New Approaches in Management and Marketing

Abstract Abstract The present study aimed to design a transformation model for AI-based B2B marketing strategies in small and medium-sized enterprises. In terms of purpose, this research is applicable, and was conducted through a qualitative approach based on the grounded theory strategy. The research population consisted of marketing managers and artificial intelligence experts. Sampling was carried out by a purposive method, and the data collection process continued until theoretical saturation was achieved, which was ultimately reached after 15 in-depth interviews. Data analysis was conducted in three stages—open, axial, and selective coding—through MAXQDA18 software. The findings indicated that causal factors, including the necessity of analyzing competitive big data, changes in the decision-making patterns of organizational buyers, and pressure to reduce marketing costs, play a key role in initiating this transformation. Contextual factors, including the organization’s digital maturity and the quality of data infrastructure, provide the necessary foundation for the successful implementation of intelligent marketing, while limitations in financial resources, technical complexities, and resistance from traditional sales experts act as intervening factors. According to the results, adopting strategies such as the implementation of intelligent account management systems, predictive analytics, and content automation leads to improved outcomes such as higher return on investment, increased loyalty among key customers, and reduced sales cycles. This study provides a systematic framework that helps managers of small enterprises gain a sustainable competitive advantage in industrial markets by understanding the complex interactions between artificial intelligence tools and marketing strategies. Introduction Today, artificial intelligence has emerged as a significant driving force across various industries (Soleymanpoor et al., 2025). The flood of data, coupled with the availability of processing power and storage in digital devices, has generated a renewed interest in artificial intelligence (AI) across various fields in recent years (Sharma et al., 2022). Intense competition among organizations worldwide has also accelerated the need for AI deployment to gain an edge over competitors (Saadisalih et al., 2025). AI is often not perceived by most managers as a core competency that organizations must cultivate to remain competitive in the long run (Hanandeh et al., 2024). One of the key areas of AI application in organizational operations has been B2B marketing (Mikalef et al., 2021). Intelligent solutions are essential for enhancing B2B marketing capabilities in a complex business environment, as B2B operations often involve extensive informational complexity and the need for rapid decision-making. In this regard, due to its ability to process increasing volumes of data and provide rich insights into business partners and key customers, AI has the potential to transform conventional activities (Karamipour et al., 2025). Furthermore, AI applications have been proposed to enable the automation of many manual processes, which can help reduce bottlenecks and increase operational efficiency in B2B operations (Emami et al., 2025). Nevertheless, despite the aforementioned benefits for large organizations, the implementation of AI in B2B marketing for small and medium-sized enterprises (SMEs) faces different complexities and obstacles. These companies, which are considered the backbone of the economy, unlike large corporations, often struggle with severe marketing budget constraints, a lack of massive datasets for training algorithms, and a shortage of AI specialists (Chatterjee et al., 2021). In the B2B world, where relationships are built on trust and long decision-making cycles, small companies need to know how AI can make their marketing processes intelligent and personalized without damaging human relationships (Zholghadr et al., 2025). Therefore, the primary issue is that despite the inevitable necessity of digital transformation, there is still no methodical model for integrating AI into B2B marketing strategies in small and medium-sized enterprises. There are many unknown aspects regarding how the managers of these companies adopt this technology and its actual impact on redefining their sales and marketing strategies. Hence, this research seeks to answer the following question: What is the role of AI in the transformation of marketing strategies? Theoretical foundations Artificial intelligence refers to a set of technologies that allow machines to simulate functions similar to human intelligence, including learning, reasoning, problem solving, and language comprehension. In fact, artificial intelligence is a system that, by complex algorithms, has the ability to process huge volumes of unstructured data to identify hidden patterns and make predictions or decisions based on them (saadisalih et al., 2025). This ability to analyze patterns distinguishes artificial intelligence from traditional data analysis tools, because artificial intelligence-based systems have the characteristic of “self-correction” and can continuously improve the accuracy of their predictions by receiving new data (seyed javadin et al., 2025). (Soleymanpoor et al., 2025) in a study entitled "Providing a model of effective factors in the socialization of artificial intelligence technologies", the results of the analysis showed that both independent and dependent variables have a direct positive and significant effect. (Rostam zadeh ganji et al., 2025) in a study entitled "Providing a model of developing employee cognitive trust in artificial intelligence", the results show that causal factors include transparency, training and awareness, ethical compliance and defining common roles and goals, contextual factors such as organizational culture and resources; intervening factors such as employee resistance and system complexity, and strategies including employee training and empowerment are known as key tools in improving human-machine interactions. Research Methodology The present study is applicable in terms of purpose, and qualitative in nature with an exploratory-explanatory approach, designed and implemented based on the grounded theory strategy. The main objective of this study is to identify key components and explain the relationships among artificial intelligence technologies, the transformation of B2B marketing strategies, and their outcomes at the level of small and medium-sized enterprises (SMEs). The statistical population of the study included marketing managers, sales strategists in the business-to-business sector, AI implementation specialists, and senior managers of small and medium-sized enterprises participated in the digitalization process of their commercial activities. Sampling was conducted by a purposive (judgmental) method to ensure that the participants possessed lived experience and a deep understanding of the challenges and opportunities of AI in the B2B environment. The data collection process continued until the stage of theoretical saturation, which was ultimately achieved through 15 in-depth interviews. Data analysis was carried out in three stages—open, axial, and selective coding—by MAXQDA10 software. Research Findings The research findings indicated that the transformation of B2B marketing strategies is a pivotal phenomenon shaped by causal factors such as the necessity of big data analysis and competitive pressure. This process, within the context of the organization’s digital maturity and through the management of intervening factors such as employee resistance and financial constraints, is realized through strategies like intelligent personalization and predictive analytics. Ultimately, the implementation of this paradigm model leads to prominent strategic outcomes, including the enhancement of competitive performance, reduction of the sales cycle, and improvement of the organizational customer experience. Discussion and Conclusion The research findings indicate that competitive pressure acts as one of the primary drivers steering small and medium-sized enterprises (SMEs) toward artificial intelligence. In today’s industrial markets, acquiring strategic customers is no longer feasible through traditional methods, and the necessity of analyzing competitive big data has forced organizations to adopt intelligent tools. This finding is consistent with the results of Mikale et al. (2021). Profound transformations in the structure of industrial markets, including supply chain complexity and the urgent need to minimize human error in sales forecasting, act as external drivers. The findings of the current study suggest that due to high volatility in B2B markets, traditional forecasting models have lost their effectiveness. This aligns with the research by Seyedjavadin et al. (2025), which emphasizes the role of business culture in formulating AI-driven strategies. According to the extracted model, value creation through intelligent insights is the defining milestone that distinguishes leading firms. This finding is closely related to the results of Labin et al. (2024), who identified six emerging clusters in smart marketing (including dynamic market strategies), demonstrating that AI shifts the nature of “strategy” from a static state to a dynamic, data-driven one. Data analysis revealed that success in marketing transformation is highly dependent on digital maturity and the culture of innovation adoption within managerial echelons. The quality of customer data and the availability of cloud platforms provide the necessary foundation for executing the model. This outcome aligns with the research of Bagheri et al. (2024), who investigated the level of digitalization in knowledge-based companies. Indeed, without structured data infrastructures and specialized human resources, even the most advanced AI tools cannot bring about strategic transformation. The research findings showed that at the operational level, the resistance of traditional sales staff and the lack of technical expertise constitute the most serious operational challenges. These barriers, identified as intervening factors in the model, indicate that strategic transformation extends beyond technology acquisition and requires a shift in the organization’s human capital. This finding is consistent with the results of Rostamzadeh Ganji et al. (2025) regarding the importance of employees’ cognitive trust; until employees trust the accuracy and functionality of AI, they will resist the implementation of algorithm-based strategies. Furthermore, this is closely related to the study by Soleymanpour et al. (2025) on the necessity of “technology socialization,” meaning that small firms must establish the necessary cultural and educational foundations for AI adoption within the organization prior to model deployment to prevent early-stage project failure. The results demonstrated that factors such as financial constraints, regulatory gaps, and particularly the unique challenges of the Iranian business environment—such as “sanctions and lack of access to international tools”—affect the model’s outcomes as critical and intervening limitations. The findings of the present study indicate that due to weak financial resources, SMEs lack the capability to compete with large organizations in acquiring licensed technologies. This finding matches the results of Ahmad (2025) regarding ethical and infrastructural challenges, as well as the research by Zolghadr et al. (2025), which emphasized the role of export and technological capabilities in electronic industries. The research findings revealed that one of the primary strategies for transformation is the re-engineering of traditional processes and aligning them with the requirements of the digital age. This re-engineering encompasses a shift in managerial attitudes and redesigning decision-making structures from intuitive to data-driven modes. This finding is consistent with the research of Seyedjavadin et al. (2025), which highlights the role of business culture in formulating AI-driven strategies. At the operational level, the findings suggest that firms must move toward intelligent tactics such as strategic personalization at scale and the utilization of predictive analytics in B2B interactions. These tactics allow the organization to focus on opportunities with higher success probabilities by accurately predicting the needs of strategic customers. This outcome is closely related to the research of Emami et al. (2025) regarding the design of an AI-based Customer Relationship Management (CRM) model. It is also aligned with the findings of Saadisalih et al. (2025) regarding innovative models for technology implementation in target markets. Ultimately, the implementation of these strategies leads to the key outcome of enhancing organizational performance and establishing a competitive advantage. The results showed that the synergy between process re-engineering and intelligent tactics not only increases sales force productivity but also places the firm in a superior position relative to competitors by creating value through intelligent insights. This finding is fully consistent with the results of Karampour (2025), which emphasized the impact of AI capabilities on organizational performance in B2B environments.

Abstract Abstract The aim of this study is to present a model for ranking insurance companies based on brand equity. In terms of implementation, this research is qualitative and was conducted by the content analysis method. The statistical population of the study consisted of 10 experts, including managers, specialists, and faculty members, selected through purposive sampling. Sampling continued until theoretical saturation of categories was achieved. The data collection instrument was semi‑structured interviews. For data analysis, first, by means of a qualitative approach and the content analysis technique—including open coding, axial coding, and selective coding—the components related to brand equity were extracted. Subsequently, MAXQDA software was applied for data analysis. The findings indicated that 36 components were identified and an initial model was developed. Based on the final conceptual model, the most important factors influencing brand equity in the ranking of insurance companies include service characteristics, customer characteristics, financial performance, profitability, credibility and public trust, innovation and technology, investment and financial assets, symbolic characteristics, and the service environment. Introduction In the contemporary era, many organizations have come to recognize that one of their most valuable assets is the brand of their products and services. Numerous studies have shown that building a strong brand is one of the key factors in achieving competitive advantage and ensuring long-term survival in competitive markets (Santos et al., 2023). A strong brand creates value not only for customers but also for the organization. On one hand, brands serve as effective tools for simplifying the process of product or service selection and purchase, facilitating information processing for customers and thereby increasing perceived value. On the other hand, while product design and production processes can often be easily imitated, the brand image and positioning—formed through years of marketing activities and customer experiences—cannot be easily replicated (Keller, 2017). Therefore, organizations relying on strong brands can implement higher pricing strategies, create more effective commercial leverage, increase their profit margins, and be less vulnerable to competitors (Aaker, 2025). Over the past two decades, the concept of brand equity has attracted considerable attention in marketing literature. Factors such as rapid technological innovations, the globalization of markets, and the increasing power of retailers have all emphasized the importance of understanding and measuring brand equity. Despite the existence of multiple definitions, there is a theoretical consensus that brand equity represents the added value that a product or firm obtains solely through its brand name (Hunt, 2019). Accurately measuring brand value has always been associated with challenges, as marketing decisions are often made in uncertain and dynamic environments, and numerous factors contribute to the formation of brand value. Under such conditions, the application of fuzzy approaches can serve as an effective tool for reducing uncertainty and improving the accuracy of evaluations. The findings of the present study can contribute scientifically by paving the way for future research in the field of insurance company ranking and by enriching the existing literature in this area. From a practical perspective, it can assist insurance companies in identifying performance criteria and aligning their performance accordingly. In the Iranian insurance industry, although all stakeholders—including insurance company managers, policyholders, and regulatory authorities—recognize the importance of branding, there is no standardized, quantitative, and widely accepted model for measuring and ranking the brand equity of insurance companies based on scientific dimensions tailored to the specific conditions of the Iranian market. Evaluations are generally conducted based on traditional financial and operational indicators such as written premiums, loss ratio, and operating profit. Therefore, aiming to propose a model for ranking insurance companies based on brand equity, the present study holds multidimensional importance and necessity, and seeks to answer the following question: What is the appropriate model for ranking insurance companies based on brand equity? Theoretical Framework Brand Equity Brand equity is of critical importance because it enables a brand to achieve greater significance and revenue in competition with rivals. Brand equity is a complex construct that encompasses numerous parameters such as brand image, brand identity, brand awareness, brand loyalty, brand associations, and others. Although this construct is largely subjective and qualitative in nature, it can be represented quantitatively. Brand equity is formed through marketing strategies, sustained efforts over time, and consistency, which collectively lead to customer perceptions and brand knowledge that may be either positive or negative. Positive perceptions result in increased brand equity. Effectively communicating product benefits to customers contributes to brand building. Companies invest substantial amounts in advertising through integrated marketing communication channels to promote their goods and services (Saputra & Margareta, 2020). Derisi et al. (2026) examined the development of a brand reputation model aimed at enhancing brand equity in the pharmaceutical industry. The qualitative findings revealed that five main categories constitute the brand reputation model: causal conditions (proven quality, drug safety monitoring systems, credible scientific data), contextual conditions (institutional collaboration, scientific–economic interaction with policymakers, international standards), intervening conditions (geopolitical factors, multi-level competition, technological developments), strategies (scientific excellence, transparency, crisis management, indigenous innovation), and consequences (sustained trust, market position, scientific credibility, and policy bargaining power). In the quantitative phase, the results indicated high validity of the extracted model. Sabzvari et al. (2025) investigated the identification of brand equity creation components in the Iranian apparel industry. According to the findings, four components—internal brand strength, brand awareness, positive brand image, and perceived value—are the most critical determinants of brand equity creation in Iran’s apparel industry, and the proposed model demonstrated an acceptable level of fit. Research Methodology This study was conducted through a qualitative approach and the content analysis method. The statistical population consisted of 10 experts, including managers, specialists, and faculty members, selected through purposive sampling. The sampling process continued until theoretical saturation of the categories was achieved. The data collection instrument was semi‑structured interviews. Research Findings For data analysis, first, by means of a qualitative approach and the content analysis technique—including open coding, axial coding, and selective coding—the components related to brand equity were extracted. Subsequently, MAXQDA software was employed for data analysis. The findings indicated that 36 components were identified and an initial model was developed. Based on the final conceptual model, the most important factors influencing brand equity in ranking insurance companies include service characteristics, customer characteristics, financial performance, profitability, credibility and public trust, innovation and technology, investment and financial assets, symbolic characteristics, and the service environment. Conclusion The present study was conducted with the aim of developing a model for ranking insurance companies based on brand equity. The findings of this study are consistent with the results of Derisi et al. (2026), Sabzvari et al. (2025), Borjalilou and Emadinasab (2025), Mohammadnezhad and Rezaei Dizgah (2025), Nam et al. (2023), Lurriro and Miranda (2023), Jontonton et al. (2022), Yazdani Kachuei et al. (2022), Mohammad (2022), Balmer and Podnar (2021), and Sarker et al. (2021). Borjalilou and Emadinasab (2025) demonstrated that corporate brand identity has a positive and significant effect on employee-based brand equity dimensions, including brand citizenship behavior, employee satisfaction, word-of-mouth promotion, and intention to continue collaboration. Furthermore, the dimensions of corporate brand identity—namely brand visual identity, brand personality, employee and customer orientation, and sustainable communications—also have a positive and significant impact on employee-based brand equity, although these effects are not strong. Among these dimensions, employee and customer orientation and sustainable communications, which are more closely related to organizational human resource policies, exert stronger effects on employee-based brand equity. In contrast, brand visual identity and brand personality, which reflect broader and more general meanings of brand identity, have comparatively weaker effects on employee-based brand equity. Based on the research findings, it is recommended that companies disclose the results of such evaluations in their annual reports to shareholders in order to enhance accountability and transparency.

