Journal of value creating in Business Management

Abstract Abstract This study aims to explain the indigenous and cultural characteristics influencing the formation and development of hybrid entrepreneurship in Mazandaran province, with a particular focus on the integration of tourism and agriculture. A qualitative research approach, specifically phenomenology using Colaizzi’s method, was employed to deeply understand the lived experiences of local hybrid entrepreneurs and to identify the cultural and indigenous factors shaping their entrepreneurial paths. Data were collected through in-depth semi-structured interviews, using purposive and snowball sampling among 25 local tourism-agriculture entrepreneurs, and continued until data saturation. Data analysis followed Colaizzi’s seven-step process, emphasizing the extraction of fundamental concepts and thematic clusters. Research findings were categorized into three main themes: environmental and local variables, hybrid entrepreneurship-related variables, and facilitating factors. The results indicate that the interplay of cultural, geographical, and climatic characteristics, along with local networks and support structures, provides a suitable foundation for the emergence and continuity of hybrid entrepreneurship in Mazandaran. Accordingly, strengthening support infrastructures, focusing on education and knowledge transfer, and leveraging indigenous cultural capital are recommended as practical strategies. Introduction In recent years, hybrid entrepreneurship, as a new approach in which individuals engage in entrepreneurial activities alongside their main job, has attracted much attention in Iran and the world. Despite the significant development of this type of entrepreneurship, the role of environmental, social, and cultural factors in the formation and success of hybrid entrepreneurs has not yet been fully elucidated. Mazandaran Province, with its specific local and cultural characteristics (such as cooperative culture, family values, strong social connections, and ethnic diversity), has created a unique context for examining the impact of these variables on entrepreneurial behavior. However, most of the research conducted so far has focused more on economic or motivational factors and has paid less attention to explaining regional cultural and indigenous characteristics on the behavior and sustainability of hybrid entrepreneurs. This research gap becomes more serious when tangible differences in the success rate, motivations, barriers, and opportunities of hybrid entrepreneurship are observed among different regions of Iran. As a result, the main issue of the present study is to explain the specific cultural and indigenous characteristics of Mazandaran province, the path of formation and continuation of hybrid entrepreneurship. The answer can, in addition to enriching the theoretical literature, also provide the necessary basis for policymaking, empowerment, and targeted support for hybrid entrepreneurs in Mazandaran. Theoretical Framework Hybrid Entrepreneurship Hybrid entrepreneurship refers to the situation of individuals who simultaneously work in a formal job (either full-time or part-time) and launch or manage an independent entrepreneurial business (Uriarte et al., 2024). The distinctive feature of this type of entrepreneurship is the dual employment of individuals, the reduction of risk arising from the failure of a new business by maintaining a fixed job income, and the possibility of flexible planning for dividing time and resources between organizational and entrepreneurial activities (Crider et al., 2024). Hybrid entrepreneurs, by utilizing the knowledge and experiences gained in both fields, can transfer learnings and skills between their main job and their business and benefit from the profites of both. The process of forming hybrid entrepreneurship usually proceeds gradually; in such a way that the individual first works with his main job, then starts a new business on a small scale or as a side project, and then continuously evaluates the success of the new business by managing both activities in parallel (Carr et al., 2023). If the new business is established and profitable, some of these individuals decide to leave their main job and enter the entrepreneurial field completely. This gradual nature of the path, reduced risk, and the possibility of continuous assessment of conditions have made hybrid entrepreneurship a desirable option in societies with unstable economic conditions or conservative work cultures, and has led many entrepreneurs to prefer this path to full-time entrepreneurship (Demir et al., 2020). Research Methodology The statistical population of this study consists of all hybrid entrepreneurs active in the field of agritourism in Mazandaran province. Hybrid entrepreneurs here are people who simultaneously play a role in two specific fields—i.e., agricultural activities (agriculture, horticulture, animal husbandry, and rural by-products) or tourism (such as setting up ecotourism lodges, agritourism, or ecotourism). This group plays an important role in developing the local economy and preserving the socio-cultural characteristics of the province by linking local businesses and the new wave of entrepreneurship based on regional capacities. To select statistical samples for interviews, a purposive sampling method with maximum diversity was used, followed by a snowball sampling method to identify key individuals with diverse experiences. The inclusion criteria for samples included: simultaneous activity in two tourism or agriculture sectors, such that both sectors constitute a significant portion of the individual's time and income; a history of at least three years of continuous activity in both sectors; being native or long-term resident of Mazandaran province; willingness and readiness to participate in in-depth interviews and transfer lived experiences; geographical diversity (different cities/villages in the province), gender, education level, and age (to increase data richness). After initial identification of samples through local communication networks, unions, and recommendations from informed individuals; semi-structured interviews were conducted with 25 entrepreneurs (based on the principle of data saturation). The data analysis process was conducted based on the seven stages of the Colaizzi’s method: (repeated study of the collected data for general understanding, extraction of meaningful expressions, formulation of fundamental concepts and meanings, classification of meanings into conceptual clusters, formulation of a comprehensive description of the phenomenon under study, structural description (semantic construction) of experiences, validation of findings through feedback from participants). The validity and reliability of the research were ensured by criteria such as participant review, data richness, and use of a research collaborator. Specialized MAXQDA software was used to analyze textual data. Research Findings The findings of the present study are categorized into three main categories and several related clusters, each of which explains different aspects of the process of formation and continuation of hybrid entrepreneurship in Mazandaran Province. The first category, “Environmental and Local Variables,” emphasizes the role of indigenous cultural values ​​and beliefs and the geographical and climatic conditions of the province in creating opportunities and shaping entrepreneurial behavior; in such a way that indigenous culture and natural conditions of the region provide a unique platform for the development of hybrid businesses and, as an advantage and sometimes a challenge, are influential in policymaking and the choice of business model. In the category of “Variables Related to Hybrid Entrepreneurship”, the motivations and incentives for entering this field are highlighted and show that individual preferences, livelihood requirements, the search for economic sustainability, and the desire to maintain and strengthen cultural identity have been among the most important factors driving entrepreneurs to launch and continue hybrid businesses. Also, the issue of business model and structure explains the diverse approaches in managing and organizing these activities, which are designed in accordance with the local characteristics of the region and the necessities of integrating the two fields of tourism and agriculture. Finally, the category of “enabling and facilitating factors” reveals the determining importance of government and local supports and facilities, as well as the role of education and knowledge transfer (formal and informal). These results imply that access to support facilities and the creation of appropriate learning platforms both lead to strengthening the individual capabilities of entrepreneurs and facilitate the sustainability, development and effectiveness of hybrid businesses at the regional level. In this way, the classification of findings reflects the systematic and multilayered link between cultural-environmental components, local management motivations and models, and the role of supporting policies and infrastructure. Conclusion According to the results of the present study and comparison with previous studies, it is suggested that training programs and specialized workshops focusing on the transfer of local knowledge, successful experiences, and new skills of hybrid entrepreneurship in the field of tourism-agriculture be held for activists and interested parties at the provincial level. This work can be done with the participation of universities, local entrepreneurs' networks, and institutions in charge of rural and urban development to facilitate the transfer of experience and practical knowledge to the new generation and interested parties. It is also suggested that by creating support packages based on the specific needs and characteristics of hybrid entrepreneurs (such as financial support, access to infrastructure, reducing bureaucracy, and receiving expert advice) and focusing on utilizing eco-oriented capacities; the formation and continuity of hybrid businesses can be facilitated. It is suggested that these supports, in addition to economic aspects, be applied in the field of market access, marketing, and facilitating networking. It is recommended that provincial policymakers and planners, when designing entrepreneurial development models, pay special attention to strengthening the link between hybrid businesses and indigenous values, beliefs, and rituals of Mazandaran, because strengthening local identity will increase the sustainability and differentiation of entrepreneurial models. Platforms for cooperation between business owners in different fields (tourism, agriculture, information technology, etc.) and the development of interdisciplinary networks along with local entrepreneurial clusters can pave the way for the transfer of experience, innovation, and acceleration of the growth of successful hybrid entrepreneurial models in the province. These networks, with the support of government and academic institutions, can play a facilitating role in interactions and social capital.

Abstract Abstract The purpose of this research is to evaluate the five senses and the perceived value of customer loyalty in the insurance industry using the Fuzzy Hierarchy Analysis (FAHP) method. The research method is applicable in terms of purpose and descriptive-survey based on the method of data collection. The statistical population of the research included 30 professors and specialists in the field of marketing management, and sampling was done in a targeted manner. The information and data needed for the research were collected through a questionnaire, and the paired comparison technique was used to prepare the questionnaire. The fuzzy hierarchical analysis technique was used to analyze the data. The results of fuzzy hierarchical analysis showed that the most important factors affecting customer loyalty are: perceived value, and five senses. It also showed that the most important dimensions of the five senses in the insurance industry are: sight, hearing, touch, smell, and taste, respectively; and the results showed that the most important dimensions of perceived value in the insurance industry are economic value, social value, perceptual value, and emotional value; and the most important dimensions of customer loyalty in the insurance industry are: behavioral loyalty, attitudinal loyalty, and emotional loyalty respectively. Also, the results of the statistical analysis showed that perceived value (economic value, social value, perceptual value, and emotional value) has a positive and significant effect on customer loyalty. Introduction In order to create a positive relationship with customers, businesses must effectively manage marketing strategies as a tool to meet customer needs and build customer loyalty. While customer retention is an essential element in strengthening the company's profitability; loyalty is created with the aim of creating a long-term relationship between companies and their customers (Hwang et al, 2019). Previous articles have examined different ways of measuring loyalty and related factors that can strengthen the predictive power of the loyalty process (Baloglu et al, 2017, Tanford et al, 2013). Customer loyalty is the most important output of product and service providers (Lewin et al, 2015). One of the important factors in the formation of customer loyalty to products, services, and in general is the perceived value of a brand imprinted in the minds of customers. (Geuens et al, 2009). In his mind, each customer assigns one or more characteristics of human personality to each brand. Brand personality is a special characteristic that is perceived by the consumer, and is defined as a unique and valid term of the effort to give meaning to the creation of identity in the brand market. A number of insurance companies have increased the volume of their operations. In addition, insurance companies have enthusiastically adopted advanced information technologies in their operations. Since the central service is almost standardized and there is no doubt about the claim that competition in the insurance industry and other forms of it, such as service quality, takes time; therefore, competition is based on their ability to provide higher quality services to customers (Mualla, 2011). Thus, the main question of the current research is: what are the five senses and the perceived value of customer loyalty in the insurance industry using the Fuzzy Hierarchy Analysis (FAHP) method? Theoretical Framework Perceived value The perceived value of the brand through a complex process and a comprehensive approach is necessary to guide towards the desired repeat purchase behavior, and finally the perceived value is the consumer's overall assessment of the desirability of a product based on the perceptions he has of the receipts and payments. (Salehi Seghiani et al, 2019). Customer commitment Customer loyalty to the organization is a category that is affected by many and diverse factors and conditions inside and outside the organization, the effect extent of which varies according to the type of organization from one organization to another. Accurately recognizing these factors and determining the effectiveness of each of them in helping managers to make the right decision is very important (Shiri et al, 2017). Five senses Sensory marketing is the process of identifying and satisfying customer needs and interests in a profitable way to engage them in two-way communication that brings the brand's personality to life and creates added value for target customers. In marketing, conducting research on emotions in consumer behavior has created a new chapter called sensory marketing; the way in which it engages the consumer's emotions and affects his judgment perception and behavior (Hasali Ashtiani & Deilmi Moazi, 2015). Akbari et al, (2024) investigated the purpose of the current research; predicting consumer loyalty through the role of flow experience, perceived value, and corporate social responsibility. The results showed that attention, concentration, and the concept of time have a significant effect on the flow experience. Other results showed that flow experience, perceived value, and corporate social responsibility have a significant effect on consumer loyalty at the p<0.05 level. In this regard, it can be said that this company personalizes its customer experience by using the available information and creates it according to the individual needs of the customers. In this way, customers will feel that the necessary attention is given to them. Khatami Firoz Abadi et al, (2023) investigated the identification and prediction of factors affecting customer loyalty in Iranian insurance companies using confirmatory factor analysis and artificial neural networks. After analyzing the results of the confirmatory factor analysis method; commitment factors, perceived quality, trust, perceived value, empathy, brand image, attractiveness of other options, customer satisfaction had an effect on customer loyalty in Iranian insurance companies, and the switching cost factor had little effect on customer loyalty. Finally, the target model of the research was designed to predict fidelity with 8 input neurons, 110 middle layer neurons, and 1 output; with an error level of 0.00992 and a regression of 0.98694. Research methodology The research method is applicable in terms of purpose and descriptive-survey based on the method of data collection. The statistical population of the research included 30 professors and specialists in the field of marketing management, and sampling was done in a targeted manner. The information and data needed for the research were collected through a questionnaire, and the paired comparison technique was used to prepare the questionnaire. Research findings The fuzzy hierarchical analysis technique was used to analyze the data. The results of fuzzy hierarchical analysis showed that the most important factors affecting customer loyalty are: perceived value, and five senses. It also showed that the most important dimensions of the five senses in the insurance industry are: sight, hearing, touch, smell, and taste, respectively; and the results showed that the most important dimensions of perceived value in the insurance industry are economic value, social value, perceptual value, and emotional value; and the most important dimensions of customer loyalty in the insurance industry are: behavioral loyalty, attitudinal loyalty, and emotional loyalty respectively. Also, the results of the statistical analysis showed that perceived value (economic value, social value, perceptual value, and emotional value) has a positive and significant effect on customer loyalty. Conclusion The current research was conducted with the aim of evaluating the five senses and the perceived value of customer loyalty in the insurance industry using the Fuzzy Hierarchy Analysis (FAHP) method. The results of this research are in agreement with the results of Akbari et al, (2024), Khatami Firoz Abadi et al, (2023), Zyad Alzaydi (2023), Rashed et al, (2023), Bahrami et al, (2022), Behruzi & Sohrabi (2022), Asgari & Fazeli (2022), Lv et al, (2020), and Hwang et al, (2019). Akbari et al, (2024) showed that attention, concentration, and the concept of time have a significant effect on the flow experience. Other results showed that flow experience, perceived value, and corporate social responsibility have a significant effect on consumer loyalty. In this regard, it can be said that this company personalizes its customer experience by using the available information, and creates it according to the individual needs of the customers. In this way, customers will feel that the necessary attention is given to them According to the results of the research, the following suggestions were made: 1- By specifying the goals of the organization, organizational processes, the support of the managers of the organization to the employees, the system of rights and benefits, and the promotion system in the organization are among the things that can affect the perceived value. 2- Adequate knowledge of the buyers and target customers should be done because, in order to be able to create excellent and superior value for them, it should be done continuously to ensure the customer's interests.

