New Approaches in Management and Marketing

Abstract Abstract The present study aims to analyze the mediating role of organizational ambidexterity in the effect of corporate social responsibility (CSR) and knowledge management infrastructure on corporate sustainable performance. In terms of purpose, the study is applicable; and methodologically, it follows a survey design. The statistical population consisted of all managers and headquarters experts of Dr. Tamin Company (N = 300). Using simple random sampling and Cochran’s formula, a sample of 168 participants was selected. Data were collected through standard questionnaires, whose validity was confirmed through expert judgment in the field of management, and whose reliability was verified via Cronbach’s alpha coefficients. Data analysis was performed through structural equation modeling by SmartPLS 3 software. The findings indicated that the path coefficient of CSR to organizational ambidexterity was 0.417 (T = 5.081); the path coefficient of knowledge management infrastructure to organizational ambidexterity was 0.379; the path coefficient of CSR to sustainable performance was 0.513 (T = 11.980); the path coefficient of knowledge management infrastructure to sustainable performance was 0.439; and the path coefficient of organizational ambidexterity to sustainable performance was 0.571—all statistically significant at the 95% confidence level. Moreover, results revealed that organizational ambidexterity mediates the effect of CSR on sustainable performance by 0.751, and also mediates the effect of knowledge management infrastructure on sustainable performance by 0.655. Accordingly, fostering an organizational culture that reinforces social and environmental values and encourages employee participation in CSR initiatives can significantly enhance corporate sustainable performance. Introduction With the emergence of increasingly complex challenges such as global competition, social problems, and environmental degradation, companies are required to simultaneously respond to dual demands and manage internal tensions in order to balance conflicting objectives (Annosi, 2024). In this regard, the closest concept in managing dual structures is organizational ambidexterity (O’Reilly & Tushman, 2013), which has attracted extensive scholarly attention. Organizational ambidexterity is widely regarded as a key concept referring to an organization’s capability to engage simultaneously in both exploration and exploitation. In this context, an ambidextrous firm is not only capable of identifying external barriers but can also effectively reallocate its resources to provide appropriate solutions, thereby securing its competitive advantage as a market adapter (Rahman et al., 2026). In other words, this concept is defined as the organization’s ability to pursue different—and often competing—strategic actions simultaneously. Evidence suggests that ambidextrous organizations not only respond effectively to conflicting demands but also achieve superior performance (Annosi, 2024). Furthermore, organizational ambidexterity refers to situations in which firms, under conditions of environmental volatility and uncertainty, simultaneously seek to combine incremental and radical innovation practices—namely exploitation and exploration (Adler, 2009). This construct helps organizations maintain strategic agility by aligning with the current environment while adapting to potential turbulence (Clauss et al., 2021). According to Duncan (1976), businesses seeking the successful adoption of creative ideas and long-term profitability must adopt a dual structure based on exploration and exploitation. Consequently, organizational ambidexterity creates a favorable environment for developing structures that promote seemingly contradictory yet complementary perspectives (Mankgele, 2023). However, achieving organizational ambidexterity requires the institutionalization of social and environmental practices through corporate social responsibility (CSR), as well as the establishment of an ecosystem based on idea exchange and decentralized structures, namely knowledge management infrastructure. Such conditions ensure that knowledge does not remain restricted and that employees feel comfortable sharing new ideas, which ultimately leads to organizational innovation and enhanced sustainable performance (Martínez‑Falcó et al., 2023). Sustainable performance represents a combination of environmental, economic, and social performance that not only benefits the natural environment and society but also generates economic advantages and long‑term competitive benefits for firms (Mokbel Al Koliby et al., 2024). To enhance this type of performance, organizations strengthen their ambidexterity by fostering innovations that enable the simultaneous exploration and exploitation of knowledge. While exploitation‑based innovations improve efficiency and economic profitability, exploration‑based innovations can develop new practices that enhance social and environmental well‑being, thereby improving the triple‑bottom‑line dimensions of sustainable performance (Martínez‑Falcó et al., 2023). Although international studies have examined the constructs of this research (e.g., Martínez‑Falcó et al., 2023), a review of the literature reveals a lack of empirical evidence regarding the mediating role of organizational ambidexterity within this causal chain in the context of online building‑materials supply platforms in Iran. This issue remains largely overlooked in the literature of this industry—an industry characterized by unique features such as high operational risk, the need for specialized information, and close interaction with contractors. Therefore, the present study, for the first time, re‑examines this model within Dr. Tamin Company, the first online platform for supplying building materials in Iran, with the aim of validating prior findings and enriching the literature on organizational survival strategies under different economic conditions and contexts. Addressing this contextual gap and localizing the CSR and knowledge management infrastructure model within the online building‑materials supply industry highlights the necessity and novelty of the present study. Accordingly, the main research question is formulated as follows: Do corporate social responsibility and knowledge management infrastructure have a positive and significant effect on the sustainable performance of Dr. Tamin Company, considering the mediating role of organizational ambidexterity? Theoretical Framework Organizational Ambidexterity Organizational ambidexterity is considered a relatively recent concept in organizational management studies (Mankgele, 2023). In the literature on strategic management and corporate entrepreneurship, an organization’s ability to simultaneously leverage the benefits of two seemingly contradictory behaviors is referred to as ambidexterity (Hill & Birkinshaw, 2014). The term was first introduced by Duncan in 1976 to describe the dual nature and structural tensions within organizations. According to Duncan, in order to achieve long‑term success, firms must adopt dual structures aligned with the different stages of the innovation process (Duncan, 1976). Corporate Social Responsibility (CSR) As the world faces increasingly complex social challenges such as malnutrition, poverty, and environmental degradation; companies are being called upon to address these issues. Such organizational engagement with social concerns is most commonly examined through the concept of corporate social responsibility (CSR) (Hill & Birkinshaw, 2014). CSR encompasses the simultaneous achievement of economic, environmental, and social outcomes (Annosi, 2024). This concept represents a set of ethical, legal, and business principles that guide organizations not only toward meeting their economic needs but also toward improving the well‑being of the society in which they operate (Akbari et al., 2023). In essence, CSR entails a series of voluntary actions undertaken by companies to enhance the economic and social welfare of the local communities in which they conduct business (Martínez‑Falcó et al., 2023). Knowledge Management Infrastructure Knowledge management refers to the systematic and coordinated creation, sharing, and application of knowledge to enhance innovation and generate added value within organizations (Naji, 2025). This process is increasingly supported by technological solutions—commonly referred to as knowledge management systems—which are employed to streamline organizational activities (Zamanifard et al., 2025). Corporate Sustainable Performance In the era of globalization, the emphasis on corporate sustainability has grown significantly (Shahzad et al., 2020), and organizations have become increasingly aware of the importance of sustainable performance. Today, many companies have committed to adopting more comprehensive performance criteria to evaluate their operations. In this regard, the most comprehensive and effective tool for assessing a company’s performance is the set of sustainable performance indicators, which simultaneously emphasize social, environmental, and economic dimensions (Mankgele, 2023). Research Methodology The present applicable study employs a descriptive‑survey design. The statistical population consisted of 300 managers and employees of Dr. Tamin Company in 2024, from which 168 individuals were selected as the sample by simple random sampling and Cochran’s formula. The data collection instrument was a standardized questionnaire comprising 4 variables, 11 components, and 49 items, all measured on a five‑point Likert scale. Research Findings In accordance with recent methodological literature (Henseler et al., 2015), the HTMT (Heterotrait–Monotrait Ratio) criterion was employed to ensure the absence of spurious correlations between constructs. The obtained HTMT values for all construct pairs were below the threshold of 0.85, confirming strong discriminant validity in the research model. Since all T‑statistics exceeded 1.96, all five direct research hypotheses were supported. Accordingly, corporate social responsibility and knowledge management infrastructure both exert positive and significant effects on organizational ambidexterity and sustainable performance. Additionally, organizational ambidexterity itself significantly influences sustainable performance. The presence of this causal chain necessitates examining indirect effects. Results showed that the direct effect of corporate social responsibility on sustainable performance (0.513) and its indirect effect through organizational ambidexterity (0.238) were both significant (T = 5.12). Since the direct effect remains significant and the confidence interval of the indirect effect does not include zero, organizational ambidexterity plays a partial mediating role in this relationship. Consequently, the total effect of corporate social responsibility on sustainable performance was calculated as 0.751. Findings also indicated that the direct effect of knowledge management infrastructure on sustainable performance (0.439) and its indirect effect through organizational ambidexterity (0.216) were significant (T = 3.95). Given the continued significance of the direct effect and the absence of zero within the confidence interval of the indirect effect, the partial mediating role of organizational ambidexterity in this relationship was likewise confirmed. In this pathway, the total effect of knowledge management infrastructure on sustainable performance was determined to be 0.655. Conclusion The present study aimed to examine the impact of corporate social responsibility and knowledge management infrastructure on corporate sustainable performance, considering the mediating role of organizational ambidexterity in Dr. Tamin Company. Based on the research findings, all research hypotheses were supported. The results are consistent with evidence reported in previous studies, including Martínez‑Falcó et al. (2023), Zaragoza‑Sáez et al. (2023), Mankgele (2023), Tabatabaeian et al. (2022), Restuputri et al. (2024), Elashry and Ali (2024), and Shahzad et al. (2020). In light of the findings and with the aim of improving firms’ sustainable performance and strengthening their ambidextrous capabilities, several practical recommendations are proposed. First, educational courses and seminars on organizational ambidexterity and sustainable performance should be organized for entrepreneurs, business owners, and employees in order to familiarize them with the positive outcomes of ambidexterity and enhance their ambidextrous capabilities. Second, establishing, developing, and promoting corporate social responsibility programs that focus on community needs, environmental protection, and employee well‑being can be highly constructive. Such initiatives may include environmental projects, support for education and local community development, and improvements in working conditions. Third, organizations should promote a culture that reinforces social and environmental values and encourages employees to participate in CSR programs, as such a culture can contribute to improved innovation and productivity. To address the limitation related to sample size and to enhance statistical power, future studies are recommended to test this conceptual model using larger statistical samples (above 200 or 300 participants). This would increase the statistical power of structural equation modeling analyses and improve the model’s sensitivity in detecting weaker relationships among variables.

Abstract Abstract
This study aims to examine the experiences and influential factors shaping young people’s transitions from traditional employment to freelancing. The statistical population consists of 28 Iranian experts in the field of freelancing. Data were collected through semi‑structured interviews using purposive sampling and continued until theoretical saturation was reached. The qualitative method applied in this study was thematic analysis based on Braun and Clarke’s standard framework, and coding and analysis were conducted using Atlas.ti software.
The analysis of findings revealed four strategic dimensions that provide a comprehensive perspective for understanding freelancing:
(1) push factors and decision‑making in career transition;
(2) significant economic and financial challenges during the transition process;
(3) skill development and professional networking; and
(4) economic empowerment outcomes for youth.
The results indicate that transitioning from traditional employment to freelancing is a complex and multilayered process requiring precise and purposeful planning, strong social and professional network development, and the establishment of supportive governmental and political environments. The comprehensive strategic framework presented in this study can serve as a practical and evidence‑based guide for policymakers, entrepreneurs, and stakeholder organizations—including chambers of commerce and labor unions—to more effectively support young individuals during this career transition and to improve conditions for the development of freelancing.
Introduction
Over the past decade, a transformative shift—both morphological and structural—has emerged in the global labor market, placing independent entrepreneurship and freelancing at the forefront as key solutions. In Iran, as the knowledge‑based economy, modern digital technologies, and internet‑driven economic infrastructures rapidly expand and evolve (Khodaparast & Emadi, 2025), young people are increasingly and noticeably gravitating toward freelancing, autonomous work, and self‑directed employment, considering these pathways as preferred career choices.
These new forms of employment and career transitions, despite offering advantages such as flexibility and autonomy, are accompanied by complex and profound challenges that pose difficulties both for young individuals and for the global economy (Yang et al., 2024). Income instability, lack of access to conventional employment benefits such as health insurance and unemployment insurance, high levels of economic vulnerability, and the absence of formal support structures are among the most significant challenges (Lu et al., 2024).
Accordingly, the central research question is formulated as follows: How does the transition of Iranian youth from traditional employment to freelancing lead to economic empowerment?
Theoretical Foundations
Independent Entrepreneurship
Independent entrepreneurship, or freelancing, refers to a modern approach to business and autonomous work in which an individual, as an independent entrepreneur, undertakes various projects for multiple clients. In this model, the individual operates as a self‑directed professional, performing diverse tasks for different and numerous clients (Baitenizov et al., 2025; Öberg, 2018).
Career Path
Various perspectives exist regarding career paths, each emphasizing one or several dimensions of occupational development. Holland’s Theory of Career Choice highlights the alignment between an individual’s personality and their work environment, proposing that people are naturally drawn to occupations that match their personality types—such as realistic, investigative, artistic, social, enterprising, or conventional. This theory conceptualizes career choice primarily as a voluntary and individually driven process based on person‑environment fit; however, it pays limited attention to external pressures and involuntary career transitions.
Economic Empowerment
Economic empowerment is a broad and multi-layered concept that refers to the process by which individuals acquire and activate the ability, authority, and capacity to plan, make independent decisions, and control their own economic resources (Golara et al., 2025; Saffari Darberazi et al., 2025).
Research Methodology
This qualitative study employed the thematic analysis method within the standard framework developed by Braun and Clarke (2006), utilizing the Atlas.ti software. A qualitative approach was selected for this research to gain an in-depth, internal understanding of the lived experiences, motivations, emotions, and meanings held by participants (Clarke & Braun, 2021). This established framework for thematic analysis has garnered over 20,000 academic citations to date and represents a scientific and systematic method for identifying semantic patterns and latent themes within qualitative data (Foroozanfar et al., 2025).
The study utilized purposive sampling. The statistical population comprised individuals holding at least a bachelor’s degree and currently engaged in freelance work. In this research, the criterion for concluding the sampling process was the achievement of theoretical saturation, as defined within Braun and Clarke’s (2006) thematic analysis framework. The sampling process unfolded iteratively and in parallel with the coding phase. Following approximately 22 initial interviews, no significant new codes were added to the initial coding structure, and the primary themes began to repeat. Subsequently, conducting 6 additional interviews provided richer data and new contextual examples but did not yield any novel conceptual codes or themes, indicating theoretical saturation at the level of the main themes.
Research Findings
Through the analysis of 28 interviews conducted with the target population and audience, followed by a textual analysis of the interview transcripts, 11 initial themes were identified. In aggregate, these sub-themes represent four main dimensions, illustrating that the transition from traditional employment to freelancing among Iranian youth is not a simple linear process. Instead, it is a chain of transitions where push factors and liberating motivations, in conjunction with economic and institutional challenges, are navigated through skill and network development. This ultimately leads to outcomes of relative empowerment or [outcome not specified in text]. Consequently, freelancing in this study is understood not merely as an opportunity nor solely as a threat, but as a dynamic and conditional field whose quality of experience is contingent upon a specific combination of individual, network, and structural circumstances.
Discussion and Conclusion
The results of this research indicate that the transition from traditional, conventional employment to freelancing for Iranian youth is a complex, multi-layered, and arduous process that demands considerable attention, meticulous and purposeful planning, and substantial support. The key findings are as follows: Firstly, diverse and varying push factors are significant—negative push factors (fear of instability, dissatisfaction) are as important as positive ones (freedom, income). Secondly, considerable economic challenges, such as income instability and the lack of benefits, represent fundamental dilemmas requiring innovative solutions. Thirdly, essential social capital, along with strong professional and social networks are crucial for success and economic empowerment. Fourthly, balanced economic empowerment and genuine self-sufficiency necessitate a combination of financial independence, a sense of control, and economic security.
In comparison to previous research, the findings of this study regarding the importance of networking underscore the vital and fundamental role of social capital in freelancing. These results corroborate Golara’s (2025) research on the role of “weak ties” and professional networks in job seeking. Furthermore, our findings concerning the essential role of social and professional networks, the importance of continuous skill development and learning, and the fear and concern regarding income instability show significant similarities with studies conducted in other countries (Baitenizov et al., 2019; Morris et al., 2025). However, fundamental differences also exist. It can be argued that in Iran, negative push factors are stronger, whereas in developed countries, positive factors are more prevalent.
The findings of this research have important implications for policymakers and support organizations, presented below based on the study’s outcomes:
Based on the findings and themes from the push factors and decision-making dimension, a significant portion of youth transitioning to freelancing stems from dissatisfaction with the rigid structures and bureaucracy of traditional employment. Therefore, it is recommended that governmental and private organizations revise their human resource policies. By developing flexible work models (remote work, flextime, project-based work) and reducing unnecessary procedures, they can mitigate some of the push pressures. This would help transform freelancing from an option often driven by compulsion or escape from structure into a conscious and strategic choice.
Based on the themes from the economic challenges dimension, considering the fundamental role of income instability and the lack of benefits in undermining the economic empowerment of freelancers, it is proposed that policymaking institutions such as the Ministry of Labor, the Social Security Organization, and the Chamber of Commerce move towards designing insurance and support mechanisms tailored for freelancers. This includes flexible health and retirement insurance and adaptable premiums based on actual income, thereby partially reducing the gap between occupational freedom and social security.
Furthermore, findings based on the skill development and networking dimension indicated that success in freelancing depends on a package of technical, soft, and business skills, as well as robust professional networks. Consequently, it is recommended that universities, skill training centers, and science and technology parks design and implement comprehensive freelancing preparation courses, including training in negotiation, project management, personal marketing, and professional networking skills. They should also foster the development of social capital for freelancers by creating networking spaces, such as internal events and platforms.
Also, based on the themes from the economic empowerment outcomes, given that economic empowerment is experienced as relative and conditional by participants, it is suggested that policymakers and freelancing platforms develop indicators to monitor the quality of freelance livelihoods, including relative income stability and access to social support. Based on these indicators, programs to enhance the welfare and job security of freelancers should be designed, ensuring that the transition to freelancing systematically leads to improved well-being rather than merely a change in the form of insecure work.
Based on the results of this research, suggestions for other researchers interested in the field of freelancing are presented as follows: conducting longitudinal studies to track the career paths of youth over longer periods; examining the potential of freelancing for women and conducting specific research on the unique barriers faced by female freelancers; analyzing the role of new technologies, artificial intelligence, and automation on the future of freelancing; investigating public sector support policies; conducting cross-country comparative studies to benchmark and assess efficient and diverse freelancing methods; and examining the social effects of freelancing on family and social relationships.

