درخت حوزه‌های تخصصی

Purpose : This study aims to explore the development and implementation of knowledge management (KM) in Iranian government-affiliated research centers. The primary focus is on identifying key factors, challenges, and opportunities in adopting effective KM practices, with the goal of proposing strategic frameworks and practical recommendations to optimize KM processes in these organizations. Method : A qualitative research design was employed, utilizing a population of scientific articles on KM in Iranian research centers. Through purposive sampling, 55 articles were selected for analysis. The primary tool used was a “Document Review Checklist”. Data were analyzed using a systematic content analysis method, which facilitated the identification of recurring themes and patterns related to KM implementation. Findings : The study identified six global themes critical to KM implementation: Maturity, Knowledge-Based Decision Making, Structural Flexibility, Knowledge-Centricity, Socialization, and Localization of Cloud Computing. While managers and experts recognized the value of KM and demonstrated a willingness to establish relevant systems, significant barriers - such as limited IT infrastructure, insufficient incentives, cultural resistance, and a lack of comprehensive policies hindered effective implementation. The findings also highlighted the importance of fostering a knowledge-sharing culture, providing continuous training, and leveraging advanced technologies like Artificial Intelligence and localized cloud computing to enhance KM processes, data accessibility, and collaboration. Conclusion : The study concluded that nurturing an organizational culture that encourages knowledge sharing, providing adequate training and incentives, and adopting advanced technologies are essential for successful KM implementation. Additionally, leveraging international models and applying continuous improvement strategies can significantly enhance research capacities and elevate organizational performance. Addressing cultural, structural, and technological barriers is crucial for unlocking the full potential of KM in Iranian government-affiliated research centers, ultimately improving decision-making, innovation, and international collaboration.

The study was carried out to investigate effects of ChatGPT on 21st century skills of university students. Since there are several 21st century skills, the study focused on only five (i.e. critical thinking, communication, collaboration, creativity, and problem-solving skills). The study employed systematic review of literature in which PRISMA framework was employed to facilitate selection of studies included in the review. Findings revealed that ChatGPT poses effects to students on each of the 21st century skills. However, the type and magnitude of effects depends on the type of skill. ChatGPT was found to bring more positive impacts to university students’ communication skills while more negative impacts on collaboration skills. While that was the case, the study found that ChatGPT can bring either positive or negative impacts on critical thinking, creativity and problem-solving skills. The effects depend mainly on the magnitude of students’ dependency on it. Those that depend more on it are likely to be more negatively affected. For instance, the more the students depend on ChatGPT, the less the ability for them to think critically and solve problems. The study recommends, therefore, that universities should embrace ChatGPT instead of avoiding it. However, they should find ways to reduce students’ over-reliance on it, among other recommendations.

The aim of this research is to analyze and map international scientific publications related to Customer Lifetime Value (CLTV). This study adopts an interpretive paradigm and employs a descriptive approach using a systematic review method. By utilizing specific search terms in the Web of Science database, covering the period from 1985 to 2024, and after thorough screening and qualitative assessment of the studies, the final analysis was conducted on 639 articles. An in-depth examination of the selected articles revealed a notable increase in international research in this field, particularly during the last twenty years. However, there have been periods of decreased research activity in years such as 2008, 2017, and 2023. The primary focus of this research has been on customer lifetime value and customer segmentation, with a significant association to the keyword "data mining," highlighting the importance of this technique in the discipline. Moreover, it was found that countries like Iran, Canada, and Turkey have lower average citation rates, whereas the United States, France, and Germany exhibit higher average citation rates. This suggests different patterns of co-authorship among these countries. By examining the most and least productive countries and researchers through scientometrics, new research opportunities in the field of customer lifetime value can be identified, providing insights for Iranian researchers to enhance the visibility of their findings on an international scale.

