quality management
Yeganeh Kamranmehr; Ezzatollah Asgharizadeh; Fatemeh Saghafi
Abstract
In recent years, artificial intelligence (AI) has emerged as a transformative force across industries, offering unprecedented capabilities in data analysis, process automation, and customer engagement. The insurance sector, inherently data-driven and operationally complex, stands to benefit significantly ...
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In recent years, artificial intelligence (AI) has emerged as a transformative force across industries, offering unprecedented capabilities in data analysis, process automation, and customer engagement. The insurance sector, inherently data-driven and operationally complex, stands to benefit significantly from AI adoption. While insurers in developed markets have begun to integrate these technologies strategically, organizations in emerging economies like Iran continue to face structural challenges in implementation. This study explores the potential of AI in improving the quality of insurance services within Iran’s Social Security Organization (SSO). Through an empirical model grounded in multi-criteria decision-making, the research identifies twenty-two AI-driven applications and evaluates their impact using expert feedback. By applying FARE (Fuzzy Analytic Relationship) for indicator weighting and MARCOS (Measurement Alternatives and Ranking According to Compromise Solution) for application prioritization, the study offers a structured pathway for digital transformation. The findings suggest that AI applications focused on data analysis and intelligent processing hold the highest potential for service enhancement. In contrast, functions like cybersecurity and customer interaction, while essential, play more supportive roles. This research contributes to the growing body of knowledge by contextualizing AI integration within Iran’s insurance landscape and proposing a practical decision-making framework for public sector innovation.IntroductionArtificial intelligence has become more than a technological trend—it is now a cornerstone of innovation in finance, healthcare, manufacturing, and insurance. As organizations contend with increasing volumes of data and rising expectations for personalized, efficient services, AI offers tools that not only automate routine tasks but also augment strategic decision-making. Within the insurance sector, AI applications span a wide array of functionalities, including predictive underwriting, fraud detection, customer service automation, and real-time risk evaluation. These advancements are reshaping insurance from a reactive, compensation-focused industry to a proactive, preventive service model. In Iran, despite visible interest and scattered efforts, the deployment of AI remains unsystematic and largely unstructured. The Social Security Organization, covering more than 45 million individuals, is a prime candidate for AI transformation. Yet the absence of a prioritized roadmap has hindered progress. This study responds to that gap by asking: Which AI applications should the SSO pursue to maximize improvements in service quality?Research BackgroundGlobally, the insurance industry has witnessed a surge in AI adoption. Companies such as AXA and Allstate utilize machine learning models to personalize policies and predict claims. Progressive and Lemonade implement chatbots and natural language processing to accelerate customer support. Zurich Insurance leverages robotic process automation to reduce operational latency, while Swiss Re and Munich Re deploy anomaly detection algorithms to prevent fraud and improve pricing accuracy.These examples underscore the strategic role AI plays, not only in optimizing internal processes but also in transforming customer engagement. International literature consistently identifies several application domains: predictive analytics, cybersecurity, automated claims processing, customer personalization, and intelligent document handling. However, within the Iranian context, existing studies have tended to focus narrowly—often examining isolated applications or technologies without offering a comprehensive prioritization framework. Moreover, public insurance institutions lack access to sector-specific evaluations that link technology capabilities to strategic outcomes like customer retention, operational cost reduction, and service satisfaction. This research seeks to bridge that divide by evaluating the functional relevance and strategic value of AI applications from an organizational perspective.MethodThe study adopts a quantitative and applied research design, supported by a positivist philosophical foundation. It uses a single cross-sectional survey to collect expert judgments and applies a hypothetico-deductive approach for analysis. The sample consists of ten carefully selected professionals affiliated with the SSO, each with proven expertise in insurance and artificial intelligence. These experts evaluated twenty-two performance indicators extracted from a comprehensive review of literature and industry practice. Indicators included operational metrics (e.g., fraud detection efficiency, speed of data processing), service quality measures (e.g., customer satisfaction, claim resolution accuracy), and strategic outcomes (e.g., risk prediction, loss minimization). To quantify the relative importance of each indicator, the FARE method was used. This enabled the assignment of nuanced weights based on expert consensus. Subsequently, each AI application was evaluated using the MARCOS ranking methodology, which integrates weighted scores with ideal and non-ideal performance scenarios to establish priority levels. This hybrid decision-making model ensures that each application is assessed on technical feasibility as well as on its strategic alignment with service enhancement goals.Discussion and ResultsThe analysis revealed clear priorities among the AI applications considered. The domains of data analysis and processing intelligence emerged as the most impactful in improving insurance service quality. These functions directly contribute to accurate risk assessment, efficient pricing, and streamlined claims management—areas that hold the greatest operational and strategic significance for public insurers. Applications such as predictive modeling, historical data mining, and intelligent classification of new information received the highest scores from experts. These tools allow insurance entities to transition from static, rule-based decision-making toward dynamic, data-driven strategies that respond to real-time conditions.In contrast, applications within cybersecurity and customer services, while essential, were deemed supportive rather than primary. Technologies such as chatbots, fraud detection systems, and encryption tools provide safeguards and enhance user experience but rely heavily on underlying analytical systems to deliver consistent value. One of the study’s conceptual contributions lies in distinguishing between direct AI applications and indirect effects, which enhance service through ancillary functions. This distinction has practical implications for implementation, especially in resource-constrained environments where prioritization is critical.Furthermore, the study highlighted a significant gap between global best practices and the current technological posture of Iran’s insurance sector. While international organizations experiment with generative AI and blockchain–AI hybrids, domestic insurers have yet to establish basic integration frameworks. This research, therefore, not only ranks applications but also serves as a blueprint for catching up with global innovation trajectories.ConclusionArtificial intelligence is no longer optional; it’s central to the future of insurance. For Iran's Social Security Organization, strategic investment in AI must begin with data analytics and intelligent processing, which offer the most immediate gains in speed, accuracy, and service quality. Applications in cybersecurity and customer service are also valuable, but their full potential depends on a solid analytical foundation. This study provides more than a ranking; it offers a practical mindset shift, from reactive service delivery to proactive, data-driven decision-making. The proposed framework is adaptable across sectors and scalable with future technological advances. Real success, however, hinges not on algorithms alone, but on how thoughtfully they are embedded into human-centered systems led by vision, collaboration, and continuous learning.
