Journal of value creating in Business Management

Abstract Abstract The purpose of this research is to design a commercialization model for knowledge-based products in the country's pharmaceutical industry. The current research is applicable in terms of its purpose, mixed (qualitative-quantitative) in terms of implementation, type of thematic analysis, and an exploratory research in terms of its nature. The statistical population of the research included 12 experts in the qualitative part by non-probability sampling with multiple strategies (intensity and snowball), and in the quantitative part included 151 employees of knowledge-based companies. The research collection tools are semi-structured interviews and questionnaires. To analyze data through coding, MAXQDA software and SPSS and PLS software were used in the quantitative part. The results showed that the formation of the concept of commercialization indicators of knowledge-based goods in the country's pharmaceutical industry includes 67 concepts in the form of 9 major concepts. During the investigated relationships, the conceptual model of the research has been implemented. The results of the research show that the influence between the components in the quantitative study had a necessary and appropriate level. Conclusion of the commercialization reforms of knowledge-based products in the country's pharmaceutical industry is very important in the economic and social development of countries. By improving commercialization and related reforms, it is possible to achieve added value, sustainable development, and improvement of social competencies. Introduction Rapid progress in such different fields of knowledge, in addition to the significant increase in the economic value of companies, also causes the economic and technical growth of society. Thus, the commercialization of knowledge-based goods is described as a vital factor, because it can guarantee the success and survival of an organization. This clearly shows the importance of commercialization in knowledge-based industries, especially the pharmaceutical industry (Amini et al, 2016). Knowledge-based companies refer to those companies that have emerged with large profits and high added value from products and services. Such companies and organizations have established their mission and main basis based on knowledge and benefiting from ideas, creativity, and innovation (Taifeh & Abedi, 2023). The process of commercialization in knowledge-based companies is highly influenced by the level of knowledge of the economic base. With the advancement of knowledge, knowledge-based economies continue to develop with greater dependence on the production, distribution and application of knowledge to production and employment in high-tech industries (Azma et al, 2020). But there are also challenges in the commercialization of knowledge in the pharmaceutical industry. One of the most important challenges is the development of the commercialization process. Most knowledge-based companies face these challenges in achieving the desired commercialization performance (Maghsoudi Ganjeh et al, 2019). The importance of this issue becomes more important, especially in a critical industry such as pharmaceuticals, which is based on intellectual property rights. In fact, innovation is the biggest driving force in the pharmaceutical industry, so attention to innovation and new drug production is essential for the development of this industry (Liu & Lyu, 2020). Therefore, the main research question is: What is the commercialization model of knowledge-based products in the country's pharmaceutical industry?  Theoretical Framework The importance of commercialization It is important to pay more attention to the category of commercialization and to choose and design a suitable model and strategy for commercialization because acquiring the ability to transform market-oriented research ideas into documented technical and economic technologies can transform a third world country from a seller of raw materials to an advanced country and seller of technical and economic knowledge; therefore, the commercialization of products based on knowledge and technology is the main priority of the country today to improve the economic cycle (Asadi et al, 2023). Considering the importance of the issue of commercialization and the existence of challenging obstacles in the commercialization process of products and created ideas, it is very necessary to emphasize more on the commercialization process in the industries of our country (Tabatabaian et al, 2018). The importance of paying attention to commercialization models Choosing the right commercialization model is essential for achieving financial success and exploitation of intellectual property. For this purpose, in order to succeed, a company must be able to choose and use the appropriate mechanism to implement the commercialization process of its new knowledge and technology (Gbadegeshin, 2017). Innovation process from conception to commercialization Commercialization is an important part of the innovation process in which new technologies and innovations are transferred from the development and testing stage to the production stage and widespread commercial supply to the markets. No technology and product can successfully enter the market without passing through this section. Creating platforms for the commercialization and supply of knowledge and technology, in addition to providing significant economic values for companies, also leads to the economic and technological growth of society (Safarzad et al, 2020). Characteristics of knowledge-based companies Knowledge-based companies have unique characteristics compared to other companies and traditional industries; for example, the ratio of active professional forces in these companies to their total employees is higher than in other companies, universities have a stronger role in their management; also, technological changes and attention to the research and development process are more in these companies; their competitive advantage is based on the knowledge base and innovation in technology, which increases the speed in conquering new markets (Ramzanpour Nargesi et al, 2022). Aghababayi et al, (2023) conducted a research entitled "Identification of factors of commercialization of technological projects in new knowledge-based companies". The findings of the research show that it is important to pay attention to the 5 criteria of technology, skill, knowledge, market, and policy and law for the commercialization of technological projects in new knowledge-based companies. Asadi et al, (2023) conducted a research entitled "Providing a model for commercializing knowledge-based ideas in companies located in science and technology parks". The findings of this research indicate that with the influence of various factors such as individual and team competence, the nature of innovation and technology, market readiness, the level of technological readiness and supporting factors, science and technology parks and government support: knowledge-based companies are able to Commercialize the ideas based on knowledge. This commercialization leads to sustainable entrepreneurship, product quality improvement, sales promotion and better profitability, and better service to society.  Research methodology The current research is applicable in terms of its purpose, mixed (qualitative-quantitative) in terms of implementation, type of thematic analysis, and an exploratory research in terms of its nature. The statistical population of the research included 12 experts in the qualitative part by non-probability sampling with multiple strategies (intensity and snowball), and in the quantitative part included 151 employees of knowledge-based companies. The research collection tools are semi-structured interviews and questionnaires. Research findings To analyze data through coding, MAXQDA software and SPSS and PLS software were used in the quantitative part. The results showed that the formation of the concept of commercialization indicators of knowledge-based goods in the country's pharmaceutical industry includes 67 concepts in the form of 9 major concepts. During the investigated relationships, the conceptual model of the research has been implemented. The results of the research show that the influence between the components in the quantitative study had a necessary and appropriate level. Conclusion of the commercialization reforms of knowledge-based products in the country's pharmaceutical industry is very important in the economic and social development of countries. By improving commercialization and related reforms, it is possible to achieve added value, sustainable development, and improvement of social competencies. Conclusion The current research was conducted with the aim of designing a commercialization model of knowledge-based products in the country's pharmaceutical industry. The results of this research are in agreement with the results of Aghababayi et al, (2023), Asadi et al, (2023), Stiri & Mehraayin (2022), Amiri & Rezaee (2022), Fakhari (2021), Masama Khosrowshahi & Sultanzadeh (2021), Papi et al., (2021), and Kapoor et al, (2020). Stiri & Mehraayin (2022) showed that the 5 main themes classify the key success factors of knowledge-based companies in 5 levels of individual characteristics, organizational factors, environmental factors, company strategy, and supply and allocation of resources; which interpretative structural modeling has been used to interpret and specify the relationship between factors and specify the role of each variable. Based on the results of the survey, it seems that after the environmental factors, the provision and allocation of appropriate financial resources (the amount and time of allocation) and the appropriate human resources (educated, experienced, skilled, creative and innovative) are more important than other factors. It was also found that individual characteristics and organizational factors have a mutual effect, and the company's strategy has the most influence over other factors. Finally, the best implementation method was determined among the five characteristics of the research to employ managers to achieve success, growth and development of knowledge-based companies. According to the results of the research, the following suggestions are presented: The commercialization of knowledge-based products in Iran's pharmaceutical industry can help the development of research and innovation in this industry. It is suggested to conduct research in the field of knowing more about diseases and their treatment methods by producing and supplying innovative drugs. This research can lead to scientific and medical progress in the country and improve treatment methods.   

Abstract Abstract This research aims to address the challenge of universities in commercializing technology and strengthen cooperation between universities and industry, presenting a comprehensive model for technology transfer from universities to industry. The research method is a mixed method, applicable in terms of purpose, and exploratory in nature. The statistical population in the qualitative section was articles in the field of technology transfer and academic commercialization published in the two databases Scopus and Web of Science from 2013 to 2023. The data collection tool was a systematic review of previous studies, selection of articles based on the criteria of the PRISMA method; and the data analysis method was the meta-synthesis method. In the quantitative part, a researcher-made questionnaire and library resources were used to collect data, and its statistical population was Iranian public universities in 2024 and the sampling method was purposive. SPSS and Smart PLS software were used to analyze and evaluate the results. In the qualitative section, first, the environmental factors affecting technology transfer from university to industry were categorized into 5 main categories: "technology supplier characteristics", "technology recipient characteristics", "technology characteristics", "region characteristics", and "laws and regulations". Then, their relationship to each other and to technology transfer methods was evaluated. The results showed that among the identified environmental factors; "laws and regulations" have the greatest impact on the methods of technology transfer from university to industry. Also, "technology characteristic" has a direct effect on "sales-based technology transfer methods", and "technology supplier characteristic" has a direct effect on "consultation-based technology transfer methods". However, the "technology recipient characteristics" and "regional characteristics" do not directly affect the methods of technology transfer. Introduction Today, the mission of universities is no longer limited to education and research, and their mission to contribute to the economic growth and social well-being of the regions in which they are located is obvious (Abramo & D’Angelo, 2022). In this regard, one of the duties of universities is technology transfer. Technology transfer is an iterative, continuous, and strategic process that requires close collaboration between all stakeholders (Bustamante et al., 2021). Despite the many benefits of technology transfer from university to industry, knowledge diffusion processes become more complex over time (Wang & Liu, 2022). Numerous evidences indicate that the complexities of the commercialization process and the existence of various obstacles in its path mean that despite the technical success of a large number of research projects, few of them successfully pass the commercialization stage (Battaglia et al., 2021). Each of the previous studies has looked at this issue from a specific perspective and by considering some factors. But the process of technology transfer from university to industry takes place in the dynamic environment of the "innovation ecosystem," where all interactions between its different elements influence the ultimate success of this process. Therefore, identifying all factors affecting technology transfer (internal and external factors of universities and industry) and examining the simultaneous relationship between these factors can lead to the identification of important factors in this process. In addition, different universities use different methods to transfer technology to industry. Therefore, examining whether universities can use any desired method for technology transfer, regardless of their environmental characteristics, can facilitate interaction between universities and industry in technology transfer policymaking. For this purpose, this research aims to provide a quantitative model to predict and determine the relationships between factors and measure their effects. In this regard, the main research question is: To what extent do environmental factors affect each other and the methods of technology transfer in technology transfer from university to industry? Theoretical framework Features of the technology provider (University) There are several factors in universities that influence the commercialization of academic technology. Contextual factors such as the nature, size, and geographical location (Zhao et al., 2020), the diversity of academic disciplines (Abramo & D’Angelo, 2022), interdisciplinary research (Hsu et al., 2015), the online development of academic services (Petrunia et al., 2019), the university’s skills in communicating with the community and identifying entrepreneurial opportunities (Rocha et al., 2022), university human capital (Marozau & Guerrero, 2016), and individual characteristics of researchers such as age, gender, academic rank, and research performance (Abramo & D’Angelo, 2022) can influence academics’ opportunities to interact with industry. Also, technology intermediary structures are considered a resource for facilitating the transfer and commercial exploitation of academically produced knowledge (Marozau & Guerrero, 2016). Technology Receiver Features (Industry) According to the World Bank report, although universities play an important role in innovation systems, the importance of their role in most industrial economies is significantly influenced by factors such as the structure of the domestic industry, the size of the industry, the number and composition of human and social capital, and the business model of industrial firms (Calcagnini & Favaretto, 2016). Technology features The most important component in creating a link between university and industry is the existence of university technology with characteristics and features that meet the needs of industry and society (Amirghodsi et al., 2020). Academic technology will be valuable when it results in the production of a product that is competitively priced, in line with foreign models, environmentally friendliness, ergonomic, and resource-saving (Chukhray & Mrykhina, 2018). Regional features Factors such as the existence of social ties (Taheri et al., 2016), mutual benefits of innovation ecosystem stakeholders (Dell’Anno & del Giudice, 2015), interaction between regional universities (Rocha et al., 2022), the existence of a strong innovation network (Perkmann et al., 2013), the diversity of industrial sectors in geographical proximity to universities (Perkmann et al., 2013), and the characteristics and advantages of… (Agasisti et al., 2023) can enhance technology transfer from university to industry. Rules and regulations In university-industry relations, many policies and programs of the higher education sector; at national, regional, and university levels, have a direct impact on technology transfer.
The government's general policies towards higher education should be formulated according to the specific situation of the region and pay attention to regional factors that are beyond the control of university administrators (Agasisti et al., 2023). Institutional policies including financial incentives such as an inventor's share of the revenue from the commercialization of knowledge, or non-financial incentives such as the impact of patenting on faculty performance evaluation and promotion or awarding prizes for commercialized inventions, can foster university-industry collaboration (Cullen et al., 2020). Research methodology The present study is applicable in terms of purpose, exploratory in nature; and a mixed method including meta-synthesis and structural equation modeling was used to conduct the research. In the qualitative part, a systematic review of sources was conducted in two databases, Scopus and Web of Science, between 2013 and 2023. In the quantitative part, library sources (secondary data) and a researcher-made questionnaire were used to collect data. The sampling method was purposive sampling and the questionnaire was completed by research assistants, directors of industry liaison centers, directors of growth centers, or selected faculty members of public universities. Research findings In the qualitative part of the research, factors affecting technology transfer from university to industry were categorized into 137 concepts, 30 subcategories, and 5 main categories including "university characteristics," "industry characteristics," "technology characteristics," "regional characteristics," and "laws and regulations" (Saberi et al., 2025). The quantitative results showed that "technology characteristics" are related to "technology transfer methods based on sales"; "technology supplier characteristics" to "technology transfer methods based on consulting"; and "laws and regulations" to "technology transfer methods based on sales, consulting, joint ventures and service provision". The five main categories are also interconnected. “Technology supplier characteristics” are related to “technology characteristics”, “regional characteristics” are related to “technology recipient characteristics”, and “laws and regulations” affect all other factors. Conclusion The present study aimed to present a model of technology transfer from university to industry with an emphasis on environmental components. The findings of this study showed that the higher the level of a technology (Amirghodsi et al., 2020), the greater the demand from industrial companies or foreign entrepreneurs to acquire it. Technology supplier characteristics such as university reputation (Shen et al., 2022), researcher expertise (Rocha et al., 2022), marketing skills (Kalantaridis & Küttim, 2023), university's history of collaboration with industry (Carayannis et al., 2016), and the university's ability to brand technological products (Burkholder & Hulsink, 2022) are among the characteristics that increase industry's sense of trust in the university. Laws and regulations at three levels: internal university guidelines and regulations (Gu, 2023), regional guidelines (Agasisti et al., 2023), and national laws (Xia et al., 2022) were evaluated as factors influencing the success of technology transfer. Some other characteristics of technology suppliers, such as the diversity of academic fields at the university (Ma et al., 2022), interdisciplinary research (Kalantaridis & Küttim, 2023), specialized laboratories and advanced equipment (Gachanja, 2023), adequate financial resources (Kalantaridis & Küttim, 2023), and management support for researchers (Rosdi et al., 2022) can lead to the acquisition of valuable technologies. Regional characteristics such as population (Petrunia et al., 2019), poverty and crime rates in the region (Gachie & Govender, 2017), gross regional product (Mascarenhas et al., 2019), exports (Wang & Liu, 2022), local market capacity (Amirghodsi et al., 2020), ability to export outside the region (Calcagnini & Favaretto, 2016), and the number of industrial companies present in the region (Fedosova & Babikova, 2017) are also characteristics that can affect the presence of investors and large industrial companies in the region. In this regard, for the success of technology transfer from university to industry, the consideration of these factores is suggested: the entrepreneurial motivation of researchers during recruitment, the requirement to formulate a sustainability annex for university technologies, strengthening alumni associations, the existence of an appropriate structure for technology intermediary centers in universities and the establishment of branches in faculties, strengthening interdisciplinary research, creating necessary incentives for researchers, allocating financial resources to applicable research in line with the priorities of the universities' strategic document, the existence of an independent center in universities for the social and economic valuation of university research, identifying and coding the research priorities of organizations and industries to guide student theses and research projects, and establishing a liaison organization between universities and industry in the governorates.

Abstract Abstract
The purpose of this research is to design a model of artificial intelligence competencies on organizational performance, taking into account business-to-business marketing capabilities. The research method is exploratory (qualitative-quantitative). In the qualitative part, it is considered with the Shannon entropy approach, and in the quantitative part, it is descriptive-survey. The participants of the present research in the qualitative part are faculty members and elites of artificial intelligence and marketing and management, which was conducted with 14 people based on the theoretical saturation rule, and in the quantitative part, the 540number of executive directors of industrial towns in northern Iran, of which 190 were selected as statistics sample. The data collection tool was semi-structured interview in the qualitative part, and researcher made questionnaire in the quantitative part. The method of data analysis was selected in the quantitative part of confirmatory factor analysis tests using SmartPLS software. The results showed that the mechanisms of artificial intelligence competencies have an effect on business-to-business marketing capabilities and organizational performance, and also the model of artificial intelligence competencies on organizational performance is confirmed considering the aspect of business-to-business marketing capabilities.
Extended Abstract
Introduction
The huge increase in the amount of data along with access to processing power capabilities and storage space on digital devices, have attracted new attention in the last few years to artificial intelligence in several fields and scientific courses (Enholm et al., 2021). The intense competition among organizations around the world has also accelerated the need to use artificial intelligence to achieve a competitive advantage over competitors (Ransbotham et al., 2018). Most C-level executives do not see AI as a core competency that organizations must employ to remain competitive in the long term (Kietzmann & Pitt, 2020). One of the key areas of using artificial intelligence in organizational activities has been business-to-business marketing (Mikalef et al., 2021). Smart solutions are needed to enhance business-to-business marketing capabilities in a complex business environment, because business-to-business operations are often associated with enormous information complexity and the need to make quick decisions. In this sense, artificial intelligence has the potential to revolutionize the way of performing common activities due to the ability to process increasing amounts of data and provide rich insights about key business partners and customers (Bag et al., 2021). In addition, it has been stated that artificial intelligence applications enable the automation of many manual processes, and this can help eliminate bottlenecks and increase operational efficiency in business-to-business activities (Paschen et al., 2020). In fact, a recent survey study on corporate executives conducted by Garner showed that the majority believe that artificial intelligence is likely to become a key development in their organization in the next few years (Shin & Kang, 2022).
Therefore, the main questions of this research are: What effect does artificial intelligence competencies have on organizational performance considering the B2B variable? Through what mechanism, the effects of artificial intelligence competencies on organizational performance are realized? And finally, what is the model of artificial intelligence competencies on organizational performance considering the aspect of B2B marketing capabilities?
Theoretical framework
Until now, there is not a complete understanding of how organizations should plan the development of artificial intelligence and turn it into a strategic asset applicable to achieve a competitive advantage. This issue is very evident and prominent in the field of business-to-business marketing, because there is still very little knowledge about the impact of artificial intelligence and the potential mechanisms of value generation from such technologies (Huang et al., 2019). Understanding the value of AI in business-to-business marketing and how to achieve it is critical to reducing the number of failed initiatives within organizations, as well as accelerating the development of AI in these types of operations. Similarly, recent survey studies of industry professionals show that there are still a number of significant bottlenecks preventing the adoption and use of AI that go beyond technical challenges. In addition, from the point of view of many managers, the value of adopting artificial intelligence is still not clear and certain, which prevents its further application in key organizational operations (Bhalerao et al., 2022). A recent study by McKinsey noted that the most popular use cases for AI in organizations relate to optimizing business-to-business marketing and service processes, and this is where respondents placed the most value. However, there are still several challenges associated with realizing such value by investing in AI and specifically with creating an AI competency that can always support business needs (McKinsey, 2022). To address this gap, this study uses the core competence theory (Prahalad, 1993) and provides a definition of the use of artificial intelligence within organizational boundaries, following the key followers of this theory. Especially, we express the concept of artificial intelligence competence as a central competence of organizations, which indicates the need for creative and coordinated use of artificial intelligence. The stated theories explain that organizations able to develop an artificial intelligence competency are organizations that are able to realize a competitive advantage over their competitors; because the application of artificial intelligence is unique in nature and requires comprehensive efforts from various organizational entities to produce artificial intelligence applications difficult to imitate and add value.
Methodology
In the current research, a systematic approach is used. This approach inductively uses a systematic set of procedures to formulate a theory in relation to a phenomenon. Content analysis is one of the documentary methods that deal with the systematic, objective, quantitative and generalizable examination of communication messages. This method is considered a concealer in the classification of methods, and it is used to check the obvious content of the messages in a text, and as a result, it does not enter into the interpretation and semiotics of the message content. The Delphi method is a structured communication method or technique that was originally invented and developed for the purpose of systematic and interactive forecasting by relying on the deliberation of experts. This method used in future research mainly pursues goals such as discovering innovative and reliable ideas or providing appropriate information for decision making. The Delphi method is a structured process for collecting and classifying the knowledge available to a group of experts, which is done through the distribution of questionnaires among these people and the controlled feedback of the answers and opinions received. Research method is fundamental-applied according to the goal; mixed (qualitative-quantitative) of sequential exploratory type according to the type of data; cross-sectional according to the time of data collection; and descriptive-survey according to the method of data collection or the nature and method of the research.
Discussion and Results
According to the results of Shannon's entropy technique for evaluating the capabilities of artificial intelligence, it can be seen that all the dimensions and indicators were higher than the average level of the group's weight, and remain in the model. Therefore, using the results of semi-structured interviews and inspired by the theoretical and empirical literature of the research, the identified categories will be categorized as described in the table below. As mentioned in the previous discussions, the sample size in the qualitative part of the research follows the principle of theoretical saturation, which is reflected in the next table on how to reach theoretical saturation.
Conclusion
The main goal of this research was to design a model of artificial intelligence competencies on organizational performance, considering the aspect of business-to-business marketing capabilities. The research method was exploratory (qualitative-quantitative). It was taken into account with the Delphi technique approach in the qualitative part, and descriptive-survey in the quantitative part. The participants of the present research in the qualitative part were academic faculty members and elites of artificial intelligence and marketing and management, which was conducted with 14 people based on the theoretical saturation rule; and in the quantitative part, 540 people of the industrial managers of the industrial towns in northern Iran, of which 190 people were selected a statistical sample. The data collection tool was a semi-structured interview in the qualitative part, and a researcher-made questionnaire in the quantitative part. The method of data analysis was carried out in the quantitative part of confirmatory factor analysis tests using SmartPLS software. One of the goals of this research was to try to understand whether, and under what conditions, artificial intelligence can lead to organizational value for companies. In order to answer this question, we grounded artificial intelligence based on the theory of core competencies. Therefore, the competencies of artificial intelligence were considered from the aspect of one of the key organizational capabilities that has the potential to create a competitive advantage for organizations. Therefore, the competencies of artificial intelligence are not only understood from the technical aspect, but also include management's ability to creatively anticipate applications that add value to the organization and include the ability to experience and test new methods of using artificial intelligence. Also, based on this approach, AI competency is considered as a core competency that organizations should strive to enhance, rather than just an ancillary and auxiliary set of capabilities that can support certain operations.
 