Abstract Abstract The aim of the present study is to identify the architecture of the entertainment structure in modern advertising; identifying core variables with word co-occurrence analysis. The present study is descriptive in terms of its purpose and developmental in terms of its application, and it was conducted through the meta-synthesis approach and scientometric techniques (synonymous analysis and co-authorship). This study applies a combined approach including "systematic review" and "word co-occurrence network analysis" to redefine the internal architecture of the entertainment structure. The meta-synthesis method was applied to analyze the findings, and the scientometric approach and VOSviewer software were utilized to combine, cluster, and structurally analyze the extracted concepts. The results showed that by analyzing 211 key vocabulary units extracted from reputable scientific sources, 9 fundamental variables in behavioral and experiential dimensions were identified and explained in the form of a comprehensive classification. While criticizing the ineffectiveness of classical models in explaining the modern complexities of advertising, the findings of this study provide an evidence-based framework for operationalizing the construct. This research not only contributes to the development of consumer behavior theories, but also provides a strategic tool for marketing managers to design and evaluate advertising content. The results of this research provide a solid basis for future empirical research to measure the effectiveness of these dimensions in platform-based environments. Introduction Rapid changes in media, content consumption patterns, and the ways in which audiences encounter commercial messages have highlighted the role of entertainment in advertising effectiveness more than ever before. In an age of message saturation, fast scrolling, short videos, and skippable ads; only those messages are likely to be seen and processed that create an enjoyable, engaging, and meaningful experience (Shafiee et al., 2024; Teixeira, 2014). Recent research suggests that entertaining elements such as humor, narrative, digital aesthetics, creativity, and cognitive stimulation can increase attention, enhance depth of processing, and improve the experience of encountering an ad (Gallo, 2023; Simonetti, 2024; Huang, 2023). This evidence highlights the importance of entertainment as one of the main drivers of the advertising experience in contemporary media environments. Despite this importance, the existing literature suggests that the concept of entertainment remains fragmented, multifaceted, and lacks theoretical coherence. Studies have explained this construct in different formats: some have considered it equivalent to cognitive engagement (Cline, 2007; Makienko, 2014); some have analyzed it in the context of narrative (Green & Brock, 2000; Wang, 2021); and others have limited it to emotional or aesthetic experiences. More recent studies also show that entertainment in digital, algorithmic, and OTT media has a more multifaceted nature and requires redefinition (Yu, 2024; Malik, 2024; Song, 2022). This conceptual heterogeneity highlights the need to review and integrate the literature. Accordingly, the aim of the present study is not to present a predetermined model, but to conduct a systematic review based on scientometric analysis of the last four decades and to identify the core variables of advertising entertainment; variables that can serve as the basis for developing new theoretical frameworks, valid measurement tools, and more accurate modeling in future research. This redefinition not only contributes to the theoretical richness of the literature, but also allows for the alignment of existing models with contemporary media conditions, including digital, algorithmic, interactive, and short-narrative advertising. Therefore, the main question of this research is: how does the architecture of the structure of entertainment in modern advertising; the identification of core variables by word co-occurrence analysis look like? Theoretical Framework Entertainment in Advertising Advertising as an effective tool for raising awareness and introducing companies, goods, services, and even ideas and perspectives has undergone extensive changes over time. In the contemporary world, advertising has become an integral element of the structure of organizations and even cultural and social arenas, such that the continued effective activity of many businesses and social institutions depends largely on the level of success in information and advertising (Sinha, 2021). Advertising is a purposeful activity that introduces and promotes products or services through advertising messages to increase sales and strengthen brand recognition (Smith & Johnson, 2022). Entertainment in advertising has been considered one of the most effective mechanisms for shaping the audience's experience and increasing the effectiveness of the message over the past four decades. However, a systematic review of the literature shows that this construct has been explained over time with different theoretical approaches and there is still no unified and agreed-upon framework for defining and measuring it. Research in this area can be categorized into several main conceptual clusters, each of which sheds light on part of the nature of entertainment, but none of which alone is capable of providing a comprehensive picture of it. Mir Motahari et al. (2025) investigated the evaluation of the role of outdoor advertising on the use of eye movements towards the brand. In this study, causal conditions (advertising content; quality of outdoor advertising and advertising features); main contextual factors (advertising slogan, billboard elements and analysis of customer eye movements); intervening conditions (online advertising, advertising costs, advertising through media and mass communication); pivotal categories (environmental advertising status, development of advertising effectiveness and effective advertising on customer intentions); strategies (attractions of advertising message, evaluation of the environmental advertising selection process) and three categories of competitive advantage, value creation and promotion of strategic marketing decisions are classified as the consequences of environmental advertising based on the use of eye trackers. Danaei (2025) investigated the effect of media (radio) advertising effectiveness on the degree of consumers' tendency to consume domestic goods. The results of this study showed that the effectiveness of media advertising (radio) has a positive and significant effect on consumers' tendency to consume domestic goods. Research Methodology The present study is descriptive in terms of purpose and developmental in terms of application, and was conducted by a meta-synthesis approach and scientometric techniques (synonymous analysis and co-authorship). This study applies a combined approach including "systematic review") and "word co-occurrence network analysis" to redefine the internal architecture of the entertainment construct. Research Findings The meta-synthesis method was employed to analyze the findings, and the scientometric approach and VOSviewer software were employed to combine, cluster, and structurally analyze the extracted concepts. The results showed that by analyzing 211 key lexical units extracted from reliable scientific sources, 9 fundamental variables were identified in behavioral and empirical dimensions and explained in the form of a comprehensive classification. While criticizing the inefficiency of classical models in explaining the modern complexities of advertising, the findings of this study provide an evidence-based framework for operationalizing the construct. This research not only contributes to the development of theories of consumer behavior, but also provides a strategic tool for marketing managers to design and evaluate advertising content. The results of this research provide a solid basis for future empirical research to measure the effectiveness of these dimensions in platform-based environments. Conclusion The present study aimed to identify the core variables of the construct of entertainment in modern advertising by analyzing the co-occurrence of words. The results of this study are consistent with the results of Mir Motahari et al. (2025), Danaei (2025), Mohammadi et al. (2024), Hessani Khabr et al. (2025), Shen & Wang (2024), Bagher Mosavi & Fadai (2023), Ahadi & Ghasemi (2020), and Gordon et al. (2019). Shen & Wang (2024) showed that short video users' personality perception from each dimension has a significant positive effect on shared value creation. Therefore, this study examines the internal correlation and mediating mechanism between short video users' personality perception and purchase intention, establishing mediating and moderating variables. First, shared value creation plays a mediating role between personality perception and purchase intention on short video platforms. Second, two individual-level moderator variables play a moderating role between shared value creation and purchase intention on short-form video platforms: monitoring focus and social presence. Using research variables as a monitoring mechanism helps managers make more informed strategic decisions regarding media selection and message content to maximize the return on advertising spending. Despite methodological rigor, the focus on English-language articles and a specific time period may have overlooked some of the local literature or very emerging research. Also, the findings remain at the theoretical level and need to be empirically validated. Accordingly, it is suggested that researchers design questionnaire items and test their reliability and validity through factor analysis in the next step.

Abstract Abstract The aim of this study is to design a model for improving commercialization in the PVC industry with an emphasis on the role of emerging technologies. The research method is applicable in terms of its purpose, and exploratory in terms of qualitative approach and data analysis. The statistical population of the study consisted of 15 experts in the PVC industry, including technical and production managers of factories, formulation and R&D specialists, and faculty members in the fields of polymer and technology management, selected by purposive and snowball sampling methods. Semi-structured interviews were applied to collect information. Thematic analysis approach and MAXQDA software were applied to analyze the data. The results showed that the initial codes extracted were categorized into five organizing themes including “Environmental Pressures on Commercialization”, “Technological Capabilities and Capacities”, “Organizational and Management Factors”, “Market and Commercialization Processes”, and “Consequences and Achievements of Commercialization”. Finally, the overarching theme of the research was identified as “Improving Commercialization through Emerging Technologies in the PVC Industry”. The research findings showed that utilizing new technologies, enhancing technological capacities, strengthening knowledge management, developing market-oriented interactions, and improving organizational infrastructure play a decisive role in increasing the effectiveness of the commercialization process in the PVC industry. The results of this research can be used as a practical framework for industrial managers, policymakers, and polymer activists to develop the commercialization of innovative products and technologies. Introduction In recent decades, emerging technologies have played a prominent role in improving organizational performance, increasing competitiveness, and sustainable development as key drivers in the transformation of the structure of various industries. In the meantime, the polymer industry, and especially polyvinyl chloride (PVC), has become one of the most important sub-sectors of the petrochemical industry due to its unique characteristics, including reasonable price, high durability, flexibility in application, and recyclability. The development of new technologies in this area can lead to the production of products with better performance, reduced energy costs, improved environmental properties, and expanded the range of industrial applications (Delogu et al., 2020). Internationally, several models for technology commercialization have been developed that focus on interaction and synergy between key stakeholders such as universities, the private sector, government institutions, and investors. For example, the “triple helix” model emphasizes the synergy of knowledge, policy, and economics to facilitate the process of transforming science into products (Etzkowitz & Leydesdorff, 2022). In these frameworks, knowledge management, market analysis, technology assessment, and business model design are key elements for successful commercialization. However, adapting these models to the economic, cultural, and structural environment of countries such as Iran requires localization and redesign based on local realities (OECD, 2021). New approaches such as open innovation and technology co-creation platforms can be used as components that strengthen the commercialization model. By facilitating participation among stakeholders, these approaches help accelerate the idea-to-market process and can lead to the creation of a sustainable value chain in the PVC industry (Chasbro, 2020). Although the international literature has extensively addressed business models in the polymer materials and chemical industries, a review of recent articles shows that existing research is often either generic and cross-industry, without focusing on the specific characteristics of PVC such as high environmental and regulatory sensitivity, or solely on sectors such as market, technology or regulation, rather than on a comprehensive commercialization model. In particular, the integration of sustainability, circular economy and digitalization dimensions in the design of commercialization models for PVC technologies is less visible in a structured manner. Therefore, the main question that this research seeks to answer is: what does a commercialization improvement model look like in the PVC industry with an emphasis on the role of emerging technologies? Theoretical Framework Commercialization in the PVC Industry In PVC extrusion and granulation lines, monitoring indicators include a set of process, equipment, quality and maintenance variables that comprehensively describe the behavior of the production system. Variables such as temperature, pressure, screw speed, equipment vibration, product weight and color, cutting blade speed, and production rate are among the indicators whose deviation from the optimal range can lead to quality degradation or line shutdown. The thermal sensitivity of PVC makes precise control of temperature and pressure in different areas of the extruder particularly important, as any fluctuations can lead to thermal degradation of the material and an increase in defective products (Pinto et al, 2020). Adomako & Tran (2026) examined Innovation versus Chance; The impact of regulatory challenges on technology commercialization performance and product innovation. Using two-wave, multi-informant survey data from 336 SMEs in Vietnam, the results confirm the mediating role of commercialization potential. Furthermore, while intangible resource advantage strengthens the link between commercialization and innovation, environmental hostility unexpectedly weakens it, indicating that external pressures can overshadow firms’ commercialization efforts. These findings contribute to regulatory science and innovation research by highlighting commercialization potential as a critical mechanism through which institutions influence innovation. The present study provides insights for managers seeking to overcome institutional barriers and for policymakers seeking to create more innovative regulatory environments. Pujotomo et al. (2025) examined a scenario of accelerating technology commercialization in a research university, applying a systems dynamics approach. The results show that TTO participation can reduce delays by directly matching university inventions with early adopters. Furthermore, given that TTOs coordinate initial grants, competitions, and external support; the results also increase funding prospects. Meanwhile, additional funding for inventors can further influence the speed of development, emphasizing the importance of team performance and resource availability. Overall, team dynamics, including expertise and time allocation, are crucial for rapid progress towards TRL levels. Active participation of end-users in design and testing also accelerates improvements. Based on these insights, we provide a practical scenario guide for technology transfer office managers to address budget shortfalls, optimize resources, and shorten commercialization timelines. Research Methodology The research method is applicable in terms of its purpose, and exploratory in terms of qualitative approach and data analysis. The statistical population of the study consisted of 15 experts in the PVC industry, including technical and production managers of factories, formulation and R&D specialists, and faculty members in the fields of polymer and technology management, selected by purposive and snowball sampling methods. Semi-structured interviews were applied to collect data. Research Findings Thematic analysis approach and MAXQDA software were utilized to analyze the data. The results showed that the initial codes extracted were categorized into five organizing themes including “Environmental Pressures on Commercialization”, “Technological Capabilities and Capacities”, “Organizational and Management Factors”, “Market and Commercialization Processes”, and “Consequences and Achievements of Commercialization”. Finally, the overarching theme of the research was identified as “Improving Commercialization through Emerging Technologies in the PVC Industry”. The research findings showed that utilizing new technologies, enhancing technological capacities, strengthening knowledge management, developing market-oriented interactions, and improving organizational infrastructure play a decisive role in increasing the effectiveness of the commercialization process in the PVC industry. The results of this research can be used as a practical framework for industrial managers, policymakers, and polymer activists to develop the commercialization of innovative products and technologies. Conclusion The present study was conducted with the aim of designing a model for improving commercialization in the PVC industry with an emphasis on the role of emerging technologies. The results of this study are consistent with those of Adomako & Tran (2026), Pujotomo et al. (2025), Sato et al. (2025), Banoo Farhang & Ansari (2025), and Momiyond et al. (2024). Pujotomo et al. (2025) showed that TTO participation can reduce delays by directly matching university inventions with early adopters. Furthermore, given that TTOs coordinate initial grants, competitions, and external sponsorships, the results also increase funding prospects. Meanwhile, additional funding for inventors can further impact development speed, emphasizing the importance of team performance and resource availability. Overall, team dynamics—including expertise and time allocation—are critical for rapid progress toward TRL levels. Active participation of end users in design and testing also accelerates improvements. Based on the research results, the following suggestion was made: PVC industry managers should create a structured “environmental monitoring” system to continuously collect, analyze, and link changes in the market, competitors, standard requirements, and environmental policies to technological decisions. This system should be able to provide early warnings and prepare the organization to face external threats. Such an approach prevents the organization from being caught in the middle of sudden changes and makes technological decisions more rational and forward-looking.