Abstract Abstract The purpose of this study is to investigate the role of open innovation, social capital, collaborative knowledge creation, and cooperation with foreign partners to increase agility in today's turbulent world, and provide a new model for applying open agility enablers in selected small and medium enterprises of Yazd Industrial City. The research method used in this research is applicable in terms of purpose, and it is a descriptive-survey type of research. The statistical population of this research includes experts from top and middle managers of 17 selected small and medium companies in Yazd Industrial City, totaling 92 people. Due to the limited availability of the population, the census method was used to collect data; and finally 89 questionnaires were returned. The data collection tools include the questionnaires of collaborative knowledge creation by Al-Amoush et al., (2020), organizational agility by Lee et al., (2015), social capital by Liu et al., (2016), cooperation with external partners by Rezazadeh and Nobari (2018), and open innovation by Chen and Liu (2018). Smart PLS software was used to check the validity, reliability and fit of the conceptual model of the research, and the validity and reliability of the questionnaire questions were confirmed. The research results showed that all 4 research hypotheses were confirmed based on the significant values ​​of the hypotheses. Therefore, organizational agility is significantly influenced by open innovation followed by cooperation with foreign partners and the creation of collaborative knowledge, and social capital has a positive and significant effect on the creation of collaborative knowledge in small and medium-sized companies selected in Yazd Industrial City. Introduction In a rapidly changing business environment, organizations often face challenges such as market turbulence, competitive pressures, and unexpected disruptions (Baškarada & Koronios, 2018). To progress in today's dynamic environment, organizations must have the ability and agility to adapt to these changes and make new adjustments and increase their innovation capacity (Audretsch & Belitski, 2023), which agility plays an important role in the activities of different areas of the organization to help responding to changes (Rezaei Sadrabadi & Karimi, 2022). The goal of organizational agility is to create satisfied customers and employees and a set of necessary capacities to respond to changes in the business environment, therefore, increasing organizational agility is necessary (Nikkhah, 2022). But traditional competitive strategies are ineffective in facing the uncertainty in today's working conditions (Arsawan et al, 2020) and more openness of the business model and the dissolution of closed organizational boundaries are obviously needed to maintain competition in this evolving landscape (Redlich et al, 2008), and companies need agility, innovation, and flexibility with an open approach to maintain their share in the market. This is especially important for small and medium-sized companies that have limited financial resources, human resources, and research and development. Since creating agility and innovation requires a lot of investment in the field of human resources and research and development, agility and innovation with an open approach is a fast and efficient way to acquire the necessary resources and capabilities (Mirfakhradini & Rezaei Sadrabadi, 2020). Considering that many small and medium companies in Yazd Industrial City use closed management methods and processes and avoid communication with the external environment and cooperation with foreign partners, it prevents them from acquiring more knowledge, agility and sufficient flexibility. Therefore, in order to achieve open capabilities in agility, more research should be done on the prerequisites and enablers of open agility in today's new conditions and in accordance with the small and medium enterprises of Yazd Industrial City. Therefore, in this research, we are looking for an answer to this question: what is the effect of open enablers on the agility of selected small and medium companies in Yazd Industrial City? Theoretical Framework Organizational agility and dynamic capabilities in small and medium enterprises Organizational agility is rooted in the two main concepts of reactive adaptation and organizational flexibility (Sherehiy et al, 2007), which shows the ability to recognize specific environmental conditions and quickly deal with changes in resources, business processes, and organizational strategies (Žitkiene & Deksnys, 2018). In the sector of small and medium enterprises, adaptation and quick response to issues and problems will be necessary for future development (Liu & Yang, 2020). Basically, organizational agility can be introduced as the ability to respond with the aim of identifying and implementing a more efficient approach in a complex environment (Panda & Rath, 2016). Social capital and collaborative knowledge creation Researches in the past investigated the function of social capital in supporting knowledge management to achieve sustainable performance (Tu, 2020). Social networks in the organization can act as a channel for transferring and integrating knowledge, in a way that improves the creation of dynamic ideas, new values ​​and their sharing (Ode & Ayavoo, 2020). Collaborative knowledge creation is introduced as a collaborative approach (Calantone et al, 2002) in creating and developing knowledge between partners in order to improve insight into changes (Zhao et al, 2020). Open innovation and organizational agility Open innovation reduces the risks associated with trial and error by providing access to diverse and complementary knowledge. It acts as a catalyst for a company's innovation engine, and offers exceptional flexibility (Bianchi et al., 2016). In addition, the approach of using external resources fosters the continuous exchange of knowledge with colleagues, thereby increasing knowledge diffusion (Scuotto et al., 2017). Open innovation enables companies to save significant time and resources needed to develop market-specific knowledge to exploit opportunities (Lee et al, 2015). Collaboration with external partners and agility Collaboration is considered as a useful strategy for making companies agile thanks to access to the other party's resources and knowledge during the implementation of joint projects (Sanchez & Nagi, 2001). Cooperating with an agile company makes the main capabilities and competencies of the other party, which are more adaptable and responsive to the demands and rapid changes of customer markets, to be jointly exploited (Yusuf et al, 2014). Motwani & Katatria (2024) in a review study investigated the concept of organizational agility and its relevance in today's dynamic business environment in order to identify the key factors affecting organizational agility and the benefits associated with cultivating agility. The results showed that organizational agility is important in three basic aspects: strategic level or market investment level, internal operation level, and individual level. Arifin & Purwanti (2023) investigated the role of leadership agility, organizational culture, and motivation on organizational agility. In general, this research emphasizes the importance of leadership agility, organizational culture, and motivation in guiding and sustaining organizational agility. The findings emphasize the importance of training agile leaders, fostering a supportive culture, and fostering employee motivation to enhance an organization's ability to adapt, innovate, and thrive in a dynamic business environment. Research methodology The research method used in this research is applicable in terms of purpose, and it is a descriptive-survey type of research. The statistical population of this research includes experts from top and middle managers of 17 selected small and medium companies in Yazd Industrial City, totaling 92 people. Due to the limited availability of the population, the census method was used to collect data; and finally 89 questionnaires were returned. The data collection tools include the questionnaires of collaborative knowledge creation by Al-Amoush et al., (2020), organizational agility by Lee et al., (2015), social capital by Liu et al., (2016), cooperation with external partners by Rezazadeh and Nobari (2018), and open innovation by Chen and Liu (2018). Smart PLS software was used to check the validity, reliability and fit of the conceptual model of the research conceptual model. Research findings Based on the significant values ​​obtained in the hypothesis test, all 4 research hypotheses were confirmed. The findings of the research show that open innovation as far as 55.4%, cooperation with foreign partners 20.6%, and collaborative knowledge creation 24.3% impact on the organizational agility of small and medium-sized companies selected in Yazd Industrial Town; and social capital 84.8% impacts on the creation of cooperative knowledge of selected small and medium companies in Yazd Industrial Town. Conclusion The current research was conducted with the aim of identifying and investigating the impact of open enablers on the organizational agility of small and medium-sized companies selected in Yazd Industrial City, and investigated the impact of cooperation with foreign partners, collaborative knowledge creation, and open innovation on agility. The results of this research are consistent with the results of Chung et al, (2019), Al-Omoush et al, (2020), Cepeda & Arias-Pérez (2019), Ravichandran (2018), and Ahmadi & Ershadi (2021). The results of the first hypothesis show that the existence of social capital in selected small and medium companies of Yazd Industrial City can lead to an increase in collaborative knowledge creation in them. Therefore, creating a mechanism for the interaction of all social resources directly and indirectly for the use of social capital in selected small and medium companies of Yazd Industrial City will lead to the production of more collaborative knowledge. The results of the second hypothesis show that the creation of collaborative knowledge in selected small and medium companies of Yazd Industrial City can lead to an increase in organizational agility. Therefore, in order to use collaborative knowledge capital, companies must have an open and cooperative model and increase agility. The results of the third hypothesis show that cooperation with foreign partners can lead to the improvement of organizational agility in selected small and medium-sized companies in Yazd Industrial City. Therefore, by cooperating with foreign partners, companies are more successful in achieving agility and responding to the market due to access to more external resources. The results of the fourth hypothesis show that open innovation can lead to an increase in organizational agility in selected small and medium-sized companies in Yazd Industrial City. Therefore, by using open innovation, companies can identify and use more resources and opportunities and increase agility. According to the results of this research, the following suggestions are presented: Companies should seek to identify suitable external partners and provide opportunities for collaborative knowledge creation and create online knowledge bases for storing and sharing knowledge and information, creating an organizational culture suitable for open innovation, and creating an atmosphere of cooperation with universities, research centers, and other companies to develop innovative ideas.

Abstract Abstract
The purpose of this research is to identify the primary elements and components effective on electronic customer relationship management. According to its purpose, the research method is applicable; and in terms of nature, it is descriptive; and its implementation method is Delphi technique. The statistical population of the research includes 35 lecturers, professors, and faculty members of the Faculty of Management, as well as senior experts and managers of private banks; and the interviews continued until reaching theoretical saturation. Delphi panel members were selected through purposeful sampling. A semi-structured interview was used to collect information. The Delphi method was used to analyze the data and to reach a consensus on the components obtained from previous research and articles published on scientific sites in the field of electronic customer relationship management. The results showed that variables related to causal factors including human factors, technology factors, support factors; and background factors including cultural factors and industry factors; organizational factors including organization design factors and customer factors were identified and categorized as effective variables on electronic customer relationship management. Also, satisfaction and loyalty were recognized as consequences of using electronic customer relationship management.
Introduction
Fierce competition, globalization, increasing customer demand, and exposure to higher credit risks have forced banks to provide the best possible services in a fast and efficient manner to retain their customer base and turn them into loyal patrons (Mang'unyi et al, 2018) and try harder to improve their profitability (Joju et al, 2017). At the same time, product homogeneity has also added to the burden of the banking industry and has proven to be a challenge for banks to maintain customer loyalty during such a significant change in technological behavior as well as in consumer behavior (Singh & Chauhan, 2022). Also, the changes caused by the rapid transformation in technology and adaptation by customers, followed by the greater penetration of the Internet and the increase in the use of smart phones, have put banks under serious challenges in countless ways (Mathew et al, 2020). As the traditional banking model declines, the traditional approach to electronic customer relationship management may not be compatible with the new banking model. Electronic customer relationship management is a reactive approach that lacks transparency, so it needs improvement. The emergence of electronic customer relationship management is one of these phenomena. Electronic customer relationship management, as a preventive approach, is the only solution to the current situation in which banks can operate (Singh & Chauhan, 2022). Electronic customer relationship management enables customers to interact with their banks remotely through Internet communication platforms and devices. Electronic customer relationship management increases profitability through customer retention, cost reduction, and valuable engagement (Abu-Shanab & Anagreh, 2015). Stating that customer retention is the main driver of a company's profit growth, a 5% increase in retention leads to an increase in profit in the range of 50% to 100% (Bezhovski & Hussain, 2016).
In this regard, the main question of the research is: What are the primary elements and components that affect the management of communication with electronic customers?
Theoretical framework
Electronic customer relationship management
Electronic customer relationship management involves thorough investigation and practice of knowledge among customers to sell products and services (Ling & Yen, 2001; Hong Kit Yim et al, 2004). Given that in today's competitive environment, companies pay more attention to meeting customer needs through digital platforms, many different companies are implementing electronic customer relationship management systems with the aim of meeting growing customer service expectations (Azila & Noor Neeraj, 2011).
Electronic customer relationship management and customer satisfaction
When customers are satisfied with the services provided by their service providers, this relationship becomes stronger, which further leads to positive word-of-mouth advertising (Adnan et al, 2021, Mulyono & Situmorang, 2020). Electronic customer relationship management services help customers interact with their bank, which increases their satisfaction level and thus leads to the creation of a loyal customer base (Mulyono & Situmorang, 2020).
Customer relationship management and loyalty
Organizations that seek to implement customer relationship management to gain customer loyalty in their organization should consider the benefits of having loyal customers to get a more tangible picture of the customer. Loyal customers can lead to increased revenue for the company by spending more money than non-loyal customers. In addition, loyalty leads to less customer turnover to other competitors; it also creates positive word of mouth advertising and a plays supportive role in the decision making of others (Al-Shoura et al, 2017).
Customer satisfaction and loyalty
Customer satisfaction is considered as one of the key factors that lead to customer loyalty (Kaur Mokha, 2022). In fact, when customers are satisfied with the services provided by banks, they are willing to recommend it to others (Mulyono & Situmorang, 2018). Customer satisfaction leads to customer retention, customer loyalty, high revenue, and high profits. Long-term customer loyalty is useful for creating a familiar environment for customers who have few problems and objections about commercial products (Al-Shoura et al, 2017).
Singh & Anuradha (2022) investigated the operation of electronic management of customer relations and customer satisfaction in the insurance sector. The results of their findings showed that among the thirteen identified components; five factors: brand popularity to create attractiveness for customers, innovative product offering, quick and honest response, relationship building, and financial security are the most effective factors affecting customer satisfaction. They have the greatest effect on buying an insurance policy and increasing satisfaction.
Kumar & Kaur Mokha (2022) investigated the interactions between electronic customer communication management and customer experience, satisfaction and loyalty. The results of the findings showed that all relationships were meaningful and positive, and also customer experience and customer satisfaction are mediators in the relationship between E-CRM and customer loyalty. They claimed that the empirical results of their research had both theoretical and managerial implications that provided useful insights for bank managers to improve their long-term relationships with customers.
 
Research methodology
According to its purpose, the research method is applicable; and in terms of nature, it is descriptive; and its implementation method is Delphi technique. The statistical population of the research includes 35 lecturers, professors, and faculty members of the Faculty of Management, as well as senior experts and managers of private banks; and the interviews continued until reaching theoretical saturation. Delphi panel members were selected through purposeful sampling. A semi-structured interview was used to collect information.
Research findings
The Delphi method was used to analyze the data and to reach a consensus on the components obtained from previous research and articles published on scientific sites in the field of electronic customer relationship management. The results showed that variables related to causal factors including human factors, technology factors, support factors; and background factors including cultural factors and industry factors; organizational factors including organization design factors and customer factors were identified and categorized as effective variables on electronic customer relationship management. Also, satisfaction and loyalty were recognized as consequences of using electronic customer relationship management.
Conclusion
The current research was conducted with the aim of identifying the primary elements and components effective on electronic customer relationship management. The results of this research are in agreement with the researches of Ahmadi et al, (2024), Gholipour domyeh (2023), Singh & Anuradha (2022), Kaur Mokha (2022), Kumar & Kaur Mokha (2022), Kumar et al, (2021), Naim & Faiz Khan (2021), Rao & Reddy (2021), Ebrahimi & Yegangi (2021), and Baskabadi & Tosli (2021). Kumar & Kaur Mokha (2022) in their research considered customer experience and customer satisfaction as prerequisite tools for improving and strengthening long-term relationships with customers and showed that all relationships are meaningful and positive, as well as customer experience and customer satisfaction are mediators in the relationship between E-CRM and customer loyalty. They claimed that the empirical results of their research had both theoretical and managerial implications that provided useful insight for bank managers to improve their long-term relationships with customers.
According to the results obtained from the research, the following suggestions are presented:

Aggregation and software integration of technologically integrated interactive channels for customer relationship management;
Determining the training system for customers and managing the analysis of customer needs based on interaction through social media in electronic customer relationship management;