Abstract Abstract Geomarketing is a new interdisciplinary knowledge that has been increasingly used with the development of technology and tools for collecting location data, especially in organizations developed in the geographical area. In fact, GeoMarketing should be considered as a kind of engineering of the neglected element of location in minimizing the uncertainties of marketing policies, so that it has the ability to challenge the traditional models of developing marketing strategies. GeoMarketing should be considered as a scientific and operational strategy in measuring the spatial acceptability of policy feedback before adopting them on the basis of smart maps. The purpose of this article is to examine more closely the capabilities of the development of the Place element on the basis of the GeoMarketing strategy in relation to each elements of marketing mix (price, product and promotion). which by providing a new definition of this topic along with the basic explanation, Implementation steps of Tehran's standard geomarketing project to guide the policies of a business..
In this article, by collecting information and processing them in 7 business components, in relation to the extraction of neglected potentials in the development of the branches of a clothing brand, by implementing multi-criteria decision analysis and the weighted average result of collecting the opinions of 11 experts in the field of clothing on the software platform ArcGIS was implemented. In the end, the three areas of Artesh Boulevard, Abshenasan and Shohada Square were identified as areas of potential with pedestrians and left for interpretation. It is obvious that this new topic is in the early stages of its maturity and it is suggested to discover its practical capabilities in other business topics. Extended Abstract Introduction Geomarketing was first proposed as a new marketing method in 1990, and research-oriented articles mention the advantages of including geographic information in marketing methods (Shweta & Rohit, 2023). This method was used over time with the development of technologies related to location calculation, such as the Global Positioning System (GPS), even for the banking industry and the development of urban tourism (Sabet et al., 2021). In general, geomarketing is the set of activities in which location information is used to improve the accuracy and efficiency of marketing programs. It is worth noting that this information are extracted from various sources such as GPS; field observations; aerial and satellite images; digital maps; local data; etc. (Shweta & Rohit, 2023). Choosing optimal locations in a store development program is one of the main conditions for the success rate of a store. The problem that was tried to be answered in this research with a scientific and operational solution by the basic implementation of the geomarketing strategy in the branch development program of one of the clothing brands in Tehran. In line with the basic explanation of geomarketing services, the superiority of the element of place for each and every elements of the marketing mix has been elaborated; and then the implementation steps of a geomarketing project were presented in order to find the most productive locations in a store branch development program in Tehran's clothing field. For this purpose, by considering 6 main components in the optimal location selection of store locations in the clothing field, in relation to the collection of spatial data and their processing, a multi-criteria decision-making model was implemented. Then, the processed components were weighted based on the opinion of active experts in this field, and the most potential locations were extracted in the developed model. Research literature The main goal in the new geomarketing strategy is to scientifically consider the neglected element of location in business decisions. Philip Kotler, the father of modern marketing science, was the first to codify the marketing mix concept as it is today under the title of 4P (Perreault & McCarthy 2002); and 7P, 15P, and 44P models were also developed later. Meanwhile, the combination of four elements including location, promotion, price, and product is the main reference of researchers in this field such as Singh (2012). Understanding that every customer is located in a geographical point of the earth having the nature of location is the main key in defining and explaining geomarketing services in each and every elements of marketing mix. In fact, geomarketing should be called the engineering science of location, the result of the intersection of marketing science and geographic information science (GIS). It is obvious that explaining the concept of geomarketing requires examining the services and capabilities that can be provided in each and every marketing mix elements (García et al., 2022). In the following, considering the research articles of Ramadan et al., (2017); Farmanesh et al., (2017); Cliquet & Baray (2020); and Baray & Pelé (2020) to derive a standard mechanism in how to define, collect and process business indicators in a geomarketing project was discussed with a case study on the optimal location of new branches of a clothing brand in Tehran, considering 6 components including household density; financial ability; foot traffic; away from competitors; micro business density; and the accesses were investigated. Research methodology The present research was carried out by implementing the practical steps of geomarketing with the sample of Tehran. After defining the project and assessing the need, determining the information layers determined in the previous stage and taking action to collect and update of the available data was performed by using different tools from different sources. In the preparation phase, the spatial data that was collected from different sources in the previous phase; sometimes in different formats, was integrated; and after cleaning, necessary measures were taken to prepare them in order to implement advanced spatial analysis, which is known as data cleaning and GeoData preparing. GIS Ready, GeoData Referencing, and GeoDatabase Implementation can be mentioned among the measures taken in this stage. In the processing phase, the raw data collected and prepared with the processes required by a project and explained and defined in the analysis phase of the need and definition of the project are upgraded to the maturity level of information. Thermal maps or heatmaps are among the most widely used outputs at this level, which are referred to under the Spatial Analyst analysis collection in the user space of spatial data science software (DANIELE, 2019). In the conducted research, a number of outputs derived from the above analysis on the data collected in Tehran city were mentioned. The household density component taken from the latest census of the Statistics Center, Financial ability component derived from the average rental price of the neighborhoods of Tehran, foot traffic component derived from the implementation of proximity analysis on the data of commercial centers and shopping centers in Tehran, and the distance component from competitors through the collection of the location of more than 1000 store locations of garment area in Tehran city from Google Map data, micro business density component based on more than 36,000 micro businesses registered in Google Map and accesses including the integration of parameters of main roads with maximum impact, secondary roads at the second level of importance, taxi stations, buses, subway and parking lots at the third level of importance, were collected with weights of 40, 35 and 25, respectively. In order to process the six data mentioned in the extraction of high-yield points in the aforementioned store development planning, a specific placement model based on the multi-criteria decision-making algorithm or MCDM was developed based on the Raster Calculator analysis of the Spatial Analyst analysis package in the ArcGIS software space with the obtained weights. In the special integrated model developed in the processing of components in order to implement multi-criteria decision analysis, the blue circles represent the variables (six components and their subsets), and the yellow rectangles represent the processing steps in the data processing, which is implemented in the last part of the decision analysis. Research findings The results of the weights assigned to each of the 6 components acquired from the average expert opinion of 11 people active in the field of clothing were presented in a table. Also, the results of the output in the integrated model indicate the existence of three areas full of neglected potential in the field of clothing in Tehran, including the first area (Artesh Blvd.) between Sohanak and Oshan Boulevard; the second area (Abshenasan highway) between Ashrafi Esfahani and Bakeri highways; and the third area (Shohada Square to Imam Hossein Square). All the above three cases were located in the commercial centers of Tehran city, while in the field surveys, the accumulation of the presence of medium and large shopping centers including Parnian Center, Mahtab, Nakhl, Morvarid and Shemiran Center was only within the determined potential range of the first range. The occurrence of the same issue in the second area, including Qaim, Maryam, Ghadir shopping centers, as well as Goldis, Bahar and Zomorrod shopping centers in the third area shows the high potentials of the clothing field in the proposed areas. Conclusion Managers of any business always make strategic decisions to determine the survival and success of that business. Ignoring each of the different success parameters makes decisions' success faced with challenge. Decision-making algorithms and analyzes in GIS not only consider all the influential parameters in the form of information layers, but also determine and define the impact of each one by the ability to weight each one (Tu Le et al., 2023). In practice, at this level, the intra-organizational, peripheral, and extra-organizational information layers that were prepared and processed in the previous stages were modeled in different scenarios and weight combinations using advanced spatial data science analyzes so that the feedback measurement and spatial acceptability measurement of decisions and policies makes it possible on the basis of the map. In this strategy, it is important to minimize uncertainties by optimally allocating resources to the most productive points in the geography of the market. The uniqueness of geomarketing evolves by integrating functions and spatial analysis in order to solve problems related to the development of local, regional and extra-regional markets with different social, economic and political approaches as the driving force of modern marketing knowledge (Tkhorikov et al., 2020). Marketing mix elements are one of the main topics that are always cited and evaluated in the formulation of marketing strategies (Thabit & Manaf 2018). Location is one of these pillars that, due to the development of information infrastructure and software based on GIS knowledge, is able to provide unique capabilities along with the development of businesses and brands in global markets (Matheus et al., 2020). The authors of the article believe that the absence or lack of standard location data has made it difficult to refer to the achievements and outputs of geomarketing in practice. In this article, by explaining a comprehensive definition, it was tried to investigate the geomarketing solutions that can be presented in each of the elements of the marketing mix, and its applications were introduced by determining the implementation steps with reference to Tehran's business data. It is suggested that case studies on the implementation of the services of this strategy to be implemented in other business areas such as restaurants, banking industry and broadcasting industry by the researchers of this field, and geomarketing services investigated specifically for each of the elements of the marketing mix based on real business data.  

Abstract Abstract
The purpose of the current research is to provide a model of ecotourism and sustainable tourism development with an emphasis on foreign exchange and economic development of Qeshm Island. The research method is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of its implementation. The statistical population in the qualitative section includes 19 experts, including managers and experts in the field of tourism, hoteliers, travel agencies, tours and ecotours; by means of the snowball sampling method, and in the quantitative section, it includes 244 employees of the Cultural Heritage Organization, handicrafts and tourism of Qeshm Free Zone. Data collection in the qualitative part was done using semi-structured interviews of the members of the statistical community, and data collection in the quantitative part was done through a questionnaire. In the data analysis of the qualitative part, the coding process in the MAXQDA 2018 software was used, and in the quantitative part, SPSS 16 and AMOS software were used; and the data was analyzed using the factor analysis method. The results in the qualitative section showed that 207 interview codes, 68 subcategories, and 14 categories were extracted. Causal factors include cultural and social factors, the potential of Qeshm Island for tourism and economic and political factors, background features including geographical location, capacity and potential facilities of Qeshm and geosites, intervening conditions including lack of strategy and proper planning and infrastructural problems as well as strategies and interactions include goal-setting and strategy determination to achieve the goal, education and culture building, and providing suitable infrastructure for tourism. The results of quantitative analysis show the significance of measurement models and structural equations at the confidence level of 95%.
Extended Abstract                                          
Introduction
Ecotourism generally refers to tourism activities that are environmentally accountable and whose purpose is to protect the environment, improve people's knowledge about the environment, and local economic development. (Buckley, 2009) Ecotourism is usually related to natural areas and wild environments, and protects natural resources and wild species, and promotes the local economy (De Grosbois & Fennell, 2021). Ecotourism has become an integral part of tourism to increase awareness about environmental protection and reduce the negative effects of tourism while using sustainable natural and cultural tourism attractions (Darda & Bhuiyan, 2022). Sustainable development is a new concept that was formed after the industrial revolution and the problems created in connection with the industrialization of cities and the technological development of cities and the economic, cultural, political and social connection and special attention to ecological considerations (Masrouri Jannett & Falahat, 2016). Sustainable tourism is a sustainable method in the tourism industry and looks at all the effects of tourism, either positive or negative, with the aim of maximizing positive effects and minimizing negative effects (Mohammed, 2022). Ecotourism is a sustainable alternative to degraded livelihoods around protected areas in developing countries, and its contribution to poverty reduction is evident in terms of employment and income generation. As a result, studies of ecotourism partnerships evaluating have traditionally focused on the accumulation and distribution of economic benefits (Agyeman et al, 2019). Based on the said material, the researcher is trying to answer the question: How are ecotourism and sustainable tourism development with an emphasis on foreign exchange and economic development of Qeshm Island?
Theoretical framework
Tourism and ecotourism
Tourism is an economic and social activity that includes the travel and stay of people in places and areas outside of their residence. This activity includes observing tourist attractions, culture and entertainment in different destinations. Tourism can help the economic and cultural development of different regions and play an important role in creating jobs and increasing local income (Camilleri, 2018). Ecotourism is considered as a branch of tourism that focuses on environmental protection, sustainable development and promotion of local economy. Ecotourism pays attention to natural areas and pristine environments and tries to preserve them. This type of tourism emphasizes social and environmental responsibility and emphasizes the direct relationship with local communities and the protection of natural resources (Fennell, 2014).
Sustainable Development
Development and sustainability is one of the relatively new concepts in the world development literature, and was first used in the United Nations Summit under the title of Human Environment in Stockholm in 1372. Sustainability is an effort to achieve the best results in human and natural environment programs that are carried out for the present and unlimitedly for the future. Sustainability is a local, sensible, cooperative, and balanced process; performed in a balanced ecological environment, without export its problems to the surrounding areas or leave them on the shoulders of future generations (Asadiyan, 2023).
Currency exchange production and economic development
Currency production means less import and more export of goods and services. Economic development means increasing production, employment, welfare and economic growth. These two concepts are directly related to each other (Cypher, 2014).
Ruzbeh et al, (2023) investigated the presentation of the green governance model with a sustainable development approach in the health system (the case study of hospitals in Kerman city). The research findings showed that the causal factors include: balanced and integrated management, service provision, and financial resources; background factors Include: legal environment, political environment, cultural and social environment, and technology; intervention factors include: government policies; management of change strategies include: green governance affairs, contractual affairs, and partnership affairs; consequences include: creating alignment between policy goals and structure and culture in the health sector, promotion of administrative and environmental health, dynamism and adapting to changes and developments in the health system.
Nikfar (2023) investigated the evaluation and analysis of the role of ecotourism in urban development (case study: Zone 1 of Tehran). The results of the research showed that Tehran region 1 has the capabilities and potential of many natural attractions to attract ecotourism, and economic, institutional, social and cultural factors are considered to be the most important obstacles and challenges for the development of ecotourism in Tehran region 1, and ecotourism in Tehran region 1 can also play an effective role in the goals of urban development of Tehran city.
Research methodology
The research method is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of its implementation. The statistical population in the qualitative section includes 19 experts, including managers and experts in the field of tourism, hoteliers, travel agencies, tours and ecotours; by means of the snowball sampling method, and in the quantitative section, it includes 244 employees of the Cultural Heritage Organization, handicrafts and tourism of Qeshm Free Zone. Data collection in the qualitative part was done using semi-structured interviews of the members of the statistical community, and data collection in the quantitative part was done through a questionnaire.
Research findings
In the data analysis of the qualitative part, the coding process in the MAXQDA 2018 software was used, and in the quantitative part, SPSS 16 and AMOS software were used; and the data was analyzed using the factor analysis method. The results in the qualitative section showed that 207 interview codes, 68 subcategories, and 14 categories were extracted. Causal factors include cultural and social factors, the potential of Qeshm Island for tourism and economic and political factors, background features including geographical location, capacity and potential facilities of Qeshm and geosites, intervening conditions including lack of strategy and proper planning and infrastructural problems as well as strategies and interactions include goal-setting and strategy determination to achieve the goal, education and culture building, and providing suitable infrastructure for tourism. The results of quantitative analysis show the significance of measurement models and structural equations at the confidence level of 95%.
Conclusion
The current research was conducted with the aim of providing a model of ecotourism and sustainable tourism development with an emphasis on foreign exchange and economic development of Qeshm Island. The results of this research are in agreement with the results of Ruzbeh et al, (2023), Nikfar (2023), Mobasheri et al, (2023), Ghasemlu et al, (2022), Salman et al, (2022), Darda & Bhuiyan (2022), Hashemi Asl (2020), and Agyeman et al, (2019). Nikfar (2023) showed that the city of Tehran has many capabilities and potentials of natural attractions to attract ecotourism, and economic, institutional, social and cultural factors are considered to be the most important obstacles and challenges for the development of ecotourism in the 1st region of Tehran, and ecotourism in Tehran region 1 can also play an effective role in the goals of urban development of Tehran city.
According to the results of this research, it is suggested that the development of sustainable tourism in Qeshm Island requires extensive and coordinated measures. Among the important actions, we can point out the examples of the experiences of successful countries and cities, protecting tourist places by establishing laws, promoting ecotourism and encouraging ecotourism activities. Also, the development of suitable infrastructure for tourism, the use of renewable energy sources, and the support of local communities and economies are also of great importance. These measures, if implemented correctly, can help develop sustainable tourism and preserve the environment and local culture in Qeshm Island.

Abstract Abstract The present study aims to explain the vital role of smart geomarketing in export development. This study has a mixed approach (qualitative-quantitative) and an applicable research method. In the qualitative part, the statistical population included government experts, major exporters of kiwi products, and also academic experts in Gilan province (Iran), of whom 15 people were considered as a sample after data saturation. The sampling method was purposive. The data were analysed using the Grounded theory methodology. Then, the research strategy in the quantitative part was conducted as a survey. The statistical population included 316 individuals and legal entities active in kiwi exports, of which 173 people were selected as a sample through the Cochran formula in a limited population using non-probability sampling. The data collection tool was a questionnaire. The results of structural equation modelling using Smart PLS software showed that management capabilities, government support policies, export development marketing, environmental factors, and kiwi cultivation characteristics have a significant effect on smart geomarketing. Smart geomarketing has a significant effect on the development of marketing capabilities, training and persuasion of officials for export development, and the development of spatial data infrastructure. The development of marketing capabilities, the development of spatial data infrastructure, and the training and persuasion of officials for export development have a significant effect on increasing share in global markets, increasing global reputation, developing brands in foreign markets, and increasing foreign exchange. Of course, training and persuasion of officials do not have a significant effect on increasing global reputation. Introduction Marketing plays a key role in export development. With the help of marketing, companies can identify their target market, understand the needs of foreign customers, and market their products or services in a way that is well-received in the target markets (Chishty & Sayari, 2024). Among them, agricultural products, especially horticultural products and fruits, are of great importance (Publication, 2024). Iran has many agricultural products, of which kiwi is an important export product mainly grown in the northern regions of the country, especially in Gilan province, but the cities of Rudsar, Talesh, and Astara are the production hubs of this product. However, despite high production, Iran's share in the global kiwi fruit markets is very low and less than 0.7 (Khalqi Eshkalek et al., 2021). In this regard, some studies have shown that the export sector of this product has not been able to develop well due to the lack of awareness of exporters about the locational indicators of consumers, including purchasing patterns, geographical location of purchases, service area and region, addresses and delivery locations, consumption levels, expectations, attitudes, etc. Today, with the growth and development of the market, the importance of locational indicators has increased (Oliveira & Spinola, 2020). Geomarketing allows marketers to better understand the needs and preferences of customers by focusing on specific regions and markets. By analysing geographic information, marketers can identify the best places to locate branches or stores, and as a result, access to customers will be easier and more effective (Alaoui & Abdelali Fateh, 2024). One of these intelligent methods is called Spatial Data Infrastructure (SDI), whose advantage over the previous method is that data is generated and collected only once, as a result of which parallelism is eliminated from the spatial data generation process. Additional costs are not spent on reproducing existing data (Bansal & Singh, 2024). Therefore, the exporting company must acquire the necessary and complete knowledge of the international marketing environment in order to increase its chances of success (Fathi Bajestani et al., 2025). This knowledge increases the company's ability to market and sell products and services effectively and efficiently according to the location of consumers. In fact, this type of knowledge includes familiarity with customer needs and local market characteristics, familiarity with business conditions, and the knowledge and skills required to promote offerings in export markets (Bartoli et al., 2021). Several studies have been conducted in this field. Of course, few studies were found that examined smart geomarketing in the export of kiwi products. Because most recent studies had conducted location marketing and geomarketing to examine the weekly market (Nath et al., 2024), hotels (Gu et al., 2024), and food products (Bartoli et al., 2021). Therefore, given this research gap and considering that currently, due to the high performance of the kiwi product and the acceptance of the gardeners of Gilan province, there are good capabilities in the field of exporting this product; but if the necessary measures are not taken to realize and develop the export of this product with respect to location marketing, the possibility of losing this golden opportunity is tangible. Therefore, it must be accepted that due to the need to develop the export of non-oil products and, in particular, the export of kiwi produced in Gilan province, which global markets have welcomed, and also due to the lack of comprehensive research related to the location marketing of this product, the present study was conducted with the aim of explaining the vital role of smart geomarketing in the development of exports. Research Methodology This study has a mixed approach and applicable research method. The statistical population included government experts, major exporters of kiwi products, and also academic experts in Gilan province, 15 of whom were considered as a sample after theoretical saturation. The sampling method was purposive. In total, about 450 minutes of interviews were conducted over a period of more than 3 months through coordination with them. The average interview with each person was about 30 minutes. The data were analysed manually using the data-driven method and three-stage coding (open, axial, and selective). Then, the research strategy in the quantitative part was conducted as a survey. The statistical population included 316 individuals and legal entities active in kiwi exports, of whom 173 were selected as a sample through the Cochran formula in a limited population using a non-probability sampling method. The data collection tool was a questionnaire. The data were tested through structural equation modelling using Smart PLS software. Research findings In the qualitative part, the results showed that the central phenomenon is geomarketing. Causal factors include management capabilities, government support policies, and export development marketing. Contextual factors include environmental factors and kiwi cultivation characteristics. Strategic conditions include developing marketing capabilities, training and persuading officials to develop exports, and developing SDI infrastructure. Intervening conditions include government weakness. Outcomes include increasing share in global markets, increasing global reputation, developing brands in foreign markets, and increasing foreign exchange. In the quantitative part, it showed that management capabilities, government support policies, export development marketing, environmental factors, and kiwi cultivation characteristics have a significant effect on smart geomarketing, and smart geomarketing has a significant effect on developing marketing capabilities, training and persuading officials to develop exports, and developing spatial data infrastructure. Developing marketing capabilities, developing spatial data infrastructures, and training and encouraging officials to develop exports have a significant impact on increasing share in global markets, increasing global reputation, developing brands in foreign markets, and increasing foreign exchange. Of course, training and encouraging officials do not have a significant impact on increasing global reputation. The weakness of the government only interferes with some relationships. Conclusion The present study was conducted with the aim of explaining the vital role of smart geomarketing in export development. The results of this study are in line with the results of Turan and Abdiu (2024); Wanzala, Marwa, and Luanga (2024); Zhou et al. (2024); Zahir and Lu (2024); Kho, Nagya and Nam (2023); Ipek, Biçakçıoğlu-Pınarçı and Kobra Hizarci (2023); Tayen-Höke et al. (2022); Budlage and Ketter (2022); Pük et al. (2022); Zoğur and Qülu (2022); Keskin et al. (2021); and Alegre Vidal et al. (2022).According to the results obtained, the following suggestions are made: It is suggested to optimise production processes. For optimisation, it is recommended to analyse the processes in detail, because by carefully examining all stages of production, it is possible to identify points where waste, time and energy are. It is also better to design and implement lean production systems. Lean production systems, such as the Toyota system, focus on eliminating waste and resource wastage in all stages of production. By implementing these systems, efficiency and productivity can be significantly increased. To deliver goods and services to customers without using marketing intermediaries, it is better to use various distribution channels using up-to-date technologies such as GPS, SDI, big data analysis, machine learning and artificial intelligence, the Internet of Things, augmented reality, virtual reality, etc., to improve interaction with the consumer.To reduce supply chain challenges, it is recommended to carefully review the personality traits model of distributors before concluding a contract.