Industrial management
laya olfat; seyed sourosh ghazinoori; maghsood amiri; TAHEREH MIRHOSEINI
Abstract
The limited natural resources of the Earth have presented various industries, including the dairy industry, with increasing challenges in the sustainable and efficient use of resources. The high consumption of water, energy, and raw materials, as well as the significant production of waste—especially ...
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The limited natural resources of the Earth have presented various industries, including the dairy industry, with increasing challenges in the sustainable and efficient use of resources. The high consumption of water, energy, and raw materials, as well as the significant production of waste—especially organic waste—in this industry, has exposed it to environmental pressures. To improve organizational performance, new approaches for the optimal use of resources have been introduced, one of the most important of which is the concept of the circular economy. In recent years, researchers have attempted to combine this concept with other management frameworks to create an effective approach for enhancing organizational performance. In this regard, the present study aims to identify and model the concepts of the circular economy from the perspective of the EFQM 2020 Excellence Model in the dairy industry. For this purpose, first, the key indicators of the circular economy were identified using a systematic literature review and interviews with experts, and then these indicators were classified within the EFQM framework with the help of a panel of experts. Finally, confirmatory factor analysis was used to assess the validity of the designed model. The results indicate an appropriate fit of the model and meaningful relationships between the components. The findings of this study can serve as a model for establishing an excellence approach in the dairy industry based on a circular economy and can play an effective role in formulating national and industrial policies.IntroductionAs industrialization has progressed, the production sector has increasingly faced the challenge of diminishing non-renewable resources (Akhimien et al., 2021). Given the finite nature of these resources, it is crucial to adopt diverse strategies to maximize their use, as they are essential to maintaining economic activity. This has led to a growing focus on the interaction between economic and ecological systems. A new approach is needed to manage resource consumption and pollution to ensure the sustainability of both biological and socio-economic systems (Pourasghar et al., 2019). Historically, economies have operated on a linear model—based on extraction, production, and disposal. In contrast, the circular economy has emerged as a modern paradigm aimed at reducing the negative impacts of the linear model. It focuses on minimizing waste and maximizing resource efficiency (Suhaib & Fayaz, 2023). This concept promotes reducing, reusing, and recycling materials throughout production, distribution, and consumption processes to achieve sustainable development (Pourasghar et al., 2019). Given these considerations, this research seeks to identify and model key indicators related to the circular economy by reviewing relevant literature, including books, scholarly articles, and online resources, as well as conducting interviews with field experts. The identified indicators are then categorized within the EFQM 2020 Organizational Excellence Model. Finally, the model’s statistical validity is evaluated using confirmatory factor analysis, with input from industry managers.Research BackgroundResearch on the circular economy is still in its early stages, characterized by considerable diversity and fragmentation. Some studies focus on redesigning business models according to circular economy principles (Bocken et al., 2016), while others explore product design (Atlason et al., 2017) or examine strategic shifts and the identification of implementation barriers (Bjørnbet et al., 2021; Grafström & Aasma, 2021; Karali & Shah, 2022; Koide et al., 2022; Padilla-Rivera et al., 2021; Rincón-Moreno et al., 2021; Salmenperä et al., 2021). The expanding research in this area highlights the increasing integration of circular economy principles with other management frameworks, particularly in the realm of organizational excellence. The shift of manufacturing firms toward circular practices, coupled with substantial changes in industrial operations, has broadened the scope for research in this field. These transformations are not merely extensions of existing practices; they require fundamental shifts in organizational thinking, operations, and business logic. However, many organizations still struggle with how to implement circular economy principles effectively. This highlights the need for a comprehensive and practical model. The present study addresses this gap by proposing a model grounded in organizational excellence, specifically using the EFQM 2020 framework.MethodThis study employed a mixed-methods approach, combining a systematic literature review, expert interviews, and confirmatory factor analysis. The first phase involved conducting a systematic literature review to identify and explore the key dimensions and components related to circular economy excellence. Systematic reviews are essential for assessing the current state of knowledge in emerging research fields (Boland, 2013; Templier & Paré, 2015). In addition to the literature review, semi-structured interviews with field experts were conducted to gain a more nuanced understanding of circular economy principles and how they can be applied effectively. In the second phase, qualitative data were