Abstract Abstract The aim of this study is to explore artificial intelligence in the automotive industry through a bibliometric analysis, systematic review, and identification of future research horizons. In this research, 179 international articles indexed in the Web of Science and Scopus databases were analyzed. The methodology consisted of two stages: first, a bibliometric analysis was conducted by VOSviewer software to identify thematic clusters in the literature related to artificial intelligence and the automotive industry. Second, a qualitative systematic review was carried out to provide deeper insights into these clusters. The findings revealed a significant focus on autonomous vehicles, deep learning, and machine learning as key artificial intelligence technologies. Four thematic clusters were identified: AI-based automotive ecosystems, core AI technologies and security, connectivity and resource management, and advanced vehicle technologies. In particular; safety, resource optimization, and legal frameworks emerged as key areas within these clusters. This study identified four emerging research areas that will play important roles in shaping the future of artificial intelligence in the automotive industry and provide transformative opportunities to address existing knowledge gaps. Introduction The automotive industry, which stands at the forefront of technological revolution, is undergoing a profound transformation through the integration of artificial intelligence (AI) (Gao & Bian, 2021; Nascimento et al., 2020). The role of AI as a key driver of innovation, efficiency, and safety in this industry has become increasingly evident, as vehicles are no longer merely means of transportation but are evolving into intelligent entities capable of perception, decision‑making, and communication (Li et al., 2019). This transformation heralds a new era in the transportation sector in which AI‑enabled vehicles provide enhanced safety, improved efficiency, and superior user experiences (Demlehner et al., 2021; Naz et al., 2022). The convergence of the automotive industry and artificial intelligence has led to the rapid expansion of AI‑based solutions and applications, including autonomous driving, predictive maintenance, and advanced driver‑assistance systems (ADAS) (Li et al., 2023). These innovations have significantly improved road safety, optimized energy consumption, and redefined transportation solutions (Banerjee et al., 2023; Mehta et al., 2024). The successful implementation of AI in this industry promises technological advancement, economic growth, environmental sustainability, and improved social welfare (Dumitrascu et al., 2023; Shahedi et al., 2023). From advanced driver‑assistance systems to in‑vehicle voice recognition technologies, AI‑driven technologies have enhanced vehicle performance, safety, and comfort (Mehta et al., 2024). Moreover, consumer expectations for AI‑enabled features in vehicles have increased, prompting automotive companies to invest more heavily in research and development (Demlehner et al., 2021). The market for artificial intelligence in the automotive industry continues to expand, reflecting the growing recognition of its transformative potential (Jain & P. Kulkarni, 2022). Recent review studies have examined the use of artificial intelligence techniques, such as machine learning and deep learning, to address various challenges in the automotive industry. For instance, Damaj et al. (2021) focused on the application of AI in vehicle maintenance and diagnostics, proposing AI‑based models for fault detection, prediction of remaining useful life, and maintenance of automotive components. Bibliometric analysis quantitatively maps publication trends and identifies key patterns and influential research areas, while systematic review provides a deeper examination of thematic clusters and conceptual developments (Van Eck & Waltman, 2017). This combined approach offers a more comprehensive understanding of the field and helps researchers and practitioners navigate the evolving landscape of artificial intelligence in the automotive industry more effectively. Therefore, the main research question of this study is: How can artificial intelligence in the automotive industry be explored through bibliometric analysis, systematic review, and the identification of future research horizons? Theoretical Framework Artificial Intelligence Artificial intelligence enables the transformation of data into information, information into knowledge, and knowledge into intelligent action, thereby facilitating the development of decision‑support systems in conditions characterized by uncertainty and intense competition. From a theoretical perspective, artificial intelligence can be considered a strategic resource within the frameworks of the Resource‑Based View (RBV) and dynamic capabilities theory, through which organizations enhance their capacity for analysis, innovation, and responsiveness to environmental changes (Mahmood, 2023). Hayatmehr et al. (2026) examined the impact of the application of artificial intelligence and intelligent learning on the strategic thinking skills and academic performance of management students, considering the moderating role of individual ethics. The results indicated that the use of artificial intelligence tools has a positive effect on strategic thinking (including systems thinking, creative thinking, future‑oriented thinking, and critical thinking), intelligent learning, and academic performance. The mediating role of strategic thinking—particularly critical thinking and future‑oriented thinking—was confirmed in the relationship between the use of artificial intelligence and academic performance, as well as between intelligent learning and academic performance. Furthermore, individual ethics not only positively influences intelligent learning but also plays a moderating role in the relationship between intelligent learning and strategic thinking (systems thinking, critical thinking, and future‑oriented thinking). This study provides novel insights into the application of artificial intelligence tools in developing strategic thinking skills and performance, offering valuable implications for researchers, managers, students, and organizations. Zolghadr et al. (2025) investigated the development of a model for the application of artificial intelligence in the export of electronic industry products. The findings showed that all composite reliability indices were above 0.7 and the convergent validity values for most constructs exceeded 0.5. The results of the hypothesis testing also indicated that all relationships among the model’s constructs were fully supported at a significance level of p < 0.001. Moreover, to evaluate the overall model fit and measure the structural model, the Goodness‑of‑Fit (GoF) index was applied. The GoF value was reported as 0.815, indicating a strong model fit. Research Methodology In this study, 179 international articles indexed in the Web of Science and Scopus databases were analyzed. The research methodology consisted of two stages. First, a bibliometric analysis was conducted by VOS viewer software in order to identify thematic clusters within the literature related to artificial intelligence and the automotive industry. Subsequently, a qualitative systematic review was carried out to provide deeper insights into these identified clusters and to further interpret the main research trends and developments in this field. Research Findings To analyze the findings, VOS viewer software was employed to identify thematic clusters within the literature related to artificial intelligence and the automotive industry. The results revealed a significant concentration of research on autonomous vehicles, deep learning, and machine learning as core artificial intelligence technologies in the automotive domain. Four major thematic clusters were identified: AI‑based automotive ecosystems Core AI technologies and security Connectivity and resource management Advanced vehicle technologies Within these clusters, safety, resource optimization, and regulatory frameworks emerged as particularly prominent and influential areas. The findings indicate that research in this field is increasingly moving toward integrated, intelligent, and sustainable mobility systems. Furthermore, the study identified four emerging research domains that are expected to play a critical role in shaping the future trajectory of artificial intelligence in the automotive industry. These areas provide transformative opportunities to address existing knowledge gaps and to advance both theoretical development and practical implementation in AI‑driven automotive systems. Conclusion The present study was conducted with the aim of exploring artificial intelligence in the automotive industry through a bibliometric analysis, systematic review, and identification of future research horizons. The findings are consistent with prior studies (Hayatmehr et al., 2026; Zolghadr et al., 2025; Ahmadi Alinoudehi et al., 2025; Heidariyan et al., 2025; Haghighi, 2024; Rahimi Klor et al., 2024; Hasan & Ojala, 2024; Akbari Emami et al., 2023; Etemadi et al., 2023; Neethirajan, 2023), confirming the expanding strategic role of artificial intelligence across industries. In particular, Hasan and Ojala (2024) demonstrated that AI management contributes to improved resource reconfiguration, reduced transaction costs, and the advancement of global sustainable development. These findings reinforce the view that artificial intelligence is not merely a technological tool but a transformative strategic capability within the automotive ecosystem. Moreover, legal concerns surrounding the development of AI‑driven vehicles require further scholarly attention. For instance, in the event that an autonomous vehicle causes an accident, critical questions arise regarding liability—whether it lies with the manufacturer, the software developer, or the vehicle owner. Existing legal frameworks may struggle to keep pace with rapid technological advancements. Given the diversity of regulatory systems across countries, this issue poses additional challenges for automotive companies operating in multiple jurisdictions. Therefore, aligning legal regulations with technological progress in the automotive industry represents a significant and promising avenue for future research.

Abstract Abstract The present research aims to systematically identify and refine the components of audience-centricity in the field of art. This study, employing a sequential exploratory mixed-methods approach, was executed in two main stages. In the qualitative stage, using the qualitative content analysis method and purposeful sampling, 19 credible scientific articles were deeply reviewed. The coding process, utilizing NVIVO software, resulted in the extraction of 39 open codes, 9 axial categories, and 4 core components. In the quantitative stage, to refine these components within the specialized context of art, the fuzzy screening technique was employed, utilizing the opinions of 12 experts. Data were collected and analyzed using a researcher-made questionnaire. The research findings indicated that out of the initial 39 indicators, 11 were confirmed as the final components of audience-centricity in art. Among these, three indicators—“Investing in Continuous Learning and Capability Development,” “Respecting Privacy and Ethics in Data Usage,” and “Transparency in Actions and Communications”—received the highest importance scores. This framework, by transitioning from “production-centric art” to “trust-centric art,” opens a new horizon for sustainable and meaningful interaction between artists, cultural managers, and audiences in the face of future uncertainties and complexities. Introduction In today’s hyper-competitive world, brands strive to increase their share of their target customers’ minds, their share of customers’ wallets, and ultimately, a larger percentage of customer lifetime value (Faraji et al., 2025). Within the marketing toolkit of these brands, audience-centricity and the precise and deep identification of the needs, desires, and behaviors of target customers are considered among the most important effective tools for achieving these brand objectives. Target audiences in different industries possess distinct and unique characteristics. Due to the special nature of artistic products and considering artists as personal brands, the target audience in art differs significantly from that of non-artistic brands. This includes brands active in the art scene such as gallerists, curators, or art exhibition organizers. Understanding these audiences and meeting their needs and desires requires a different perspective. On the other hand, in the contemporary world, art, as a dynamic and multidimensional phenomenon, is constantly evolving in interaction with social, cultural, and technological contexts. One of the key pillars of this transformation is the shift in the “audience’s” position from a passive element to an active agent, interpreter, and participant in the process of creating and perceiving the artwork (Wald-Fuhrmann et al., 2026). The main issue here is that the prevailing paradigm in the scientific examination of audience behaviors, attitudes, desires, and needs is still influenced by the paradigms governing marketing science in conventional manufacturing or service industries, and it does not precisely consider the fundamental differences within the art domain. This is while traditional survey methods and static analyses are no longer sufficient for the needs of brands active in the field of art. The necessity of revising the indicators of audience-centricity is increasingly felt due to the diversity in message reception and the mismatch between source encoding and receiver decoding within the context of art (Sampah et al., 2025). Accordingly, the present paper seeks to answer two main questions: First, what are the components of audience-centricity through in-depth exploration and content analysis of credible scientific texts and articles? And second, which of these components hold greater importance by employing the fuzzy screening technique and expert opinions in the field of art? Theoretical Foundations Ontological Transformation of the Concept of Audience; From Mass Passivity to Active Subjectivity The concept of the audience, throughout the history of communication theories, has transformed from a mere receiver to a central actor (Ghorbannezhad et al., 2024). In classical and linear perspectives, the audience was positioned at the end of the communication chain, playing a passive role in message reception. However, with the emergence of new paradigms, the audience is considered an active subject who selects and interprets the work based on their needs, cultural backgrounds, and lived experiences (Abdou, 2025). Audience-Centricity as a Strategic Paradigm in the Communications Age Audience-centricity, in its broadest sense, is not merely a marketing technique; rather, it is a philosophical paradigm whose core is the transition from “production-centricity” to “human-centricity.” This approach is based on the principle that true value lies not in the commodity or message itself, but in the experience and meaning created for the end-receiver (Hegen, 2017). Audience-Centricity in the Specialized Arena of Art; Aesthetic and Economic Layers In the realm of art, audience-centricity acquires deeper dimensions that distinguish it from other fields. This concept in art is analyzed at three levels: the aesthetic-philosophical level, which emphasizes the “semantic openness” of the work, allowing the audience to complete the work through their own interpretation (James, 2024); the communicative-experiential level, which focuses on the quality of interaction and the creation of inclusive pathways for understanding art, where participatory and interactive art transforms the audience’s presence into an essential element of the work (Kasiyan, 2019); and the economic-organizational level, which is shaped by new models such as “crowdfunding” and the “sharing economy,” turning the audience into a creative and material stakeholder of the work (Ersöz, 2015) Research Methodology The present research is philosophically based on the pragmatism paradigm, emphasizing the applicability of knowledge. This research is developmental in terms of its objective and exploratory-analytical in nature, conducted with a mixed-methods approach of the exploratory sequential type. In the first step, dedicated to identifying components (qualitative part), the qualitative content analysis method with an inductive approach was used to extract the components of audience-centricity. The statistical population for this section included all documents, scientific records, and research articles published in reputable domestic and international databases within the timeframe of 2015 to the present, addressing topics of audience and art. The sample consisted of 19 articles selected using purposive sampling. Data analysis was performed using NVIVO software through three stages of open, axial, and selective coding. In the second step, component screening (quantitative part), the aim was to refine the components identified within the specialized context of art, utilizing the fuzzy screening technique. The statistical population for this step comprised art experts, including university faculty members, distinguished artists, and senior cultural managers. A sample of 12 individuals was selected using snowball purposive sampling and analyzed via a researcher-made fuzzy screening questionnaire. The content validity of the questionnaire was reviewed by professors and 5 prominent experts (outside the main sample). Furthermore, for reliability in the fuzzy section, instead of Cronbach’s alpha, the method of agreement and consensus among expert groups was used. Research Findings A qualitative content analysis, through an in-depth study of 19 articles, resulted in the extraction of 39 initial open codes. These codes were key sentences, concepts, or phrases that directly or indirectly referred to dimensions of audience, interaction, technology, economy, or the future of art. In the axial coding stage, these 39 codes were categorized into 9 general themes based on semantic and conceptual similarities. The results of the fuzzy screening indicated that out of the initial 39 indicators, 11 components were selected as the “golden components” of audience-centricity in art. The three components “Investing in continuous learning and capability development,” “Respecting privacy and ethics in data usage,” and “Transparency in actions and communications” received the highest degree of importance from the experts. Additionally, the component “Personalization of product features” received the lowest level of importance, suggesting a different approach to audience-centricity in art compared to other fields. Discussion and Conclusion The findings of this study, beyond merely presenting a refined list of indicators, propose a profound paradigm shift in the philosophy of contemporary art: a transition from an instrumental view of the audience toward an “ontological co‑creation.” Within this emerging framework, the audience is neither a “target” for marketing nor even a simple “participant,” but becomes an inseparable component of the very being of the artwork. The fuzzy refinement of the initial 39 indicators into 11 golden indicators—ultimately distilled into three core criteria (“investment in continuous learning,” “data ethics and privacy,” and “transparency”)—outlines a roadmap in which the future of art is fundamentally tied to trust as its most valuable intangible asset. This implies that, in the artistic ecosystems of tomorrow, the success of an artwork will not be measured by the number of viewers or sales, but by the depth of the existential bond formed between the artwork and its audience within an ethical and transparent environment. While the results of this study reinforce and validate previous research, they also open new theoretical horizons. The alignment between the final fuzzy‑filtered components and the existing literature can be analyzed at three levels: First, the confirmation of foundational theories such as market orientation (Wut et al., 2026) and value co‑creation (Ranjan & Upadhyay, 2025). Indicators such as “understanding the needs and preferences of the target audience” and “designing for multisensory and immersive experiences” directly emphasize the need for systematic information gathering and the creation of unique experiences—fully consistent with Ersöz (2015) regarding participatory arts and the stimulation of the five senses. Second, the extension of these theories within the domain of art. For example, the finding of Bruen et al. (2025) regarding the influence of audience awareness of AI technologies on the valuation of artworks appears in this study as the highly significant indicator of “utilizing digital platforms and social media”—reflecting the acceptance of technology not merely as a tool, but as a component of the artwork’s ontology. Third, and most importantly, the study introduces innovative ethical‑strategic indicators. While domestic research (e.g., Fallah et al., 2023) emphasized “message source credibility” and “avoiding biased perspectives,” fuzzy screening in this research highlights indicators such as “investment in continuous learning,” “respect for privacy and ethical data use,” and “transparency in actions and communications” as golden priorities. These findings not only bridge gaps in previous fragmented case studies (such as Leow & Ch’ng, 2021 in virtual reality, or Mokhtari Dehkordi, 2023 in multimedia art), but—drawing on theories of the experience economy (Esagala & Ntale, 2026) and relationship marketing (Madruga et al., 2024)—provide a comprehensive and integrative framework applicable across all artistic fields. A key point is the strong alignment with Wald‑Fuhrmann et al. (2026) regarding the influence of performance formats on audience experience, as well as Zuo & Au’s (2026) theory of “media richness,” where the highly significant indicator “offering products/services through multiple channels (physical, online, hybrid)” reinforces the necessity of multi‑modal artistic engagement in the digital era. Thus, by combining qualitative content analysis with fuzzy logic, this study not only consolidates previously scattered elements into a coherent system but also advances beyond existing theories by introducing data‑driven ethics and continuous learning as foundational pillars of audience‑centricity—thereby offering a roadmap for transitioning from mass‑oriented art to trust‑oriented art in the future.