Abstract Abstract The aim of the present study is to identify and prioritize the challenges of implementing digital marketing in knowledge-based companies located in Qom and Markazi provinces. The research follows a mixed-methods approach, utilizing both qualitative and quantitative techniques. Qualitative data were collected through a literature review and interviews with fourteen managers of knowledge-based companies, selected via snowball sampling until the stage of theoretical saturation. These data were analyzed by the thematic analysis method to identify and categorize the issues. For qualitative data organization, coding, and analysis, MAXQDA 2020 software was employed. Initially, 42 issues were identified and classified into six general categories: cognitive and behavioral issues of managers, financial and funding issues, content-related issues, human resource issues, marketing-oriented issues, and issues related to changes in social platforms and networks. In the second stage, the issues were screened by the Fuzzy Delphi technique in Excel. The screening resulted in 22 final factors, which were then prioritized by the MARCOS technique. The prioritization results indicate that “weakness in retaining expert and specialized human resources” and “lack of marketing research tailored to the digital environment” held the highest priority. Introduction The market is a dynamic, living, and evolving phenomenon (Azizi et al., 2020, p. 4). As the science of responding to human needs within a turbulent market environment, this academic discipline must maintain flexibility and alignment with environmental changes to address corporate requirements. One of the most significant transformations is the remarkable progress and expansion of cyberspace. Given the substantial growth of information and communications technology, which has influenced all aspects of social life and induced extensive changes in various work methods, it can be stated that digital communication tools have encompassed nearly all facets of modern human life (Iran Aghideh, 2021, p. 121; Farzinmehr et al., 2025, p. 102). The widespread use of the Internet and digital tools presents a unique commercial opportunity for managers. The integration of social life with the digital sphere has led to the emergence of a new concept termed digital marketing (Iran Aghideh, 2021, p. 121). As a branch of marketing management and advertising, digital marketing encompasses all tools and activities related to the supply and promotion of products across digital platforms (Gholipour et al., 2016, p. 8; Mohseni et al., 2023, p. 229). This form of marketing refers to the utilization of the Internet and digital technologies such as computers, websites, mobile devices, applications, and email (Ayush et al., 2020, p. 225). Digital marketing, as a new paradigm, has revolutionized the marketing system and marketing knowledge. Given that it is conducted within the context of the Internet and through its associated techniques, it can be considered the most effective way to introduce products and improve the performance of knowledge-based companies. Consequently, the introduction and marketing of products via digital marketing are decoupled from their place of production; customers can be found from any point in Iran or even the world, and they, in turn, can establish connections with various manufacturers (Shirdel et al., 2022, p. 3). On the other hand, knowledge-based businesses are on the rise and are considered vital to the economy; the knowledge-based economy accounts for a large share of the economy in many developed countries (Hayes, 2021). Therefore, knowledge-based companies can be viewed as the core and driving force of the knowledge-based economy. These companies, which often seek to generate wealth through high technologies, are critical drivers of growth. These firms, like any other, require marketing functions, and specifically, the position of digital marketing is vital and essential in advancing their objectives. Digital marketing can bring numerous benefits to knowledge-based companies that produce and provide high-value-added goods and services based on knowledge and technology (Center for the Growth of Knowledge-Based Companies and Institutions, 2024). Thus, the implementation of digital marketing in knowledge-based companies can significantly contribute to their progress. However, the use of digital marketing in these companies faces challenges and problems that have not been adequately addressed in previous research. Given the necessity of market development and increasing sales for knowledge-based companies, it is imperative to conduct precise and effective research to achieve these goals; identifying these challenges will pave the way for their resolution. Therefore, the present study seeks to address the issue of identifying the obstacles and problems hindering the use and implementation of digital marketing in knowledge-based companies. The research question is: What are the challenges and issues of implementing digital marketing in knowledge-based companies, and which of these hold higher priority? Theoretical Framework Digital Marketing Since the emergence of buying and selling in human relations, the concept of marketing has existed (Mohseni et al., 2023, p. 227). Having transitioned through eras such as hunting, agriculture, and the first to third industrial revolutions, we are now witnessing the Fourth Industrial Revolution. This revolution is essentially considered a revolution in communications, linked to internet technologies that have transformed the world; thus, organizational agility in responding appropriately to changes can be regarded as the key to their success (Petrosian, 2023). Some believe that the emergence of digital marketing coincided with the advent of radio. In 1990, only a few individuals had access to the internet, a number that gradually increased until there were approximately 18 million internet users by 1995. In 1999, the Salesforce website introduced the first customer management system for storing user information, tracking their behavior on the site, and monitoring campaigns; this software is recognized as the core foundation for the formation of digital marketing (Mohseni et al., 2023, p. 228). In simple terms, digital marketing can be defined as the execution of marketing plans and strategies through the internet and other related digital platforms. In fact, it can be defined as a marketing approach that primarily relies on the internet to communicate with target audiences through various digital media channels and platforms. In digital marketing, marketers utilize the internet, mobile devices, email, social media, search engines, video streaming platforms, and other channels to reach customers. At the same time, some marketing experts argue that digital marketing should be viewed as an entirely different and new endeavor that requires novel and distinct methods for approaching, communicating with, and understanding customers. Since digital technology lies at the heart of all companies, digital marketing serves as an essential tool for brands and marketers ydigital marketing provides organizations with the opportunity and space to strive for success in utilizing local and domestic markets (Nejati, 2022, p. 25). Research Methodology The present study adopts an inductive approach and falls within the category of exploratory and applicable research. Its strategy is survey-based, and in terms of implementation, it was conducted by a mixed-methods design. Data collection instruments included documents and semi-structured interviews in the qualitative phase, and a questionnaire in the quantitative phase. The theoretical population consisted of knowledge-based companies located in the Science and Technology Parks of Qom and Arak. Fifteen companies were selected as the sample for interviews, and data collection continued until theoretical saturation was achieved. Research Findings The thematic analysis method and MAXQDA 2020 software were employed for data analysis. The findings revealed 150 initial codes which, through consolidation, resulted in 42 basic themes. these were categorized into six organizing themes: cognitive and behavioral issues of managers, financial and funding issues, content-related issues, human resource issues, marketing-oriented issues, and changes in social platforms and networks. In the second step, due to the high number of basic themes, the Fuzzy Delphi method was applied to screen the issues. A questionnaire containing 42 issues was sent to 10 experts; ultimately, 22 issues with a defuzzified value greater than 0.5 were selected as the final challenges. For the final ranking, the MARCOS method was utilized. Conclusion The present study aimed to identify and prioritize the dimensions and components of implementing digital marketing in knowledge-based companies. The results of this study are consistent with the findings of previous studies of Shiri & Moradnejadi (2024); Gholipour et al. (2016); Papi et al. (2022); and Farast (2020). Based on the prioritization results, factors such as weakness in retaining expert and specialized human resources, lack of marketing research aligned with the digital environment, the lack of up-to-date digital marketing education in universities in line with the latest scientific and technological developments, and insufficient attention to the long-term outcomes of digital marketing were identified as the most significant issues. As noted by Shiri and Moradnejadi (2024), efficient human resources equipped with sufficient knowledge, who can address societal needs—including those of knowledge-based companies—through their expertise and contribute to national development, are of great importance. As long as there is no adequate and accurate understanding of the market and competitors, the application of marketing knowledge will face challenges. Excessive focus on innovation and product uniqueness (without attention to the needs of online customers), along with insufficient awareness of marketing and digital marketing and the failure to conduct marketing research appropriate to the digital environment, will collectively lead to difficult and costly outcomes for businesses. Based on the research findings, the following practical recommendations are proposed: Designing a comprehensive system for the retention and development of digital human capital Institutionalizing data-driven digital marketing research Redesigning the university education system with a skills-oriented approach Enhancing managers’ strategic literacy regarding digital marketing Reforming organizational structures and consolidating the position of digital marketing Diversifying digital channels and managing platform-related risks Improving infrastructures related to trust, security, and digital financial transparency

Abstract Abstract The aim of this study is to investigate the effect of augmented reality features on customer behavior by considering the role of mediating customer interaction. This study is applicable in terms of its purpose, and descriptive-survey in terms of its data collection method. The statistical population of this study is an unlimited number of all online customers of Panberiz Company in Tehran. According to the Krejci Morgan table, the number of statistical samples in the unlimited population was 384 people, selected through simple random sampling. The main tool for collecting information is a questionnaire. In the data analysis section, descriptive and inferential statistics were employed to test the hypotheses, and the analyses were performed with SmartPLS 3 and SPSS 26 software. The research findings showed that augmented reality features can affect customer behavior and this effect is strengthened by increasing customer interaction. In other words, the more desirable the quality and features of augmented reality, the higher the level of customer engagement and, as a result, the more positive their behavior towards the product or service will be. Finally, the research results emphasize that the use of augmented reality can be an effective tool for attracting, engaging, and guiding customer behavior, and the targeted use of this technology can lead to improved customer experience and enhanced desired behavioral outcomes. Introduction Augmented reality is an innovative technology that overlays digital content on the physical world to enhance users’ understanding and interaction with their environment. The integration of virtual elements with the real-world environment creates immersive experiences that enrich consumer interaction with the product and service providers (Mubdir et al., 2025). In retail, augmented reality creates opportunities for seamless interaction through virtual trials and product visualization, and enhances the shopping experience through personalization and immersion. Augmented reality capabilities are being extended by mobile devices, such as smartphones and tablets, enabling users to interact with products or service providers in a virtual environment. Thus, augmented reality features are highlighted to increase customer engagement, facilitate decision-making, and reinforce positive customer behavior (Doroudi & Babaei, 2025). More engaged customers can experience long-term relationships with brands and businesses, and recommend products and services to other customers, bringing the greatest return to the company at the lowest cost. Despite the growing interest in augmented reality marketing, there are still gaps in understanding its impact on customer behavior. Existing studies have focused on technical and entertainment applications or the role of augmented reality in education, such as enhancing fashion design skills and advancing virtual design practices. However, limited research examines the broader impact of augmented reality on key dimensions such as customer behavior, especially purchase decisions, customer engagement, or brand loyalty (Sarkis et al., 2025). Panbereez Company, as a company active in the cosmetics and health care field and a wide range of customers, can provide customers with more information that is more in line with their interests, desires, and needs, and that better matches reality by using augmented reality technology. Accordingly, considering what was stated, the question is: What is the impact of augmented reality features on customer behavior? What is the role of customer interaction in this? Theoretical Framework Augmented Reality Features Augmented reality features are defined as features such as system quality, perceived information, and consistency with reality in the use of augmented reality technology, which play a pivotal role in guiding hedonic and utilitarian experiences (Barta et al., 2025). Perceived Information Perceived information refers to the extent to which augmented reality applications provide customers with valuable, relevant, and reliable product information (Du et al., 2024). System Quality System quality refers to the technical performance and reliability of the application, including responsiveness, ease of use, and overall usability (Treinen & Kolla, 2024). Customer Engagement Customer engagement behaviors are “the positive cognitive, affective, and behavioral activity of a customer associated with a brand during or in connection with key consumer/brand interactions” (Winell et al., 2023). Customer Behavior Customer behavior refers to the study of how people make decisions about purchasing, using, and disposing of goods and services. It includes the actions, motivations, and influences that shape consumers’ purchasing decisions. Understanding customer behavior is crucial for businesses to tailor their marketing efforts and product offerings to customer needs and preferences (Kaur et al., 2024). Adinda et al. (2025) investigated the effect of social media marketing on purchase intention by mediating customer engagement. The results of the study show that all the tested factors do not have a significant effect on consumer engagement. Monitoring does not affect customer engagement, while information sharing, social interaction, and attractiveness do. In addition, customer engagement significantly affects purchase intention. Indirectly, information sharing, social interaction, and attractiveness have a positive effect on purchase intention through consumer engagement, while monitoring does not. Asante et al. (2025) investigated the use of online multi-channel commerce to increase customer engagement in the era of digital transformation. The results showed that the use of online multi-channel commerce is effective in increasing customer engagement in the era of digital transformation. Research Methodology This research is applicable in terms of purpose, and descriptive-survey in terms of data collection method. The statistical population of this study is all online customers of Panbereez Company in Tehran in unlimited numbers. According to the Krejci Morgan table, the number of statistical samples in the unlimited population was 384 people, selected by simple random sampling. The main tool for collecting information is a questionnaire. Research findings In the data analysis section, descriptive and inferential statistics were used to test the hypotheses, and the analyses were performed with SmartPLS 3 and SPSS 26 software. The research findings showed that augmented reality features can affect customer behavior and this effect is strengthened by increasing customer interaction. In other words, the more desirable the quality and features of augmented reality, the higher the level of customer interaction and, as a result, their positive behaviors towards the product or service also increase. Finally, the research results emphasize that the use of augmented reality can be an effective tool for attracting, engaging, and guiding customer behavior, and the targeted use of this technology can lead to improved customer experience and enhanced desired behavioral outcomes. Conclusion The present study aimed to investigate the impact of augmented reality features on customer behavior by considering the role of mediating customer interaction. The results of this study are consistent with the results of Adinda et al. (2025), Asante et al. (2025), Anwar et al. (2025), Dağ et al. (2025), Xiao & Chen (2025) Sarkis et al. (2025) Masfer & Helmi (2025) Mubdir et al. (2025), Naveen et al. (2025), Taneja & Dutta (2024), Rajendran & Usha (2024), Seidi et al. (2024), Doroudi & Babaei (2025), Zarin Negar et al. (2025), and Por Hoseini & Farahbakht Fomani (2024). Sarkis et al. (2025) showed that augmented reality positively influences purchase decisions, significantly increases customer engagement, and strengthens brand loyalty, highlighting its transformative potential in shaping consumer behavior in the fashion industry. It also increases understanding of how augmented reality technologies can be used to enhance purchase decisions, foster deeper customer engagement, and create stronger brand loyalty, thereby influencing long-term customer relationships with the brand. Based on the research findings, it is suggested that companies improve the quality of augmented reality content delivery by reducing latency, optimizing device and software performance, and creating accurate and natural overlays with the real environment to improve customer behavior. Also, by means of solutions such as focusing on problem solving, accepting and compensating for errors, following up on requests, providing alternatives, using customer relationship management systems, training employees, and better designing the customer experience can help strengthen customer trust, satisfaction, and positive behavior.