synthesized into a structured questionnaire. Quantitative data were then collected and analyzed to examine the relationships between the identified variables (Creswell, 2005). Confirmatory factor analysis was used to assess the validity of the indicators and to verify the overall structure of the proposed model.Discussion and ResultsThe findings suggest that the proposed model fits the data well, with all paths showing statistical significance. The model consists of three main components:Orientation: Organizational Culture and Leadership, Purpose and StrategyExecution: Stakeholder Engagement, Sustainable Value Creation, Performance ManagementOutcomes: Stakeholder Perceptions, Strategic and Operational ResultsKey performance indicators identified include waste management, the use of renewable energy, employee engagement, and circular product design. The model achieved a Goodness-of-Fit (GOF) index of 0.707, indicating strong empirical validity. By combining the EFQM 2020 framework with circular economy principles, this study presents a comprehensive model that supports both environmental and economic sustainability. The results emphasize the importance of fostering a culture of innovation, implementing green strategies, and maintaining effective communication with stakeholders. Additionally, the use of advanced technologies and information systems can enhance decision-making capabilities. The model offers a strategic roadmap for the dairy industry and other sectors within the food industry.ConclusionThis study provides a validated framework for implementing circular economy practices in the dairy industry, grounded in the EFQM 2020 organizational excellence model. By integrating these two approaches, organizations can drive continuous improvement and achieve long-term sustainability. Organizations are encouraged to adopt the identified indicators and take concrete actions to reduce waste, optimize resource use, and create sustainable value. Future research should explore the application of this model in other industries and test its effectiveness with larger and more diverse samples to increase generalizability.
Industrial management
maryam parandvarFoumani; r m; abbas Tolouie Ashlaghi
Abstract
Rapid and extensive developments in the field of information and communication technologies (ICT), especially in recent decades, have created a widespread foundation for profound and fundamental transformations in managerial, social, and organizational structures. These changes have not only revolutionized ...
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Rapid and extensive developments in the field of information and communication technologies (ICT), especially in recent decades, have created a widespread foundation for profound and fundamental transformations in managerial, social, and organizational structures. These changes have not only revolutionized the way work is conducted but have also redefined the work environment, organizational relationships, and interpersonal interactions. In this context, the emergence of the Fifth Industrial Revolution has played a pivotal role as a turning point in the history of technological advancement. This revolution, characterized by the intelligent integration of physical, digital, and biological worlds—particularly through technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data, cyber-physical systems, and advanced automation, while maintaining human-centric values and capabilities—is driving unprecedented changes in production, service delivery, and management patterns. This integration has not only enhanced organizational productivity and efficiency but has also challenged traditional concepts of work and physical presence. Under these circumstances, remote work has gained increasing attention as one of the most significant concepts in contemporary management and a novel approach to organizing job activities. Remote work is defined as performing job duties outside the organization’s central office, typically through digital environments and virtual communication. This work model became particularly prominent during the COVID-19 pandemic, emerging as an unavoidable necessity in many organizations, while simultaneously being recognized as an opportunity to increase flexibility, reduce operational costs, and improve employees’ work-life balance. Today, remote work is no longer an option—it is a necessity. However, the successful implementation of remote work requires careful attention to multiple factors spanning technological, organizational, psychological, and social dimensions.Research ObjectiveThe aim of this study is to examine the executive and structural requirements necessary for the effective utilization of remote work in organizations operating in the era of the Fifth Industrial Revolution. Given that remote work is no longer a temporary or limited option, but rather an established global and sustainable trend in the modern workplace, identifying its challenges and implementation necessities—particularly within the framework of Fifth Industrial Revolution concepts and human-centered approaches—has become a key priority in contemporary management. This research aims to provide a systematic and scientific framework to analyze the factors influencing the success or failure of remote work in organizations. It seeks to assist organizations in efficiently and sustainably managing this structural shift by identifying existing challenges and proposing practical solutions.Research MethodologyThis study employs Soft Systems Methodology (SSM) as its primary methodological framework. SSM is a qualitative, systems-thinking-based approach particularly suited to complex and multi-faceted situations where precise