Abstract Abstract The purpose of this study is to present a performance evaluation management model for the internal supply chain applying a hybrid approach of DEMATEL and the Analytic Network Process (ANP). In terms of purpose, the research is applicable, and regarding data collection it is descriptive–survey in nature. The statistical population of the study consisted of 12 experts, including 5 members from the hospital’s internal logistics unit and 7 managers from Namazi Hospital. To determine the relationships among 13 indicators within the five main components of the hospital’s internal supply chain, the DEMATEL method was employed. These relationships were subsequently incorporated into the Analytic Network Process model, and the weights of the 13 indicators were calculated by Super Decisions software. The indicators were ranked as follows: performance of completing patient treatment through access to comprehensive treatment resources, delay‑free patient treatment performance, patient waiting time, service provider supply time, professional value‑added productivity, error‑free performance in patient safety, continuity of the clinical treatment supply process, reliability and accessibility of suppliers of healthcare service resources, communication with patients, overall logistics cost management in the clinical treatment supply chain, reliability of investment and growth and revenue of the clinical treatment process, improvement of hospital staff skills to prevent errors, and development of organizational culture. The results of this study indicated that the hospital’s internal supply chain has a positive, direct, and significant impact on improving hospital performance. The findings of this research can be highly useful for optimizing managerial practices and supporting effective decision‑making among hospital managers and administrators. Introduction Supply chain management refers to the coordination and management of a complex network of activities aimed at delivering the final product to the end consumer or customer. It is a critical function in business operations and includes processes such as the procurement of raw materials, manufacturing and assembly of products, order registration and tracking, distribution through various channels, and ultimately delivery to customers. The structure of a company’s supply chain consists of external suppliers, internal organizational operations, external distributors, and customers (Glavas et al., 2018). Compared with traditional management approaches that focus on managing individual members separately, supply chain management emphasizes managing relationships and seeks solutions to reduce supply, production, and distribution cycles while increasing flexibility and responsiveness. This approach aims to improve existing processes and facilitate the development of new products capable of meeting continuously changing customer demands. To achieve this, organizations need to adopt and implement supply chain technologies and information systems in order to reduce supply chain cycles and integrate their operational processes (Talaei & Ziaian, 2022). Christmann and Taylor (2012) state that exporting and selling to international customers are two primary drivers for improving firms’ environmental performance. When companies export their products, they often face environmental or “green” requirements, which can motivate manufacturing firms to implement supply chain management practices. In many countries, exporting products requires obtaining specific certifications demonstrating that the use of these products does not harm the sustainable development of the destination country. In addition, consumer pressure also encourages organizations to adopt supply chain management practices. The effects of drivers for implementing supply chain management can be evaluated through three dimensions of firm performance: environmental performance, operational performance, and economic performance (Zoogah, 2011). Environmental performance has become increasingly important for managers of manufacturing companies due to regulatory requirements, public awareness, and the pursuit of competitive advantage. Recent studies have provided insights into potential patterns of supply chain relationships that can enhance environmental performance. However, investment in such programs can be substantial, and the pressure for environmental investment may be high, while improvements in environmental performance resulting from the adoption of these practices are not always guaranteed (Kim et al., 2019). Therefore, considering the growing importance of environmental supply chains and the performance of internal supply chains, as well as the increasing attention these topics have received in recent years and their associated benefits, this study investigates the indicators affecting internal supply chain performance. Accordingly, the main research question addressed in this study is: **What is the model for managing the performance evaluation of the internal supply chain using a hybrid approach of DEMATEL and the Analytic Network Process (ANP)? Theoretical Framework The supply chain is an essential network within the business infrastructure significantly facilitating production and distribution processes in companies that market their products. This network plays a vital role in creating numerous employment and professional opportunities for many individuals (Zinati et al., 2025). Nouri et al. (2025) examined the impact of supply chain integration dimensions on new product innovation performance through supply chain agility in Pegah Pasteurized Milk Company in the northwest region of the country. The results of the study, using path analysis techniques, indicate that internal integration, customer integration, and supply chain integration have a significant effect on supply chain agility. Furthermore, supply chain agility, market knowledge, and product development significantly influence product innovation capability. In addition, internal integration, customer integration, and supply chain integration significantly affect product innovation capability through the mediating role of supply chain agility. Zinati et al. (2025) also investigated the design of a maturity model for the Fourth Industrial Revolution in the banking service supply chain and the development of digital banking using a grounded theory approach. According to the findings, the identified categories included ten categories in the causal conditions, seven categories as components of the core phenomenon, four categories related to contextual conditions, eleven categories in the strategies section, seven categories representing intervening conditions, and finally eight categories in the outcome section of the model. Research Methodology In terms of its objective, this research is applicable, and in terms of data collection, it is descriptive–survey in nature. The statistical population of the study consisted of 12 experts, including five specialists from the internal logistics unit of the hospital and seven managers from Namazi Hospital. Research Findings To determine the relationships among the 13 indicators of the five main components of the hospital’s internal supply chain, the DEMATEL method was employed. The identified relationships were then incorporated into the Analytic Network Process (ANP) model, and the weights of the 13 indicators were calculated by Super Decisions software. The ranking of the 13 indicators, in order of priority, was as follows: completion of patient treatment through full access to medical resources; on-time treatment performance without delay; patient waiting time; service provision time; professional value-added productivity; error-free performance in patient safety; continuity of the clinical treatment supply process; reliability and accessibility of medical service resources; communication with patients; overall logistics cost management in the clinical treatment supply chain; reliability of investment, growth, and revenue of the clinical treatment process; enhancement of hospital staff skills to prevent errors; and development of organizational culture. The results of this study indicate that the hospital’s internal supply chain has a positive, direct, and significant impact on improving hospital performance. The findings provide valuable insights for hospital managers and decision-makers in optimizing management practices and enhancing overall operational effectiveness. Conclusion The present study was conducted with the aim of proposing a model for performance evaluation management of the internal supply chain employing a hybrid approach of DEMATEL and the Analytic Network Process (ANP). The findings of this research are consistent with the results of previous studies conducted by Nouri et al. (2025), Zinati et al. (2025), Mirshekar et al. (2024), Alizadeh (2024), Mousavi (2023), Samiei et al. (2023), Amozadeh et al. (2023), Davoodi and Sazgari (2022), Haddadzadeh and Motwalian-Bafeghi (2022), Zhang et al. (2022), Jetton (2022), and Dachyar and Mahendra (2020). Nouri et al. (2025) investigated the impact of supply chain integration dimensions on new product innovation performance through supply chain agility in Pegah Pasteurized Milk Company in the northwest region of the country. The results, obtained using path analysis techniques, showed that internal integration, customer integration, and supply chain integration have significant effects on supply chain agility. Furthermore, supply chain agility, market knowledge, and product development significantly influence product innovation capability. In addition, internal integration, customer integration, and supply chain integration significantly affect product innovation capability through the mediating role of supply chain agility. Based on the findings of the present study, several recommendations are proposed. Since hospital supply chain processes must be aligned with clinical care processes and supporting processes, it is necessary to consider all these aspects of performance simultaneously within clinical care processes, process efficiency support, and patient safety. Priorities should be determined according to the calculated weights of the indicators. Identifying key performance aspects enables supply chain managers to focus on the most critical factors when seeking to improve hospital performance.

Abstract Abstract The purpose of this study is to present a model for the typology of consumer behavior in response to packaging messages in the Iraqi market. In terms of purpose, this research is a developmental study; in terms of data collection, it is a survey; and in terms of nature, it is exploratory. The study was conducted quantitatively employing structural equation modeling (SEM). The statistical population consisted of managers, owners, marketing and production managers, as well as customers of these products in Iraq. Based on Cochran’s formula, a sample size of 476 participants was selected through simple random sampling. Data were collected by a questionnaire. Data analysis was performed by SPSS 27 and SmartPLS 4.0 software. The findings indicated that packaging color has a significant effect on environmental awareness, packaging retention, social influence, and packaging value choice. Environmental awareness also affects social influence and packaging value choice. Furthermore, packaging imagery influences packaging value choice, while packaging retention also has a significant effect on packaging value choice. In addition, quick information scanning influences environmental awareness, packaging retention, social influence, and packaging value choice. Information-seeking behavior also affects packaging value choice, and social influence has a significant impact on packaging value choice. Introduction The dynamic and highly competitive conditions of today’s business environment have compelled companies to differentiate their products from those of their competitors. A fundamental element of this environmental instability is consumers, who have become significantly different from the past and increasingly seek personal development within their social environment. Changes in lifestyle and the expansion of individual skills among the general public have raised the level of awareness regarding the content and characteristics of consumer goods. Consequently, companies can successfully sell products only when they properly understand and respond to changes in consumer behavior (Yokokawa et al., 2021). Packaging is considered the outermost layer of a product, and consumers often evaluate both the product and its contents based on it. If the packaging aligns with their needs and behavioral preferences, they are more likely to make a purchase (Shukla et al., 2022). Packaging is one of the essential elements of a product and refers to the container that comes into direct contact with the product, holds and protects it, provides relevant information about it, and facilitates transportation and commercialization (Afif et al., 2022). Industry analysts suggest that the visual attractiveness of packaging design plays a crucial role in influencing customer decision‑making at the retail level. Brands typically have only about seven seconds to create a positive impression before customers move on to the next option. As retailers increasingly recognize the impact of point‑of‑purchase decisions, they invest heavily in packaging design (Shukla et al., 2022). Studies indicate that consumers have certain expectations of successful packaging that contribute to its sustainable competitiveness compared with rival products and its survival in the turbulent market environment (Habib et al., 2023; Dilucia et al., 2020). A careful analysis of these studies suggests that customer expectations and behaviors when encountering packaging can be categorized into two main groups: technical and environmental criteria. Technical criteria relate to the physical, mechanical, chemical, or biological properties of packaging and the product itself. These include dimensions, weight, strength, durability, compatibility, stability, permeability, biodegradability, and similar attributes. Environmental criteria, on the other hand, concern the impact and interaction of packaging and products with the natural and social environment. These include energy consumption, greenhouse gas emissions, waste generation, recycling potential, toxicity, safety, and regulatory compliance. In other words, consumers increasingly demand packaging that is sustainable and friendly to both human health and the environment. In response to these expectations, companies often conduct packaging tests and evaluations to ensure that their products meet these requirements. Accordingly, the present study seeks to answer the following research question: What is the appropriate model for the typology of consumer behavior in response to packaging messages in the Iraqi market? Theoretical Framework Product Packaging Packaging, often described as a silent salesperson, represents the final and frequently the most critical point of contact between a brand and a potential consumer before a purchase decision is made. It serves as the container of brand identity, a canvas for communicating brand promises, and a tangible interface within the otherwise intangible world of marketing messages. Through its visual and informational elements, packaging conveys product characteristics and brand values while simultaneously shaping consumer perceptions. While marketing literature is rich with studies examining the influence of individual packaging elements—such as color, typography, or material—on consumer perception and purchase intention, a significant gap remains in understanding how consumers respond to the holistic gestalt of packaging as distinct behavioral types (Moniri Moghadam, 2022). In other words, most existing studies focus on isolated design components, whereas fewer investigations explore how the combined set of packaging messages collectively influences different patterns of consumer behavior. Consumer Behavior Consumer behavior refers to the decisions made by consumers regarding the consumption and use of goods, services, time, and ideas by human decision‑making units. Therefore, entrepreneurs and businesses must identify the goods and services that consumers need. In particular, firms should focus on understanding the actions consumers take when purchasing products and the factors that influence their buying and consumption behavior. Several key factors affect consumer behavior, including cultural, social, personal, and psychological factors (Baskabadi & Rasti, 2021). Dhawan and Garga (2025) examined the influence of demographic variables on consumer preferences for purchasing electronic products through online and offline channels. Their findings indicate that younger consumers with higher disposable incomes tend to prefer online shopping, whereas older individuals and lower‑income groups rely more on offline retail stores. Furthermore, factors such as trust, convenience of purchase, pricing, product variety, and product information play significant roles in shaping consumer choices. The study highlights the importance for businesses to adopt a multichannel strategy that integrates the strengths of both online and offline platforms. Marketers can utilize these insights to design targeted campaigns, improve customer experience, and enhance consumer engagement. Amirhosseini and Najafi (2025) investigated the impact of packaging elements of Donic sports products on consumers’ purchasing behavior. The results showed that packaging materials and packaging design significantly influence the post‑purchase behavior of consumers of Donic sports products, explaining approximately 7 percent of the variance in consumers’ pre‑purchase behavior. Research Methodology In terms of purpose, this study is developmental; in terms of data collection, it adopts a survey method; and in terms of nature, it is exploratory. The research was conducted by a quantitative approach and structural equation modeling (SEM). The statistical population consisted of managers, owners, marketing and production managers, as well as customers of these products in Iraq. Using Cochran’s formula, a sample size of 476 participants was determined and selected through simple random sampling. The primary instrument for data collection was a questionnaire. Research Findings Data analysis was conducted by SPSS 27 and SmartPLS 4.0 software. The results of the structural equation modeling indicated several significant relationships among the study variables. The findings revealed that packaging color has a significant effect on environmental awareness, packaging retention, social influence, and packaging value choice. Additionally, environmental awareness significantly influences both social influence and packaging value choice. The results further showed that packaging imagery has a significant impact on packaging value choice, and packaging retention also significantly affects packaging value choice. Moreover, quick information scanning significantly influences environmental awareness, packaging retention, social influence, and packaging value choice. In addition, information‑seeking behavior has a significant effect on packaging value choice, and social influence itself significantly affects packaging value choice. Overall, the findings highlight that both visual and informational packaging cues, along with cognitive and social factors, play a crucial role in shaping consumers’ packaging value selection in the Iraqi market. Conclusion The present study aimed to propose a model for the typology of consumer behavior in response to packaging messages in the Iraqi market. The findings of this study are consistent with the results reported by Premendra (2024), Su and Wang (2024), Hallez et al. (2023), Ketelsen et al. (2020), Mahmoud et al. (2022), Liu et al. (2025), Charif Hamdar (2018), Oloyede and Lignou (2021), Boz et al. (2020), Zhang et al. (2023), Chirilli et al. (2022), Mokha (2018), Nguyen et al. (2020), Herbes et al. (2020), Seo and Scammon (2017), Zhao et al. (2021), Liang et al. (2024), and Miao et al. (2025). Premendra (2024) demonstrated that packaging color, particularly when influenced by children’s preferences, can affect parents’ purchasing decisions, illustrating a form of family‑based social influence. Based on the findings of this study, the following recommendation is proposed: managers should avoid overloading packaging with dense textual information that requires active investigation by consumers. Instead, resources should be focused on visual and easily scannable cues, which can simplify consumer decision‑making while also reducing printing costs and maintaining effectiveness in fast‑paced retail environments.

