Journal of value creating in Business Management

Abstract The aim of this research is to design, develop, and evaluate a Multi-Agent Artificial Intelligence (MAS) framework that significantly increases the efficiency of banking services marketing processes by utilizing a distributed architecture. This study specifically focuses on operational data and digital transformation challenges at Rafidin Bank in Iraq. This study uses the applied-developmental research methodology and intelligent systems modeling. The proposed framework consists of four independent key agents: Data Analysis Agent (DAA), Offer Personalization Agent (PA), Campaign Execution Agent (CEA), and Coordinator Agent. The agents interact and collaborate with each other through algorithms such as behavioral clustering, reinforcement learning for offer optimization, and linear programming for budget allocation. The effectiveness of the framework was measured through simulation on Rafidin Bank’s historical data and by comparing financial and operational metrics (ROMI, conversion rate, and CAC) with traditional approaches. The results indicated the high effectiveness of the MAS framework in optimizing marketing processes. This framework was able to: Increase Return on Marketing Investment (ROMI) by 36.1%. Improve the Conversion Rate of target customers by 46.4%. Reduce Customer Acquisition Cost (CAC) by 29.1%, which is due to precise targeting and optimal channel management. These improvements are the result of the system's ability to make prescriptive decisions and execute operations in a distributed and real-time manner.

Abstract The purpose of this study is to validate the social media marketing model to influence the purchasing decision-making process of potential customers of residential and commercial buildings. In this study, an attempt has been made to examine and validate the paradigmatic model of social media marketing to influence the purchasing decision of customers, considering the real needs of potential customers of residential and commercial construction projects in the Mashhad metropolis. The research method is applied in terms of purpose and descriptive-survey in terms of nature. The statistical population included customers, citizens, and residents of residential and commercial neighborhoods and complexes in the city of Mashhad. To analyze research data structural equation modeling and PLS software were used. The proposed model was validated by distributing 420 questionnaires among the members of the statistical population. The results of structural equation analysis indicate that the social media marketing model is valid for influencing the purchasing decision-making process of potential customers of residential and commercial buildings. It was also found that the pivotal phenomenon and contextual conditions have a significant impact on strategies, and strategies also play an important role in improving marketing outcomes.

Abstract The aim of this research is to design a marketing process model for customer relationship management in digital platforms of the banking industry. The research method is applied in terms of its purpose and qualitative in terms of its implementation. The statistical population of the study included 16 marketing experts, academics, and banking industry managers, selected through purposive or snowball sampling. Semi-structured interviews were used to collect data. Data analysis and coding were conducted in grounded theory. Based on the results of qualitative analysis, five categories of overarching categories were identified, including: causal factors of the customer relationship management model in digital platforms in the banking industry (technology, organizational, individual, technological factors) Phenomenon-oriented factors (electronic channels, company organization, employee empowerment) Strategic factors (electronic marketing, customer interaction, internal exchanges, learning and innovation) Contextual factors (trust-building behavior) Intervening factors (political factors, organizational factors) The outcomes (customer satisfaction and loyalty, customer trust) were identified. The results of the quantitative section, while confirming the research hypotheses, showed that the proposed model has appropriate validity.

Abstract The present study aims to identify the components of digital marketing and investigate their impact on marketing performance, considering the mediating role of knowledge adoption and the moderating role of organizational innovation in Small and Medium Enterprises (SMEs) in Iraq. In terms of objective, the research is applied-developmental; in terms of implementation, it follows a mixed-methods (qualitative-quantitative) approach; and by nature, it is exploratory-confirmatory. The statistical population in the qualitative phase consisted of 15 digital marketing experts in Baghdad, selected through judgmental and snowball sampling methods. The quantitative population included 108 marketing specialists from companies operating in the online food ordering industry in Baghdad, selected via simple random sampling. Data were collected using semi-structured interviews and questionnaires. For data analysis, the Fuzzy Delphi method was employed in the qualitative phase, while SPSS and PLS software were used for the quantitative phase. Qualitative results identified 55 indicators categorized into five main components: operational agility, data-driven personalization, brand credibility, customer interactive experience, and digital marketing strategies. The findings revealed that all digital marketing components have a positive and significant impact on marketing performance. Furthermore, knowledge adoption plays a significant mediating role in strengthening this relationship, and organizational innovation moderates the intensity of digital marketing’s impact on marketing performance. The results suggest that the strategic integration of digital marketing with knowledge management mechanisms and the promotion of an innovation culture can lead to enhanced marketing performance and the creation of a sustainable competitive advantage for Iraqi SMEs.