Abstract Abstract The purpose of the current research is to design the pattern of compulsive purchase of consumers in the TV shopping industry using the data-based theory. To answer this question, a mixed research method (qualitative and quantitative) was used; the data-based theory was used in the qualitative part, and the descriptive-correlative research method was used in the quantitative part. For data analysis; MaxQDA2020 software was used in the qualitative part, and Lisrel software was used in the quantitative part. The sampling method in the qualitative part is of a targeted type, which was saturated by conducting 10 interviews with professors and experts in the fields of marketing and sales, management, sociology and psychology; and in the quantitative part, data was collected using a questionnaire from the statistical population of television buyers in Mashhad. 384 questionnaires were collected based on Morgan's table and based on random sampling. The validity of the questionnaire was confirmed using face and content validity, and reliability using Cronbach's alpha. The results and findings of the research showed that the central phenomenon of compulsive buying from television has two main categories, including quick and thoughtless buying and irrational and emotional buying, and we have identified the factors that directly and indirectly cause this behavior. Some of the most important factors included appropriate marketing mix design for television sales, marketing capabilities of television sales companies, individual demographic characteristics, personality causes, psychological causes, lifestyle, family, and the role of cultural and social structures. Extended Abstract Introduction Compulsive shopping occurs when there is a constant worry about shopping or an irresistible urge to buy unnecessary items and products, especially when the purchases are more than a person can afford. This creates serious negative consequences for the individual and the people around him (Otero-López et al, 2021; Brito et al, 2021; Müller et al, 2019). But until now, only compulsive buying has not been given much attention in terms of marketing (Horvath & Joosten, 2021). Due to this context and the scarcity of studies that examine compulsive buying behavior (Brito et al, 2021); therefore, there is a need to conduct research to identify possible causes that cause, maintain or promote this purchase behavior (Otero-López et al, 2021). Of course, it should be kept in mind that the knowledge of any phenomenon can be understood with a detailed knowledge of that phenomenon in its context. For this reason, it is necessary to know the causes of the phenomenon of compulsive buying from television, this phenomenon itself should also be correctly identified, along with the background conditions (the platform that creates it). Based on this, the use of data-based theory as a suitable qualitative method has been used in this research to provide appropriate solutions for the use of marketing managers and manufacturers by understanding the causes, background, interfering conditions and the phenomenon of compulsive buying. Ultimately, by applying appropriate and ethical strategies that are explained based on Iranian and Islamic culture and the native environment of Iran, the consequences of this pattern should be identified. This research intends to use its findings to solve the problem of compulsive buying and to provide suitable solutions for the purposeful and ethical use of this phenomenon by Iranian marketing and producers. Therefore, the main question in this research is: what is the compulsive buying pattern among TV buyers? Theoretical Framework Compulsive buying is defined as an unconventional and unusual type of shopping that is characterized by a strong, uncontrollable and frequent desire to buy without considering its consequences (Ong et al., 2021). Compulsive buying behavior represents "...a response to an uncontrollable urge or desire to obtain, use, or experience a sensation, substance, or activity that repeatedly leads a person to engage in behavior that ultimately results in and/or harm others." Note that in the consumer research literature; this phenomenon is also defined as compulsive consumption or compulsive buying (Tarka & Babaev, 2020). Compulsive buying behavior is a modern disorder that has received increasing attention from scientists. This dysfunctional shopping behavior has grown rapidly in recent decades, especially among young people, and has resulted in harmful psychological and financial consequences (Ong et al, 2021). Research Methodology In terms of the fundamental goal and from the perspective of the result, the upcoming research is one of the exploratory researches carried out using a mixed (qualitative-quantitative) approach. The sampling method in the qualitative part was purposeful and judgmental. The method of collecting information in the qualitative section was to conduct an in-depth interview. In the quantitative part of the research, the model extracted from the qualitative part was tested. Based on this and based on the open codes identified in the qualitative section, a questionnaire was designed. The statistical population of the quantitative part was television buyers in Mashhad, who were selected as a sample by using available sampling method and based on the method of determining the sample in structural equation modeling (384 people as sample). Research Findings After conducting the interviews and transcribing them, the text of the interviews was entered into the qualitative data analysis software MaxquQDA2020 for analysis and open coding. The following components were the output of the qualitative section and codings of the research with the foundation's data theory method. Causal conditions: the causal conditions of the research that cause the occurrence of the phenomenon of compulsive purchase from television by television audiences and television buyers, were placed in eight categories of appropriate marketing mix design for television sales; Marketing capabilities of television sales companies; demographic characteristics of the person; personality causes; psychological causes; life style; the family, and the role of cultural and social structures. The phenomenon orientation: In this research, the phenomenon orientation is compulsive buying from television, which is divided into two main categories, including quick and thoughtless buying, and irrational and emotional buying. Background conditions: In the current research, ten main categories were identified as existing categories in the background conditions. These categories include the level of awareness in society; knowledge of people in the field of TV products and shopping; attitudes and trends of society; cultural civilization of society; economic conditions; society's view of national media and television; maturity of the television ecosystem; the growth and development of technology in society; maturity of the marketing and sales ecosystem, especially in television; maturity of the complements of the television sales company. Intervention conditions: In this research, intervention conditions have eight main categories, including the attractiveness of television in the eyes of the audience; broadcasting policies; policies of television sales companies; belief and trust in national television; awareness and knowledge of the person in the field of buying products; community culture in the field of shopping; the individual's situational components; and the conditions governing the society and the individual's life. Strategies: In the current research, strategies were divided into two main categories: human strategies, and structural and organizational strategies. Consequences: Consequences of implementing strategies to reduce the negative effects of compulsive TV shopping were placed in three main categories including consumer consequences; Consequences for families; and consequences for society. In the quantitative part, correlation test and then structural equations were used to evaluate the extractive model. The results of the correlation test showed that the correlation value between the causal conditions and the compulsive buying phenomenon is equal to (0.686); correlation value between compulsive buying phenomenon and strategies is equal to (0.561); the correlation value between intervention components based on strategies is equal to (0.937); The correlation value between contextual components and strategies is equal to (0.71); and finally the correlation value between strategy variables and outcomes is equal to (0.55). This amount of correlations is significant (0.000) and it should be considered that this value is lower than the standard value of 0.05. Structural equation modeling was also used to evaluate the model. The results showed that the effect of causal conditions and compulsive buying phenomenon was equal to 0.44. The effect of compulsive purchase on strategies was equal to 0.48. The influence of contextual components on strategies was equal to 0.27. The effect of strategies on outcomes was equal to 0.46. Conclusion The development process of urbanization and industrialization has brought major changes in the lifestyle and shopping of Iranian society. These changes are not necessarily positive; a negative one created in Iranian and Islamic culture is the creation of immediate, compulsive, addictive, and hedonistic shopping styles that are derived from the culture of Western society. This has intensified under the influence of intense media activities and has been institutionalized as a part of people's lives. In recent years in the country, television sales companies have sold their products directly from television channels. The nature and type of this style of sales includes immediate, quick, unplanned purchases, based on fleeting emotions and based on emotional advertisements, which causes television viewers to quickly buy the advertised products when this style of television commercials is aired. This style of shopping is a type of compulsive shopping style that has many negative consequences for customers and society. TV products are increasing rapidly, which indicates a good sales situation for TV direct selling companies. However, due to the extent of the compulsive purchase phenomenon in the country, as well as among the buyers from TV channels, no research has been done in this field in the country. For this reason, this research aims to design the pattern of compulsive buying by consumers in the television shopping industry using data base theory in order to properly understand this phenomenon, provide strategies to reduce its negative consequences, identify the background and interfering components affecting the strategies, and also identify the consequences resulted from the implementation of these strategies in the society. Therefore, the opinion of 10 experts in this field and the interview process were used to design a model based on data-based theory. The experts were selected based on purposeful sampling. The model was designed in the qualitative part and tested in the quantitative part. This research had limitations such as the small number of experts in this field, access to experts, and the impossibility of face-to-face interviews with experts due to the Corona situation. Finally, it is recommended for future research to develop practical knowledge in this field for the country by quantitatively testing the research model as well as increasing more research in the field of instant shopping, shopping addiction, and shopping frenzy from the television sales industry.

Abstract Abstract The main goal of this research is to design a causal model of factors affecting the Lars supply chain (lean, agile, resilient and sustainable). This research is applicative-developmental in terms of purpose. In line with the purpose of the research, firstly, each of the supply chain paradigms was examined using the theme analysis method of the research literature, and then, by using which; the dimensions of sustainable supply chain, resilient supply chain, agile supply chain, lean supply chain, and 16 indicators were also specified. Their differences were also identified. Dimtel method was used to determine the effectiveness of each of the indicators, and for this purpose, the Dimtel questionnaire was completed by professors and experts in this field, and finally, the relationships between the indicators were determined in the Cartesian coordinate system. The results of this stage showed that economic factors, agility, continuous improvement and flexibility are, respectively, the most influential, and on the other hand, supplier management is the most influential among the Lars supply chain indicators. Finally, in order to present a suitable conceptual model of the Lares supply chain, using the method of structural equations, the validity of the relationships of the model provided by 20 experts in this field was quantitatively evaluated. Extended Abstract Introduction Lars supply chain is trying to bring lean, agile, resilient and sustainable approaches together in the supply chain in order to benefit from the advantages of each of them and cover their shortcomings at the same time. Agility in the supply chain allows business partners to react to changing markets with visibility into customized services and customized products. Unlike the "lean" paradigm, the "flexible" paradigm responds to unexpected disruptions to achieve competitive advantage. Although a resilient supply chain may not be the least expensive supply chain, it is efficient in unpredictable turbulence (Raut, Mangla, Narwane, Dora, & Liu, 2021). In lean supply chain management, the effort is to bring the inventory level to zero (Carvalho & Cruz-Machado, 2011), but it is noteworthy that the application of each of the paradigms alone will not lead to significant results for the organization in the current competitive environment, and many researchers have stated that the implementation of only one approach such as the lean approach is not the most appropriate supply chain because focusing on minimum inventory and more detailed planning and even only agile implementation may not be cost-effective for companies, and since in today's market, companies want to be flexible and responsive in a cost-effective way, they implement a combination of the most suitable paradigms as a hybrid strategy in accordance with the organization's strategy to improve the supply chain as much as possible (Ahmed & Huma, 2021). Also, implementing any combination of paradigms allows organizations to reduce costs and increase quality, flexibility, and responsiveness to customer demand (Ambe, 2009). Naylor, Naim and Berry (1999) introduced the concept of integrating each paradigm in a supply chain, that is, the acceptable supply chain paradigm. By implementing an acceptable paradigm, one can take advantage of the advantages of each different paradigm (Naylor, Naim, & Berry, 1999). Next, Azevedo et al. implemented agile and resilient paradigms in the supply chain, and this combination of paradigms influenced sustainability and promoted sustainability performance (Azevedo, Carvalho, & Cruz-Machado, 2016). Trade-offs between lean, agile, resilient, and sustainable management paradigms are real issues and help supply chains become more efficient, streamlined, and sustainable. Lean in the supply chain maximizes profits through cost reduction, while agility maximizes profits by providing exactly what the customer needs. Resilient supply chains may not be the least expensive, but they are more capable of dealing with an uncertain business environment. Also, to ensure the sustainability of the management system, the environmental measures should be paid attention. Considering that the tile and ceramic industry is one of the main industries of Yazd province and has the potential to export its products to other countries, the design of the Lars supply chain model is of particular importance in this industry. Considering the importance of this issue, the current research aims to present the Lars model in the supply chain in the current industry so that lean, agile, resilient and sustainable approaches are used side by side in order to benefit from their advantages in supply chain management. Therefore, the researcher asked the main question: what is the design of the causal model of the factors affecting the Lars supply chain? Literature Lean supply chain Lean management approach, developed by Ohno (1998) at Toyota Motor Corporation in Japan, forms the basis of Toyota's production system with two main pillars of "automation" and "just-in-time production". Lean manufacturing is described as the integration of manufacturing systems to maximize capacity utilization while minimizing buffer stock by minimizing system variability (Swenseth & Olson, 2016). Agile supply chain Agility means using market and corporate knowledge to exploit profitable opportunities in an unstable market, which agility is the essential characteristic of the supply chain needed to survive in turbulent and unstable markets. Since customer needs are constantly changing, the supply chain must be adaptable to future changes to properly respond to market needs and changes. The agile paradigm aims to develop the ability to quickly respond effectively to unpredictable changes in markets and increasing levels of environmental turbulence, both in volume and variety (Agarwal, Shankar, & Tiwari, 2007). Resilient supply chain Resilient supply chain is a topic that has attracted the attention of researchers, especially when a trend such as globalization has increased risks for supply chains. Regarding the issue of globalization, the increasing complexity of the supply chain in the global world has caused more uncertainty (Tordecilla, Juan, Montoya-Torres, Quintero-Araujo, & Panadero, 2021). Resilient supply chain is related to the system's ability to return to the initial state or a new and more favorable state after disruption, and avoid failure states. In other words, the resilient supply chain is not only the system's ability to control performance changes when faced with disruption, but also the ability to adapt and sustainably respond to sudden and significant changes in the environment in the form of demand uncertainty (Kamalahmadi & Parast, 2016). Resilience strategies aim to reduce disruptions that threaten the continuity of operations in the supply chain. These strategies can be categorized as proactive or reactive, and from another perspective they can be strategies of flexibility, robustness or redundancy (Gholami-Zanjani, Klibi, Jabalameli, & Pishvaee, 2021). Sustainable supply chain The globalization of supply chains has increased the number of network units and transportation between them, and has led to more greenhouse gas emissions including carbon dioxide emissions, and energy consumption. Therefore, in order to design the supply chain in the future, some necessary measures must be taken, which include adopting a sustainable approach, efficient in energy consumption, reliable and resistant to disruption conditions (Lotfi, Mehrjerdi, Pishvaee, Sadeghieh, & Weber, 2021). Also, other objectives such as environmental impacts, including carbon dioxide emissions and energy consumption, and social welfare have been added to the literature to consider the sustainability problem more comprehensively (Kadambala, Subramanian, Tiwari, Abdulrahman, & Liu, 2017). Research Methodology This research is applicable-developmental in terms of purpose, because it seeks to design a suitable Lars model for the service supply chain. In this regard, by using the theme analysis method, the structure of the researches in each of the lean, agile, resilient and sustainable supply chain approaches were evaluated, and then the most important indicators were identified in accordance with the studies of the previous researches. After the evaluation, the influence nature of these indicators was determined using Dimtel method. Finally, structural equation modeling was used to present the Lars supply chain framework; so that this framework will show the implementation indicators of Lars supply chain and the great effect of these indicators. In the following, the steps of this research and the methods used were explained. Research Findings The findings showed that the Lars supply chain dimensions include the dimensions of sustainable supply chain, resilient supply chain, agile supply chain, lean supply chain, along with 16 indicators of supplier management, supporting suppliers, multiple distribution channels, waste elimination, timely production, logistics management, continuous improvement, flexibility, competence, speed, communication with customers, responsiveness, agility, economic, environmental, and social. Conclusion The aim of the current research is to design a causal model of factors affecting the Lars supply chain. In order to achieve the goal of the research, after reviewing the literature and the background of the research, 16 factors affecting the Lars supply chain were identified in the form of 4 dimensions. In the following, these indicators were evaluated using Dimtel's method to be effective or influential, and then the relationships of these indicators were drawn in the Cartesian coordinate system. Finally, the conceptual model of the Lares supply chain was modeled according to the different dimensions of this chain, and the indicators were evaluated as items in this conceptual model. Then the structural equation method was used to quantitatively evaluate this model. The results showed that sustainability is directly related to communication with the external environment and process and production management. Also, sustainability will directly and indirectly affect the supply chain design. The results of this research are aligned with the results of Khan et al, (2022), Aityassine et al, (2022), Piya et al, (2022), Kazancoglua et al, (2022), Juan, (2022), and Hung, Salehi & Ostvar (2022). The results of this research show that economic indicators, agility, continuous improvement, and flexibility are, respectively, the most influential indicators; and supplier management is the most influential indicator of the Lars supply chain. On the other hand, the multiple distribution channel index, speed, and economic factors are, respectively, the most influential index among the indicators that management decisions in other indicators will have the greatest impact on this aspect of the Lars supply chain, and the management and performance of this index will be in proportion to the performance and impact of other indicators, and the results of the above researches are in line with the confirmation of the results of the present research. Also, Shamout (2019) showed in his research that supply chain analysis has a significant effect on supply chain innovation, but does not have a significant effect on its strength. But supply chain innovation has a significant effect on robustness. In other words, supply chain innovation can play a mediating role in the relationship between supply chain analysis and robustness. Tarafdar & Qrunfleh (2017) showed that chain agility has a significant mediating role. The results of their research confirm the results of the present research. The evaluation of the indicators showed that indicators such as economic index, agility, continuous improvement and flexibility are the indicators that have the greatest impact on other indicators, so according to the obtained results, it is suggested that by managing these indicators, other indicators and their performance can be better evaluated and predicted. In other words, if the economic criteria are at a suitable point in the Lars supply chain, the agility of this supply chain will be maintained, and at the same time, continuous improvement will be created along the supply chain, and it can be hoped that other criteria and indicators of the supply chain Lars will operate well, and so the Lars supply chain will continue to operate with a good performance and will have a promising result. According to the factor loadings of the presented conceptual model, it can be concluded that the indicators of these criteria can play a significant role in the design of the Lars supply chain, and it is suggested that each of the criteria be properly managed and planned in order to achieve the goals. For future researches, it is suggested to use other methods such as fuzzy cognitive map, SD, or interpretive structural modeling to draw the conceptual model of the research and examine how the variables influence each other and compare the results with the results of the current research. Also, researchers can implement in other industries.        

Abstract Abstract The aim of this study is "The Effect of Electronic Banking Advertisements on Customer Brand Loyalty with Emphasis on the Mediating Role of Perceived Value in the Shahr bank". To achieve this goal, a statistical population consisting of the Shahr bank customers in Tehran and a sample of 350 people were studied using a convenience (non-probability) method. Data collection was carried out by a questionnaire whose validity was confirmed by experts and its reliability by Cronbach's alpha coefficient. SmartPLS3 statistical software was used to analyze data and test hypotheses. This is a descriptive-survey and applicable research, and a correlational type in terms of relationships between variables. The findings of this study show that. Electronic advertising has a positive and significant effect on customer loyalty to the bank brand, and perceived value also plays a mediating role in the relationship between banking electronic advertising and customer loyalty to the brand. Introduction The growing trend of information technology in banking and other businesses has led to the computerization of banking and other business transactions. This information technology-based development has created new ways for business organizations to communicate with their customers, which supports the improvement of banking and financial services (Raza et al., 2020). Banks and financial institutions started the business of “home banking” through touch phones in the 1970s, and cable television was considered an ideal tool for home banking in the 1980s (Yang et al., 2023). Electronic banking is an innovative method for banks and financial institutions. It offers several services such as accessing their account balance, transferring funds from one account to another, paying various bills, purchasing goods and services without cash, and sending checks to its customers (Ayinaddis et al., 2023). Banks and financial institutions allow users to conduct financial transactions digitally rather than physically, which can increase e-customer satisfaction and loyalty to increase the perceived value of the business. E-banking is transforming the financial services sector by promoting innovation, fostering growth, and enhancing domestic and foreign competition (Yang et al., 2023). Advertising can create value for the intended brand, which can also increase customer loyalty and satisfaction. The term brand is considered a complex concept for marketers. Some perceptions imply the ratio of what is received in a transaction versus what is paid. Traditional perceptions of value have been largely self-centered, passive, and randomly determined (Salameh et al., 2022). Value is actually a unique concept composed of quality and satisfaction. Perceived value is the core driver for delivering the right products and services to the right customers at the right time and in the right way. Perceived value also provides organizations with a relative value-price opportunity. The role of perceived value has increasingly attracted the attention of customers and marketers because customer perceived value is one of the most powerful forces in today's market (Norouzi et al., 2021). Theoretical framework of the study The concept of customer brand loyalty One of the most famous and perhaps most important marketing concepts today, which was first formulated in the 1980s, is the concept of customer brand loyalty. However, the emergence of this concept for marketing professionals includes both positive and negative aspects. The positive aspect is that customer loyalty to the brand highlights and emphasizes the importance of the brand in the marketing strategy and maintains its focus on research activities and ultimately responding to the demands of the organization's top management (Gao et al., 2024). However, its negative aspect is that it has been presented in various ways and with different purposes, and there is still no single approach to the method of introducing, depicting and measuring customer loyalty to the brand. Perceived value Customer perceived value is one of the prerequisites for customer satisfaction, trust, commitment and customer loyalty. Perceived value is defined as the ratio of perceived benefits to perceived disadvantages. Perceived disadvantages are all the costs that the buyer faces when making a purchase, including items such as purchase price, acquisition costs, transportation, installation, ordering, repair and maintenance, risk of failure or poor performance. Perceived benefits are a combination of physical features, service features, and technical support associated with using a product. Customer perceived value is defined as a customer’s comprehensive assessment of the desirability of a product or service based on their perceptions of what they have gained and what they have lost (Asgarnezhad et al., 2019). Perceived value has been identified as one of the main predictors of customer loyalty and all dimensions of loyalty, including word-of-mouth, repurchase intention, and price insensitivity. The higher the perceived value of a service, especially in service industries, the greater is the customer loyalty (Rezaei et al., 2016). Electronic Banking Internet banking is actually a virtual, 24-hour branch of a bank that allows customers to make financial transactions regardless of time and place. Internet banking does not just include simple web pages used solely for information purposes. It is a two-way transaction system and a bank branch in the home of each customer. With the growth of the Internet in the world, the expansion of financial services is also obvious. Internet banking allows customers to carry out all their banking transactions without using cash, with just a click of the mouse button at home or at work. Internet banking is the performance of financial activities and transactions using the Internet through the bank's website; or in other words, it is the provision of banking services through a personal computer at home or at work, without the need to visit bank branches. Therefore, success in Internet banking requires providing financial services that are tailored to the needs, preferences, and expected quality of customers. Online banking is the format for conducting monetary and financial transactions in the digital age. The necessity of digital transformation in the banking system and financial management is the use of online banking (Siamul et al., 2025).  Research Method The present study is a descriptive-survey type of correlation that aims to investigate the effect of electronic banking advertising on customer loyalty to the brand with the role of perceived value mediator in the Shahr bank. First, by explaining the theoretical foundations of the research and describing the existing conditions by designing and distributing a questionnaire, the necessary information was collected and then analyzed by statistical software. Given that the purpose of this study is to investigate the effect of electronic banking advertising on customer loyalty to the brand with the role of perceived value mediator in the Shahr bank, this research can be considered applicable. The statistical population of the study consists of the Shahr bank customers in Tehran, from which a sample of 350 people was selected by a convenience (non-probability) method, and a questionnaire was distributed among them. In this study, structural equation modeling using partial least squares and PLS software was used to test the hypotheses and model accuracy. PLS is a variance-based approach that requires fewer conditions compared to similar structural equation techniques such as LISREL and EMON, and its main advantage is that it requires fewer samples compared to LISREL modeling. Research findings Modeling in PLS is carried out in two stages. In the first stage, the measurement model should be examined through reliability and validity analyses; and in the second stage, the structural model was analyzed by estimating the path between variables and determining the model fit indices. According to the results obtained from the path coefficient (indicating the intensity and type of relationship between two latent variables) and the t-statistic, electronic advertising has a positive and significant effect on customer loyalty to the bank brand, and perceived value plays a mediating role in the relationship between electronic banking advertising and customer loyalty to the brand. Discussion and Conclusion In this study, the results of data analysis indicate that all the main hypotheses of the study have been confirmed; meaning that the positive and significant effect of electronic banking advertising on both customers perceived value and their loyalty to the bank brand, as well as the mediating role of perceived value in this relationship, has been statistically proven. These findings indicate that the more targeted, transparent, and with an emphasis on the practical and emotional benefits of services, the more value customers receive from interacting with the bank increases, which in turn leads to strengthening their commitment and loyalty to the bank brand. These results are in line with the studies of Vajdani et al., (2024) and Wu & Wang (2024). Zyad Alzaydi (2023) also stated that branding plays a key role in achieving customer loyalty in the Saudi banking sector. Khajeh Saeed et al., (2022) also reached results in line with the results of the present study. In further analysis of the findings, it can be said that improving the quality and content of e-banking service advertisements leads to increased trust, transparency, and perception of innovation by customers, which directly increases perceived value in functional (convenience, security, speed) and emotional (satisfaction, confidence, sense of worth). This improvement in perceived value, in turn, strengthens the likelihood of re-selecting bank services and recommending the brand to others; a topic that has also been confirmed in previous studies. In the competitive environment of Iranian banks, where most services are very similar, the orientation of advertising towards explaining benefits and unique user experience can create competitive advantage and sustainable loyalty. Also, the mediating role of perceived value showed that even if advertisements are extensive, without real promotion of customer expected values (including efficiency, security and usefulness), a sustainable impact on loyalty will not be formed; therefore, banks should focus on the quality of messages and their suitability with the real needs of customers in addition to the volume of advertisements. In conclusion, the research findings highlight the necessity of a strategic link between effective e-banking advertisements and the promotion of real customer experiences; in such a way that perceived value, as the leader of loyalty motivations, is continuously strengthened; and banks move beyond mere information provision and focus on creating practical and emotional added value for customers. This approach is considered vital, especially for Iranian banks, given the digital transformation and the need for brand differentiation.