definitions and definitive solutions are absent. Positioned within the paradigm of Soft Operational Research (Soft OR), this method emphasizes the analysis of diverse stakeholder perspectives, human-centered problem definition, and the development of conceptual models to better understand socio-organizational systems. In this study, key SSM tools such as CATWOE analysis (Customers, Actors, Transformation process, Worldview, Owners, and Environmental constraints) were used to identify and analyze stakeholders, objectives, and limitations of the remote work system. This tool enabled the research team to examine the problem from multiple perspectives and formulate appropriate implementation strategies. Data collection was conducted through semi-structured interviews with nine experienced experts in the fields of human resource management, information technology, organizational psychology, and digital transformation. Participants were selected from governmental, private, and international organizations to ensure diverse viewpoints and comprehensive findings. The interviews were conducted in-depth, focusing on practical experiences, encountered challenges, and successful strategies in implementing remote work. Subsequently, qualitative content analysis was employed to extract conceptual categories and identify key patterns.Research FindingsThe findings indicate that the implementation of remote work in the era of the Fifth Industrial Revolution faces numerous challenges at individual, organizational, and technological levels. At the individual level, role conflicts, reduced work-life balance, and stress due to social isolation were identified as three primary challenges. Many employees experience longer working hours, job burnout, and feelings of loneliness due to the lack of separation between work and home environments. Additionally, the reduction in face-to-face interactions with colleagues leads to weakened organizational belonging and diminished motivation. At the technological level, infrastructural limitations such as lack of access to high-speed internet, weak cybersecurity measures, absence of intelligent tools for remote work management, and high costs of necessary equipment and software are among the main barriers to the productivity and effectiveness of remote work. Furthermore, the lack of smart systems in both work and home environments—such as automated time management systems, intelligent monitoring tools, and integrated communication platforms—negatively impacts employee performance. These technological gaps hinder seamless collaboration, reduce operational efficiency, and increase the risk of data breaches and work disruptions, particularly in organizations that rely heavily on real-time coordination and data sensitivity. At the organizational level, the absence of transparent and integrated remote work policies, inadequate support structures (such as psychological counseling, continuous training, and technical support), and insufficient tools for evaluating employee performance were identified as other significant challenges. Many organizations still rely on traditional performance evaluation systems based on physical presence, which are incompatible with the nature of remote work and can lead to perceptions of unfairness, reduced motivation, and diminished trust between employees and management. Without clear guidelines, measurable objectives, and outcome-based assessment mechanisms, remote work can result in ambiguity in roles, accountability issues, and inconsistent performance across teams.Discussion and ConclusionThe study demonstrates that remote work, as a global transformation in the world of work, despite its numerous challenges, holds high potential for improving employees' quality of life and enhancing organizational efficiency. The success of this transformation depends on organizations’ ability to adapt to new conditions, leverage emerging technologies, and foster a supportive and trust-based organizational culture. The recommendations derived from this research include the development of clear and comprehensive remote work policies, investment in technological infrastructure, strengthening psychological and social support systems, and designing performance evaluation frameworks based on outcomes rather than physical attendance. Such a model can serve as an operational framework for organizations to systematically identify gaps, set improvement goals, and implement targeted strategies for continuous enhancement of remote work systems. Ultimately, this research emphasizes that remote work is not merely a spatial shift—it represents a structural and cultural transformation that requires changes in mindsets, policies, and managerial systems. By adopting systemic approaches such as Soft Systems Methodology (SSM) and paying close attention to the diversity of stakeholder perspectives, organizations can turn this transformation into a strategic opportunity. This includes increasing operational flexibility, attracting and retaining skilled talent, improving employee well-being, and building resilience to future challenges in the evolving world of work. Remote work, when implemented thoughtfully and inclusively, can become a cornerstone of sustainable and human-centered organizational development in the era of the Fifth Industrial Revolution.
uncertainty
Maryam Tajik Khaveh; Maryam Daneshvar; Seyed Hossein Razavi Hajiagha
Abstract
Route selection in multimodal transport networks is a key issue in transport management and planning that requires advanced modeling and optimization approaches due to the multimodal nature and complexities arising from uncertainty. This study aims to develop a multi-objective mathematical model for ...