Abstract Abstract The aim of this study is to present a career and organizational promotion model for middle managers with a meritocracy strategies approach. The research method is applicable in terms of purpose, and of descriptive-exploratory research type. The statistical population of the study includes 12 experts and specialists of Bushehr Petrochemical Company, and the sampling method is purposive. Based on the summary of previous studies and the data obtained from the implementation of the Delphi technique among Bushehr Petrochemical experts, four criteria of meritocracy strategy were identified for career and organizational promotion, meritocracy thinking, organization qualification, and talent management; and by a hierarchical approach, a hierarchy tree was first formed through this method, and then the criteria were ranked by Expert Choice software. The results of this study showed that meritocracy in managers and employees in Bushehr Petrochemical is as follows: meritocracy strategy for career and organizational promotion has a value of 0.362, meritocracy thinking for career and organizational promotion has a value of 0.384, organization qualification has a value of 0.152, and finally talent management has a value of 0.102, and the compatibility rate of 0.004 is also in the acceptable range. These results can transform managers' decisions and cause changes in their attitudes. Introduction Today, given the extremely intense and complex competition, unstable environment, lightning-fast technological advances, the increasing development of information and communications, and... almost no organization can survive without knowledge-based components, which are specialized and experienced managers and employees. The era of information technology and the digital revolution has also doubled the need to pay attention to the value of these precious elements in the life cycle of companies and organizations, and perhaps it can be safely claimed that the progress and excellence of any organization is due to the unique abilities and competencies of its human resources. An organization is a social environment whose cultures and values ​​provide space for the creativity and ingenuity of its employees. Without creating this creative environment, the hope for the growth and flourishing of elites and the emergence of their talents will be an illusion (Rastgar, 2024). On the other hand, the competence of managers in an organization causes the emergence of organizational competence. Therefore, the competence relationship between the individual and the organization is a two-way relationship, which, due to the major benefit from the organization, must naturally grow from the whole to the part, that is, from the organization to the individual. In simpler terms, organizations need to create a suitable space for the activities of competent individuals and the emergence of their competencies (Hajili, 2022). One of the main solutions to get rid of the inefficiency of Iranian organizations is to value competent managers and make appropriate use of their competencies by appointing them to appropriate jobs. However, to establish this system, there is a need to make fundamental changes in the values ​​and culture of organizations, but the role of the government in promoting this thinking correctly and in a principled manner is undeniable (Vahidi Rad et al. 2026). To develop and promote this thinking, it is also necessary to be well acquainted with its obstacles and try to overcome them. On the other hand, the need to establish this system in organizations must become a widespread belief and senior organizational managers must accept it as the only model for saving their troubled organizations. In addition, familiarity with the requirements for implementing this system provides them with more operational solutions and helps them in the correct and comprehensive implementation of this thinking in organizations (Daniyali Deh Hoz et al. 2018). In meritocracy, the latent talents of individuals are usually identified and then cultivated through the creation of a talent base and made available to the organization's goals. Meritocracy begins with the production of competence in employees and managers, then evolves in the context of opportunity creation. In meritocracy, criteria such as commitment, expertise, skill, ability, experience, and compassion are the criteria for selecting and promoting individuals in the organization. The degree of excellence, popularity, attachment, and the use of components in planning, decision-making, and their implementation are in line with the achievement of the organization's goals (Wade & Obwegeser, 2019). When a company helps employees in career promotion, employees are less willing to leave the company. Career promotion can increase employee morale, also increase productivity, and on the other hand help the company become more efficient (Rastiani, 2024). Considering the above, it can be said that the main purpose of conducting the research is to answer the main question: what is the career and organizational promotion pattern of middle managers with the approach of meritocracy strategies? Theoretical Framework Meritocracy Meritocracy is a social system in which the highest power and social status are entrusted to those with the most ability. Meritocracy is a social system in which progress in society is based on individual abilities and competencies, not derived from family wealth or social background (Nasiri & Jafari, 2026). Vahidi Rad et al. (2026) studied the design and presentation of a sustainable human resource management model with a meritocracy promotion approach in seven education districts of Mashhad. The findings in the sustainable human resource management model with a meritocracy promotion approach include 5 dimensions and 24 components, which include merit-based recruitment, sustainable human resource competency development-based improvement, human resource retention with a merit-based approach, sustainable and merit-based human resource performance evaluation, and a performance-based payment system. In the quantitative section, the results showed that the aforementioned model has the necessary validity. According to the results of the research, managers of the seven education districts of Mashhad can promote meritocracy in the organization by developing and strengthening sustainable human resource management. Nasiri & Jafari (2026) studied the identification of the underlying factors and challenges in irresponsible appointments and away from meritocracy in education. The results of data analysis led to the identification of 47 basic themes and 7 organizing themes in the context of the underlying factors in irresponsible appointments and away from meritocracy in education, which were classified into 4 comprehensive themes: management in the shadow of politics; organizational affiliation bottlenecks; conflict between economy and educational strategy; and fundamental structural faults. Also, 105 basic themes and 9 organizing themes were identified in the context of the challenges in irresponsible appointments and away from meritocracy in education, which were classified into 4 comprehensive themes: managerial and organizational crises in the education system; social and cultural crisis in education; inequality crisis in education; and qualitative crisis in education were classified. Considering the identification of the underlying factors and challenges in irresponsible and meritocratic appointments in education in this research, implementing the solutions presented in it can facilitate and bring about the realization of the appointment of competent people in education. Research Methodology Regarding its purpose, the research method is applicable based on its objective, and a descriptive-exploratory research type. The statistical population of the research includes 12 experts and specialists of Bushehr Petrochemical Company, and the sampling method is purposive. Research findings Based on the summary of previous studies and the data obtained from the implementation of the Delphi technique among experts in Bushehr Petrochemical, four criteria of meritocracy strategy for career and organizational promotion, meritocracy thinking, organization competency, and talent management were identified. Through a hierarchical approach, a hierarchy tree was first formed by this method, and then the criteria were ranked by Expert Choice software. The results of this study showed that meritocracy in managers and employees in Bushehr Petrochemical is as follows: meritocracy strategy for career and organizational promotion has a value of 0.362, meritocracy thinking for career and organizational promotion has a value of 0.384, organization competency has a value of 0.152, and finally talent management has a value of 0.102. The compatibility rate of 0.004 is also within the acceptable range. These results can transform managers' decisions and cause changes in their attitudes. Conclusion The present study aimed to present a career and organizational promotion model for middle managers with a meritocracy strategies approach. The results of this study are consistent with the results of Baffour Gyau et al. (2024), Wang et al. (2023), Eskandarany (2024), Alotaibi (2024), Byambaa et al. (2025), Svoboda (2024), Lin et al. (2024), McKinsey (2020), Abdulsalam & Tajudeen (2024), Deloitte (2024), and Khan et al. (2024). The results of Baffour Gyau et al, (2024) study on the dynamic relationship between AI technology innovation in banking and finance and financial performance of banks in 20 countries show that AI technology innovation in banking and finance positively affects the return on assets of banks and highlights its role in increasing financial performance. The interaction term between AI innovation and economic growth emphasizes their joint positive effect on financial performance. Mediation analysis highlights the role of information and communication technology development in transforming AI innovation into improved financial results. According to the results of the study, the following suggestions were made: Training comprehensive AI literacy to bank employees: the findings showed that the bank should shift its focus from training skills in simple tools to training comprehensive AI literacy and enable its workforce to transform from passive users to active participants in the AI ​​ecosystem.

Abstract Abstract The aim of this research is to formulate a human resources development scenario for the traffic police with a foresight approach. This research was applicable-developmental in terms of purpose, and qualitative in terms of research method. The statistical population of the research includes 14 traffic police managers. Individuals were selected by a purposeful method based on at least 10 years of management experience and complete familiarity with this field. The MICMAC and Wizard Scenario methods were utilized to analyze the findings. The results showed that 11 drivers of new traffic police technologies, behavioral and social changes in drivers, traffic police economic policies, revision of traffic police laws, global convergence in traffic management, demographic and aging challenges, development of traffic culture, public demands on traffic police, management of traffic police emergencies, road traffic safety, and sustainable development of traffic management are effective in developing a human resources development scenario for the traffic police. Also, based on the results of Scenario Wizard, a total of eighty-five scenarios are presented to the traffic police. However, two scenarios of progress and development and awareness raising have been introduced as the most important and likely paths to achieving the goals, which have higher priority than others. Introduction Globalization and the rapid growth of information and communication technology have brought a fundamental change to the human resource management process, and the role and function of human resources have been marginalized by the strong presence of information and communication technology, and it is even feared that some day human resource management will be realized without the role of humans in all organizational areas and dimensions (Naji & Alirezaei, 2025). Human resources, as the most valuable organizational resource, are the main axis of the organization's approaches and activities and play a fundamental role in achieving the organization's goals and ideals (Babaei Meybodi & Alirezaei, 2020). Like many organizations, the police organization depends on efficient and capable human resources. The nature of police work and being in stressful situations requires efficient human resources (Parsay et al. 2020). Training and development are critical to the success of an organizational HRM strategy, yet there is abundant evidence that shows some organizations fail to achieve desired business outcomes through training interventions (Dixit & Sinha, 2020). In general, several studies have argued that training and development are critical to the success of an organizational HRM strategy, yet there is abundant evidence that shows some organizations fail to achieve desired business outcomes through training interventions. According to estimates from the American Association for Training and Development, organizations in the United States spend more than $126 billion on employee training and development each year. This amount has increased to $135 billion by 2010 (Dixit & Sinha, 2020). Employing a foresight approach and developing plausible scenarios can help identify trends, key uncertainties, and future skill needs and provide a basis for designing human resource development strategies in traffic police. Despite the importance of this issue, limited studies in the research literature have specifically addressed the scenario development of human resources in the traffic police with a foresight approach; therefore, this study seeks to identify possible scenarios for the development of human resources in the traffic police by utilizing foresight methods and provide a framework for strategic planning in this area. Considering what has been said, this study seeks to answer the question: how is the formulation of a scenario for the development of human resources in the traffic police with a foresight approach? Theoretical framework Human resource development Human resource development is a set of programs, activities, components and systems designed and implemented to improve performance. Traditionally, improving the performance of the organization is facilitated through three broad areas including training and development, evaluation, program and development in the organization. It can be decisively stated that these fields are not just concepts of human resource development and only part of the human resource development literature (Abili et al. 2020). Abdulkazem Helu Al Rakabi et al. (2025) conducted a research with the aim of providing a model for improving human resource management with an emphasis on technology-oriented education in the era of digital transformation. The research results showed that there is a 17-factor model with 88 indicators for improving human resource management in the age of digital transformation. This model includes seven causal factors, six background factors, two intervening factors and two strategies. The findings showed that there are significant relationships between all the factors of the model and its two strategies. Also, the presented model has a strong overall fit. This research can be useful for managers and policymakers by taking advantage of technology-based training to improve human resource management in their organizations in the era of digital transformation. Rajabi Farjad et al. (2025) conducted a research with the aim of identifying and prioritizing the challenges of human resource management in the digital age. The research is applicable in terms of purpose, and descriptive in terms of method. The research method in the study is Delphi in the qualitative part and descriptive research in the quantitative part. Statistical population of the research consists of specialists in human resources management and information technology, Tehran's Asan-Pardakht company. The sample size in both sections (Delphi and TOPSIS Fuzzy), consists of 10 experts. The main tool for collecting information was a questionnaire based on theoretical foundations and conducting three rounds of the Delphi method, the validity of which was based on the technique of long-term engagement with long-term contact with the participants. Then, weighting was carried out by TOPSIS algorithm according to the opinions of experts. The findings of the research showed that the sub-indices of the need for a revolutionary change in society and organization, digital investment, promotion of value creation, information sharing and facilitating organizational changes were ranked first in the research. In addition, the sub-indices of understanding the need to change behavior, communication and interaction with the customer, human resources as a business partner, promoting innovation, the ability to work with tools in the digital field and designing organizational change processes are the factors that have the least impact on the challenges of human resource management in the digital era. Research methodology This research was applicable-developmental in terms of purpose, and qualitative in terms of research method. The statistical population of the research includes 14 traffic police managers. Individuals were selected by a purposeful method based on at least 10 years of management experience and complete familiarity with this field. Research findings MICMAC and Wizard Scenario methods were applied for the analysis. The results showed that 11 drivers of new traffic police technologies, behavioral and social changes of drivers, economic policies of traffic police, revision of traffic police laws, global convergence in traffic management, population and aging challenges, development of traffic culture, public demands from traffic police, management of emergency situations of traffic police, road traffic safety, sustainable evolution of traffic management are effective in formulating the development scenario of traffic police human resources. Also, based on the results of scenario wizard, a total of eighty-five scenarios are facing the traffic police. However, the two scenarios of progress and development and raising awareness have been introduced as the most important and most likely paths for achieving the goals, which have a higher priority than others. Conclusion The present study was conducted with the aim of formulating the human resources development scenario of the traffic police with a foresight approach.. The results of this research are somewhat in agreement with the findings of Abdulkazem Helu Al Rakabi et al. (2025), Rajabi Farjad et al. (2025), Lotfollahi Haqi et al. (2025), Reza Alizadeh et al. (2025), Mehraban et al. (2025), Shariati et al. (2025), Gheiravani et al. (2023), Banmairuroy et al. (2022), Nnadi et al. (2021), Vahabi et al. (2019), Taheri Damaneh et al. (2014), Vahabi et al. (2019), and Staller et al. (2023). Mehraban et al. (2025) showed that in order to maximize the effectiveness and efficiency of the organization's people, while familiarizing them with the environment and justifying them based on their needs, managers and officials should develop appropriate training courses for human resources in education and training. In the end, based on the findings of the research, it is suggested that artificial intelligence be used to analyze historical data (accidents, weather, traffic) in order to identify accident-prone points and deploy agents proactively. Also, drones equipped with thermal cameras should be used to cover blind areas and monitor violations on inaccessible roads.