Abstract Abstract The aim of the current research is to design a joint branding model using the structural-interpretive modeling method. The research method of this study is exploratory in terms of nature, with a mixed approach. In order to collect data and identify factors; the method of content analysis and review of related texts was used, as well as interviews with ten selected experts using a non-random method and snowball technique to achieve theoretical saturation. To achieve the research findings, a matrix questionnaire was constructed. Findings were compiled to determine the relationships between indicators. The data obtained from the questionnaire were analyzed using structural-interpretive modeling and depicted on three levels in an interactive network, as a result of which the factor of banking dynamics was placed at the highest level. Also, the amount of influence and the degree of dependence of these factors on each other were investigated in the matrix of influence-dependency. According to the results, the dynamic index of banking and target customers are located in the matrix of influence-dependency in the dependent area, i.e., the highest degree of dependency; and the least influence and service evaluation factors, human force dynamics, organization development, mental norm are located in the connected area, i.e., the highest power of influence and the highest interdependence. Introduction One of the latest brand development approaches is co-branding. Based on the studies, it is expected that joint marketing measures will increase significantly in the coming years. Surveys show that 38% of companies carry out joint marketing measures, including joint branding, at an increasing rate (Nygaard & Dahlstrom, 2023). Rao & Ruekert (2017) defined co-branding as the integration of two or more products in short-term or long-term periods. Of course, these researchers redefined their definition of joint branding in the following years. According to them, this method occurs when two or more existing and independent brands join together in a common product or are marketed together in a similar form (Abbaszadeh et al, 2018). Considering that the current research is about joint branding in the banking industry, the banking industry is one of the businesses that have grown rapidly in recent times, so that accessibility of the banks is easier than other trades, in any area of an urban environment, and this is also due to many researches that have been done in the field of availability of bank branches; but why should there be so many branches of this industry in a human society, while in modern banking, borders have disappeared? Now, due to the merger of five banks and military financial institutions (Ansar, Qavamin, Kotsar, Hekmat and Mehr Eghtesad Iran) in Sepeh Bank, one of the main concerns is creating a suitable image of the Sepeh brand and presenting a common model for the brand, because neglection to this may have negative consequences on other bank businesses, such as the decrease in deposits, operating profit and the bank's rating in this industry. Based on the mentioned cases, what is the basic problem of the current research of the joint branding model in the banking industry? Theoretical framework Despite the different definitions that have been presented in relation to branding, all experts agree that the brand is a very important intangible asset for organizations, and plays an effective role in gaining a competitive advantage (Ruschman, 2020). A brand enables an organization to differentiate its market offerings from competitors. Consequently, strong brands are very important for any type of product, service or organization, including those in the public sector (Drostkar et al, 2022). Co-branding is a kind of strategic alliance between parties. In recent years, creating strategic alliances with co-branding has become common in many industries, because the co-branding strategy has the ability to achieve significant synergy, which focuses on the unique strengths of each of the co-brands. Rao and Ruckert, at the beginning of their research, defined co-branding as the integration of two or more products in short-term or long-term periods (Labrović et al, 2021).  Research methodology The purpose of this research is to provide a local model of joint branding in the banking industry. Therefore, in terms of nature, the research is exploratory with a mixed approach. Interpretive Structural Modeling (ISM) has been used to determine the sequence and relationships between the identified elements. In order to collect data and identify factors, the method of content analysis and review of sources, books and interviews with participants, which include university experts and organization experts who have a series of characteristics such as doctorate degrees and scientific productions (books, articles, etc.) and the background of high performance in this field was used. Also, non-random sampling and snowball technique were used to select experts until theoretical saturation was achieved. MATLAB software was used for interpretive structural modeling (ISM). Research findings In this section, the relationships between research factors were analyzed in pairs and couples, and led to structural-interpretive modeling and the use of the relationship between experts' concepts, using the following symbols to determine the relationships between factors. The structural interaction matrix itself is composed of research dimensions and factors and their comparison through four modes of concept relations. The final accessibility matrix (FRM) is formed by applying the existing multiplicative relationships among the factors. In this way, the next stage of the implementation of the ISM methodology can be completed. In this matrix, secondary relationships between factors are controlled. In order to analyze the power of penetration and the degree of dependence (MICMAK), the factors are classified into four groups. In order to calculate the influence of factors, it is enough to add the number of 1's in each row of the final access matrix.  Conclusion In this research, joint branding factors have been identified. After identifying the factors through the achievement matrix, an attempt was made to examine the factors affecting co-branding. Based on the results of the research, the factor of banking dynamics is located at the first level, the factor of target customers at the second level, and the factors of service evaluation factors, human force dynamics, organization development, and mental norm at the third level. Co-branding to support a new product or service has emerged as a legitimate and legal way to develop a brand, especially to establish or maintain competitive advantages, which represents the long-term cooperation strategy of a product by two brands. Considering the increasing intensity of competition in the market, joint branding between companies has become a tool to achieve the interests of both sides of strategic partners. Co-branding can influence consumer perceptions such as trust, confidence, satisfaction and commitment. Therefore, it should be noted that the relationship between partners can be considered as a process, so that the common brand is trusted when that brand is similar to the consumer. This reduces the feeling of discomfort and inconsistency, thereby making the brand more reliable. The ability to trust the product will bring consumer satisfaction, and the consumer in the next step will commit to the brand. And in order to maintain this process, it is necessary to create a competitive advantage by adopting creative and innovative strategies, and competitors cannot imitate it.

Abstract The profit predictability of the company's future performance is based on accounting information. It uses the information obtained from these forecasts to determine the company's value from the point of view of users of financial statements. Therefore, the current research was conducted with the aim of designing and explaining the profit predictability assessment Model in Companies active in the financial industry. This qualitative research was compiled using thematic analysis; In this research, by using semi-structured interviews with 18 experts in the field of accounting, as well as reviewing related researches, the findings were combined and the present model was designed. Based on this, by analyzing the content of the interviews and researches using the MaxQda2020 software, the relevant dimensions were extracted and the importance and priority of each was determined using Shannon's entropy technique. Based on the research approach, 28 components were extracted and the company's information environment, deviation analysis, profit variability and financial leverage analysis obtained the highest coefficient of importance using Shannon's entropy technique. In this research, the profit predictability evaluation model was presented in the form of 28 components. Since a comprehensive model has not been provided to evaluate the predictability of profits, this research can be useful in the direction of emerging challenges, profitability and improving the ability of Companies active in the financial industry to create scenarios. Extended Abstract Introduction Profit is one of the most prominent and significant items on financial statements that attracts financial statements users' attention. Investors, lenders, managers, company employees, analysts, the government, and other users of financial statements use profit as a basis for investment decisions, loan granting, interest payment policies, evaluating companies, calculating taxes, and other decisions pertaining to the company (Rauličkis et al., 2019). Profit, from an informational standpoint, represents the result of economic activities. Numerous investors favor companies with a consistent, profitable trend. Moreover, investors believe companies with fluctuating profits are riskier than those with stable profits (Gedviliene et al., 2018). In other words, profit is one of the most accurate measures of economic activity. Shareholders, as the most significant group of users of financial statements, scrutinize profit-related information. Investment, decision-making, and forecasting are guided by profit. Among the qualitative characteristics of profit are its sustainability, predictability, relevance, timeliness, and conservatism (Cheema et al., 2017). Forecasting is a crucial aspect of economic decision-making. Investors, creditors, management, and others rely on predictions and expectations in making economic decisions. Since investors and financial analysts use profit as a primary criterion for evaluating companies, they measure future profitability when deciding whether or not to retain or sell their shares. They evaluate the status of a company based on profit projections (Tian et al., 2021). The current and future profitability level of companies is the most important criterion for investors when selecting investment companies, so investors make decisions regarding various investment strategies based primarily on profitability. From the perspective of users of financial statements, the ability to predict the company's future performance based on accounting data and to utilize the information derived from these forecasts is crucial for determining the company's value. Therefore, accounting profit and its related components are among the factors individuals consider when making decisions. Information about the predictability of future profits is essential and helps forecast future profits. Based on the above background, the present research aims to design and explain a model for assessing the predictability of profit in companies active in the financial industry between 2011 and 2019. Valuable information regarding the predictability of profits aids in predicting future profits. This information typically covers the existing literature and research that determines a company's value from the financial statement users' perspective. Therefore, accounting profit and its related components are among the factors individuals consider when making decisions. The information related to the profit predictability is important and helps the future profit predict. The current study presents a useful model for developing research literature and will close the existing research gap. In this regard, this study sought to answer the following research question: How do companies active in the financial industry evaluate the predictability of their future profits? Theoretical framework Profit prediction is the ability of current profit to predict future profit in the short and long term. Profit projections provide management with information about the future of the company. Profit volatility is one of the factors that should be considered in profit forecasting; predictability has an inverse relationship with profit volatility (Cai et al., 2017). This criterion is defined as profit's ability to predict its own future. Predictability is one of the relevant components of the Financial Accounting Standards Board's (FASB) theoretical framework. Therefore, profit is desirable from the standard-setter's perspective (Ali et al., 2017). As a quality and time series characteristic of profit, profit predictability is the capacity of current profit to predict future short- and long-term profit. Among the factors that influence the relationship between volatility and profit predictability are economic and accounting factors (Skvarciany & Simanavičiūtė, 2018). Profitability predictability is one of the quality characteristics that enhance the relevance of accounting information. Examining the relevant literature reveals that numerous researchers have included predictability as one of the characteristics of profit time series. Several analysts view predictability as a distinct indicator of profit quality. On the other hand, others place predictability under a separate heading referred to as qualitative characteristics of accounting information criteria. Methodology The current research is based on qualitative research in the inductive paradigm and is applicable in terms of purpose. This study's statistical population consisted of accounting experts, including professors of the disciplines above, and managers and business owners in the financial sector. Per the study's objectives, 18 individuals were sampled in a targeted manner using the snowball method. The sample size was determined using the principle of theoretical saturation so that no new factors were observed after interviewing the 16th and 17th individuals, and the process of interviewing the 18th individuals was completed. Face-to-face interviews with open-ended questions were conducted, and then, using the coding procedure, 28 factors affecting the assessment of profit predictability were identified. MAXQDA 2020 software was utilized for coding. To ensure the coding and concept extraction accuracy, the codes obtained from the interviews were provided to the interviewees once more to obtain their approval of the extracted codes. The objective was to discover the interviewee's main point. In addition to expert interviews, domestic and international publication databases were examined in this study, focusing on articles relating to the ability to predict profit based on the reflection of previous studies in articles published between 2010 and 2022. Discussion and Results This research identified and categorized five concepts and 28 components of profit predictability evaluation based on interviews analyzed using the method of theme analysis and a review of previous research. This stage's findings indicate that such a systematic and exhaustive study has not yet been conducted. Each study has focused on a particular aspect and has not been presented comprehensively and systematically. Components of profit predictability evaluation include profit smoothing, company information environment, deviation analysis, disclosure requirement, biases, company market value, profit process complexity, environmental consciousness, volatility, time series techniques, unsystematic risk, expectations analysis, profit variability, operating cycle length, managers' opportunism, news accumulation, point forecasting, financial statement items, information accumulation, financial leverage analysis, forecasting horizon, past forecasting behaviors, company size, identification of stable components, and information symmetry are all factors that can influence the accuracy of projections, where conservatism is the estimation of items and the quality of matching income and expenses. Conclusion Accounting and the preparation of financial statements serve to provide decision-makers with useful information. The capacity to predict financial statement items is one manifestation of this usefulness. Investors, managers, financial analysts, researchers, and lenders have long been interested in forecasting accounting profit and its impact on economic decisions. This interest is a result of the use of profit in stock evaluation models, which contributes to the efficient functioning of the capital market, solvency assessment, risk assessment, economic unit performance evaluation, and the use of profit forecasts in the discussion of profit smoothing for management decisions, as well as the use in economic, financial, and accounting research. This study was conducted to design and explain the model for evaluating the ability to predict profit. According to the results of Shannon's entropy applied to the company's information environment, deviation analysis, profit variability, and financial leverage analysis are the most crucial factors. The subject of the research was the absence of a comprehensive model in the studied society and the neglect of the study gap's effective components, which prompted the researchers to develop a model. In this study, through expert interviews and a review of prior research, new dimensions of profit predictability were introduced with a more detailed and accurate look. Finally, a stage model was presented to design the model of this phenomenon according to the native conditions of the country, thereby reducing the amount of dispersion among previous research findings and emphasizing coherence and integration. This study aimed to fill the gap left by previous research to provide a comprehensive and step-by-step model for evaluating the predictability of profit. In their research, Alarussi and Alhaderi (2018) mentioned company size and financial leverage. They concluded that there is a positive relationship between company size and profitability and a negative relationship between leverage and profitability, which support the findings of this study. Moreover, the research findings of Yohn (2018) are consistent with the use of financial statement information. Fazlzadeh et al. (2019) referenced the investigation of the effect of news on profitability and profit forecasting, which is consistent with the present study's findings. In this regard, conducting a comprehensive analysis of financial leverage and the possibility of enhancing profit predictability through its application is suggested. In leveraged companies, profitability can be achieved by analyzing and matching the return on equity to the amount of debt. If the shareholders' rights exceed the company's debt, the company's use of debt may have a negative impact on its profitability. A weak and ineffective information environment decreases participation and diminishes profits. It also affects expenses and income compared to when sufficient and complete information is available. It is recommended that the company's information environment be improved and information circulation is accelerated. The more the deviations from the profit forecast are reduced, and the management can correctly identify and direct these deviations, the better the company's image will be, and the more accurately the profit will be reflected.  

Abstract Abstract The purpose of this research is to design a model to evaluate the level of digitization of the innovation process centered on artificial intelligence in knowledge-based companies, so that the digital maturity of the innovation process in an organization can be measured. The results of 188 indicators were distributed in the form of a 5-point Likert questionnaire and by Delphi method in two times among 18 experts in this field. The result of the work was 5 components as input to the model, which was sent in the form of a questionnaire to 230 knowledge-based companies of Pardis Technology Park. 198 companies completed it and sent it back. From this number of samples, 150 data were separated for training data and 48 data as model test based on a random function. In the last stage, i.e. modeling, the adaptive neural-fuzzy inference method was used for the model. The method of grid separation or lookup table (PG) in MATLAB 2023 software was used to evaluate the performance of the model using root mean square, error (RMSE) and relative error (E). This research was able to provide an intelligent model with a very low error. As a result, it was able to achieve effective indicators in the degree of digitization of the innovation process. Extended Abstract                                           Introduction An innovation process, whether in its general form from the stage of idea formation to the stage of entering the market and commercialization, or in each of the parts separately, must be in such a way that it has the most productivity (efficiency and effectiveness combined during the process). Moving in this direction reduces financial, time and human costs. The relevance of innovation to ensure the competitiveness of companies has been confirmed among researchers and professionals (Schiuma, 2012). Also, innovation is a risky process that requires resources, competence, culture and attitudes that cannot even be promoted and managed easily (D'Este et al., 2012). Due to the mismatch in digital skills and awareness, employees are not able to understand the reasons and potential of implementing new technology and displacement. Therefore, the next challenge in the digital ecosystem is to promote and define conditions, roadmaps and management models for implementing digital innovation strategies, for managing digital knowledge and fostering continuous innovation (Nonaka & Takeuch, 2019). Innovation processes rely on external institutions, i.e. innovation intermediaries, research and development laboratories, or innovation centers (Corre & Mischke, 2005). Global and virtual competition, as well as the rapid development of digital technologies and solutions, raise efficiency standards, increase the speed of market dynamics, and reduce product life cycles (Schiuma, 2012). Each actor involved in innovation must be aware of the organization's vision, goals, and strategies in order to effectively contribute and generate value (Lianto et al., 2018; Nonaka & Takeuchi, 2019). To understand and effectively manage technology, codification and exploitation of generated knowledge, specialized skills and new governance models are required (Joshi et al., 2010). Theoretical framework Digital innovation process Completely rebuilding business around new opportunities and new demands is possible through digital technology. Eyring et al., (2022) in an article, mentioned these processes under the title of digitization. Digitization is a useful and effective necessity. It provides a vision of digital assets that offer opportunities to business or even to industry. Digitization sometimes reconceptualizes their products by serving a business model through their artifacts. Digital transformation describes a sometimes broad process of change that may have multiple goals, while innovation focuses on the moment of invention and the implementation of that invention. Innovation may cause fundamental changes and vice versa, but both are not synonymous. Advancement of digitization and digital changes may be started as an innovation action. This may be catalyzed by new business opportunities, but ultimately must reach beyond the innovation function to reshape the entire organization (Globe, 2018). Businesses need a dynamic tool to support their digital innovation management efforts. Artificial intelligence Artificial intelligence is sometimes called machine intelligence, refers to the intelligence shown by machines in various situations, which is in contrast to the natural intelligence in humans; in other words, artificial intelligence refers to systems that can react similar to human intelligent behavior, including understanding complex situations, simulating human thinking processes and reasoning methods and successfully responding to them, learning and having the ability to acquire knowledge and reason to solve problems (Teece et al., 1997). The scientific study of algorithms and statistical models are used by computer systems that use patterns and inference to perform tasks rather than using clear instructions. Machine learning is the science of making computers learn about a specific subject without the need for an explicit program. As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or "training data" in order to predict or make decisions without overt planning (Du et al., 2019). Research methodology The sampling method was carried out in the form of theoretical saturation at the stage that no new material was obtained from the articles as a new index, and other indices were common in meaning and concept. In this study, Pardis Technology Park companies and its branches, including Azadi Factory and Hi-Wi centers were considered. Because these companies are active within the innovation ecosystem, they are familiar with the literature in this field, which facilitated the completion of the questionnaires. Questionnaires were sent through the Press Line program and within social network groups, and for some through phone and email. All these companies had the approval of knowledge-based company level 1 to 3 and the completers had educational qualifications of at least bachelor's degree to doctorate. After examining the opinions of the experts, it was determined that a consensus was reached by having an average above 4 for all indicators. In adaptive neural fuzzy inference, the network separation method or lookup table (PG) was used in MATLAB 2023 software. In this method, the number of membership functions is 5 functions representing very low, low, medium, high and very high. Research findings A number of 290 articles were selected among the whole, which had the citation higher than 1. The texts of these 290 articles were studied, and finally 149 articles related to the selected topic and the literature and background of this research were used. In fact, the general goal and main question of this research was to model the innovation process centered on artificial intelligence, was carried out successfully with a very low model error during the test. As a result of reviewing these 149 articles and specialized texts, 189 indicators related to the issue of digital innovation were extracted. As the next goal, 42 main and effective indicators were obtained from this literature Conclusion In addition to the main goal and special goals of the research that were achieved through the presentation of the model, by examining the relationships obtained according to the surface diagrams, the sensitivity and impact of each component can be determined. "Variables" in the output, i.e. the degree of digitalization of the innovation process were analyzed. According to the findings and comparing them with the background and findings of previous researches, it can be stated that the sensitivity rate and impact of input 1, that is, the benefit of digital technologies based on artificial intelligence, is more than the other 4 inputs on the digital level of innovation process, in the sense that having these technologies is the main axis. It is less effective to benefit from the output; and if it is not present in the laboratory, from other components. The lowest sensitivity and impact on the output in the model belonging to the third and fourth components, i.e. network and smart learning, were identified next to the component of benefiting from technologies, and this could mean that in the conditions of benefiting from digital technologies, according to the findings of the research, it is suggested to design the intelligent model of innovation in different industries and for each industry separately for future research. In the literature, researchers encountered a wide range of these digital innovation models such as banking, schools and institutions of higher education, health, etc. The second suggestion is that future researches can design a separate model for each of the input components so that the input component of this research is placed in the output position and the indicators determined in this research are used as their input. It is possible to design 5 other models according to the five components of this research, and connecting these models to develop a final and macro model can bring new achievements.

Abstract Abstract The purpose of this research is to provide a comprehensive framework for improving the export performance of small and medium Iranian companies. According to its purpose, the research method is applicable, and in terms of implementation method, it is mixed (qualitative-quantitative), and descriptive-correlative in nature. The statistical population in the qualitative section includes 20 academic experts in the field of export, chosen by judgmental and snowball sampling methods; and the statistical population in the quantitative section includes 217 people from all personnel of small and medium export companies in Tehran province, chosen by simple random sampling. Data collection tools include semi-structured interviews in the qualitative part, and researcher-made questionnaires in the quantitative part. Fuzzy Delphi method was used in the analysis of qualitative part data, and SPSS and Lisrel software were used in quantitative part. The statistical results showed that 78 variables (open codes) are effective in penetrating international markets, and statistically, all of them are effective. There are 33 factors influencing international market penetration, and all of them are statistically significant. The final components and dimensions of the model of the influencing components of penetration into the international market were identified after applying statistical tests (confirmatory factor analysis). According to the research model, penetrating the international market includes 5 dimensions, 33 components, and 75 indicators; and compared to the initial research model including 5 dimensions, 33 components, and 78 indicators; 3 indicators were removed in the final model and the rest of the components and dimensions were confirmed in the final model while having a suitable working load. Introduction The issue of competitiveness is one of the basic issues for which there are various criteria to evaluate. Competitiveness can help policymakers to evaluate the country's foreign trade situation. Competitiveness means the possibility of achieving a suitable position and stability in a regional market. Creating and supporting small and medium enterprises is one of the basic priorities in economic development programs in many developed and developing countries. Small and medium enterprises play an important role in creating employment and providing a suitable platform for innovation and increasing exports (Asgari, 2019). Small and medium industries are the backbone of the economy and have created more than half of employment and 80% of employment growth in the last decade. Therefore, it is necessary to evaluate the value of approaches such as strategic management to improve the performance of these companies in entering regional markets (Hatfi & Azari, 2021). On the other hand, due to the fact that a major share of Iran's exports belongs to oil exports, in recent years and specifically since the beginning of the first development plan, the planners' attention has been directed to the expansion policies of non-oil exports. For example, the establishment of free trade zones, the amendment of customs laws and regulations, and the establishment of private banks all indicate the direction of following the policy of encouraging exports and moving towards sustainable economic development (Hosseinnejad & Shujaei Fard, 2021). Considering the above, the main research question is as follows: What is the comprehensive framework for improving the export performance of Iranian small and medium companies? Theoretical Framework Export Export literally means the transfer of goods or the sending of goods from one place to another, whether inside or from inside of the country. In other words, exporting is communicating and working with professional markets and market specialists on the other side of the borders. Export is the starting point of communication with others, and export is also achieved to earn foreign exchange and help establish trade balance and create economic balance (Akhlaghi et al, 2019). Export performance Export performance is a variable that has attracted the attention of researchers in the last few decades. Export companies set goals to internationalize and sell their products or services in other countries; the amount of activities as well as the success and failure during this path and the results of achieving those goals are called export performance. Nowadays the field of export has attracted the attention of companies with an increasing speed and companies are moving towards globalization and seeking power. (Bashirkhodaparasti & Marzieh, 2021). Small and medium companies Small and medium enterprises (SME) are known as essential components of national development in developed and developing countries (Abrie & Doussy, 2011). This sub- part of economy in the worldwide has been significantly relevant to the employment increasing, poverty removing, equitable distribution of resources, redistribution of income, technical and technological innovation, development of entrepreneurial skills, uniform industrial and economic dispersion, and general improvement of the living standards of the people of an economic region. In addition to this, it has been mentioned as a strategy in providing food security and encouraging rapid industrialization and the reversal of rural-to-urban migration (Oyekanmi, 2003). Tajamir et al, (2024) in a research presented the model of marketing capacities in Khuzestan steel industry and its effect on financial performance in the direction of investment development. The results obtained from the analysis of theoretical bases and research interviews led to the final model of marketing capacities, which has 7 main categories of product, distribution network, market, customer, analysis of competitors, advertising, and brand. The findings indicated a good fit of the proposed model. Martos et al, (2023) in a research, investigated innovation and the legal form of the organization as factors that can affect this relationship, with the aim of determining the impact of corporate social responsibility efforts on their export performance. The results show that innovation acts as a mediator in this relationship. It was also found that companies that adopt associative legal forms (i.e. cooperatives) benefit more from their social responsibility practices than companies that adopt non-associative legal forms. Previous findings on the relationship between corporate social responsibility and profitability show that some aspects need to be clarified about this binomial. Contribute to this body of research specifically on exporters helps to understand the role that CSR may play in improving export performance. Research methodology According to its purpose, the research method is applicable, and in terms of implementation method, it is mixed (qualitative-quantitative), and descriptive-correlative in nature. The statistical population in the qualitative section includes 20 academic experts in the field of export, chosen by judgmental and snowball sampling methods; and the statistical population in the quantitative section includes 217 people from all personnel of small and medium export companies in Tehran province, chosen by simple random sampling. Data collection tools include semi-structured interviews in the qualitative part, and researcher-made questionnaires in the quantitative part. Research findings Fuzzy Delphi method was used in the analysis of qualitative part data, and SPSS and Lisrel software were used in quantitative part. The statistical results showed that 78 variables (open codes) are effective in penetrating international markets, and statistically, all of them are effective. There are 33 factors influencing international market penetration, and all of them are statistically significant. The final components and dimensions of the model of the influencing components of penetration into the international market were identified after applying statistical tests (confirmatory factor analysis). According to the research model, penetrating the international market includes 5 dimensions, 33 components, and 75 indicators; and compared to the initial research model including 5 dimensions, 33 components, and 78 indicators; 3 indicators were removed in the final model and the rest of the components and dimensions were confirmed in the final model while having a suitable working load. Conclusion The current research was conducted with the aim of providing a comprehensive framework for improving the export performance of Iranian small and medium-sized companies. The results of this research are in agreement with the results of Tajamir et al, (2024), Rahimi Kalor & Marzieh (2023), Azimi & Hosseinpoor (2023), Hatfi & Azari (2021), Faryabi et al, (2021), Najafi et al, (2021), Bagherzadeh et al, (2021), Martos et al, (2023), İpek et al, (2023), Kupta & Chahan (2020), Judege et al, (2020), and Boso et al, (2019). Hatfi & Azari (2021) showed that marketing capabilities have a positive and significant effect on export performance and marketing communications. On the other hand, organizational innovation also affects marketing communications and marketing capabilities. In the end, it was concluded that organizational innovation does not directly affect export performance, but organizational innovation has a positive effect on marketing capabilities, and marketing capabilities also improve export performance. According to the results of the research, the following suggestions were presented: 1-Establishing integration in exporting companies, especially regarding the information systems and databases of the organization, can promote the creation, sharing, and storage of knowledge in the organization, and strengthen the knowledge consequences of knowledge management in improving the export performance of the organization.

