Journal of value creating in Business Management

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

Abstract Abstract The aim of this research is to design and explain a model of marketing processes with data-driven innovation capabilities in B2B companies. The research is of a mixed type (qualitative-quantitative). In the qualitative part, the main categories and components of the model were identified by using in-depth semi-structured interviews with 10 of experts in the field of marketing and data technology, using purposive sampling. In the quantitative part, the statistical population included managers, directors, and employees active in the marketing units of B2B companies located in Tehran. Considering the size of the statistical population of 530 people, based on the Morgan table, 219 people were selected as the sample size and were examined using the relative cluster sampling method. The findings were tested using a questionnaire and confirmatory factor analysis. The results showed that causal conditions such as data-driven culture and technological infrastructure have a significant effect on the formation of data-driven marketing processes. These processes also lead to outcomes such as increased marketing innovation, improved business performance, and improved customer satisfaction through data-driven strategies. The relationships between conditions, processes, strategies, and outcomes were statistically significant. As a result, the presented model can be used as a framework for developing innovative data-driven approaches in B2B companies. The findings emphasize that by strengthening a data-driven culture and using predictive analytics, companies will be able to make more effective marketing decisions and gain a more sustainable competitive advantage. Introduction In today's world, data-driven marketing is considered one of the key approaches to optimizing marketing processes in B2B companies. This approach is especially important in complex and dynamic competitive conditions, as it is able to improve strategic decision-making based on the analysis of customer data, market trends, and collected information (Rosário et al., 2023). Data-driven marketing allows companies to more accurately identify their customers’ needs and design their marketing strategies based on reliable information and predictive analytics (Akter, 2021; cao et al., 2021). In this regard, B2B companies, especially in complex industries requiring customized solutions, should improve their marketing processes using data-driven innovation (Ghayour baghbani et al., 2024). One of the biggest challenges in this area is implementing data-driven innovation models that not only make optimal use of existing data but also exploit new technologies such as artificial intelligence (AI) and machine learning (De Luca, 2020). Specifically, these models must provide a deep understanding of customers and accurate predictions of their future needs so that marketing processes can be designed effectively and optimally (Länsipuro, 2020). In order for B2B companies to benefit from this approach, they need comprehensive models that include processes such as data collection, data analysis, and strategy development (Mohaisen et al., 2024). These models should be designed in a way that not only helps optimize marketing processes but also improves customer experience and increases overall company efficiency (Johnson et al., 2019). In this regard, Big Data analysis and the use of advanced analytical tools allow companies to adjust their marketing strategies based on more accurate predictions and continuously improve them (Becha et al., 2021). On the other hand, the exploitation of data-driven innovations in B2B companies should be implemented considering the specific characteristics of this type of business and the more complex relationships they have with customers (Bagheri et al., 2024). These characteristics require companies to design their marketing processes based on accurate and reliable analyses of customer and market data to create a sustainable competitive advantage in addition to improving the customer experience (Alghamdi & Agag, 2024). Data-driven marketing models should also consider that data is regularly updated and that they have the ability to respond quickly to customer needs in conditions of rapid market changes (De Luca, 2020). Considering the above, the present study seeks to present a data-driven and innovative model for optimizing marketing processes in B2B companies; a model based on advanced technologies that both responds to the specific needs of this environment, is simple for companies to implement, and allows for continuous updating and adaptation to rapid market changes. Accordingly, and in accordance with the research objective, this study seeks to answer the main question: How can a comprehensive and implementable model be designed that, by relying on data and technological innovation, significantly increases the effectiveness of B2B marketing processes while reducing costs and remaining flexible to environmental changes?  Theoretical foundations In this study, data-driven marketing refers to the use of customer data collected from various sources (including online interactions, CRM systems, social media data, and market research) to formulate marketing strategies and implement advertising campaigns in B2B companies (Al-Khatib, 2025), and data-driven innovation refers to the use of data and their analysis to create new and improved products, services, and marketing processes in B2B companies (Mirzaei and Thompson, 2024). Paying attention to the capacities of data-driven innovation as one of the most important factors in improving marketing performance can directly affect the promotion of companies' competitive advantage (Mahmoudi, 2024). On the other hand, using data as one of the key resources in marketing decision-making processes can create new opportunities and reduce risks associated with undocumented decisions (Kardani Maliki nejad et al., 2024). In addition, this trend allows marketing strategies to be planned more carefully and allows businesses to gain significant competitive advantages in competition with other companies (Al-Khatib, 2025; lamminparras, 2022). Al-Khatib et al. (2025) examined “How do big data-based organizational capabilities shape innovation performance?”, empirically found that data-driven organizational capabilities have a positive and significant relationship with intellectual property and play a moderating role in internal and