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Route selection in multimodal transport networks is a key issue in transport management and planning that requires advanced modeling and optimization approaches due to the multimodal nature and complexities arising from uncertainty. This study aims to develop a multi-objective mathematical model for optimal route selection in multimodal transportation networks, simultaneously minimizing transportation costs, carbon emissions, and delivery time deviations while preserving cargo value. This model, by considering time windows and uncertainty management, seeks to provide sustainable solutions to improve the transport system’s performance. In this model, transport capacity and demand are assumed to be fixed in each period, and costs and time are uncertain. The output of the model determines optimal routes and transport modes to achieve the defined objectives. Also, a robust optimization approach is used to manage uncertainty and provide a model that maintains its reliability even under uncertain conditions. In order to validate the model, a numerical example of a multimodal transportation network is solved using the goal programming approach. The results show that the proposed model, using robust optimization, has the necessary flexibility to adapt to changes and can help improve the quality of service and reduce operating costs. Also, using the robust optimization approach in a multimodal transportation network leads to increased resilience and network efficiency.IntroductionThe advancement of economic globalization and information technology has significantly facilitated global communication. A singular mode of transportation is insufficient to satisfy the demands of the transportation market, leading to the emergence of multimodal transportation (Peng et al., 2023). The route selection strategy of a multimodal transportation network is a complex multi-objective decision-making problem that has become a key aspect of multimodal transportation systems (Elbert et al., 2020).In this research, the multimodal transportation structure is a network structure with nodes (terminals) and edges (transportation) representing multiple modes of transportation. Also, in the research conducted, the objectives related to reducing travel time have been considered, while in the real world, arriving on time is preferable to arriving early. Hence, adding a time window to the objective functions is one of the innovations of this study.Considering uncertainty factors in the decision-making process is essential for designing optimal routes. Robust optimization, as one of the approaches in the field of uncertainty management, has the ability to provide models that enable better decision-making by maintaining stability and efficiency in uncertain conditions. In this study, a multi-objective robust optimization model is developed to minimize the total transportation cost, delivery delays, and carbon emissions while maintaining the value of perishable goods, considering the time window for timely arrival of goods.According to the above, the innovations of this paper are as follows:Adding time windows to objective functions.Defining the value function of perishable goods.Combining uncertainty in time and cost with the perishability factor.Considering uncertainty with a robust optimization approach. Research backgroundMultimodal freight transport means the transport of goods by at least two different modes of transport (UNECE, 2009). This type of transport ensures the efficiency of transport in terms of the timely availability of products and raw materials. This method usually involves a combination of land (truck, train), sea (ship), and air transport. The main feature of multimodal transport is that even if several modes of transport are used, the transport process is managed under a single contract or general responsibility, which helps to reduce delays, improve efficiency, and reduce transport risks. Other advantages of this transport method include cost reduction through the optimal use of different modes of transport, the possibility of using the fastest transport methods in specific conditions, and increased transparency and damage reduction through integrated management. The combination of methods can also help reduce energy consumption and greenhouse gas emissions. For a transport system to be efficient, it must be multimodal to meet different needs. Hence, the importance of multimodal transport lies in its ability to improve efficiency, reduce costs, and enhance delivery speed by strategically combining different modes of transport (Udomwannakhet et al., 2018).MethodologyThe main objective of this research is to develop a mathematical model for route selection in a multimodal transportation network. Therefore, this research is applied research conducted within the positivist paradigm. In order to collect research data, information related to multimodal transportation networks has been extracted from reports, databases, and scientific articles. Since the routing problem formulated in this paper is a multi-objective problem, weighted goal programming (GP) is used to solve it. To account for the uncertainty in the parameters of transportation cost and time, the Bertsimas and Sim robust optimization approach is applied, and a robust goal programming model has also been formulated. Discussion and ResultsTo verify the validity of the model, real data and numerical scenarios have been used. The results of solving the model show that the proposed model is able to provide optimal paths considering uncertainty and multiple objectives (cost reduction, carbon emission reduction, and product value preservation). Solving the model and specifying the values of the decision variables indicate the structural coherence and feasibility of the model. One of the main validation criteria in mathematical modeling is the ability of the model to provide a justified and optimal solution. In addition, the model results are consistent with the logic of the problem and the defined constraints. Also, the proposed model has been solved using the goal programming method, which is one of the valid methods for solving multi-objective optimization problems. Furthermore, the sensitivity analysis of key parameters shows that the model behaves stably in response to changes in input parameters and that its outputs are reasonable and reliable. ConclusionThe findings indicate that integrating multiple transportation modes and optimizing routing decisions can significantly reduce total costs. Furthermore, the incorporation of time windows and the reduction of delivery time deviation enhance customer satisfaction compared to conventional models. The study confirms that the robust optimization model can recommend routes that maintain high levels of stability and efficiency, even in the presence of uncertainty. The model also accounts for the perishability of goods, thereby contributing to waste reduction. Overall, the proposed model improves the performance of transportation systems under uncertain conditions, lowers costs, improves productivity, and offers a practical solution applicable across various industries.
Industrial management
Mahdiye Ghorbani; Hossein Sayyadi; salim karimi takalo
Abstract
The existence of challenges such as the continuous circulation of information, the active presence of governments in the online environment, ecological changes in work and lifestyle, and the problem of electronic wave pollution have made the role of governments in green management both essential and ...