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 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 aim of this study is to present a performance management model based on BSC and EFQM models in Golgohar Mining industrial companies. The research method is applicable-developmental, and the research method is descriptive-correlational, using structural equation modeling. The statistical population of the study includes 940 employees of Golgohar Mining and Industrial Company, of whom 270 were selected by simple random sampling using the Cochran formula. To collect research data, three questionnaires; BSC, EFQM, and performance management, were used, whose validity and reliability were appropriate and acceptable. Data analysis was performed using SPSS and AMOS statistical software. The results of structural equation modeling show that the conceptual model of performance management using the BSC and EFQM models in Golgohar Mining and Industrial Company has an acceptable fit, and the balanced scorecard and organizational excellence models, if integrated can provide a suitable framework for performance management in Golgohar Mining and Industrial Company by covering each other's weaknesses. Introduction Undoubtedly, what distinguishes our world today from the world of organizations a few decades ago is the unstable and complex environment, increasing competition, rapid change and developments, and the increasing development of communications (Siraj & Hágen, 2023); and, on the other hand, the remarkable and continuous developments in management knowledge that have made the existence of an effective performance management system inevitable for organizations (Ohlig et al, 2021); in such a way that the lack of evaluation in various dimensions of the organization (including the evaluation of the use of resources and facilities, employees, goals and strategies) is considered as one of the symptoms and diseases of the organization (Shen et al, 2021). Therefore, all organizations are somehow involved in the issue of organizational performance evaluation; but what they do not agree on are the frameworks, methods and processes (Ehmann et al, 2023). Performance management is a system for measuring the performance of human resources based on defined and agreed indicators (De Rooij et al, 2019). The goal of such a system is an economic and exploratory assessment of the various activities of an organization (Cvetkoska et al, 2023). In an organization, each individual needs to be aware of their position and performance in order to achieve the set goals and progress in their work [including reducing costs and waste, increasing productivity and efficiency and profits, increasing the satisfaction of those who refer to the organization with the services provided, improving the organization's output results, etc.] (Leviäkangas, 2021). Such awareness allows individuals to be aware of the weaknesses and strengths of their performance and behaviors, and to take the necessary measures to make their efforts more effective (Nguyen & Dao, 2023). BSC establishes a connection between strategic goals and criteria, and is responsible for planning goal setting and strategic alignment (Sarraf & Nejad, 2020). EFQM, as a comprehensive model for measuring the performance of organizations through the design and implementation of an organizational performance assessment system, analyzes their level of intelligent mobility in the optimal design of the movement path, the optimal implementation of goals, the review of the results obtained, and the measurement of the effectiveness of the actions taken; and determines the level of success of organizations in achieving excellence (Romero del Castillo et al, 2021). EFQM is also a methodological framework for evaluating the performance of organizations in two areas: processes and the results of these processes. The achievements of the assessment in this model are the strengths of the organization and its areas for improvement, which suggest a list of prioritized programs to achieve improvements. Based on the lessons learned from Total Quality Management (TQM), paying attention to the eight fundamental values ​​and concepts is a prerequisite for the success and continuous improvement of organizations (Bourillon et al, 2020). Based on the above, the researcher tries to address the main question: What is the performance management model based on the BSC and EFQM models in mining and industrial companies? Theoretical Framework Performance Management Performance management is a process for achieving the business and overall goals of the organization through greater employee participation in activities, and performance appraisal [in the realm of performance management] is defined as an effective means of monitoring and developing employees in work groups (Dolatiet al, 2020). Performance management is a systematic approach that leads to improved organizational performance through the processes of setting strategic goals, measuring, collecting and analyzing data, reviewing performance data reports, and applying its results (Bitkowska, 2018). Shariati et al, (2024) investigated the identification of factors affecting employee performance management with a human resource development approach in research and technology organizations. According to the interviews conducted, 6 dimensions of the paradigm model, 23 components, and 105 indicators were extracted. Then, according to the paired comparison questionnaire to identify influential and influenced dimensions, it was determined that among the 23 components, the components of the workplace, laws and regulations, socio-cultural factors, human resource planning, role clarity, performance measurement, organizational climate, reward system, employee communication, training, performance evaluation, professional ethics, leadership style, and motivational factors are the most influential, respectively. Nasiri et al, (2024) investigated the design of a general employee management model with an emphasis on performance management. The findings of the qualitative section showed that the major and core categories in the form of six dimensions of causal conditions, pivotal phenomenon, contextual conditions, intervening conditions, strategies and consequences were upgraded to a higher abstract level, and finally the paradigmatic model of the research was presented. In the quantitative section, the general management model was also approved. Research Methodology Regarding its purpose, the research method is applicable-developmental, and descriptive-correlation research method, of structural equation modeling type. The statistical population of the research includes 940 employees of the Golgohar Mining and Industrial Company, of whom 270 were selected by the Cochran formula using simple random sampling. To collect research data, three questionnaires were used: BSC, EFQM and performance management, whose validity and reliability were appropriate and acceptable. Research findings Data analysis was carried out using SPSS and AMOS statistical software. The results of structural equation modeling show that the conceptual model of performance management using the BSC and EFQM models in Golgohar Mining and Industrial Company has an acceptable fit, and the balanced scorecard and organizational excellence models, if integrated, can provide a suitable framework for performance management in Golgohar Mining and Industrial Company, by covering each other's weaknesses. Conclusion The present study was conducted with the aim of presenting a performance management model based on the BSC and EFQM models in Golgohar mining and industrial companies. The results of this study are consistent with the results of Shariati et al, (2024), Nasiri et al, (2024), Mahdavian sadr et al, (2022), Ismail & Amin (2021), Karaevli (2021), Shirtaheri et al, (2020), Kim (2020), Baruch (2019), and Deft (2019). Kim (2020) concluded that the performance evaluation model, a combination of the balanced scorecard and organizational excellence models, is effective in managing the performance of the insurance industry. Baruch (2019) showed: Prioritizing the improvement projects proposed by the EFQM model, according to the strategic and long-term goals of the organization, seems necessary. Karaevli (2021) in a study showed: the balanced scorecard (BSC) and the European Quality Model (EFQM) are tools for evaluating the performance of the organization with the aim of continuous organizational improvement. Given the expansion of organizational performance management methods, it is necessary to pay sufficient attention to choosing a method that will result in the highest possible return on investment. According to the results of the study, the following suggestions are made: - It is recommended to the managers of Golgohar Mining and Industrial Company; Considering the relationship between strategic goals (BSC goals) and the criteria of the Excellence Model (EFQM) to determine the level of achievement of goals and also to increase the efficiency of these goals, use the EFQM criteria. For example: to achieve the goal of growth and learning, pay special attention to the criteria of leadership, strategy, employees and partners. - It is recommended; special attention should be paid to the criteria of the excellence model because these factors can provide a reliable understanding of the assumptions, needs and expectations of customers, global markets for greater competitiveness, as well as continuous improvement of processes based on the precise identification of situations and demands through the analysis of customer data, operational data and external benchmarks, etc.

Abstract Abstract The present study attempts to identify research gaps and new topics by analyzing and visualizing international research conducted on customer journey. This study is a descriptive one conducted using a systematic review method and was examined using a search term defined in the Web of Science database in the title of articles published in the period 1990-2023. After searching, screening, and qualitative evaluation of the studies, a final analysis was conducted on 241 articles and an in-depth analysis of the studies showed that most of the research in the field of customer journey is related to the year 2021. The information required to achieve the research objectives was explored using the software "VOS viewer" version 11.6.1; a software in the field of scientometrics. Maps were drawn and analyzed. The results of the word co-occurrence showed the most frequent words in this field. Also, according to the results of co-authorship, the countries that had the most scientific cooperation were identified. By examining the publication trend of articles, it can be seen that the field of customer journey has been on an upward trend. Also, the map of co-authorship of countries shows the isolated role of Iran among other countries. As a result, paying attention to the most and least active countries, words and researchers through scientometrics can reveal research opportunities and weaknesses in the field of customer journey process and illuminate the horizon of progress of Iranian researchers to shine their research results at the international level. Introduction The customer journey is defined as a process that the customer goes through in all stages and the touchpoints that constitute the customer experience; that is, as long as customers invest their resources (such as time, emotions or financial resources) in their brand interactions, their journey with the company will continue (Lemon & Verhoef, 2016). The customer journey explores the understanding of customers’ behavior, emotions, and motivations along the journey (Fallast & Vorbach, 2019). Understanding the distinct stages, customers’ encounters with an organization, and the factors that influence them is of considerable importance for any business that aims to increase customer orientation. These encounters are called touchpoints and refer to distinct entities such as places, people, or tools that customers encounter during their journey to purchase a product or service (De Keyser et al, 2020). The present study has dedicated its mission to reviewing published articles in the field of customer journey, which is one of the important and widely used areas of the management discipline, using scientific maps and providing scientometric analyses. A scientific map shows a spatial representation of the relationship between articles, terms, and their findings. Theoretical Framework Customer Journey The customer and his satisfaction are key issues that can lead to the advancement of a group in the current competitive world. In this regard, it is necessary and important to pay attention to what can be effective in increasing customer satisfaction more accurately and completely. Today, the quality of services and products and customer satisfaction during the customer journey have become the most important core of marketing; because they are the prerequisites for customer loyalty and cause repeat purchases. They can attract more customers and, by providing more specific services to users than their other competitors, witness greater loyalty and attract more customers. (Behrozi, 2022). The customer journey is a tool for understanding the behavior and motivation of customers when purchasing, and is also used to analyze customer paths in order to identify problems when providing services. By looking at the role of each customer journey, fundamental insights are gained into how customers use the service offerings, portfolios, programs offered, and touchpoints that exist (Mucz & Gareau-Brennan, 2019). Research Methodology The method of conducting the present study is fundamental in terms of orientation, as it seeks to find the intellectual paradigm of international researchers in the field of customer journey. The approach of the present study is inductive, as it goes from part to whole, and its formulation is quantitative. From the perspective of the purpose of the research, the exploratory approach dominates the research, which is carried out in a single cross-sectional manner. Based on this protocol, on 4/10/2023, a systematic study was conducted of all research articles published in the database (Web of Science) regarding the customer journey, which were published between 1900 and 2023, and the data analysis was carried out using the software (VOS viewer). In the Web of Science database, the initial search results included 404 articles, which were screened based on the researchers' scientific judgment in three stages. After screening and final analysis, 241 articles were analyzed. Research findings In the present study, according to Table 1, out of all 241 articles published by different authors, according to the Web of Science database report, an H-index of 44 was reported, which indicates the strength of the scientific credibility of the research conducted in the field of customer journey. Citation or use of scientific materials of an author by another author who has referred to it in his article or book indicates the high credibility of that article. Increasing referencing to a researcher also has a direct relationship with increasing the H-index of that researcher. The citation index in the present study, according to the Web of Science database report, is 5582. Based on the year of publication of the articles, Chart 2 shows that during the years 2011 to 2023, the research trend and growth of scientific production on this subject have been on an upward trend, and the number of articles in this field has grown significantly. In this study, it was found that many researchers are engaged in research on the subject of customer journey. In Table 3, ten authors who have published more research in reputable journals in the field of customer journey are shown. In the present study, the co-authorship network of researchers in the field of customer journey is drawn according to Figure 3. In this regard, for a more detailed understanding, the connections between authors who have scientific collaborations were identified through the Vis Viewer software, which are classified into 4 clusters, and each color in the image represents a cluster. In Figure 5; the network map, each circle represents a keyword analysis unit. The purpose of the present study in drawing this map is to understand the structure of the relationships between concepts related to the customer journey that have been used by the authors in the articles. In this way, by considering the main axis of each of the research conducted on the subject under study, in addition to identifying new concepts, it is possible to discover remote but important terms. The overlap map shows the relationship between terms associated with the research period. Figure 6 depicts the year-by-year trend of research conducted on the customer journey. Based on the navigation bar on the right side of the map, we can say that the yellow color represents new terms and the older terms are blue. In other words, keywords that are dark blue are related to articles published around 2018 and before, and conversely, yellow keywords are related to articles published around 2020. In the vocabulary overlap map, we can see that most of the map area has a green color tending to yellow. This indicates the growth of scientific research on the customer journey. Conclusion Statistics and figures show that in the field of customer journey in the business domain, the largest number of articles is about 61%, in the field of management, 50% of the articles are included, and there are articles in other fields as well; but to a lesser extent, which indicates that there is a long way to go for the development of customer journey in the field of health and medicine, which researchers have not yet reached. In the map of co-authorship of countries, 67 countries had published at least 1 article. Based on the results of this map, it was determined that the United States is known as the largest research center with the publication of 55 articles and 4107 connections. As mentioned, Iran does not have a place in this map. This indicates the lack of cooperation of Iran with other countries on the subject under study; of course, this result is not far-fetched considering the theoretical background. It is also ranked second by a small margin from the United Kingdom with the publication of 49 articles and 1295 connections. The trend of studies in this field showed that although the amount of research has increased continuously in the period under review, scientific production has increased significantly from 2011 to 2023; and in the year ending in 2021, the number of articles published has reached its maximum, and out of the 241 articles reviewed, 53 articles have been published, which indicates the research interests of scientists in this topic. The co-authorship network drawn from authors in the field of customer journey studies showed that the active and top authors in this field are Jaakkola, Elina\ Rauschnabel, Philipp A.\ Lemon, Katherine N\ Bonfanti, Angelo\ Verhoef, Peter C. In the network map, each circle represents a unit of keyword analysis, and by examining the aforementioned map, it can be seen that the words customer journey and customer experience are among the most frequently used words with 25 and 17 repetitions, respectively. In the word overlap map, the co-occurrence of words shows the relationship between terms associated with the research area. In recent years, concepts such as “consumers”, “identity”, “technology”, “strategy” and “augmented reality” have been in focus, while more recent concepts such as “customer journey”, “customer experience”, “satisfaction” and “impact” have become widespread (trending). In the maps of co-authorship network and co-authorship density of countries, it examines the countries active in the field of research on the subject of customer journey. According to the aforementioned map, it can be found that the United States, the United Kingdom, Australia, France, Norway, Finland and Sweden can be considered pioneers and founders in the field of scientific production. If researchers are willing to conduct research in this field, the least used keywords are an opportunity to do something new, and if they are willing to improve the citation index of their articles, choosing the most used keywords can provide them with this opportunity. Thus, this study has played a significant role in promoting knowledge in this field by introducing the emerging trend of scientific research in the field of customer journey. According to the findings of the research on international research and co-authorship maps of countries on the subject of customer journey, it is suggested that Iranian authors have scientific cooperation with other foreign countries, especially the United States and the United Kingdom, in order to improve both the position of authors among international authors and the position of Iran in the co-authorship map of foreign countries. Also, considering the results of the co-occurrence of words, it is suggested that researchers take advantage of emerging words in this field such as "customer journey", "customer experience", "satisfaction", and "consumption rate"; and thereby fill the existing research gaps. The most recent and emerging themes identified in the word co-occurrence maps should be tested through quantitative methods. The customer journey path should be analyzed in other reputable scholarly databases such as Google Scholar, Science Direct, and Scopus using other bibliometric and scientometric techniques.