Abstract Abstract The aim of this study is to determine the relationships and hierarchical levels of optimal risk control factors in the medical laboratory supply chain using an Interpretive Structural Modeling (ISM) approach. In terms of implementation, this research adopts a mixed-methods design (qualitative–quantitative); and in terms of purpose, it is an exploratory study conducted through a descriptive–survey method. The statistical population of the study consisted of 15 senior managers and experts working in medical laboratories across the supply chain, selected by the snowball sampling method. Data were collected through semi-structured interviews. For data analysis, the Fuzzy Delphi method, Interpretive Structural Modeling (ISM), and MICMAC software were employed. The results of the interpretive structural analysis using the exploratory model indicated that the proposed model consists of four main factors and thirteen sub-factors identified across four levels. The fourth level involves supply chain risk identification, including the dimensions of financial and legal risks, quality risks, and supplier and logistics risks. The third level includes failure mode and effects analysis, encompassing the identification of weaknesses, analysis of failure effects, and risk prioritization. The second level involves risk response and management strategies, including supplier diversification, emergency planning, and quality control supported by information technology. Finally, the factors and dimensions identified in the previous three levels lead, at the first level, to achieving optimal risk control, which includes risk and failure reduction, increased supply chain efficiency, continuous improvement, and performance monitoring. Introduction The extensive use of medical and laboratory technologies requires a significant number of resources for the procurement of equipment and materials; an issue that must be given considerable attention in these organizations. It is evident that if the procurement of equipment and materials is carried out without a precise and structured mechanism, the quality of purchases will not align with the actual needs of healthcare centers. Therefore, the procurement process in medical centers is of great importance and must be continuously monitored. Risk assessment is a systematic process for measuring quantitative and qualitative hazards associated with materials, processes, activities, personnel, equipment, and the environment (Rahmanifar, 2014). Utilizing the findings of this research on the supplier side can lead to the development of structures that ensure timely procurement and provide an understanding of risks and potential failure points in the laboratory supply process. As a result, suppliers can devise various methods to meet laboratory needs. Considering that the researcher is also active in the laboratory supply sector, if the findings are validated, they can be presented to the laboratory community through multiple sessions, thus contributing to the dissemination and expansion of the research results. Given that multiple methods and individuals are involved in the procurement of laboratory necessities, this analytical approach can also be applied in this study, representing one of its innovative aspects. Failure Mode and Effects Analysis (FMEA) is one of the most widely used tools for enhancing the reliability and safety of products or processes (Radpour & Karbasian, 2011). This tool is particularly used in the design and development of new products (Fattahi, 2009). Various techniques exist for risk identification, and no single method serves as the best approach; instead, an appropriate combination of techniques should be utilized. Moreover, evaluating risks solely based on their probability of occurrence and severity of impact is insufficient, as these two criteria do not fully capture all influential aspects of risk. FMEA is an analytical technique that combines technology and expertise to prevent the procurement of substandard products and protect the reputation of the organization (Radpour & Karbasian, 2011). Therefore, aligned with this objective, the main research question of the present study is: What are the relationships and hierarchical levels of optimal risk control factors in the medical laboratory supply chain using an Interpretive Structural Modeling approach? Theoretical Framework Risk Management Supply chain risk management focuses on controlling risks that affect the efficient flow of information, materials, and products across different segments of the supply chain. Risk management is a key component of supply chain management; and given the diverse objectives within supply chains, it can be utilized when risk is considered a multidimensional phenomenon. Factors such as uncertainty in supply and demand, market globalization, shorter technology and product life cycles, and the increasing use of outsourcing have been identified as major reasons for the importance of supply chain risk management (De Oliveira et al., 2024). Wang et al. (2025), in their study on Strategic Behavior in Multi-Criteria Sorting with a Trust-Relationship-Based Consensus Mechanism: Application to Supply Chain Risk Management, examined strategic behavior using a cost-based consensus mechanism within a social trust network. First, a piecewise cost function dependent on trust relationships was formally defined and formulated, taking into account the influence of trust on preference adjustments required to achieve consensus sorting. Based on this trust-dependent cost structure, a strategic preference manipulation model was proposed to enable strategic behavior in consensus sorting by minimizing the adjustment costs incurred by decision-makers. Considering that the social trust network affects strategic manipulation, an extended collaborative strategic manipulation model was introduced to guide decision-makers in adjusting both their preferences and their social trust networks. Furthermore, the practical applicability of the proposed strategic manipulation models was demonstrated through a supply chain risk management case study, and their performance was validated through simulation analysis. Mobo et al. (2025) investigated Supply Chain Risk Management in the Trade Sector: A Modern Perspective. Their findings indicate that studying supply chain sustainability strategies is crucial, especially when the risks faced by commercial farms within the supply chain are increasingly complex, diverse, and sudden—making prediction and probability assessment more difficult than ever. This underscores the significance of supply chain management. It was found that in order to gain competitive advantages within the market, the trade sector must improve supply chain management practices, which leads to reduced costs from production to delivery, optimized timing, and enhanced customer satisfaction. Their study also concludes that an effective risk assessment mechanism in the supply chain for the trade sector must serve as an appropriate tool for evaluating the effectiveness of risk management from the perspective of all business segments involved in the supply chain. Research Methodology This study was conducted using a mixed-methods approach (qualitative–quantitative) and, in terms of purpose, followed an exploratory and descriptive–survey design. The statistical population consisted of 15 senior managers and experts working in medical laboratories across different stages of the supply chain, selected through the snowball sampling method. Data were collected by semi-structured interviews. Research Findings For data analysis, the Fuzzy Delphi method, Interpretive Structural Modeling (ISM), and MICMAC software were used. The results of the interpretive structural analysis using the exploratory model revealed that the model includes four main factors and thirteen sub-factors distributed across four hierarchical levels. The fourth level involves supply chain risk identification, including financial and legal risks, quality risks, and supplier and logistics risks. The third level consists of failure mode and effects analysis, including identifying weaknesses, analyzing failure impacts, and prioritizing risks. The second level encompasses risk response and management strategies, including supplier diversification, emergency planning, and quality control supported by information technology. Finally, the factors and dimensions identified in the previous three levels lead, at the first level, to achieving optimal risk control, characterized by risk and failure reduction, increased supply chain efficiency, continuous improvement, and performance monitoring. Conclusion The present study aimed to determine the relationships and hierarchical levels of optimal risk control factors in the medical laboratory supply chain using an Interpretive Structural Modeling approach. The results of this research are consistent with the findings of Wang et al. (2025), Mobo et al. (2025), Shishehgarkhaneh et al. (2024), Choudhary et al. (2023), Ganesh & Kalpana (2022), Qaraghanabadi & Sarkardeh (2024), Arab Cham Khalifa & Alavi (2023), and Fokerdi & Talavari (2021). Mobo et al. (2025) demonstrated that an effective risk assessment mechanism in the supply chain for the commercial sector must serve as an appropriate tool for evaluating the effectiveness of risk management from the perspective of all business units involved in the supply chain. Based on the findings of the study, the following recommendations are proposed: Organizations should prioritize reducing risks and preventing failures in order to achieve optimal risk control. Implementing preventive processes, continuously analyzing risks, and executing proactive programs can help reduce the likelihood of failure causes and, in turn, ensure the efficiency and continuity of the supply chain.

Abstract Abstract The aim of the present research is to validate the social marketing model for banking services, emphasizing consumer behavior patterns in Rafidain Bank, Iraq, through testing structural relationships. This research is applicable-developmental in terms of objective, and descriptive-survey in terms of method, employing a quantitative approach. The statistical population of the study consists of 384 customers of Rafidain Bank, Iraq, selected via random sampling. To validate the extracted model, a researcher-made questionnaire was developed and distributed among the statistical population. Data analysis was conducted by AMOS 20 software and Structural Equation Modeling (SEM). The results of this study indicate that at Rafidain Bank, Iraq, contextual conditions play the most significant role in shaping social marketing strategies, while the core phenomenon (consumer behavior patterns) serves a complementary and reinforcing role. This model suggests that the success of social marketing for banking services within the Iraqi context depends more on the degree to which strategies align with the cultural, institutional, and structural realities of society than on merely understanding customer behavior. Introduction A brief review of the performance of the banking market in Iraq indicates that despite the growing trend of financial services and the increasing public awareness of banks, the current status of banking service marketing still shows a considerable gap from the ideal level (Azar et al., 2025). For example, the penetration rate of modern banking services such as digital banking, financial advisory services, and credit instruments remains lower than the global average. This is while in leading countries, a large portion of people’s financial interactions with banks takes place through customer-oriented approaches and social marketing strategies (Babaee et al., 2025). One of the main reasons for this gap, in addition to technical and infrastructural challenges, relates to an insufficient understanding of consumer behavior, lack of attention to the psychological and social preferences of customers, and weaknesses in creating a positive experience in interactions with banks (Babalola et al., 2020). In many cases, banks’ inability to establish effective communication with customers leads to reduced loyalty, trust, and continuous use of banking services. This not only negatively affects the financial performance of banks but also results in negative word-of-mouth advertising and ultimately weakens the banking brand (Baghmalek et al., 2025). Rafidain Bank of Iraq, as one of the largest and most long-standing banks in the country, faces similar challenges (Baicu et al., 2020). Although the bank has made efforts in recent years to modernize its services, the absence of effective social marketing strategies and the neglect of behavioral, cognitive, and emotional dimensions of consumers have prevented the achievement of its intended goals (Biroscak et al., 2025). Under such circumstances, banks need to adopt modern marketing models such as sensory marketing in order to establish deeper and more effective relationships with customers (Borges et al., 2019). Many marketing activities have merely been limited to creating awareness and visual identity, which has little impact on customer loyalty and choice (Dietrich et al., 2022). A strong mental image and a credible brand can be among the most effective tools for creating sustainable differentiation in the banking industry (Enworo, 2023). In addition, a strong brand plays a significant role in reducing customers’ perceived risk and provides banks with a sustainable competitive advantage (Ezechi et al., 2025). Nevertheless, most banks have mainly focused on financial performance and have neglected a deep understanding of customer perceptions and behaviors (Faludi, 2025). This gap in understanding consumer behavior and the weakness in developing effective social marketing represent a fundamental challenge for banks, particularly Rafidain Bank of Iraq. Therefore, the present study aims to design and present a comprehensive model of social marketing for banking services with a particular emphasis on consumer behavior patterns in Rafidain Bank. Through this approach, the bank may enhance its position in the competitive Iraqi market and increase customer satisfaction and loyalty. Accordingly, the main research question of the present study is: How can validate the social marketing model for banking services with an emphasis on consumer behavior patterns in Rafidain Bank of Iraq? Theoretical Framework Social Marketing Social marketing has been defined as the application of marketing principles and tools to achieve desired social objectives, with its aim being the benefit of society rather than personal goals or organizational interests (Kamin et al., 2022). Social marketing is a business perspective that respects the ethics of employees, society, and the environment; and it is a comprehensive strategy capable of improving an organization’s competitive position. Companies and marketing managers should adopt a philosophy based on social responsibility and adherence to ethical principles. This ethical perspective and insight help marketing managers rationally deal with many complex issues faced by marketing and other human activities (Wang, 2023). Kikhah et al. (2025) investigated the role of social marketing in improving bank performance through institutionalized knowledge management opportunities, with a case study of Mehr Iran Qard Al-Hasana Bank branches in Sistan and Baluchestan Province. The analysis of hypotheses utilizing structural equation modeling showed that, in the presence of institutionalized knowledge management, the standardized coefficient for the relationship between social marketing and employee performance increased from 0.502 to 0.617, meaning an increase of over 30% in the standardized coefficient, while remaining significant. Ahmadi and Ghane Bagheri Baghal (2025) examined the impact of social marketing on bank profitability with the moderating role of social responsibility. The results indicated that social marketing is related to bank profitability. Social responsibility moderates the relationship between social marketing and bank profitability. Research Methodology In terms of purpose, this study is applicable–developmental; in terms of method, it is descriptive–survey, with a quantitative approach. The statistical population of the study consisted of 384 customers of Rafidain Bank in Iraq, selected through random sampling. In order to validate the extracted model, a researcher‑made questionnaire was developed and distributed among the statistical population. Research Findings AMOS 20 software and Structural Equation Modeling (SEM) were employed to analyze the research findings. The results of this study indicate that, in Rafidain Bank of Iraq, contextual conditions play the most significant role in shaping social marketing strategies, while the central phenomenon (consumer behavior patterns) plays a complementary and reinforcing role. This model suggests that the success of social marketing for banking services in the Iraqi context depends not merely on understanding customer behavior, but more importantly on the extent to which strategies are aligned with the cultural, institutional, and structural realities of society. Conclusion The present study was conducted with the aim of validating the social marketing model for banking services with an emphasis on consumer behavior patterns in Rafidain Bank of Iraq through the testing of structural relationships. These results are consistent with the findings of Kikhah et al. (2025), Ahmadi & Ghane Bagheri Baghal (2025), Safaei et al. (2025), Nguyen (2025), Haqtalab (2024), Allahyari et al. (2023), Cao & Weerawardena (2023), Onuorah et al. (2022), and Kamin et al. (2022). Haqtalab (2024) demonstrated that social marketing and its dimensions (social marketing mix, pro-social behavior, customer orientation, and nature of services) influence the behavioral reactions of customers in Tejarat Bank branches of Torbat-e Jam. Furthermore, employees’ pro-social behavior and; subsequently, customer orientation have the highest impact and importance. Based on the findings of this research, it is recommended that Rafidain Bank of Iraq place its primary focus on strengthening contextual conditions in the formulation and implementation of social marketing strategies; this includes enhancing institutional trust, aligning marketing messages with the cultural values and norms of Iraqi society, and improving technological and digital infrastructure. Additionally, targeted investment in intervening conditions—such as enhancing customers’ financial literacy, empowering employees in the field of social marketing, and developing interactive digital channels—can significantly increase the effectiveness of these strategies.