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The existence of challenges such as the continuous circulation of information, the active presence of governments in the online environment, ecological changes in work and lifestyle, and the problem of electronic wave pollution have made the role of governments in green management both essential and inevitable. For this reason, along with accelerating the development of information technology in organizations, governments pay special attention to the protection of biological resources to ensure stakeholder trust in electronic services and adhere to environmental principles as a strategic approach. The present study was conducted with the aim of designing a model for green e-government. For this research, after reviewing the related literature and utilizing experts’ opinions, a snowball sampling survey was conducted among subject-matter experts, with a final sample size of 13 determined according to the rule of theoretical saturation. Forty-three components were identified within eight dimensions. The factors affecting the implementation of green e-government include citizen communication management, service delivery management, human resource management, process management, legal requirements, financial management, strategic management, and cultural management. Then, using the fuzzy cognitive map (FCM) methodology, the relationships among the factors were explained. In this process, the concept of cultural management had the highest influence (4.54), and citizen communication management had the highest susceptibility (4.49). The results also showed that strategic management, with the highest degree of centrality, is the most fundamental among key concepts. Therefore, focusing on this concept in creating green e-government is not only an undeniable necessity but also a vital requirement for managers and innovators in all areas of decision-making and planning. The proposed model provides an essential tool for policymakers to develop successful strategies for green e-government.IntroductionGlobal economic shifts and growing environmental challenges have pushed organizations and governments toward embracing sustainability and green initiatives (Espejo & Espinosa, 2015; Chofreh & Goni, 2017). Sustainable development aims to meet present needs without compromising the needs of future generations (Glasser, 2016). Today, improving efficiency, trust, service quality, and reducing corruption have become government priorities (Dwivedi et al., 2017). Global environmental crises such as pollution and loss of biodiversity have brought “green” issues to the forefront of public agendas (Too & Bajracharya, 2015; Laasch & Conaway, 2014). Alongside technological developments, the expansion of e-government has accelerated, yet it presents fresh environmental problems including e-waste, increased energy consumption, and digital pollution (Andreopoulou, 2012). To address these risks, implementing green strategies in government processes and managing electronic waste through green technologies are critical (Masud et al., 2012; Nurdin et al., 2022). The main challenge in Iran is the absence of a comprehensive framework that integrates environmental sustainability into e-government structures. Therefore, this study presents an applied, integrated model based on expert knowledge to support decision-makers in advancing green e-government practices.MethodologyThis research was conducted using a mixed-method design. After reviewing relevant literature, 13 academic experts in e-government and sustainability were selected by snowball sampling for interviews and surveys, reaching theoretical saturation. Important factors for green e-government were identified and validated, resulting in 43 components within eight main dimensions. Using Fuzzy Cognitive Mapping (FCM), the causal relationships and relative influence of these factors were analyzed. The process included scoring the importance of each dimension, converting these scores into fuzzy values, and constructing matrices that mapped the direct and indirect influence among factors. Analytical tools such as Excel, FCMapper, and Pajek were used for data management and visualization (Rodriguez-Repiso et al., 2007).ResultsThe findings of this research led to the identification of 43 major components classified into eight primary dimensions: citizen communication management, service delivery management, human resource management, process management, legal requirements, financial management, strategic management, and cultural management. Noteworthy components included environmentally-focused complaint handling, citizen engagement in green communications, empowerment of e-citizens for energy efficiency, green IT literacy, rapid response to environmental needs, employee training for green skills, awareness of environmental protection, readiness for green software, pollution control, resource adaptability, network and cybersecurity, and waste management. The legal, financial, and strategic dimensions included the development of supportive laws, financial incentives for environmental protection, green budgeting, convergent organizational structures and strategies, senior management support, and eco-friendly infrastructure. The cultural management dimension emphasized promoting a green culture, upholding ethical standards, and empowering culture for green digital services.Based on the expert assessments and the FCM model, cultural management was identified as the most influential factor (impact score: 4.54), and citizen communication management was the most susceptible (influenceability: 4.49). Among all, strategic management emerged as the most central factor in the cognitive map, indicating its fundamental bridging role in the system and its importance for practical policy and implementation (Rodriguez-Repiso et al., 2007). These findings underscore the need for an integrated managerial, legal, financial, strategic, and cultural approach to developing green e-government initiatives tailored to the national context (Schein, 2012; Schleager & Stepan, 2017).ConclusionThe research provides a structured understanding of the causal relationships among the multifaceted dimensions of green e-government. The results emphasize that successful implementation of green e-government in Iran demands special emphasis on cultural management—as the greatest driver of system-wide change—and strategic management, which facilitates effective alignment of policy and practice. Policymakers and managers must therefore devote particular attention to nurturing a green culture across government entities and building strategic capability for environmental policy integration. The fuzzy cognitive map approach has proven to be a robust method for unraveling complex, interdependent systems, enabling prioritization of interventions and formulation of actionable, context-aware strategies for sustainable digital governance.
modeling and simulation
Fariba Ahmadpanah; Abbas khamseh; Seyyed Javad Iranban Fard
Abstract
Nowadays, due to technological advances, many technology recipients act as technology providers, which has created new complexities in the technology transfer process. Therefore, this study seeks to answer the question of what the dimensions and components are that affect technology transfer from the ...
