Food security is one of the most challenging topics globally (Akbari et al., 2022a), and genetically modified foods (GMFs) remain a contentious issue worldwide (Boccia and Punzo, 2021) but the growing population of the world and the increasing demand for food in recent decades have led to the use of new genetic methods in the production of foods called GMFs (Erol, 2021; Ferrari et al., 2021; Owusu and Kwesi Ndzebah Dadzie, 2021). GMFs refers to modifying or improving the genetic structures of foods by the application of modern biotechnological methods. GMFs are applicable in many sectors, such as animal husbandry, the health system, and especially the agriculture and food sectors (Lefebvre et al., 2019; Guo et al., 2020; Erol, 2021). GMFs have a production method that production method of GMFs resembles natural foods. GMFs have a comparative advantage such as resistance to pests and insects, high yield, better nutritional value, etc., which are caused by changes in undesirable genes (Erol, 2021; Ferrari et al., 2021; Hwang and Nam, 2021; Levi, 2022).

Although the utilization of new biotechnology for food production has opened new opportunities for the food industry, raised numerous questions about the potential dangers of producing and consuming such foods (Ortega et al., 2022) and have raised numerous questions about their potential effects, such as allergenicity, increased resistance to antibiotics, destructive environmental effects, and horizontal gene transfer in society. Therefore, the acceptance of GMFs depends on the consumers' perception of the risks and benefits of these food products (Boccia, 2016; Deng and Hu, 2019; Erol, 2021; Hwang and Nam, 2021; Owusu and Kwesi Ndzebah Dadzie, 2021; Ortega et al., 2022). Therefore, despite the potential perceived advantages and disadvantages of GMFs mentioned in the literature, public opinion on GMFs remains largely skeptical and controversial (Miles et al., 2005; Lusk, 2011; Varzakas and Tzanidis, 2015; Boccia et al., 2018; Lefebvre et al., 2019; Guo et al., 2020; Xu et al., 2020; Boccia and Punzo, 2021; Fozouni Ardekani et al., 2021; Owusu and Kwesi Ndzebah Dadzie, 2021; Levi, 2022; Ortega et al., 2022). This is the main reason why the GMFs literature includes conflicting conclusions on consumer response to gene technology and especially to GMFs (Xu et al., 2020; Boccia and Punzo, 2021).

Various research has been done in the field of customer behavior concerning GMFs with different perspectives like consumer response, consumers knowledge, consumer perception, consumer acceptance, consumer trust, risk perception, and corporate social responsibilities (CSR; Deng and Hu, 2019; Ardebili and Rickertsen, 2020; Xu et al., 2020; Ferrari et al., 2021; Fozouni Ardekani et al., 2021; Hwang and Nam, 2021; Vindigni et al., 2022). Although there are several studies on GMFs that include some influential determinants of consumer behavior, still the field of consumer behavior toward GMFs is assumed to be uncertain and this uncertainty makes it difficult to provide solutions for the development of relevant industries (Rodríguez-Entrena and Salazar-Ordóñez, 2016; Guo et al., 2020; Erol, 2021). In this regard, the purpose of this study is to investigate the acceptance of GMFs while considering influential consumer behavior factors such as corporate social responsibilities (CSR), Regulatory Focus Theory (RFT), and Self-Determination Theory (SDT) in order to assist the literature and related industry in clarifying consumer behavior toward GMF. Hence, the combined model of Rough Set Theory (RST) and Flow Network Graph (FNG) is used to generate rules and demonstration that exist among these factors.

This study provides a noticeable contribution to the existing literature on GMFs. Consumers' behavior is considered a noticeable determinant for the future of GMFs and their applications, but the number of studies conducted on this subject is very limited in developing countries (Akbari et al., 2019; Owusu and Kwesi Ndzebah Dadzie, 2021). So, we chose Iran to enrich the related literature on this subject in developing countries. Furthermore, the existing literature on consumer behavior and GMFs includes studies that have investigated the effects of one or some factors on consumer behavior toward GMFs separately. With the means of RST and FNG, not only influential factors were studied together, but also some rules were extracted that have a meaningful role in predicting consumer behavior toward GMFs. Regarding literature, the field of consumer behavior toward GMFs is assumed to be uncertain due to the different results of studies in this field, which makes it difficult to provide solutions for the development of relevant industries (Rodríguez-Entrena and Salazar-Ordóñez, 2016; Guo et al., 2020; Erol, 2021). Here, using RST is a privilege since it is a method that is known for its clear and straightforward outputs, which are demonstrated through rules and show the way. So, predicting consumer behavior toward GMFs by the means of RST is important from a macro perspective since it will help governmental policymakers, farmers, and agricultural biotechnology firms make effective decisions for the future of this industry. Finally, it is also worthwhile to mention that, to the best of our knowledge, none of the previous studies on GMFs used RST for their investigations.