external supply chain integration. This relationship, in addition to the indirect effect of supply chain innovation capabilities, improves firms’ innovation performance. Torabi et al. (2024) examined “A Critical Review of Intelligent Marketing Strategies: Challenges Between Data-Driven Marketing and Human Experience in the Age of "Encompassing Technologies" used document analysis to find that using data alone without considering the psychological complexities of customers leads to superficial decision-making. Combining data and human insight increases the effectiveness of campaigns and customer satisfaction. Challenges include privacy protection and analyzing customer behavior. Research Methodology Research Methodology of this study is based on a mixed approach (qualitative-quantitative) and aims to design a model of marketing processes with data-driven innovation capabilities in B2B companies. In order to achieve this goal, the research was conducted in two consecutive and complementary stages. In the first stage, a qualitative and exploratory approach was used to identify the main dimensions and components of the model. Therefore, the grounded theory method was used. The statistical population in this section included academic experts and senior managers of B2B companies who had significant experience and knowledge in the field of data-driven marketing and organizational innovation. Purposive and snowball sampling was used to select individuals with the most knowledge and insight into the subject. After conducting semi-structured interviews with 10 of these experts, the data were analyzed through open, axial, and selective coding and key concepts were extracted. In the second stage, to verify and test the model obtained from the qualitative section, the research entered the quantitative phase. The statistical population in this section included managers, directors, and employees active in the marketing units of B2B companies located in Tehran. Considering the size of the statistical population (530 people), based on the Morgan table, 219 people were selected as the sample size and the relative cluster sampling method was used. The data collection tool in this stage was a researcher-made questionnaire designed based on the findings of the qualitative stage and was provided to the respondents after confirming the content and construct validity. Cronbach's alpha coefficient was used to measure the reliability of the questionnaire, and the structural equation modeling method in Smart PLS software was used to analyze the data.  Research findings The research findings show that effective networking and building ongoing relationships in marketing between companies is a key factor for sustainable success, and that stakeholder identification, a culture of collaboration, and data analytics play a vital role. Also, data-driven innovation capabilities and big data analytics increase organizational efficiency by enhancing marketing agility and competitive advantage, and the convergence of data-driven with human experience makes marketing decision-making more comprehensive and intelligent. Data-driven strategic orientations and the development of organizational capabilities enhance innovation performance and sustainable value creation, and intelligent human resource management and big data provide the necessary infrastructure for the operationalization of innovations. Overall, the integration of networking, data-driven innovation, and a customer-centric approach provides an integrated and effective framework for improving marketing performance and achieving sustainable competitive advantage. Discussion and Conclusion The results of this study show that data-driven marketing and data-driven innovation capabilities, as a multidimensional phenomenon, are influenced by a set of causal, contextual, and intervention factors that ultimately lead to improved marketing performance and sustainable competitive advantage through scientific and creative strategies. In the causal dimension, the results indicate that the existence of technological infrastructure, data-driven culture, and top management support are key prerequisites for the formation of data-driven marketing. This result is consistent with the findings of Al-Khatib et al. (2025) who emphasized that organizational capabilities based on big data are the foundation for the development of intellectual property and innovation in manufacturing companies. In comparison with the research of Koivuniemi (2020), both emphasize the role of human resources and technology as the main resources in applying the data-driven approach. The main focus of the research is on data-driven marketing innovation, which shows how to transform data into actionable insights. The findings indicate that combining data and human creativity takes marketing from the level of mere numerical analysis to the level of intelligent decision-making. This finding is consistent with the study of Torabi et al. (2024), which emphasized that relying solely on quantitative data without considering human experience leads to superficial decisions. In the contextual dimension, the findings showed that organizational structure, learning culture, digital maturity, and management policies play an important role in the success of data-driven marketing. Organizations that have an open culture and flexible structure reach data maturity faster. This result is consistent with the research of Länsipuro (2020), who identified structural and cultural barriers as the most important challenges of data-driven marketing. It is also consistent with the findings of Aljumah et al. (2024). In the intervention conditions, barriers such as resistance to change, weak analytical skills, and lack of technology budget were identified. However, the results showed that targeted training, employee empowerment, and knowledge management can greatly reduce these barriers. This result is similar to the findings of Kardani Maliki Nejad et al. (2024) who emphasized the role of expert validation and training in the implementation of data-driven innovation. It is also in line with the research of Lamminparras (2022) who pointed out the need to develop dynamic capabilities in data-driven decision-making. The strategies identified in this study include data analysis for service personalization, designing value propositions, and creating targeted communications with customers. Implementing these strategies allows companies to use data not only to describe the past, but also to predict the future.