Abstract Abstract The aim of the present study is to determine a pattern for identifying uncertainty and risk and increasing flexibility in capital budgeting decisions with an investment discretionary approach. The research method is applicable-developmental in terms of its purpose, and qualitative in terms of the nature of the data. The statistical population of this study included 18 power plant experts from Mejmar who had at least 10 years of teaching, research, and management experience in power plants. Purposive sampling was used in this study. The data collection method was referring to documents and semi-structured interviews. Atlas ti software was used to code the interviews for data analysis. Based on the results obtained, 6 constructive themes and 14 basic themes were identified. The 6 constructive themes include political and international factors, legal and regulatory factors, financial and budgetary factors, technological and information factors, organizational structure and culture, and economic factors. The dimensions of the organizational structure and culture factors include: the structure and organization domain, the human resources domain, and the management domain. The dimensions of the technological and information factors include: the information technology environment domain and the information sharing domain. The dimensions of the economic factors include: the economic structure and the economic environment. The dimensions of the political and international factors include: the political structure and environment and transnational and international factors. The dimensions of the legal and regulatory factors include: general legal factors and specific legal factors. The dimensions of the financial and budgetary factors include: the human factors and individual capabilities domain, the intra-organizational requirements and factors domain, and the extra-organizational factors and requirements domain. Introduction Since the late 1970s, along with the spread of advanced financial techniques such as internal rate of efficiency and net present value at the firm level, many researchers have offered criticisms regarding the use of these techniques in valuing strategic investment decisions (Dai et al., 2021). The first criticism is related to the inability of these techniques to correctly value uncertain investments, under conditions where the firm has a degree of flexibility in decision-making (Alipour and Behdadian, 2020). In particular, early proponents of the option theory of capital assets criticized the NPV method for considering decisions only in terms of whether or not they are or are not, without considering the value of flexibility (choosing between different options when receiving new information). Using the theoretical framework presented for pricing contingent claims, financial researchers have proposed the option theory of capital assets as an alternative to the NPV method to overcome its shortcomings. Since the aforementioned conditions are often related to the structure of strategic decisions, the option theory of capital assets claims to be the best method for valuing such decision-making problems (Cheong, 2021). Recent research shows that about 20 percent of industrial projects are terminated before completion, and less than a third of them are completed on time and based on budget. Effective risk management is absolutely essential to prevent these problems from occurring. In fact, flexibility is a key factor in project success (Dai et al., 2021), because by it, senior management can design and develop risk coping tools according to the requirements and conditions of the risk. The option method allows project managers to be aware of the consequences of risk, deal with it in case of adverse events and use it in the best way if conditions are favorable, select the best capital allocation option and justify it to senior management and financial stakeholders. Therefore, this research, considering the Majmar Power Plant Group, seeks to answer the question: What is a model for identifying uncertainty and risk, and increasing flexibility in capital budgeting decisions with the investment option approach? Theoretical Literature Risk In the past, people used to rely only on financial information obtained from statements prepared based on historical values ​​and analyzed by experts to make decisions about investment locations. However, since the collapse of large companies such as Enron and WorldCom due to the failure to disclose financial scandals by company managers, investors have paid more attention to the prominent role of the governance system and its principles (Komar et al, 2017). On the other hand, after the separation of the legal personality of commercial enterprises from the real personality of their owners and the development of global trade and the owners' need for financing sources, the discussion of multiple owners in companies took shape and led to the creation of joint-stock companies. In the meantime, each of the shareholders; especially those who had more influence (which was mainly due to the high volume of their capital), tried to direct the financial decisions of the companies towards their own interests (Cerchiello et al, 2016). For many years, economists assumed that all groups in a corporation work for a common goal. However, in the past 30 years, many cases of conflicts of interest between groups and how companies deal with such conflicts have been raised by economists. These cases are generally expressed under the title of “agency theory” in management accounting. According to Jensen and Meckling’s definition: an agency relationship is a contractual relationship in which a principal or owner appoints a representative or agent on his behalf and delegates decision-making authority to him (Chen et al, 2019). The greater the number of major shareholders in the company’s ownership structure, the more supervision and control is shared among the major shareholders and the conflict of interest between them decreases; therefore, the company’s efficiency on equity increases (Kou et al, 2019). Research Methodology The present study is an applied-developmental study in terms of its purpose, and a content analysis study in terms of its research implementation method. The statistical population of this study includes 18 Iranian power plant experts who have at least 10 years of teaching, research, and power plant management experience. The sampling method used in this study was purposive. Qualitative content analysis can be considered a research method for the subjective interpretation of textual data through systematic classification, coding, and theme-building processes or designing known patterns. After taking interviews from participants and writing line by line the text related to the interviews, the researcher analyzed the texts; in fact, in this method, codes, concepts, and categories were identified through a systematic classification process, and then a model of uncertainty and risk and increased flexibility in capital budgeting decisions with an investment discretionary approach was presented. The snowball technique was also used to select participants, and each interviewee was asked to provide the researcher with a list of people who were willing and specialized to participate in a study. Coding was used to analyze the data obtained from the interview and theoretical foundations. Atlas ti software was used for analysis. Research findings Based on the results obtained from thematic analysis, 6 constructive themes, 14 basic themes and 132 initial codes were identified. The 6 constructive themes include political and international factors, legal and regulatory factors, financial and budgetary factors, technological and information factors, organizational structure and culture, and economic factors. The dimensions of the organizational structure and culture factors include: structure and organization, human resources, and management. The dimensions of technological and information factors include: information technology environment and information sharing. The dimensions of economic factors include: economic structure and economic environment. The dimensions of political and international factors include: political structure and environment, and transnational and international factors. The dimensions of legal and regulatory factors include: general and general legal factors and specific legal factors. The dimensions of financial and budgetary factors include: human factors and individual capabilities, intra-organizational requirements and factors, and extra-organizational factors and requirements. Discussion and Conclusion The aim of this study was to develop a model for identifying uncertainty and risk and increasing flexibility in capital budgeting decisions with an investment discretion approach (study in the Mejmar power plant group). The coding and text analysis process of the interviews was carried out in the Atlas ti qualitative data analysis software. Based on the results obtained from thematic analysis, 6 constructive themes, 14 basic themes, and 132 primary codes were identified. The results of this study are somewhat consistent with the results of Mashhadizadeh et al, (2020) and confirm the results of this study. According to the results of the study, it is suggested: to increase flexibility in the organizational structure, a map of the current organizational structure can be prepared and factors such as organizational hierarchy, different units, and the relationships between them can be examined. Then, according to the needs and market conditions, optimization of the organizational structure can be proposed and the necessary reforms can be applied. This includes modifying hierarchies, creating matrix teams, increasing coordination, and transferring responsibilities and competencies. By planning and implementing training and professional development courses for employees, they can be improved and provided with the necessary capabilities to deal with changes and risks. By encouraging teams and employees to actively participate in decision-making and budgeting processes, the flexibility of the organization can be increased.

Abstract Abstract The aim of this research is to sociologically explain the bottlenecks and challenges affecting the economic development of small and medium-sized enterprises with a data-based approach. The research method is applicable developmental in terms of purpose, descriptive exploratory in terms of strategy, and qualitative in terms of implementation method, with a grounded theory approach. The statistical population of the research includes 14 experts and academic scholars in the economic-sociological field. The sample size was selected using the snowball sampling method. A semi-structured interview was used to collect information. A grounded theory approach was used to analyze the data. The validity of the interview questions was confirmed by the content method; and its reliability by implementing the test-retest method (0.897). Based on data-based theorizing; causal conditions (including interactional/identity conditions and generational gap conditions), the central category (the process of lack of economic development and growth), the context makers (including weak cultural capital and lack of competitiveness), contextual factors (including lack of development thinking and low investment rates), strategies (including lack of purpose-centeredness and lack of value-centeredness), and consequences (including continued underdevelopment and business collapse) were explained in the paradigmatic model, and finally the relationships between them were identified in the selected model. Introduction Sociological components and factors cause differences among humans in ethics, responsibility, spirituality, dynamic management, entrepreneurial initiatives, risk-taking, and many other behaviors that are likely to affect the economic success of individuals (Throsby, 2001). Iran's economy is currently facing bottlenecks in various areas, for which a solution must be found for each of them. Perhaps one of the most important challenges is in the field of production. Although production and how to make it flourish is itself an important challenge, we will also face other challenges to make production flourish. The fragile recession caused the closure of many economic enterprises in the country; so much so that the government's 16 trillion rial financial assistance could not seriously solve the problems of small and medium-sized enterprises (Rezaei & Safa, 2016). The interest of economists and sociologists in the institutional manifestations of small enterprise development and their role in economic development has recently increased. This socio-economic concern is well reflected in the fact that all theories of entrepreneurship tend to trace the sources of economic change to the activities exerted by certain types of individual or collective agents (Gruzina et al, 2021). Unlike economics, sociology has been interested in analyzing companies as social institutions or social organizations from the very beginning, and a sophisticated analysis of this tradition remains today. Max Weber initiated this tradition, which has continued in the field of industrial sociology and today in the sociology of organizations, and is still alive in the field of industrial sociology and related fields, namely the sociology of work (Swedenborg, 2012). The most important influential factor is the marketing power of these companies to sell their products; therefore, the lack of sufficient information of companies in Arak city about the needs of the market at home and abroad is weak, which has faced these companies with the problem of lack of demand for their products, and this has an impact on sales and profitability. The results of comparisons of the implementation performance of the regulations on quick-turnover enterprises in Markazi province during the period under review also show that, in general, the industry and mining sector has had a negligible contribution to most of the indicators examined in this study. Due to the industrial nature of this city, it is desirable that by implementing effective support policies, the effectiveness of industry in the province’s GDP and employment can be realized. Therefore, serious support for early-stage industrial projects, removing existing obstacles in small and medium-sized enterprises, using the effective model of priority industries in Arak city, and providing the grounds and preparations for more investment in this city can be effective in increasing the share of small and medium-sized enterprises and their role in the development of the province. Accordingly, the present study seeks to answer this question: What are the sociological bottlenecks and challenges affecting the economic development of small and medium-sized enterprises with a data-based approach? Theoretical Framework Economic Development Economic development is economic growth accompanied by fundamental changes in the economy and an increase in production capacities, including physical, human, and social capacities. In economic development, a small growth in production will be achieved, but along with it, social institutions will also change. Economic development has goals that include: increasing the wealth and welfare of the people of society, reducing poverty, and creating employment, which are goals for achieving social justice. The view of economic development in developed and underdeveloped countries is different. In underdeveloped countries, the main goal is to increase the welfare and opportunities of the people, while in underdeveloped countries, the main goal is to eradicate poverty and increase social justice (Vahed Moghadam & Kaykha, 2015). Small and Medium Enterprises Small and medium enterprises are considered the backbone of an economy because they play an important role in poverty reduction, employment generation, promotion of foreign trade and technical innovation, and also significantly contribute to the growth of developing economies (Elahi Shirvan et al, 2023). Taromi (2024) studied the impact of small and medium enterprises and their role in economic development. The results showed that what is effective in the efficiency of these units in the economy of any country is that these units provide their goods or services to consumers in a competitive environment with the lowest cost and time, while complying with various standards of transportation - storage - sale. Rostamian et al, (2023) studied the effects of small and medium enterprise credit on economic growth and employment (a dynamic computable general equilibrium model). The results of the analysis of the employment rate in small, medium (and large) enterprises showed that the employment rate increased during the period from 2005 to 2012), considering the credit ceilings of 20, 30, 40 and 50 percent made by the government. Therefore, it can be said that the amount of credit allocation has a direct relationship with the production rate of small, medium (and large) enterprises and consequently the amount of employment. The amount of employment in large enterprises was higher compared to small and medium enterprises, which is due to the centralization of the integrated management system and the greater type of support that the government provides to these types of enterprises. Research Methodology The research method is applicable developmental in terms of purpose, descriptive exploratory in terms of strategy, and qualitative in terms of implementation method, with a grounded theory approach. The statistical population of the research includes 14 experts and academic scholars in the economic-sociological field. The sample size was selected using the snowball sampling method. A semi-structured interview was used to collect information. Research findings A grounded theory approach was used to analyze the data. The validity of the interview questions was confirmed by the content method; and its reliability by implementing the test-retest method (0.897). Based on data-based theorizing; causal conditions (including interactional/identity conditions and generational gap conditions), the central category (the process of lack of economic development and growth), the context makers (including weak cultural capital and lack of competitiveness), contextual factors (including lack of development thinking and low investment rates), strategies (including lack of purpose-centeredness and lack of value-centeredness), and consequences (including continued underdevelopment and business collapse) were explained in the paradigmatic model, and finally the relationships between them were identified in the selected model. Conclusion The present study was conducted with the aim of sociologically explaining the bottlenecks and challenges affecting the economic development of small and medium-sized enterprises using a grounded theory approach. The results of this study are consistent with the results of Taromi (2024), Rostamian et al, (2023), Muthuraman et al, (2021), Pedraza (2021), Nazari et al, (2021), Kushins et al, (2020), Kawaguchi (2019), and Bani Fatemeh et al, (2017). Nazari et al, (2021) showed that business cultural components have a positive effect on the production-commercial performance of industrial units. The research findings also indicate 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. One of the causal conditions affecting the bottlenecks and challenges affecting the economic development of small and medium-sized enterprises from a sociological perspective is the interaction/identity conditions. Maintaining power is highly valued in collectivist societies; therefore, there is no desire to delegate authority and involve colleagues in decision-making. In this regard, industrial managers are suggested to place the types of management on a bipolar scale according to the amount of authority delegated to subordinate colleagues. If these types of economic enterprises want to develop and continue in generations, they must first remove the obstacles and problems within the organization in terms of realizing meritocracy, prioritizing expertise, training members, etc. by observing organizational principles and management hierarchy. They should try to provide easier generational transition areas by using non-family specialist managers and by separating ownership from management.

Abstract Abstract The purpose of this study is to provide the pattern of effective factors in the socialization of artificial intelligence technologies. The present research is applicable in terms of purpose, quantitative in terms of implementation, and of descriptive-survey type in terms of nature. The statistical population of the research included 20 board members, financial managers, accountants and auditors, as well as financial statements and engineers in the field of artificial intelligence in the hierarchical method, selected by judicial and targeted sampling method; and, 342 of all employed accountants in the structural equation sector, by random sampling. Research collection tool is a questionnaire. A hierarchy of Expert Choice software as well as structural equations (PLS) was used for data analysis. The results of the analysis showed that independent variables and dependent variables have directly a positive and significant effect. The results also showed that increasing efficiency and productivity is the first priority, creativity and innovation is the second priority, saving time and money is the third priority, analyzing financial data is the fourth priority, collaboration and cooperation is the fifth priority, matching transactions is the sixth priority, transparency is the seventh priority, determining training needs is the eighth priority, trust in financial tools is the ninth priority, ease of use is the tenth priority, user-friendly tools are the eleventh priority, automation of repetitive processes, real-time financial analysis and financial protection and security are the twelfth priority, improving financial reporting is the thirteenth priority, and awareness, technical knowledge, and the capabilities of financial tools are the fourteenth priority. Introduction The advancement and utilization of artificial intelligence technologies transforms conventional patterns of life and work, thereby creating indelible changes in the social environment to adapt to the current society in which information is rapidly evolving. All disciplines and professions are rebuilding or improving their strategies, organizations, products, and approaches. Accounting is no exception (Concept et al, 2019). Accounting can now use electronic accounting, data mining, and multidimensional data analysis. However, accounting technologies and methods are merely a subsector which is being changed by artificial intelligence, and can have a significant impact on accounting goals (Yubin et al, 2021). Artificial intelligence is an essential component for the implementation of international accounting law. Accounting rules require systems support in complex risk coverage programs, and law enforcement is one of the important advantages of artificial intelligence in the accounting profession (Cong et al, 2018). Makridakis (2017) believes that artificial intelligence will soon replace traditional accounting and auditing. This change will lead to the separation of traditional accounting and auditing and will help accounting staff to improve their work. Accounting posts, structure optimization, restructuring as well as work quality and the ability of settings are the advantages of using artificial intelligence. Accounting needs to be modified to be more reliable in the community. For example, by means of artificial intelligence, accounting uses accounting robot to build simulator models from the environment. The financial robot has an instantaneous implementation speed. Information and automation of the accounting process under instantaneous response and conditions improves the efficiency of accounting activity. Proper programming of the financial robot can ensure the decline and specifications of each link, and can effectively reduce the occurrence of errors according to the designated methods. The financial robot only executes financial employees and legal steps. It performs data entry and step by step operation, thereby reduces artificial operations in accounting, and greatly prevents artificial operations (Suleiman et al, 2020). In this regard, the main question of the research is: What is the pattern of influencing factors in the socialization of artificial intelligence technologies in accounting? Theoretical framework Artificial Intelligence Artificial intelligence is a branch of computer science that examines the computational requirements of actions such as perception of reasoning and learning, and provides a system for doing so. Its main roots and ideas should be sought in the philosophy of linguistics, mathematics, psychology, neurology and physiology, and it has various applications in computer science, engineering sciences, biology and medicine sciences and many other sciences (Han et al, 2023). Socialization The socialization stage is the stage where an individual learns social norms. This stage is the same as the assimilation stage in the psychoanalytic system of form and the learning stage according to Blumer. After socialization of the assimilation or socialized learning stage, the socialized stage begins, meaning accepting social and cultural norms and in other words, becoming in tune with them or, according to Blumer, unifying and melting into them (Arizi & Barati, 2017).  Karamipour (2023) investigated the design of artificial intelligence competencies on organizational performance, taking into account the commercial marketing capabilities. The results showed that the mechanisms of artificial intelligence competencies influence the business-to-business marketing capabilities and organizational performance, and also the artificial intelligence ​​competencies model is validated on organizational performance by considering the aspect of business-to-business marketing capabilities. Pourshahabi (2023) examined the presentation of a systematic model of employee training using artificial intelligence. The research findings show that the inputs of the model include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- workplace data. The model process also includes 1- determining needs and goals, 2- data collection, 3- data preprocessing, 4- training the artificial intelligence model, 5- model evaluation and improvement, 6- implementation and deployment, and 7- monitoring and updating. Finally, the outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance. Research Methodology The present research is applicable in terms of purpose, quantitative in terms of implementation, and of descriptive-survey type in terms of nature. The statistical population of the research included 20 board members, financial managers, accountants and auditors, as well as financial statements and engineers in the field of artificial intelligence in the hierarchical method, selected by judicial and targeted sampling method; and, 342 of all employed accountants in the structural equation sector, by random sampling. Research collection tool is a questionnaire. Research findings A hierarchy of Expert Choice software as well as structural equations (PLS) was used for data analysis. The results of the analysis showed that independent variables and dependent variables have directly a positive and significant effect. The results also showed that increasing efficiency and productivity is the first priority, creativity and innovation is the second priority, saving time and money is the third priority, analyzing financial data is the fourth priority, collaboration and cooperation is the fifth priority, matching transactions is the sixth priority, transparency is the seventh priority, determining training needs is the eighth priority, trust in financial tools is the ninth priority, ease of use is the tenth priority, user-friendly tools are the eleventh priority, automation of repetitive processes, real-time financial analysis and financial protection and security are the twelfth priority, improving financial reporting is the thirteenth priority, and awareness, technical knowledge, and the capabilities of financial tools are the fourteenth priority. Conclusion The present study was conducted with the aim of providing a model of effective factors in the socialization of artificial intelligence technologies. The results of this study are in line with the results of Harikumar et al, (2023), Pourshahabi (2023), Karamipour (2023), Shirzad et al, (2023), Anca (2022), Noordin et al, (2022), Mahdavi (2022), and Jakob & Luciano (2021). Harikumar et al, (2023) concluded that artificial intelligence in e-commerce and financial industries has been used to achieve better customer experience, efficient supply chain management, improve operational efficiency and reduce material waste with the aim of designing standard and reliable methods of product quality control and finding new ways to reach customers and serve customers at low cost. Among the applications of artificial intelligence in e-commerce; corporate management and finance, sales growth, profit maximization, sales forecasting, inventory management, security, fraud detection and portfolio management are some of the main applications of artificial intelligence. According to the results of the research, the following suggestion is made: Accountants are advised to use artificial intelligence in the accounting and reporting process because the artificial intelligence system is completely proficient and has sufficient data in this regard. It can easily provide fast reporting and can also easily provide accurate reporting; that is, it can provide accurate reporting at any time. According to the conditions that the artificial intelligence system has, we can get from it, and it is enough to have data and it can obtain a lot of data itself; that is, it can be placed in the path of the information exchange system and exploit reports and data and it can itself provide accurate financial reporting in any style that the organization needs.

