New Approaches in Management and Marketing

Abstract The purpose of the present study is to propose a model for the career and organizational advancement of middle managers based on meritocracy strategies. In terms of its purpose, this research is applied, and methodologically it is a descriptive–exploratory study. The statistical population consists of 12 experts and specialists of Bushehr Petrochemical Company, selected through purposive sampling. Based on a synthesis of previous studies and the data obtained from implementing the Delphi technique among the experts of Bushehr Petrochemical Company, four meritocracy strategy criteria for career and organizational advancement were identified: meritocratic thinking, organizational meritocracy development, and talent management. Using a hierarchical approach, a hierarchy tree was first constructed, and then the criteria were ranked with the help of Expert Choice software. The findings indicated that the weights of meritocracy-related dimensions among managers and employees in Bushehr Petrochemical Company are as follows: meritocracy strategies for career and organizational advancement (0.362), meritocratic thinking for career and organizational advancement (0.384), organizational meritocracy development (0.152), and talent management (0.102). The consistency ratio of 0.004 falls within the acceptable range. These results can transform managerial decision‑making and lead to changes in managers’ attitudes.

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 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 study is to present and validate a sustainable consumer behavior model based on good digital governance in the banking industry (case study: Parsian Bank). The research method is developmental-applicable in terms of targer, mixed in terms of implementation method, and of descriptive-exploratory type. The statistical population in the qualitative section includes 9 experienced professors in the field of research and senior and knowledgeable managers in the banking industry and Parsian Bank across the country, selected purposefully (judgmentally); and in the quantitative section includes 358 branch managers across the country, 186 of whom were selected using the Cochran formula and simple random sampling method. The tool for collecting findings in the qualitative section is a semi-structured interview and in the quantitative section is a questionnaire. MAXQDA software was used to analyze the data in the qualitative section and SPSS, and PLS software in the quantitative section. After coding, 23 subcategories were identified, including organizational values ​​supporting the formation of responsible behavior, Parsian Bank's macro and strategic goals, the level of education of the community and the expansion of new digital technologies at the banking level, influential factors (causal factors), providing personalized services, continuous training and awareness, reviewing digital policies, creating secure digital banking channels, and managing digital assets in the bank (pivotal factors). Considering the extracted components, the final research model explains a way to create sustainable consumer behavior and determine strategies and operational plans for the sustainable development of the country and improving bank performance. Introduction Good digital governance is recognized as a key framework for aligning digital processes with sustainable development goals. Recent studies show that the effective integration of technologies such as artificial intelligence and data analytics into banking systems not only increases operational efficiency, but also plays a decisive role in shaping sustainable consumer behavior. In particular, the report (Wang & Zhang, 2025) emphasizes that the use of artificial intelligence in integrating ESG (environmental, social and governance) data with banking decision-making processes leads to improved transparency and accountability (Wang & Zhang, 2025). On the other hand, research of Stauropoulou et al. (2023) shows that online banking plays a pivotal role in increasing financial inclusion and empowering underserved communities through personalized financial management tools by reducing access barriers. These findings suggest that designing digital systems based on good governance principles can lead to building customer trust and loyalty. Another study by Khosrpour et al. (2024) emphasizes the need to adopt digital transformation governance frameworks in commercial banks and proposes the “self-empowerment” strategy as an efficient model for coordinating between centralized and decentralized structures. Also, changes in lifestyle and the emergence of new technologies have led to different needs of customers who demand personalized services. In this competitive industry, banks must have strategies to maintain their competitive position. Good digital governance can improve consumer behavior by communicating with customers in a two-way manner and conveying necessary information about sustainable financial solutions to customers (Lucas & Basuki, 2015). By using digital technologies, banks can provide faster services and more diverse products, which increases customer interaction with the bank. In this regard, there are theoretical gaps about the role of good digital governance in the banking industry and its impact on sustainable consumer behavior. Also, differences in the definition and interpretation of sustainable consumer behavior can lead to a variety of perceptions and theoretical gaps among researchers (Zulfikar et al. 2020). The present article, focusing on Parsian Bank as a case study, seeks to combine research findings in the field of digital governance and sustainable consumer behavior to present a new model that is capable of adapting to rapid technological developments and increasing stakeholder expectations. Accordingly, the present study seeks to answer the following question: How does the presentation and validation of a sustainable consumer behavior model based on good digital governance in the banking industry look like in Parsian Bank?  