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 research is to investigate the effect of Sanctions’ Reduction and the financial strength of companies on the development of garment exports, considering the mediating role of direct exports and indirect exports. The research method is practical in terms of purpose and descriptive-survey-correlation in terms of method. The statistical population of this research includes the managers of garment manufacturing and exporting companies, and the sample size was determined to be 344 people using random sampling method. To analyze the data, the method of structural equations modeling has been used using Smart PLS software. The tool used for data collection is standardized questionnaire. The findings of this research show that the Sanctions’ Reduction has a direct and significant effect on the development of garment exports. Also, the financial Strength of companies has a direct and significant impact on the development of garment exports. Direct exports play a mediation role on the relationship between the Sanctions’ Reduction and the development of garment exports, the mediation role of indirect exports on the relationship between the Sanctions’ Reduction and the development of garment exports was also confirmed.
Extended Abstract
Introduction
A review of the latest statistical report on world trade, which was recently published by the World Trade Organization, shows that the dollar value of the world's textile and clothing exports was 315 billion and 505 billion dollars respectively in 2018, the ratio of the previous year increased 6.4 and 1.11 respectively, the increase was the fastest growth in the world's textile and apparel trade since 2012. It is also predicted that the annual growth rate of about 5% from 2019 to 2025 will bring the world's textile and clothing exports to about 1207 billion dollars in 2025. In Iran, many companies are active in the country's textile and clothing industries with operating licenses from the Ministry of Mining Industry and Trade, which constitute a significant percentage of all active industrial enterprises in the country. Also, these industrial units account for a significant percentage of the country's industrial employment. It is worth mentioning that this amount is related to industrial units, and due to the large number of small and trade units, the number of employees in the country's textile, clothing and leather industry is a significant amount of the entire industry (Ebadi et al, 2021). While the industries upstream of the apparel industry are also very important, the fashion industry is $1.3 trillion global business that employs more than 300 million people worldwide and represents a significant economic force and a significant driver of worldwide GDP (Gazzola et al, 2020). Considering that the export of clothing has high value added and foreign exchange earnings, it is also considered as one of the industries that have a very high employment generation capacity, and its capital-intensiveness considering its created sustainable employment is much lower compared to other industries (oil and petrochemical). Also, the growth of this industry affects other industries, and exports in this field can be driving force of upstream and downstream industries. Neglecting attention to Iran's garment exports has resulted that Iranian companies are not identifying the procedures and influencing factors for entering and stabilizing in these markets for consecutive years, and the managers of these industries have no effective strategy to develop their exports and overcome sanctions and improve their financial strength. Therefore, with the existing assumptions, this research seeks to answer the question of whether sanctions and the financial strength of companies can affect the development of garment exports, and whether direct export and indirect export can play a mediation role on the relationship between the Sanctions’ Reduction and the development of garment exports.
Theoretical Literature
Financial strength is a scientific process that helps an organization measure the effective use of company resources to maximize financial resources (Bei & Wijewardana, 2012). In other words, financial strength is an indicator to measure the probability of a company needing the support of third parties such as shareholders, banks, government, or official institutions to finance and pay the company's debt (Salimi et al, 2017).
Export is a set of actions and activities that are carried out to transfer the goods and services of commercial or governmental companies from one country to another for receiving currency or exchanging it for other goods and services (Moshabaki & Khademi, 2012). The most important export methods are direct export and indirect export. Direct export is an export in which goods and services move directly to foreign markets (Grozdanovska et al, 2017). Indirect export means the export of goods through intermediaries. They can be agents or companies that carry out the export. Agents act as brokers or establish a relationship between the exporter and foreign buyers (Grozdanovska et al., 2017).
Export development programs include all commercial, informational, and educational actions. In addition to sourcing, these programs also evaluate the export performance of the current period compared to the previous period. Despite planning for export development, these export programs are always accompanied by obstacles (Malca et al, 2020).
Sanctions are sets of restrictive measures applied by a country or a group of countries against a country that violates international laws or has violated accepted moral standards (Khaledi & Ardestani, 2022).
Research Methodology
The current research is applicable in terms of the purpose, and descriptive-survey-correlation in terms of the method. The tool used for data collection is a standard questionnaire (5-point Likert scale) as a result of the Alidaie et al, (2023) qualitative research work. The statistical population of this research includes the managers of garment manufacturing and exporting companies (N = 987); random sampling method was used to select the sample and finally, by Cochran's formula, at least 276 garment industry managers were selected to conduct the research, and 344 questionnaires were collected.