This paper is organized as follows: Section 2 covers a background on the consumers' acceptance of GMFs. The elaboration of RST, FNG, and their combinations are mentioned in the methodology. Section 4 includes the results obtained from the combined model of RST and FNG. Eventually, in Section 5, the conclusion and recommendations for future studies will be delivered.

Consumer behavior is one of the influential factors that are important in product success and growth. In this regard, consumer demand is also a key factor shaping the government and firms' strategies or policies and is a determinant for GMFs' acceptance in the market (Deng and Hu, 2019; Ortega et al., 2022). According to Siegrist and Hartmann (2020), due to the lack of knowledge about most novel food production technologies, the term of “black box” is used for consumer demand since it is influenced by simple heuristics and is unstable in different situations.

In the GMF literature, there are plenty of studies that investigate the attractiveness, or better to say acceptance of GMFs due to some determinants like culture, sexuality, age, risks and benefits, concerns, CSR, and knowledge (Siegrist and Hartmann, 2020; Boccia and Punzo, 2021; Fozouni Ardekani et al., 2021; Pruitt et al., 2021; Rabbanee et al., 2021; Zheng and Wang, 2021). Siegrist and Hartmann (2020) showed in their study that cultural issues are related to the acceptance of various risks. In that direction, Soroka et al. (2021) demonstrated that consumer's attitude is different from country to country in the European market. A bulk of studies on GMFs found that Asian consumers were unsure about how to use GMFs because there were only a few studies about them (Siegrist and Hartmann, 2020; Fozouni Ardekani et al., 2021). A study found that education of students about GMF leads to improvement of their scores of knowledge and attitude (Asadi et al., 2021).

According to Siegrist and Hartmann (2020), consumer intentions are also influenced by food neophobia, disgust sensitivity, and cultural values. It is not only true for GMFs but also for novel food technologies. The findings of Fozouni Ardekani et al. (2021) discovered that net benefit and compatibility are significant antecedents of consumers to accept GMFs. Additionally, self-efficacy has an indirect influence on acceptance of GMFs. Akbari et al. (2019) found attitude toward GMFs, trust in GMFs, concerns about GMFs, and CSR the most significant variables of buying intentions for GMFs. Another study confirmed the significant influence of CSR in the relationship between trust in GMFs and consumers' intention toward such products. They have emphasized the role of attitude which is considered the main predictor of intention. Also, they concluded that Iranian consumers have a relatively negative approach to GMFs. Furthermore, they found some differences between males and females for their promotion focus, perceived behavior control, and concerns about choosing GMFs. Attitudes toward GMFs are also determined by environmental concern and objective knowledge (Ferrari et al., 2021). Regarding sexuality effects, Rabbanee et al. (2021) concluded that female consumers in comparison to their male counterparts pose relatively more favorable attitudes and repurchase intention.

GMFs, besides all their new opportunities for the food industry, have led to concerns among consumers due to their perceived potential dangers (Ortega et al., 2022) which have raised numerous questions about their potential side effects, such as allergies, increased resistance to antibiotics, destructive environmental effects, and horizontal gene transfer in society. So, the acceptance of GMFs depends on the risks and benefits that consumers perceive (Boccia, 2016; Deng and Hu, 2019; Erol, 2021; Hwang and Nam, 2021; Owusu and Kwesi Ndzebah Dadzie, 2021; Ortega et al., 2022). Yet, consumers' behavior toward GMFs is vague due to the quite complex technology behind their production (Deng et al., 2019; Palmieri et al., 2020; Siegrist and Hartmann, 2020; Boccia and Punzo, 2021). In this regard, an investigation revealed that a negative approach toward GMFs is related to a lack of knowledge about genetic modifications and their effects (Palmieri et al., 2020). In this case, Ortega et al. (2022) reported that decreasing consumers' fear of novel food technologies improves consumer valuation and acceptance of GMFs.

Consumers would like to make an informed choice. Not only does the existence of labels include product information, but also the amount of time consumers spend reading label information is an important aspect in determining consumers' intentions. It helps consumers in their decisions to buy food products (Macall et al., 2021). Within the scope of GMFs labels, Zheng and Wang (2021) described using a labeling system for differentiating GMFs as important since it is considered one of the most influential factors that drive the buying intention. Similarly, Pruitt et al. (2021) supported consumers knowing that clear health benefits are more accepting of biotechnology products. In addition, Ardebili and Rickertsen (2020) found that information is an important factor that can change attitudes toward GMFs and reduce resistance to these novel technologies over time. To elaborate on the influence of knowledge, Hwang and Nam (2021) showed that higher education and income cause overestimation of actual knowledge level, which subsequently leads to higher risk perception, lower benefit perception, and lower intention to use. Regarding risk perception, Hakim et al. (2020) concluded that risk perception is being moderated by social trust and perceived quality, and as a whole, willingness to pay for GMFs is influenced by price (Hakim et al., 2020; Macall et al., 2021). Finally, Deng and Hu (2019) in this field conclude that safety issues are the main barrier to the acceptance of GMFs by consumers.