Abstract Abstract The aim of the current research is to identify the components of brand equity creation in Iran's garment industry. The research method is of a fundamental type with an exploratory purpose; and in terms of the implementation method, it is mixed (qualitative-quantitative). The statistical population in the qualitative section is made up of managers who are members of the Supreme Council of Iranian Clothing Brands, 16 of whom were selected based on the snowball method; and in the quantitative section, the statistical population is all customers who refer to Iranian clothing shopping centers, which due to the unlimited nature of the population, with Using Cochran's formula, 387 people were selected as the sample size by simple random sampling. Data collection was carried out by interviews in the qualitative part; and by a researcher-made questionnaire in the quantitative part, and the validity of the questionnaire was confirmed by the professors' confirmation method, and its reliability was confirmed by Cronbach's alpha. Coding, along with grand theory method was used in the qualitative data analysis; and SPSS and AMOS software in the quantitative part. According to the results obtained in this research, the four components of internal brand power, brand awareness, positive image of the brand, and perceived value are the most key components of creating brand value in Iran's clothing industry, and the obtained model has a good fit. Introduction In today's world, which is the world of consumerism, companies try to attract more audiences through different tricks, but in the end, it is the mind of the customer that determines the power of a brand and turns it from a simple brand to a brand (Rastgar et al., 2016). In other words, brand is a perceptual concept that is rooted in the realities of the product on the one hand, and expresses the perceptions and personal characteristics of the consumer on the other. In fact, the concept of brand is something beyond the product (Keller, 2012). Brands are defined by individuals, not companies. Because the inner feeling and perception of each person is different and in the end each person creates his own version of the brand; as a result, the brand is a completely relative concept that part of which is in the mind of the individual and other part in the mind of society (Moghaddam, 2023). Customers are the most important and fundamental source of brand value determination. If a company can imitate the production processes and product features, it certainly cannot imitate the positive experiences and memories of customers that have been obtained from years of buying and consuming products. Thus, brand equity helps companies gain competitive advantages, and creating, maintaining, improving, and preserving brand equity is one of the most privileged abilities of a brand manager. Therefore, the higher the value of a brand, the more loyal customers and fans it will have (Mohabbat Talab & Rezvani, 2018). On the other hand, the fashion industry is one of the growing industries that is constantly changing and evolving. Considering that we live in the age of information and communication, people can easily access the latest information in various fields, including fashion (Solomon, 2019). Identifying the components of the brand equity in any industry can, in addition to increasing the credibility and reputation of the brand, help to make the market more competitive, change the mentality of mistrust of goods made in Iran - which clothes are not excluded - and provide a basis for revising the incorrect macro-policies in support of Iranian quality products, so as to a big step be taken to develop and strengthen the national brand in the clothing industry. In this research, we are looking for an answer to the question: how to identify the components of brand equity creation in Iran's clothing industry? Theoretical Framework Brand equity The definition of the international marketing dictionary of brand equity defines the values, assets, funds and perceptions related to a product, service, or idea that are assigned to it and promoted by the creator of that product, service, or idea (Khalilnejad, 2021). After the correct implementation of branding and influence in the mind of the audience, the sum of the activities that lead to influence in the customer's heart and favor and customer's loyalty of the brand is called the brand equity (Hoseinzade & Baktash, 2018). Amiri & Rezaei (2023) investigated the analysis of consumer buying behavior influenced by awareness of sustainability in the fashion industry (case study: clothing industry). The results showed that the cognitive component including knowledge and awareness, the emotional component including emotions and feelings, and the behavioral component including the purchase decision; as dimensions of attitude towards the concept of sustainability, affect the purchasing behavior of fashion industry consumers in the field of clothing. Mohammadi et al, (2023) investigated the understanding of the phenomenon of brand courage in the fashion industry. The results showed that the model of brand courage phenomenon in the fashion industry includes five components: "brand-related features", "brand social actions", "advertising-related features", "competitor-related actions" and "customer-related actions". Research methodology The research method is of a fundamental type with an exploratory purpose; and in terms of the implementation method, it is mixed (qualitative-quantitative). The statistical population in the qualitative section is made up of managers who are members of the Supreme Council of Iranian Clothing Brands, 16 of whom were selected based on the snowball method; and in the quantitative section, the statistical population is all customers who refer to Iranian clothing shopping centers, which due to the unlimited nature of the population, with Using Cochran's formula, 387 people were selected as the sample size by simple random sampling. Data collection was carried out by interviews in the qualitative part; and by a researcher-made questionnaire in the quantitative part, and the validity of the questionnaire was confirmed by the professors' confirmation method, and its reliability was confirmed by Cronbach's alpha. Research findings Coding, along with grand theory method was used in the qualitative data analysis; and SPSS and AMOS software in the quantitative part. According to the results obtained in this research, the four components of internal brand power, brand awareness, positive image of the brand, and perceived value are the most key components of creating brand value in Iran's clothing industry, and the obtained model has a good fit. Conclusion The current research was conducted with the aim of identifying the components of brand equity creation in Iran's clothing industry. The results of this research are aligned with the results of Amiri & Rezaei (2023), Mohammadi et al, (2023), Taleghani et al, (2022), Yazdani Kachuei et al, (2022), Rezaeian & Asgari (2021), Azimi et al, (2021), Ghorbani dolatabadi et al, (2021), Khademi et al, (2022), Ishaq & Di Maria (2020), Beig & Nika (2019), and Molse et al, (2019). Ishaq & Di Maria (2020) showed that sustainability in brand equity is effective in reducing consumer cynicism and removing significant flaws in the current conceptualization of brand equity. According to the results of this research, the following suggestions are presented: It is suggested that the supporting roles of the government to implement the brand equity model in Iran's garment industry should be further investigated. It is suggested that the training needs of employees of Iranian clothing brands should be taken into consideration to improve the internal strength of the brand.

Abstract Abstract The present study aims to provide a model for developing employee cognitive trust in artificial intelligence. The statistical population of this study includes senior managers of companies in Tehran province, 17 of whom were selected as samples using non-probability purposive sampling. This study was conducted with a qualitative approach and using grounded theory. In-depth semi-structured interviews were used to collect data, and data analysis was performed using open and axial coding methods. Interviews continued until data saturation and were then analyzed using MAXQDA 2022 software. The results show that there are16 subcategories in the form of six classes: the causal factors include transparency, education and awareness, ethical compliance, and defining common roles and goals, are among the factors that help strengthen employee trust. Contextual factors are organizational culture and resources; and intervening factors are employee resistance and system complexity. Strategies include employee training and empowerment as key tools in improving human-machine interactions. Ultimately, the outcomes of this process include AI adoption, improved human-machine interactions, and increased organizational performance. The results of this analysis emphasize the importance of creating transparency, reducing system complexity, and improving employee understanding of the mechanisms and benefits of AI to increase cognitive trust. Introduction The creation of value through digital technologies depends on users’ trust in these technologies. Trust in this context is recognized as a key factor for the acceptance and utilization of new digital technologies. Numerous previous studies have examined the relationship between the transparency of AI systems and human trust in this technology and have obtained mixed results. Some studies have reported a positive relationship. For example, transparency in music recommendation systems can increase user trust (Mehrotra et al., 2024). Given the significant advances in the field of artificial intelligence, how employees interact with these technologies and the level of trust they have in them has become one of the key challenges in workplaces. Cognitive trust is recognized as one of the fundamental pillars in human-machine relationships, which is based on employees’ rational and logical assessments of the capabilities and competencies of artificial intelligence systems. This type of trust has a great impact on decision-making processes, group collaboration, and overall organizational performance (Lukyanenko et al., 2022(. Cognitive trust in artificial intelligence is of great importance because employees need to be confident in the capabilities of artificial intelligence so that they can benefit from it in their decision-making processes and in performing their tasks. Research has shown that when employees trust the capabilities of artificial intelligence systems, their acceptance and effective use in workplaces increases. This trust is particularly important in environments where AI is used as a decision-making support tool (Yu & Li, 2022). Consequently, developing and strengthening employees’ cognitive trust in AI in the workplace, especially given the technical and psychological complexities of this technology, is a key pillar in the process of its effective adoption and use. Given the existing evidence and new research, it is clear that transparency, continuous training, and performance-appropriate assessments of AI can help increase this trust. Thus, a more accurate understanding of how cognitive trust affects employee interactions with AI can not only lead to improved productivity and job satisfaction, but also generally facilitate decision-making processes and improve organizational performance. Therefore, future research should examine these components in more detail and provide more practical models for improving cognitive trust in interactions with AI. The present study seeks to answer the question: what is the model for developing cognitive trust in AI? Theoretical Framework Artificial Intelligence and Trust in Organizations Artificial intelligence, sometimes called machine intelligence, refers to the intelligence displayed by machines in various situations, which is in contrast to the natural intelligence in humans (Bagheri et al., 2024). The use of AI in organizations can generate great value and significantly improve the productivity and effectiveness of organizational performance. In particular, AI can improve the accuracy of recommender systems, gain user trust in these systems, and provide a better user experience (Cicek et al., 2025). Employee Cognitive Trust Cognitive trust is one of the main pillars in organizational relationships that is formed based on employees' rational assessments and conscious analyses of the capabilities, honesty, and predictability of others' behavior. In contrast to affective trust, which is based on emotions; cognitive trust focuses more on competence and professional capabilities (Cicek et al., 2025). In fact, this type of trust is formed when people evaluate the capabilities and honesty of others through rational evidence and previous experiences. Research has shown that cognitive trust has a significant impact on job performance and team interactions and, in complex organizational situations, plays a fundamental role in reducing conflicts and promoting cooperation (Rajabi-Farjad & Atapour, 2021). In various theoretical models, cognitive trust typically includes the dimensions of competence, honesty, and predictability. Competence refers to an individual's ability to perform tasks, integrity refers to fair and ethical behavior, and predictability refers to employees' expectation that others' behaviors will be predictable in different situations (Choudhury, 2022). These dimensions simultaneously affect the formation and strengthening of cognitive trust in workplaces. Research Methodology The present research approach is qualitative and its strategy is based on grounded theory. At the heart of this method, a systematic approach was used to achieve a paradigmatic model. The statistical population of this study included all senior managers of companies in Tehran province; consultants in this field, and academic experts. The sampling method was non-probability purposive sampling and snowball sampling. Research findings 17 interviews were analyzed. In the open coding stage, 613 initial concepts were reduced to 90 primary open codes and 45 secondary open codes after reviewing the data and merging similar concepts. In the second stage of axial coding, secondary codes were classified based on their relationship to similar topics and placed into 16 subcategories (components). In the last stage of open coding, the previously obtained components or subcategories were placed into more abstract categories or categories based on similarities, conceptual connections, and common characteristics between open codes and concepts. In the axial coding stage, the components obtained from the open coding stage were linked together in the form of causal conditions, pivotal phenomena, contextual factors, intervening factors, strategies, and consequences in a paradigmatic pattern. It should be noted that due to the length of the open coding stages, only secondary open codes are referred to for each category. Conclusion The findings of this study indicate that employee training and empowerment are key tools in improving human-machine interactions, which ultimately include the adoption of artificial intelligence, improving human-machine interactions, and increasing organizational performance. At the causal level, a set of key components were identified that provide the context for the formation of cognitive trust. Transparency and explainability of AI performance play an important role in the correct and reassuring understanding of employees, because users trust them more easily when they are aware of how the systems make decisions. The enabling factors also have a profound impact on the formation or weakening of this type of trust, as cultural and organizational contexts. A learning, collaborative, and technology-oriented organizational culture facilitates the faster adoption of new technologies and trust in AI systems. In addition, organizational resources, including expert human resources, budget, technical infrastructure, and senior management support, are factors that, if present, facilitate the path to trust building and, if absent, are considered an obstacle to it. In line with ethical compliance and preventing bias, the results of the study also showed that employees trust AI systems more to use accurate and transparent data for decision-making and also to comply with ethical rules and principles. According to the results of the study, the following suggestions were made:  Designing formal and informal communication platforms such as inter-unit meetings, organizational social networks, and collaborative software to facilitate effective communication between employees and different department  Establishing standard information security frameworks and implementing data quality controls periodically to ensure the accuracy, completeness, and updating of information used in decision-making  Designing user-friendly user interfaces and providing training on working with intelligent systems for employees, so that understanding, controlling, and predicting system behavior is simple and reliable for humans