Abstract Abstract
The purpose of this research is to identify the determining factors of green product branding, a model for Iran's food industry. The research method is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of implementation method, and survey-exploratory in terms of data collection method. The statistical population of the research in the qualitative part includes 15 managers of green brand food companies with master's and PhD educations in the field of management, agriculture and entrepreneurship, as well as professors of business management and environment at the university, who were selected for an interview by means of judgmental sampling. The statistical population in the quantitative part is the consumers of green products in the food industry; 384 people were selected using available sampling and answered the questions of the questionnaire. Interviews and questionnaires made by the researcher and taken from the qualitative section were used to collect information. In the qualitative section, the data obtained from the interviews were coded and analyzed in three main stages: open coding, axial coding, and selective coding. In the quantitative section, SPSS software was used for analysis and PLS was used for structural equations. The results in the qualitative section showed that 214 open codes, 85 concepts and 26 subcategories were identified and extracted from the conducted interviews. The results in the quantitative part showed that the model has a suitable fit and can be used for branding green products in the country's food industry.
Extended Abstract
Introduction
Today, in response to the increasing public interest in sustainable development, many companies have introduced green products. The characteristics of production and consumption of green products are in accordance with the concepts of economy, where waste reduction and environmental protection are the most important (Govindan & Hasanagic, 2018). In the economy, green products are increasingly popular with consumers and widely marketed. Selling green products creates domestic competition with non-green products. Green products usually have a higher quality level than non-green products. Due to the sustainable production method, green products have a higher production cost than non-green products (Shen et al, 2019). As a result, it is widely observed that green products are more expensive than non-green products (Basiri & Heydari, 2017). Consumers are also looking for a brand that has a strong planning strategy and methodology to achieve environmental sustainability in accordance with current and future regulatory guidelines and policies. Therefore, most business units have tried to incorporate sustainability into process and product or service design (Upadhyay & Kumar, 2020). Branding can be critical to a company's long-term success, especially for companies operating in markets with many clusters (many buyers and sellers) and few differentiated products. On the other hand, in recent years, climate changes along with increasing environmental awareness have changed consumers' purchasing decisions towards green and environmentally friendly products (Aivazidou et al, 2017). Green marketing is emerging as a popular advertising strategy due to increasing environmental concerns and awareness. In addition, in order to achieve a better understanding of the environmental movement of the target society, it is an important issue to test the attitude of the consumers of that country towards environmental issues and, as a result, their behavior (Mohammadi et al, 2022).
Based on this, the current research is looking for an answer to this question: What are the determining factors of branding green products, a model for Iran's food industry?
Theoretical Framework
brand
A brand is not only a symbol that distinguishes a product from others, but also includes all the features that come to mind when a buyer thinks of that brand. These characteristics are the objective, abstract, psychological and social characteristics of that product (Xiangbo et al, 2021). Green brands, green labels and characteristics of green environmental products create positive feelings in certain groups and consumers who know that a product is green and when it is better to use it. Natural brands and proper labeling are successful from a marketing point of view because of the positive overall image they create, and consumers tend to buy such products and therefore stick with them (Del Afruz et al, 2017).
Green marketing
The concept of green marketing is a business process that takes into account consumers' concerns about protecting the natural environment. Previously primarily based on environmental status, green marketing is becoming more sustainable in marketing efforts, with a primary focus on environmental and socio-economic status. However, the green market is defined as part of the market segments related to green consumption (Yoo et al, 2019).
Sandoughi et al, (2022) studied the modeling process of organic agricultural products market development in Iran with an interpretative structural approach. Based on the obtained results, the process model of organic agricultural products market development starts from the analysis of the current situation, setting goals and prospects, and ends with the stage of increasing consumption and capacity building in the market. This model can be used as a guide by policy makers and all organic field activists in various research, planning and implementation sectors.
 Sarkar et al, (2022) investigated environmental and economic sustainability through innovative green products with renewable production. The findings showed that highly innovative green products perform better than low innovative products when uncertainty in demand and supply is high. Furthermore, new green products should be introduced only when the expected benefits of the new products outweigh the losses of the existing products. New policy innovation with remanufacturing is cost-effective compared to traditional innovation policy.
Research methodology
According to its purpose, the research method is applicable; in terms of execution method, it is mixed (qualitative-quantitative); and in terms of data collection method, it is survey-exploratory. The statistical population of this research in the qualitative part is the managers of green brand food companies with master's and doctorate educations in the field of management, agriculture and entrepreneurship, as well as university professors of business management and environment. The statistical population in the quantitative section is the consumers of green products in this industry, which were considered as the sample size of 384 people using Cochran's formula and available sampling method. Collecting information in the qualitative part by the interview; and in the quantitative part of the research using the concepts obtained in the qualitative part, a questionnaire of 85 questions was used.
Research findings
In the qualitative section, the data obtained from the interviews were coded and analyzed in three main stages: open coding, axial coding, and selective coding. In the quantitative section, SPSS software was used for analysis and PLS was used for structural equations. The results in the qualitative section showed that 214 open codes, 85 concepts and 26 subcategories were identified and extracted from the conducted interviews. The results in the quantitative part showed that the model has a suitable fit and can be used for branding green products in the country's food industry.
Conclusion
The current research has been carried out with the aim of identifying the determining factors of green product branding, a model for Iran's food industry. The results of the present study are in agreement with the results of Sandoughi et al, (2022), Mohammadi et al, (2022), Sarkar et al, (2022), Jegatheesan et al, (2021), Mohammadi Far & Soleimani (2021), Pourjamshidi et al, (2020), Marvi et al, (2021), Pourjamshidi et al, (2021), and Tandon et al, (2016). Mohammadi Far & Soleimani (2021) investigated the design of a multi-level framework for the successful implementation of green marketing in food manufacturing companies. The findings of the model indicate that several factors influence the implementation of green marketing in a multidimensional and intertwined manner. These factors can be categorized in four levels. The fourth level factors form the most basic layer and include the penetration of belief in green marketing in the philosophy and vision of the company; the third level includes the support of senior managers and changes in the organization's internal procedures; the second level includes optimizing the organizational structure, improving the organizational culture, improving employees and managing the change process: and the first level, which was placed in the highest and most operational layer of the interpretive structural model hierarchy, includes changes in the marketing mix, understanding and implementing green marketing audits, and developing technology infrastructure of information.
According to the results obtained from the research, it is suggested:

Advertising programs should be developed to familiarize the general public with green products, features and benefits on the platform of social networks.
Human resource development programs and attention to the training of people in this field should be developed.
Selection of food industry experts and experts in the field of green products so that their experiences in the field of green products production can be used.
To improve the quality and safety of programs related to the production of green products and achieving health and management standards.

Abstract Abstract The purpose of this research is to design a model for improving the behavior of Iranian handicrafts e-commerce customers with the approach of improving customer trust. According to its purpose, the research method is applicable, and mixed (qualitative-quantitative) in terms of its implementation, and descriptive-exploratory in terms of data collection in the qualitative part. The statistical population of the research in the qualitative part includes 19 Iranian experts, including university professors in the fields of marketing management, handicrafts and senior managers of companies that export handicraft products, who through non-random sampling in the form of snowballs, were selected for interviews. The statistical population in the quantitative section includes 11 Iranian university professors in the fields of marketing management, handicrafts and senior managers of companies that export handicraft products, who were selected using non-probability sampling. A semi-structured interview was used to collect information in the qualitative section. To analyze the data in the qualitative part, first, content analysis was used to code the interviews, and in the quantitative part of the research, the self-interaction matrix was used for interpretive structural modeling (ISM). In the qualitative section, 16 sub-themes and seven main themes (indices) were extracted. In the quantitative part, a four-level model was obtained, and the most effective indicator of this model is electronic communication and interaction with customers. Also, its most effective indicator at the seventh level is confidence in purchasing. Therefore, 5 other criteria also play the role of interface factors in this model. Extended Abstract Introduction Today, advances in science and technology and the development of new technologies have created new competitive conditions for production and service organizations, so that quality and customer satisfaction and trust are the most important factors in global competition. Attracting and retaining customers in an organization is a category that is affected by various factors and conditions inside and outside the organization, the importance of which varies according to the type of organization and from one organization to another (Nguyen et al, 2020). Trust is defined as the trust of one party (trustee) to another person (trusted or trusted third party) (Yeon et al, 2019). Trust plays an important role in interactions and is important for companies and for developing relationships with consumers (Astono, 2021). Trust is the consumer's belief that the transaction will be carried out according to the plan. The way customers think about trust can be one-dimensional or multi-dimensional (Papas, 2018). On the other hand, in the late 1990s, the Internet promoted the formation of e-commerce, and the development of information technology in the middle years has caused the rapid development of e-commerce in the past 20 years (Baylok, 2021). E-commerce and information technology (IT) have a positive relationship. E-commerce and information technology are measured by evaluating the value of their business; the more the company gains positive value and trust, the more buyers it attracts (Alam & Osly, 2021). Electronic commerce is a set of technologies, applications, and business processes that connect companies, consumers, and communities through electronic transactions and electronic commerce of goods, services, and information (Rafiah, 2019). Based on this, the current research is looking for an answer to this question: What is the model for improving the behavior of Iranian handicrafts e-commerce customers with the approach of improving customer trust? Theoretical Framework Customer trust There are many definitions about trust. The multiplicity of definitions of trust in the theoretical texts of the subject probably originates from two reasons; first, trust is an abstract concept and sometimes it is synonymous with concepts such as authenticity, trustworthiness, or reliability. Second, trust is a multifaceted concept that has different perceptual, sensory and behavioral dimensions (Latifi & Momenkashani, 2014).   electronic commerce E-commerce refers to a broader definition of traditional business, which, in addition to buying and selling goods and services, includes welfare services for customers, cooperation with business partners, conducting electronic learning, and conducting electronic transactions in an organization (Torban & Lonino 2020). Iranian handcraft Iran's handicrafts experts believe that handicrafts refer to a set of arts and crafts that mainly use local raw materials and carry out some of the basic production steps with the help of hands and hand tools. In each unit, the artistic taste and intellectual creativity of the manufacturer are manifested in some way, and this factor is the main distinguishing feature of such products from similar machine and factory artifacts (Kazemi, 2019). Zolfaghar Dolabi (2023) investigated the factors affecting customer loyalty and electronic trust in electronic commerce in Tehran Infrastructure Company. The results of the research in the Tehran infrastructure company showed that the quality of the user interface has a positive and significant effect on customer satisfaction and trust. The quality of information has a significant effect on customer satisfaction, but it does not affect trust. Keeping privacy and security is not important for customer satisfaction, but it is important for trust. Customer satisfaction and trust have a positive relationship with each other and mediate customer loyalty with the determinants of service quality. The results of regression analysis show that the dependent variable of e-commerce loyalty is influenced by e-customer satisfaction and e-trust. Sharifi & Mardani (2022) investigated the identification of the key success factors in e-commerce during widespread crises (the case study of DJ Kala online sales company). The findings of the research showed that besides paying attention to hardware and software factors related to information technology in e-commerce, paying attention to human factors can increase the efficiency of e-commerce even more. Research methodology The research method is applicable according to its purpose, and mixed (qualitative-quantitative) in terms of its implementation, and a qualitative part of the descriptive-exploratory type in terms of the data collection method. The statistical population of the research in the qualitative part includes 19 Iranian experts, including university professors in the fields of marketing management, handicrafts and senior managers of companies that export handicraft products, who were selected for interviews using non-random sampling in the form of snowballs. The statistical population in the quantitative section includes 11 Iranian university professors in the fields of marketing management, handicrafts and senior managers of companies that export handicraft products, who were selected using non-probability sampling. A semi-structured interview was used to collect information in the qualitative section. Research findings Analysis was used in the coding part of the content analysis; and in the quantitative part, the combined method of Dimetal and Interpretive Structural Modeling (ISM) was used. The findings from the qualitative part of the research showed that 16 sub-themes and 7 main themes (indicators) of the desired model were extracted using content analysis and interviews of 19 experts. In the quantitative part, a four-level model was obtained, and the most effective indicator of this model is electronic communication and interaction with customers. Also, its most effective indicator at the seventh level is confidence in purchasing. Therefore, 5 other criteria also play the role of interface factors in this model. Conclusion The current research has been conducted with the aim of designing a model for improving the behavior of Iranian handicrafts e-commerce customers with the approach of improving customer trust. The results of the present research are in accordance with the results of Zolfaghar Dolabi (2023), Hashempor (2023), Sharifi & Mardani (2022), Yazdi (2022), Jalali (2021), Sutia et al, (2020), Bozic & Kuppelwieser (2019), Nekooeezade & Amini (2019), Issam (2016). Zolfaghar Dolabi (2023) showed that the quality of the user interface has a positive and significant effect on customer satisfaction and trust. The quality of information has a significant effect on customer satisfaction, but it does not affect trust. Keeping privacy and security is not important for customer satisfaction, but it is important for trust. Customer satisfaction and trust have a positive relationship with each other and mediate customer loyalty with the determinants of service quality. The results of regression analysis show that the dependent variable of e-commerce loyalty is influenced by e-customer satisfaction and     e-trust. According to the results obtained from the research, it is suggested: 1- The development of Iranian handicrafts in the context of e-commerce should be considered as one of the important strategies to increase the market share of these industries, because unlike traditional retail, a craft e-commerce store can be up and running with just a few clicks. The e-commerce platform makes it easy and simple for craft business owners to create attractive and reliable sites with minimal effort. 2- Training and promoting the use of sales in the context of e-commerce by relevant organizations should be prioritized. Buyers search for the desired product in the e-commerce space for various reasons such as comparing prices, comparing brands, knowing the opinions of previous buyers, checking the amount of inventory and more. The only way to ensure potential buyers of your products is to have a persistent presence in the e-commerce space. Even if buyers are looking for the store's working hours or its address, access to the desired information through e-commerce is very important.

Abstract Abstract The current research was conducted with the aim of providing a model of consumer behavior in creating brand attachment, emphasizing the packaging component of food industry companies. It is considered a qualitative-quantitative (mixed) research method. The sample of the qualitative section with 12 interviews with marketing, academic and food industry experts, and the quantitative section with the Cochran formula of limited societies included 384 managers and senior experts of Tehran food industry companies. In the first step of the research, the coding of specialized research interviews was performed using thematic qualitative analysis with MAXQDA 20 software and fuzzy Delphi method with MatLab software, structural-interpretive analysis with MicMac software; in the next step, the results of confirmatory factor analysis with Smart PLS 3.0 software. Based on the results of qualitative analysis, three comprehensive categories including packaging, brand attachment and consumer behavior were identified as the main components. Based on the influence-dependence diagram, the structures of communication factors, logistic factors, economic factors, bio-social factors have high influence power and are under little influence, and are placed in the area of independent structures. The constructs of brand loyalty, brand awareness, perceived quality, and brand associations also have high dependence but little influence, so they are considered dependent constructs. The results of the quantitative part, while confirming the research hypotheses, showed that the proposed model has good validity. Extended Abstract                                           Introduction According to the opinions and findings of recent researches, it is clear that the packaging characteristics might have a major contribution in creating brand attachment, but this problem should be done with a careful and precise examination of the consumer's behavior, because only by this way can correct decisions be made regarding the packaging component. Therefore, this study aims to help brand managers who are trying to improve brand performance in order to create attachment to their product and collection. In addition, this research can be an effective step in the field of improving brand management with an emphasis on creating effective value in the field of packaging. According to the above, the main innovation of this research may be considered in entering the field of brand attachment through packaging, and providing a completely native model suitable for the domestic business environment. Finally, in this research, we are looking for an answer to this question: What dimensions and components form the consumer behavior model in creating brand attachment with an emphasis on the packaging component of food industry companies? Theoretical framework Literature A person's behavior is how he acts or behaves in a certain situation. Each person has different views, opinions, desires, tastes and needs; hence, consumer behavior deals with the way consumers spend their income on various goods and services. For example, if a consumer has $2,000 and has different options such as movies, clothes, and food to spend the money on, there are different ways to spend the money. He may spend the entire amount on one option, or divide it between two or more options. The way a consumer uses his money shows his consumer behavior (Khan, Sheikh, Ashraf & Yu, 2022). Taylor et al (2023) conducted a study titled the effect of packaging on brand association in tobacco products. This research was carried out as a survey among a sample consisting of teenagers (2469 people) and adults (12046 people) in the UK. Based on the results obtained, it was found that the use of attractive packaging has a positive effect on brand associations and brand loyalty. Romeo-Arroyo, Jensen, Hunneman & Velasco (2023) conducted a study titled evaluating the effect of symmetry, curvature and packaging mark on the perception of brand superiority. In this survey study, the effect of different dimensions of packaging including curvature, symmetry, and marking was measured on consumers' perception of superiority in four different categories of food products (chocolate, coffee, jam, and ice cream). Overall, a significant positive effect of symmetry, and a significant effect of brand on brand superiority perception were identified. It seems that the effect of sign and curvature on consumers' perception of superiority is more influenced by the texture and classification of the product. Research background Taylor et al (2023) conducted a study titled the effect of packaging on brand association in tobacco products. This research was carried out as a survey among a sample consisting of teenagers (2469 people) and adults (12046 people) in the UK. Based on the results obtained, it was found that the use of attractive packaging has a positive effect on brand associations and brand loyalty. Romeo-Arroyo, Jensen, Hunneman & Velasco (2023) conducted a study titled evaluating the effect of symmetry, curvature and packaging mark on the perception of brand superiority. In this survey study, the effect of different dimensions of packaging including curvature, symmetry, and marking was measured on consumers' perception of superiority in four different categories of food products (chocolate, coffee, jam, and ice cream). Overall, a significant positive effect of symmetry, and a significant effect of brand on brand superiority perception were identified. It seems that the effect of sign and curvature on consumers' perception of superiority is more influenced by the texture and classification of the product. Tabatabai Yeganeh (2021) conducted a study titled investigating the effects of brand experience, brand image and brand trust on brand attachment and purchase intention. The findings of the research showed that brand image has a direct and significant effect on customers' purchase intention and customer trust. Also, brand experience has shown a significant direct effect on brand image, brand trust, and customers' dependence on the brand. Finally, the trust of the brand leads to the increase of the customers' dependence on the brand, and the customers' dependence on the brand also increases the motivation of the customers to buy. Research methodology The purpose of this research is in the field of developmental-applicable research. The statistical sample in the qualitative stage included 12 experts and university professors in the fields of marketing management, experts in the field of food industry companies, and consumer behavior. The sample volume is determined based on reaching theoretical saturation. According to the topic and objectives of the research, the semi-structured interview method has been used to collect data. In the quantitative phase of the research, the statistical population in this phase of the research includes 300 food industry companies, which includes an unlimited number of food industry companies' customers. Using Cochran's formula for unlimited communities, the required number of 384 people was calculated and the final samples were selected through random-cluster sampling with proportional distribution. To ensure obtaining sufficient data, 400 questionnaires were distributed, of which 393 complete questionnaires were returned. In order to determine the validity and reliability of the interviews, two methods of re-testing and double-coder agreement were used. In this research, thematic analysis method was used to analyze qualitative data. In the next step, the results of confirmatory factor analysis are presented. Then the existing categories are leveled with the structural-interpretive method and the initial research model is designed. Finally, partial least squares method has been used to validate the model. Qualitative analysis was performed with MAXQDA 20 software, structural-interpretive analysis with MicMac software and partial least squares method with Smart PLS 3.0 software. Research findings In order to evaluate the validity and reliability of the interviews, two methods of coder agreement and retest reliability were used. As shown in Table 3, the coefficients obtained in both methods were higher than the threshold of 0.6; therefore, the reliability and validity of the interviews are confirmed. Qualitative content analysis was done with thematic analysis approach in six consecutive steps. The present research has identified 42 subcategories by examining and categorizing the descriptive codes obtained from the interview texts which, according to their semantic similarity and affinity, in the main concepts were identified as follows: packaging (communication factors, logistic factors, economic factors, environmental factors and social responsibility), brand attachment (brand loyalty, brand awareness, perceived quality and brand associations), consumer behavior (personal, cultural, psychological (motivational) and marketing mix). The results of the quantitative part of the research showed that the proposed model has good fit and validity. Conclusion
Based on the results of qualitative analysis, three main dimensions including packaging, brand attachment and consumer behavior were identified. And based on the components detected in relation to these three dimensions, the structures of the primary model of consumer behavior in creating brand attachment were identified as follows: communication factors, logistics factors, economic factors, bio-social factors, brand loyalty, brand awareness, perceived quality, brand associations, personal, cultural, psychological, marketing mix. In relation to the test of the first and second hypotheses, which indicates the effectiveness of the marketing mix, the following suggestions are presented: reducing the price of products, using online distribution channels (such as a contract with Snap Food, etc.), improving product quality by using high-quality raw materials. These findings are consistent with the results of Rojas-Méndez & Khoshnevis (2023), Vila-Lopez & Küster-Boluda (2021), Chen (2021), Moodie, et.al. (2022), Chakraborty & Dash (2023), Chan & Chiu (2022), Vila-Lopez & Küster-Boluda (2021), Cleff, Lin & Walters (2021), Hwang, Choe, Kim & Kim (2021), Fathi, Torabi & Shayghi Azarzad (2021), Gómez-Suárez & Veloso (2020), Nguyen, Parker, Brennan & Lockrey (2020), Gefen & Straub (2014), Amini & Kaidi (2014), Das, Agarwal, Malhotra & Varshneya (2019), Shukla, Misra & Singh (2023), Moody, et al. (2022), Shetty & Fitzsimmons (2022), Tabatabai Yeganeh (2021), Chan & Chiu (2022), Hwang, Choe, Kim & Kim (2021), Ho & Chung (2020), and Gómez-Suárez & Veloso (2020). In relation to the results obtained from the third to fifth hypotheses regarding the impact of psychological factors, the following suggestions are presented: the use of merry and attractive visual elements to attract young customers; using attractive and challenging billboard ads to attract customers' attention; using sensory marketing techniques. In relation to the results obtained from the analysis of the sixth to ninth hypotheses regarding personal factors, the following suggestions are presented: customer segmentation, using CRM software to improve customer relations, conducting surveys among customers, personalizing products for different customer groups. In relation to the result obtained from the analysis of the tenth hypothesis in relation to social factors, the following suggestions are presented: creating a customer club, creating pages in the space of social networks to increase interaction with customers, inserting a slogan related to social responsibility on the packaging  In relation to the result obtained from the analysis of the eleventh and twelfth hypotheses in relation to communication factors, the following suggestions are presented: the use of attractive colors in product packaging; providing detailed information regarding the ingredients used in the product's manufacture; using the appropriate logo on the packaging. In relation to the result obtained from the analysis of the thirteenth hypothesis in relation to economic factors, the following suggestions are presented: cooperation with domestic companies to reduce the total cost of packaging, designing packaging according to the size of the product, using social media for marketing. In relation to the result obtained from the analysis of the fourteenth hypothesis in relation to logistics factors, the following suggestions are presented: use of three-layer packaging to increase product durability, use of materials that prevent product spoilage. In relation to the results obtained from the analysis of the fifteenth and sixteenth hypotheses regarding cultural factors, the following suggestions are presented: the use of elements of Iranian culture in advertising, focusing on the use of nostalgic elements in the introduction and advertising of products (due to the strong sense of nostalgia among people of Iran).