Abstract Abstract The aim of the present study is to analyze the organizational policy model based on social responsibility in the oil-rich areas company. The research method is fundamental-applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of implementation method and of exploratory type. The statistical population in the qualitative section includes 14 managers and experts in the field of social policy-making and social responsibility, faculty members, managers of the oil-rich areas company, selected purposefully; and in the quantitative section includes 191 managers and experts of the Iranian Copper Company who are experts in the field of corporate social responsibility and were selected by simple random sampling. The tool for collecting findings in the qualitative section is a semi-structured interview, and in the quantitative section is a researcher-made questionnaire. For data analysis in the qualitative section, based on the data-driven method, NVIVO version 11 software was applied; and in the quantitative section, SPSS and PLS software were applied. The results of the qualitative section showed that the extracted concepts included 89 open codes, 30 central codes, and 15 selected codes that constitute the model of social policies based on social responsibility in the National Iranian Copper Company. The results of the quantitative section show that the causal conditions of the model of implementing social policies based on social responsibility, the background factors affecting the model of implementing social policies based on social responsibility, the intervening conditions of social policies based on social responsibility, the consequences of the model of social policies based on social responsibility, and the strategies of social policies based on social responsibility are the central categories that constitute the model of social policies based on social responsibility. Introduction Given the progress made in recent decades regarding the implementation of social policies, its importance for policy analysts has been clarified to some extent. But it has not yet been able to achieve its true place in policy-making and is referred to as the “missing link” in many countries, especially third world countries (Faliza & et al., 2019). Many of these countries are trapped in the cycle of traditional bureaucracy and authoritarian management system that stems from the old thinking of separation of administration from politics, and the implementation of social policy remains an unsolvable issue for them. Accordingly, most government programs in these countries face failure and frustration in the implementation stage (Alvani & Shalviri, 2016). In our country, although the extensive changes and political development process that have attracted the attention of the authorities in the last few years have partially closed the gap between those who formulate social policies, implementers of the system, and the general public, and important steps have been taken to reform and transform the administrative system; many policies, guidelines, and laws are still formulated and approved in a vacuum and sometimes far from the realities of society, which naturally leads to numerous problems in implementation and in the practical stage (Zainivand Moghadam et al., 2021). Undoubtedly, part of these damages are due to the failure to consider appropriate social and cultural development for the industrialization of oil-rich regions in the country, such as Kerman Province. Although some companies have taken a few steps in recent years, the companies' actions to end this situation have been insufficient, irresponsible, and late, and some of them have only taken initial steps (Zare Behnimiri et al., 2022). In these areas, in addition to the construction and expansion of its phases, it has caused the loss of people's sense of place in the villages, as well as the arrival of countless immigrants from all over the country to the region and the undesirable reception and accommodation methods in labor camps, the lack of healthy recreation, the distance of workers from their family environment, the multiculturalization of the environment, and the lack of adequate social control and supervision. Such issues have caused great harm to the people of local communities, and with many social and cultural issues such as social insecurity, the risk of contracting various diseases caused by biological contamination, and the uncontrolled entry and exit of workers who have suspicious interactions (Salari-Eskari & Shafiei, 2022). Therefore, the main question of this research is: What is the model for implementing social policies based on social responsibility in the National Iranian Copper Industries Company? Theoretical Framework Social Policy Social policy is a set of interrelated decisions made by groups or social and political actors of organizations with the aim of achieving specific goals (Maadani et al., 2018). Social Responsibility Corporate Social responsibility means that organizations, in addition to carrying out economic activities and producing goods or services, must act in a way that contributes to the well-being and development of society and prevents social and environmental harm. This responsibility includes complying with laws, respecting employee rights, producing safe and quality products, protecting the environment, and participating in public benefit activities (Por hoseini & Farahbakht Fomani, 2024). Pazhouhan et al. (2026) analyzed the mediating role of organizational ambidexterity in the effect of corporate social responsibility and knowledge management infrastructure on sustainable corporate performance. The research findings showed that the path coefficient of corporate social responsibility on organizational ambidexterity was 0.417, the path coefficient of knowledge management infrastructure on organizational ambidexterity was 0.379, the path coefficient of social responsibility on sustainable performance was 0.513, the path coefficient of knowledge management infrastructure on sustainable performance was 0.439, and the path coefficient of organizational ambidexterity on sustainable performance was 0.571, which were significant at the 95% confidence level. The results also showed that organizational ambidexterity can mediate the effect of social responsibility on sustainable performance by 0.751. In addition, organizational ambidexterity can mediate the effect of knowledge management infrastructure on sustainable performance. Accordingly, promoting an organizational culture that strengthens social and environmental values ​​and encourages employees to participate in corporate social responsibility programs will lead to sustainable company performance. Akbari et al. (2023) investigated the prediction of consumer loyalty through the role of flow experience, perceived value, and corporate social responsibility. The results showed that attention, focus, and the concept of time have a significant effect on flow experience. Other results showed that flow experience, perceived value, and corporate social responsibility have a significant effect on consumer loyalty. Research Methodology The research method is fundamental-applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of implementation method and of exploratory type. The statistical population in the qualitative section includes 14 managers and experts in the field of social policy-making and social responsibility, faculty members, managers of the oil-rich areas company, selected purposefully; and in the quantitative section includes 191 managers and experts of the Iranian Copper Company who are experts in the field of corporate social responsibility and were selected by simple random sampling. The tool for collecting findings in the qualitative section is a semi-structured interview, and in the quantitative section is a researcher-made questionnaire. Research findings For data analysis in the qualitative section, based on the data-driven method, NVIVO version 11 software was applied; and in the quantitative section, SPSS and PLS software were applied. The results of the qualitative section showed that the extracted concepts included 89 open codes, 30 central codes, and 15 selected codes that constitute the model of social policies based on social responsibility in the National Iranian Copper Company. The results of the quantitative section show that the causal conditions of the model of implementing social policies based on social responsibility, the background factors affecting the model of implementing social policies based on social responsibility, the intervening conditions of social policies based on social responsibility, the consequences of the model of social policies based on social responsibility, and the strategies of social policies based on social responsibility are the central categories that constitute the model of social policies based on social responsibility. Conclusion The present study was conducted with the aim of an organizational policy model based on social responsibility in the oil-rich areas company. The results of this study are consistent with the results of Pazhouhan et al. (2026), Akbari et al. (2023), Kashanipour & Ghorbani (2022), Mighi fard (2021), Barshad et al. (2021), Behnam et al. (2021), Ramzan et al. (2021), Liua et al. (2021), and Svetlana et al. (2020). Barshad et al. (2021) showed that there is a negative and significant relationship between social responsibility and information asymmetry. Social responsibility performance, according to organizational policy, can reduce information asymmetry when information related to this performance of companies is made available to the market in some way (through the preparation and publication of social and environmental performance reports or through the announcement and publication of the score or rank of corporate social responsibility by financial institutions, etc.). Based on the results, the following suggestions were made: - Strengthening civil and local institutions and involving them in the company's missions and viewing them as strategic partners in achieving a common goal - In order to meet social expectations from organizations and reduce information inequality, necessary and effective policies related to corporate social responsibility should be examined.

Abstract Abstract This research aimed to predict the products required by e-commerce customers in Kaleh Meat Products Company using machine learning algorithms. The present research was conducted with an applicable and quantitative approach in terms of purpose. The data used included historical information on online purchases of Kaleh Company customers, which included the variables "product price", "past weight purchase volume", "product type", "past purchase frequency" and "past Rial purchase amount". To predict the "required product" as an output variable, four machine learning algorithms including artificial neural network, random forest, decision tree and K-nearest neighbor were implemented and evaluated with the criteria of Accuracy, Precision, Recall and F1-Score. The results of the model evaluation showed that the artificial neural network algorithm achieved the highest score in all evaluation criteria (Precision: 95.1%, Accuracy: 94.2%, Recall: 95.9% and F1-Score: 95.5%). The results showed that the artificial neural network has the best performance in predicting the products needed by customers; the research models were implemented and validated in the Python version 3.x programming environment and using the specialized libraries Scikit-learn and Keras based on TensorFlow. After that, the random forest, decision tree and K-nearest neighbor algorithms were used, respectively. Also, the analysis of the importance of the variables showed that "past weighted purchase volume" and "past purchase frequency" had the greatest impact on the model's prediction. The proposed model based on the artificial neural network has the ability to become an accurate and efficient product recommendation system for the Kale company. Implementing this model can lead to optimized inventory management, increased customer satisfaction, and ultimately sales growth by accurately predicting future customer demand. Introduction Accurately predicting customer purchasing behavior is recognized as one of the most central capabilities of market-oriented organizations in the era of digital transformation, because in order to achieve sustainable competitive advantage, companies must predict future customer needs and allocate their resources to the production and distribution of products that are most likely to be purchased; otherwise, they will face phenomena such as accumulation of low-demand inventories, increased holding costs, lost sales opportunities, and ultimately reduced profitability (Bhavishya et al., 2025). This challenge is even more critical in industries with perishable and time-sensitive products such as meat products, because any forecast error not only leads to direct financial losses, but also to damage to brand reputation and reduced customer trust (Satyanarayana et al., 2025; Jauhar et al., 2025). With the rapid expansion of e-commerce and the development of digital infrastructure, a huge amount of transactional big data including purchase records, order frequency, product type, prices, and purchase time has been generated, which has provided a suitable platform for the application of advanced data analysis methods (Wen & Liu, 2026; Jain, 2025). In the meantime, machine learning algorithms are known as powerful analytical tools in the field of demand forecasting and recommender systems due to their ability to identify hidden patterns and nonlinear relationships (Syamsuri et al., 2025). Despite the large volume of transaction data in the e-commerce system of Kale Meat Products Company, no data-driven model has been designed to predict the products needed by customers, and managers often face shortages of high-demand products or overproduction of low-demand products. Therefore, the main objective of this study is to design and evaluate a model based on machine learning algorithms to predict the products needed by customers in the e-commerce platform of Kale Company and to identify the best algorithm in terms of accuracy, precision, recall, and F1 score. Theoretical Framework The theoretical foundations of this study are based on the intersection of e-commerce, consumer behavior, and machine learning. Machine learning enables the extraction of complex patterns from large data and data-driven decision-making (Breiman, 2001). Algorithms such as support vector machines are used in the analysis of customer behavioral data due to their ability to model nonlinear relationships (Cortes & Vapnik, 1995). Customer Buying Behavior in E-Commerce (Digital RFM Model) E-commerce platforms are a huge source of customer behavioral data (Kechri et al., 2025). Past purchasing patterns are considered the strongest predictor of future behavior (Li & Hu, 2026). The RFM model analyzes customer behavior based on purchase recency, purchase frequency, and transaction value (Gupta & Agarwal, 2024). In this study, past purchase frequency and past purchase amount are adapted from this theoretical structure. Customers with higher purchase frequency have stronger behavioral attachment to the brand (Fang et al., 2025). The financial volume of transactions reflects the customer lifetime value (Akter et al., 2025). Combining these variables allows for dynamic personalization (Vuppala & Prasad, 2026). Supply Chain: Demand Forecasting in Perishable Food Industries Meat products face logistical challenges due to their high perishability (Jauhar et al., 2025). Maintaining a safe inventory is costly due to the risk of expiration (Padmanabhan et al., 2025). The imbalance between supply and demand creates the bullwhip effect (Letto & Orsini, 2025). Predictive logistics emphasizes the shift from push to pull mode based on actual demand (Thiel et al., 2023). In this study, past weighted purchase volume and product type are entered into the model as supply chain indicators. Analytical Tools: Recommender Systems and Python Computing Platform Recommender systems play a key role in guiding customer choices (Koren et al., 2009). The entire data pipeline is developed in Python. Artificial neural networks extract hidden patterns of transactions (Hochreiter & Schmidhuber, 1997). Random forest is predicted using decision trees and criteria such as Gini impurity (Da, 2025). K-nearest neighbor algorithm also operates based on behavioral similarity. Research methodology The present study is applicable in terms of purpose, quantitative in nature, and based on a data mining approach. The statistical population includes all online customers of Kale Company, and a sample of 15 thousand purchase records (after cleaning and preprocessing) was extracted from the company's online shopping transaction database by simple random method. The dependent variable "customer's required product" was defined as binary (select/not select the target product) and the independent variables included product price, past purchase weight volume, product type, past purchase frequency and past purchase amount in Rials. Four classification algorithms including Artificial Neural Network (ANN), Random Forest, Decision Tree and K-Nearest Neighbor (KNN) were implemented using Python 3.x and Scikit-learn and Keras libraries (based on TensorFlow). The implementation process included data preprocessing (cleaning, normalization of quantitative variables and encoding of nominal variables), data splitting into training (80%) and test (20%) parts, training the models using cross-validation to avoid overfitting, and finally evaluating the performance of the models on the test data. The evaluation was performed using the criteria of accuracy (Accuracy = (TN+TP)/(TN+FP+TP+FN)), precision (Precision = TP/(FP+TP)), recall (Recall = TP/(FN+TP)) and F1 score (F1 = 2(PrecisionRecall)/(Precision+Recall)). In order to check the stability of the results, each algorithm was run in 15 consecutive iterations. Research findings The results of implementing four machine learning algorithms on the data of 15 thousand online customers of Kale Company are presented in Table 1. As can be seen, the artificial neural network algorithm obtained the highest values ​​in all four criteria of precision, accuracy, recall and F1 score; and is recognized as the superior algorithm. After that, random forest, decision tree and K-nearest neighbor are in the next ranks, respectively. Table 1 Comparison of machine learning algorithms used in the present study Algorithms Accuracy Precision Recall F1-Score Decision Tree 0.94 0.92 0.958 0.938 random forest 0.946 0.932 0.958 0.945 artificial neural network 0.951 0.942 0.959 0.955 K-nearest neighbor 0.931 0.912 0.944 0.922 In addition to comparing the main performance, the stability of the results was checked in 15 consecutive repetitions. The results showed that the accuracy of all four algorithms had a slight and stable improvement process; so that the artificial neural network reached 94.3% accuracy in the first iteration to 95.1% in the 15th iteration, and the random forest improved from 93.9% to 94.6%, which indicates proper convergence and the lack of strong dependence of the results on the randomness of the data. The confusion matrix also showed that the artificial neural network and the random forest have the lowest type 1 and type 2 errors. In the ranking of the input variables based on the analysis of the importance of features in the artificial neural network algorithm, the variable "weight or weighted volume of purchase" had the greatest impact on the prediction with an approximate share of 35%, followed by "past purchase times" (28%), "product type" (18%), "product price" (12%) and "previous purchase amount in riyals" (7%). This ranking in random forest and decision tree algorithms was also observed to be almost the same, which indicates the high validity of the findings. Conclusion The findings of this research are consistent with previous research. Turatti (2025) showed that deep learning models have a high ability to analyze big data and predict online shopping behavior, which is confirmed by the accuracy of 1.95% of the artificial neural network in the current research. Iseal & Michael (2025) also considered analytical models based on behavioral data to be effective for estimating the probability of purchase. Mirasçı & Aksoy (2025) reported the use of machine learning models in the optimization of effective purchasing strategies, and the random forest in the current research showed a good performance with an accuracy of 94.6%. Esmeli & Gokce (2025) emphasized the importance of explainable models and the ranking of the input variables in the current research is considered a step towards clarifying the model. Also, Karimi & Mahmoodi Ranai (2025) confirmed the positive impact of artificial intelligence adoption on business performance, which reinforces the necessity of implementing the proposed model. Darvish et al. (2026) also introduced machine learning algorithms as key technologies. Due to the superior performance of the artificial neural network as well as the acceptable and stable performance of the random forest, these two algorithms are proposed as the main options for the implementation of the intelligent recommender system in Calais. Such a system can reduce perishable product waste, optimize warehouse inventory, increase sales conversion rates, and improve customer satisfaction. For future research, it is suggested to test the proposed model in similar companies, add new variables such as demographic characteristics and purchase time patterns to the model, use more advanced deep learning approaches (such as LSTM and attention-based models), and perform sensitivity analysis on the architecture of the artificial neural network (number of layers, neurons, activation functions) to increase the accuracy and stability of the model.