Abstract Abstract The aim of the present study is to identify and investigate the effectiveness of supply chain risk indicators of online business activities in the food industry through machine learning method using single support vector algorithm. The research method is applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of implementation method. In the qualitative part, interviews with experts active in the food industry with complete information and sufficient experience in the supply chain of this industry have been used until theoretical saturation, which were 10 people of relevant experts in large companies in this field. In the quantitative part, the field method and questionnaire were used to collect data with statistical methods, and this number was also 114 people selected as a sample from the statistical population. Considering the data conditions and the application of machine learning in the supply chain, the support vector machine algorithm, one of the most powerful algorithms in the field of artificial intelligence, was used. The results showed that customer satisfaction has a negative effect in the research model. Supply chain coordination has a positive effect in the research model. Factors affecting costs have a positive effect, but its amount is moderate. Economic and market conditions have a positive effect in the model. Internet infrastructure has limited importance in the model. Environmental risks have a positive effect. Product quality has a negative effect in the model. Introduction Today, risk management is one of the effective factors in every industry and business activity in economic enterprises around the world. Therefore, for this purpose, it is first necessary to identify the relevant risks (Rajendran & Ravindran, 2019). Companies are always looking for ways to deal with work uncertainties. In this regard, risk management has been introduced as an efficient tool for organizational managers. Risk identification and management is a new approach used to strengthen and improve the effectiveness of organizations (Ghaderi & Tariverdi, 2020). Risk management is a logical and systematic method for analyzing, assessing, and dealing with risk related to any type of activity that enables organizations to minimize losses while taking advantage of opportunities. (Rahnamaye Rudposhti & Soleimani, 2021). Increasing costs and complexities in organizations, along with increasing uncertainty and risk, have led managers to use risk management to reduce risk-taking and deviation from goals (Jalali & Moghadamnia, 2022). Identifying supply chain risks based on minimizing and managing these risks has always been an important challenge for industries and organizations. Supply chain risks increase the likelihood of unexpected events occurring in this chain that may cause significant losses to the organization (Mehrmanesh & Safavi Mirmahalleh, 2020). In this article, we decided to identify these factors in order to determine these risks in general and specifically in our country's market and in the food industry. On the other hand, by analyzing data in artificial intelligence methods such as machine learning, human error can be significantly reduced. Accordingly, the present study seeks to answer the question: What is the effectiveness of supply chain risk indicators in online business activities in the food industry through machine learning methods using the unit support vector algorithm? Theoretical framework Supply chain A supply chain is defined as a set of functional activities (transportation, inventory control, etc.) that are repeated many times along the flow channel and by which raw materials are converted into final products and value and reach the consumer. Since globalization has opened new markets and intensified competition, organizations have been able to reduce production costs by developing more complex supply chains to compete in the global market (Kamalahmad & Mrllat-Parast, 2016). Risk Management In the conventional sense, risk management means compensating for known risks by managing them. In the past, risk or danger was seen as a result of natural causes that could not be predicted. However, in a modern, managed thinking based on current science in the field of risk management, a view has been presented that risk can be measured and controlled provided that there are effective and efficient systems (Sepahvand & Vaghfi, 2021). Ahmadi et al., (2023) studied the design of a distribution channel selection system in the oil industry supply chain using a combination of adaptive neural-fuzzy network and metaheuristic algorithms (case study: National Petroleum Distribution Company of the West Azerbaijan Dual Regions). In order to analyze the data, confirmatory factor analysis, adaptive neural-fuzzy network in the basic mode, and adaptive neural-fuzzy network combined with genetic and particle swarm optimization algorithms were used. In this study, a hybrid distribution channel selection system was first designed and then evaluated based on the input scores using the system designed based on the least error, traditional distribution channel, and fuel station branding design. The results show that the best system for distribution channel selection was the adaptive neural-fuzzy network combined with the particle swarm algorithm. By comparing the performance of the branding plan and the traditional method, it was determined that the branding plan performed better and was a suitable distribution channel for the National Oil Products Distribution Company of the West Azerbaijan Dual Regions. Brusset et al., (2023) addressed this issue in a study as a dynamic method for the effects of re-understanding the supply chain during the pandemic. In this study, they created and used the dynamic method in which they redrawn the dynamic model using the optimal control model. Their model is a combination of optimal control and a pandemic model (such as Corona); and in fact, their model was a combination of these two models, which are older and more time-consuming than machine learning methods. Research Methodology The research method is applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of implementation method. In the qualitative part, interviews with experts active in the food industry with complete information and sufficient experience in the supply chain of this industry have been used until theoretical saturation, which were 10 people of relevant experts in large companies in this field. In the quantitative part, the field method and questionnaire were used to collect data with statistical methods, and this number was also 114 people selected as a sample from the statistical population. Research findings Due to the data conditions and the application of the field of machine learning in the supply chain, the support vector machine algorithm; which is one of the very strong algorithms in the field of artificial intelligence, was used. The results showed that customer satisfaction has a negative effect in the research model. Supply chain coordination has a positive effect in the research model. Factors affecting costs have a positive effect, but its amount is moderate. Economic and market conditions have a positive effect in the model. Internet infrastructure has limited importance in the model. Environmental risks have a positive effect. Product quality has a negative effect in the model. Conclusion The present study aimed to identify and investigate the effectiveness of supply chain risk indicators of online commerce activities in the food industry through machine learning method using the single support vector algorithm. The results of this study are consistent with the results of Ahmadi et al., (2023), Samiei et al., (2023), SpieskeAlexander et al., (2023), Akkerman et al., (2023), Brusset et al., (2023), Burgess et al., (2023), Ozdemir et al., (2022), Khorram Ruz., (2022), Sheydaei (2022), Pellegrino et al., (2022), and Zeng et al., (2019). Ozdemir et al., (2022) examined the effects of the pandemic on the supply chain of store goods, and finally examined and evaluated their presented model using covariance. The results indicated that in the field of supply chain vibration control, innovation can be greatly affected, so they used statistical methods for their research method. Considering the research topic, it is suggested that researchers use other machine learning algorithms, such as random forest and decision tree, and estimate the necessary evaluations. In addition, in each of these models, the accuracy of the models can be compared, and the effectiveness of each indicator in other models can also be examined. These algorithms and evaluations can also be used in industries other than the food industry.

Abstract Abstract The aim of the current research is to design a strategic co-creation model using social media in small and medium-sized industrial businesses. According to its purpose, the research method is applicable; and in terms of implementation, it is qualitative; using the data-based method. The statistical population includes 8 experts in the field of digital marketing in the B2B sector, selected by snowball sampling. The data collection tool includes semi-structured interviews. Data analysis was carried out using the coding and data-based method and MAXQDA software. Based on the findings of the research, central phenomenon is influenced by 60 indicators or causal factors; and 11 indicators or contextual factors along with 18 intervening factors are able to influence strategic co-creation strategies in the social media environment. In addition, the results led to the identification of 14 diverse strategies in this field, which can contribute to achieving 36 diverse consequences of the strategic use of social media in the           co-creation actions of small and medium-sized industrial businesses. The phenomenon of strategic co-creation in the environment of social media and industrial customers depends on a set of factors and the adoption of a set of strategies, among these factors, the set of customer perceptions as the most frequent causal factor, and the outsourcing of customer relationship management measures as the most important strategies show the importance and the position of these two issues in the field of increasing the effectiveness of the central phenomenon of the research. Introduction Customers and their cooperative behaviors are among the most important tools to improve business marketing functions, improve the quality of products and services in accordance with market expectations, needs and trends, and reduce overall marketing and advertising expenses of businesses (Xie et al., 2016). In today's highly competitive markets, those businesses can achieve success that are capable of creating value and are able to provide higher and more creative values ​​for their customers, and create higher incentives for customers to encourage stronger and more creative interactions. On the other hand, according to Nasution et al, (2014), due to the increase in the level and intensity of competition in the productive and industrial markets; in the both local markets and international markets, the customers have the higher right to choose and the bargaining power compared to the past. Therefore, in order to maintain a competitive position and gain commercial success, businesses have to provide more value to their customers compared to their competitors. But one of the most important survival conditions in today's competitive and turbulent business markets is to focus on environmental changes and create innovation and create joint value with customers in business (Nasution et al, 2014). The strategic use of social media in industrial markets consists of programmings, plans and broad and general goals of industrial businesses in the field of using these media in dealing with industrial customers (Cartwright et al. et al., 2021). Beyond the tactical use of social media, the present study is focused on a more comprehensive aspect of using social media in the field of adding value through the customer, that is, the strategic use of social media. The issue of the need to present a comprehensive and general plan in the field of strategic use of social media in co-creation activities, along with the severe limitation of the domain of knowledge in the strategic use of social media in specific areas such as synergy with customers among industrial customers, leads the study towards providing a paradigmatic model in the field of designing a strategic co-creation model using social media in industrial businesses. Especially, studies such as Foltean et al, (2019) and Iankova et al, (2019) stated that there is limited knowledge in the field of strategic social media marketing and its various dimensions in the field of industrial marketing. Based on the explanations presented, the main research question can be raised as follows: What is the model of strategic co-creation using social media in small and medium-sized industrial businesses? Theoretical Framework Strategic co-creation Co-creation of value as a strategy includes diverse but similar meanings, concepts, and contexts. In the initial definitions of this concept, Prahad & Ramaswamy (2004) introduced co-creation of value as the joint creation of value by business and customers through exchange activities of knowledge, information, and awareness, and considered these actions as a factor in creating, modifying, and updating products and services in line with the needs and expectations of customers (Piller et al, 2010). Social media and online co-creation Studies in the field of the role of social media in value co-creation, especially among industrial customers, are developing and still in their early stages. These studies are moving from the what question to the how question stage (Martini et al, 2017). Today, large companies and even emerging and small businesses spend a significant part of their budgets on digital marketing measures and developing interactive communications in the form of synergistic strategies with customers. These measures are taken while the real benefits of these measures are not certain for businesses, and the measures are accompanied by trial and error. Especially the position and role of social media in improving the innovation level of businesses and improving and revising their products and services is still being investigated and studied, and studies in this field are on an exponential growth path. This issue shows that the field of customer relationship management in social media and more specifically synergy in the context of social media is a subject that still has significant hidden angles (Rashid et al, 2019). Bashokouh Ajirlo & Ghasemi Hamedani (2023) investigated the role of influencing factors on value co-creation through technologies equipped with artificial intelligence and knowledge management in the tourism industry. The results showed that the significant effect of customer-based factors of technologies equipped with artificial intelligence and knowledge management on the effectiveness of value co-creation was confirmed, and customer-based factors could mediate the relationship between technologies equipped with artificial intelligence and the effectiveness of value co-creation. Finally, technologies equipped with artificial intelligence were able to mediate the relationship between customer-based factors and the effectiveness of value co-creation. Shirkhodaie et al, (2023) investigated the identification of effective factors of co-creation in social media brand communities. The variables of customer participation in social media brand communities and social support have positive and significant effects on the mediating variable of communication quality, and the quality of communication has a positive and significant effect on brand co-creation. On the other hand, social support and customer participation in social media brand communities have a positive and significant effect on brand loyalty, and brand loyalty has a positive and significant effect on the dependent variable of brand co-creation, and there is no significant relationship between the quality of communication and loyalty. Research methodology According to its purpose, the research method is applicable; and in terms of implementation, it is qualitative; using the data-based method. The statistical population includes 8 experts in the field of digital marketing in the B2B sector, selected by snowball sampling. The data collection tool includes semi-structured interviews. Research findings Data analysis was carried out using the coding and data-based method and MAXQDA software. Based on the findings of the research, central phenomenon is influenced by 60 indicators or causal factors; and 11 indicators or contextual factors along with 18 intervening factors are able to influence strategic co-creation strategies in the social media environment. In addition, the results led to the identification of 14 diverse strategies in this field, which can contribute to achieving 36 diverse consequences of the strategic use of social media in the co-creation actions of small and medium-sized industrial businesses. The phenomenon of strategic co-creation in the environment of social media and industrial customers depends on a set of factors and the adoption of a set of strategies, among these factors, the set of customer perceptions as the most frequent causal factor, and the outsourcing of customer relationship management measures as the most important strategies show the importance and the position of these two issues in the field of increasing the effectiveness of the central phenomenon of the research. Conclusion The current research was conducted with the aim of designing a strategic co-creation model using social media in small and medium-sized industrial businesses. The findings are in line with the results of Bashokouh Ajirlo & Ghasemi Hamedani (2023), Shirkhodaie et al, (2023), Hasan et al, (2023), Zhang et al, (2023), Asgharzadeh et al, (2023), Taleghani et al, (2022), Yazdani Kachuei et al, (2022), Ghomi Kazemi & Vaziri (2021), Cartwright et al, (2021), and Cartwright et al, (2021). Zhang et al, (2023) showed that the tool has satisfactory reliability and validity. This work contributes to theory and practice by providing a context-specific and accurate conceptualization of the value of customer co-creation experience with a reliable and valid survey tool. According to the results of the research, the following suggestions are provided: It is suggested that future studies, while focusing on the results of the current research, apply their views and tools in the field of developing strategic co-creation models using social media in small and medium-sized industrial businesses, and thus carry out a comparative research, independent of the results of the current research, while achieving other results.