Abstract Abstract The present study was conducted with the aim of modeling the drivers of artificial intelligence-based knowledge commercialization in high-tech startups. In terms of the purpose, this study is an applicable-developmental research, and based on the method of data collection, it is considered a cross-sectional survey. In order to achieve the goal of the research, an exploratory mixed research design was used. The community of participants of the qualitative part includes theoretical experts (university professors) and experimental experts (managers of hi-tech startups). Purposive method was used for sampling, and theoretical saturation was achieved after 17 interviews. The statistical population of the quantitative section includes experts in the technical department of high-tech startups. The sample size was estimated to be 132 people using Cochran's formula, and sampling was done by cluster-random method. Qualitative theme analysis was used to identify the categories of the model. Partial least squares method was used to validate the model. Data analysis in qualitative phase was done with Maxqda20 software, and in quantitative phase with Smart PLS software. According to the theme analysis method based on Etrid-Sterling's (2001) six-step method, 201 codes were identified in the open coding stage, and 11 main themes and 71 secondary themes were obtained through axial coding. The results showed that there are environmental factors, networking, technical factors, managerial factors, customer factors, digital factors, strategic factors, technological opportunities, entrepreneurial knowledge, entrepreneurial awareness, and entrepreneurial characteristics that affect the commercialization of knowledge based on artificial intelligence in high-tech startups. Technical, environmental and networking factors play the most important role in the commercialization of knowledge based on artificial intelligence. Introduction In recent years, a new approach of economic development has been placed on the agenda of societies, which is in line with the growth, expansion and application of knowledge, which is referred to as knowledge-based economy. In this new economic approach, knowledge is the main source of wealth creation and is considered a source of competitive advantage (Bahari & Taheri rouzbahani, 2023). On the other hand, knowledge has economic value when it leads to improvement in the production of products and providing services, otherwise it will have no value. This statement points to the importance of a new concept called "commercialization of knowledge" (Alizadeh et al, 2022). Among various businesses, startups are the most interested in commercializing knowledge. Startup companies do not have a strong economic base, but their scientific and technological base is strong. Therefore, if these companies can commercialize knowledge and research in a market-oriented way, they can attract the desired capital (Polidoro & Jacobs, 2024). The subject of innovation and commercialization in knowledge-based startups is more necessary than ever. In fact, technology is the main way to enter the business field, the main element of which is commercialization and added value resulting from it (Feiz et al, 2023). Theoretical framework Commercialization of knowledge The commercialization of knowledge started with discussions of industry and university cooperation in 1862 and refers to the activities of academic staff members and university researchers to take advantage of market opportunities by using knowledge and research (Yaghubi et al, 2021). Commercialization of knowledge means converting new findings and research ideas into processes, technologies, services and products that can be presented to the market. This concept includes all the efforts made in order to sell research achievements with the aim of gaining profit and connecting education and research with economic and social goals as much as possible (Maurseth & Svensson, 2021). Various theories have been proposed about the commercialization of knowledge, some of the most important theories are: Linear theory of knowledge commercialization: This theory inspired the first researches about knowledge commercialization. In this theory, the process of knowledge commercialization is drawn as a pattern that starts from idea generation and technology development in academic centers and continues until patenting and providing certificates to knowledge-based businesses and startup companies (Pohle, 2023). Inverse linear theory of knowledge commercialization: Along with the growth of researches and field activities, inverse linear theory was formed. Based on this theory, the problems and issues in the industry are considered as the starting point of the knowledge commercialization process. When the issues and problems of the industries occur, knowledge enhancement and knowledge development in academic centers or business research and development are done to answer these problems. In this way, the resulting knowledge is used to solve industry problems (Leitner et al, 2021). Knowledge Commercialization Interaction Theory: In this theory, knowledge commercialization is described as including the interaction between various actors in a network of intertwined relationships. This theory rejects the linear approach and emphasizes the role of networks, interactions, collaborations and mutual learning between academia and industry. Interactive models of technology transfer actually imply the joint development of technology between the academic sector and businesses. This theory describes a process that includes a network of factors involved in the production, dissemination and application of knowledge (Heighton & Gaubert, 2021). Hi-tech startups A startup (new company) is a business that has recently been created as a result of entrepreneurship, has rapid growth and is formed to provide an innovative and sustainable solution to meet a need in the market (Vazifeh Doost et al, 2024). To put it better, startups are a business model whose development is an inseparable part of them, and unlike pure entrepreneurship, they try to get rid of individuality and by attracting capital, they employ many employees and demand expansion and scalability (Hsu & Tambe, 2023). □ Artificial intelligence Artificial intelligence was proposed since 1950 with the study of Alan Turing, a British mathematician. Turing raised the question "Can machines think?". After this initial question, artificial intelligence was formally proposed and defined as a new field of research at the Dartmouth Academic Conference in 1956. Then, in 1965, John McCarthy introduced the concept of artificial intelligence in its current common sense. Then came the first blossom of artificial intelligence, when the field was rapidly applied in various contexts (Grzybowski et al, 2024). Commercialization drivers: Commercialization drivers is a process during which ideas and results or products obtained from research departments in universities, research centers and industrial departments are transformed into products, services and processes that can be offered in the market and through which the findings of research are brought to the market. And new ideas are expanded into new products and services or technologies that can be distributed around the world. Research methodology The current study is an applicable research conducted with the aim of modeling the drivers of commercialization of knowledge based on artificial intelligence in high-tech startups in the spring of 2013. Also, based on the method of data collection, it is a non-experimental (descriptive) study that was conducted with a cross-sectional survey method. In order to achieve the goal of the research, an exploratory mixed research design was used. The population of participants of the qualitative part includes theoretical experts (university professors) and experimental experts (managers of hi-tech startups) who have enough experience in the field of knowledge commercialization system. Based on the view of Miller et al, (2010), five criteria of key-role playing, popularity, theoretical knowledge, diversity, and participation motivation were used to select the participants. Sampling was done with a purposeful method and theoretical saturation was obtained with 17 interviews. In the quantitative part, the statistical population includes managers and experts in the technology sector of high-tech startups. For this purpose, Science and Technology Park of Tehran University, Shahid Beheshti, Amirkabir, and Technology and Innovation Center of Azad University (SINTEC) were monitored. The power analysis rule (Cohen, 1992) and G*Power software were used to calculate the sample size. Using the rule of power analysis, the minimum sample size of 132 people was estimated at the confidence level of 95% with the effect size of 0.15 and the statistical power of 80%. A cluster-random method was used for sampling in the quantitative part. Data collection tools are interviews and questionnaires. The interview included 6 questions and the research questionnaire included 11 main topics and 71 secondary topics with a five-point Likert scale. Based on the analysis of the research questionnaire, 11 hypotheses were created and validated. The results of the aforementioned analysis are presented in Tables 2 and 3. To identify the categories of artificial intelligence-based knowledge commercialization drivers, qualitative content analysis and Maxqda20 software were used, and partial least squares method and Smart PLS software were used to validate the model. Research findings The results of the interviews were conducted with thematic analysis method based on the six-step method (Attride-Stirling, 2001): and 201 primary codes were identified in the open coding stage, and 11 main themes and 71 secondary themes were obtained through axial coding. Based on the results, it was determined that environmental factors, networking, technical factors, managerial factors, customer factors, digital factors, strategic factors, and technological opportunities affect the commercialization of knowledge based on artificial intelligence in high-tech startups. It was also found that entrepreneurial knowledge, entrepreneurial awareness, and entrepreneurial characteristics also affect the commercialization of artificial intelligence-based knowledge in high-tech startups. The results showed that technical, environmental and networking factors play the most important role in the commercialization of knowledge based on artificial intelligence. Conclusion Considering the importance of the issue of commercialization and on the other hand, despite the obstacles in the commercialization of created products and ideas (such as financial obstacles, government obstacles, etc.), it is necessary to emphasize more on the commercialization process in high-tech startups in our country. Since commercialization is one of the main links of the innovation process and attention is mostly paid to creating innovation and commercialization in commercial complexes of the country and solving the existing problems of commercialization in third world countries and especially Iran, we should improve commercialization in high-tech startups so that we will be able to achieve innovation and technology transfer to other industries and countries in addition to commercialization of ideas created in research and development and universities. Increasing the rate of technology commercialization brings many achievements for society, organizations and innovators, the most important of which are: raising standards and quality of life, generating national/organizational/individual wealth, creating competitive advantage, productivity growth, success in market and innovation in processes and products, and development of industries and products related to technology/inventions. Therefore, the present research is innovative and value-creating in providing practical results in this field.

Abstract Abstract The purpose of this research is to provide a model for determining the value of startups, focusing on fintech startups. The existing literature lacks models that integrate both quantitative and qualitative dimensions for fintech, which highlights the necessity of conducting this research. This research is applicable in terms of purpose, descriptive-analytical in nature, and mixed (qualitative-quantitative) in terms of type. In the qualitative part, by using the theme analysis technique and interviews with 11 startup investment and valuation experts who were selected by purposive sampling, the main and secondary themes were extracted using MAXQDA software. In the quantitative part, based on the identified themes, a questionnaire with 199 questions was designed and distributed to the statistical population consisting of 220 managers, experts, founders and managers of fintech startups, using available sampling method. The data were analyzed using confirmatory factor analysis by partial least squares method in Smart-PLS software. The final findings included 194 extracted codes in 24 sub-themes and 6 main themes, which in order of importance are business enterprise, environmental factors, technology and innovation, risks, business team, and financial industry. It was found from the results that the valuation of startups in the fintech field is very important for different stakeholders. Therefore, it is imperative to develop comprehensive valuation models that are specifically designed for the unique characteristics of fintech startups. These models should integrate both quantitative measures and qualitative factors to provide a comprehensive assessment of startup value. Introduction The valuation of startups is very important for various stakeholders, including founders, investors, employees, and even legislators. According to the National Association of American Startups in 2022, more than 60% of startups failed due to lack of sufficient financing. In addition, according to the report of the Organization for Economic Cooperation and Development in 2023, fintech startups in the member countries of this organization have accounted for 18% of venture investments (OECD, 2023). These statistics and figures show that the accurate valuation of startups, especially in the field of fintech, is of great importance. Only 28% of fintech companies have managed to achieve a market value of more than one billion dollars (CB Insights, 2021). This statistic shows that existing valuation methods may be insufficient in accurately estimating the growth potential and true value of fintech startups. While important steps have been taken in understanding the valuation of startups, there is a significant research gap, especially regarding fintech startups (Wallace, 2022). Previous researches have mainly focused on conventional valuation methods applicable to general startup contexts, without paying attention to the distinctive features and nuances of fintech venture valuations (Muchtar et al., 2023). This research seeks to fill the identified research gap by developing a structural model of startup valuation with a focus on fintech startups. The main question of the current research is: what are the dimensions and indicators affecting the value of fintech startups? Theoretical Framework Startup Startups are entrepreneurial ventures characterized by innovative ideas, agility and the pursuit of growth in a dynamic market environment. These startups are often founded by individuals or small teams with innovative visions that seek to address unmet needs or disrupt existing markets with new solutions (Oliva & Kotabe, 2019). The key feature of startups is their risky nature, limited resources, and emphasis on their scalability. Startups usually operate in sectors ranging from technology and health to finance (Aldianto et al., 2021). Fintech Fintech, short for financial technology, refers to the integration of technology into financial services to simplify processes, increase efficiency, and improve the customer experience. Fintech companies use advanced technologies such as artificial intelligence, big data analytics, and cloud computing to provide innovative solutions that challenge traditional financial institutions (Mention, 2019). Fintech startups Fintech startups focus on using technology to provide innovative financial products or services. These startups operate at the intersection of finance and technology with the aim of addressing the inefficiencies of traditional financial systems or introducing completely new business models (Zarrouk et al., 2021). Fintech startups generally exhibit the common characteristics of startups, such as agility, innovation, and scalability; while also specializing in finance and technology. (Kijkasiwat, 2021). Startup valuation Startup valuation refers to the process of determining the economic value of a start-up company at a specific point in time. Valuation is very important for various stakeholders, including founders, investors, and buyers, because it provides insights about the company's value and potential investment returns (Köhn, 2018). Research background Rahimi Klishadi (2017) stated that some startup businesses are only ideas that have very little or even zero income and operational flows. Menon & James (2022) stated that the boom of startups has witnessed the emergence of alternative sources of financing such as venture capitalists, angel investors, etc. Dhochak et al., (2024) stated that strategic management theories have been used to develop a prediction model based on the artificial neural network technique that predicts the valuation of startups before fundraising. Hidayat et al., (2022) stated that financial information (revenues) and non-financial information (social media) as well as sectoral and technological differences affect startup stock value. Hammami et al., (2023) designed a model to evaluate profit predictability in companies active in the financial industry and identified 28 components in this field, in which the company's information environment, analysis of deviations, variability of profit, and analysis of financial leverage have the highest coefficient of importance. Golshani et al., (2023) by examining the technology valuation strategies of Iranian startups, have identified 7 categories including the development and promotion of the technology valuation discourse, the transformation of existing knowledge in the field of technology into desirable and valuable knowledge, leadership and idea management, comprehensive technology evaluation system, culture creating, regulation in the technology market, and localization of technology valuation. Research method The current research is applicable in terms of purpose, descriptive-analytical in nature, and mixed (qualitative-quantitative) in terms of type. In the qualitative part, thematic analysis technique was used, based on interviews with experts and based on the six-step approach of the model (Braun & Clarke, 2006). For sampling, the purposeful sampling method was used, which was based on theoretical saturation. Based on this, a semi-structured interview was conducted with 11 experts. To analyze the findings from the interviews, the method of thematic analysis and qualitative data coding was used in the MAXQDA 2020 software. The validity of the qualitative data was confirmed using the Newman validation method. In the quantitative section, a questionnaire with 199 questions was designed and distributed based on the results of the theme analysis section, which was used for analysis. The content validity of the questionnaire was confirmed by experts, and Cronbach's alpha coefficient was used to check the reliability of the questionnaire, and its value was higher than 0.7. Confirmatory factor analysis was used for construct validity. For confirmatory factor analysis and evaluation of test content from the point of view of structural validity and fit of the research model, structural equation model with partial least squares method was used in SMART-PLS software. Research findings The findings of the research included 194 extracted codes in 24 sub-themes and 6 main themes, which in order of importance are business enterprise, environmental factors, technology and innovation, risks, business team, and financial industry.  Conclusion Research findings show that the most influential issue in startup valuation is "business enterprise". The findings of this research are in line with studies of Suwarni et al., (2020), Passaro et al., (2020), and Lee (2022). It also highlights "environmental factors" as the second most important dimension in the valuation of startups, which is in line with the research results of Alänge et al., (2022), Kim et al., (2023), and Savin et al., (2023). On the other hand, it emphasizes the importance of "technology and innovation" as the third dimension, which is in line with the studies of Zorzetti et al., (2022), Koning et al., (2022), and Sibińska (2022). Research findings highlight "risks" as a fourth dimension. This finding is in line with the studies of Laksmana & Permana (2023), Saravistha & Sancaya (2022), and Oliva et al., (2022). Research findings emphasize the importance of "business team" as the fifth dimension. This finding is in line with Aryadita et al., (2023), Honoré (2022), and Wise et al., (2022). The findings of the research show that the "financial industry" dimension has the lowest rank in terms of importance among the dimensions identified in the valuation of startups, and is in line with the studies of Stevy et al., (2023), Berman et al., (2022), and Sreenivasan & Suresh (2024). According to the results of this research, the following suggestions are presented: While revenue is an important indicator of financial performance, stakeholders should take a holistic approach to startup evaluation that considers a diverse range of factors beyond revenue generation, including market potential, growth prospects, competitive position, and regulatory compliance. On the other hand, startups should focus on diversifying revenue streams and increasing revenue generation capabilities through innovative business models, strategic partnerships, and value-added services. By offering a set of financial products or services tailored to customer needs and preferences, startups can maximize revenue generation opportunities and strengthen their value proposition in the competitive fintech landscape. Startups must prioritize building scalable and sustainable business models that can adapt to changing market dynamics and regulatory environments while delivering value to customers and stakeholders. By aligning revenue generation strategies with broader business goals and market dynamics, startups can enhance their appeal to investors, increase growth, and achieve long-term success in the financial industry.

Abstract Abstract
The purpose of this research is sociological analysis of the scenarios of the business environment of Isfahan province with the future research approach of 2029 horizon. This research is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of method. The statistical population of the research includes 17 experts consisting of Isfahan province specialists selected by purposeful sampling. The tools of data collection are interviews and questionnaires. Data analysis was done using Delphi method as well as Scenario Wizard-MICMAC software. Using the mik-mak method, 8 key factors affecting the business environment of Isfahan province up to the horizon of 2029 were extracted, including good governance, stability of laws, international political and non-political interactions, expected inflation, priority of politics over economy, tax system, banking system, expansion and development of virtual space; based on which, 5 scenarios (from the most optimistic to the most pessimistic) have been compiled. The results show that sociological components, along with economic elements, play a decisive role in the future of the business environment in Isfahan province and the decision-making of national and provincial officials. Based on this, it is recommended to rely on good governance, stability of laws, international political and non-political interactions, etc. to provide the necessary platform for improving the business environment of the province. Also, there should be mutual trust in the relations between them. In the field of improving the business environment, it is possible to benefit from educational programs or to use environmentally stimulating advertisements, and the management in different sectors should be based on people's demands.
Introduction
The business environment is one of the indicators that determine the economic status of each country, which can be used to analyze the economic conditions of each country. The more transparent and competitive the business environment in the countries, the more increase in the economic health of the countries and the adoption of favorable policies, which will lead to the improvement of economic indicators. In this regard, examining the status of business indicators, both micro and macro, and explaining the existing challenges can be fruitful in creating and developing the business environment in the country (Nozari, 2017). What is certain is that reforming the business climate and improving the aforementioned indicators in the global arena is not only a positive and fundamental step in the direction of strengthening the private sector's participation in the economy and improving the level of employment and production, but also, definitely, from the perspective of foreign investors are among the most important indicators for entering the host country and a necessary condition for promoting and facilitating the flow of technology entering the country (Masovic, 2018).
Businesses have both economic and social goals. The business environment, development of plans and scenarios related to it, innovation in the production and sale of products or services cause a lot of diversity and transformation in the society and economy of any country, so sociological investigation of the subject is important. And Weber was the first sociologist who answered the question of why successful and innovative businesses flourished in the areas where Protestant ethics and Calvinist spirit prevailed in the book "Protestant Ethics and Spirit of Capitalism" (Nozari, 2017). According to Weber, the success of people in new businesses is the product of specific cultural and social conditions. According to him, social characteristics are the determining factor of entrepreneurial spirit. In other words, the issues and problems of economic development are non-economic.
What can be imagined for the future is that the society will change from a managerial state to an entrepreneurial state. In other words, in future societies, people will make an unprecedented effort to control many educational, economic, cultural and etc. variables (Nazari Sheikh, 2022). Therefore, in this research, the researcher intends to answer the basic question: what is the sociological analysis of the scenarios of the business environment of Isfahan province with the future research approach of 2029 horizon?
Theoretical Framework
Business space
Business environment is a set of policies, legal conditions, institutions, and regulations that govern business activities. Macroeconomic stability, the quality of the country's infrastructure, the quality of executive bodies, the system of making laws and regulations, the cost and the possibility of accessing information and statistics, work culture, and other such factors are among the factors that affect the performance of economic units, while managers of economic units cannot have much influence on them. Inappropriate business environment increases the cost of economic enterprises and causes the loss of investment motivation and also the country's producers lag behind global competitors (Bakhtiari & Shayesteh, 2012).
 Futurology
Futurology is a systematic and collaborative process that provides information about the future and creates medium-term to long-term perspectives, in a way that aims to make decisions and mobilize joint actions. In other words, in future studies, a picture of the future is depicted so that planners can design the way to reach it (Fazli & Gholizadeh, 2020).
Shafaei et al, (2024) investigated the effect of knowledge management on organizational performance considering the mediating variable of business process management. The findings of the research showed that all three hypotheses were confirmed based on the significant values ​​related to the hypotheses. The effect of knowledge management on business process management was 0.742, the effect of knowledge management on organizational performance was 0.422, and the effect of business process management on organizational performance was 0.652. Therefore, the most important proposal of the research is to pay attention to the preservation, sharing and application of knowledge, which is effective both in managing business processes and in increasing the level of organizational performance.
Nazari Sheikh et al, (2022) in his research entitled "Sociological analysis of the effect of cultural components of business on the production-commercial performance of industrial units (Ardebil province)"; which was conducted with the participation of 184 entrepreneurs active in the industrial sector, came to the conclusion that cultural-social components have a positive effect on the production-commercial performance of industrial units. Also, the findings of the research indicated that the components of low power distance, individualism, low uncertainty avoidance, long-term orientation, and masculinity have a positive effect on the production-commercial performance of industrial units.
Research methodology
This research is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of method. The statistical population of the research includes 17 experts consisting of Isfahan province specialists selected by purposeful sampling. The tools of data collection are interviews and questionnaires.
Research findings
Data analysis was done using Delphi method as well as Scenario Wizard-MICMAC software. Using the mik-mak method, 8 key factors affecting the business environment of Isfahan province up to the horizon of 2029 were extracted, including good governance, stability of laws, international political and non-political interactions, expected inflation, priority of politics over economy, tax system, banking system, expansion and development of virtual space; based on which, 5 scenarios (from the most optimistic to the most pessimistic) have been compiled. The results show that sociological components, along with economic elements, play a decisive role in the future of the business environment in Isfahan province and the decision-making of national and provincial officials. Based on this, it is recommended to rely on good governance, stability of laws, international political and non-political interactions, etc. to provide the necessary platform for improving the business environment of the province. Also, there should be mutual trust in the relations between them. In the field of improving the business environment, it is possible to benefit from educational programs or to use environmentally stimulating advertisements, and the management in different sectors should be based on people's demands.
Conclusion
The current research was carried out with the aim of sociological analysis of the scenarios of the business environment of Isfahan province with the future research approach of 2029 horizon. The results of this research are in agreement with the results of Shafaei et al, (2024), Sahraei & Mafibalani (2023), Nazari Sheikh et al, (2022), Dvorský & Petráková (2021), Soni et al, (2021), Farzin et al, (2020), Safar & Bahram (2019), Shams et al, (2019), Nozari (2017), Zabetpour & Aghajani (2016), Ritter & Pedersen (2019), and Masovic (2018). Shams et al, (2019) showed that elements including the growth of the urban middle class, the large and attractive market of the service sector, the government's support for start-up businesses, the change in lifestyle in Iran and the world, the organizational culture of start-up businesses, and transformation of social problems were identified as social factors (environmental culture or ecology) affecting future businesses.
According to the results of the research, the following suggestions were presented:
 According to the results of the study, 8 factors have been identified as key factors affecting the business environment of the province, based which, 4 scenarios explain the business environment of the province until the horizon of 2029 in a storytelling method.
It is obvious that in the wide range of challenges faced by the rulers and the multitude of possible solutions to improve the business environment, identifying the main factors of changing the business environment will prevent wasting time and national funds.

