the food insecurity did not affect their ethical commitments. The descriptive results also showed that farmers' level of responsibility was below average. In other words, farmers did not consider themselves responsible for food insecurity, and they did not consider it their moral duty to work for the food security of the people of their country. This result contradicts previous findings^55,56,61. Possible reasons for this may be the farmers' perspective on the impact of other variables, including international sanctions, natural disasters, price fluctuations, and the increase in food prices on food insecurity. Additionally, insufficient knowledge about digital technologies in farmers can lead them to not consider themselves responsible for food insecurity. In this regard, it is possible to improve farmers' belief in being responsible for food insecurity by managing the factors affecting food security (from farmers' point of view) such as price fluctuations and food price increases. In this way, we may strengthen their ethical commitments to adopt technology.

According to the results, perceived risk through feeling guilt had a direct, positive, and significant effect on intention. If farmers come to believe that their compatriots are at risk of food insecurity and the spread of many diseases is due to excessive use of chemical inputs and their works matter in creating food security; they may develop a negative feeling of guilt. Therefore, to deal with this feeling and the risks caused by food insecurity, their willingness to accept digital agriculture may increase.

One of the variables affecting intention was ethical norms, which directly and indirectly (through feelings of guilt and pride) affected intention, as confirmed by Harland et al.⁵⁵, Onwezen et al.⁵⁷, and Han⁶². The more farmers perceive the adoption of digital technology as their ethical duty to create food security, consider it a morally correct and conscientious act, and feel better about it, the more positive their intention to adopt DAT will be. In other words, they should consider themselves ethically obliged to use agricultural digital technologies for food security. EN also affects intention through Feeling guilt and pride. Therefore, farmers who morally approve of using digital technology to reduce food insecurity feel more guilt and regret if they do not use the technology. On the other hand, if they use digital technology, they experience more pride and satisfaction, ultimately leading to an increase in their intention to adopt it. Considering the religious beliefs of Iranians and the fact that the majority of respondents expressed higher-than-average religious beliefs, coupled with the emphasis on food security for the public in Islam, religious beliefs of farmers in Iran can be utilized to evoke feelings of pride and guilt, as well as to instigate ethical commitments, facilitating the adoption of digital technologies. Since ethical behavior requires education, it appears that increasing ethical education can be effective.

Implications

This research has specific research and practical applications.

Research implications

The findings outline a path for other researchers in different countries to investigate the adoption of innovations, particularly DAT, using the developed Norm Activation Model. The measurement section's findings indicated that after eliminating two items related to outcome assessment, the remaining items used for estimating the model's components adequately represented them. Therefore, the employed items in this study serve as an effective tool for measuring the model's components, demonstrating high reliability and validity. Based on this, it is recommended that researchers using the Norm Activation Model in various investigations utilize the tools presented in this research. On the other hand, the proposed model in this research can elucidate a significant portion of the intention to adopt digital agricultural technologies for food security among farmers. In practical applications, it seems possible to emphasize the components of this model to strengthen intentions further. In general, it can be noted that the presented model, considering fit indices, is an acceptable and validated model that can facilitate the innovation adoption process in the target community.

The findings of this research contribute to fostering a divergent mindset among researchers interested in this field, potentially leading to the recognition of new approaches towards the acceptance of DAT related to food security. Researchers can assess the effectiveness of the results in promoting the acceptance of DAT and thus challenge or validate the findings by implementing them. In addition, this study proposed a new approach to the diffusion of innovations. The fact is that the diffusion of innovations only through education does not guarantee sustainable adoption, but it is also necessary to arouse the ethical commitments and altruistic behavior of farmers. In other words, education should be ethical. Considering the significant effect of the two variables of risk perception and social capital (which have not been considered in previous studies), it is necessary to consider perceived risk in addition to improving farmers’ social capital. In general, the presented model can be used in the context of diffusing new innovations.

Practical implications

The research findings can serve as a guide for FAO policymakers in formulating strategies to combat food insecurity by facilitating the extension of DAT. The research suggests to decision-makers at FAO that, before introducing innovations such as DAT, it is crucial to consider the ethical commitments of the target community.

Conclusions

The impact of variables like ethical commitments, on the acceptance of DAT towards food security (a global challenge and issue) was investigated. It was demonstrated that the developed Norm Activation Model, being a predictor of the intention to adopt DAT, can be utilized in fostering the acceptance of various types of innovations, especially those related to pro-social behaviors. Indeed, incorporating two components, perceived risk and social capital, into this model enhanced its predictive power and made it more effective in promoting the acceptance of innovations, particularly DAT. Furthermore, considering that digital technologies represent a novel innovation in various countries, the results of this study can also be applicable to other countries with similar conditions.

Ethical commitments exerted the most significant influence on the intention to adopt DAT through the feeling proud and guilt. Furthermore, contrary to other studies, we found that two variables, mental norm and responsibility, did not have a significant impact on farmers' ethical commitments toward the adoption of DAT. This not only reflects farmers' dissatisfaction and lack of trust in governmental and non-governmental sectors in Iran but also indicates that Iranian farmers have not yet embraced the belief that their actions directly and indirectly affect the food security of their country. The results of this section of the study may vary in different countries depending on their specific conditions and government policies. The lack of awareness among Iranian farmers about the positive or negative impact of their actions on food security is a key concern for the sustainable management of food systems. Additionally, due to the profit-driven and short-term interests of Iranian farmers, their actions are mostly aligned with short-term economic gains. Therefore, the government should attempt to alter the farmers' perception of food insecurity risks so that farmers can understand and consider their actions, including the acceptance of digital technology, as effective in addressing these risks. To achieve this, education should not be limited to informational campaigns but should be practical, demonstrating the results of technologies.

It was demonstrated that to enhance the impact of farmers' ethical commitments on their intention to adopt DAT, positive and negative emotions such as pride resulting from the positive effects of their actions and guilt resulting from the negative consequences of their actions should be emphasized and made certain for farmers. This can make the adoption of new innovations, including digital agricultural technologies, "easier," "faster," and "more cost-effective". Such cost savings can then be redirected to providing low-interest loans or subsidies to farmers for the purchase of digital agricultural technologies, aiming to ensure sustainable food security.

Author contributions

A.A: suggested a title and monitored the data collection, analysis, and article revision. M.T wrote the manuscript.

Data availability

The raw data and collated data supporting the findings of this study could be made available from the corresponding author upon judicious request.

Competing interests

The authors declare no competing interests.