Theoretical Framework Sustainable Consumer Behavior Sustainable consumer behavior refers to the conscious and intentional actions of individuals as consumers to minimize the negative environmental, social, and economic impacts associated with their purchasing decisions. This includes choices that support sustainable practices, products, and businesses with the aim of promoting environmental protection, social equity, and economic well-being. By adopting this behavior, individuals can contribute to positive changes that create a more sustainable and just society (Milfont & Markowitz, 2016). Digital Governance Digital governance includes strategies and methods for managing and optimizing advertising activities, content marketing, digital public relations, data analytics, and customer communications. These actions help businesses improve, strengthen customer relationships, and ultimately increase sales and profitability. For example, using data analytics to better understand business strengths and weaknesses, identify customer behavior patterns, and optimize marketing strategies is an important aspect of digital governance in the marketing space. In general, digital governance in the marketing space plays a fundamental role in customer engagement, advertising, and online sales, and provides businesses with enormous opportunities to improve performance and growth (Shmok, 2022). Mohammadi et al. (2024) examined the design of a regulatory model based on sustainable development governance. They believe that the governance-based monitoring model for sustainable development includes three overarching themes (contextual, content, and monitoring) and five organizing themes (policy-based factors, economic factors, social factors, audit and reporting factors, and environmental factors) and 28 basic themes (formulation and implementation of economic, environmental, and social development policies, annual GDP growth, full employment, transparency, fiscal discipline, economic stability, social justice, access to renewable energy, combating desertification, waste management, sustainable forest management, sustainable use of financial resources and terrestrial ecosystems, reducing air pollution, minimizing the release of hazardous chemicals, poverty alleviation, social responsibility, combating corruption, health, intergenerational commitments, education, participation, promoting security, public welfare, budget and analytical reports, and audit reports). Hael et al. (2024) examined the trends in the literature on consumer behavior and sustainability: insights from a bibliometric analysis approach. They concluded that the three components The main ones, namely attitude, mental norms and perceived behavioral control, together shape the behavioral intentions of the individual and behavioral intention is the closest determinant of human social behavior and can have a significant impact on prediction. Research Methodology The research method is developmental-applicable in terms of its purpose, mixed in terms of implementation method, and descriptive-exploratory. The statistical population in the qualitative section includes 9 experienced professors in the field of research and senior and knowledgeable managers in the banking industry and Parsian Bank nationwide, which were selected purposefully (judgmentally) and in the quantitative section includes 358 branch managers nationwide, 186 of whom were selected using the Cochran formula and simple random sampling method. The tool for collecting findings in the qualitative section is a semi-structured interview and in the quantitative section is a questionnaire. Research Findings MAXQDA software was used to analyze data in the qualitative section and SPSS and PLS software were used in the quantitative section. After coding, 23 subcategories were identified, including organizational values ​​supporting the formation of responsible behavior, Parsian Bank's macro and strategic goals, the level of education of the community and the expansion of new digital technologies at the banking level, influencing factors (causal factors), providing personalized services, continuous training and awareness, reviewing digital policies, creating secure digital banking channels, and managing digital assets in the bank (central factors). Considering the extracted components, the final research model explains a way to create sustainable consumer behavior and determine strategies and operational plans for the sustainable development of the country and improving bank performance. Conclusion The present study was conducted with the aim of presenting and validating a model of sustainable consumer behavior based on good digital governance in the banking industry (case study: Parsian Bank). These results are consistent with the results of Mohammadi et al. (2024), Hael et al. (2024), Akpan Obong et al. (2023), Rezaei Lori et al. (2022), Husta & Zabkar (2021), Al-Ansari et al. (2021), Velenduck et al. (2017), Wu et al. (2016). Rezaei Lori et al. (2022) stated that; holistic responsibility is a factor for the formation of good governance and along with it, knowledge linkage, innovation platforms and innovative actions lead to sustainable development in social, economic and environmental dimensions. According to the research results, the following suggestions were made: Parsian Bank should evaluate and continuously improve its integrated channels by receiving consumer feedback periodically with the aim of their participation in the digital service improvement processes. This can be done through surveys, online comments or in-app feedback systems. 