The global demographic shift toward an aging population has driven the growth of senior tourism as a vital and expanding part of the tourism industry. This systematic review synthesizes empirical and theoretical studies published from 1980 to 2025, examining the main motivations, travel preferences, and challenges faced by senior tourists. Using the PRISMA protocol, 31 peer-reviewed studies were systematically identified and analyzed through thematic content analysis in NVivo software. The findings show that senior tourists are mainly motivated by intrinsic factors, such as relaxation, nostalgia, social engagement, and personal growth. At the same time, extrinsic motivators—like destination features and leisure activities—also play a role but are less explored. Preferences among senior travelers are quite diverse, influenced by age, health, socioeconomic status, and cultural background, and reflected in different choices regarding accommodation, travel timing, activities, and services needed. Key barriers to senior travel include physical and health limitations, financial constraints, psychological fears, and infrastructural issues, all of which affect participation and satisfaction. The literature indicates a regional focus on studies from Asia and Europe, with notable gaps in the Middle East and other underrepresented areas. Quantitative methods dominate, highlighting the need for more qualitative and mixed-method research to better understand the lived experiences and changing needs of senior tourists. This review provides an integrated thematic perspective that enhances understanding of senior tourism behavior and lays a foundation for creating more accessible and inclusive travel options that address the complex needs of aging populations.

Objective : This study aims to identify and prioritize cybersecurity risks associated with IoT applications in the retail sector, an area critical to digital transformation and operational resilience. Given the challenges managers face in evaluating threats under uncertainty, the study introduces a novel methodological framework to enhance risk-based decision-making and strategic resource allocation.  Methodology: A hybrid approach combining Pythagorean fuzzy SWARA (PF-SWARA) and an alternative ranking technique based on adaptive standardized intervals (PF-ARTASI) within the FMEA framework is proposed. PF-SWARA is used to weight evaluation criteria, and PF-ARTASI ranks the identified risks. The model is applied to a case study in Iran’s retail sector. Sensitivity and comparative analyses are conducted to validate the robustness and effectiveness of the method. Results : The findings show that "Insecure Firmware/Software and Inadequate Patch Management" is the top cybersecurity risk, followed by "Lack of Standardization and Interoperability Issues" and "Physical Security concerns". The proposed PF-SWARA–ARTASI approach outperforms traditional FMEA and PF-MOORA methods in terms of result consistency, robustness, and practicality under uncertain conditions. Conclusion : This research makes four contributions: (1) It proposes the first integration of PF-SWARA and PF-ARTASI within FMEA; (2) applies a novel ranking method for risk prioritization; (3) provides an actionable list of prioritized cybersecurity risks in IoT-enabled retail; and (4) validates the model through extensive sensitivity and comparative analysis. The study provides a valuable decision-making tool for IT managers and contributes to the existing literature on fuzzy risk assessment in retail contexts.

Forecasting financial variables, especially the returns of debt instruments, plays a vital role in economic decision-making and risk management. Although the forecasting literature in financial markets is extensive, few studies have focused on predicting the returns of Islamic Treasury Bonds with unconventional structures. Moreover, despite the importance of these bonds, very limited work has been done using machine learning in the debt market. This study aims to predict the returns of Islamic Treasury Bonds using three models: Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLP), and Radial Basis Function Neural Network (RBF). Monthly data from 2018 to 2023 were collected using Excel and Python. The training and evaluation of the models were carried out in MATLAB. Eleven influential variables were selected based on previous studies and expert opinions. The models' performance was evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The findings indicate that the Multilayer Perceptron Neural Network model has higher accuracy in predicting the returns of Islamic Treasury Bonds compared to Multiple Linear Regression and Radial Basis Function models. These results suggest that neural network models can serve as more effective tools in financial and economic analyses, significantly enhancing forecasting accuracy.

Purpose : The purpose of this paper was to critically analyze the textbook "Management of Digital Libraries: A Practical Guide" written by Mitra Samiei (2023) from Chapar Publications and evaluate it based on the standards and criteria of university textbooks. Method : This was an applied research study in terms of purpose and mixed-method in terms of approach, which used critical qualitative analysis and descriptive survey. A checklist was employed for qualitative evaluation of the content and formal structures of the textbook on a 5-point Likert scale. Findings : According to the analysis results, the textbook scored 54/70 (72%) for formal criteria, which was acceptable. In contrast, it scored 40/70 (53.33%) and 47/70 (67.14%) for structural and writing criteria, respectively, which were unacceptable. It scored 66/95 (69.47%, almost 70%) for content criteria. In general, the textbook under criticism can be suitable and practical for students as a work that collects all the materials available in digital libraries by solving the stated problems. Conclusion : Among the factors that can help improve the present work are modifying photo captions, the publisher's innovative cover design, footnotes for jargon, required persons and concepts, citations, glossary and index, and a list of tables and figures. Therefore, the author is recommended to provide practical examples and new instances, employ up-to-date sources, reduce textbook volume, and avoid duplicate content to improve the current work in the next editions. Also, among the strengths of this textbook is a separate chapter dedicated to digital librarianship, organizational structure, human resources in digital libraries, and the author's credibility and expertise.

