Management Research in Iran

Abstract The aim of this research is to develop a model of the consequences of digital tourism memorable experience in selected Iranian tourism destinations through destination appeal. The present study is applied in terms of purpose and mixed in terms of method. In the qualitative part, semi-structured interviews were used to collect data. The statistical population of this part consists of university professors, experts, and managers in the tourism industry. Judgmental and accessible sampling was used to identify experts, and the sample size was determined through theoretical saturation of 18 people. The results obtained from theme analysis and Max Quda software led to the identification of 135 codes that were classified into 18 categories. Also, in the quantitative part, 420 questionnaires were distributed among tourists who experienced the tourism destination through the digital platform, and the data were analyzed using SmartPLS and SPSS software. The results of the research led to the creation of a framework for the consequences of digital tourism memorable experience. Destination appeal is one of the most important antecedents of a memorable digital tourism experience, which can help tourism planning and tourism policy development, and has consequences for the tourist and the destination.

Abstract This article elucidates effective academia-industry linkage for developing technology-driven services. It proposes a comprehensive framework to enhance this interaction, positioning Business Intelligence (BI) as the core decision-making factor. In terms of classification, this study is applied and descriptive-survey, adopting a mixed-methods approach integrating qualitative and quantitative paradigms. During the qualitative phase, thematic analysis was employed, commencing with literature review and semi-structured interviews with subject-matter experts. Extracted data were categorized through open and selective coding. For the quantitative phase, a researcher-developed questionnaire was designed based on the identified components and distributed among 152 managers, specialists, and seasoned experts within the technology-focused industrial landscape. Instrument reliability was confirmed via Cronbach's alpha (0.93) and composite reliability, while convergent validity was demonstrated using Average Variance Extracted (AVE > 0.50). Quantitative data analysis was performed using Structural Equation Modeling with the Partial Least Squares approach (PLS-SEM) via Smart PLS 3.0. The empirical findings robustly indicate that six major determinants, at a significance level of 0.05 and with t-statistics exceeding 1.96, exert a very substantial influence on university-industry collaborations. These critical factors encompass Business Intelligence capabilities, Management Strategy, Structural elements, Cultural norms, Educational competencies, and Managerial-Financial resources. Collectively, they play an indispensable role in facilitating synergistic cooperation and accelerating the deployment of innovative technological services. The distinct innovation of this research resides in proposing a multidimensional model that places Business Intelligence at its conceptual core, while holistically encompassing six internal and external variables through an analytical-managerial lens to ultimately optimize strategic decision-making within the university-industry ecosystem.

Abstract The main objective of this research is to "improve production performance" in the spinning and single-ply yarn production section in the textile industry using artificial intelligence and appropriate machine learning algorithms and to apply an optimal algorithm to make the Business Intelligence in this industry. The data was analyzed by regression algorithms to improve the performance of yarn production with different fineness in the Worsted system and minimize stops, waste, and thus increase efficiency. The results show that machine learning models can successfully predict production performance improvement criteria in the textile industry to reduce waste and unplanned stops, thereby improving and increasing production efficiency in the single-ply yarn production stage in ring spinning machines in Guilan Spinning Company. In this study, the “Categorical Boosting Regression” model with R² Score and RMSE fitness indices of 0.938 and 22.182 respectively has the best accuracy and performance for data analysis and prediction. The advantage of using this model over other models is the reduced probability of overfitting, the ability to process batch data (classified) without the need for extensive preprocessing, high accuracy, fast processing speed, and less time for training. Accordingly, using the SHAP technique, it was determined that the most important factors affecting "production performance" based on their value in production performance are, respectively, yarn count, main cause of stoppage, amount of waste, duration of stops, and explanation of the main cause of stops. The main focus for “improving production performance” should be on “root causes of stop time” and “waste management” under production pressure conditions.