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Nowadays, due to technological advances, many technology recipients act as technology providers, which has created new complexities in the technology transfer process. Therefore, this study seeks to answer the question of what the dimensions and components are that affect technology transfer from the perspective of the transferor. In this study, Sandelowski and Barroso's meta-synthesis qualitative approach was used. During a systematic review of 188 initial articles, 33 articles related to the research objective were finally selected. The inclusion criteria comprised articles published from the perspective of the technology provider between 2008 and 2024. The kappa coefficient was used to assess the reliability of the findings, which showed an acceptable reliability of 0.75. To validate the proposed model and increase its acceptability, review methods were used by experts in this field. The results identified 127 components in 12 main dimensions. They are categorized into laws and policies, technology absorptive capacity, culture, economic factors, cooperation and communication, technology commercialization, human resources, training, intellectual property, technology adaptation, technology assessment, and technology absorption.IntroductionOver the past two decades, the trend of technology transfer has changed significantly. Emerging economies, such as Brazil and India, once primarily recipients of imported technologies, have emerged as key players in the global technology transfer landscape, increasingly acting as transferors (Lee, 2013). Despite extensive research on technology transfer, the literature has predominantly focused on the recipient’s perspective. This study aims to address this research gap by systematically identifying and categorizing the dimensions and components that impact technology transfer from the perspective of the transferor. By conducting a systematic literature review, this study develops a theoretical framework that not only lays the groundwork for future measurement tools but also ensures practical applicability in developing economies, such as Iran, through expert validation. This research provides a structured categorization of key factors, offering both theoretical insights and practical implications for improving policymaking in technology transfer initiatives.esearch BackgroundThe literature on technology transfer from the transferor’s perspective, though limited, provides critical insights into the factors influencing the process. Olakada et al. (2024) investigated the impact of technology transfer on the performance of Nigeria’s oil and gas sector through technology licensing, emphasizing dimensions such as the type of technology, licensing conditions, technical support, transparency, trust, and the institutional environment. Similarly, Sampson (2024) identified the risk of imitation, value chain type, contract type, intellectual property policies, technology development, and institutional frameworks as key determinants affecting technology transfer from the transferor’s viewpoint. In the pharmaceutical industry, Pajayatil et al. (2023) highlighted factors critical to successful technology transfer from the transferor’s perspective, including documented knowledge, employee training, analytical methods, collaboration and coordination, initial assessments and redesign, production and packaging methods, and equipment and machinery. Hipp et al. (2024), in their examination of barriers to technology transfer, underscored the influence of economic factors, international collaborations, knowledge production, accumulation, and dissemination on the transfer process.MethodThis study, aiming to identify the dimensions and components influencing technology transfer from the transferor’s perspective, employed an applied, qualitative approach based on systematic literature review and meta-synthesis, following the seven-stage model of Sandelowski and Barroso (2007). Data were collected from credible journals, databases, and institutional websites. The time frame covered English articles from 2008 to 2024 and Persian articles from 1398 to 1403. From an initial set of 188 articles, 59 were selected based on relevance and alignment with research objectives. These were evaluated using the 32-item COREQ checklist, leading to the selection of 33 high- and medium-quality articles for final analysis. To assess reliability, the kappa coefficient was applied, yielding a value of 0.75, indicating strong inter-rater agreement. Model validation involved a multi-stage expert review process. A panel of five experts—with master’s or doctoral degrees and over a decade of experience in technology transfer—was purposefully selected. Data were gathered through in-depth interviews across several sessions using triangulation, iterative reviews, and peer validation. Experts were chosen based on academic background and practical experience. Their feedback refined the conceptual framework, ensuring both reliability and contextual applicability, particularly in developing countries. Discussion and ResultsThis study aimed to identify the dimensions and components influencing technology transfer from the provider’s perspective, offering a comprehensive view of its complexity. Economic factors—such as export volume, inflation, and exchange rates—directly impact provider decisions by shaping financial frameworks and offering participation incentives. Supportive legal structures and transparent policies are also essential, as they foster trust and streamline bureaucratic processes. Organizational culture emerged as another key factor; unmanaged cultural differences between provider and recipient can hinder effectiveness. Strengthening mutual understanding and intercultural communication is thus vital. Additionally, the recipient’s absorptive capacity significantly affects success. Organizations with robust infrastructure and knowledge systems are better positioned to integrate new technologies. Qualified and continuously trained human resources are foundational for transferring both explicit and tacit knowledge. Effective commercialization is equally important; technology must have market viability and economic potential, necessitating solid marketing strategies and opportunity assessments. Intellectual property rights also play a central role—clear legal protections enhance provider willingness by minimizing risks of misuse or unauthorized replication. Technology assessment—including competitiveness, complexity, and applicability—is crucial for preparing technologies for transfer and reducing uncertainties. The provider actively participates in the absorption phase by delivering structured knowledge, including models, design elements, and technical components, aligned with contractual terms. Technological adaptation is another critical dimension. Aligning imported technologies with local standards, culture, and resources improves their effectiveness and sustainability. Finally, successful transfer depends on collaboration among governments, providers, recipients, research centers, and industries. Enhancing such cooperation supports knowledge sharing, process refinement, opportunity recognition, cost efficiency, and productivity growth.ConclusionThis study employed a meta-synthesis approach to explore the key dimensions and components influencing technology transfer from the provider’s perspective. Through the systematic review of 33 selected articles, 127 initial conceptual codes were extracted, leading to the identification of 12 core components forming a conceptual framework that clarifies the complex dynamics of the transfer process in both national and international contexts. The findings emphasize the crucial role of organizational culture, effective collaboration with recipients, and human capital development. These components, if optimized, can significantly enhance technology transfer initiatives within Iranian tech-based organizations. Policymakers can also draw on the conceptual insights to refine support structures and policies. Legal and regulatory frameworks, intellectual property rights, commercialization, and economic factors emerged as vital enablers. These dimensions help identify structural barriers and shape targeted interventions, particularly in cross-border technology transfer projects. While grounded in qualitative analysis and theoretical modeling, this framework offers practical applications—such as developing assessment checklists and operational indicators based on the identified components. However, the absence of empirical validation marks a key limitation. Future studies should employ field-based methods and structured techniques like fuzzy Delphi and agent-based simulation (e.g., using AnyLogic) to test and localize the framework. Additionally, since the components were not prioritized, multi-criteria decision-making methods like WASPAS are recommended to rank factors and guide the development of a more effective, context-specific operational model for technology transfer.