Postmodern tourism is gradually shifting away from mass tourism toward more personalized, experience-based, and interest-driven forms of travel. In response to this trend, many destinations are increasingly emphasizing local cuisine and developing culinary tourism as one of the most dynamic and creative sectors of the tourism industry. However, the successful development of culinary tourism is not possible without a clear understanding of culinary tourists’ behavior. Accordingly, the aim of this study is to explain the behavior of product-oriented culinary tourists, the most specialized segment of culinary tourism, by developing a behavioral model using a mixed-method approach. In the qualitative phase, a meta-synthesis method was applied, while the quantitative phase employed structural equation modeling (SEM) using SmartPLS 4 to identify key dimensions, components, and indicators, as well as to examine the relationships among them. The sample size was determined using the Cochran formula and consisted of 285 participants, who were randomly selected from saffron culinary tourists visiting the Qaenat region. The findings indicate that tourists’ motivation, destination image, and perceived service quality have direct effects on satisfaction and indirect effects on behavioral intentions. Notably, perceived service quality also shows a direct and significant influence on behavioral intentions. Furthermore, the results highlight several key destination characteristics that strongly influence the quality of culinary tourism tours. These include cultural and culinary involvement, accommodation quality, farm-based experiences, food and beverage quality, reliable service delivery, a sense of safety and tranquillity, effective tour organization, and the quality of tour guide services .

The Neighborhood Rough Set (NRST) method is a valuable approach for selecting a subset of features from a complete dataset, enabling us to preserve the essential information that the entire feature set provides. In financial datasets, which often contain high-dimensional input features, effective feature selection techniques are crucial to identify the features that yield the most predictable results. In this work, we use neighborhood concepts to discover data dependencies and reduce the number of features in a financial dataset based solely on the data itself, without relying on additional information. This process also includes removing extra features. To facilitate a simple algorithm, we use the properties of neighbourhood rough sets to formulate a Binary Integer Linear Programming (BILP) model. Optimal solutions to these problems are obtained using genetic algorithms. Our approach allows for feature reduction from minimum to maximum cardinality. We demonstrate the efficiency of our proposed method compared to other techniques through various tables showing the results on several benchmark datasets characterized by unbalanced class distributions. The financial dataset used in the present study is taken from the UCI Machine Learning Repository.

Objective : In the competitive global retail industry, achieving sustainable competitive advantage is a key factor for long-term success. This advantage arises when companies effectively utilize their unique resources and capabilities to outperform competitors. Operational efficiency and financial performance are critical for evaluating competitiveness and investment attractiveness. Data Envelopment Analysis (DEA) is a standard method for measuring efficiency, but classical DEA cannot fully rank efficient units. Integrating DEA with multi-criteria decision-making (MCDM) methods addresses this limitation, considering investor-relevant financial ratios. This study proposes a two-stage approach to evaluate and rank retail companies comprehensively.  Methodology: In the first stage, an input-oriented CCR model of DEA is applied, with assets, operating expenses, and the number of employees as inputs, and total revenue and net profit as outputs, to assess relative efficiency. In the second stage, financial indicators—asset turnover, dividend yield, return on equity (ROE), return on assets (ROA), and return on investment (ROI)—alongside DEA efficiency scores are evaluated using the PROMETHEE II method to generate a complete preference-based ranking of retailers. Results : DEA in the first stage provides relative efficiency insights but cannot rank efficient units. Employing PROMETHEE II in the second stage, and considering financial ratios, overcomes this limitation and produces a comprehensive ranking. Validation against DEA, hybrid DEA–PROMETHEE II, and hybrid DEA–AHP rankings demonstrates a strong alignment of the results with the actual market positions of retailers. Conclusion : The proposed method enables investors to identify high-performing companies and provides retailers with a strategic tool to monitor competitiveness, identify strengths and weaknesses, optimize resource allocation, and achieve a sustainable competitive advantage.