Abstract Abstract The aim of this study is to validate the factors affecting the smart marketing information system in Refah chain store. This study is applicable in terms of its purpose, and its implementation method is quantitative with a descriptive-survey approach. The statistical population of the study includes 384 managers and marketing and information technology experts of selected Refah store branches. The sampling in this study is simple random. A researcher-made questionnaire was utilized to collect data. Structural equation modeling with SmartPLS4 software was employed to fit the designed model. The research findings indicate that business value strategy, smart product management, customer behavior analysis and shopping psychology, smart customer communications, responsive logistics and supply chain, smart customer experience, data technologies and infrastructures, performance measurement and metrics, organizational adaptability, ethical framework, and data have a positive and significant effect on the design of smart marketing information system. As a result, designing and deploying such a system requires a smart integration of advanced analytical technology capabilities with business strategy, ethical considerations, and organizational context preparation. This study provides an operational framework for convenience store chain managers to transform data into actionable insights to not only increase operational efficiency and profitability, but also to play their social role in providing the desired basic goods to society in an efficient manner. Introduction In the digital age, marketing is no longer simply aligned with sales, but has become a comprehensive, customer-centric, and data-driven philosophy in which decisions are made based on evidence, not intuition. In this context, the marketing information system, as one of the fundamental pillars of marketing management, is a vital tool for collecting, organizing, and analyzing marketing-related data that enables managers to make informed decisions (Aggarwal et al., 2025). These systems not only record raw data, but also dissect it and store it for advanced analysis and more accurate decision-making in the future (Abu Anzeh et al., 2024). In today's competitive and dynamic environment, fast and efficient access to marketing information is a strategic source for gaining sustainable competitive advantage. Chain stores, especially in developing countries like Iran, are faced with a huge volume of data generated from daily interactions with millions of customers, thousands of suppliers, tens of thousands of products and hundreds of branches (Habibi Machiani, 2020). Smart management of this data is not only an opportunity to optimize operations, but also the key to a deeper understanding of customer behavior, demand forecasting and designing targeted campaigns. In this regard, intelligent marketing information systems, through advanced data mining techniques and machine learning algorithms, are able to discover patterns hidden in data and provide effective predictions based on customer behavior and market conditions (Liu et al., 2023). The necessity of this research is to design and validate a realistic, adaptable, and localized model that is not only technically feasible, but also aligned with the organizational structure, work culture, and social values ​​of Refah. This model should be able to analyze data related to customers, products, suppliers, and branches in an integrated framework and provide behavioral analysis and practical predictions to improve marketing decisions. Therefore, this article aims to validate the intelligent marketing information system model in Refah chain store. This research is a response to the gap in the literature: designing a model that, while taking advantage of global advances in the field of marketing intelligence, is also compatible with the organizational and social realities of Iran. The results of this study not only provide an operational framework for Rehf, but can also be a model for other retail organizations in the country that are seeking responsible and sustainable digital transformation. Therefore, the main question of the present study is: How to validate the factors affecting the intelligent marketing information system in the Rehf chain store? Theoretical Framework Marketing Information System A marketing information system is a structured set of people, technologies, processes, and procedures designed to collect, analyze, store, and present market-related information in a timely manner to help marketing decision makers identify opportunities, respond to challenges, and design effective strategies. This system is a continuous and dynamic process that transforms raw data into actionable information (Aggarwal et al., 2025). Agarwal et al. (2025) investigated an unsupervised visual data-based marketing information system for the management of the transportation industry. They showed that the integration of digital technologies in information systems can improve decision-making in operational departments and confirmed its generalizability to other industries (such as hospitals). In a study in Hormozgan province by Dehghani (2025), a positive and significant relationship was shown between the use of a marketing information system and the improvement of productivity indicators, sales growth, and production capacity in industrial units, and the deployment of such systems was introduced as a key strategy for improving economic performance. Research Methodology This research is applicable in terms of its purpose, and its implementation method is quantitative with a descriptive-survey approach. The statistical population of the research includes 384 managers and marketing and information technology experts of selected branches of the convenience store. The sampling in this research is simple random. A researcher-made questionnaire was utilized to collect data. Research findings To fit the designed model, structural equation modeling with SmartPLS4 software was applied. The research findings indicate that business value strategy, intelligent product management, customer behavior analysis and shopping psychology, intelligent customer communications, responsive logistics and supply chain, intelligent customer experience, data technologies and infrastructures, performance measurement and metrics, organizational adaptability, ethical framework and data have a positive and significant effect on the design of an intelligent marketing information system. As a result, the design and implementation of such a system requires the intelligent integration of advanced analytical technology capabilities with business strategy, ethical considerations, and organizational platform preparation. This research provides an operational framework for managers of the Refah chain store to transform data into actionable insights, not only to increase operational efficiency and profitability, but also to effectively play their social role in providing the community with the desired basic goods. Conclusion The present study was conducted with the aim of validating the factors affecting the smart marketing information system in the Refah chain store. The results of this study are consistent with the results of Agarwal et al. (2025), Dehghani (2025), Torabi et al. (2024), Khojastehpour & Abedi (2024), Trianti et al. (2024), Shekhawat (2023), Liu et al. (2023) Chen & Chang (2023), Iravani et al. (2023), Tarahomi & Keshtgar (2022), Jin et al. (2022), Khojastehkhosro et al. (2022), and Tota & Negro (2021). Tota & Negro (2021) showed that focusing on value proposition and competitive differentiation is the basis for the success of marketing information systems in the competitive retail environment. The study of Jin et al. (2022) emphasized that the pricing strategy based on the perceived value of the customer has a direct impact on the performance of the smart marketing information system. These results indicate that focusing on high-quality basic goods and creating a sense of value for families is the basic foundation for designing the smart marketing information system in convenience stores. Based on the findings of this study, it is recommended that convenience chain stores set up a smart marketing command center that integrates all the influential factors identified in this study into an integrated platform. This center should enable the implementation of smart and personalized marketing campaigns in real time by creating a live connection between customer data from the loyalty system, supply chain inventory information, and behavior analysis system.

Abstract Abstract The purpose of this research is to examine the presentation of an artificial intelligence (AI) model at Rafidain Bank to ensure the enhancement of marketing capabilities. This study is developmental in terms of objectives, survey-based in data collection, exploratory in nature, and quantitative in execution. The statistical population consists of all employees of Rafidain Bank in Iraq, from which 450 individuals were selected using Cochran’s formula and stratified random sampling. The data collection instrument is a questionnaire. SPSS and SmartPLS software were used for data analysis. The results indicated that AI has a significant effect on the bank’s effective performance. AI capability affects AI adaptability. AI capability affects the bank’s effective performance. The experience of using AI has no effect on AI adaptability. The experience of using AI affects AI capability. The experience of using AI affects the bank’s foresight. The experience of using AI affects the bank’s effective performance. AI knowledge affects AI adaptability. AI knowledge affects the bank’s foresight. AI sustainability affects AI adaptability. AI sustainability affects the bank’s foresight. AI sustainability affects the bank’s effective performance. The bank’s foresight has no effect on AI adaptability. The bank’s foresight affects AI capability. The bank’s foresight affects the bank’s effective performance. Introduction The banking sector is undergoing a fundamental transformation, driven by the integration of AI into financial service processes. AI enhances operational efficiency, risk management, and customer engagement (Deloitte, 2022). As financial institutions worldwide strive to remain competitive in a digital-centric economy, AI technologies—particularly machine learning and natural language processing—have emerged as vital tools for processing massive datasets, predicting customer behavior, and automating complex processes. Simultaneously, marketing activities in banking have evolved from traditional advertising to sophisticated, data-driven strategies that leverage AI to deliver personalized customer experiences (Chaffey & Smith, 2022). Applications such as fraud detection, credit scoring, and chatbot-based customer service rely on machine learning algorithms to analyze historical data and make real-time decisions (Fethi & Pasiouras, 2010). Researchers further note that natural language processing has enhanced conversational AI, enabling more natural customer interactions (Hirschberg & Manning, 2015). While predictive analytics have refined risk assessment models, these advancements have positioned AI as a strategic asset for banks seeking to navigate competitive and regulatory challenges (Noreen et al., 2023). Marketing activities in banking have been equally transformed by AI’s ability to analyze consumer behavior and deliver targeted campaigns. Deloitte’s report on AI in the banking sector indicates that banks utilizing AI-driven marketing strategies achieve a 30% increase in customer retention and a 25% improvement in campaign conversion rates (Deloitte, 2022). In line with this objective, the main question of the present study is: How does the artificial intelligence model at Rafidain Bank ensure the enhancement of marketing capabilities? Theoretical Framework The Evolving Landscape of Banking and Finance In 2025, banks are increasingly leveraging artificial intelligence (AI) and generative AI to enhance productivity and customer personalization, with over 75% of them planning to invest in data management and cloud infrastructure. This trend is evident in Asia, where digital banking and fintech have increased resilience following crises and strengthened financial inclusion through mobile payments and central bank digital currencies. In Europe, asset tokenization on the blockchain is on the rise, promising faster settlement and reduced costs, supported by initiatives such as the UK’s Digital Securities Sandbox. Meanwhile, the African banking sector is leading in mobile banking adoption, accounting for nearly half of global mobile accounts, which bolsters inclusion and robust brand growth, contributing an average of 22% to brand value. Globally, revenue streams are shifting; non-interest income from payments and wealth management is expected to rise, while net interest margins face pressure from declining rates (Deloitte, 2024). AI Application in the Banking Industry AI in digital marketing for banks not only increases customer acquisition and retention but also improves risk management, fraud detection, and overall financial performance. In banking, AI-driven segmentation utilizes machine learning to categorize customers based on behavior, transaction history, and demographic information. This enables targeted marketing, such as promoting credit cards to high-spending segments or savings programs to conservative savers (Sardjono & Perdana, 2023). Ahmadi Alinoudehi et al. (2026) examined the dimensions and components of AI-based digital transformation management. The results indicated that AI-based digital transformation management is designed within four overarching categories: “contextual requirements,” “digital infrastructure,” “digital transformation management process,” and “organizational capital,” encompassing 12 organizing categories: “ethical requirements,” “cultural requirements,” “organizational requirements,” “hard digital infrastructure,” “soft digital infrastructure,” “digital transformation management process,” “digital transformation planning,” “prototyping,” “learning,” “human capital,” “process capital,” “structural capital,” “social capital,” and 73 basic categories. Deshmukh (2025) investigated the impact of AI on customer relationship management practices in retail banking, focusing on data-driven personalization to enhance customer engagement. Using a mixed-methods approach, including surveys and interviews with banking professionals, this study explores how AI technologies, such as machine learning and predictive analytics, enable banks to analyze customer data, improve segmentation, and tailor marketing strategies. Research Methodology This study is developmental in terms of its objectives, survey-based in its data collection method, exploratory in nature, and quantitative in execution. The statistical population consists of all employees of Rafidain Bank in Iraq, from which 450 individuals were selected using Cochran’s formula and stratified random sampling. The data collection instrument used is a questionnaire. Research Findings Data analysis was performed using SPSS and SmartPLS software. The results indicated that artificial intelligence (AI) has a significant effect on the bank’s effective performance. AI capability affects AI adaptability. AI capability affects the bank’s effective performance. The experience of using AI has no effect on AI adaptability. The experience of using AI affects AI capability. The experience of using AI affects the bank’s foresight. The experience of using AI affects the bank’s effective performance. AI knowledge affects AI adaptability. AI knowledge affects the bank’s foresight. AI sustainability affects AI adaptability. AI sustainability affects the bank’s foresight. AI sustainability affects the bank’s effective performance. The bank’s foresight has no effect on AI adaptability. The bank’s foresight affects AI capability. The bank’s foresight affects the bank’s effective performance. Conclusion This study was conducted with the aim of examining the presentation of an artificial intelligence (AI) model in Rafidain Bank to ensure the strengthening of marketing capabilities. The findings of this research are consistent with those of Baffour Gyau et al. (2024), Wang et al. (2023), Eskandarany (2024), Alotaibi (2024), Byambaa et al. (2025), Svoboda (2024), Lin et al. (2024), McKinsey (2020), Abdulsalam and Tajudeen (2024), Deloitte (2024), and Khan et al. (2024). In particular, the findings of Baffour Gyau et al. (2024) on the dynamic relationship between AI technological innovation in banking and finance and banks’ financial performance across 20 countries show that AI technological innovation in banking and finance positively affects banks’ return on assets, highlighting its role in improving financial performance. The interaction term between AI innovation and economic growth also emphasizes their combined positive effect on financial performance. Mediation analysis further highlights the role of information and communication technology development in transforming AI innovation into improved financial outcomes. Based on the research findings, the following recommendation is proposed: · Comprehensive AI literacy training for bank employees: The findings showed that the bank should shift its focus from training employees in simple tools toward comprehensive AI literacy, enabling its workforce to move from passive users to active participants in the AI ecosystem.