Research Findings
To analyze the hypotheses or the conceptual model of the research, Smart PLS 3 software was used, and the results showed that the effect of sanctions’ reduction on the development of garment exports was calculated as (0.415), which indicates a favorable effect. The t-test statistic was also obtained (7.586), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of financial strength on the development of Iran's garment export has been calculated as (0.468), which indicates a desirable effect. The t-test statistic was also obtained (4.557), which is greater than the critical value at the 5% error level, i.e. (1.96), and it shows that the effect is significant. The effect of indirect export on the development of garment export has been calculated equal to (0.457), which indicates a favorable effect. The t-statistic was also obtained (4.497), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of direct garment export on the development of garment export has been calculated equal to (0.581), which indicates a relatively strong effect. The t-test statistic was also obtained (9.211), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of sanctions’ reduction on direct export of clothing has been calculated as (0.927), which indicates a very strong effect. The t-test statistic was also obtained (81.106), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of sanctions’ reduction on indirect export of clothing has been calculated as (0.574), which indicates a favorable effect. The t- test statistic was also obtained (43.805), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of sanctions’ reduction on financial strength has been calculated as equal to (0.451), which indicates a favorable effect. The t-test statistic was also obtained (3.144), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of direct export on financial strength has been calculated equal to (0.414), which indicates a favorable effect. The t-test statistic was also obtained (4.711), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of indirect export on financial strength has been calculated equal to (0.515), which indicates a desirable effect. The t-test statistic was also obtained (4.379), which is greater than the critical value at the 5% error level, i.e. (1.96) and shows that the effect is significant. The effect of direct exports on the relationship between the sanctions’ reduction and the development of garment exports shows that the partial mediation of direct exports is confirmed with an intensity of 0.56. The effect of indirect export on the relationship between the sanctions’ reduction and the development of garment exports also indicates that the partial mediation of indirect export is confirmed with the intensity of the effect of 0.33.
Conclusion
The results of the hypothesis "the sanctions’ reduction impacts on the development of garment exports" are confirmed. The results of this hypothesis are in line with the research of Pourebadollahan et al, (2019) and Jafari et al, (2023). The results of the hypothesis "financial strength on the development of Iran's garment export" are confirmed. The results of this hypothesis are in line with the research of Rasoulinezhad & Kazemnia (2019) and Khorshidi et al. (2015). The results of the hypothesis "direct exports play a mediation role on the relationship between the sanctions’ reduction and the development of garment exports" are confirmed. Therefore, the direct export plays a mediating role. Also, the results of the hypothesis "indirect exports have a mediation role on the relationship between the sanctions’ reduction and the development of garment exports" are confirmed, which indicates the mediation role of indirect exports. Considering that the current research is exploratory and has innovation, it does not have a similar research history in the field of garment export.
The policy makers of the country should try to reduce the sanctions as much as they can, and in the situation of sanctions, it is recommended to the garment manufacturers and exporters to switch to indirect exports if there is a problem with direct exports. Increasing communication with brokers in this field is the key to the success of these companies in indirect export. These companies can increase their financial strength through appropriate and optimal financing methods and select target markets that have high export value added. These companies can transform their distribution network into internet sales and distribution networks in the export target markets and do brand development in the export target markets and avoid exporting products with fake brands.