Abstract Abstract The purpose of this study was to develop a qualitative model of innovation in modern banking services based on customer knowledge management. The research was applicable‑developmental in purpose, and qualitative in method, employing thematic analysis due to its exploratory nature. To gather and select relevant scientific literature, the Scopus, Web of Science, and Google Scholar databases were used. In the first stage, an extensive search was conducted across these databases, yielding 198 articles. After carefully reviewing the titles, abstracts, and keywords; 32 eligible articles were selected for coding and qualitative analysis. Data analysis was carried out by MAXQDA software through coding processes and theme extraction. The findings of the thematic analysis revealed that the qualitative model of innovation in modern banking services based on customer knowledge management is structured around five key elements: intelligent technological infrastructures and capabilities, customer knowledge management processes, innovation in the design and delivery of banking services, value co‑creation and knowledge‑driven customer interactions, and environmental dynamics and institutional requirements for banking innovation. Together, these elements form a coherent framework for developing innovative banking services and can serve as a practical foundation for banking managers and decision‑makers seeking to enhance customer experience, strengthen competitive advantage, and achieve sustainable innovation. Introduction In recent years, technological advancements and increasing competition in the banking industry have driven financial institutions toward leveraging both explicit and tacit customer knowledge to create added value and deliver differentiated services (Ahinful et al., 2024). Customer knowledge management; with its focus on collecting, analyzing, and utilizing customer data and experiences, provides the foundation for developing innovative services that can effectively meet the growing demands of the market (Tseng et al., 2021). The rise in customer expectations and the growing complexity of their needs have compelled banks to move beyond traditional service delivery (Aspiranti et al., 2023). Under such conditions, customer‑knowledge‑based innovation can serve as an effective tool for predicting behavioral patterns, uncovering hidden needs, and identifying individual customer preferences. This approach not only enhances banks’ competitive capabilities but also enables the development of highly customized, value‑added services (Diener & Spacek, 2021). Applying customer knowledge management is especially crucial for banks seeking to strengthen long‑term relationships with their clients (Ahmadi & Safari, 2022). It contributes to increased loyalty, satisfaction, and a deeper understanding of customer needs. Being able to design new services based on customer data analysis, the banks can achieve sustainable competitive advantage in the digital banking landscape (Al‑Dmour et al., 2020). When banking services are designed based on accurate customer insights, the user experience becomes smoother, faster, and more efficient. This enhances customer satisfaction and ultimately strengthens public trust in the banking system. With the growing complexity of the competitive environment, service innovation in banking has reached a level at which success is unlikely without a deep understanding of customer needs, preferences, and expectations (Yasmeen et al., 2019). Digital banking services, mobile banking, artificial intelligence, and big‑data analytics are among the areas whose development has been significantly shaped by knowledge gained through customer interactions (Stefanelli & Manta, 2022). In the Iranian banking system, competitive pressures, technological changes, and rising customer expectations have motivated banks to move toward designing innovative services (Hasanvand et al., 2022). However, challenges persist regarding the accurate identification of customer needs, analysis of customer knowledge, and translation of this knowledge into effective innovation (Grassi et al., 2022). Therefore, based on the above discussions, the main research question is as follows: What is the qualitative model of customer‑knowledge‑based innovation in modern banking services? Theoretical Framework Concept and Nature of Service Innovation As one of the key components in enhancing organizational competitive advantage, service innovation refers to the processes of creating, developing, or improving services that can provide greater value to customers. Unlike product innovation—which primarily focuses on tangible features—service innovation emphasizes the intangible aspects of services and the quality of interactions between the organization and its customers (Yang et al., 2024). Service innovation involves developing solutions that enhance the customer experience, address customer needs more accurately, and increase the efficiency of service delivery processes (Nguyen & Harrison, 2018). Customer Knowledge Management As one of the most important strategic approaches in modern organizations, customer knowledge management focuses on collecting, organizing, analyzing, and utilizing customer‑related knowledge to create added value and enhance the organization’s competitive capability. This concept goes beyond mere data collection and involves a deep understanding of customers’ needs, preferences, behaviors, and experiences (Bratianu et al., 2022). Jafari Dehkordi et al. (2026) examined the identification of customer‑knowledge‑management factors with a digital‑marketing approach in the banking system and proposed a model using thematic analysis. The findings indicated that customer knowledge management with a digital‑marketing perspective in banking must include elements such as customer knowledge, knowledge‑management capabilities, the implementation of digital banking marketing, and digital innovation training and development. Ultimately, these results were categorized into 12 basic themes, 4 organizing themes, and one overarching theme. It can be concluded that customer knowledge management in the banking system is a vital process that, through digital marketing methods, offers effective communication and superior value to customers. This approach not only improves the customer experience but also enhances bank–customer relationships and improves business performance. Through this approach, banks can achieve sustainable and competitive growth in the digital era. Moeini et al. (2025) investigated the impact of modern banking services on customer attraction and the improvement of banking system efficiency in Maskan Bank branches in Abhar. The results showed that modern banking services increased customer satisfaction in these branches, but did not lead to improved operational efficiency (reduced staff time and costs). Research Methodology Based on its purpose, this study is applicable–developmental; in terms of execution method, it is qualitative using thematic analysis approach; and is exploratory in nature. To collect and select relevant scientific literature, the databases Scopus, Web of Science, and Google Scholar were utilized. In the first stage, an extensive search was conducted across these databases, resulting in the identification of 198 articles. After careful examination of the titles, abstracts, and keywords; 32 articles were deemed eligible for coding and qualitative analysis. Research Findings The data analysis process was conducted using MAXQDA software through stages of coding and theme extraction. The results of the thematic analysis revealed that the qualitative model of customer‑knowledge‑based innovation in modern banking services is built upon five key elements. These elements include intelligent technological infrastructures and capabilities, customer knowledge management processes, innovation in the design and delivery of banking services, value co‑creation and knowledge‑driven customer interactions, and environmental dynamics and institutional requirements for banking innovation. Together, these elements interact to form a cohesive framework for developing innovative banking services and can serve as a practical foundation for banking managers and decision‑makers to enhance customer experience, strengthen competitive advantage, and achieve sustainable innovation. Conclusion The present study was conducted with the aim of proposing a qualitative model of customer‑knowledge‑based innovation in modern banking services. These results are consistent with the findings of Wang and Xu (2018), Salunke et al. (2019), Manohar et al. (2019), Zahrawi et al. (2025), Abrar et al. (2022), Bhat and Gupta (2024), and Al‑Dmour et al. (2022). The findings indicated that achieving sustainable innovation in banking services requires alignment among advanced technologies, effective knowledge‑management processes, customer‑centric service design, bidirectional interaction with customers, and consideration of environmental and institutional conditions. These factors not only create a foundation for generating added value but also enhance organizations’ ability to respond to rapid market changes and rising customer expectations. The results show that the combination of these five key elements can shape a coherent strategic framework for banks—one that simultaneously strengthens service personalization, improves customer experience, and increases organizational flexibility. Based on the findings of this study, it is recommended that banks implement the following practical actions to reinforce customer‑knowledge‑based innovation in modern banking services: First, by investing in integrated customer information systems, digital infrastructures, and intelligent platforms, banks should enable real‑time collection and analysis of customers’ transactional and behavioral data. By leveraging big data and artificial intelligence, they can generate applicable insights for designing personalized services. Simultaneously, customer knowledge‑management processes should be operationally strengthened; for example, customer data and knowledge should be systematically stored, categorized, and shared, enabling different organizational teams to use this knowledge in strategic and operational decision‑making.

Abstract Abstract The purpose of this research is to present a localized model for brand authenticity in the healthcare industry. Initially, through a systematic literature review and qualitative content analysis using NVIVO software, 15 main factors of brand authenticity were identified in this study. In the next step, these components were localized for the Iranian healthcare services context using the Delphi method and surveying academic and industry experts (including 12 individuals), resulting in the extraction of 12 final factors (including transparency and honesty, brand heritage, existential authenticity, and brand social responsibility). The findings indicate that consistency between the stated values and the actual performance of healthcare organizations is the primary basis for the perception of authenticity in the minds of patients. This research, by focusing on the localized components, provides a scientific framework for managers of organizations active in the health sector to foster patient network trust and enhance the quality of the treatment experience by fundamentally shifting decision-making from an intuitive state. Introduction In the last decade, the healthcare industry has faced fundamental changes in how it interacts with stakeholders. Today’s patient is no longer a passive consumer; rather, through digital media and online feedback, they actively participate in evaluating healthcare services (Araújo et al., 2025). This transformation has paved the way for the emergence of “brand authenticity,” a construct referring to the patient’s perception of the healthcare organization’s honesty, transparency, and alignment between its stated values and reality (Apriyana et al., 2024). The necessity of studying brands active in the healthcare industry stems from the fact that brand authenticity in this sector is shaped by factors that are objective, behavioral, and observable. These factors rely more on the brand’s actual conduct, credible backing, and direct interaction with patients rather than on symbolism, claims, or historical narratives (Farzinmehr et al., 2025). Such a model indicates that patient trust is formed when a brand can demonstrate its authenticity in practice, not merely in discourse or mental imagery. Furthermore, in high-risk service environments like healthcare, patient trust is not only a prerequisite for treatment but also a key variable in the quality of the patient experience (Ghaffar et al., 2025). Despite the importance of this construct, scientific research on the precise role of brand authenticity in the healthcare industry is limited and scattered. Many managers, due to the lack of a localized framework, operate based on intuitive perceptions. This research seeks to answer the main questions: What are the scientifically-based factors of brand authenticity? And which of these factors, with an emphasis on trust-building, should be prioritized in the healthcare industry? Theoretical Foundations Brand Authenticity In contemporary marketing literature, brand authenticity is defined as a psychological and strategic construct that extends beyond the technical quality of services. This concept pertains to stakeholders’ perceptions of the alignment between the brand’s declared values and the organization’s actual behaviors (Hyun et al., 2024). Authenticity, in its true sense, means being genuine, honest, and avoiding hypocrisy. It is achieved when a brand exhibits consistent performance over time and adheres to its promises. This characteristic imbues brands with a unique identity that is inimitable (Dedeoğlu et al., 2019; Crisci, 2024). Customer Trust Trust, as a key construct in relationship marketing, reflects the sense of security a consumer experiences when interacting with a product or service (Bae & Kim, 2023). This construct is formed on the perception that the brand is reliable and responsible for the customer’s interests and well-being. In essence, trust is a form of consumer belief and reassurance that they will be served in the long term to meet their needs and interests, acting as a lever of credibility for repeat purchases (Bhattacharjee, 2025). In a study by Deng et al. (2025) titled “Exploring the Role of Brand Authenticity on Customer Loyalty in Sport Brands,” it was found that all dimensions of brand authenticity (real-to-ideal, real-to-reality, and real-to-self) have significant positive effects on brand attachment, trust, and loyalty. In research conducted by Anubha & Roy (2025) titled “Explaining the Role of Consumer-Perceived Brand Authenticity in the Context of Masstige Brands,” findings indicated that all three dimensions of authenticity (including brand heritage, commitment to quality, and sincerity) influence customer purchase intention through the mediating variable of trust. In a study by Kim et al. (2021) titled “Examining Consumer-Based Brand Authenticity in the South Korean Coffee Shop Market,” it was found that the dimensions of commitment to quality, heritage, and sincerity have significant impacts on brand credibility, ultimately leading to strengthened brand loyalty. Another study titled “Investigating the Impact of Green Brand Storytelling on Authenticity and Trust” was conducted by Huang & Guo (2021). The results showed that rhetorical strategies (such as symbolism) enhance narrative immersion, which in turn positively influences perceived brand authenticity and trust in the brand. Research Methodology Regarding its research philosophy, this study is situated within the pragmatic paradigm, which relies on a subjectivist approach. In terms of objectives, this research is developmental with an inductive approach; and in its nature, it is considered an exploratory-analytical study. From a methodological perspective, this paper employs a mixed-methods research design, utilizing a qualitative content analysis strategy to identify factors, and the Delphi technique for the localization of these components. The statistical population for this study is defined in two parts. The first part comprises articles related to brand authenticity. For the second part, intended for localization, the population included industry experts and academic faculty members with over 15 years of experience, possessing expertise in the core concepts of the research, as well as a history of publishing interdisciplinary research in business and healthcare; 12 of whom were selected by purposive sampling. Qualitative content analysis was conducted using open and axial coding in NVIVO 12 software, while the Delphi data analysis, including the calculation of means and the assessment of consensus, was performed using Excel. Research Findings The findings of this study led to the identification of 15 initial factors, which were ultimately localized and finalized into 12 key factors influencing brand authenticity within the context of Iran’s healthcare industry. These factors include transparency and sincerity, uniqueness, brand authenticity protection, adherence and commitment to customers, brand distinctiveness, alignment with brand identity, existential authenticity, sustainability and corporate social responsibility (CSR), enduring brand heritage, core brand values, rich background and history, and strategic participation and engagement with customers. Discussion and Conclusion The results indicate that in the healthcare industry, brand authenticity is not merely a marketing construct, but an ethical and strategic foundation for building networked trust and improving the treatment experience. Organizations that successfully embed these components into their identity and operations can, under current complex conditions (including sanctions and international limitations), foster domestic trust, serve as an effective substitute for foreign brands, and achieve a sustainable competitive advantage. This is consistent with studies by Anubha & Roy (2025), and the brand heritage component is also emphasized in the study by Kim et al. (2021). Furthermore, the results of this research underscore the importance of transparency as a dimension of brand authenticity in building patient trust; the study by Dehghan et al. (2022), consistent with our findings, emphasizes the role of transparency and brand storytelling in strengthening authenticity. The path of this study—redefining the position of brand authenticity and establishing it as the cornerstone of patient trust—elevates the trust-oriented perspective from the level of individual interactions (physician-patient) to the level of identity and macro-behavior of healthcare organizations. Specifically, this research highlights components such as uniqueness, commitment to customers, brand distinctiveness, and customer engagement, which have received less attention in other studies, as constituent elements of brand authenticity in this industry. Existential authenticity refers to the internal authenticity of a brand, reflecting a commitment to its unique essence and origin. This factor is vital in creating deep and lasting trust in patients, as the brand must consistently and genuinely demonstrate and defend its values. Behavioral consistency, another component emphasized in this study, refers to the degree of harmony, continuity, and integrity of a brand’s behaviors, decisions, and actions over time and across all touchpoints with stakeholders. This concept indicates the extent to which a brand acts in accordance with its stated identity, values, and promises without experiencing fluctuations or behavioral contradictions. This factor serves as a powerful tool for increasing brand credibility in patients’ minds, particularly in therapeutic contexts where a high level of patient trust is required. Based on these findings, the following recommendations are provided to managers of organizations active in the healthcare industry: Developing a Charter of Transparency and Sincerity: Healthcare organizations should formalize and implement an official charter based on transparency in treatment, financial, and communication processes. Publishing periodic performance reports and patient feedback can be an effective step toward increasing public trust. Developing Brand Identity Based on Authentic Values: Managers should institutionalize brand authenticity in patients’ minds by redefining their brand identity based on core values (e.g., social responsibility, commitment to quality, and organizational heritage) and aligning all communications and services with this identity. Creating Strategic Engagement Mechanisms with Patients: Designing digital and in-person platforms for active patient participation in treatment decisions and service improvement can increase the sense of belonging and trust. Training and Empowering Human Resources: As brand ambassadors, employees must receive continuous training in professional ethics, transparent communication, and behavior based on authenticity. Utilizing Authentic Narrative and Storytelling: Utilizing real, human-centric narratives regarding the organization’s history, treatment successes, and social initiatives can strengthen the perception of brand authenticity. Continuous Monitoring and Evaluation of Authenticity Indicators: Establishing a periodic monitoring system based on the 12 identified factors and gathering feedback from patients and stakeholders will assist managers in continuous improvement. Turning Brand Authenticity into a National Competitive Advantage: At the macro level, policymakers can help differentiate and elevate the status of domestic providers by introducing authenticity-based accreditation marks (e.g., “Authentic Health Brand”).