Abstract This study aimed to understand the lived meaning of managerial error from the perspective of managers in an Iranian public university. While existing literature has mostly treated error as a quantitative variable and examined it through typologies and individual, group, and organizational antecedents, less attention has been paid to how managers experience and make sense of error within specific historical–cultural contexts. Using a qualitative design and Gadamerian hermeneutic phenomenology, we conducted in-depth semi-structured interviews with managers of various units at Vali-Asr University and analyzed the data through the hermeneutic circle and interpretive thematic analysis. The findings showed that managerial error, in the participants’ horizon of understanding, is not an exception but a normal part of organizational life, emerging at the intersection of a patronage-based appointment structure, a culture of “Roodarbaisti” (face-saving), relational considerations, and weak evaluation and documentation systems. Managers simultaneously perceived error as a threat to reputation and trust, and as a potential opportunity for learning; however, in the absence of an error management culture, this positive potential largely remains at the level of individual experience and fails to translate into formal policies and procedures. This research demonstrates that the meaning of error in this context is the product of the interweaving of managers' interpretive horizons with the specific cultural and institutional contexts of the university under study. Accordingly, recommendations are offered for revising appointment procedures, establishing evidence-based evaluation systems, and creating institutional platforms for dialogue around error.

Abstract The aim of this study was to identify the dimensions and assessment models of professional qualifications in digital marketing jobs. This research employed a systematic review method, focusing on studies published in international databases between 2016 and 2025. Ultimately, eight selected articles were approved, analyzed, and synthesized. findings revealed that assessing soft skills—such as analytical thinking and problem-solving, communication skills, teamwork, management skills, personal attributes, and interpersonal abilities—plays a significant role in evaluating the professional qualifications of individuals in digital marketing roles. another key finding was that integrated models like DigComp are recognized as some of the most comprehensive frameworks for assessment in this field. In addition to general qualifications, DigComp covers five core areas: information and data literacy, communication, content creation, safety, and problem-solving. Furthermore, the 21st Century Skills Model, which includes learning skills, digital literacies, and life and career skills, along with performance-based situational questionnaires and assessments, can serve as effective, multidimensional tools for evaluating individuals’ qualifications in these occupations.

Abstract Development of key personnel, as one of the basic components of human resource management, plays a pivotal role in improving the efficiency, effectiveness, and learning of organizations. The aim of the present study is to design a talent development model in the Court of Accounts of the country. This study was conducted with an interpretive paradigm, a qualitative approach, a case study strategy, and a content analysis method. The research population included organizational experts in key positions who were identified and clustered based on the experts' opinions. To collect data, semi-structured interviews were conducted with 19 experts selected through purposive sampling, and the data were analyzed until theoretical saturation was reached. The findings showed that the talent development model in the Court of Accounts consists of four main parts: (1) goals and strategies, (2) what and areas of development, (3) how and mechanisms of development, and (4) individual and organizational results. The studies showed that key forces have been developed in some areas, such as personal and technical competencies, but for the future they also need to pay attention to other areas. Also, the experts emphasized the difference in mechanisms, level and implementation between key forces and other employees. The identified talent development mechanisms fall into four main categories: job-based, relationship-based, formal program-based and informal/informal mechanisms. This model can be used as a practical basis for designing and improving talent management programs in the Court of Accounts and similar organizations.

Abstract In recent years, alongside fundamental changes in the nature of the work environment at both international and domestic levels, as well as the rapid pace of change in employees' skills, the concept of "skill intelligence" has emerged as a data-driven and technology-oriented approach in scientific literature and organizational applications. However, existing studies have largely been conducted in a fragmented, siloed manner, focusing on specific aspects of skills, human resources, or technology, and this emerging topic still lacks a comprehensive, integrated, and organization-centered framework. This study aims to propose a comprehensive framework for skill intelligence systems in organizations by employing a systematic literature review method. To this end, scientific studies related to skill intelligence, skill management, skills analytics, and data-driven and AI-based systems in the field of talent management were systematically identified, screened, and analyzed. In the analysis phase, the extracted components were clustered using a structured approach, and the conceptual relationships among them were examined.

The findings led to the identification of key components of skill intelligence systems and their organization into a five-layer reference architecture. Furthermore, the proposed framework can serve as a theoretical and practical foundation for researchers and organizational managers in designing, evaluating, and developing skill intelligence systems.