quality management
Ali Ebrahimi Kordlar; Hossein Safari; Helyeh Sadat Aghamiri; Fatemeh Sharifi Tabar; Mohsen Moradi Moghaddam
Abstract
In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational ...
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In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational capabilities of the Mobile Communications of Iran (MCI) have not been assessed using the latest EFQM model, this study aims to identify key capabilities and develop a mathematical optimization model. Using a descriptive survey with an applied purpose, the research targeted academic experts, excellence practitioners, and MCI specialists. First, a systematic literature review identified and categorized critical capabilities. Then, expert judgment and a fuzzy inference system modeled causal links between capabilities and the EFQM framework. Mathematical equations quantified each capability, forming an integrated model. A genetic algorithm was used to optimize parameters and determine the best capability combination. The study concludes with practical implementation recommendations and suggestions for future research.IntroductionIn today’s globalized and competitive environment, organizations face increasing competition and a dynamic external landscape. To survive and lead, organizations must differentiate themselves by creating a competitive advantage through innovation. This requires management excellence models that help organizations adapt to these changing conditions. The competitive environment, characterized by geographical dispersion and organizational innovation, demands unique capabilities known as dynamic capabilities, which help organizations create, expand, and maintain their core resources. The 2020 edition of the EFQM model, based on design thinking, has evolved from an assessment tool into a vital framework for addressing the changes and disruptions organizations face daily. Its strategic focus, combined with operational performance and a results-oriented approach, makes it an ideal framework for examining the alignment of an organization's ambitions. The aim of this article is to develop a mathematical model of organizational capabilities within the EFQM 2020 excellence model, helping organizations evaluate and improve their current performance.Materials and MethodsThis research is applied in nature with a comparative approach. It follows a quantitative methodology and is based on library research. The research strategy is survey-based, and from a goal perspective, it falls under the descriptive category. Data collection is conducted through interviews and questionnaires. Organizational capabilities are first extracted from scientific sources, then matched with the sub-criteria of the model by reviewing guidelines. Based on the findings, if-then rules and a fuzzy inference system are designed using MATLAB software.Meta-heuristic methods are used to solve complex optimization problems where classical optimization and heuristic methods are ineffective. Among these, the genetic algorithm is commonly used as a function optimizer. In this model, due to the complex, non-linear, and fuzzy relationships in the fuzzy inference system within the objective function, it can be compared to a neural network. The genetic algorithm is then applied to solve the model.ResultsThe desired capabilities for the fuzzy inference system are determined by specifying the capabilities of each criterion and sub-criterion of the EFQM model. A fuzzy inference system is defined for each of the 23 sub-criteria, and at the criterion level, the systems of the sub-criteria are combined. Sensing, learning, integration, coordination, and reconfiguration routines are used to measure the capabilities of the EFQM excellence model.This research focuses on MCI. By comparing the current values with the target and the scores obtained from the genetic algorithm, it is found that, within the budget limits, the desired goal can be achieved for 38 capabilities. For capabilities such as sensing, abduction, business model development, reporting, environmental management, networking, modeling, and social responsibility, the values fall within the target range. However, for three capabilities—organizational governance development, transformation management, and improvement—the target values fall outside the selected range. These differences are minor and can likely be ignored. The transformation management capability score (25.6) is close to the minimum value of 26, indicating that improvement is not feasible within the current budget for this sub-criterion. Increasing the budget could raise the score. The organizational governance development score differs by almost 4 points, which may be due to the fuzziness in scoring and inaccuracies in the budget values assigned to each sub-criterion.ConclusionOrganizational excellence models are generally frameworks that organizations use to develop a culture of excellence, and each model attempts to provide a set of management principles that are generally employed by organizations in their geographical areas of influence. Organizational resources and capabilities are the key success factors for the organization. In this research, using the fuzzy inference system, the combination of organizational capabilities in the sub-criteria of the EFQM 2020 excellence model was designed, and the mathematical model was developed using linear programming. Finally, a genetic meta-heuristic algorithm was used to solve the model. Each sub-criterion is a fuzzy inference system composed of the organizational capabilities related to it. A set of organizational capabilities makes up each of the sub-criteria of the excellence model, and we have a point limit for each capability. The budget limit defined in this model consists of the total budget dedicated to each organizational capability constituting the relevant sub-criterion. A case study was used to check the validity of the model and its practical application in an internal organization. In this research, the studied organization is MCI.