Abstract Abstract The present research aims to identify and prioritize the factors affecting the success and failure of platform ecosystems in technology companies. The research method is applied-developmental in terms of its objective, and exploratory mixed-methods (qualitative-quantitative) in nature. The statistical population includes 15 industry and academic experts in the qualitative section and 12 experts in the quantitative section. The data collection instrument is semi-structured interviews. For data analysis in the qualitative section, thematic analysis was conducted using MAXQDA 2024 software, and in the quantitative section, the factors were prioritized using the fuzzy Analytic Hierarchy Process (AHP). Sensitivity analysis using a four-scenario approach, along with Friedman and Spearman tests, was employed to confirm the robustness of the results. The qualitative results identified 36 open codes, 12 sub-themes, and ultimately 6 success factors and 6 failure factors. The quantitative results showed that the most important success factors are, respectively, “positive network effects” (weight 0.382), “transparent and fair governance” (0.245), and “effective management of complementors” (0.163). In contrast, the most important failure factors are “multi-homing migration” (0.351), “unsustainable revenue model” (0.228), and “reverse network effects” (0.175). The inconsistency ratios of the matrices (CR = 0.05 for success and CR = 0.07 for failure) indicate satisfactory reliability of judgments. A proposed three-layer integrated framework for success (foundational prerequisites, core drivers, sustaining factors) and failure (greatest threat, structural threats, contextual threats) was presented. Platform managers should prioritize strengthening positive network effects, governance transparency, and countering multi-homing migration. Introduction In recent years, the emergence and expansion of digital platforms have fundamentally transformed the landscape of technology businesses. Platform ecosystems, formed through dynamic interactions among different groups of actors (developers, suppliers, customers, and complementors), have become one of the pillars of the digital economy (Alt, 2022). Despite the remarkable success of some global platforms, many platform ecosystems in technology companies face a high failure rate, and only a limited number of them are able to sustain and scale (Fan et al., 2021). This issue is of particular importance in Iran due to the rapid growth of platforms such as Snapp, Digikala, Torob, and Albatros, coinciding with challenges of survival and sustainable profitability. According to industry reports, the customer churn rate in Iranian platforms is estimated at over 40% annually on average, indicating structural problems in governance, revenue, and user interaction models. Previous research in the field of platform ecosystems has primarily focused on aspects of architecture, governance, and competitive strategy (Engert et al., 2022). Some studies have addressed the role of complementors (Blasco-Arcas et al., 2020; Deilen & Wiesche, 2021), while others have emphasized the importance of platform leadership (Leong et al., 2019). In this regard, recent studies published in reputable domestic journals have emphasized the need to localize success and failure models in digital contexts. For example, Torkanloo et al. (2025), in a study titled “Increasing Purchase Intention in Chain Retail Stores Using an Integrated Channel Strategy,” demonstrated that customer experience and perceived risk play a decisive role in the success of digital channels. Additionally, Aalami et al. (2025), in designing a customer experience model in the retail industry with an emphasis on Generation 4.0, highlighted the importance of bilateral interactions and co-creation of value as the primary drivers of success. In this context, the main research question is formulated as follows: How can the factors affecting the success and failure of platform ecosystems in technology companies be identified and prioritized? Theoretical Framework Platform Ecosystem A platform ecosystem refers to a set of independent and semi-independent actors organized around a shared technological core (the platform) who, through repeated interactions, exchanges, and mutual collaboration, create value beyond the sum of the individual components. Unlike traditional hierarchical organizations, which are based on ownership and direct control, platform ecosystems operate through indirect orchestration and network governance (Kretschmer et al., 2022). Rong et al. (2025) examined the all-in-one platform strategy, focusing on building a multi-product ecosystem to lock in users. The results showed that features such as the integration of diverse services, inter-product synergies, and centralized data management influence platforms’ ability to create sustainable user dependence and strengthen their competitive position in multi-sided markets by increasing switching costs and enhancing a unified user experience. Schreieck et al. (2022) investigated the governance of platform ecosystems through inward and outward governance in traditional companies, using SAP as a case study. The findings indicated that factors such as managing conflicts between internal units, designing open application programming interfaces (APIs), and creating reward mechanisms for business partners, through their influence on platform governance structure, affect the success of leading firms in building innovation ecosystems and maintaining competitiveness in digital markets. Research Methodology The research method, in terms of objective, is applied-developmental, and in terms of nature, it is exploratory mixed-methods (qualitative-quantitative). The statistical population in the qualitative section consisted of 15 industry and academic experts, while in the quantitative section it included 12 experts. The data collection tool was semi-structured interviews. Research Findings For data analysis, thematic analysis was employed in the qualitative section using MAXQDA 2024 software. In the quantitative section, factors were prioritized using the fuzzy Analytic Hierarchy Process (AHP). Four-scenario sensitivity analysis, along with Friedman and Spearman tests, were used to confirm the robustness of the results. The qualitative analysis revealed 36 open codes, 12 sub-themes, and ultimately identified 6 success factors and 6 failure factors. The quantitative analysis indicated that the most significant success factors, in order of importance, are: “Positive network effects” (weight 0.382), “Transparent and fair governance” (0.245), “Effective management of complementors” (0.163). Conversely, the most critical failure factors identified are: “Multi-homing migration” (0.351), “Unsustainable revenue model” (0.228), “Reverse network effects” (0.175). The matrix inconsistency ratios (CR=0.05 for success and CR=0.07 for failure) suggest a favorable reliability of the judgments. A proposed three-layer integrated framework for success (foundational prerequisites, core drivers, sustaining factors) and failure (greatest threat, structural threats, contextual threats) was presented. Platform managers should prioritize strengthening positive network effects, governance transparency, and countering multi-homing migration. Conclusion The present research was conducted with the aim of identifying and prioritizing the factors affecting the success and failure of platform ecosystems in technology companies. The findings are consistent with the results of Zeng et al. (2025), Bontems & Chaves (2025), Tiwana & Konsynski (2024), Schreieck et al. (2024), Busalim et al. (2024), Hao & Fan (2026), Rong et al. (2025), Schreieck et al. (2022), Marković et al. (2022), Gawer (2022), Mirzaee Azandariani & Arya (2022), Jalali (2021), Kretschmer et al. (2022), and Calabrese et al. (2021). Specifically, Rong et al. (2025) demonstrated that features such as the integration of diverse services, inter-product synergy, and centralized data management—by increasing switching costs and enhancing a unified user experience—influence the ability of platforms to create sustainable dependence and stabilize their competitive position in multi-sided markets. Based on the research findings, the following recommendations are proposed: Strengthening positive network effects alongside managing reverse effects: Utilize loyalty mechanisms, quality ranking, and supply-demand balancing to prevent value degradation caused by network congestion. Transforming governance transparency into a competitive advantage: Publicly declare transparent commission rules, fair pricing, and dispute resolution procedures, and institutionalize adherence to these standards.

Abstract Abstract The present study aims to design a competitive model for streaming platforms, specifically subscription-based video and audio services, with an emphasis on the future of media management. In terms of objective, the research is applied, and in terms of implementation, it is mixed-methods (qualitative-quantitative) with a sequential exploratory design. The qualitative population consists of 15 experts in media management in Iran. The quantitative population includes 378 active users of streaming services in Iran (Filimo, Namava, Shadava, and Beeptunes), and the sampling method used in this study was convenience sampling. The data collection instruments were semi-structured interviews and questionnaires. For data analysis, coding was used in the qualitative section, including three stages of basic, organizing, and overarching themes, supported by MAXQDA software. In the quantitative section, structural equation modeling with the partial least squares approach (PLS-SEM) was employed. The findings showed that three dominant competitive models include local exclusive content production, bundling with mobile operators, and a hybrid advertising-subscription model. The phenomenon of multi-homing was observed in more than three-quarters of users and showed a positive and significant correlation with churn rate. The PLS results indicated that bundling with operators has the greatest effect on reducing churn. The qualitative analysis identified seven main categories. The most likely future scenarios are platform consolidation and alliances, or the stabilization of existing platforms. It is concluded that media managers should adopt a combination of retention strategies and prepare themselves for managing integrated and large-scale platforms. Introduction The rapid growth of internet communication technologies has transformed the traditional structure of various industries, including media. In this context, subscription-based video and audio streaming services, by offering on-demand and personalized content, have changed audience consumption patterns from linear viewing to non-linear viewing (Aalami et al., 2025). This transformation has not only altered media consumption habits, but also posed a serious challenge to traditional business models based on television advertising and linear broadcasting. Platforms such as Netflix, Amazon Prime, and Disney Plus globally, and Filimo, Namava, Shadava, and Beeptunes in Iran, are examples of this paradigm shift. In this new model, users pay a monthly or annual fee to access a large content library, and platforms, instead of generating momentary ad-based revenue, seek stable and predictable income streams through subscriptions (Koul & Suresh Ambekar, 2020). However, this very feature, namely the ease of canceling subscriptions and the low cost of switching between platforms, has confronted media managers with a new strategic challenge: churn rate. From an academic perspective, the literature on media management and platform economics in Iran requires localized models that reflect the unique characteristics of the domestic market, including the role of mobile operators as gateways for payment and distribution, the high prevalence of VPNs as a hidden competitor, and centralized regulation by SATRA. International studies, such as Tang and Wei (2023), have shown that media management strategies in Western and Eastern contexts differ due to cultural and regulatory differences; Netflix emphasizes global content production and simultaneous release across countries, while Chinese platforms such as Tencent Video focus on local content and adaptation to domestic policy. Therefore, the main research question is: How can a competitive model for streaming platforms, specifically subscription-based video and audio services, be designed with an emphasis on the future of media management? Theoretical Framework Streaming Platforms and Subscription-Based Services Streaming platforms refer to digital environments that provide video and audio content directly via the internet, bypassing traditional broadcasting networks (cable, satellite). By leveraging subscription business models, these platforms have successfully transformed media consumption patterns from “linear viewing” to “on-demand viewing” (Steffiani & Irwansyah, 2021). The Evolution of Media Management in the Platform Era The emergence of global streaming platforms has shifted media management from the traditional model of “linear broadcasting and content scheduling” toward “algorithmic ecosystem management and audience engagement” (Khalafi et al., 2019). In this new landscape, media managers must grapple with four primary challenges: changing audience consumption habits (shifting from TV channels to on-demand platforms), competing with cross-border giants, the need for massive investment in exclusive content, and the integration of data-driven marketing strategies (Pandey et al., 2020). Sarbakhsh et al. (2025) conducted research titled “Explaining the Content of Popular Animations on Streaming Media (A Study of Netflix, Amazon Prime, and Disney+ Works).” Their findings indicated that most Netflix animations center on female protagonists or women, often progressing without reliance on male characters. Amazon Prime productions continue to focus on older high-audience content within the themes of adolescence and maturity, while the newcomer Disney+, relying on the productions of its parent company (Disney), offers fragmented content in the form of spin-offs. Cheng et al. (2025) conducted a study titled “Advanced Customer Churn Prediction for a Music Streaming Service via Enhanced Deep Learning.” The results showed that user behavioral characteristics (such as decreased engagement and changes in listening patterns) and user-experience-related features—by influencing loyalty indicators and perceived satisfaction—significantly impact consumers’ churn intention (service abandonment) in online streaming environments. Research Methodology In terms of its objective, this research is applied, and in terms of its execution method, it is mixed-methods (qualitative-quantitative) using a sequential exploratory design. The qualitative statistical population includes 15 experts in the field of media management in Iran. The quantitative population consists of 378 active users of streaming services in Iran (Filimo, Namava, Shadava, and Beeptunes), selected through convenience sampling. The research data collection tools include semi-structured interviews and questionnaires. Data analysis was conducted using coding (comprising three stages: basic, organizing, and overarching themes) with MAXQDA software in the qualitative section, and structural equation modeling with a partial least squares approach (PLS-SEM) in the quantitative section. Research Methodology The research method, in terms of its objective, is applied. In terms of execution, it is a mixed-methods (qualitative-quantitative) approach with a sequential exploratory design. The qualitative population includes 15 experts in the field of media management in Iran. The quantitative population comprises 378 active users of streaming services in Iran (Filimo, Namava, Shadava, Beeptunes), and the sampling method used was convenience sampling. The research data collection instruments were semi-structured interviews and questionnaires. Research Findings Data analysis was performed using coding (including three stages: basic, organizing, and overarching themes) with MAXQDA software in the qualitative section. In the quantitative section, structural equation modeling with the partial least squares (PLS-SEM) approach was utilized. The findings revealed three dominant competitive models: Production of exclusive local content, Bundling with mobile operators, A hybrid model of advertising and subscription. The phenomenon of multi-homing was observed in over three-quarters of users and showed a significant positive correlation with churn rate. The PLS modeling results indicated that bundling with operators has the greatest impact on reducing churn. The qualitative analysis identified seven main categories. The most probable future scenarios involve the merger and alliance of platforms or the consolidation of existing platforms. It is concluded that media managers should employ a combination of retention strategies and prepare themselves for managing integrated and large-scale platforms. Conclusion The present study was conducted with the objective of designing a competitive model for streaming platforms (subscription-based video and audio services) with an emphasis on the future of media management. The results of this research are consistent with those of Sarbakhsh et al. (2025), Cheng et al. (2025), Wu (2025), Benesbordi & Khalili (2024), Mohammadi et al. (2024), and Yahya Zadeh et al. (2023). Specifically, Sarbakhsh et al. (2025) demonstrated that the majority of Netflix animations center on female protagonists or women, often progressing without reliance on male characters. Amazon Prime’s productions continue to focus on previously popular content within the themes of adolescence and maturity, while the newcomer Disney+—relying on the productions of its parent company, Disney—offers fragmented content in the form of spin-offs. Based on the findings, the following recommendations are proposed: 1. Strategic Bundling: Establishing long-term exclusive contracts with mobile operators to reduce payment friction. 2. Data-Driven Customization: Investing in recommendation systems tailored to indigenous behavioral data.