Abstract This study aims to identify the barriers to implementing corporate governance in Iranian state-owned enterprises (SOEs) using the three-pronged model. Given the strategic role of SOEs in achieving national macroeconomic and social goals, and ongoing challenges in transparency, accountability, and managerial independence, a detailed analysis of these inhibiting factors is increasingly essential. The research employs a qualitative approach with thematic analysis as the main method. Fifteen academic experts and senior SOE executives were purposively selected and interviewed through semi-structured interviews. Data were systematically coded and classified into basic, organizing, and global themes.The findings reveal that barriers fall into three major categories: contextual factors (e.g., weaknesses in legal and regulatory frameworks, political interference, institutional misalignment, and political economy constraints); structural factors (including inefficiencies in governance bodies and committees, and deficiencies in risk management and financial reporting); and behavioral factors (such as conflicts of interest, resistance to change, non-professional managerial culture, and managerial instability). The interaction among these levels creates a cycle of inefficiency that reduces governance effectiveness in SOEs. Addressing these barriers requires an integrated, cross-sectoral approach. By offering a context-specific understanding of corporate governance barriers in Iran, this study provides a foundation for policymaking, regulatory design, and improving supervisory mechanisms in Iranian SOEs.

Abstract Purpose: This study develops and validates a strategic human resource development model tailored to the requirements of Iran’s aviation industries. Methodology: An applied-developmental, exploratory-validation mixed-method design was used. Initial strategies were derived from a targeted literature review and an analysis of human, technological, and safety challenges, and were refined with 60 managers, senior experts, and specialists. Fifteen experts then assessed model comprehensiveness, structural logic, and implementability using a nine-item questionnaire and open-ended responses. The formative model was tested using partial least squares structural equation modeling. Findings: Mean scores for comprehensiveness, structure and relationships, implementability, and overall evaluation were 4.65, 4.40, 4.20, and 4.42 out of 5, respectively. Convergent validity was 0.78, variance inflation factors ranged from 1.85 to 2.95, and all formative weights were significant at the 95% confidence level. R2 was 0.68 and SRMR was 0.072. Digital skill development and technology localization had the largest relative weights; however, approximate pairwise Wald tests did not confirm statistically distinct weights. Conclusion: The final model organizes ten strategies into foundational drivers, organizational enablers, and strategic outcomes. It offers an actionable framework for competency development, talent retention, burnout reduction, and technological capability building under the resource and safety constraints of specialized aviation organizations.

Abstract Product design in complex and dynamic environments requires an understanding of the simultaneous interactions among the problem, solution, knowledge, and stakeholders, while linear design models lack the capacity to explain such dynamics. Despite the development of the co-evolutionary design literature, there remains a gap in terms of an integrated framework for identifying and analyzing the relationships among the drivers affecting this process. Therefore, the present study was conducted with the aim of identifying, classifying, and modeling the key drivers of co-evolutionary product design. The research adopted a qualitative-developmental approach and was carried out in two stages. In the first stage, the key drivers were extracted and organized from the relevant literature using systematic meta-synthesis; in the second stage, the causal relationships and dynamics among them were modeled and analyzed using fuzzy cognitive mapping. The findings showed that co-evolutionary product design is grounded in the networked interaction of cognitive, adaptive, technological, and social drivers. Among these, the co-evolutionary process, adaptive mechanisms, and learning play the most significant role in the dynamics and performance of the system. Scenario analysis further demonstrated that the proposed model possesses an appropriate level of stability and resilience under different environmental conditions. By providing an integrated framework and dynamic modeling of the relationships among the drivers of co-evolutionary design, this study contributes to the advancement of the product design literature in complex and uncertain conditions and can serve as an analytical tool to support decision-making and innovation management in the design process.