Abstract Abstract The aim of this study was to design an optimization model for the coexistence of artificial intelligence and human capital in the provision of urban services in the municipalities of Sistan and Baluchestan Province. This research was conducted in two phases: qualitative and quantitative. In the qualitative phase, document analysis, interviews, and the Delphi technique were employed with the assistance of 20 experts to identify key dimensions and develop the initial model. The software used in this phase was MAXQDA. In the quantitative phase, data were collected from 300 employees of the municipalities of the province through a researcher-made questionnaire, the validity and reliability of which were tested and confirmed, and the data were analyzed through Structural Equation Modeling (SEM). The findings indicated that the final research model demonstrated a satisfactory fit and comprised six main constructs, including contextual factors, artificial intelligence components, human capital components, mediating factors, coexistence strategies, and desirable outcomes. The strongest relationship in the model was observed between contextual factors and artificial intelligence components, with a path coefficient of 0.98. This study provides an indigenous model for optimizing the collaboration between humans and artificial intelligence in the municipalities of underprivileged regions. Introduction The rapid growth of artificial intelligence in the public sector has created unprecedented opportunities to improve the delivery of urban services. However, the successful implementation of artificial intelligence is not merely a technical challenge; rather, it represents a profound organizational transformation that critically depends on effective interaction with human capital (Wilson & Daugherty, 2018; Wirtz et al., 2019). In many developing regions, this challenge is intensified by infrastructural deficiencies, skill gaps, and unique socio‑cultural contexts. Municipalities in Sistan and Baluchestan Province of Iran face specific difficulties, including dispersed populations, severe financial constraints, a shortage of digitally skilled personnel, and inadequate communication infrastructure (Jafarinia et al., 2021; Deputy for Management Development and Resources of the Governorship of Sistan and Baluchestan Province, 2022). Preliminary observations indicate that although there is willingness to adopt artificial intelligence, employees often fear job displacement, and the existing level of digital literacy is significantly lower than the national average (Planning and Budget Organization of Sistan and Baluchestan Province, 2023). Moreover, general artificial intelligence models developed for metropolitan areas are often unable to account for local knowledge, cultural nuances, and geographical dispersion. Despite the growing body of literature on artificial intelligence in smart cities (Sun & Medaglia, 2019; Singh & Shah, 2025), most studies assume the presence of strong infrastructure and well‑prepared human capital, leaving a significant gap regarding context‑dependent and resource‑constrained environments. Therefore, the necessity of the present research lies in developing a localized model that promotes a symbiotic (co‑evolutionary) relationship, rather than a substitutive one, between artificial intelligence and human employees. The main objective is to design an optimal coexistence model that enables municipalities in a less‑developed province to benefit from artificial intelligence while simultaneously empowering their existing workforce. Theoretical Framework In this study, three complementary theoretical frameworks are integrated. First, Becker’s Human Capital Theory (Becker, 1964) posits that knowledge, skills, and experience are productive assets that generate economic and organizational returns. In urban management, human capital—including technical skills, creativity, organizational commitment, and awareness of local culture—constitutes a prerequisite for successful digital transformation (Pahlavani Rad, 202). Second, the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) explain technology adoption through the constructs of performance expectancy, effort expectancy, social influence, and facilitating conditions—factors highly relevant to the adoption of artificial intelligence in public services. Third, the framework of Collaborative Intelligence proposed by Wilson and Daugherty (2018) moves beyond the notion of substitution toward coexistence, where artificial intelligence performs data‑driven tasks while human beings contribute ethical judgment, creativity, and contextual understanding. Recent empirical research confirms that trust, transparency, and algorithmic explainability are essential for such collaboration (Glikson & Woolley, 2020; Raftopoulos & Hamari, 20). Taken together, these theories provide the foundation for a model composed of contextual factors, artificial intelligence components, human capital dimensions, mediating factors, coexistence strategies, and desirable outcomes. Research Methodology This study employed an exploratory sequential mixed-methods design. In the qualitative phase, following a systematic literature review, semi-structured interviews were conducted with 15 participants (senior managers, IT specialists, and AI experts) from the municipalities of Sistan and Baluchestan Province, selected through snowball sampling until theoretical saturation was reached. Subsequently, a three-round Delphi technique involving 20 experts was employed to reach a consensus on the model’s components. Thematic analysis by MAXQDA software led to the extraction of 127 primary codes, 6 main themes, and 18 sub-themes. In the quantitative phase, a researcher-made questionnaire (using a five-point Likert scale) was administered to a simple random sample of 300 municipal employees (drawn from a population of approximately 1,200). The validity of the instrument was confirmed through content validity, convergent validity (AVE > 0.5), and discriminant validity (Fornell-Larcker criterion). Reliability was also verified by Cronbach’s alpha and composite reliability (CR > 0.7). Data were analyzed by SPSS software for descriptive statistics and Smart-PLS software for Structural Equation Modeling (SEM), including bootstrapping to test the significance of path coefficients. Research Findings The structural model demonstrated that all hypothesized paths were statistically significant (p < 0.05), confirming the multidimensional nature of human–AI coexistence in this context. The strongest relationship was observed between contextual factors—including hardware and software infrastructure, managerial support, adequate budget, and ethical considerations—and AI components (decision-making accuracy, reliability, and explainability), with a path coefficient of 0.986. This finding aligns with recent international studies emphasizing that foundational infrastructure and ethical governance are indispensable pillars for AI adoption in smart cities (Ben Dhaou et al., 2024; Thomas et al., 2025). Notably, without managerial commitment and sufficient funding, even well-designed AI systems are likely to fail. Human capital components—particularly awareness of local culture (0.937), technology acceptance (0.797), and problem‑solving skills (0.923)—had strong direct effects on mediating factors (trust in system accuracy, equitable distribution of benefits, and a clear managerial vision) as well as on desirable outcomes. These results confirm previous domestic studies (Nikfarjam & Soleimani, 2023; Karimi & Naderi, 2023) while extending them by quantifying the relative importance of local knowledge, a factor often overlooked in general AI models. In underdeveloped regions, employees’ familiarity with geographical and cultural characteristics becomes a strategic asset that AI cannot easily replicate. Furthermore, coexistence strategies—including intelligent task division, continuous training, and the use of decision-support systems—significantly improved outcomes such as service quality (0.873), citizen trust (0.894), crisis resilience (0.881), and reduced operational costs (0.877). These findings strongly support the collaborative intelligence model (Wilson & Daugherty, 2018) and recent empirical evidence from South Korea and Singapore (Yang & Lee, 2025; Singh & Shah, 2025), where human‑in‑the‑loop designs have enhanced both efficiency and public acceptance. However, this study uniquely demonstrates that in resource‑constrained environments, continuous training and intelligent task allocation are not optional enhancements but core drivers of effective coexistence. Compared with earlier international studies that focused primarily on leading smart cities such as Barcelona and Singapore, this model reveals that contextual factors play a disproportionately larger role in less developed regions. While Ferreira dos Santos et al. (2025) identified inclusiveness and trust as key drivers, the present study adds that budget limitations and deficiencies in basic infrastructure can completely hinder AI initiatives unless they are explicitly addressed. Similarly, although ethical concerns (such as privacy and algorithmic bias) are global issues (Engstrom et al., 2020), in Sistan and Baluchestan Province, the absence of any formal ethical framework emerged as a primary concern raised by experts. From a theoretical perspective, this study proposes a third model beyond the dominant views of “AI as replacement” or “AI as complement.” It provides empirical evidence for coexistence as a distinct, context‑dependent relationship in which AI and human capital co‑evolve through mutual adaptation. From a practical perspective, the final model—comprising six components and 21 indicators—offers an operational roadmap for municipalities facing similar structural and resource constraints. Conclusion Using a mixed-methods approach, this study designed and empirically tested a localized coexistence model for artificial intelligence and human capital in the municipalities of Sistan and Baluchestan Province, Iran. The final structural model demonstrated a good fit (SRMR = 0.07, NFI = 0.91) and confirmed that contextual factors, AI components, human capital, mediating factors, and coexistence strategies collectively generate desirable outcomes. The strongest effects were found, respectively, in the relationship between contextual factors and AI components, and between human capital and coexistence strategies. Accordingly, the model provides a practical and evidence-based framework for optimizing human–AI collaboration in under-resourced urban service environments. The findings showed that achieving an optimal coexistence between artificial intelligence and human capital in the municipalities of Sistan and Baluchestan requires simultaneous attention to technical infrastructure, capable human capital, and cultural–organizational strategies. Domestic studies such as Zamani et al. (2024) and Naseri & Ahmadi (2023) emphasized the role of AI in improving the quality of urban services, including waste management and transportation. Consistent with those studies, the present research found that AI components—namely decision-making accuracy, reliability, and algorithmic transparency—have a direct effect on desirable outcomes. Likewise, the results are aligned with the studies of Nikfarjam & Soleimani (2023) and Karimi & Naderi (2023), which identified training, skills, and employees’ psychological readiness as prerequisites for successful digital transformation. In the present model, human capital, represented by indicators such as awareness of local culture and problem-solving skills, played a central role in technology acceptance. At the international level, the findings are also consistent with Ferreira dos Santos et al. (2025), who identified inclusiveness and trust as major drivers of smart-city system adoption. Similarly, in the present model, trust in system accuracy and the perceived fair distribution of benefits emerged as key mediating factors. Furthermore, the results correspond with the findings of Singh & Shah (2025) and John et al. (2025) regarding the importance of collaborative and trust-based approaches to digital governance.

Abstract Abstract The objective of this research is to design a grounded theory model for the social acceptance of smart home technology (SHT) among Iraqi consumers. This study was conducted based on an interpretive paradigm through a qualitative approach. The statistical population consists of 15 smart home technology experts and university professors specializing in marketing, selected through the snowball sampling method, and participated in semi-structured interviews. The data were analyzed by latent content analysis tactics in MAXQDA 2024 software through three stages: open, axial, and selective coding. The findings led to the conceptualization of several categories, including “acceptance of smart home technology,” “perceptions of trust and security,” “lifestyle integration behaviors,” “economic decision-making,” and “response to social influence,” with seven main and original hypotheses established among them. The results revealed that the core category, “Social Acceptance of Smart Home Technology,” encompasses a multi-faceted construct influenced by cognitive, social, and innovative dimensions. Introduction In recent years, Iraq has faced significant challenges in the energy sector due to international sanctions and the occurrence of multiple wars (Al‑Ghabera et al., 2024). Efficient energy consumption in this country has become highly important and has been introduced by the government as a national priority (Al‑Yozbaky et al., 2022). Smart home technology represents the convergence of the Internet of Things (IoT), artificial intelligence (AI), sensors, and automation systems to create intelligent and responsive living environments that enhance residents’ comfort, security, energy efficiency, and sustainability (Yeoh Lui et al., 2025). In Iraq, where persistent challenges such as frequent power outages, security concerns, water shortages, and rapid urbanization exist, smart home systems offer an appropriate solution for optimizing resource use and adapting to local lifestyles (Mustafa et al., 2025). On the other hand, an examination of Iraq’s demographic composition indicates that the majority of the country’s population consists of young people, many of whom pay particular attention to innovation in their purchasing decisions and tend to prefer products that possess a superior mental image compared with competing alternatives (Ahmed et al., 2020). Furthermore, according to reports from Iraq’s National Housing Sector, approximately 75 percent of Iraqi households are interested in adopting modern and innovative architectural approaches in order to improve their quality of life (Al‑Khafaji & Kamaran, 2019). A cultural analysis of Iraq based on Hofstede’s cultural dimensions shows that the country is characterized by high power distance, a collectivist culture, masculinity, and a high level of uncertainty avoidance (Sardar, 2015). Analytical studies indicate that societies with high power distance often demonstrate lower resistance to smart home technologies, as many households in such cultures are willing to adopt innovative architectural ideas to enhance comfort and welfare (Bao et al., 2014; Wang et al., 2024). Regarding collectivism, researchers argue that positive social attitudes toward smart home technology spread rapidly within society, and once the technology gains collective acceptance, resistance declines and consumers quickly move from early adoption to more rational and widespread use (Liu et al., 2021; Mouzaidis et al., 2023). Analysis of the masculinity–femininity dimension suggests that in more feminine cultures there is a greater tendency to adopt practical and modern technologies such as remote-control systems, graphical user interfaces, and smart electricity technologies (Jeong et al., 2013). Finally, studies on uncertainty avoidance indicate that in such cultures consumers often view new technologies with caution and tend to accept smart home technologies only after they become widely adopted within society (Furszyfer Del Rio et al., 2021; Chang & Chiu, 2025). Accordingly, the present study seeks to answer the following main research question: What is the grounded theory model of social acceptance of smart home technology among Iraqi consumers? Theoretical Framework Social Acceptance of Smart Home Technology Social acceptance refers to the processes through which individuals, groups, and societies approve, adopt, or integrate innovations, technologies, or social phenomena into their norms, behaviors, and institutional structures (Moeremans & Dooms, 2025; Shuhaiber & Mashal, 2019). The theory of social acceptance is not a single unified framework; rather, it is a multidimensional concept that has evolved across different disciplines, particularly in social psychology, energy transition studies, and innovation research. It encompasses psychological needs for belonging, the social validation of innovations, and the institutional transformations required for systemic change (Ji & Chan, 2020; Alshammari & Alkhwaldi, 2025). Norouzzadeh et al. (2025) examined the role of the Internet of Things (IoT) in residential smart home technologies. Their findings indicated that social influence was not a significant factor in the adoption of IoT-based smart home technologies. They attributed this result to habit-related barriers and the level of effort required from users within the studied sample, suggesting that technology acceptance depends more on individual usability than on social norms. Moghbal Naseri et al. (2023) investigated human behavior based on sleep–wake patterns using smart home sensors. In this study, smart home technologies were used to monitor the behavior of elderly individuals who faced physical or psychological challenges and lived independently or with pets. Using data collected from smart home sensors, the daily behaviors of individuals during waking and sleeping periods were predicted and analyzed through computational approaches. The researchers developed an algorithm capable of modeling sleep and wake behavior patterns and enabling behavioral prediction. By analyzing data from the CASAS smart home dataset, this approach was presented as an effective method for improving the ability of individuals with physical or mental limitations to live independently. Research Methodology This study was conducted based on the interpretive paradigm through a qualitative research approach. The statistical population consisted of 15 experts in smart home technology and university professors specializing in marketing. Participants were selected through the snowball sampling method and took part in responding to the interview questions. The data collection tool was semi‑structured interviews. Research Findings The collected data were analyzed by latent content analysis techniques in MAXQDA 2024 software through three stages: open coding, axial coding, and selective coding. The findings led to the identification and development of several key categories, including smart home technology acceptance, perceived trust and security, lifestyle integration behaviors, economic decision‑making, and responses to social influence. Seven main and novel hypotheses were established among these categories. The results indicated that the core category, “Social Acceptance of Smart Home Technology,” represents a multidimensional construct influenced by cognitive, social, and innovation‑related dimensions. Conclusion The present study aimed to design a grounded theory model for the social acceptance of smart home technology among Iraqi consumers. The findings of this research are consistent with previous studies conducted by Moghbal Naseri et al. (2023), Nakhalet al‑Jannah and Ghaffarian (2022), Abbasi (2022), Pirzada et al. (2022), Farah Hussin et al. (2022), Arar et al. (2021), Pal et al. (2021), Gross et al. (2020), Mashal et al. (2020), and Ko et al. (2020). Pal et al. (2021) found that user characteristics, particularly user skills, have the greatest influence on privacy concerns, followed by legal and policy aspects and technological dimensions. In addition, physical and psychological privacy were considered more important than concerns related to informational privacy. Based on the results of the study, the following recommendation is proposed. Managers and stakeholders should recognize the lack of a significant relationship between economic decision‑making and perceptions of trust and security. This insight should encourage them to separate financial incentives from security‑focused campaigns. Instead, they should develop independent educational initiatives that address cost–benefit analysis separately, ensuring that trust‑building efforts are not influenced by perceptions of economic trade‑offs.

