جستجوی مقالات مرتبط با کلیدواژه "n" در نشریات گروه "صنایع"

With the advancement of technology and rapid changes in the financial industry, electronic banking has emerged as an innovative solution for providing financial services and facilitating financial interactions. This research examines and presents a business model for electronic banking aimed at improving mutual collaboration with financial startups. Accordingly, this study utilized content analysis, interviews, and grounded theory methods for data collection and information gathering. In this phase, after four rounds of refinement, out of 79 studies, 64 were eliminated, and 15 research papers were selected for data analysis. Following the review of the theoretical and empirical content, the coding of interviews was conducted at three levels: open, axial, and selective coding. During the open coding phase, approximately 266 concepts were identified as initial concepts from the interview texts, which were categorized into 63 subcategories and 6 main categories. Based on the results, the proposed business model includes key elements such as value proposition, cost structure, revenue sources, and distribution channels. This model helps banks leverage the innovations of startups to provide better services to customers, while startups can also benefit from the existing infrastructure of banks. Ultimately, the findings of this research can contribute to enhancing inter-organizational collaboration and increasing competitiveness in the financial market.

This study aims to examine the factors affecting the issuance of Central Bank cryptocurrency (CBDC). This research was conducted using a mixed-methods approach (qualitative-quantitative). The qualitative section employed the grounded theory method through semi-structured in-depth interviews with 15 experts in the fields of payment systems and financial technologies, selected through purposeful and snowball sampling. In the qualitative phase, after three stages of coding, the affecting components were identified, comprising six main categories (axial, causal conditions, intervening conditions, contextual conditions, strategies, and outcomes), along with 37 subcategories and 190 concepts. In the quantitative section, to validate the categories derived from the qualitative phase, a fuzzy Delphi approach was utilized with input from a panel of experts. Additionally, to test hypotheses based on the validation of relationships among the categories, a Partial Least Squares (PLS) approach was employed. The statistical population for the quantitative research consisted of all experts, deputies, and managers at the central bank, from which 110 individuals in the payment systems department were selected as the research sample. The results of the quantitative phase confirmed the validity of the identified components. The findings of this research not only identify multiple factors affecting the issuance of token-based central bank digital currency but also affirm that the issuance of digital currency paves the way for its social acceptance, emphasizing the necessity for precise policymaking. Therefore, it is recommended that managers and policymakers at the central bank utilize the model presented in this study.

This study explores the co-creation of value in technology startups, emphasizing the role of customer engagement in identifying needs from the outset. The research follows a qualitative approach, employing meta-synthesis to analyze 48 selected studies from a review of 200 scientific articles. Additionally, the Shannon entropy method ranks the identified sub-categories. Key categories for value co-creation include product/service indicators, interactions, organization, customers/target market, and development actions, with 22 sub-indicators. The most influential factors are human resources and training, organizational structure, marketing and sales, product/service type, innovation and quality improvement, and customer relationships. Findings highlight that collaboration, knowledge sharing, and stakeholder engagement enhance value creation. These elements drive efficiency, innovation, and sustainable growth. Furthermore, strong networks with customers, partners, and institutions contribute to increased value for startups, emphasizing the importance of interactive relationships in developing high-quality, customer-centric offerings.

The tourism industry is a highly customer-centric sector where the quality of customer service plays a pivotal role in determining organizational success. Key Performance Indicators (KPIs) are critical metrics for evaluating and improving customer service standards. However, prioritizing these KPIs is challenging, given the complexity of customer expectations and service delivery frameworks. This research examines the criteria for prioritizing KPIs in customer service selection within the tourism industry. By systematically analyzing quantitative and qualitative methodologies, we aim to develop a robust framework for KPI prioritization. The study explores customer satisfaction, service efficiency, and loyalty as primary KPIs and assesses their impact on business performance. Findings from this study provide actionable insights for stakeholders in the tourism sector, enabling them to make informed decisions that align service goals with customer expectations. Recommendations for implementation and future research directions are also discussed.