Abstract The rapid advancement of clean energy technologies has made the integration of sustainable marketing and renewable energy development a central issue in the sustainable development literature. However, the application of conventional marketing approaches in this field remains challenging due to the capital-intensive nature of renewable energy projects, institutional complexities, and dependence on government policies. This study aimed to design and analyze a renewable energy development model based on sustainable marketing using the Fuzzy Cognitive Mapping (FCM) approach. The research is applied in terms of purpose and employs a mixed-methods (qualitative–quantitative) design. In the qualitative phase, semi-structured interviews were conducted with 21 experts in renewable energy, biosystems, and marketing, and the initial model was developed using the Grounded Theory approach. In the quantitative phase, 40 of the 41 identified components were validated through the Fuzzy Delphi method. Subsequently, the relationships among factors, strategies, and outcomes were analyzed using Fuzzy Cognitive Mapping based on the judgments of 14 experts participating in a focus group.The findings revealed that data-driven and information-based policymaking was the most influential factor (centrality = 5.90; outdegree = 2.98), while structural transformation in the energy system exhibited the highest indegree (3.03). Furthermore, improving regional and local policies (centrality = 4.49) emerged as the most significant strategy, whereas regional and local transformation (centrality = 4.32) was identified as the most significant outcome. Regional analysis further indicated that data-driven and information-based policymaking and the improvement of regional and local policies function as key leverage points within the system. The findings indicate that renewable energy development in Iran requires not only technological advancement but also the redesign of information systems, the strengthening of smart governance, institutional capacity building, and the localization of sustainable marketing strategies to create economic, social, and environmental value.

Abstract The purpose of the present study is to validate a model of the factors affecting the improvement of the commercialization of emerging technologies in the PVC industry using structural equation modeling (SEM). In terms of its objective, this research is applied; in terms of implementation, it is quantitative; and in terms of approach, it is classified as sequential exploratory research.The statistical population includes experts and managers مرتبط with the PVC industry who are active in the areas of production, technical operations, research and development, quality control, and senior industrial management. Given that the statistical population was considered unlimited, Cochran’s formula was used, and the sample size was determined to be 384 participants. The sampling method used in this study was simple random sampling.The research data were collected using a researcher-made questionnaire. For data analysis, SPSS and LISREL software were employed. The findings indicate that improved commercialization in this industry is a function of five key components: “technological capabilities and capacities,” “organizational and managerial factors,” “environmental pressures,” “market processes,” and “outcomes and achievements.”The results show that environmental pressures play the strongest inhibiting role when not managed properly, whereas technological capabilities and managerial support are the main drivers of successfully crossing the technology “valley of death.” This model helps managers and policymakers facilitate the path toward the commercial exploitation of innovations in the polymer sector through careful management of these drivers.

Abstract The aim of this study is to present the factors of young consumers' distrust of brands. Customer distrust is one of the fundamental challenges in maintaining sustainable relationships between organizations and their stakeholders. The present study, using a qualitative approach and data-driven theory, has identified different dimensions of customer distrust and provided a theoretical framework for understanding it. Semi-structured interviews with 17 customers who had direct experience of brand distrust were used to collect data. Participants were selected purposefully and with maximum diversity, and the sampling process continued until theoretical saturation was reached. The results with maxqda software show that causal factors such as deceptive advertising, brand infringement, negative reputation, negative experience, poor service, and negative opinions of others play a key role in the formation of distrust. Also, contextual factors (such as unhealthy competitive environment and general culture of distrust), intervening factors (such as psychological characteristics and customer awareness level), coping strategies (such as avoidance, information seeking), and consequences such as negative word-of-mouth advertising play a role in this process. The extracted framework can be the basis for developing marketing strategies, customer experience management, and rebuilding trust in organizations.

Abstract Abstract
The aim of this study is to fit and validate a model of spanning export marketing capabilities in Iran's cement industry using an exploratory mixed-method approach. The research is applied in purpose and descriptive-survey in nature. In the qualitative phase, the grounded theory method based on Strauss and Corbin's approach was employed. Data were collected through semi-structured interviews with 13 experts in the field of cement export using purposive sampling and were analyzed with MAXQDA 2020 software through three-stage coding (open, axial, and selective). Based on the coding process, 64 open codes, 35 axial categories, and one core category were identified. To ensure the trustworthiness of the qualitative data, the criteria of credibility, confirmability, and transferability were applied. In the quantitative phase, experts, scholars, exporters, specialists, and marketers active in cement exports were selected through purposive sampling, and a sample of 70 individuals was chosen. A researcher-made questionnaire was used in this phase. Face and content validity were confirmed by experts, and construct validity was assessed through AVE, CR, and Cronbach’s alpha. Data analysis using structural equation modeling (SEM) with the partial least squares (PLS) approach indicated a good statistical fit of the conceptual model’s paths. The results identified internal and external causal features, contextual and intervening conditions, export strategies, and consequences such as branding, optimization of production lines, and national economic development. This model can serve as a foundation for developing strategic export programs, export policymaking, international marketing planning, enhancing competitive advantage, and increasing export efficiency in Iran’s cement industry.