Therefore, this paper aims to investigate the acceptance of GMFs considering influential factors of consumer behavior like socio-demographics, CSR, concern, promotion, and prevention focus, autonomy, competence, and relatedness to help the literature and related industry consequently in clarifying consumer behavior toward GMFs. Hence, a combined model of RST and FNG is used to generate rules and demonstrate relations that exist among these factors. For this, the intention to use GMFs was used to measure consumer behavior output because intention influences willingness to fulfill a behavior (Jiang et al., 2016; Fozouni Ardekani et al., 2021). The independent variables cover the determinants of RFT, SDT, and consumers' concerns toward GMFs as psychographic variables. Also, in this study, the determinants of CSR, including social equity, environmental, economic, legal, ethical, and philanthropic factors, determine the role of a social variable in consumers' intention to use GMFs.

As mentioned before, various studies have been conducted in the field of consumer behavior toward GMFs with different perspectives like consumer response, consumer knowledge, consumer perception, consumer acceptance, consumer trust, risk perception, and CSR (Deng and Hu, 2019; Ardebili and Rickertsen, 2020; Xu et al., 2020; Ferrari et al., 2021; Fozouni Ardekani et al., 2021; Hwang and Nam, 2021; Akbari et al., 2022b) and again as it is shown in Table 1, there are several studies on GMFs that include some influential determinants of consumer behavior but still the field of consumer behavior toward GMFs is assumed to be uncertain and this uncertainty makes it difficult to provide solutions for the development of relevant industries (Rodríguez-Entrena and Salazar-Ordóñez, 2016; Guo et al., 2020; Erol, 2021). In this regard, here we are going to investigate the acceptance of GMFs while considering influential consumer behavior factors such as corporate social responsibilities (CSR), Regulatory Focus Theory (RFT), and Self-Determination Theory (SDT) in order to assist the literature and related industry in clarifying consumer behavior toward GMF. For this, we used the combined model of Rough Set Theory (RST) and Flow Network Graph (FNG) in order to generate rules and demonstration that exist among these factors.

To meet the purpose of this study, the proposed methodology approach for this research combines RST and FNG. Toward that direction, we consider questionnaire-based survey research according to the practical concept of this research problem that occurs in daily life. This part encompasses (1) variables' selection and item measurement; (2) population and sample; and (3) analytical methods.

Within the scope of RST, after preparing the data, an information table can be constructed to carry out further analysis (Table 3). The first outcomes extracted from the coded information table based on rough sets analysis were the description of equivalence classes, the approximation of the decision classes, and the quality of classifications. It is considered that the classification accuracy is used to represent the percentage of correctly classified and the approximation accuracy is used to describe the degree of intention to use GMFs.

As illustrated in Table 4, our samples are categorized into three classes, (1) low, (2) medium, and (3) high intention to use GMFs. To display the rigor and robustness of results, the accuracy of classifications has been shown for the three decision classes. In this study, we have a total of 377 equivalence classes. Furthermore, in terms of consumers' intention to use GMFs, the results indicate high accuracy equal to 1 for all three decision classes. It means correct classification of all samples.

To get the reduced attribute sets and increase the classification rate, the existing algorithms in RST analysis are employed to determine the core attributes and reduce conditional attributes. Due to the result of 1.0 for the value of the positive region, the reduced set include all 12 attributes {x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, and x12} and there is no redundant attribute in our analysis. Furthermore, since the core includes the essential and most influential attributes, our results display the importance of these 12 variables in anticipating consumer intent to use GMFs.

This research used a new approach to predict consumers' intention to use GMFs in a developing country by combining RST and FNGs. The reason behind that is to respond to the research gap that has been addressed by the scholars (Rodríguez-Entrena and Salazar-Ordóñez, 2016; Guo et al., 2020; Erol, 2021) to realize hidden information in consumer characteristics and predict the decision rules of consumers' intention to use GMFs. This approach, is specifically useful for the classification of multi-attribute problems, as well as suited for the problems of business success or failure prediction (Dimitras et al., 1999). Moreover, as emphasized by Cheng et al. (2013) the FNGs like a bridge could connect the pathway of decision rules and the degree of their interdependency. As such, this study utilized these features to extract more influential factors and important patterns of consumers' intention to use GMFs.

By considering the results of the FNG, it was found that consumers' perception of CSR is the factor that has been influential in all degrees of consumers' intention to use GMFs. Moreover, it can be claimed that consumers' concerns, occupational status, and autonomy are other sensitive factors that affect consumer behavior toward GMFs. So, concerning the results of the present study, these factors influence consumer decision-making toward GMFs consumption more than other influential factors. Additionally, the influence of consumers' age, education, and prevention focus has been predicted to reinforce consumers' intention. A summary of these dimensions, derived from the FNGs, is given in Figure 5. It is worth noting that the lines represent the relationships between the variables.