Abstract Abstract The objective of this research is to investigate the mindsets of experts regarding the application of blockchain technology in government organizations using Q methodology. This study is applicable in its objective, and employs a mixed-methods approach for its execution. The statistical population of the research consists of managers in government organizations. Using purposive sampling and based on the principle of theoretical sufficiency, 19 individuals were selected as the statistical sample. With respect to the research approach, the qualitative phase initially involved 19 interviews to establish discourse; and the sample, Q-options, and finally the Q-set were derived through their perspectives and opinions. Subsequently, in the quantitative phase, the data obtained from the qualitative phase were analyzed and examined using SPSS. The findings indicate that transparency, increased productivity, enhanced agility, corruption prevention, elevated trust levels, improved electronic voting, secure identity management, and improved innovation are the eight mindsets of managers concerning blockchain technology in government organizations. Introduction The contemporary era of digitalization is placing significant pressure on administrative sectors, both in the private and public domains, to initiate and advance their digital transformation agendas (Hammad et al., 2023). Blockchain technology is one such technology that can be utilized within administrative systems (Cagigas et al., 2022; Seyedsayamdost & Vanderwal, 2020; Tandon et al., 2021). Some of the areas where blockchain is being tested for government services include: cryptocurrency/payments, land registration, identity management, document authentication, supply chain tracking, healthcare, education, company registration, data management, auditing, energy markets, taxation, voting, and the management of legal entities (Muafiq, 2024; Tan et al., 2022). The increasing adoption of blockchain in public sectors indicates that this technology possesses a broader capacity to enhance trust, accountability, and operational efficiency in governance functions. Particularly, the decentralized and immutable nature of blockchain offers a compelling solution to the long-standing challenges in these domains in areas such as digital identity management, adherence to requirements and regulations, and the provision of public services (Chen et al., 2026). It is claimed that blockchain will profoundly transform the process of producing and delivering public services (Rana et al., 2022). With key features such as decentralization, persistence, transparency, privacy and security, accuracy, and notably, cost and network savings, this technology significantly enhances the value in accessing data and minimizing intermediation in digital processes (Dowlatabadi, 2025; Hammad et al., 2023; Jamali, 2023; Rana et al., 2022). Furthermore, blockchain contributes to improving process efficiency through automation with smart contracts. These self-executing contracts can streamline administrative tasks, reducing the time and costs associated with manual processes. Moreover, the decentralized nature of blockchain technology enhances trust among participants by eliminating the need for intermediaries and providing a secure and transparent platform for transactions. Integrating blockchain into administrative processes can lead to increased trust through information transparency, predictability, and efficiency. By leveraging blockchain’s capabilities such as data aggregation through smart contracts and ensuring data security via cryptographic algorithms, administrative processes can be simplified and become a more reliable tool (Muafiq, 2024). Therefore, considering the perspectives of experts, this research seeks to answer the question: What are the mindsets of experts in the application of blockchain technology in government organizations, using Q methodology? Theoretical Framework Blockchain in Government Organizations Blockchain is a combination of existing technologies such as distributed ledgers, cryptography, hashing, and consensus protocols. All transaction records in a blockchain are stored in a chain of data packets (blocks) and distributed across a peer-to-peer network. All nodes in the network possess a copy of the blocks (Batubara & Janssen, 2018). This technology can resolve or mitigate issues related to transparency, trust, public policies, and service quality. The adoption, implementation, or integration of blockchain technologies by governments and public institutions can be beneficial for all stakeholders. For instance, blockchain can help optimize data management among public service provider organizations, not only in terms of interoperability, trust, and transparency; but also in terms of data accuracy, coordination, traceability, and integrity. It is also suggested that this technology can aid in preserving existing organizational and management structures. Therefore, blockchain is presented as a solution for governments to address significant public sector challenges such as transparency and fairness in processes and procedures (Fosso et al., 2024). Chen et al. (2026), in their research, conducted a comprehensive review of blockchain applications in government, demonstrating that blockchain can play a transformative role by enhancing transparency, efficiency, and security in public services (e.g., healthcare, e-voting, and registration systems). They