Abstract Abstract
The aim of the current research is to design a strategic co-creation model using social media in small and medium-sized industrial businesses. According to its purpose, the research method is applicable; and in terms of implementation, it is qualitative; using the data-based method. The statistical population includes 8 experts in the field of digital marketing in the B2B sector, selected by snowball sampling. The data collection tool includes semi-structured interviews. Data analysis was carried out using the coding and data-based method and MAXQDA software. Based on the findings of the research, central phenomenon is influenced by 60 indicators or causal factors; and 11 indicators or contextual factors along with 18 intervening factors are able to influence strategic co-creation strategies in the social media environment. In addition, the results led to the identification of 14 diverse strategies in this field, which can contribute to achieving 36 diverse consequences of the strategic use of social media in the           co-creation actions of small and medium-sized industrial businesses. The phenomenon of strategic co-creation in the environment of social media and industrial customers depends on a set of factors and the adoption of a set of strategies, among these factors, the set of customer perceptions as the most frequent causal factor, and the outsourcing of customer relationship management measures as the most important strategies show the importance and the position of these two issues in the field of increasing the effectiveness of the central phenomenon of the research.
Introduction
Customers and their cooperative behaviors are among the most important tools to improve business marketing functions, improve the quality of products and services in accordance with market expectations, needs and trends, and reduce overall marketing and advertising expenses of businesses (Xie et al., 2016). In today's highly competitive markets, those businesses can achieve success that are capable of creating value and are able to provide higher and more creative values ​​for their customers, and create higher incentives for customers to encourage stronger and more creative interactions. On the other hand, according to Nasution et al, (2014), due to the increase in the level and intensity of competition in the productive and industrial markets; in the both local markets and international markets, the customers have the higher right to choose and the bargaining power compared to the past. Therefore, in order to maintain a competitive position and gain commercial success, businesses have to provide more value to their customers compared to their competitors. But one of the most important survival conditions in today's competitive and turbulent business markets is to focus on environmental changes and create innovation and create joint value with customers in business (Nasution et al, 2014). The strategic use of social media in industrial markets consists of programmings, plans and broad and general goals of industrial businesses in the field of using these media in dealing with industrial customers (Cartwright et al. et al., 2021). Beyond the tactical use of social media, the present study is focused on a more comprehensive aspect of using social media in the field of adding value through the customer, that is, the strategic use of social media. The issue of the need to present a comprehensive and general plan in the field of strategic use of social media in co-creation activities, along with the severe limitation of the domain of knowledge in the strategic use of social media in specific areas such as synergy with customers among industrial customers, leads the study towards providing a paradigmatic model in the field of designing a strategic co-creation model using social media in industrial businesses. Especially, studies such as Foltean et al, (2019) and Iankova et al, (2019) stated that there is limited knowledge in the field of strategic social media marketing and its various dimensions in the field of industrial marketing. Based on the explanations presented, the main research question can be raised as follows: What is the model of strategic co-creation using social media in small and medium-sized industrial businesses?
Theoretical Framework
Strategic co-creation
Co-creation of value as a strategy includes diverse but similar meanings, concepts, and contexts. In the initial definitions of this concept, Prahad & Ramaswamy (2004) introduced co-creation of value as the joint creation of value by business and customers through exchange activities of knowledge, information, and awareness, and considered these actions as a factor in creating, modifying, and updating products and services in line with the needs and expectations of customers (Piller et al, 2010).
Social media and online co-creation
Studies in the field of the role of social media in value co-creation, especially among industrial customers, are developing and still in their early stages. These studies are moving from the what question to the how question stage (Martini et al, 2017). Today, large companies and even emerging and small businesses spend a significant part of their budgets on digital marketing measures and developing interactive communications in the form of synergistic strategies with customers. These measures are taken while the real benefits of these measures are not certain for businesses, and the measures are accompanied by trial and error. Especially the position and role of social media in improving the innovation level of businesses and improving and revising their products and services is still being investigated and studied, and studies in this field are on an exponential growth path. This issue shows that the field of customer relationship management in social media and more specifically synergy in the context of social media is a subject that still has significant hidden angles (Rashid et al, 2019).
Bashokouh Ajirlo & Ghasemi Hamedani (2023) investigated the role of influencing factors on value co-creation through technologies equipped with artificial intelligence and knowledge management in the tourism industry. The results showed that the significant effect of customer-based factors of technologies equipped with artificial intelligence and knowledge management on the effectiveness of value co-creation was confirmed, and customer-based factors could mediate the relationship between technologies equipped with artificial intelligence and the effectiveness of value co-creation. Finally, technologies equipped with artificial intelligence were able to mediate the relationship between customer-based factors and the effectiveness of value co-creation.
Shirkhodaie et al, (2023) investigated the identification of effective factors of co-creation in social media brand communities. The variables of customer participation in social media brand communities and social support have positive and significant effects on the mediating variable of communication quality, and the quality of communication has a positive and significant effect on brand co-creation. On the other hand, social support and customer participation in social media brand communities have a positive and significant effect on brand loyalty, and brand loyalty has a positive and significant effect on the dependent variable of brand co-creation, and there is no significant relationship between the quality of communication and loyalty.
Research methodology
According to its purpose, the research method is applicable; and in terms of implementation, it is qualitative; using the data-based method. The statistical population includes 8 experts in the field of digital marketing in the B2B sector, selected by snowball sampling. The data collection tool includes semi-structured interviews.
Research findings
Data analysis was carried out using the coding and data-based method and MAXQDA software. Based on the findings of the research, central phenomenon is influenced by 60 indicators or causal factors; and 11 indicators or contextual factors along with 18 intervening factors are able to influence strategic co-creation strategies in the social media environment. In addition, the results led to the identification of 14 diverse strategies in this field, which can contribute to achieving 36 diverse consequences of the strategic use of social media in the co-creation actions of small and medium-sized industrial businesses. The phenomenon of strategic co-creation in the environment of social media and industrial customers depends on a set of factors and the adoption of a set of strategies, among these factors, the set of customer perceptions as the most frequent causal factor, and the outsourcing of customer relationship management measures as the most important strategies show the importance and the position of these two issues in the field of increasing the effectiveness of the central phenomenon of the research.
Conclusion
The current research was conducted with the aim of designing a strategic co-creation model using social media in small and medium-sized industrial businesses. The findings are in line with the results of Bashokouh Ajirlo & Ghasemi Hamedani (2023), Shirkhodaie et al, (2023), Hasan et al, (2023), Zhang et al, (2023), Asgharzadeh et al, (2023), Taleghani et al, (2022), Yazdani Kachuei et al, (2022), Ghomi Kazemi & Vaziri (2021), Cartwright et al, (2021), and Cartwright et al, (2021). Zhang et al, (2023) showed that the tool has satisfactory reliability and validity. This work contributes to theory and practice by providing a context-specific and accurate conceptualization of the value of customer co-creation experience with a reliable and valid survey tool.
 According to the results of the research, the following suggestions are provided:
It is suggested that future studies, while focusing on the results of the current research, apply their views and tools in the field of developing strategic co-creation models using social media in small and medium-sized industrial businesses, and thus carry out a comparative research, independent of the results of the current research, while achieving other results.

Abstract Abstract

The aim of the present study is to design a customer relationship management model based on artificial intelligence in digital marketing of services in the health tourism industry. The research method is applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of its implementation method. The statistical population of the qualitative part of the study includes 14 experts and scholars in the field of marketing and artificial intelligence selected by the snowball sampling method. The statistical population in the quantitative part includes experts and marketing managers related to health tourism in Tehran. Given that their exact number cannot be calculated, a maximum number of 384 people was considered based on the Morgan and Cergesi table. Data collection in the qualitative part was carried out through semi-structured interviews, and in the quantitative part through questionnaires. The coding method was used in the qualitative part data analysis, and SPSS and Lisrel software were used in the quantitative part. The results of the study showed that the causal conditions in the study include improving market competition, improving relationships, automated data analysis, and empowerment; and the background conditions include customer data management and intelligent services. Also, the intervening conditions include efficient planning, saving resources, and managing customer behavior. The strategies in the study include solving the integration problem, solving the information management problem, and solving planning problems; and the outcomes include increasing customer satisfaction, increasing financial strength, customer loyalty, and saving time. The results of the structural equations show that the dimensions are well loaded on the research variables and can provide a suitable description of the variables.

Introduction

The activity of customer relationship management includes collecting, managing, and intelligently using data with the support of technology solutions to develop long-term customer relationships. Data obtained from all customer touchpoints, if managed well, can support companies in creating personalized marketing responses, generating new ideas, tailoring products and services, and thus delivering high customer value and gaining competitive advantage (Agarwal et al., 2021). In the digital age, the increase in the volume, velocity, and variety of data, as well as its processing capacity, has led to new technological solutions, including the advancement of artificial intelligence techniques, which refers to the ability of a system to correctly interpret large amounts of data, learn from this data, and use this learning to achieve specific goals and tasks (Ahmed et al., 2020). Artificial intelligence seems to be the future of the industry, and the focus of this technology on putting consumers at the center of health and well-being and ensuring that patients’ daily patterns and healthcare professionals’ needs are observed to provide improved guidance, support, and feedback, and ultimately customer relationship management in the health tourism industry will be of great importance in the future (Al Sayed, 2024).

A review of the role of data in the health tourism sector shows that the data required for the impact of AI on service delivery, especially in health tourism, is still limited (Dalkıran, 2023). Artificial intelligence can provide opportunities for health tourism service providers (Cubric, 2020). New customer relationship management features, such as personality insight services, website formation, chatbot services, programmatic advertising, and facial, image, and face recognition technologies, require significant data to be collected in real time, which is almost impossible to implement without advances in AI. Along with the relevance of AI in the business world, universities also claim that AI is the next step towards a new and more powerful customer relationship management (Rabbi, 2024).

Therefore, this research seeks to answer the question: How does the design of an AI-based customer relationship management model in digital marketing of services in the health tourism industry look like?

Theoretical Framework

Digital Marketing

Digital marketing means using the Internet, mobile devices, social media, search engines and other channels to reach customers. Some marketing experts consider digital marketing to be a completely new endeavor that requires a new way of approaching customers and new ways of understanding how customers behave compared to traditional marketing (Barone, 2021(.

Customer Relationship Management

Customer relationship management involves the intelligent collection, management and use of data supported by technology solutions to develop long-term customer relationships and exceptional customer experiences (Ledro et al., 2022).

Artificial Intelligence on Customer Relationship Management

Artificial intelligence is impacting customer relationship management. Artificial intelligence has revolutionized customer relationship management by automating tasks, providing deeper insights, and creating more personalized experiences for customers. Artificial intelligence enhances the automation process in customer relationship management, freeing up human resources to focus on more complex issues in the work (Ahmed, 2025).

Ponomarenko et al. (2024) in their research on the topic “Application of Artificial Intelligence in Digital Marketing” reported that it is important to identify the main directions of using artificial intelligence to optimize marketing strategies of companies in the digital environment in conditions of intensifying competition on the Internet. Artificial intelligence is considered as a tool for qualitative transformations in the use of digital marketing tools based on various information generated in the global network. The methodological basis of this study is a comprehensive analysis of scientific approaches to the implementation of artificial intelligence in the field of digital marketing, the formation of a database for modeling and identifying optimal machine learning algorithms to ensure the competitiveness of brands. A scheme of the main sources of information that should be used by the company to implement artificial intelligence algorithms in the process of increasing the effectiveness of the use of digital marketing tools is developed on the Internet. Digital marketing tools are presented to be utilized to communicate with the target audience in the long term and ensure the economically feasible level of conversion. The main stages of interaction of companies with audiences on the Internet using modern machine learning algorithms are presented. The main directions of using artificial intelligence in digital marketing have been identified, which enables the company to achieve a high level of loyalty among users based on personalized interaction models.

Baran et al. (2023) in their research on “Next Generation Technologies in Health Tourism” reported that the developments in the field of digitalization in health tourism were initially focused on e-health technologies and suggested that managers and employees should be prepared for the profound transformation created by technology.

Research Methodology

The research method is applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of its implementation method. The statistical population of the research in the qualitative section includes 14 experts and specialists in the field of marketing and artificial intelligence, selected by the snowball sampling method. The statistical population in the quantitative section includes experts and marketing managers related to health tourism in Tehran, and given that their exact number cannot be calculated, the maximum number was considered to be 384 people according to the Morgan and Gergesi table. Data collection in the qualitative part was done through semi-structured interviews, and in the quantitative part through questionnaires.

Research findings

Coding method was used in the qualitative part data analysis, and SPSS and Lisrel software were used in the quantitative part. The research results showed that the causal conditions in the research include improving market competition, improving relationships, automatic data analysis, and empowerment; and the background conditions include customer data management and intelligent services. Also, the intervening conditions include efficient planning, saving resources, and managing customer behavior. The strategies in the research include solving the integration problem, solving the information management problem, and solving planning problems; and the outcomes include increasing customer satisfaction, increasing financial strength, customer loyalty, and saving time. The results of structural equations show that the dimensions are well loaded on the research variables and can provide a suitable description of the variables.

Conclusion

The present study aimed to design an AI-based customer relationship management model in digital marketing of services in the health tourism industry. The results of this part of the study are consistent with the findings of Abdollahi (2021), Ghasemi (2019), Ribeiro et al. (2021), Ramon Saura et al. (2021), Al Sayed (2024), and Ponomarenko et al. (2024). AI in customer relationship management involves the integration of intelligent technologies to analyze customer data, predict behaviors, and automate interactions. This integration enhances the capabilities of traditional customer relationship management systems, making them more efficient and responsive to customer needs. AI-based customer relationship management systems provide the tools needed to achieve these goals and provide insights and automation that were previously unavailable.

According to the research results, the following suggestion was made:

Based on the role of artificial intelligence in customer data management and automated data analysis, it can be suggested to develop artificial intelligence in tourism service marketing because with artificial intelligence, businesses can use historical data to predict customer behavior and anticipate customer needs.

Abstract Abstract The aim of this study is to present a tourism industry development model in Iran. The research method is fundamental in terms of its purpose, and mixed in terms of its implementation (qualitative-quantitative), with an approach based on grounded theory. The statistical population in the qualitative section includes 23 experts in the field of theoretical and practical foundations of the tourism industry; including university faculty members and tourism managers selected in a snowball method; and in the quantitative section includes 374 experts in the field of tourism. The tool for collecting findings is a semi-structured interview in the qualitative section, and a researcher-made questionnaire in the quantitative section. Data analysis in the qualitative section carried out based on the grounded method by MAXQDA software, and in the quantitative section by SPSS software. The results of the study showed that, according to the determined goal; tourism infrastructure, international relations, social factors, and governance attitudes are among the effective factors that must be managed. Factors such as presence in the global tourism scene, facilities and infrastructure, tourist attraction policies, and existing potentials are considered as the basis and context for the development of the tourism industry in Iran and need to be improved. Introduction Tourism is an expanding industry and its importance is constantly increasing, and more and more people are getting involved with it. The United Nations World Tourism Organization states in its report that the tourism industry is the world's largest service industry in the 21st century and will maintain this position in the future (Amini et al., 2018). Tourism is a set of activities of people who travel to places outside their usual community environment for leisure, entertainment, business, or any other purpose and stay there for a while. The importance of tourism is 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 (Yahya Zadeh et al., 2023). Tourism is vital to the success of many economies around the world. The experience of the last two decades has shown that some countries, despite the lack of natural resources, have been able to achieve very high incomes by investing in the tourism industry (Qiu et al., 2020). Since Iran is a country with pristine nature and great potential in the health, trade, and historical-cultural sectors of the Middle East, the present study tries to minimize the gap in expectations between tourists and legislative institutions, and in this regard, using a data-based approach based on the opinions of relevant experts, an attempt was made to present a paradigmatic model for the development of the tourism industry. In the second stage of the study, using the opinions of experts, the executive factors in the development of the tourism industry were examined, and the results are presented in the following description of the findings so that it can be effective in the socio-economic development of the country in accordance with the national development plan and related perspectives. Accordingly, the present study seeks to answer this question: What is the development pattern of the tourism industry in Iran? Theoretical Framework Tourism Industry The tourism industry is one of the important socio-economic phenomena with cultural, political and environmental impacts. In recent decades, tourism has been growing and diversifying and has been one of the largest economic sectors in the world (Veicy, 2018). The tourism industry is an important source of income and an effective factor in cultural exchanges between nations and societie; and as the world's largest service industry, it has a special economic position (Hoseini & Mosavi, 2024(. Fani (2025) examined the influential and effective factors of marketing to tourism industry customers in Iran using the fuzzy DEMATEL technique approach. The findings showed that 3 dimensions, 8 components, and 41 indicators were identified. The extracted dimensions consist of the service quality dimension, including the components of satisfaction with tourism services, infrastructure facilities, tourism costs; the marketing policy dimension including the components of macro-policy, planning and management; and the tourism experience dimension including the components of tourists' feedback, tourism culture, and advertising and marketing. Momeni et al. (2025) examined the design of a smart tourism model with a meta-synthesis approach. The present study evaluated 128 articles and sources in the field of smart tourism using the meta-synthesis method. During the stages, 34 sources and articles were consistent with the accepted criteria. As a result of combining the findings, 8 subcategories were extracted, including improving cost management in tourism, providing smart tourism services, smart cloud services, an online service system for tourists, quality of services and facilities, the Internet of Things, identifying customer needs in a smart way, and dynamic pricing. Finally, for the development of smart tourism in Iran, it is recommended to adopt a comprehensive perspective considering both the micro and macro levels. At the macro level, more attention should be paid to raising the priority of smart tourism development in the long term, national development policies, paying more attention to planning, coordination, and monitoring, and improving the infrastructure required for the development of smart tourism. Research Methodology The research method is fundamental in terms of its purpose, and mixed (qualitative-quantitative) in terms of its implementation method, with an approach based on grounded theory. The statistical population in the qualitative section includes 23 experts in the field of theoretical and practical foundations of the tourism industry; including university faculty members and tourism managers selected in a snowball method; and in the quantitative section includes 374 experts in the field of tourism. The tool for collecting findings is a semi-structured interview in the qualitative section, and a researcher-made questionnaire in the quantitative section. Research findings Data analysis in the qualitative section is based on the grounded theory method and MAXQDA software was used, and in the quantitative section, SPSS software was used. The research results showed that, according to the determined goal; tourism infrastructure, international relations, social factors, and governance attitudes are among the effective factors that must be managed. Factors such as presence in the global tourism scene, facilities and infrastructure, tourist attraction policies, and existing potentials are considered as the basis and context for the development of the tourism industry in Iran and need to be improved. Conclusion The present study was conducted with the aim of providing a model for the development of the tourism industry in Iran. The results of this study are consistent with the results of Fani (2025), Momeni et al. (2025). Mansoori et al. (2024), Ghodrati et al. (2024), Rajabi et al. (2024), ,Kwabi et al. (2023), Yahya Zadeh et al. (2023), Al Fahmawee & Jawabreh (2023), and Asadpour Kordi et al. (2022). Mansoori et al. (2024) showed that the nine main factors affecting the formation of higher education tourism in Iran, in order of influence, are: dynamic political exchanges with the world at the national level, the existence of national macro-policies in the field of academic interaction, facilitating the admission process in political and administrative dimensions, the existence of economic and technical infrastructure for foreign students, the international language level of faculty members and staff and a dynamic and receptive higher education structure, the existence of a sense of security in social, security and political dimensions for foreign students, and the factors of being a brand of universities and introducing and presenting historical, cultural and religious attractions to the world. According to the results obtained, it is suggested that the development of the tourism sector in both categories through changes in infrastructure and extensive advertising at the national and international levels should be a priority for the authorities, and the authorities should pay special attention to the foreign tourism sector to generate foreign exchange, growth, and development of the tourism sector. It is suggested to the government, ministries and relevant institutions, considering the experiences of neighboring and similar Islamic countries such as Turkey and Malaysia, to try to see the tourism sector as one of the most important sources of income in order to move away from budgeting based on oil and taxes and pressure on domestic factors.