Abstract Abstract
The purpose of the present study is to identify factors affecting the delivery points, risk transfer and costs in international commerce with emphasis on Incoterms 2020. The research method is applicable-operational in terms of its purpose, and mixed (qualitative) and exploratory research in terms of implementation. The statistical population in the qualitative sector consisted of 12 academic experts and experts in the field of international commerce, international transportation and Incoterms, selected by targeted sampling and snowball; and statistical population consists of 10 experts in the field of commerce. Data collection tools were semi-structural interview in the qualitative section, and a researcher-made questionnaire in the quantitative section. The study was conducted in two stages, including the data-based method and the Delphi technique. Maxqda20 software was used to analyze the qualitative data, and SPSS software in the qualitative part. The results in the qualitative section showed that 7 effective factors, 13 components, 11 strategies, and 2 consequences were identified and extracted. In the quantitative section, 33 effective factors were identified in the four stages of the Delphi interview technique. These findings will be able to provide a tool for senior executives of international interactions in effective planning for the use of Incoterms Regulations to optimize delivery points, risk transfer and costs in international supply chain management, given the buyer and seller's relationships in transboundary transactions.
Extended Abstract                                          
Introduction
Supply Chain Management in production, inventory control, location, distribution, logistics management and transportation between supply chain factors is to achieve the best responsive, efficient and profitability of market success (Song et al, 2022). Companies are more at risk when more dependent on the other dealing party (Hallikas, 2004, Swink & Zsoidisin, 2006). With the increase in the volume and complexity of the business, especially globally, the likelihood of disputes or misunderstandings of the supply chain partners has also increased; therefore, a set of commercial terms, called Incoterms rules, is designed by the International Chamber of Commerce to standardize the performance of businesses when concluding a contract for the sale of goods (Davis & Vogt, 2021). Incoterms, which is being updated periodically, determines who is incurred in international trade, cost and risk. Choosing the delivery method caused by these bylaws is one of the most important issues that should be consciously considered for both the buyer and the seller (Erdogan & Kavas, 2020). The important thing is that these laws are not mandatory, they only play the role of supporting actors involved in international trade, but if the parties agree to use them, they become mandatory (Mirică & Tudor, 2022). Incoterms' terms support trade growth and facilitate their use of international and domestic trade (Davis & Vogt, 2021), to the extent that they are known as the columns of international trade (Coetzee, 2010). The responsibility and how to settle costs and risks in international trade is an essential aspect of international competitiveness (Baena-Rojas & Cano, 2022). Therefore, according to the above, the researcher seeks to answer the question: What are the factors affecting the delivery, risk transfer and costs in international commerce with emphasis on Incoters 2020?
Theoretical Framework
Supply Chain Management
Supply chain management is known as a network management of interconnected businesses that are involved in providing final product packaging and service to the customer; as a result, the supply chain management covers all the necessary displacements and warehousing of the raw materials between the process and the finished products from the basic point to the point of consumption (Ghadri et al, 2019)
Delivery of goods in international trade
Delivery is one of the most important and basic principles in any foreign sales agreement. For each party, the delivery has its own specific place. On the one hand, the seller calls for more freedom on the terms and conditions of delivery, and on the other hand the buyer calls for more confidence in delivery of the goods in terms of quantity and qualitatively and in accordance with the raised conditions; in this context, the documentation or other documents related to the products order are important. Some commentators have defined the delivery as any action that allows the buyer to dominate the goods' buyer (Abadi & Azadi, 2017).
 
Incoterms
Incoterms is part of a business contract between the seller and the buyer. Proper business terms improve logistical engineering, reduce risk, save the company's money; and successful transportation provides solid bases for transnational transactions (Jimnez, 2012).
KIM (2022) in a study entitled "Some Critical and Controversial problems about Incoterms 2020 for International Trade" argues that Incoterms has been revised for the eighth time since its initial release in 1936. Incoterms 2020 (eighth edition) was required on January 1, 2020. Like the 2010 Incoterms (Seventh Edition), Incoterms 2020 provides eleven law (seven laws for multiple transport and four laws for marine transport), but Incoterms 2020 added DPU law by deleting the DAT law. The 2020 Incoterms seems to be improved and better organized, but it also brings some important and controversial issues. This article discusses some of the most important and controversial issues about Incoterms 2020.
Karimi et al, (2022) investigated the impact of supply chain strategic management on performance and orientation of supply chain with the role of resilience mediation (case study of the Oil Industry Offshore Sector). The results showed: 1- Supply chain strategic management has a direct and significant impact on the supply chain orientation, 2- Supply chain strategic management has an indirect and significant impact on supply chain orientation (through agility and chain strength), 3- Supply chain strategic management has no direct and significant impact on the performance of the supply chain, 4- Supply chain strategic management has an indirect and significant impact on supply chain performance (through agility and chain strength), 5- Chain agility plays a significant mediating role in the relationship between strategic management of supply chain and chain performance and orientation. 6- The chain strength has a mediating significant role in the relationship between strategic management of the supply chain and chain performance and orientation.
Research methodology
The research method is applicable-operational in terms of its purpose, and mixed (qualitative) and exploratory research in terms of implementation. The statistical population in the qualitative sector consisted of 12 academic experts and experts in the field of international commerce, international transportation and Incoterms, selected by targeted sampling and snowball; and statistical population consists of 10 experts in the field of commerce. Data collection tools were semi-structural interview in the qualitative section, and a researcher-made questionnaire in the quantitative section. The study was conducted in two stages, including the data-based method and the Delphi technique.
Research Findings
Maxqda20 software was used to analyze the qualitative data, and SPSS software in the qualitative part. The results in the qualitative section showed that 7 effective factors, 13 components, 11 strategies, and 2 consequences were identified and extracted. In the quantitative section, 33 effective factors were identified in the four stages of the Delphi interview technique. These findings will be able to provide a tool for senior executives of international interactions in effective planning for the use of Incoterms Regulations to optimize delivery points, risk transfer and costs in international supply chain management, given the buyer and seller's relationships in transboundary transactions.
Conclusion
The purpose of this study was to identify factors affecting the delivery points, risk transfer, and costs in international commerce with emphasis on Incoterms 2020. The results of this study are consistent with the results of samiei et al, (2023), ahmadi et al, (2023), KIM (2022), Karimi et al, (2022), Durdağ & Delipinar (2021), Miltikbaevich & Kizi (2021), Stojanović & Ivetic (2020), Erdogan & Kavas (2020), Shafei et al, (2021), Islami Tabar et al, (2020), Ranjbarzadeh (2020), and Herghelegiu et al, (2019). KIM (2022) argue in a study entitled "Some Critical and Controversial issues abort Incoterms 2020 for International Trade", that Incoterms 2020 provides eleven laws (seven laws for multiple transport and four laws for maritime transport). Incoterms 2020 improved and better organized
It is recommended that companies with international transactions during international negotiations on the purchase or sale of goods and prior to the contract of sale, transportation, insurance and inspection, should consider effective factors and extracted indicators in this study to:
1- Reduce product transfer costs and reduce risk between buyer and seller.
2- Bring the country's economic growth and development by removing the ambiguities of transactions and increasing the quality of transactions.
3-Develop willingness to foreign direct investment by improving the international business environment.
 

Abstract Abstract
The aim of the present study was to mathematically model the forecast of iron ore prices and its by-products. The present study is applicable in terms of its purpose, and survey in terms of data. The main data collection methods in the present study are library methods. The daily price of Iranian oil was obtained by referring to the website of the Organization of the Petroleum Exporting Countries (OPEC). The daily price of iron ore stocks was extracted from the website of the Commodity Exchange, and the price of Bahar Azadi coins and the dollar rate were extracted from the Central Bank of Iran. The indices were first extracted from library studies. In this study, the statistical population includes the daily price of iron ore stocks for 2,058 working days. Given that severe fluctuations in stock prices will affect the forecast; the statistical sample used in this study includes daily iron ore stock prices during the period of companies entering the stock exchange from 20/03/2016 to 19/03/2022. Matlab and Dematel software were used for predictions made by fuzzy inference. Based on the findings of the studies, 12 variables were extracted as predictor variables to design the prediction model. Dematel results showed that 7 factors: other suppliers' prices, seasonal effect of order registration, prices of past periods, government tariff rate, exchange rate, oil price, and world iron ore price were the most influential factors. Adaptive neural fuzzy approach is one of the important approaches for comparing the effectiveness of different factors. The results showed that the exchange rate has the highest frequency among the seven available variables, followed by the world iron ore price.
Introduction
Continuous and sustainable economic growth in any economy requires the optimal mobilization and allocation of resources at the national level. In economic literature, capital is considered the lifeblood of the economic system, and its formation has been emphasized as one of the determining factors of economic growth and development. Basically, the rate of economic growth and development depends on capital accumulation on the one hand; and on the other hand, on the productivity factor in economic activities. These two basic factors depend on the nature of the investment process; therefore, one of the most important tasks of financial markets is to facilitate capital formation. Capital markets can well handle both of the aforementioned tasks of capital accumulation and increasing economic productivity (Farajian & Farajian, 2022).
The iron ore trade in the world has faced major changes with the rapid growth of developing economies in regions such as China, India, and South Korea as key growth centers in this sector, and the industrialized economies of the European Union and North America are gradually losing their dominant role in this market. Currently, the developing regions of Asia are the center of growth in steel production and consumption. Most steel-producing regions import most of their iron ore resources, and some others have insignificant or uneconomic iron ore resources. The most prominent of these are the steel industries of China and Japan. The growth in demand for iron ore imports has led to a significant increase in production in countries such as Brazil and Australia, as a combination of large, high-quality iron ore resources accessible to ports, and iron ore resources for the export market have been developed. In view of what has been said, the present study seeks to answer the question: what are the effective factors in determining the price of iron ore and how is the comparison of effective factors using the fuzzy neural network method?
Theoretical literature
The iron ore industry plays a key and influential role in the growth and development of a country. On the one hand, this industry is fundamental in development, and on the other hand, it is considered a benchmark for the industrialization of countries. Therefore, its improvement and development is of particular importance. Basic industries such as transportation, construction, machinery manufacturing, mining and other industries related to energy production and transmission are dependent on products made from iron ore. Therefore, global demand for iron ore is high and will remain stable in the future, if not increase (Hao et al, 2018). After the 2008 financial crisis, when supply and demand fell sharply, supply and demand were on an upward trajectory. Of course, so far the growth rate of supply has been greater than demand, and it is likely that the iron ore surplus will continue to grow, which could ultimately have a significant impact on iron ore prices. World crude iron ore production in 2010 reached 1.238 billion tons, with a growth of 15% compared to 2009. This number was 1.694 and 1.8 billion tons in 2013 and 2014, respectively. The apparent consumption of iron ore in 2010 was 144.8 million tons, which was an increase of 13.2% compared to the previous year. This number was 168.1 and 171.5 million tons in 2013 and 2014, respectively (Hao et al, 2018).
Factors Affecting Iron Ore Prices
Economic Factors
The economic situation of each country and the global economic situation in general have a major impact on the level of domestic and foreign demand for iron ore, and as a result, it has a significant impact on the export of iron ore in exporting countries (Azimi & Afrough, 2015).
Political Factors
Other factors affecting the trend of iron ore exports are political factors and trends. These factors are often accompanied by economic burdens. Regional and international political crises and the increasing acceleration of arms races can also be a powerful factor in increasing the production and export of iron ore (Azimi & Afrough, 2015).
Substitute goods
Although iron ore substitutes do not have an immediate impact on iron ore exports, they can have a significant impact in the long term. With the passage of time and the advancement of science and technology, new possibilities are provided for the production of new types of petrochemical products that are not only more resistant and more malleable, but also have greater relative advantages over iron ore in terms of erosion and application methods (Farajian & Farajian, 2021).
Price levels
The price level of iron ore products is also one of the factors affecting its export volume. Among the factors that have increased the export volume of iron ore products from countries such as Japan and other common market countries to the domestic markets of the United States has been the low price level of exported products compared to the price level of domestic production in the United States (Farajian & Farajian, 2021).
Exchange rate changes
The exchange rate is one of the most important variables affecting exports. It is important to examine the speed of the impact of these variables on exports. As trade between countries increases, exchange rate fluctuations are considered one of the most important sources of corporate risk (Mojdeganlou & Hosseini, 2021).
Research Methodology
The present study is applicable in terms of its purpose, and survey in terms of data. The main data collection methods in the present study are library methods. The statistical population includes elements, components, and individuals or units that share at least one attribute. In this study, the statistical population includes the daily price of iron ore stocks for 2058 business days. Given that severe stock price fluctuations will affect the forecast, the statistical sample used in this study includes the daily prices of iron ore stocks during the period of companies' entry into the stock exchange from 20/03/2016 to 19/03/2022. For the predictions made by the fuzzy inference system, MATLB and DEMATEL software were used.
Research findings
In this section, the adaptive neural fuzzy approach, which is one of the important approaches for comparing the effectiveness of different factors, is used. The important point is that in this section, 7 influential factors that are the result of the DEMATEL method are examined and explored, and their effect on the final or output variable, i.e. the price of iron ore, is examined in pairs; and finally, the factors that have the highest frequency are listed. First, the influencing factors (prices of other suppliers, seasonal effect of order registration, prices of past periods, government tariff rate, exchange rate, oil price, world price of iron ore) are introduced as a result of the DEMETL method. According to the results, the exchange rate has the highest frequency among the seven variables available, followed by the world price of iron ore. After that, the seasonal effect of order registration and the variables of government tariff rate and oil price are followed. The least frequent variable is the price of past periods, which has only been dominant once in the permutations.
Discussion and Conclusion
The aim of the present study was to predict the price of iron ore and its by-products based on time series neural networks. For this purpose, first, library studies were conducted based on which, 12 variables were extracted as predictor variables to design the prediction model. The results of DEMETL show that 7 factors: price of other suppliers, seasonal effect of order registration, prices of past periods, government tariff rate, exchange rate, Oil price and world iron ore price were the most influential factors. The results showed that the exchange rate has the highest frequency among the seven variables, followed by the world iron ore price. After that, the seasonal effect of order registration and the variables of government tariff rate and oil price are next. The least frequent variable is the price of past periods, which has only been dominant once in the permutations. The results of this finding are somewhat consistent with the results of Lee et al., (2017) and Lin & Si (2021).

Abstract Abstract The present study was conducted with the aim of designing and validating a sustainable marketing model based on consumer behavior management in the automotive industry. In terms of the purpose of this study, it is an applicable-developmental research, and based on the method of data collection, it is also considered a cross-sectional survey. In order to achieve the goal of the research, an exploratory mixed research design was used. The community of participants of the qualitative section includes theoretical experts (marketing professors) and experimental experts (automotive industry managers). Purposeful method was used for sampling, and theoretical saturation was achieved after 17 interviews. The statistical population of the quantitative part includes the managers and experts of the marketing and sales department of the automotive industry. The sample size was estimated to be 131 people using Cochran's formula, and sampling was done by cluster-random method. Thematic analysis was used to identify the categories of sustainable marketing model based on consumer behavior management. The partial least squares method was used to validate the model. Data analysis was done by Maxqda20 software in qualitative phase; and Smart PLS software in quantitative phase. Based on the research findings, 298 codes were identified in the open coding stage. Finally, 5 overarching themes, 11 organizing themes, and 55 basic themes were obtained through axial coding, and the results showed that environmental factors, organizational factors, and customer factors affect sustainable infrastructure. Sustainable infrastructure leads to sustainable consumption behavior by influencing sustainable consumption strategy and social responsibility. Sustainable consumption behavior also leads to sustainable marketing by influencing environmental sustainability, economic sustainability, and social sustainability. Introduction Consumer behavior is a critical success factor in the implementation of marketing strategies, especially new marketing topics in the field of social and environmental issues, which is referred to as sustainable marketing. Sustainable marketing is a method of marketing that emphasizes market-oriented efforts to achieve business profitability while respecting society's rights and protecting the environment (Yadav al et, 2024). In the last two decades, the integration of sustainability in marketing research has grown rapidly and widely. According to a McKinsey Institute survey, companies have actively integrated sustainability principles into their business plans in order to contribute to society and the environment in addition to economic benefits (Rastogi et al, 2024). Now the concept of marketing and its underlying philosophy has undergone a fundamental transformation. In today's era, the social responsibility of businesses is very important, and protecting the environment and preserving resources for the future generation is at the center of human thought. Therefore, companies try to use sustainable marketing as a key strategy to introduce their products and services (Fuxman et al, 2022). This approach includes efforts to create and facilitate exchange processes to respond to the needs and demands of customers in such a way that satisfying the needs and demands of customers brings the least negative consequences for society and the environment (Machado et al., 2023). The goal of sustainable marketing is to meet the needs of consumers in the best possible way and to serve the long-term interests of customers and society. In this approach, profitability and obtaining economic benefits of business should be done by adhering to environmental requirements and respecting the social rights of the entire society (Trang al et, 2023). On the other hand, the automobile industry of the country is one of the basic industries that play a significant role in creating employment, public welfare, and economic development of the country, so in the path of industrial development, the automobile industry of the country should not be neglected. In fact, the current condition of the country's automobile industry is very challenging and worrying for a wide range of people, especially policy makers (Sheikh et al, 2023). Automotive industry has the largest contribution to the country's economic growth in the industrial sector and ranks sixth among the 20 largest companies in the country. The largest amount of national GDP in the industry sector is related to automobile manufacturing, which has grown faster in recent years (Arjamandi et al, 2023). Therefore, the automotive industry has created a very large market; one of the main pillars of its success depends on the management of consumer behavior. Analyzing the buying behavior of consumers is one of the basic principles in analyzing market opportunities in the automotive industry. The importance of this issue is to the extent that today the field of marketing is based on the principle of consumer priority. Consumer behavior is a process. Most marketers have recognized that consumer behavior is a continuous process, not something that happens at a moment, and based on which people buy the goods and services they need through money or credit cards (Martin & Peattie, 2021). In general, it can be said that the country's automobile industry is one of the basic industries that plays a significant role in creating employment, public welfare, and economic development of the country, so in the path of industrial development, one should not neglect the country's automobile industry. Government support, green human resources, and corporate social responsibility are the basic factors effective on sustainable marketing in the country's automobile industry, which plays a strategic role in sustainable marketing of these industries. Therefore, by emphasizing on sustainable marketing in the automotive industry of the country, the behavior of the consumers of this industry can be shaped. Meanwhile, so far, the independent study has not examined all the elements of sustainability in the market-oriented efforts of the automotive industry in a single whole. In other words, what has been neglected from the point of view of researchers is the conceptualization of sustainable marketing in the context of consumer behavior, and the present studies are an effort to fill this research gap. In this regard, the dimensions of sustainable marketing based on consumer behavior will be identified first. Then the causal relationships between the dimensions will be identified, and at the end, the validation of the final model of sustainable marketing based on consumer behavior will be presented. The present study answers the key question: what is the sustainable marketing model based on consumer behavior management? Theoretical framework - Sustainable marketing The term sustainability was proposed for the first time in 1986 by the World Committee for Environmental Development under the title of meeting the needs of the present without compromising the resources of the future generation to meet their needs, and this concept is expanding until today. One of the areas affected by this movement is marketing, and now sustainable marketing has become a dominant approach in market management and communication with customers (Siano et al, 2022). Based on a general definition, "sustainable marketing" is the efforts and activities of a business to introduce and sell environmentally friendly products and services. This method of marketing is in line with the goals of sustainability and sustainable development, which ultimately brings a sustainable competitive advantage to the organization (Yadav al et, 2024). - Management of consumer behavior Consumer behavior management is all the activities that people engage in when choosing, buying, using, and disposing of disposable goods in order to satisfy their needs (Mowen & Minor, 2022). Based on another definition of consumer behavior, with the aim of meeting the needs and desires of different individuals and groups, they examine effective processes during the selection, purchase and use of products, services, ideas and experiences (Raji et al, 2024). The consequence of fierce competition in the market is the ever-increasing power of the customer. With the increase in the power of customers, their expectations from production and service organizations have also increased. Organizations have to offer the most valuable products and services at the most appropriate price. As a result, organizations are constantly looking for new methods and innovation in creating and providing value for customers. Due to the complexity of customers' needs and demands and their deep inner motivations, acquiring information and knowledge in the field of customers' demands, mental perceptions, purchases, and purchasing behavior is of fundamental importance for marketers (Barbe et al., 2023).  Research methodology This study is an applicable-developmental research and based on the method of data collection, it is also a non-experimental (descriptive) research that was conducted with a cross-sectional survey method. In order to achieve the goal of the research, a mixed exploratory research design (qualitative-quantitative) has been used. The community of qualitative sector participants includes theoretical experts (professors of marketing management) and experimental experts (managers of the country's automotive industry) who have sufficient experience in the field of sustainable marketing system. Sampling was done with a purposeful method, and theoretical saturation was obtained with 17 interviews. In the quantitative part, the statistical population includes managers and experts of the marketing and sales department of the country's automobile industry, numbering 131 people. In the qualitative part, the theme analysis method (TAM) was used with the proposed approach (Attride-Stirling, 2001). In the quantitative part, partial least squares method was used in Smart PLS software to validate the model. Research findings In the open coding phase, 298 codes were identified. Finally, 3 overarching themes, 11 organizing themes, and 68 basic themes were obtained through axial coding. Conclusion Based on the results of the qualitative part of the research; 68 sub-themes were classified in the form of 11 main themes including organizational factors, customer factors, environmental factors, sustainability infrastructure, social responsibility, sustainable consumption behavior, sustainable consumption strategy, sustainable marketing, environmental sustainability, economic sustainability, and social sustainability category. Based on the results, it was determined that environmental factors, organizational factors, and customer factors affect sustainable infrastructure. In the study of Wang & Udall (2023), it was also shown that moral self and group identity encourage sustainable consumption behaviors. Altruistic values ​​predict self and group moral identity, and the relationship between altruistic values ​​and sustainable consumption behaviors is fully mediated by moral self and group identity. Also, the results showed that sustainable infrastructure leads to sustainable consumption behavior by influencing sustainable consumption strategy and social responsibility. In the results of the study of Gong et al., (2023), it was also determined that a useful tool for long-term purchase intention is interaction with customer, and the company's social responsibility plays an important role in strengthening consumers' intention to make sustainable purchases in the automotive industry. Finally, it was found that sustainable consumption behavior leads to sustainable marketing by influencing environmental sustainability, economic sustainability, and social sustainability. In the results of the study of Peterson et al, (2021), it is also mentioned that "... in the philosophy of social marketing, the goal is to respect the social rights of individuals in addition to the economic interests of companies. In addition to this issue, the efforts of environment activists and public awareness about the importance of the environment caused respect for nature to be placed on the agenda of companies and business owners." 