This study also discovered multiple interesting and considerable patterns from our data as following:

Pattern 1: the greater the consumers' perception of CSR, the greater the chance that they will use GMFs (94 supports).

In this respect, other scholars (van der Wees and Moonen, 2011; Pino et al., 2016; Boccia and Covino, 2021) referred to CSR initiatives that have a powerful effect on consumer decisions. Furthermore, in many developing and developed countries, the emphasis on biotechnological foods has recently increased (Alphonce et al., 2020), which has led to the growth of social problems. These social problems mainly focus on challenges such as the environment, ethics, philanthropy, and the law.

Pattern 2: consumers with high concerns about GMFs probably fall into the low or medium intention class of using GMFs (78 supports).

With the evolution of the GMFs domain, one of the vital concepts is consumers' concerns about GMFs. The influence of consumers' concerns on the adoption of GMFs has been studied in prior research to some extent. For example, findings by scholars (Hakim et al., 2020; Davies et al., 2021; Pakseresht et al., 2021; Zheng and Wang, 2021) reveal that consumers' concerns positively affect consumer adoption of GMFS. Additionally, findings from Frewer et al. (2011) confirm that those foods characterized as being “bioactive” boost particular concerns, including ethical concerns, uncontrolled use, and unpredictable effects.

Pattern 3: consumers with the occupational status of students usually fall into the low intention class, while self-employed consumers usually fall into the medium intention class (66 supports).

One of the most fundamental elements of GMFs is their focus on and interest in individuals with higher incomes. The finding obtained by Chege and Groote (2008), Zheng and Wang (2021), and Waterfield et al. (2020), suggest that the high-income category of consumers were more likely to purchase GMFs. The reason behind this is consumers' income levels could moderate attitudes toward bioengineered food (Levi, 2022).

Pattern 4: the degree of autonomy is higher for low intention consumers rather than for consumers with medium intention to use GMFs (60 supports).

The SDT is one of the most important theories that has imported the psychological concepts from the GMFs literature to explore consumers' characteristics toward these biotechnological foods (Rubel and Streiffer, 2005; Mwathi, 2015; Martinez-Ribaya and Areal, 2020). In this study, the authors claim consumers' autonomy to choose GMFs has a greater effect on consumers' adoption compared to other elements in SDT. Similarly, Siipi and Uusitalo (2011) support that consumers' autonomy will be a crucial driver for the adoption of GMFs. Moreover, other patterns that have fewer support values are listed below:

Pattern 5: consumers' aged 30–39 are more likely to be in the high intention class (16 supports).

Pattern 6: high intention consumers usually have a medium level of prevention focus (16 supports).

Pattern 7: diploma consumers usually fall into the high intention class (16 supports).

This study combined the RST and FNG to construct network graphs of consumers' intentions to use GMFs. Current research extracts decision rules from the initial data based on RST. Moreover, by considering the extracted rules, non-linear relationships to analyze the influence of consumer characteristics on the intention to use GMFs have been provided by the FNG. Therefore, in this study, the researchers, looked at possible routes and useful information about how likely people were to eat GMFs.

According to the analysis of RST, and considering the support values and other indexes introduced in the Rough Set Theory, 13 rules have been selected from 289 initial rules in three segmented classes. Using the RST analysis, in the first class (low intention to use GMFs), seven decision rules from 116 rules were selected; in the second class (medium intention to use GMFs), two decision rules from 76 rules were selected; and in the third class (high intention to use GMFs), four decision rules from 97 rules were selected. Using the FNG approach, the total inflow of the graph and the top-4 determinants of consumers' intention to use GMFs in all three decision classes were identified and described. Moreover, by combining these two methods and investigating all three classes, the main rules and patterns governing the behavioral intention of consumers toward GMFs adoption were identified. The overall results confirm that corporate social responsibilities, consumer concerns, occupational status, and consumer autonomy are more important than other observed dimensions in consumers' decision-making. Moreover, findings illustrate that combining Rough Set Theory and Flow Network Graph could predict customers' intention and provide valuable information for policy-makers in related active industries.

Like any other study, this research has some limitations that we must acknowledge for the direction of future research. (Christofi et al., 2021) use the term “tri-axial” including theory, context, and methodology, to signify that future research is positioned along these axes. Concerning the results of the present study, we suggest fruitful areas for further advancement of future GMFs research based on these three axes.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

MA, NM, and FM: methodology. NM and FM: software. MA, NM, FM, and PM: analysis, writing—original draft, and writing—review and editing. MA: investigation and project administration. All authors contributed to the article and approved the submitted version.