also identified driving forces and barriers to adoption through force field analysis and emphasized the need for interdisciplinary research and regulation/collaboration for successful implementation. Murano et al. (2026), through their research, found that despite blockchain’s capacity to enhance transparency, efficiency, and trust in public services, its adoption remains slow due to organizational and technical barriers. They identified specific public sector challenges, such as interoperability and a lack of technical expertise, through a systematic review and proposed tailored strategies for effective implementation. Research Methodology This research is applicable in terms of its objective, and was conducted using a mixed-methods approach. The statistical population of the research consists of managers in government organizations. Using purposive sampling and based on the principle of theoretical saturation, 19 individuals were selected as the statistical sample. With respect to the research approach, the qualitative phase initially involved 19 interviews to establish discourse. Research Findings A sample, a Q-sort, and finally a Q-set were obtained using the experts’ insights and opinions. In the quantitative phase of the research, the data obtained from the qualitative section were analyzed using SPSS. The findings indicate that transparency, increased productivity, enhanced agility, corruption prevention, improved trust levels, better e-voting, secure identity management, and improved innovation are the eight mental models of managers regarding blockchain technology in government organizations. Conclusion The present research was conducted with the aim of examining the mental models of experts in the application of blockchain technology in government organizations, using the Q methodology. The results of this research are aligned with the findings of the studies by Chen et al. (2026), Murano et al. (2026), Hammad et al. (2026), Mueller et al. (2026), Rubino et al. (2026), Sánchez-obando et al. (2025), Fosso et al. (2024), Akhmetbek & Špaček (2021), Sung & Park (2021), Brauner & Janissek-muniz (2020), Reddic et al. (2019), Navadkar et al. (2018), and Qian et al. (2017). Reddic et al. (2019) state that blockchain prevents the excessive concentration of power in the hands of a few, possesses a more transparent legal framework, and provides citizens with more information. This higher level of transparency allows citizens to monitor public transactions. They emphasized that with a greater degree of transparency and security, undesirable behaviors can be quickly identified and curbed. Based on the research results, the following suggestions are presented: To enhance and empower government organizations in achieving their objectives, it is recommended that managers pay special attention and consideration to blockchain technology. Blockchain can minimize the risk of using unreliable systems and provide rapid access to information for stakeholders, thereby improving the performance of public sector institutions.

Abstract Abstract The purpose of this study is to analyze the dimensions and components of digital transformation management based on artificial intelligence. This research was conducted qualitatively using the thematic analysis method. The statistical population consisted of 12 experts and specialists in the fields of human resource management and information technology management across the country (including university faculty members in HRM and IT management, as well as general directors of provincial education departments). Participants were selected using purposive sampling. The data collection instrument was semi‑structured interviews. Data analysis was performed through thematic analysis using MAXQDA software. The findings revealed that AI‑based digital transformation management includes four overarching themes—contextual requirements, digital infrastructure, digital transformation management process, and organizational capital; twelve organizing themes—ethical requirements, cultural requirements, organizational requirements, hard digital infrastructure, soft digital infrastructure, digital transformation management process, digital transformation planning, prototyping, learning, human capital, process capital, structural capital, and social capital; and a total of 73 basic themes. Introduction The rapid advancements in new technologies in the current era, particularly in the fields of artificial intelligence, machine learning, big data, and smart technologies, have caused fundamental changes in the structures and operations of organizations. These transformations are not limited to economic and industrial sectors but also significantly impact educational and cultural institutions (Mostafaei et al., 2024). Many experts believe that the future of organizations depends on their ability to manage digital transformation. Digital transformation is a process that goes beyond equipping organizations with IT infrastructure; it involves rethinking missions, structures, governance methods, and even organizational culture (Wang et al., 2025). In this context, the education system, as the most fundamental social institution responsible for nurturing human capital, requires a reinvention and transformation of its processes more than ever before. In Iran, the education sector faces challenges such as centralization, inefficiency in some administrative procedures, underutilization of educational