Abstract Abstract The aim of the present study was to identify the dimensions and components of the model for assessing financing policies. This study has a qualitative approach that is applicable-developmental in terms of purpose and survey in terms of nature and method. The data collection method in this study was a combination of library and field studies, and the data collection tools were referring to documents, interviews with experts, and a questionnaire, the validity and reliability of which was confirmed by a high percentage of interviews and questionnaires. The statistical population of this qualitative study includes university professors in the field of financial management and experts in the field of assessing financing policies of industrial enterprises in the north of Kerman province; sampling in this study was snowball type. Based on the subject of the desired data through preliminary studies, semi-structured interviews were collected and sampling continued until theoretical sufficiency and saturation, which was estimated to be 15 people; The interviews were also examined and coded using the content analysis method. The information obtained was analyzed using the 2020 MAXQDA software. The results showed that the dimensions of the model include laws and policies, feasibility and estimation, support and interaction, consolidation and development, promotion and optimization, feedback and performance analysis, risk taking and evaluation, monitoring and control, management and implementation, allocation and financing. Introduction In the world of management, if the government does not do something, it is based on a policy and if governments decide to do something, it is also based on a policy and the national will is always implemented in the light of public policies. Governments seek to identify and solve public problems of society (2017, Matoufi et al). The stages of policy-making include setting the agenda, policy formulation, decision-making, policy implementation, and then policy evaluation (Lane et al., 2020). Policy implementation is the implementation of major political decisions, usually in line with the law, and may also include the form of major executive orders or judicial decisions. Over the course of the twentieth century, the problems facing policymakers have arguably become much more complex and their structure more difficult to understand. Solving these problems requires the use of powerful tools and processes to obtain the strongest possible evidence of the causal relationship between policies. One important process to achieve this goal is policy evaluation (De La Cruz et al., 2020). Financing production activities has always been one of the serious concerns of the production sector for business development (Moradi et al., 2020), so that in the monitoring of the country's business environment, which is published quarterly by the Chamber of Commerce, Industries, Mines and Agriculture of Iran, financing has been ranked high as a challenge. This issue becomes more acute at times when the country is facing various shocks, such as Corona, oppressive sanctions, etc. According to surveys, in 2018 and the first five months of 2019, the financing of the Iranian economy, as well as the industrial and mining sectors, continued to be focused on the banking system (with a share of 78%), followed by the capital market, government budget development credits, and foreign direct investment with shares of 18, 3, and 1%, respectively. The main issue is that the country's banking system has several problems, such as high non-current receivables, low capital adequacy, and a high share of non-cash assets, which limits the possibility of expanding financing from this market (Bahadoran-Baghbaderani & Mohamadi, 2021). According to the studies conducted, the main challenges of the country's manufacturing enterprises from the capital market can be presented as follows: Capital market fluctuations due to the instability of macroeconomic variables (inflation and exchange rate) and government policies regarding economic instability and international sanctions have distorted the prospects for financing manufacturing enterprises from the capital market due to the lack of trust in many retail investors (who intend to exit the market). For example, since the end of 2019 and the first 5 months of 2020, the 2020 capital market index experienced rapid growth, but subsequently and in the late summer of 2020, the stock exchange index began a downward process. Evidence shows that part of the growth of the capital market was related to the growth of inflation and exchange rates in recent years, which reflected their effects on the capital market with a slight delay, and the other part was related to government policies regarding divestitures and, consequently, the entry of retail and new investors with not very high financial literacy and a short-term view of the market, which caused emotional behavior. Therefore, this research seeks to provide a model for evaluating the financing policies of the Ministry of Industry, Mines and Trade in industrial enterprises. Theoretical foundations of the research Capital accumulation plays a significant role in providing financial resources to countries because capital has been one of the important factors of production. In developed countries, capital accumulation and concentration of capital have been formed, but in developing countries, growth and development have not been achieved due to lack of concentration of capital. In order to better understand the importance of capital formation in economic growth, we will refer to the opinions of some economists. In the development theory of classical economists, capital played a central role and capital accumulation was the main determinant of economic growth and progress. For them, the discussion of investment is a discussion of savings. Therefore, from their point of view, the existence of savings was a sufficient condition for the emergence of investment (Seifollahi, 2023). Research Background: Naseri Zakir et al. (2024) conducted a study with the aim of presenting a new model for implementing export financing policies in the form of buyer credit facilities. The sampling method was purposeful and snowball, and data coding and analysis were performed using MAXQDA software. Then, the thematic network (basic themes, organizing themes, and overarching themes) was identified and a model for implementing export financing policies in the form of buyer credit was presented by combining two policy implementation models of Callista and Maitland. Ghabdian et al. (2024) conducted a study entitled Presenting a Model for Evaluating Public Policies Based on the Dimensions of Financial Transparency. The results showed that the dimensions of the model included 1- Management factors; 2- Expertise and skills; 3- Laws and regulations; 4-Organizational environment; Administrative health; 6-Government budget; 7-Organizational resources; 8-Reform strategies; 9-Organizational consequences; and 10-General consequences. The results also showed that management factors, expertise and skills, laws and regulations, organizational environment and administrative health affect each other. Research methodology The method of the present research is data-based analysis. The statistical population of the research includes specialists and experts familiar with organizational behavior issues. The researcher first qualitatively examined the research topic with limited participants and then, based on the qualitative findings, proceeded to create the desired tool. The technique used in sampling in this research was snowball. Semi-structured interviews, used to collect and sample until theoretical sufficiency and saturation; and after 15 interviews, data analysis indicated that no new data was added to the previous data. To verify scientific accuracy using reliability, several strategies are used, such as tracking the type of account, reviewing at the time of coding, classifying or confirming the results by returning to the subjects, confirming research colleagues, qualitative case analysis, confirming the structure, and adequacy of the referenced sources. In order to verify the validity, all documentation related to the research was preserved. Among the documents, we can mention the full text of the interviews conducted and the outputs of the MAXQDA2020 software and the relevant Excel files prepared. To ensure the transferability of the research findings, three experts outside the research were consulted. Research findings In this study, 17 interviews were conducted, and the following are the coded tables that include part of the interviewer's statements, semantic codes, categories and related concepts. Based on the results of the qualitative part, it can be claimed that the evaluation model of the financing policies of the Ministry of Industry, Mines and Trade in industrial enterprises includes the dimensions of laws and policies, feasibility and estimation, support and interaction, stabilization and development, promotion and optimization, feedback and performance analysis, risk-taking and evaluation, monitoring and control, management and implementation, allocation and financing. Discussion and Conclusion The aim of the present study was to identify the dimensions and components of the evaluation model of financing policies. The results showed that the dimensions of the model include laws and policies, feasibility and estimation, support and interaction, stabilization and development, promotion and optimization, feedback and performance analysis, risk-taking and evaluation, monitoring and control, management and implementation, allocation and financing. The results of this study are somewhat consistent with the findings of Ghabdian et al. (2024) and Naseri Zakir et al. (2024), and confirm the results of this study. Laws and policies refer to the legal framework and policies related to the model. This includes laws, regulations, standards, and guidelines that guide the implementation of the model. Feasibility and estimation involve assessing the feasibility of implementing the model and estimating the resources required (e.g., time, cost, manpower). The goal is to ensure that the model is realistic and feasible. Support and engagement emphasizes the importance of supporting key stakeholders (e.g., managers, employees, customers) and creating positive interactions between them for the model to succeed. Stabilization and development refers to the process of stabilizing the model after implementation and developing it over time. This involves ensuring that the model is continuously working and improving. Enhancement and optimization focuses on continuously improving the model to increase its efficiency, effectiveness, and value.  

Abstract Abstract The purpose of this research is to provide a model to identify the role of social communication capital and the ability to take advantage of international business opportunities. The research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of execution method, and descriptive-survey in terms of nature and method. The statistical population of the research in the qualitative part includes 17 experts from the country's petrochemical industry, and in the quantitative part, it includes 108 experts from the international trade department of the petrochemical industry; selected by a simple random sampling method. Data collection in the qualitative part was carried out by semi-structured interviews; and in the quantitative part by the questionnaire. Qualitative data analysis was done using the method of theme analysis and coding and MAXQDA software, and in the quantitative part, it was done using SPSS and Smart PLS software. In the qualitative section, 90 open codes and 14 categories were identified. In the quantitative part, confirmatory factor analysis was used to examine the validity of the identified elements and components of the model of the role of social communication capital in the ability to take advantage of international business opportunities in the petrochemical industry. The results of the research showed that the model of the role of social communication capital in the ability to take advantage of international business opportunities in the petrochemical industry has two main factors and their indicators. This model consists of indicators that are the core and heart of this model. Also, the GOF index was obtained 0.44, which indicates the good fit of the model. Introduction The variety of needs and demands, and the change in the pattern of production and consumption and, as therefore, life as a result of significant advances in technology have made it impossible to live within the borders of a country, so that internal border relations have turned into international relations in a very broad sense, and even the  disappearance of these borders is also expectable. Different nations with different cultures, policies and economic and social conditions need to exchange goods and services to meet their diverse needs. Due to these new conditions and new needs, different societies have started trading with other countries and at this time opportunities have been created to increase commercial activities. Among these opportunities, we can mention export, non-oil export, and joint cooperation (Ahoei, 2019). International business opportunities are the possible opportunities that organizations face due to their presence in foreign markets (Muzychenko, 2008). Various factors can contribute to its non-development. In this research, social communication capital is one of the factors whose role is investigated in using international business opportunities. Social capital or social bonds that are placed in different cultural contexts are important and valuable resources that bring the meaning of identity to people's minds. This type of identity concept will give them a positive emotional experience (Shan & Tian, ​​2022). Social capital is a comprehensive concept that takes into account the social aspect of human interaction and enables access to the resources of members of the association or network due to their membership in the association (Christy et al, 2022). Social capital refers to the characteristics of social organization such as trust, norms, and networks that can improve the efficiency of society by facilitating coordinated actions (Salisua et al, 2019). Therefore, this research aims to take an effective step in the direction of improving social communication capital and consequently empowering the performance of the country's petrochemical industry. As mentioned, today's organizations are under the influence of factors such as increased global competition, sudden transformations, the need for quality and after-sales services, and the existence of limited resources and under a lot of pressure. After years of experience, the world has come to the conclusion that if an organization wants to be a leader in its economy and business affairs and not lag behind in the field of competition, organizations must be empowered and be able to use this empowerment in line with practical action. Therefore, in this research, we are looking for an answer to this question: how to identify the role of social communication capital and the ability to take advantage of international business opportunities? Theoretical Framework Social capital Social capital refers to features of social organization such as trust, norms, and networks that are able to improve the efficiency of society by facilitating cooperative actions. It is stated that social capital can be simply defined as the existence of a certain set of norms with informal values ​​that the members of a group with cooperation among them, share in it (Alwani, 2015). International marketing opportunities Internationalization is a step-by-step process of international business development, whereby a company is increasingly involved in international business operations through specific products in selected markets. To adapt the organization to the needs and preferences of customers, marketing knowledge is required to be created and disseminated among functional departments within an organization (Muzychenko, 2008). The export company must acquire the necessary and complete knowledge of the international marketing environment in order to increase the possibility of its success. The marketing environment includes forces that directly and indirectly affect the performance of the organization. For an organization, changes in the marketing environment create uncertainty, threats, and opportunities (Alaghehmand Shandi & Joybari, 2023). Mahmoudi & Pourshahabi (2023) investigated the effect of the value of financial intelligence on the risk-taking of Zahedan National Bank employees with the mediating role of social capital. Analyzing the model and carrying out structural equations showed that financial intelligence with the mediating role of social capital cannot affect employees' risk taking. Also, the results showed that financial intelligence has a separate effect on the two variables of risk-taking and social capital. Alaghehmand Shandi & Joybari (2023) investigated ethical challenges and opportunities in international business: a look at ethical values ​​as the most important assets of international businesses. The results show how ethical values ​​can be considered as the most important assets of international businesses. Some of the main ethical barriers in international business include corruption, unfavorable working conditions, and lack of respect for human rights. Also, some of the opportunities in international business include strengthening cultural connections, observing ethical principles in business relationships, and promoting transparency and accountability. Finally, this paper concludes that adherence to ethical values ​​can serve as a competitive advantage for international businesses and help local and global communities to improve. Research methodology The research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of execution method, and descriptive-survey in terms of nature and method. The statistical population of the research in the qualitative part includes 17 experts from the country's petrochemical industry, and in the quantitative part, it includes 108 experts from the international trade department of the petrochemical industry; selected by a simple random sampling method. Data collection in the qualitative part was carried out by semi-structured interviews; and in the quantitative part by the questionnaire. Research findings Qualitative data analysis was done using the method of theme analysis and coding and MAXQDA software, and in the quantitative part, it was done using SPSS and Smart PLS software. In the qualitative section, 90 open codes and 14 categories were identified. In the quantitative part, confirmatory factor analysis was used to examine the validity of the identified elements and components of the model of the role of social communication capital in the ability to take advantage of international business opportunities in the petrochemical industry. The results of the research showed that the model of the role of social communication capital in the ability to take advantage of international business opportunities in the petrochemical industry has two main factors and their indicators. This model consists of indicators that are the core and heart of this model. Also, the GOF index was obtained 0.44, which indicates the good fit of the model. Conclusion The current research was conducted with the aim of providing a model to identify the role of social communication capital and the ability to take advantage of international business opportunities. The results of this research are in agreement with the results of Mahmoudi & Pourshahabi (2023), Alaghehmand Shandi & Joybari (2023), Shariatnejad et al, (2023), Tajpor et al, (2022), Troise et al, (2020), Noruzi et al, (2019), and Pinho (2016). Noruzi et al, (2019) showed that social capital in a specific market and social capital at the international level have a direct and positive effect on taking advantage of business opportunities; on the other hand, social capital at the international level has a positive and meaningful effect on social capital in a specific market. According to the results of the research, the following suggestions were presented: Based on the research findings and confirmation of the effectiveness of inter-organizational cooperation, it is suggested that the training of human resources and empowerment in the field of inter-organizational cooperation in the organization should be included in the organization's programs, and increase employees' knowledge of different cultures through training courses and holding scientific meetings and scientific journals and... so that employees believe in it and respect it in the work environment.