Abstract Abstract The purpose of this research is to provide a customer loyalty model based on the five senses with the mediating role of perceived value in the insurance industry (case study of life and investment insurance). The research method is applicable in terms of purpose, and descriptive-survey in terms of the conducting method. According to the subject nature of the research model and professors' opinions, the statistical population of the research is the insurance buyers; and due to the limited statistical population, 384 people were selected using Cochran's formula; and the random sampling method is simple. The collection tool is a researcher-made questionnaire, taken from the qualitative part of the research. SPSS and PLS software were used for analysis. Also, confirmatory factor analysis was used to show the reliability of the questionnaire. The results showed that the five senses with the mediating role of perceived value have a positive and direct effect on customer loyalty, and the fit of the proposed model for the relationship of the variables has been confirmed. Extended Abstract 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). The main reason for the importance and loyalty of customers for service companies is that with the increase of loyal customers, the number of visits and purchases increases, which leads to a larger market share for that company. In fact, in order to survive in tough competition and keep existing customers, predicting the potential of loyal customers has become one of the main tasks of strategic managers (Khan et al, 2019). Another factor in customer loyalty research literature is the perceived value of customers, which is assigned to the perception that a person has about a product or service. It is possible that this belief is based on the thought of the experience of the individual's point of view, which is measured according to the ratio of the cost paid for that product or service and the value received (Ashraf et al, 2018). These beliefs, based on past studies of factors, have been effective on customer loyalty An era in which companies, regardless of whether they sell traditional consumer goods or provide services, consider effecting and influencing customers in new, stimulating, innovative and creative ways. Marketing enters this era in a situation where tested ideas and concepts are being revised. Traditional marketing is slowly disappearing and giving way to new methods such as sensory marketing. Sensory marketing emphasizes the use of five human senses (sight, hearing, smell, taste and touch) in the field of marketing. The ultimate goal of sensory marketing is to create a sensory experience with the help of the five human senses (Hamacher & Buchkremer, 2022). According to the mentioned points, the researcher is trying to answer the main question of how to present the customer loyalty model based on the five senses with the mediating role of perceived value in the insurance industry. Literature The perceived value of the brand is necessary, through a complex process and a comprehensive approach, to lead towards the desired repeated 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). Loyalty is the total amount of feelings and attitude that makes the customer buy again certain goods and services from the company (Shahid et al, 2022). In general, customer loyalty is always defined as a sales frequency with a relative volume of purchases from the same branch (Jenneboer et al, 2022). The goal of most organizations is to achieve customer satisfaction. Customer satisfaction with the services provided leads to recommending the product or service to other customers (Chisam et ai, 2022). In the customer's interaction with the company, environmental information is received by the five senses. Compatibility or lack of compatibility of the characteristics of the environment with the sensory tastes of the customer can cause satisfaction or dissatisfaction of the customer. Bahrami et al, (2022) investigated the impact of citizenship behavior and cooperative behavior of customers on their perceived value and satisfaction. The findings showed that citizenship behavior and cooperative customer behavior have a positive and significant effect on the perceived value of customers. The perceived value of customers has a positive and significant effect on their satisfaction. Also, the mediating role of perceived value in the relationship between citizenship behavior-customer satisfaction and cooperative behavior-customer satisfaction was confirmed. Asgari & Fazeli (2022) investigated the impact of mixed sensory marketing on customer loyalty in Iran's clothing industry. The results of the research showed that all sensory marketing components, except the interaction component, have a positive and significant effect on customer loyalty. Research methodology This research is applicable in terms of purpose, and descriptive-survey in terms of implementation method. According to the subject nature of the research model and professors' opinions, the statistical population of the research is the insurance buyers, which in the current research is a study on life insurance and investment. According to the obtained statistics, the number of the population was about 384 people, due to the limited statistical population, Cochran's formula was used and the random sampling method is simple. The tool for data-collecting is the questionnaire made by the researcher, which is related to the investigation of the relationship between the dimensions of the five senses and the perceived value on customer loyalty in the insurance industry, and is taken from the qualitative section; which includes 4 dimensions for perceived value (economic value, social value, perceptual value and emotional value), 5 dimensions for five senses (sense of sight, sense of hearing, sense of touch, sense of smell, and sense of taste), and 3 dimensions for customer loyalty (behavioral loyalty, attitudinal loyalty, and emotional loyalty).  Research findings Data analysis was done using SPSS and PLS software. The present research has seventeen hypotheses, all of which were confirmed, and the results showed that the five senses with the mediating role of perceived value have a direct and significant effect on customer loyalty, and the fit of the proposed model for the relationship of the variables has been confirmed. Conclusion The current research was conducted with the aim of presenting the customer loyalty model based on the five senses with the mediating role of perceived value in the insurance industry (case study of life and investment insurance). This finding is consistent with research findings of Bahrami et al, (2022), Behruzi & Sohrabi (2022), Asgari & Fazeli (2022), Lv et al, (2020), Hwang et al, (2019), and El-Adly (2018). Lumi et al, (2022) showed that the perceived image and value of customers has a positive and significant effect on the satisfaction and eventually on the attitudinal loyalty of customers, and this causes them to increase the intention to buy again. Ezati & Mazhari (2021) showed that brand equity, brand identity and brand loyalty have a positive and significant effect on repurchase intention. According to the research results, it is suggested: - Customer loyalty can play an important role in the success of organizations; finding out the effective factors on customer loyalty will help managers and employees of organizations to get closer to customers and respond to their needs faster and better. - By specifying the goals of the organization; organizational processes, the support of the managers of the organization to the employees, the system of payments and benefits, and the promotion system in the organization are among the things that can affect the perceived value. Adequate knowledge of 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.