data, and a significant gap compared to global standards in digital transformation (Azimi et al., 2024). In many educational systems, including Iran’s, the adoption of new technologies has largely remained at the level of tools and infrastructure, with less attention paid to its strategic and forward-looking dimensions. Domestic studies indicate that most digitalization projects in education are implemented in isolation and sporadically, lacking necessary coherence (Golestani, 2024). In the Gilan province specifically, field evidence suggests that although some administrative and educational processes have been digitized, these changes often face resistance from employees, a lack of digital skills, and weaknesses in macro-level policymaking. Consequently, the potential capabilities of artificial intelligence for organizational transformation have not yet been utilized effectively or operationally. Therefore, in pursuit of this goal, the main research question is: What are the dimensions and components of artificial intelligence-based digital transformation management in the education system? Theoretical Framework Digital Transformation Digital transformation is a profound, multidimensional, and strategic process through which organizations systematically and purposefully leverage digital technologies to fundamentally alter their structures, processes, business models, organizational culture, and service delivery methods. This transformation aims to enable them to achieve more effective, agile, and value-driven performance in today’s complex, dynamic, and competitive environment (Asad Amraji et al., 2020). AI-Based Digital Transformation Artificial intelligence can bring about a fundamental transformation in the analysis of the performance of educational personnel, shifting it from a subjective, periodic, and guesswork-based process to an objective, continuous, data-driven, and development-oriented one (Agrawal et al., 2018). Chen & Zhang (2025) in their examination of the impact of AI applications on the environmental, social, and governance (ESG) performance of companies, demonstrated that digital transformation can enhance the sustainable development of organizations by increasing coordination and collaboration, the demand for specialized digital knowledge, the ability to manage virtual systems, and by changing the roles and styles of managers. Mohsen et al. (2025) in a study on financial institutions, found that the integration of artificial intelligence into organizational structures can significantly improve the managerial performance and operational efficiency of these organizations. Research Methodology This research was conducted qualitatively using the thematic analysis method. The statistical population of the study included 12 experts in the fields of human resource management and information technology management at the national level. This group comprised university faculty members in the fields of human resource management and information technology, as well as general directors of provincial education departments. They were selected using a purposive sampling method. The data collection tool used was semi-structured interviews. Research Findings Data analysis was performed using the thematic analysis method with the MAXQDA software. The findings of the present study indicate that AI-based digital transformation management has been designed in the form of four overarching categories: “Contextual Requirements,” “Digital Infrastructure,” “Digital Transformation Management Process,” and “Organizational Capital.” These are further broken down into 12 organizing categories: “Ethical Requirements,” “Cultural Requirements,” “Organizational Requirements,” “Hardware Digital Infrastructure,” “Software Digital Infrastructure,” “Digital Transformation Management Process,” “Digital Transformation Planning,” “Prototyping,” “Learning,” “Human Capital,” “Process Capital,” “Structural Capital,” and “Social Capital,” along with 73 basic categories. Conclusion The present research was conducted with the aim of exploring the dimensions and components of AI-based digital transformation management. These findings are consistent with the results of previous studies, including those by Brock & Von Wangenheim (2019), Chen & Zhang (2025), Mohsen et al. (2025), Rosemary (2025), Malik et al. (2022), Alemi Pasand & Farahani (2024), Tavakoli-Rad & Zargaran-Khozani (2022), and Kitsios & Kamariotou (2021). In their research, they acknowledged that indicators such as individual prerequisites, organizational culture, organizational digital culture, data, digital ethics and privacy, hardware and software, and policy-making play a significant role in improving organizational digital leadership. Furthermore, Mohsen et al. (2025) asserted that attention to the structural dimensions of an organization can play a crucial role in improving organizational performance. Chen & Zhang (2025) also stated that focusing on changes in organizational structure and regulations significantly contributes to the development of organizational practices. Based on the research findings, it is recommended that the Education Departments of Gilan Province develop and issue a special ethical charter for artificial intelligence. This charter should explicitly include principles such as algorithmic transparency, data privacy protection, prevention of discriminatory biases, and the necessity of human oversight in decision-making processes.   

