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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.

Tourism is widely acknowledged as a potential engine of economic growth and poverty reduction, particularly in developing countries. This research investigates the impact of tourist growth on poverty reduction in Wukari Local Government Area, Taraba State, Nigeria. The study concentrated on its contributions, problems, and ideas for increasing its pro-poor effect. The questionnaire was applied to obtain data from 400 respondents. The data were examined descriptively. The findings show a clear consensus that tourism may greatly help to poverty reduction, with job creation and income generation highlighted as main advantages. However, fundamental constraints such as inadequate infrastructure, poor security, and little government backing impede tourist expansion. Respondents significantly approve policies such as government infrastructure investment, enhanced security measures, entrepreneurial training, awareness campaigns, and community participation in tourist planning. The study indicates that tourism has significant potential for poverty reduction in Wukari LGA, and that focused interventions are required to solve its obstacles for long-term and inclusive growth.

Objective : The global increase in the elderly population has heightened the need for coordinated, tailored healthcare services that address the complex needs of older adults. This study aims to conceptualize the elderly healthcare ecosystem by identifying its key actors, classifying their roles, and examining the nature of their interactions. Methodology: A multi-stage methodological approach was employed. First, an extensive literature review—focusing on healthcare ecosystems and ageing studies—was conducted to develop an initial analytical framework. Based on this, ecosystem actors were identified and categorized using Mitchell et al.’s Stakeholder Salience Model. An expert panel was then consulted to validate actor attributes and refine classifications. To analyze interdependencies and determine influential actors, a Fuzzy Cognitive Map was constructed, enabling the assessment of causal relationships and the dynamic positioning of stakeholders within the ecosystem. Results : The analysis identified seven groups of actors within the healthcare ecosystem. FCM findings reveal that the elderly, families, and medical centers are the most influential actors. At the same time, the Ministry of Health and Medical Education, insurance and pension funds, and the Ministry of Cooperatives, Labor, and Social Welfare emerge as the most influential and central stakeholders in advancing ecosystem objectives. Conclusion : The study demonstrates that the elderly healthcare ecosystem is inherently dynamic, and stakeholder classifications should not be viewed as static. Attributes such as power, legitimacy, and urgency are fluid and context-dependent. The FCM results further highlight this dynamism by illustrating how shifts in causal relationships can reposition actors across stakeholder categories, underscoring the need for adaptive policymaking.

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.

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.

Social media has become a vital communication channel in various industries, including tourism and hospitality. This study aims to investigate the impact of social media—specifically user-generated content—on the occupancy rates of 5-star hotels in Mashhad, Iran. The research follows a three-stage methodology. First, customer reviews from Instagram fan pages of selected hotels were collected using the Graph API Explorer and analyzed through sentiment analysis to classify them into positive, negative, or neutral categories. Second, hotel occupancy was estimated using the number of weekly check-ins on Instagram as a proxy due to restricted access to official occupancy data. Finally, regression analysis was applied to examine the relationship between the percentage of positive reviews and the number of check-ins. The results reveal a strong positive correlation between favorable customer comments and hotel check-ins for most of the studied hotels, suggesting that social media plays a critical role in influencing consumer decision-making and hotel occupancy. The findings emphasize the strategic importance of leveraging social media platforms for effective marketing and customer engagement in the hospitality industry.

In this study, in which a new business model was constructed to examine the reflections of digitalization on the field of authentication due to the increasing importance of digitalization day by day. This paper aims to design a business model for digital transformation using Near-Field Communication (NFC) technology to develop foreign markets for original products by creating a product authentication database. Given the global use of NFC technology for authenticity checks and market development, this research is pioneering in proposing a business model for applying this approach to authenticate original products in Iran. Beyond product authentication, this approach can facilitate extensive market research, particularly in international markets, where many handicraft and clothing products are highly successful but often overlooked by industry owners. Following a description of Osterwalder’s business model, a business model canvas is developed, and service and sales revenue models are presented. The service revenue model includes two business strategies: "service provision per tap" and "annual subscription strategies (Bronze, Silver, and Gold)".