Objective : This paper develops a single-objective and a bi-objective mixed-integer linear programming model to optimize the post-earthquake relief logistics network involving transfer points, hospitals, and relief centers in Tehran, Iran. The primary aim is to minimize the total time required to transfer injured individuals through the system, while the bi-objective model additionally minimizes penalties for failing to transfer casualties due to capacity shortages.  Methodology: The methodology involves formulating location-allocation models in which demand points, transfer points, hospitals, and relief centers are represented by specific capacity and travel-time parameters. The models are applied to two earthquake scenarios in south-central Tehran: a magnitude-6 event with lower casualties and selective facility activation, and a magnitude-7 event requiring full capacity utilization and a 30% assumed increase in hospital capacity. Results : The model’s effectiveness in optimizing the relief network is demonstrated. For the magnitude-6 scenario, the model selects 10 transfer points, 15 hospitals, and 25 relief centers to minimize total transfer time. For the magnitude-7 scenario, utilizing all available facilities, the model optimally allocates casualties despite severe capacity constraints. Conclusion : The proposed models offer a practical decision-support tool for designing efficient humanitarian supply chains in earthquake-prone urban areas. They underscore the necessity of pre-disaster planning, including establishing transfer points with triage and outpatient capabilities, increasing hospital surge capacity, and ensuring public awareness to direct casualties to designated transfer points.

Objective : This study addresses the critical gap in understanding how Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and Extended Reality (XR)—synergistically transform customer-centric supply chains in creative industries (e.g., music, fashion, film). It moves beyond fragmented analysis to develop a unified framework for customer-integrated value delivery. Methodology: A systematic meta-synthesis was conducted following established seven-stage protocols. A targeted search across Scopus and Web of Science (2016–2025) identified relevant literature. After stringent screening and quality appraisal (the Critical Appraisal Skills Programme (CASP) checklist), 59 high-quality publications were analyzed through iterative coding and thematic analysis. Results : The analysis produced a novel framework of five interdependent components: (1) Customer-Integrated Value Creation (e.g., AI co-design), (2) Omnichannel & Immersive Fulfillment (e.g., XR commerce), (3) Dynamic Value Capture Models (e.g., fan-driven financing), (4) Algorithmic & Gamified Community Engagement , and (5) Networked Co-Creation Ecosystems . The framework demonstrates how AI, IoT, blockchain, and XR interconnect to transform linear supply chains into agile, experiential, community-integrated value networks. Conclusion : This study presents the first synthesized framework for Industry 4.0-enabled, customer-centric supply chains in creative industries. It bridges technology, culture, and operations management, offering researchers a structured model for future inquiry and providing practitioners with a strategic roadmap for building responsive, community-driven operations. The research fills a significant literature gap and offers a blueprint for competitive advantage in the digital creative economy.

Purpose : The current study examined the effectiveness of gamified reading of electronic books among adolescents in school libraries. Method : A randomized sample of students aged 11 to 12 years including two control and experimental groups from four schools participated in this study. According to the Mechanics, Dynamics, and Emotions (MDE) framework, six gamification elements were implemented as group challenges. The experiences were then evaluated based on a quasi-experimental design with a post-test via the GAMEX scale. Findings : Multiple independent t-tests using SPSS 26.0 showed that the gamified experience and its relevant subscales including enjoyment, absorption, creative thinking, activation, absence of negative affect, and dominance differed significantly between the two groups. Therefore, the results revealed that implementing gamification in the reading experience within a gamified environment is highly effective and will influence adolescents' interest, motivation and ability to read in library contexts, which can be of interest to experts and policymakers in education and computer science. Conclusion : Various game mechanisms can be integrated into the educational context or platforms like electronic books to make learning interesting and motivating to the students.