Abstract Abstract This study was conducted to investigate the impact of digital technologies on the evolution of human resource management practices at the head office of Tehran Telecommunications Company. The research method is applicable in terms of its purpose, quantitative in terms of its implementation method, and descriptive-correlational in terms of its nature and method. The statistical population of the study included all managers, experts, and human resource employees of the company who interacted with digital human resource management systems. A mixed sampling method was used, included a stratified random sampling of 200 people in three organizational layers (employees, human resource experts, and managers). A standard questionnaire based on a 5-point Likert scale was used to collect research data. The content validity of the tool was confirmed by specialists and experts, and Cronbach's alpha and composite reliability were used to measure the reliability of the tool. By distributing the questionnaire, the validity of the tool was measured with three methods: construct validity (external model), convergent validity (AVE), and divergent validity. The AVE value for all variables should be greater than 0.5. SPSS and PLS software were used to analyze the data. The research findings show that all research hypotheses have been confirmed. The main limitation of the study was the focus on a specific organization, which limits the generalizability of the results. It is suggested that future studies be conducted with a wider sample and in different industries to provide more comprehensive patterns of the impact of digital technologies on the transformation of human resource management practices. Introduction The integration of digital technologies into human resource management practices has emerged as a transformative paradigm in the contemporary organizational landscape (Dyakiv et al., 2024). In the face of the increasing complexities of the digital age, the application of advanced technologies such as artificial intelligence, big data processing and cloud computing has transformed the traditional foundations of human resource management and has led to qualitative improvements in efficiency and effectiveness in various dimensions of this field (Wahyudi et al., 2023). Digital transformation in human resource management faces profound challenges that require special attention. Organizational resistance at various levels, from employees to senior managers, is considered the first and biggest obstacle. Employees are afraid of being replaced by intelligent systems, while senior managers are dependent on traditional management methods (Gupta, 2024). This resistance is intensified when the organization cannot clearly explain the real benefits of digital transformation. The next challenge is data security, which has become a red line in this process due to the sensitivity of personal and organizational information in human resource management systems. Any security flaw can have irreparable consequences for the organization (Chen et al., 2024). High implementation costs are the third major obstacle that has faced many organizations, especially small and medium-sized enterprises, with difficulties. These costs include not only the purchase of hardware and software, but also the hidden costs of training, updating and maintaining systems (Zisis & Polydoros, 2024). The skills gap among human resource management professionals is another challenge that poses a problem for the digital transformation process. The digital transformation of human resource management in Iran faces deep structural challenges rooted in the country's special economic, cultural and technological conditions. Operational solutions are proposed to overcome the challenges. Accordingly, the question arises: what impact do digital technologies have on the transformation of human resource management practices in the digital age? Theoretical Framework Digital transformation in human resource management faces profound challenges that require special attention. Organizational resistance at various levels, from employees to senior managers, is the first and biggest obstacle. Employees fear being replaced by intelligent systems, while senior managers are dependent on traditional management methods. Digital technologies and the mediating role of skills development Digital technologies, as new tools in human resource management, have been able to change traditional management frameworks and provide new ways to train, evaluate, and develop employees. According to the study by Zaborovskaia et al. (2020), digital transformation not only improves operational processes, but also has a direct impact on the transformation of human resource management practices by improving employee skills as a mediating factor. In other words, digital technologies increase the ability of organizations to adapt to environmental changes and innovations by providing tools for online training, knowledge management, and professional development of employees (Barykin et al., 2020). The use of digital technologies in human resource management equips employees with the necessary skills to work with modern tools and analytical data. These skills include the ability to work with human resource information systems, use data analysis for decision-making, and use artificial intelligence in recruitment and performance evaluation processes (Trochi et al., 2021). Employees with higher digital skills adapt more easily to organizational changes and play a more active role in achieving human resource transformation goals (Zhang et al., 2024). The moderating role of employee resistance Employee resistance to digital change is one of the main challenges in implementing HR transformation processes. This resistance can be due to fear of unemployment, lack of trust in new technologies, or lack of necessary skills. According to the findings of Barykin et al. (2020), employee resistance can reduce the impact of implementing digital technologies on HR transformation, and the moderating role of this resistance determines the extent to which an organization is successful in implementing digital changes. In addition, effective management of employee resistance includes providing appropriate training, involving employees in the change process, and creating a