Abstract Abstract The objective of the present study is to provide a model to explain the factors affecting the implementation of Electronic Customer Relationship Management (ECRM) with an emphasis on Artificial Intelligence (AI) components and its outcomes for banking customers. In terms of its objective, the research method is developmental-applicable; and in terms of its execution, it is a mixed-methods (qualitative-quantitative) study. The statistical population in the qualitative section includes 10 experiential and academic experts, and the statistical population in the quantitative section consists of 8 experiential and academic experts, selected through purposeful snowball and judgmental sampling methods. Data collection tools included semi-structured interviews in the qualitative section and an ISM questionnaire in the quantitative section. To analyze the findings, thematic analysis based on open, axial, and selective coding was applied in the qualitative section, while Interpretive Structural Modeling (ISM) and interactive matrices were employed in the quantitative section. Research findings indicated that the successful implementation of ECRM in banks is influenced by a set of key factors, including technological factors (IT infrastructure, data quality, AI tools), organizational factors (top management support, organizational culture, processes), and human factors (employee skills, technology adoption). Furthermore, the results indicate that the application of AI components in ECRM leads to a significant improvement in outcomes such as customer satisfaction, trust, loyalty, customer experience, and value creation. Introduction The digital initiatives of governments undertaken since the beginning of the last decade, coupled with the increasing penetration of the Internet in recent years, have led to a surge in the global digital population (this increase has been reported to be over 50% in the last decade) (Statista, 2020). The Internet has become a powerful tool for electronic customer relationship management (Almajali et al., 2022). As digital technology begins to blur the distinctions between data platforms, a relatively new field of science—namely, Electronic Customer Relationship Management (E-CRM)—is developing. Customers interact with companies through a variety of information channels, some of which are connected to Information and Communication Technology (ICT) applications via the Internet (Santy & Hardiyanti, 2019). Intense competition, increasing globalization, and rising consumer expectations have forced banks to provide the best possible services to their customers, both to retain customers and to increase financial profitability (Gul et al., 2025). As a result, the business model has shifted from being bank-centric to customer-centric. Product homogeneity has only added to the burden of the banking industry, becoming a major challenge for maintaining customer satisfaction and loyalty during a significant transition in technology and customer behavior. Banks utilize various ICT strategies to improve their customer relationships (Al-Dmour et al., 2019). The rapid expansion of digital banking has forced banks to transform their current strategy into an omni-channel strategy that includes the Internet, web, or physical branches (Shastri et al., 2020). Consequently, banks offer a wide range of digital products, including online banking, mobile banking, telephone banking, neobanking, self-service technology, and more. Every marketing campaign is primarily aimed at enhancing business profitability and developing and maintaining good customer relationships (Kotler & Keller, 2015). In managing a customer relationship system, it is necessary to analyze customer interaction data using appropriate tools. Today, these tools are developed and utilized through information technology capabilities. Therefore, to succeed in implementing a customer relationship management system, the factors affecting its implementation must first be examined and an appropriate model must be adopted. This will be addressed in this thesis through a mixed-methods (qualitative-quantitative) approach. Thus, the main question is formulated as follows: How can the factors affecting the implementation of Electronic Customer Relationship Management (ECRM) be explained, emphasizing artificial intelligence components and their subsequent outcomes for banking customers? Theoretical Framework Electronic Customer Relationship Management Electronic Customer Relationship Management (ECRM) is an online delivery system of sales, marketing, and service designed to identify, attract, and retain a company’s customers. The form of customer relationship management that is established through the use of information technology is referred to as Electronic Customer Relationship Management (Fuad & Abdullah, 2023). Artificial Intelligence Artificial Intelligence refers to the study of how computers can be made to perform tasks that humans currently perform correctly or better, and it is defined as the intelligence demonstrated by a machine or the computer science that attempts to create it (Zolghadr et al., 2025). Asadi et al. (2025) investigated the identification of primary elements and components affecting electronic customer relationship management, and their results showed that variables related to causal factors—including human, technological, and support factors—along with contextual factors such as cultural and industry factors, and organizational factors including organizational design and customer-related factors, were identified and categorized as influential variables on electronic customer relationship management; furthermore, satisfaction and loyalty were recognized as the outcomes of implementing electronic customer relationship management. Additionally, Emami et al. (2025) examined the design of an AI-based customer relationship management model in digital service marketing within the health tourism industry, where the research results indicated that causal conditions included market competition enhancement, relationship improvement, automated data analysis, and empowerment, while contextual conditions consisted of customer data management and intelligent services; moreover, intervening conditions included efficient planning, resource savings, and customer behavior management, while the strategies in the study involved solving integration problems, information management issues, and planning challenges, leading to outcomes such as increased customer satisfaction, improved financial strength, customer loyalty, and time savings, with structural equation modeling results demonstrating that the dimensions loaded well onto the research variables and provided an appropriate description of them. Research Methodology The research method, based on its objective, is developmental-applicable; and in terms of execution, follows a mixed-methods (qualitative-quantitative) approach. The statistical population of the study in the qualitative phase consists of 10 experimental and academic experts, and in the quantitative phase, it includes 8 experimental and academic experts selected by purposeful snowball and judgmental sampling methods. The data collection tool in the qualitative section is semi-structured interviews, while the quantitative section utilizes the Interpretive Structural Modeling (ISM) questionnaire. Research Findings To analyze the findings, thematic analysis based on open, axial, and selective coding was utilized in the qualitative phase, while the Interpretive Structural Modeling (ISM) method and interactive matrices were employed in the quantitative phase. The research findings revealed that the successful implementation of ECRM in banks is influenced by a set of key factors, including technological factors (IT infrastructure, data quality, and AI tools), organizational factors (top management support, organizational culture, and processes), and human factors (employee skills and technology adoption). Furthermore, the results indicate that the application of artificial intelligence components in ECRM leads to a significant improvement in outcomes such as customer satisfaction, trust, loyalty, customer experience, and value creation. Conclusion The present study was conducted with the objective of providing a model to explain the factors affecting the implementation of Electronic Customer Relationship Management (ECRM), with an emphasis on artificial intelligence components and their subsequent outcomes for banking customers. The results of this research are consistent with the findings of Asadi et al. (2025), Emami et al. (2025), Hosseinimanesh et al. (2025), Karimi & Mahmoodi Ranani (2025), Zolghadr et al. (2025), Shiri & Hassoumi (2024), Pousti (2024), Sahoo et al. (2024), Amin Ravan & Ferdous Makan (2023), Sarfarazi et al. (2023), and Fuad & Abdullah (2023). Karimi & Mahmoodi Ranani (2025) demonstrated that the adoption of artificial intelligence in e-commerce has a significant relationship with improving the business performance of small and medium-sized enterprises (SMEs). Furthermore, their research emphasizes the pivotal role of dynamic capabilities and entrepreneurial orientation in advancing AI adoption within the e-commerce sector, which in turn contributes to enhanced business performance; these results highlight the importance of developing technological capabilities and innovative approaches in SMEs to effectively exploit artificial intelligence and achieve growth and success. Given the dependence of intelligent ECRM on data and artificial intelligence, it is suggested that regulatory bodies facilitate the sustainable development of these systems by formulating transparent frameworks in the areas of privacy, AI ethics, and data security.

Abstract Abstract The aim of the current research is to present a model of customer participation with brands in social networks with an emphasis on cultural differences. The research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of execution method, exploratory in terms of nature, and descriptive and survey type in terms of information gathering and analysis method. The statistical population in the qualitative part includes 10 experts from the scientific community and academic specialists and experts of the top 500 companies in Iran based on the ranking of the Industrial Management Organization, who were selected purposefully; and in the quantitative part, it includes the managers of the top 500 companies in Iran based on the ranking of the industrial management organization, and the statistical sample will be selected from among the companies that have active accounts in social networks and considered as the statistical population; and based on Cochran's formula, 217 people were selected as a sample by random sampling method. Data analysis in the qualitative section is based on the content analysis method; and in the quantitative section, SPSS and PLS software are used. The results of the qualitative part show that this research includes 14 dimensions and 30 components, and the results of the quantitative part show that the dimensions and components of customer engagement with brands have an impact on social networks with an emphasis on cultural differences. Also, the results show a strong and very good fit of the model. Extended Abstract Introduction Organizations, with the intense competition in the markets and the understanding of the importance of keeping customers for organizations and at the same time as the customer orientation movement peaked, were gradually pushed to create and maintain long-term relationships with customers. Also, the emergence of new technologies such as information technology has had tremendous effects on various dimensions of the organization and has caused the emergence of an issue such as customer participation (Hosseini, 2020). Customer participation is a tool that can be used with the aim of helping organizations to establish interaction and retain customers. Using electronic customer participation and benefiting from its results can help to improve the quality level of the provided services more effectively, and subsequently, increase customer loyalty, trust and satisfaction (Mohammadi & Sohrabi, 2017). The brand includes instructions that lead to the desired perceiving of brand by the customer's mind and belief. It is important to note that the definition of the brand position and the mental image of the brand are completely different. The brand is an important thing in the development and promotion of the brand in the target market, because it will increase interest in the brand, more willingness to buy in the target market, and also increase brand loyalty (Koch & Gyrd-Jones, 2019). Brand is considered a promise and commitment from the organization to stakeholders, and a symbol that is presented to identify and differentiate products from competitors' products (Mirzaei et al, 2019). Culture, as a basis for determining values, is one of the most important factors affecting management. The nature of decision-making is also rooted in culture. Ignoring this role will result in lack of internal coherence and external compliance. In large organizations around the world, it is different who makes the decision, when the decision is made, and to what extent the decision is made in a rational way; therefore, when discussing individual methods in decision-making, the issue of culture should not be neglected (Shi'ezadeh et al, 2017). Based on this, the current research is looking for an answer to this question: What is the pattern of customer participation with brands in social networks, considering cultural differences? Theoretical Framework Customer involvement Customer participation is a key link to several measures of company success, including increasing revenue and customer loyalty and profitability. Significant links has been found between customer participation and business success (Taghiabadi et al, 2023). Brand Brand is a factor to create differentiation. It is not easy to make this distinction. In the past, quality was considered an advantage and distinction, but today, quality is a matter of course. Many similar products with different brands do the same thing for the consumer; therefore, the consumer looks for signs among a multitude of brands to encourage him to choose. This distinguishing sign is not functional features; rather, it is emotional and symbolic features, and brand personality can create such a distinction (Rasouli & Bayat, 2020).   Social Networks Social networks are Internet-based communication and collaborative channels that have been widely used since 2005 for different purposes (Kaplan & Haenlein, 2010). Cultural differences Culture can cause the formation and emergence of appropriate or inappropriate performance (Kasemsap, 2013). It determines how to perceive, think and react appropriately to internal and external environments (Shao, 2019). Culture in the last decade has been widely used in various research fields and has been recognized as one of the important factors guiding strategy formulation and implementation (Kavala et al, 2020).  Lopez et al, (2021) investigated the role of online brand community on customer relationship with the brand. The results showed that participation through the online brand community directly has a positive and significant effect on community participation and the desire to create cooperation with the brand name and positive word of mouth, and also has a positive indirect effect on brand loyalty. These results show that interaction through establishing online communities based on customer participation has a positive effect on product sales through online platforms. Khademi et al, (2021) in their study investigated the cooperation in branding through the cooperative motivation of customers in digital media. The results of the research showed that customer participation motivation in social networking sites has a positive and significant effect on customer participation in brand communities, customer participation on brand trust and brand loyalty. Meanwhile, brand trust has a positive and significant effect on brand loyalty, and brand trust and loyalty also have a positive and significant effect on brand co-creation. Finally, brand trust moderates the relationship between customer involvement and brand loyalty. Research methodology The research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of execution method, exploratory in terms of nature, and descriptive and survey type in terms of information gathering and analysis method. The statistical population in the qualitative part includes 10 experts from the scientific community and academic specialists and experts of the top 500 companies in Iran based on the ranking of the Industrial Management Organization, who were selected purposefully; and in the quantitative part, it includes the managers of the top 500 companies in Iran based on the ranking of the industrial management organization, and the statistical sample will be selected from among the companies that have active accounts in social networks and considered as the statistical population; and based on Cochran's formula, 217 people were selected as a sample by random sampling method Research findings Data analysis in the qualitative section is based on the content analysis method, and in the quantitative section, SPSS and PLS software are used. The results of the qualitative part show that this research includes 14 dimensions and 30 components, and the results of the quantitative part show that the dimensions and components of customer engagement with brands have an impact on social networks with an emphasis on cultural differences. Also, the results show a strong and very good fit of the model. Conclusion The current research has been done with the aim of providing a model of customer participation with brands in social networks with an emphasis on cultural differences. The results of this research are in agreement with the results of Savadkoohi Qudjanki & Zarbakhsh Bahri (2022), Yazdani Kachuei et al, (2022), Lopez et al, (2021), Khademi et al, (2021), Nasrollahi et al, (2020), Mashhadizadeh & Saedi (2020), and Li et al, (2020). Lopez et al, (2021) showed that participation through online brand community directly has a positive and significant effect on community participation and the desire to create cooperation with the brand and positive word of mouth, as well as a positive indirect effect on brand loyalty. These results show that interaction through establishing online communities based on customer participation has a positive effect on product sales through online platforms. According to the results of the research, the following suggestions are presented: It is suggested that in order to have a successful branding, you must constantly create positive experiences for your customers because branding is the result of fulfilling your promises; the result of gaining customers' trust that your brand will do its best to fulfill what they want or expect from you. This trust leads to your brand being chosen again by them. It is suggested to choose a diversified product development strategy because it is a combination of existing products and existing brands, in which case some product characteristics such as color, taste, shape, size and packaging will change. Even the components of the product may change slightly.

Abstract Abstract The main purpose of this research is to provide a destination branding model based on the nostalgia of the tourism industry, so the current research is in the field of applied-developmental research. Also, based on the nature and method, the current research is a descriptive-survey research with a mixed (qualitative-quantitative) approach, which is conducted cross-sectionally in terms of time. The statistical sample in the qualitative section includes 30 academic and market experts related to the tourism industry, which was purposefully non-random and finally reached theoretical saturation with the number of 30 semi-structured interviews. In the quantitative part, the statistical population includes all tourists of Tehran province in 1401, from which a sample of 384 people was selected. The data collection tool in the qualitative part includes a semi-structured interview and in the quantitative part it includes a questionnaire from the qualitative phase. Data analysis was done in qualitative method with database approach and MAXQDA 20 software. Validation of the model was done with partial least squares technique and SmartPLS software. Based on qualitative analysis, the paradigm model of research in six dimensions of causal factors (sometimes from the destination brand, destination brand image, tourist relationship management, tourist participation), background conditions (tourism infrastructure), intervening factors (host tourism culture), strategies (branding) based on nostalgia), central phenomenon (tourism brand development) and consequences (tourist travel decision) were designed. Based on the results obtained from the validation of the model in the quantitative section, it was determined that the proposed model of this research has adequate validity. Extended Abstract                                           Introduction Today, tourism activity is considered as one of the most important and dynamic activities in the world, so that the number of foreign and domestic tourists and their income generation is constantly increasing at the global level. Identifying important and effective variables in the tourism industry is effective in changing the demand pattern of many countries. Therefore, tourism marketing and specifically, branding of tourist destinations has become one of the most key concepts in the field of tourism (Adachi et al, 2022). Creating a reliable tourism brand makes customers loyal to a specific tourist destination. In an age where the competition between agencies and tourism destinations has intensified more than ever, creating loyal customers has become the golden key to the success of companies active in the field of tourism (Suhartanto et al, 2022). However, the number of researches that have specifically focused on the impact of nostalgia on customer loyalty is not very high. On one hand, the relationship between nostalgia and loyalty to a tourist destination is not a linear and straightforward relationship, and undoubtedly other factors can also play a role in this relationship. Therefore, theoretical frameworks with more complexity and elegance are needed to explain the impact of nostalgia on loyalty. On the other hand, it should be kept in mind that despite the extensive domestic studies related to tourism marketing, the importance and role of nostalgia in the tourism industry has been given less attention, which explains the need to address this category. Therefore, considering the importance of the topic, the main question of the current research is, "How is the branding model based on the role of nostalgia on the tourist's travel decision in the tourism industry?" Theoretical Framework Tourism is the set of activities of people who travel to places outside the usual environment of their society; for leisure, entertainment, business or any other purpose and stay in that place for a while. The importance of tourism is due to the fact that this field is considered a dynamic, competitive and income-generating industry. A significant part of the budget of developed and developing countries is provided by the tourism industry. Many tourist countries adopt written strategic plans to improve their performance in this industry (Cheraghi et al., 2021). Some researchers have introduced tourism destinations as a complete concept with a definition; that is, destinations are places that have made a combination of tourism products and services consumed under the brand name of the destination. According to the definition of the World Trade Organization, a destination is a unique place where the visitor spends at least one night and presents tourism products such as attractions, support services and tourism resources with specific management, physical and administrative boundaries and a well-known image (Wang et al., 2022). Nostalgia can be a powerful motivator that can influence people's behavioral intention. Nostalgia is known as an internal motivation that makes a person look for a way to need to relive past experiences whose memories cause pleasurable thoughts and feelings (Jacobsen, 2023). Research Methodology The main purpose of this research is to provide a model of destination branding based on nostalgia and tourist travel decision in the tourism industry, so the current research is in the field of applicable-developmental research. Also, based on the nature and method, the current research is a descriptive-survey research with a mixed (qualitative-quantitative) approach, which is conducted cross-sectionally in terms of time. In this research, first, the initial research model was designed using the database theory, and then the obtained model (qualitative part) was subjected to validation using a field survey. The foundation data method and three stages of coding including open, central and selective coding were used to compile the initial model. The data collection tool included a semi-structured interview in the qualitative part, and a questionnaire from the qualitative phase (including 9 dimensions and 60 items) in the quantitative part. The statistical population in the qualitative section includes academic and market experts related to the tourism industry. The selection of 30 sample people was done purposefully and non-randomly, with the snowball technique. Determining the sample size in the qualitative section has been done with the number of 30 people in a semi-structured interview, based on reaching theoretical saturation. The statistical population of the quantitative research includes all tourists of Tehran province (unlimited society), which is selected using the formula of Kochran's unlimited society; a sample consisting of 384 people. Data analysis was done in both descriptive and inferential ways, and the bootstrap method was used by Smart PLS software to test the research hypotheses. The analysis in the qualitative section was done by MAXQDA 20 software. Research Findings Based on the selective coding results of the research, tourism infrastructures (including indicators of access to places of residence, transportation services of tourist destinations, access to various natural attractions, amenities and various tourism facilities) were selected as background categories in presenting the brand development model based on the role of nostalgia on the decision of the tourist trips in the tourism industry. Also, awareness of the destination brand, image of the destination brand, relationship management with tourists, and participation of tourists were identified as causal conditions in the model of destination branding based on nostalgia in the tourism industry. Tourism brand development (including tourism destination reputation indicators, tourism destination popularity, positive word-of-mouth advertising of tourism destination, etc.) were selected as the central phenomenon category in presenting the brand development model based on the role of nostalgia on the tourist's travel decision in the tourism industry. Brand nostalgia (including indicators of paying attention to emotional components and past experiences, creating cognitive and emotional connections, paying attention to the traditional culture of the tourist destination, the ideal self-concept of the tourist destination, etc.) were chosen as a category of strategies and actions in presenting the brand development model based on the role of nostalgia on the decision of the tourist travel in the tourism industry. Host tourism culture (including indicators of respect for tourists and hospitality, respect for different ethnic groups and beliefs of tourists, the spirit of accepting tourists, compliance with individual and social norms and values, etc.) were selected as a category of intervening conditions to present a brand development model based on the role of nostalgia on the tourist's travel decision in the tourism industry. Tourist travel decision (including indicators of planning to travel to a tourist destination, recommending others to travel to a tourist destination, etc.) were selected as the consequences category in presenting the brand development model based on the role of nostalgia on the tourist travel decision in the tourism industry. And finally, according to the qualitative part and the model presented using smart pls software, the relationship between the research variables, which was designed according to the model of the research assumptions, was examined and tested, and all the research assumptions were confirmed. Conclusion The results obtained in the first and second hypotheses show that brand awareness and brand image have a positive and significant impact on tourism brand development. Based on these findings, it is clear that in order to develop the tourism brand, it is necessary to provide proper information about the destination through different channels to increase people's desire to visit these areas. Also, the image that tourists keep of a tourist destination in their mind will also affect the development of the tourism brand. Research shows that people who have the desire to visit tourist destinations and buy from these destinations are more knowledgeable about tourist destinations than ordinary people. (Keskin et al., 2022). It is worth noting that in many cases it has been reported that creating a nostalgic feeling also improves the image of the tourist destination (Chi & Chi, 2022; Wang, 2022). In the third to fifth hypotheses, it was shown that nostalgia is influenced by factors such as brand development (branding), host culture, and infrastructure. As mentioned before, people feel very good about reminiscing; therefore, by inducing a sense of nostalgia in people, a big step can be taken in the direction of branding. Specifically in the field of tourism, if a tourist destination can evoke memories of the past among tourists, people's attitude towards the brand of that destination will improve significantly. Such a finding has already been reported by other researchers (Jiang, 2019; Christou, 2020). In the sixth and seventh hypotheses, it was found that the management of relations with tourists and the participation of tourists have a positive and significant effect on the development of the tourism brand. Tourist relationship management includes all the activities that a tourist center does to create a long-term relationship with customers. These communications include advertising, receiving feedback from customers, offering discounts, etc., and thus can lead to the formation of a powerful brand in the tourism market. On the other hand, branding in the modern era is a two-way relationship between the customer and the company, and without the participation of customers, the probability of success in branding decreases. Even today, the concept of value co-creation has been proposed, which shows that brand value is the result of continuous interaction between the brand and the customer, and without the participation of tourists, a strong brand cannot be created. The obtained results are consistent with the findings of previous researchers (Xu et al, 2023; Volgger et al, 2021). According to the result obtained about the effect of nostalgia on loyalty, it is suggested that tourism managers should first identify the factors that create a sense of nostalgia in tourists, and increase their intention to visit again by focusing on such factors.