Abstract Abstract The aim of this study is to design a model for the implementation of a data‑driven human resource system by means of digital and intelligent tools. In terms of purpose, this research is fundamental; and in terms of implementation method, it is qualitative. The statistical population of the study consisted of 15 experts and specialists, including academic scholars, faculty members, and managers of the Blood Transfusion Organization, selected through purposive and theoretical (judgmental) sampling. The data collection instrument was semi‑structured interviews. MAXQDA software was applied to analyze the findings. The results showed that through axial coding, 44 initial codes were categorized into 22 axial codes, representing the key concepts and structural elements of a data‑driven human resource system. A comprehensive examination of the factors influencing the implementation of a data‑driven human resource system using digital and intelligent tools indicates that this process represents a multidimensional and strategic transformation that requires simultaneous attention to a set of causal factors (5 dimensions), contextual factors (4 dimensions), intervening factors (4 dimensions), strategies (4 dimensions), and consequences (4 dimensions). By presenting an integrated and data‑driven model, this study highlights the role of digital and intelligent tools in improving human resource processes and strategic decision‑making, and it can provide valuable practical and theoretical guidance for organizations in their path toward digital transformation. Introduction In recent decades, organizations have faced complex and rapid changes in both internal and external environments, leading to an increasing need for innovative, technology‑oriented managerial approaches. These changes include factors such as rapid advancements in information technology, the growing volume of organizational data, the increasing complexity of work processes, and the rising demand for fast and accurate decision‑making (Mujtaba, 2025). Traditional human resource systems have largely been based on administrative procedures, experience‑based practices, and limited data collection, which often resulted in incomplete, slow, and reactive decision‑making. These limitations created challenges for organizations in forecasting workforce needs, identifying the strengths and weaknesses of employees, and effectively managing their performance (Iyer, 2025). The emergence of digital technologies, intelligent tools, and advanced data analytics made it possible to collect, process, and analyze large volumes of human resource data (Panahi et al., 2024). Designing and implementing a data‑driven human resource system in organizations requires the accurate identification of key organizational components, analysis of informational needs, and the utilization of modern technologies. Such systems are capable of collecting accurate and up‑to‑date data on employee performance, training processes, motivation and job satisfaction, behavioral patterns, and future organizational needs (Zhang, 2024). Digital and intelligent tools such as Human Resource Information Systems (HRIS), data analytics software, artificial intelligence, and managerial dashboards enable complex analysis and the optimization of organizational processes (Gupta et al., 2025). The application of such tools ensures that decision‑making is no longer based solely on intuition and experience but is grounded in data and rigorous scientific analysis. This is particularly critical in organizations that deal with human health and lives, where managerial decisions can have wide‑ranging direct and indirect impacts on service quality and stakeholder satisfaction (Niu, 2024). Advances in digital technologies and artificial intelligence have created unprecedented opportunities for organizations to collect and analyze extensive and complex workforce data (Niu, 2024). These analyses enable managers to identify trends and hidden patterns within data and make strategic decisions accordingly (Varma & Dutta, 2023). In the field of human resources, such data‑driven approaches can improve processes related to recruitment, training, performance evaluation, and employee retention. The use of intelligent tools such as machine learning, predictive analytics, and human resource management systems facilitates and accelerates processes, enabling organizations to achieve their objectives more effectively and at lower cost (Dehmardeh Kamak et al., 2025). The intelligent use of data enables organizations to identify weaknesses and opportunities for improvement, while also contributing to the development of an organizational culture based on transparency, continuous learning, and innovation. Overall, such a model can serve as a transferable framework for other service and healthcare organizations and provide a practical approach for improving the quality of human resource management at the national level. Accordingly, the main research question of the present study is: What is the model for implementing a data‑driven human resource system using digital and intelligent tools? Theoretical Framework Data‑Driven Human Resource System A data‑driven human resource system refers to a set of procedures, processes, and policies in which decisions related to employee management are based on the collection, analysis, and utilization of empirical data. This system leverages accurate data on employee performance, skills, training needs, and job satisfaction to enable forecasting of workforce requirements and strategic human resource planning. Its primary objective is to enhance productivity, reduce human errors, and improve the quality of organizational decision‑making (Gupta et al., 2025). Digital and Intelligent Tools Digital and intelligent tools refer to software and systems that, through the use of information technology and artificial intelligence algorithms, collect, store, process, and analyze human resource–related data in order to enable data‑driven and goal‑oriented processes for employee recruitment, retention, development, and evaluation (Mujtaba, 2025). Khashi and Pourshahabi (2026) examined the design of a model for optimizing the coexistence of artificial intelligence and human capital in the provision of urban services in the municipalities of Sistan and Baluchestan Province. The findings indicated that the final research model demonstrated a desirable level of fit and included six main constructs: contextual factors, artificial intelligence components, human capital components, mediating factors, coexistence strategies, and desirable outcomes. The strongest relationship in the model was observed between contextual factors and artificial intelligence components, with a path coefficient of 0.98. This study presents an indigenous model for optimizing human–AI collaboration in municipalities located in less developed regions. Tizfahm Fard et al. (2026) investigated the impact of digital technologies on the transformation of human resource management practices and their consequences for employee outcomes. The findings showed that causal factors—including technology‑oriented leadership, managerial support for innovation, and a data‑driven decision‑making culture—play a key role in facilitating the transformation of human resources. Contextual factors such as an organizational learning culture and employees’ level of digital literacy provide the necessary foundation for the successful implementation of digital processes, while financial resource limitations, administrative regulations, and employee resistance function as intervening factors. Strategies such as digital empowerment of employees, interdepartmental collaboration, and the development of technological infrastructure lead to improved productivity, employee satisfaction, and organizational commitment. The study provides a comprehensive theoretical–practical framework through which organizations can effectively and sustainably implement human resource management practices by leveraging digital technologies. Research Methodology In terms of purpose, this study is fundamental; and in terms of implementation method, it is qualitative. The statistical population consisted of 15 experts and specialists, including academic scholars, faculty members, and managers of the Blood Transfusion Organization, selected through purposive and theoretical (judgmental) sampling. The data collection instrument was semi‑structured interviews. Research Findings MAXQDA software was applied to analyze the findings. The results showed that through axial coding, 44 initial codes were categorized into 22 axial codes, representing the key concepts and structural elements of a data‑driven human resource system. A comprehensive examination of the factors influencing the implementation of a data‑driven human resource system through digital and intelligent tools indicates that this process constitutes a multidimensional and strategic transformation that requires simultaneous attention to a set of causal factors (5 dimensions), contextual factors (4 dimensions), intervening factors (4 dimensions), strategies (4 dimensions), and consequences (4 dimensions). By presenting an integrated and data‑driven model, this study highlights the role of digital and intelligent tools in improving human resource processes and strategic decision‑making, and it can provide valuable practical and theoretical guidance for organizations in their path toward digital transformation. Conclusion The present study was conducted with the aim of designing a model for the implementation of a data‑driven human resource system applying digital and intelligent tools. The findings of this study are consistent with the results of Khashi and Pourshahabi (2026), Tizfahm Fard et al. (2026), Mujtaba (2025), Liu (2025), Iyer (2025), Damnjanović et al. (2025), Toghiani‑Pezouh et al. (2025), Dehmardeh Kamak et al. (2025), Mateen et al. (2024), Ajalli et al. (2023), Bahari and Taheri Roozbhani (2023), and Foroutan Eghlidi et al. (2021). Iyer (2025) emphasizes the importance of data security, legal frameworks, and a supportive organizational culture. These factors function like the pillars of a building, ensuring the stability and effectiveness of other components; without them, even the most advanced intelligent tools will not achieve the required level of efficiency. A transparent organizational structure, standardized technological infrastructure, and clearly defined legal and ethical policies enable smooth data flow and reliable analytics. Based on the research findings, the following recommendations are proposed: Design training programs for managers and decision‑makers to enhance their capability in utilizing data and predictive analytics. Invest in data analytics tools, artificial intelligence, and machine learning to improve the accuracy of human resource decision‑making. Identify areas with the greatest need for data‑driven approaches (e.g., talent acquisition, employee retention) and prioritize them accordingly.

Abstract Abstract The aim of this research was to identify the dimensions and components of a service delivery model based on Financial Technology (Fintech) with a customer-centric approach in the Iranian banking system. In terms of purpose, this research is applicable-developmental; and in terms of research method, it is qualitative. The data collection tools consisted of two parts: an investigation and review of research literature in the library section, and semi-structured interviews in the field section. The participants in the field section of this study were managers within the banking system. The selection of participants was conducted through purposive sampling. Semi-structured interviews with the participants continued until the stage of theoretical saturation. The coding process and textual analysis of the interviews were performed via MAXQDA 2020 software. The research findings indicated that the dimensions and components of the presented model include: Technology Infrastructure and Capabilities (digital infrastructure, emerging financial technologies, cybersecurity and privacy, technological integration); Customer Centricity (customer knowledge, customer experience, service personalization, communication and interaction with the customer); Digital Financial Services (diversity of Fintech services and modern tools, digital service quality, service accessibility, innovation in financial services); Banking Structure and Management (digital transformation strategy, organizational innovation culture, social responsibility and banking sustainability); and Institutional and Regulatory Environment (Fintech laws and regulations, oversight and regulation, institutional support). Introduction In an environment where customers are becoming increasingly informed and empowered with choice, neglecting their needs is no longer possible (Doumpos et al., 2024). On the other hand, electronic banking and the development of financial technology (Fintech) are among the most critical contemporary topics garnering attention within the banking industry. Electronic banking, and subsequently Fintech, as one of the most vital infrastructures for e-commerce, can further facilitate the growth and development of electronic trade by streamlining payments and the electronic transfer of funds for internet transactions. Fintech, or financial technology, is a term referring to the innovative application of technology in the delivery of financial services. In simpler terms, Fintech encompasses a set of activities in which financial services are provided with greater ease and speed through the aid of technology (Bakhshayesh Ardestani & Radfar, 2025). As one of the most significant emerging trends in the financial industry, financial technology (Fintech) has successfully transformed the banking structure by providing faster, cheaper, more transparent, and customer-centric services. By leveraging technologies such as Artificial Intelligence (AI), Big Data, Blockchain, Open Banking, and smart applications, Fintech firms have redesigned financial services to match the evolving needs of customers (Karami & Esmaeilpour, 2025). Conversely, to maintain their market position and respond to customer expectations, banks have been forced to adopt new technologies and implement fundamental changes in their business models. Consequently, banks have been at the forefront of technology adoption for the past three decades; as other sectors are influenced by financial technology (Information and Communication Technology), banks—acting as the foundation and prerequisite for development—must synchronize their strategies and policies with these transformations to mitigate upcoming challenges and remain competitive. Undoubtedly, the financial services sector is recognized today as one of the most successful and influential areas of activity globally, and focusing on marketing within financial services will lead to greater productivity and profitability for banks (Li et al., 2023). Therefore, the primary problem of this research is: What are the dimensions and components of the fintech-based service delivery model with a customer-centric approach in the Iranian banking system? Theoretical Framework Banking Service Delivery Modern banks must pay special attention to the quality of their services to remain competitive in today’s turbulent organizational environment. This focus leads to increased customer retention, the acquisition of new customers, and the improvement of financial performance and profitability. In recent decades, intensifying competition among economic enterprises to gain greater market share has driven them toward a more precise and profound understanding of customer needs and desires (Bakhtiari & Yaghoubpour, 2025). Financial Technology (Fintech) Financial technology (Fintech) refers to the innovative application of technology in the provision of financial services. Fintech is an industry within the economic landscape that encompasses companies striving to make financial services more efficient through the use of technology. Firms active in the field of financial technology are generally startups that attempt to establish themselves within financial systems and challenge traditional institutions (Rahman et al., 2024). Customer Centricity Customer centricity is a state in which a business prioritizes a positive customer experience across all stages of the customer journey. A business is considered customer-centric when it recognizes the needs, wants, and preferences of its valued customers and adapts its products and services accordingly (Gordi Baghcheh mishe et al., 2025). Zandi et al. (2026) investigated the provision of a Fintech model in platform companies using an interpretive structural modeling (ISM) approach within the Iranian environmental context. The findings from the interpretive structural modeling revealed that the exploratory research model identifies five main components and twenty sub-dimensions across three hierarchical levels. At the first level, the “Fintech Execution and Implementation” component—comprising the dimensions of current state analysis, scenario design and roadmap, architecture and execution, and monitoring and learning—is situated at the top of the ISM graph as the most influenced and dependent component. The second level includes three components: “Fintech Opportunities and Benefits for Platform Companies” (improving user experience, increasing customer acquisition and loyalty, reducing operational and transactional costs, and developing complementary services within the platform ecosystem), “Challenges and Obstacles” (technical, legal/regulatory, and economic barriers, as well as a shortage of specialist labor), and “Infrastructure and Basic Requirements” (IT infrastructure, open and standard APIs, cybersecurity and data management, and supportive laws and regulatory frameworks). These components are influenced by the third-level component and, in turn, impact the execution and implementation phase. Ally et al. (2025) examined bank efficiency in the digital age, focusing on the role of financial technology in Tanzanian banks. The results of this research indicated that the Fintech index, which measures the development of financial technology in banks, significantly increases efficiency across all banks. It has the greatest impact on large banks due to their advanced financial technology development. However, medium and small banks face challenges in developing financial technology, leading to a negative relationship between the Fintech index and bank efficiency. Research Methodology This research is applicable-developmental in terms of its objective, and qualitative in terms of its research methodology. The data collection tools consisted of two parts: a library-based review and exploration of the research literature, and semi-structured interviews in the field section. The participants in the field phase of the study were managers within the banking system. Participant selection was conducted by purposive sampling. The semi-structured interviews with participants continued until the stage of theoretical saturation was reached. Research Findings The process of coding and textual analysis of the interviews was conducted by MAXQDA 2020 software. The research findings revealed that the dimensions and components of the proposed model include: Technology Infrastructure and Capabilities: (Digital infrastructure, emerging financial technologies, cybersecurity and privacy, technology integration). Customer Centricity: (Customer knowledge, customer experience, service personalization, customer interaction and engagement). Digital Financial Services: (Diversity of Fintech services and modern tools, digital service quality, service accessibility, innovation in financial services). Banking Structure and Management: (Digital transformation strategy, organizational innovation culture, social responsibility, and banking sustainability). Institutional and Regulatory Environment: (Fintech laws and regulations, supervision and regulation, institutional support). Conclusion The present study was conducted with the objective of identifying the dimensions and components of a Fintech-based service delivery model with a customer-centric approach in the Iranian banking system. The results of this research are consistent with the findings of Zandi et al. (2026), Ally et al. (2025), Farmahini Farahani et al. (2025), Kaartti et al. (2025), Schreiber (2024), Rizvi et al. (2024), Sadraee et al. (2024), Moradi et al. (2024), and Kumar (2024). Specifically, Ally et al. (2025) examined bank efficiency in the digital age and the role of financial technology in Tanzanian banks. Their results indicated that the Fintech index, which measures the development of financial technology in banks, significantly increases efficiency across all banks. It has the greatest impact on large banks due to their high level of financial technology development. However, medium and small banks face challenges in developing financial technology, which leads to a negative relationship between the Fintech index and bank efficiency. Based on the research findings, it is recommended that banks pay special attention to the development and integration of Information Technology infrastructure and the enhancement of cybersecurity. They should utilize customer data analytics to personalize services and improve the customer experience, while expanding diversity and innovation in digital financial services. Furthermore, banks should strengthen digital transformation strategies and a culture of innovation within their organizational structures. Finally, through close cooperation with regulatory bodies, they should assist in creating flexible regulatory frameworks that support the development of Fintech within the country’s banking system.