Abstract The aim of this study is to investigate the factors affecting employees' attitudes and behavior towards the acceptance of artificial intelligence in order to achieve sustainable development. The statistical population consisted of employees and organizational experts who had experience in dealing with artificial intelligence systems. Purposive sampling was conducted and 15 human resource specialists, information technology managers, university professors, and selected employees participated in semi-structured interviews, observing the principle of theoretical saturation. The data were analyzed through three stages of open, axial, and selective coding, and 6 main categories were extracted, including causal factors, contextual factors, intervening factors, central phenomenon, strategies, and consequences. Findings using maxqda software showed that individual motivations such as the desire to learn, the need for growth, and self-efficacy, along with employees' psychological and cognitive readiness, play a pivotal role in the acceptance or resistance to artificial intelligence technology. Also, organizational culture, technological infrastructure, and leadership style as contextual factors and macro policies, competitive pressures, and social pressure as intervening factors are effective in the formation of acceptance strategies. Successful implementation of artificial intelligence increases productivity, improves job satisfaction, and strengthens sustainable development components in organizations. The findings of this study provide a conceptual model for better understanding and managing the adoption of new technologies from a psychological and organizational perspective and can be a practical guide for policymakers, human resource managers, and intelligent systems designers.

Abstract The objective of this study is to develop a model of capital market functioning based on changes in investors’ behavioral patterns and digital transformation.This research is applied and exploratory in terms of purpose and qualitative in terms of methodology.The statistical population of the study consists of 17 financial experts from the Tehran Stock Exchange, who were selected using purposeful sampling.Data were collected through semi-structured interviews.For data analysis, Fuzzy Delphi and Interpretive Structural Modeling (ISM) methods were employed, and the MICMAC software was used.The results indicate that five main components and nineteen sub-dimensions were identified across four levels.The capital market functioning component is positioned at the first level of the ISM graph and represents the most dependent and most influenced component of the model.At the second level, the change in investors’ behavioral patterns component is located, which influences the first-level component and is itself influenced by the lower-level components.At the third level, the components of qualitative and content-related disclosure characteristics and formal and process-oriented disclosure characteristics are positioned; these components affect the higher-level components and are influenced by the lower-level component.At the fourth (final) level, the digital transformation component is located, which is the most influential and dominant component of the model. This component affects all other components of the model.

Abstract The present study aims to provide a bundling model in online service industries based on digital technologies (case study: Dey Insurance). In terms of purpose, this research is applied, and in terms of implementation, it is qualitative. The statistical population of the study consisted of 20 experts and specialists who were knowledgeable about the research topic, as well as specialists in bundling in the insurance industry, who were selected through purposive sampling. The data collection tool was interviews. MAXQDA software was used to analyze the findings. The results of the data analysis showed that the bundling model in online service industries is grounded in eight key variables and dimensions. These dimensions include: “value creation and the solution-oriented logic of bundles,” “integration of the customer’s digital experience,” “technological infrastructure and data readiness,” “organizational readiness and agility,” “service mix design and selection of complementary components,” “ecosystem collaboration and environmental requirements,” “customers’ behavioral and experiential outcomes,” and “the system for measuring and monitoring bundling performance.”
The successful implementation of the bundling model in Dey Insurance requires simultaneous attention to the development of technological infrastructure, data readiness, and organizational agility. Through the intelligent design of service combinations based on ecosystem collaborations and the integration of the digital experience, customer behavioral outcomes can be improved, and through continuous monitoring, sustainable value creation and competitive advantage can be achieved.