Abstract The study attempted to identify key factors affecting the operational resilience of Iran's banking ecosystem, analyze the interrelationships between these factors, and present a systematic model for improving the resilience of banks in the face of cyber risks. The study is applied and mixed (qualitative-quantitative). In the qualitative part, data from semi-structured interviews analyzed using content analysis and coded with the MAXQDA tool. In the quantitative part, causal relationships between the identified factors analyzed using the Fuzzy Cognitive Mapping approach and modeled with the Mental Modeler tool. The statistical population of the study included digital banking experts, and sampling was purposeful and judgmental, and 20 people were interviewed until theoretical saturation was reached. The results of the present study showed that the operational resilience of Iran's banking ecosystem is subject to the complex interaction of technological, managerial, human, institutional, and cyber factors. Among them, cyber resilience, IT infrastructure, crisis management and organizational agility play the most important role in maintaining the sustainability and continuity of banking operations. The research also showed that the digital circular economy can play an important role in improving operational resilience by increasing the sustainability of infrastructure, optimizing resource consumption and improving the flexibility of systems. On the other hand, the findings indicate that operational resilience is not simply a technological issue, but human, managerial and institutional dimensions also play a fundamental role in it. Therefore, single-dimensional approaches to cyber risk management and banking resilience cannot respond to the complexities of the digital banking ecosystem.

Abstract Recent changes in consumer behavior and advancements in communication technologies have given rise to the concept of consumer activism, which indicates that today’s consumers play an active role in the market, can influence purchasing decisions and brand policies, and act as effective players in the consumption environment. The present study is designed based on a mixed-method approach, utilizing both qualitative and quantitative methods. This study falls within the deductive-inductive paradigm and is applied in terms of purpose, while being classified as exploratory research. The statistical population of the study consists of Digikala consumers in the city of Khorramabad. Sampling was conducted randomly, and based on the principle of theoretical saturation, 20 individuals were selected as the sample members. For data analysis in the qualitative section, content analysis and coding methods were employed using Maxqda software.For data analysis in the qualitative section, content analysis and coding methods were employed using Maxqda software.

Abstract This study analyzes the heterogeneous impacts of the crisis induced by the military attack against Iran in February 2026 on the economic activities of firms. The objective of this research is to classify businesses based on two dimensions: “the criticality of the product/service from the consumer’s perspective during crises” and “operational risk and supply chain vulnerability,” thereby proposing tailored managerial and policy solutions for each scenario. Methodologically, this study is applied in terms of objective and employs a multi-stage design with a predominantly qualitative approach, supplemented by a descriptive survey stage. Data were gathered through structured questionnaires and semi-structured interviews with participants possessing relevant experience and expertise. The findings indicate that, from the participants’ perspective, firms supplying essential goods and services with lower dependency on complex supply chains are in a more favorable position to maintain business continuity. Conversely, firms facing declining demand, transportation disruptions, and infrastructural instability exhibit higher operational vulnerability. Based on this classification, managerial strategies were developed across four distinct quadrants within the framework of three interpretive logics: “dynamic adaptability,” “risk decentralization,” and “social capital.” Furthermore, policy recommendations aligned with operational continuity, market adaptation, and the prevention of business cessation are proposed. The proposed framework can assist policymakers in targeting support mechanisms more effectively and guide managers in selecting adaptation strategies to mitigate operational vulnerability.

Abstract The developments of the last few decades in the economy, even capitalism and the crisis of 2007, show that it is impossible to talk about the withdrawal of the government in the development process. The level of government involvement in the development process is one of the important issues of the economy. The main goal of this research is to describe the way and scope of government intervention. The research method of this research is descriptive-analytical. Government production of goods and services, transfer payments, taxes and regulations are the four main channels of the government to influence economic activities. And the regulation of the government's optimal intervention based on investment, commercial and industrial policy has been investigated. Based on the results of the research, contrary to liberal beliefs that government intervention hinders development in East Asian countries, the conscious intervention of government agents is the main factor in the development of these countries, and the main focus in these countries is on the manner and scope of government intervention instead of interference or non-interference. In our country, the presence of the government should be comprehensive and focused on development. With the intervention of the government in the processes of development, regulation and guidance of the market mechanism, inadequacies can be eliminated and the economic system can be driven to a better situation in terms of resource allocation and economic capacity creation.