supportive organizational culture. These measures reduce concerns and increase acceptance of digital changes, and ultimately enable the effective implementation of new technologies in the organization. Studies show that employee resistance is not only a barrier but can also act as a moderating factor; that is, the level of resistance affects the intensity and direction of the impact of digital technologies on HR performance. Research Methodology The research method is applicable in terms of its purpose, quantitative in terms of its implementation method, and descriptive-correlational in terms of its nature and method. The statistical population of the research includes 420 employees, human resources experts, and managers of the Tehran Telecommunications Company's head office, 200 of whom were selected as a sample using a stratified random method using the Cochran formula. A researcher-made questionnaire based on a 5-point Likert scale was used to collect research data. The content validity of the tool was confirmed by specialists and experts, and Cronbach's alpha and composite reliability were used to measure the reliability of the tool. By distributing the questionnaire, the validity of the tool was measured with three methods: construct validity (external model), convergent validity (AVE), and divergent validity. The AVE value for all variables must be greater than 0.5. Research findings The findings showed that digital technologies, by empowering employees and improving technical and managerial skills, drive human resource processes towards efficiency, flexibility, and innovation. This finding emphasizes that technology alone is not enough to create transformation and that human empowerment is essential as an intermediary link between technological investment and managerial achievements. Therefore, organizations should simultaneously focus on implementing new technologies and developing employee skills to achieve sustainable human resource transformation. The research results show that the impact of digital technologies on human resource management practices largely depends on the level of employee acceptance or resistance. Positive employee acceptance increases the impact of technologies on innovation and improvement of human resource processes, while employee resistance is one of the main obstacles to digital transformation. This finding highlights the importance of change management, employee participation, and creating a culture of technology acceptance, and shows that the success of human resource transformation will not be achieved without considering employee attitudes and behavior. Conclusion The findings of the present study show that digital technologies, by mediating the role of skill enhancement, have an impact on the transformation of human resource management practices of experts at the head office of Tehran Telecommunications Company. Various studies indicate that digital technologies, by enhancing employees' skills, increase their ability to use modern management tools and facilitate the transformation of human resource practices. For example, Blanka et al. (2022) state that digital technologies, by providing e-learning platforms, process automation systems, and data analysis software, pave the way for employee empowerment and cause human resource management to change from the traditional experience-based and rigid rules-based approach to data-driven, flexible, and advanced approaches. From a theoretical perspective, this finding is consistent with the theory of "capability-based resources." This theory emphasizes that an organization's sustainable competitive advantage does not come solely from tools and technologies, but from their combination with human capabilities. Accordingly, digital technologies lead to value creation when they are combined with employee skills and competencies. This finding is also consistent with previous research. For example, Bresciani et al. (2022) showed that training employees in digital skills has a positive and direct relationship with the success of human resource transformation projects. Similarly, Trochi et al. (2021) state that the role of new technologies in organizations is strengthened by the development of employee knowledge and skills. These results indicate that upskilling acts as a “mediating link” between technological investment and managerial achievements. The findings of the present study show that digital technologies have an impact on the transformation of human resource management practices by moderating employee resistance of experts at the Tehran Telecommunications Company headquarters. Previous research, including Barykin et al. (2022), states that the effect of digital technologies on human resource management practices largely depends on the level of acceptance or resistance of employees. In situations where employees have a positive attitude towards technological changes and see changes as an opportunity for their professional growth and advancement, technologies can have a significant impact on improving human resource processes and innovating in management practices. From a theoretical perspective, this finding is consistent with the "Cutter Theory of Change" by Blanka et al. (2022). This theory emphasizes that motivating, communicating clearly, and engaging employees are essential to reducing resistance. Based on this framework, it can be concluded that digital technologies can become a tool for human resource transformation when organizations first deal with employee resistance and strengthen a culture of accepting change. Previous studies have also confirmed this. For example, Sousa et al. (2019) stated in their research that employee resistance to new technologies has a direct impact on the failure or success of digital transformation projects. Also, Legner (2017) showed that organizations that reduce employee resistance through change management are more successful in implementing digital human resource management systems. These results are in line with the present findings and highlight the importance of the moderating role of employee resistance. To make the most of digital technologies, it is suggested that organizations should implement continuous training and digital skills development programs so that employees have the ability to effectively utilize new technologies.