Abstract The present study aims to identify the antecedents, components, and consequences of smart home technology adoption in Iraq. This research is applied in purpose and descriptive‑exploratory in terms of nature and methodology. The statistical population consists of 15 key experts and specialists in the field of technology as well as distributors located in major cities of Iraq, selected through purposive snowball sampling. Data were collected using semi‑structured interviews. The data analysis was conducted using thematic analysis, which ultimately led to the development of a three‑tier thematic network.The findings indicate that the model of smart home adoption in Iraq is strongly influenced by local contextual factors. In the domain of antecedents (barriers), electricity instability and infrastructural challenges (93.3%) and high initial cost (86.7%) were identified as the strongest obstacles. In the domain of components (drivers), locally perceived usefulness in electricity management and stability (80%) and trust in local importers and after‑sales services (73.3%) emerged as the most critical determinants of adoption, effectively substituting the traditional construct of perceived ease of use.Finally, the most significant outcomes of adoption were reduced operational costs and energy savings (86.7%), followed by gaining social credibility and prestige (60%). This study proposes a localized technology adoption model that emphasizes “survival‑oriented usefulness” (overcoming infrastructural deficiencies) and “perceived risk reduction” (through local warranties), rather than comfort or ease of use.

Abstract Purpose: This study aimed to develop an indigenous model for public value creation in the Martyr and Veterans Affairs Foundation (MVAF), with emphasis on stakeholders’ differentiated priorities and demands.

Methodology: Adopting a qualitative approach within the interpretivist paradigm, data were collected through semi-structured interviews with 15 purposively selected senior managers and experts of the MVAF. Data were analyzed using thematic analysis based on the Attride-Stirling framework with MAXQDA 2020. Trustworthiness was ensured through peer debriefing, member checking, and inter-coder reliability assessment.

Findings: The analysis resulted in 40 open codes, classified into five overarching themes for public values and six overarching themes for stakeholder demands. The findings indicate that stakeholders prioritize public values through a hierarchy including human dignity, procedural justice, institutional accountability, service effectiveness, and social cohesion. Human dignity was identified as the foundational condition for realizing other values. Stakeholder demands ranged from dignity-based relationships to cultural development, highlighting the importance of symbolic capital and human interaction beyond material support.

Conclusion: In the context of martyrdom and sacrifice, public value is not merely functional but identity-based and existential. It is created through the interaction of dignity-centered service, integrated human competencies, and transparent accountability. The gap between legal promises and executive performance was found to be the main factor undermining social trust and service legitimacy. The proposed model emphasizes redesigning recruitment systems, empathy-based training, and employee empowerment as the core drivers of sustainable public value creation.

Abstract The rapid advancement of artificial intelligence (AI) technologies and the increasing role of data in organizational decision-making have made AI adoption in supply chain management an important research topic. This study examines the factors affecting managers’ behavioral intention to use AI in supply chain decision-making. The Unified Theory of Acceptance and Use of Technology (UTAUT) was adopted as the theoretical framework and extended by incorporating information quality, perceived privacy, and uncertainty avoidance. This applied study employed a descriptive-survey design. Data were collected from 384 managers and experts in companies located in Tehran using a structured questionnaire and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. The results indicated that performance expectancy, effort expectancy, peer influence, facilitating conditions, information quality, perceived privacy, and uncertainty avoidance all had significant positive effects on behavioral intention. Among these factors, uncertainty avoidance and information quality showed the strongest effects. The findings highlight the importance of reducing uncertainty and providing accurate, reliable information to facilitate AI adoption in supply chain management.

Abstract The purpose of this study is to investigate the impact of supply chain capabilities and knowledge management capabilities on business performance, with the mediating role of competitive advantage. In terms of purpose, this is an applied research; regarding its methodology, it is a descriptive-survey and causal study, and concerning data analysis, it is correlational. The statistical population consists of 600 managers and employees from the financial, marketing, R&D, distribution, and IT departments, as well as suppliers in the procurement sections of top-tier chain stores in Mashhad, Iran (including Shahr-e-Ma, Refah, Ofogh Kourosh, Hyperme, Ba-Ma, and Etka). Based on the Morgan table, the sample size was estimated at 234; ultimately, 215 questionnaires were completed and returned. The sampling method is non-probability convenience sampling. The data collection instrument is a questionnaire, and Structural Equation Modeling (SEM) using SPSS and AMOS software was employed for data analysis. The research findings indicate that supply chain capabilities significantly influence competitive advantage and business performance, and knowledge management capabilities also have a significant effect on competitive advantage and business performance. Furthermore, competitive advantage mediates the impact of supply chain capabilities and knowledge management capabilities on business performance.