Abstract Abstract The purpose of this research is to present a model for sustainable financial resource provision in Iran’s sports federations. This research was conducted qualitatively using a data-based theory approach. The statistical population of the research included 17 elites in the field of sports management and sports federation management. A semi-structured interview was used as the data collection tool. The grounded theory method was employed for data collection and analysis. Data analysis was carried out in three stages: open coding, axial coding, and selective coding. MAXQDA software version 24 was used for data analysis. The findings indicated that sustainable financial resource provision in Iran’s sports federations, as the core phenomenon, is shaped by factors such as dependence on government budgets, scarcity of sustainable financial resources, and the pressure of regional and global competitions, all within a context of economic limitations, managerial weaknesses, and infrastructural challenges. Factors such as government policies, the role of media, and organizational capabilities moderate the intensity and direction of these efforts. To overcome these challenges, adopting strategies such as diversifying financial resources, commercializing sports, and strengthening the branding of federations is essential. The successful implementation of these strategies leads to outcomes such as financial independence, infrastructure development, and increased competitiveness of sports federations, ultimately ensuring the sustainable growth of the country’s sports. Introduction The strong inclination of society towards sports and the demand for sports goods and services have led the sports industry to experience significant revenue generation and contribute substantially to the economies of nations. This industry, driven by major sporting events, creates opportunities for advertising across various media, thereby establishing the necessary platform for interaction between industry, commerce, and sports (Halkos & Tzeremes, 2013). The macroeconomic impact of sports, such as its contribution to Gross Domestic Product (GDP) and Gross National Product (GNP), as well as its role in job creation, compels countries to accurately and regularly assess sports' economic effects each year (Dimitropoulos & Alexopoulos, 2014). Professional clubs worldwide utilize various methods to secure financial resources and cover expenses, including attracting sponsors for commercial product advertising, player sales, ticket and merchandise sales, and offering ancillary services (Singh et al., 2016). One crucial method for funding sports activities, particularly championship sports, is attracting suitable sponsors by sports organizations and officials. In this regard, creating a healthy and conducive environment for investment by industrialists and the private sector in sports is essential and necessary. Capital owners are interested in establishing sports facilities, providing sports services, producing sportswear and equipment, and organizing profitable competitions because their fundamental goals include achieving global recognition through competitions, offering high-quality services and products, and most importantly, realizing profits through effective marketing (Andreopoulou et al., 2015). The significant presence of the government in professional sports has created challenges for both parties. On one hand, given the severe economic constraints in the country and intervening factors such as oppressive sanctions and threats to people's livelihoods, allocating substantial funds to professional sports is no longer justifiable for the government and the nation as it once was. On the other hand, professional sports, by relying on government and state budgets, has become a dependent phenomenon. Despite the abundant potential for revenue generation, sports clubs in Iran are not only unprofitable for governments and investors but also face fundamental challenges in meeting their own expenses. Therefore, the issue of financial provision, along with the existing tools, methods, and strategies for attracting capital to ensure the dynamic and efficient operation of professional sports, has become more critical than ever. Accordingly, the present research seeks to answer the question: How can a model for sustainable financial resource provision in Iran's sports federations be presented? Theoretical Framework Financial Resource Provision The financial capacity of a profit organization is defined as the ability and potential to develop and deploy financial capital that can be converted into money: revenues, expenses, assets, and liabilities (Hall et al., 2003). From the perspective of sports financing, using the allocation of public resources in the Slovak Republic, according to the study by Kucera & Nemec (2021), four forms of financing can be discussed: assignment taxes; EU budget; budgets of state-owned companies; and budgets of central government, local government, and their parts are used to finance projects that also serve sports activities (indirect public resources). Maleki et al. (2025) investigated the design of a model for innovative financing in the sports industry, focusing on the role of FinTechs. The research results indicated that the empowering drivers for sports startups are capital attraction through smart FinTech, novel revenue models with data analysis, smart contracts in transfers or sponsorships, and the application of blockchain in financial transparency, which are the five influential factors in this regard. The proposed policy recommendations focus on key influential drivers and include supporting infrastructure such as specialized accelerators in the sports and FinTech domain, access to seed capital, tax exemptions for innovators in the sports industry, and encouraging the use of artificial intelligence in sports data analysis. The results of this research can serve as a basis for policymaking to support sports startups, attract venture capital, and develop the digital sports economy. Mokhlesi et al. (2024) examined the factors influencing financing in sports clubs and presented a model based on exploratory analysis. They stated that the increasing societal inclination towards sports and the demand for sports goods and services have led the sports industry to experience significant revenue generation and play a substantial role in the economy of any country. The results show that variables such as holding raffles among spectators, dedicated administrative buildings, financial support from fans, attracting foreign investors, ticket pricing in different tiers, advertising club products and services through media, receiving interest income from banks, using their own commercial licenses for business transactions, and dedicated stadiums are of higher importance. Research Methodology This research was conducted qualitatively using the grounded theory method. The statistical population of the study included 17 experts in the field of sports management and the management of sports federations. The data collection tool was semi-structured interviews. The grounded theory method was used for data collection and analysis. Research Findings Data analysis was performed in three stages: open coding, axial coding, and selective coding. The MAXQDA 24 software was used for data analysis. The findings indicated that the provision of sustainable financial resources in Iran's sports federations, as the core phenomenon, is shaped by factors such as dependence on government budgets, lack of stable financial resources, and the pressure of regional and global competitions; within a context of economic limitations, managerial weaknesses, and infrastructural challenges. Factors such as government policies, the role of media, and organizational capabilities moderate the intensity and direction of these efforts. To overcome these challenges, adopting strategies such as diversifying financial resources, commercializing sports, and strengthening federation branding is essential. The successful implementation of these strategies leads to outcomes such as financial independence, infrastructure development, and increased competitiveness of sports federations, ultimately ensuring the sustainable growth of the country's sports. Conclusion The present research was conducted with the aim of providing a model for sustainable financial resource provision in Iran's sports federations. The results of this research are aligned with the findings of Maleki et al. (2025), Mokhlesi et al. (2024), Varmus et al. (2023), Ahmadi (2022), Guevara et al. (2021), and Ghafouri Yazdi et al. (2021). Mokhlesi et al. (2024) stated that the increasing societal inclination towards sports and the demand for sports goods and services have led the sports industry to experience a significant revenue-generating trend, playing a crucial role in any country's economy. The results indicate that variables such as holding lotteries among spectators, having dedicated administrative buildings, fan financial support, attracting foreign investors, ticket pricing in different tiers, advertising club products and services through media, receiving bank deposit interest, using own commercial licenses for business transactions, and having dedicated stadiums are of higher importance. As the core phenomenon, "The effort to provide sustainable financial resources in Iran's sports federations" is shaped by factors such as dependence on government budgets, lack of stable financial resources, and the pressure of regional and global competitions; within a context of economic limitations, managerial weaknesses, and infrastructural challenges. Factors such as government policies, the role of media, and organizational capabilities moderate the intensity and direction of these efforts. To overcome these challenges, adopting strategies such as diversifying financial resources, commercializing sports, and strengthening federation branding is essential.

Abstract Abstract The present study aimed to investigate the impact of Green Human Resource Management (GHRM) on green behavior, considering the mediating role of environmental awareness and the moderating role of managers’ green commitment in the food industry of Zahedan. In terms of its objective, the research is applicable; regarding its execution method, it is quantitative; and in terms of its nature and methodology, it is descriptive-correlational. Standard questionnaires based on a 5-point Likert scale were applied to collect research data. The content validity of the instrument was confirmed by specialists and experts, and Cronbach’s alpha and composite reliability were applied to assess the reliability of the instrument. Following the distribution of the questionnaires, the validity of the instrument was measured through three methods: construct validity (outer model), convergent validity (AVE), and discriminant validity. The Average Variance Extracted (AVE) value for all variables was estimated to be greater than 0.5. SPSS and SmartPLS 3 software were applied for data analysis. The results of structural equation modeling by SmartPLS software indicated that Green Human Resource Management, environmental awareness, and managers’ green commitment all play an effective role in strengthening employees’ green behaviors and advancing the organization’s environmental goals. Furthermore, environmental awareness as a mediating factor and managers’ green commitment as a moderating variable facilitate and reinforce the path toward institutionalizing environment-friendly behaviors and adopting a sustainable professional lifestyle. Introduction Today, environmental crises and social pressures have compelled organizations to think beyond economic goals and focus on sustainability. In this context, the food industry plays a decisive role in environmental health due to its high level of resource consumption, waste generation, and potential pollution (Jain et al., 2026). The first step toward achieving a sustainable environment is to identify the factors that threaten it and take action to reduce their impact. Accordingly, the environment has now emerged as a global issue for everyone, including organizations (Valor et al., 2025), to the extent that preventing environmental degradation and striving to improve environmental quality are among the major challenges facing organizations (Wang et al., 2025). Recently, employees’ green behavior in the workplace has become a central focus of organizational researchers, as evidence suggests that employees’ individual behavior can play a significant role in improving an organization’s environmental performance. Employees’ green behavior refers to sustainability-enhancing or sustainability-undermining behaviors displayed by employees, whether voluntarily or involuntarily, that are also measurable. To demonstrate green behavior, environmental awareness is the first step. Environmental awareness is a multidimensional concept and influences individuals’ information, knowledge, attitudes, tendencies, behaviors, intentions, actions, and practices. This awareness is linked to psychological factors and affects individuals’ willingness to engage in activities, form attitudes, and exhibit environmentally responsible behavior (Miah et al., 2025). Since the foundation and infrastructure of any organization lie in its human resources, and given the importance of considering the reciprocal relationship between human beings and the environment, the integration of environmental management into human resource practices is known as Green Human Resource Management (GHRM), which aims to help organizations improve environmental performance by enhancing the positive role of employees and their environmentally related activities (Pazhouhan et al., 2026). To achieve this goal, having truly green employees—from recruitment and selection to performance appraisal—who can assist organizations in attaining this objective is essential. Therefore, a Green Human Resource Management system has become one of the fundamental principles of organizations. In recent years, human resource managers have joined organizational green movements. Since human resource management plays an important role in shaping organizational culture, structure, strategy, and policy development, it also has a key role in achieving sustainable development. Green Human Resource Management refers to all actions undertaken in the transformation, implementation, and continuation of a system so that an organization’s human resources remain conscious of environmental concerns in both their professional and personal lives. GHRM means implementing strategies to raise awareness of green practices in order to promote and sustain sustainable business activities, thereby helping organizations create a more environmentally friendly atmosphere (Samadi et al., 2025). The mere existence of Green Human Resource Management policies—such as green training or green rewards—does not guarantee environmentally friendly behavior in the workplace. Many employees, despite the presence of such policies, may fail to understand the necessity of these actions due to insufficient environmental awareness, causing such programs to fail. In fact, the absence of a mediating mechanism capable of transforming employees’ environmental knowledge into action is one of the fundamental challenges facing organizations. Moreover, in industrial settings such as the food industry in Zahedan, the role of managers as behavioral role models is highly prominent. An issue often overlooked is that if employees observe a lack of green commitment among senior managers, they may lose motivation for green behavior even when their environmental awareness is high. The contradiction between organizational slogans and managerial practices can neutralize the effectiveness of green human resource strategies. In the food industry of Zahedan, given the region’s specific climatic conditions and the necessity of optimal resource management, this fundamental question arises: Does Green Human Resource Management affect green behavior through the mediating role of environmental awareness and the moderating role of managers’ green commitment? Theoretical Framework Green Human Resource Management and Green Behavior Some studies have shown that Green Human Resource Management (GHRM) has a clear impact on employees’ green behaviors (Daud et al., 2026). The results indicate that employee training and involvement in GHRM practices are the primary factors driving commitment, green behaviors, and organizational citizenship behaviors toward the environment. This is while many environmental management researchers have investigated environmental management practices for green behaviors and waste minimization in various sectors (Arshad et al., 2025). Green Human Resource Management and Environmental Awareness Green Human Resource Management focuses on training employees in green practices and increasing their environmental awareness, environmental efficiency, environmental participation, and environmental performance (Wang et al., 2025). Some of the underlying strategies of GHRM include investing in employees who are concerned about environmental issues, making employees aware of organizational environmental processes, empowering them to participate in these processes, and creating an eco-friendly organizational culture (Sharif et al., 2026). Kühner et al. (2026), in a study titled “Leveraging the Twin Transition: The Role of Workplace ICT Use on Employees’ Green Behavior,” found that the strategic use of technology in the workplace not only enhances operational efficiency but also acts as a key driver for strengthening employees’ green behavior. Othami et al. (2025), in a study titled “Strategic Drivers of Employee Green Behavior: The Role of HRM Alignment, Culture, and Leadership,” demonstrated that a green organizational culture acts as a powerful motivator and, through the mediating variable of green self-efficacy, leads to the reinforcement of sustainable behaviors among employees. Methodology In terms of its objective, the study is applicable, and in terms of method, it is descriptive-correlational. The statistical population comprises all employees working in the food industries of Zahedan (Industrial Town, Mirjaveh Road) who are involved in the organization’s operational and executive processes. Given that the total population size was 800, the sample size was determined as 260 through Cochran’s formula for finite populations. The research sample was selected through simple random sampling. The data collection instrument was a standard questionnaire. The results of Cronbach’s alpha and composite reliability tests, utilized to assess the reliability of the research instrument, are reported in the relevant tables. To examine the instrument’s validity, content validity (expert judgment) was employed and confirmed. Then, after distributing the questionnaire, the instrument’s validity was assessed by three approaches: construct validity (outer model), convergent validity (AVE), and discriminant validity. The AVE values for all research variables were greater than 0.5, indicating optimal validity. To test the research hypotheses, structural equation modeling was conducted through SmartPLS3 statistical software. Research Findings The analysis of the research model showed that Green Human Resource Management in the food industry of Zahedan is a key driver in institutionalizing employees’ environmental behaviors, and that this effect is facilitated through the development of green awareness as a mediating factor. The findings also confirmed that senior managers’ green commitment acts as an accelerator, strengthening the link between human resource tools and employees’ behaviors, and plays a decisive role in the effectiveness and sustainability of this behavioral model in the industry. Discussion and Conclusion The analysis of the research findings indicates, in the first step, that Green Human Resource Management (GHRM) has a positive and significant impact on employees’ green behavior. This profound link between organizational structure and individual action demonstrates that when human resource subsystems—from recruitment to rewards—are redesigned based on sustainability criteria, employees unconsciously align their behavioral patterns with the organization’s green expectations. This finding is consistent with the framework presented by Ghaemi et al. (2025) in their meta-synthesis model, as they also consider green processes to be the core pillar of forming environmental behaviors. In fact, as emphasized by Alem et al. (2022), Green Human Resource Management transforms green behavior from a marginal activity into an intrinsic duty by making sustainability a part of the professional identity. In explaining why behavior changes, it must be noted that Green Human Resource Management also has a significant impact on environmental awareness. Organizations cannot succeed by relying solely on instructions; they must change the employee’s mindset. The findings of this research in this regard showed that green human resource practices enhance employees’ knowledge and understanding of ecological challenges. These results align with the perspective of Alirezaei et al. (2022), who believe that green training and participation in environmental projects transform individuals’ level of awareness from abstract concepts into applied knowledge. The importance of this knowledge enhancement becomes clear when we realize that environmental awareness, in turn, has a significant impact on green behavior. According to the findings of Rubel et al. (2025), awareness is an essential prerequisite for action; an employee who has become aware of the destructive consequences of human activities on the Earth moves toward protective behaviors, such as waste reduction and energy consumption optimization, with greater intrinsic motivation. The results showed that environmental awareness plays a mediating role in the relationship between Green Human Resource Management and green behavior. This means that green human resources first sows the seeds of awareness in the minds of employees through training and participatory mechanisms, and it is then this awareness that acts as a psychological stimulus, leading to the emergence of sustainable green behaviors. This mediating role, which has also been confirmed in the research of Alirezaei et al. (2023), proves that without engaging employees’ perception and cognition, organizational green policies remain only at the surface level and do not lead to real behavioral change. The findings showed that senior management’s green commitment has a direct impact on green behavior. According to the theory of green transformational leadership (Wang et al., 2025), when senior managers adhere to green values in practice beyond formal statements, they serve as behavioral role models for personnel. Finally, the research findings indicated that senior management’s green commitment plays a moderating role in the relationship between Green Human Resource Management and green behavior. This finding is consistent with the view of Sharif et al. (2026) regarding the importance of the leader’s role in reinforcing green innovations.