Purpose: In the 21st century, digital libraries have emerged in new ways in the professional career of librarians and other library users worldwide. The aim of the present study is to identify the functions of artificial intelligence in digital libraries. Method: This research is a qualitative systematic review. The population of the study includes all quantitative and qualitative research articles on the identification of artificial intelligence functions in digital libraries. Findings: A total of 500 research titles were retrieved through a multi-stage search in internal and external databases. Following a three-stage monitoring process (based on title, abstract, and full-text study), 34 research sources (9 foreign research titles and 25 domestic research titles) in the form of published journals articles and conference papers were identified and analyzed using thematic content analysis. For qualitative evaluation of the studies, 8 questions were posed to the experts, resulting in 1 selective code, 8 central codes and 42 open codes. Conclusion: The results of this research show that artificial intelligence can play an important role in the functioning of digital libraries and providing services to users. The findings demonstrate that AI-based systems are useful in various areas such as reference services, database search, organization, indexing, collection management, information retrieval, convenience and development, planning, and circulation. Moreover, librarians can enhance the quality of their works by employing this technology, which contributes to user satisfaction and the improvement of library services.

Purpose : This study systematically reviews the advantages and disadvantages of using ChatGPT in higher education. Method : This systematic review adheres to PRISMA guidelines. The search was conducted using the terms "ChatGPT" and "chatbot" for the years 2021–2024 in Google Scholar, ResearchGate, Web of Science, and PubMed via Publish or Perish (version 8). A total of 365 records were retrieved, and after screening, 35 relevant studies were included. Findings : The analysis indicate that 66% of the reviewed studies highlighted the benefits of ChatGPT in higher education, including enhancements in cognitive and learning skills, support for research and writing, improvements in language and communication, and automation of certain tasks to increase efficiency. Additionally, advantages such as 24/7 availability, quick responses, topic diversity, privacy, and easy access to past interactions were noted. Among the advantages that received the most attention are optimization of academic training, enhancement of cognitive and learning skills, and assistance in the research and development process. However, 75% of the studies discussed disadvantages, including concerns about plagiarism, ethical issues, negative perceptions, lack of audiovisual communication, absence of human interaction, technical limitations, and restricted multi-dimensional engagement.  Among the most prominent disadvantages are ethical concerns, plagiarism, and lack of uniformity in responses. Conclusion : While ChatGPT offers significant benefits in education, its limitations require careful consideration to ensure responsible and effective use. Specifically, to address these disadvantages, practical measures such as the development of policies and ethical guidelines should be implemented to ensure responsible and optimal use of this technology in education.

In this research, first the financial criteria used in capital decision-making were identified and refined, then the most effective criteria were selected based on the deep learning algorithms including: RF, XGBoost, and LightGBM. In this stage, 11 factors were selected from the 35 factors found in previous research. In the next stage, based on the Forensic-Based Investigation algorithm (FBI), feasible investment options were identified and the internal rate of return was calculated over a 5-year period, and 42 companies that had an internal rate of return higher than the risk-free investment were selected as feasible investment options. During the next stage, different random combinations were used as investment portfolios using three methods: equal weight allocation, mean-variance model, and hierarchical risk preference model. Investment weights were determined for each invested share (combination) and investment returns were evaluated using different metrics. Finally, in order to validate the findings, the feasible investment options were divided into two categories of companies active in the financial industry and others, and the superiority of decision-making (higher returns) in a dynamic process was accepted.

Objective : This study investigates how waste storage, waste quality, and market dynamicity influence the economic and environmental performance of industrial symbiosis networks in Iran’s food sector.  Methodology: A hybrid simulation approach, combining agent-based modeling and discrete event simulation, is employed to analyze the dynamics of industrial symbiosis networks in the food sector in Iran. This integrated method enables a detailed examination of how waste quality, storage duration, and market dynamicity jointly affect network performance. The model is implemented and simulated using AnyLogic software. Results : The simulation results demonstrate that effective management of waste storage is essential for improving the economic and environmental performance of industrial symbiosis networks in the food sector. Extending the storage duration allows firms to better align waste supply with demand, which is particularly valuable in volatile markets. However, the benefits of longer storage depend on waste quality: for high-quality waste, additional storage costs are offset by higher exchange values, while for low-quality waste, prolonged storage mainly increases costs and reduces profitability. The study also finds that waste storage strategies can substantially buffer the negative effects of market fluctuations. Conclusion : This paper advances circular economy research by presenting an analytical framework that integrates agent-based modeling and discrete event simulation to analyze industrial symbiosis networks. The findings suggest that managing storage duration can improve economic and environmental outcomes, while waste storage strategies help firms mitigate the negative impacts of market volatility. These insights can help managers and policymakers improve waste management in Iran’s food sector.