Abstract The purpose of this study is to hierarchically structure (level) the factors of the performance budgeting model for actor-network–based management, adopting a mixed-methods approach. In terms of purpose, the research is applied, and in terms of methodology it is descriptive–exploratory; in terms of implementation, it follows a mixed-method design. The statistical population consists of 20 academic and executive experts in the fields of public budgeting and management accounting, who were selected using purposive sampling. Data were collected through semi-structured interviews. Participants were chosen purposively based on criteria such as scholarly background and professional experience in performance budgeting.For data analysis, open, axial, and selective coding were employed using MAXQDA 24, and the DEMATEL technique was used to examine the cause–effect relationships among categories. The results indicated that the study categories include: governance and strategic orientation, indicator system and performance measurement, performance information and data system, information technology infrastructure, monitoring and transparency system, human actors, non-human actors, and outcomes (financial resource management). DEMATEL results showed that the monitoring and transparency system had the highest influencing power, whereas the indicator system and performance measurement exhibited the highest degree of being influenced. The findings can serve as guidance for policymakers and managers in designing and implementing efficient performance budgeting systems.

Abstract The aim of this study is to develop a model for foresight in emerging accounting technologies based on artificial intelligence. In terms of its purpose, the research is applied, and in terms of implementation, it adopts a mixed-methods design (qualitative-quantitative). The qualitative phase of the study involved 12 experts and specialists in finance and accounting, who were selected through snowball sampling. The quantitative phase included 215 financial managers and senior accountants. Data were collected through semi-structured interviews and questionnaires. In the qualitative phase, data were analyzed using coding procedures and ATLAS.ti software, while in the quantitative phase, SPSS and PLS software were used.The qualitative findings showed that the study comprised the following categories: 1) contextual conditions (sustainability and efficiency in AI implementation) with 8 subcomponents, 2) causal conditions (digital transformation and innovation in accounting) with 14 subcomponents, 3) intervening conditions (challenges and ethical and professional consequences of digital transformation in auditing and accounting) with 16 subcomponents, 4) strategies with 7 subcomponents, 5) consequences with 14 subcomponents, and 6) core category with 2 subcomponents. The quantitative results indicated that the variables of contextual conditions, causal conditions, intervening conditions, core category, strategies, and consequences were sufficiently normally distributed. The findings also showed that all research hypotheses were supported and that the proposed model had an acceptable fit.

Abstract The aim of this study is to identify the value-creating dimensions and drivers embedded in sustainable business models based on the circular economy in Iran’s oil industry. In terms of purpose, this research is applied, and in terms of implementation, it is qualitative in nature and follows an exploratory strategy. The statistical population comprised 12 managers, experts, and specialists familiar with the fields of circular economy, sustainable business models, and Iran’s oil industry, and the participants were selected through purposive sampling. Data were collected through semi-structured interviews. Thematic analysis and MAXQDA software were employed for data analysis.The findings revealed 105 basic themes, 21 organizing themes, and 7 overarching themes. The overarching themes include economic value creation, environmental value creation, social and organizational value creation, sustainable business model, circular economy drivers, implementation challenges, and the requirements for transitioning toward sustainable value creation. The results indicate that increasing resource productivity, reducing life-cycle costs, recycling and reusing materials, developing circular services, innovating business models, expanding inter-organizational collaboration, and leveraging digital technologies are among the most important factors contributing to sustainable value creation in the oil industry. By contrast, infrastructural limitations, cultural challenges, high initial costs, and the absence of supportive policies are among the main barriers to implementing the circular economy in this industry.Based on the findings, moving toward sustainable business models grounded in the circular economy can enhance the sustainability and competitiveness of Iran’s oil industry by improving resource productivity, reducing environmental impacts, and strengthening corporate social responsibility.

Abstract The present study aims to level the factors of the sustainable human resource development model using an interpretive structural modeling (ISM) approach. The research method is applied–developmental in terms of purpose and mixed-method (qualitative–quantitative) in execution, utilizing the interpretive–structural approach and MICMAC analysis. The statistical population includes 20 experts and specialists in the fields of human resource management, sustainable development, futures studies, and digital transformation, selected through purposive and judgmental sampling. Research data were collected through a systematic literature review, interviews with HR and sustainable development experts, and pairwise comparison questionnaires. MICMAC and ISM software were employed for data analysis. The findings indicated that six major components—namely, sustainable HR governance and strategy, sustainable human capital and empowerment, green and responsible human resource management, sustainable digital transformation and artificial intelligence, workforce health and well-being, and futures orientation, organizational learning, and innovation—form the core framework of sustainable human resource development. The results revealed that the components of governance, digital transformation, and futures orientation act as drivers, while human capital and well-being serve as dependent variables representing the ultimate outcomes. Additionally, green human resource management functions as a linkage factor, mediating between independent and dependent components. The findings highlight the importance of integrating futures studies approaches and a sustainability perspective in human resource management, offering practical strategies for organizations to enhance the sustainability, innovation, and resilience of their human resources.