Abstract Abstract The aim of this study is to provide a framework for implementing AI‑based demand‑driven business intelligence in the apparel industry. In terms of purpose, this research is applicable; and in terms of methodology it follows a mixed‑methods approach (qualitative and quantitative). The statistical population in the qualitative phase consisted of 17 participants, including university professors, experts, sales specialists, managers in the apparel industry, and artificial intelligence specialists, selected through purposive sampling. In the quantitative phase, the statistical population consisted of 384 individuals, including managers, senior experts, data analysts, supply chain managers, and information technology specialists in the apparel industry, selected by cluster random sampling. Data collection instruments included semi‑structured interviews and questionnaires. For data analysis, MAXQDA 2020 software was employed in the qualitative phase, while SPSS and PLS were employed in the quantitative phase. The results of data coding showed that 675 initial codes were refined to 461 cleaned codes and subsequently categorized into 214 conceptual codes, 45 sub‑categories, 19 main categories, and 6 core categories. Based on the research paradigm model, causal factors include increasing competition, changes in customer roles and market behavior, rising internal costs, and resource constraints. Continuous learning and monitoring, as well as privacy protection, serve as positive interventions in this process, whereas factors such as lack of artificial intelligence skills, resistance to change, and legal limitations may act as inhibiting interventions. These factors influence strategies such as transformational leadership, organizational memory management, and technological investment. The final model is aligned with the internal and structural challenges faced by businesses. Introduction Today, in some market segments, the speed of production cycles has become more important than cost, because shorter production times provide the flexibility required to adapt to rapidly changing fashion trends in these market segments. Accordingly, selecting the most beneficial supply chain strategy has become an established research topic, particularly for fashion products where demand for greater variety exists within short time intervals. At the same time, managing demand and supply simultaneously is not easy, as supply chains have become increasingly complex due to outsourcing processes (Anitha & Neelakandan, 2024). The apparel industry, characterized by short product life cycles and rapid changes in customer preferences, requires highly flexible supply chains. Fast fashion brands, due to their need for rapid responses to fashion changes, require responsive and flexible supply chains, whereas traditional brands focus more on efficiency and productivity through economies of scale. The sensitivity of retail brands to fashion trends shapes their supply chain strategies. Fast fashion brands require high flexibility and speed in their supply chains in order to respond to market demand in a timely manner. Coordination between demand and supply remains challenging, and achieving mutual benefits for both manufacturers and retailers require precise supply chain management (Swaminathan & Venkitasubramony, 2024). Choosing between these two strategies requires consideration of consumer purchasing behavior as well as competitors’ strategies. The interaction of these factors leads to complex market structures that require appropriate tools for evaluating strategies. Agent‑based modeling and simulation enable the consideration of individual consumer preferences, social interactions, and competition among producers, thereby illustrating the overall behavior of the system (market) at a macro level (Rand et al., 2018). This research not only seeks to present a theoretical model but also aims to assist manufacturers and businesses active in the apparel industry in moving beyond traditional approaches toward production aligned with real demand and value creation. Such a model, while preserving cultural standards, can contribute to sustainable growth, social identity formation, and economic development. Considering the above, the present study seeks to answer the main research question: How can a framework for implementing AI‑based demand‑driven business intelligence in the apparel industry be developed? Theoretical Framework Demand‑Driven Business Intelligence Demand‑driven business intelligence refers to an approach to business intelligence that focuses on analyzing and understanding customer needs, preferences, and market behaviors in real time or near real time. This type of business intelligence utilizes both internal and external data to help organizations base their decisions on actual market demand rather than relying solely on historical data (Ben et al., 2019). Artificial Intelligence Artificial intelligence (AI) is considered a strategic approach for maximizing the use of technology and market data in order to improve customer experience. Based on previous studies, advanced AI techniques include tools such as email automation, chatbots, and various analytical systems that assist managers in introducing product features to customers on a broad scale without temporal or spatial limitations, thereby creating a better purchasing and consumption experience for customers (Hayatmehr et al., 2026). Hayatmehr et al. (2026) examined the impact of artificial intelligence applications and intelligent learning on the strategic thinking skills and academic performance of management students, considering the moderating role of individual ethics. The results indicated that the use of AI tools has a positive effect on strategic thinking dimensions (systems thinking, creative thinking, future-oriented thinking, and critical thinking), intelligent learning, and academic performance. The mediating role of strategic thinking—particularly critical thinking and future-oriented thinking—was confirmed in the relationship between AI usage and academic performance, as well as between intelligent learning and academic performance. Additionally, individual ethics not only had a positive effect on intelligent learning but also played a moderating role in the relationship between intelligent learning and strategic thinking (systems thinking, critical thinking, and future-oriented thinking). Zolghadr et al. (2026) investigated the modeling and validation of the role of artificial intelligence in enhancing the export capabilities of companies in the electronics industry using a mixed‑methods approach. The qualitative results indicated that the model consists of five main categories: causal conditions (ICT infrastructure, data quality, and technical capacity), contextual conditions (supportive policies, international collaborations, and an innovative organizational culture), intervening conditions (sanctions, rapid technological changes, and legal and customs regulatory barriers), strategies (demand forecasting, price optimization, logistics intelligence, and human resource empowerment), and outcomes (competitive advantage, penetration into global markets, increased customer satisfaction, and cost reduction). In the quantitative phase, composite reliability indices were all above 0.7 and convergent validity for most constructs exceeded 0.5. The hypothesis testing results also confirmed all relationships among the model constructs with a significance level of p < 0.001. Research Methodology This study is applicable in terms of purpose, and adopts a mixed‑methods approach (qualitative and quantitative) in terms of research design. In the qualitative phase, the statistical population consisted of 17 university professors, experts, sales specialists, managers in the apparel industry, and artificial intelligence specialists selected through purposive sampling. In the quantitative phase, the statistical population included 384 individuals consisting of managers, senior experts, data analysts, supply chain managers, and information technology specialists in the apparel industry. These participants were selected by cluster random sampling. The data collection tools consisted of semi‑structured interviews and a questionnaire. Research Findings For data analysis, MAXQDA 2020 was applied in the qualitative phase, while SPSS and PLS were applied in the quantitative phase. The results of the coding process reduced the initial 675 codes to 461 refined codes, ultimately categorized into 214 conceptual codes, 45 sub‑categories, 19 main categories, and 6 core categories. Based on the study’s paradigmatic model, the causal factors include increased competition, changes in the roles and behaviors of customers and markets, rising internal costs, and resource constraints. Continuous learning and monitoring, as well as the protection of privacy, are considered positive interventions in this process. In contrast, factors such as the lack of AI skills, resistance to change, and legal limitations can act as inhibiting interventions. These factors influence strategies such as transformational leadership, organizational memory management, and technological investment. The final model is aligned with the internal and structural challenges faced by businesses. Conclusion The present study was conducted with the aim of developing a framework for implementing AI‑based demand‑driven business intelligence in the apparel industry. The findings of this research are consistent with the results of previous studies (Hayatmehr et al., 2026; Zolghadr et al., 2026; Ghanbari Gheshlaghi et al., 2025; Liu et al., 2025; Swaminathan & Venkitasubramony, 2024; Anitha & Neelakandan, 2024; Roth, 2024; Hemati, 2024; Kunz et al., 2023; Hajipourfard et al., 2022; Wamba‑Taguimdje et al., 2020). Swaminathan and Venkitasubramony (2024), through a systematic review and comprehensive analysis of reputable studies, compared statistical methods, machine learning, and deep learning approaches, identifying fundamental challenges in the fashion industry such as short product life cycles, high variety, and demand volatility. Their findings demonstrated that AI models provide higher predictive accuracy in this industry and emphasized the growing necessity of robust validation frameworks within demand‑driven models. Based on the research results, it is recommended that apparel industries develop the necessary foundation for activating AI‑based demand‑driven business intelligence by establishing core infrastructure, gradually developing knowledge and human capital, redesigning decision‑making structures, and institutionalizing a data‑driven culture. Such a foundation is neither inherently complex nor excessively costly; rather, it can be implemented gradually and aligned with the actual capacities of the national apparel industry.

Abstract Abstract The present study aimed to level the strategic compliance factors based on the development of interactive behaviors in the tax affairs organization. The present research method is qualitative and utilizes the interpretive-structural modeling (ISM) method. The research population consisted of 21 managers and experts of the country's Tax Affairs Organization selected based on purposive sampling via the data saturation technique. In this study, data were collected by a strategic compliance questionnaire based on the development of interactive behaviors (researcher-made) and analyzed by structural-interpretive modeling and MICMAC analysis. The results showed that 16 factors (transformational leadership, behavioral competencies, tax education, transparency, taxpayer satisfaction, incentives, mutual understanding, crimes, electronic services, system legitimacy, monitoring, two-way interaction, trust, justice, belief in the government, citizen participation) are effective on strategic compliance based on the development of interactive behaviors and; based on the interpretive-structural model, it includes three levels of independent or key, relational and dependent variables. The monitoring and incentives variable, as an independent and key variable, had the least influence and the most dependence compared to the variables considered at other levels in the model. Introduction Today, tax organizations around the world face numerous challenges such as tax evasion, lack of transparency in tax processes, and taxpayer resistance to laws (Ramadanty et al., 2025). These challenges are especially exacerbated in developing countries, such as Iran, due to weak tax infrastructure and lack of efficient interactive models. In Iran, the Tax Administration, as one of the main pillars of government financing, faces problems such as high tax evasion rates, taxpayer dissatisfaction, and inefficiency in interactive processes. These issues have not only reduced tax revenues, but also undermined public trust in the tax system (Hosseini et al., 2022). In recent years, numerous studies have examined strategies for improving tax compliance. Some of these studies have focused on coercive approaches that use tools punitive and supervisory approaches, while others have focused on trust-based approaches that rely on building trust and voluntary participation of taxpayers. However, research shows that combining these two approaches in a comprehensive strategic compliance model can lead to better results (Ramadanty et al., 2025). In Iran, domestic studies have also mainly focused on identifying existing problems and have less focused on designing operational models to improve strategic compliance (Karami et al., 2021). Recent research shows that interactive behaviors, including transparency, accountability and participation, can lead to increased taxpayer satisfaction and improved tax compliance rates. Many studies have emphasized that interactive and participatory approaches in tax organizations not only reduce administrative costs, but also help create an environment based on trust and cooperation. For example, in countries such as Denmark and Sweden, the use of digital technologies and transparent tax systems has increased taxpayer satisfaction and has resulted reduced tax evasion (Mukti et al., 2025). In the Tax Affairs Organization, due to its task-oriented nature, regulatory sensitivities, and formal structure, most of the followers' behaviors have been directed towards duty-based obedience rather than strategic compliance. This has led to a decrease in the organization's agility in the face of developments such as the smart tax system and comprehensive tax plans. To overcome this situation, the development of interactive behaviors between employees and managers is a necessity; because strategic compliance does not form in a vacuum and requires a context of constructive interactions, knowledge sharing, and mutual feedback. Therefore, the main question of this research is formulated as follows: Main question: How can a strategic compliance model based on the development of interactive behaviors be designed for the Tax Affairs Organization of Iran? Theoretical foundations Strategic alignment Strategic alignment means aligning an organization's activities and decisions with its overall goals and strategies. This concept is proposed in tax organizations as an approach to increase the effectiveness and efficiency in implementing tax laws and regulations (Ramadanty et al., 2025). Strategic compliance not only includes compliance with laws, but also includes creating an organizational culture in which employees and managers actively strive to achieve the strategic goals of the organization. This concept is closely related to concepts such as strategic management, organizational culture, and interactional behaviors (Hosseini & Gholami, 2022). Ramadanty et al., (2025) conducted a study titled “Optimizing Tax Planning Strategy in Mergers and Acquisitions: A Systematic Review of the Literature”. The results showed that choosing the right tax strategy is a vital tool for managing risk and optimizing the cost structure in large transactions. The findings emphasized that diversity in selected strategies allows companies to deal with legal complexities and maximize the financial value created in the merger process. Mukti et al., (2025) conducted a study titled “Corporate Tax Compliance as a Strategy for Improving Tax Compliance of Legal Taxpayers.” The results showed that the COVID-19 pandemic had a significant impact on tax avoidance practices. Research Method This study is applicable in terms of its purpose, and qualitative in terms of nature and method, and aims to level the factors of strategic compliance in the Tax Affairs Organization. The statistical population in this study included 21 managers and experts of the Tax Affairs Organization selected through purposive sampling and were questioned until the step of data saturation. The data collection tool was a researcher-made questionnaire extracted the interrelationships of variables based on the opinions of experts. Data analysis in this study was carried out through the Structural-Interpretive Modeling (ISM) method to identify hierarchical relationships, and MICMAC analysis to determine the influence power and degree of dependence of variables. Research Findings The findings show that transformational leadership is the most fundamental cornerstone of strategic compliance in the organization, which can be achieved through improving system competencies and tools affect mid-level variables. This hierarchical chain ultimately leads to the realization of the ultimate and macro-outputs of the organization, namely trust, justice, and citizen participation at the highest level of the model. In fact, this model emphasizes that in order to achieve strategic goals, transformation must begin from the layers of leadership and behavioral competencies. Discussion and Conclusion The analysis of the findings indicates that in the Tax Affairs Organization, any structural transformation requires managers who, with a transformational approach, create the necessary motivation and vision in the body of the organization. This finding is in line with the rahmi et al. (2023) research that considers strategic management and leadership to be the main prerequisites for administrative reforms in tax systems. The behavioral competencies variable is also placed at the fundamental level of the model. It is concluded that technical knowledge alone is not enough to achieve strategic compliance and that the organization's employees must be equipped with soft skills and interactive behaviors. This finding is consistent with the results of Taling et al. (2021) regarding the importance of “perceived behavioral control.” The tax education variable acts as a link variable at the next level of the model. The analyses show that education is the intermediary that transforms fundamental capabilities (leadership and competence) into perceptual outputs. From the research perspective, tax education includes both empowering employees to act professionally and making taxpayers aware of the rules. This finding is consistent with the view of Mukti et al. (2025) that emphasizes educational strategies to manage social norms and reduce taxpayer resistance. The “transparency” variable in the middle layer of the model indicates that strategic compliance is impossible without clarity in processes. Analysis of the findings indicates that the more transparent the tax administration is in explaining the rules and how taxes are spent, the less ambiguity taxpayers will have and the more willing they will be to cooperate voluntarily. This result is consistent with the findings of Rahmi et al. (2023) who consider administrative transparency to be a critical outcome of digital reforms and modernization of tax systems. According to the research model, mutual understanding is defined as a situation in which the organization and the taxpayer reach a common understanding of each other's rights and obligations. It is concluded that mutual understanding bridges the gap between government expectations and taxpayers' economic realities. This finding is consistent with Mukti et al. (2025) research on the importance of managing taxpayers' subjective and social norms to improve tax compliance. In this layer, the variable of taxpayer satisfaction appears as a behavioral outcome resulting from transparency and mutual understanding. The analyses show that taxpayer satisfaction is not simply a positive feeling, but rather an assessment of the fairness of the services received from the organization. This finding is consistent with the theory of planned behavior in the taling et al. (2021) research; because they introduce a positive "attitude" as one of the three pillars of the formation of strategic compliance intention. In the extracted model, penalties and incentives are included as control variables along with satisfaction and transparency. It is concluded that strategic compliance is not achieved only with rewards, but also requires a fair system of punishments and incentives to adjust taxpayers' behaviors towards the organization's goals. This finding is consistent with tax optimization strategies in Ramadanty et al. (2025) research that emphasize the balance between policy instruments. Electronic services at higher levels of the model play an accelerating role in strategic compliance. The findings show that the electronicization of services reduces physical interaction and strengthens procedural justice by eliminating intermediaries. This result is fully consistent with the view of Rahmi et al. (2023) that considers the implementation of modern tax systems as a strategy for efficiency and reducing corruption. The analysis of the system legitimacy variable shows that when electronic services and transparency are combined, the organization becomes legitimate in the eyes of society. Legitimacy makes taxpayers accept tax laws not as an imposition, but as a social duty. This finding is consistent with Taling et al. (2021) research in which organizational legitimacy greatly reduces monitoring costs. In the present model, monitoring is upgraded from a police action to a systemic process. It is concluded that under the shadow of electronic services, intelligent monitoring leads to more accurate identification of taxpayers and strengthens the sense of justice in society. This is consistent with the findings of Ramadanty et al. (2025) in the field of optimizing monitoring strategies to achieve maximum tax compliance. The “two-way interaction” variable indicates the transition from a prescriptive tax system to a participatory tax system. In this layer, the organization and the taxpayer are in a continuous feedback loop. This finding is consistent with Mukti et al. (2025) research that emphasizes the importance of social pressures and interactional connections in improving tax behavior. These three variables, as high-level outputs of the model, complete the causal chain of strategic compliance. The final conclusion shows that justice and trust are not accidental, but are direct consequences of legitimacy and interactions at the lower levels. As Taling et al. (2021) has pointed out, trust in the system minimizes the risk of non-compliance and strengthens faith in the government. Citizen participation is standing on the highest level of the ISM model. This variable is known as the end of strategic compliance; a situation in which paying taxes has become part of the taxpayer's citizenship identity.