Purpose : This research aims to develop an advertising policy model in digital marketing based on eye tracking. Method : The research method is qualitative and based on grounded theory. Semi-structured interviews were used to collect information, and data analysis was performed using the Strauss and Corbin method and the paradigm model. Sampling was theoretical sampling using targeted (judgmental) techniques, based on which 15 interviews were conducted with managers and marketing and advertising experts. Findings : The findings of the research show that during the process of open, central and selective coding, the advertising policy model based on the use of eye tracking consists of Causal categories that include Advertising content, Quality of environmental advertising and Promotional features; contextual categories consist of advertising slogans,  billboard elements, analysis of customer eye movements;  intervening categories consist of online advertising,  advertising costs, advertising through media and mass communication; Central categories that consist of environmental advertising status, development of advertising influence, effective advertising on customers' intentions; category of strategies includes advertising message attractions, evaluation of target environmental advertising selection process, advertising based on customers' taste; consequence category includes competitive advantage, currency innovation, Promotion of strategic marketing decisions. Conclusion : The study concludes that the eye tracking-based advertising policy model provides a comprehensive framework for enhancing advertising strategies. The model emphasizes the importance of considering various factors such as advertising content, consumer behavior, and contextual elements in developing effective advertising campaigns. The research highlights the potential of eye tracking in optimizing advertisements and gaining a competitive edge in the market. From a managerial perspective, the model offers actionable insights for marketers to improve customer engagement and increase advertising effectiveness by leveraging eye tracking data. It also suggests that advertisements should be tailored to consumer preferences and the context in which they are viewed. The study advocates for further research in this area to broaden the application of eye tracking technology in other marketing domains and service sectors. From an academic standpoint, this research contributes to the limited body of knowledge regarding environmental advertising and the use of eye tracking in marketing. It provides a foundation for future studies to explore the relationship between visual marketing and consumer behavior, offering insights that could foster innovative advertising strategies in the digital marketing field.

The motivations driving cash holdings have a profound influence on corporate decision-making and performance. Exploring the dynamics between risk-taking behaviour, cash reserves, and their adjustment pace provides valuable insights into effective financial resource management. This study examines the impact of corporate risk-taking on the adjustment speed of cash holdings, with a focus on the moderating effect of CEO tenure. A sample of 151 firms listed on the Tehran Stock Exchange from 2011 to 2023 (1,963 firm-year observations) was analysed using multiple regression and the Generalized Method of Moments (GMM) estimator. Results indicate that the adjustment speed of cash holdings is 49.5%. A significant negative relationship exists between corporate risk-taking and the speed of cash holdings adjustment, suggesting that elevated risk-taking decelerates the alignment of cash reserves with optimal levels. Moreover, the findings highlight the moderating role of CEO tenure in the relationship between corporate risk-taking and the speed of cash holdings adjustment; in other words, in firms with longer-tenured CEOs, the negative association between corporate risk-taking and cash holdings adjustment speed is weaker than in firms with shorter-tenured CEOs. These findings suggest that risk-taking hinders swift cash adjustment, necessitating a precise determination of optimal cash levels to prevent liquidity shortages in high-risk scenarios. Additionally, the experience of long-tenured CEOs appears to facilitate better liquidity management, aligning corporate interests with strategic financial goals.

In data-driven marketing, customer behavior analysis plays a crucial role in developing targeted marketing strategies aimed at increasing return on investment, enhancing profitability, and gaining a larger market share. In this study, four clustering methods- including K-means, density-based clustering, principal component analysis, and hierarchical clustering- as well as four classification methods- including Support Vector Machine, XGBoost, Random Forest, and Gradient Boosting- are examined for customer behavior analysis. The data for this study was extracted from the "Amazon Customer Behavior Survey" dataset, which includes 23 features from 602 customers. Initially, the data was preprocessed, and then, using clustering methods, customers were divided into different groups. The performance of these methods was evaluated based on criteria such as the silhouette index, and ultimately, appropriate marketing strategies for each cluster were proposed. Additionally, to examine the possibility of predicting customer membership in the extracted clusters, the aforementioned classification models were implemented and compared. The results indicate that the K-means method performed the best in clustering, while the XGBoost model performed the best in classification. The innovation of this research lies in combining clustering and classification methods to provide targeted marketing strategies and comprehensively comparing these methods on real customer data. This study demonstrates that combining clustering and classification methods can help businesses better understand customer behavior and make more optimal marketing decisions.