Gamification is used in various fields as persuasive technology, especially in learning and education applications. Personal gamification changes or suggests the content of games and elements based on the specific characteristics of users. The purpose of this article is to create and implement a framework in personal gamification design in the field of data science learning, which uses recommender systems algorithms for the first time to improve data quality in these algorithms. This framework utilizes implicit and explicit voting in actual time and provides a dynamic and personalized environment for the enhancement quality of understanding data science learners. In this study, we developed a game environment to learn data science and its categories. Different elements of the game were considered for the challenges that existed in the process of learning. 680 students joined this system and were divided into 8 classes. After three months of users using the system, according to the collected logs and also the comments on the personalized gamification algorithm model, it was implemented by machine algorithms and suggestions were presented to the students on the site about the elements and content. With notice to root mean square error (RMSE) and mean square error (MSE) criteria, the singular value decomposition (SVD) algorithm had better results in recommender algorithms and was used in personalized gamification. The t-test and A/B test of this framework had positive effects

This study aimed to identify the indicators of human capital excellence in commercial banks of Iran. The research method was qualitative and exploratory, and in terms of purpose, it was developmental. The participants included bank managers (with at least 15 years of experience and a minimum master’s degree) and management scholars (with at least 10 years of experience and PhD). The sample size reached 11 individuals, selected through purposive sampling until theoretical saturation was achieved. Data collection was conducted via semi-structured interviews, and data analysis was performed using thematic analysis with MAXQDA software. The validity and reliability of the data were confirmed using various methods. Based on the study of the interviews, 262 initial codes were extracted in the open coding stage, which, after removing duplicate codes, were reduced to 118 core codes. Ultimately, these codes were categorized into 15 second-level sub-themes, six first-level sub-themes (character, employee well-being, competence, culture, leadership process, and human resources process), and two main themes (individual and organizational). Therefore, to succeed in developing human capital excellence, it is essential to consider the identified categories and their related concepts fully.

River ecosystems support biodiversity, regulate water resources, and foster tourism opportunities. This study examines the strategic management of the Talar River ecosystem and its impact on sustainable tourism development. Using a SWOT analysis, the research identifies key strengths, weaknesses, opportunities, and threats associated with regional tourism growth. Findings reveal that while the river holds significant potential for ecotourism and adventure tourism, water pollution, inadequate infrastructure, and climate change pose significant threats. The study proposes a TOWS-based strategic approach, integrating environmental conservation, infrastructure development, and community engagement to ensure sustainable tourism management. The results provide valuable insights for policymakers, environmentalists, and tourism developers seeking to balance economic growth with ecological preservation. This research contributes to the global discourse on sustainable river tourism by offering practical recommendations tailored to the Talar River’s unique ecological and tourism landscape.

This study aims to establish a comprehensive framework for integrating blockchain technology into the supply chain of Golrang Industrial Group. Adopting a qualitative methodology with a practical orientation, the research utilizes Strauss and Corbin's paradigm model for data-based theoretical exploration. The study's statistical population includes food industry factories affiliated with Golrang Industrial Group, with data collected through interviews with ten purposefully selected experts from the food industry and academia. Grounded analysis involving open, central, and selective coding was employed, resulting in a model comprising 54 indicators categorized into 19 concepts. The findings reveal that causal conditions include strategy design, goal setting, blockchain structure development, fostering inter-company cooperation, and financial and economic infrastructure provision. Key transformative factors involve industrial innovation, the Internet of Things, and artificial intelligence. Essential facilitators include employee training, continuous data updates, and skill-focused blockchain selection processes. Background conditions such as transformational leadership, standardization, legal frameworks, and scaling procedures are critical for optimization. These findings provide valuable insights into the potential benefits and challenges of blockchain integration in Golrang Industrial Group's supply chain, offering practical guidance for similar endeavors in the industry.

High-speed presses are critical in modern manufacturing but face challenges due to wear and unplanned downtime. This study introduces an innovative multi-objective framework integrating Multi-Objective Particle Swarm Optimization (MOPSO) with real-time reliability monitoring for preventive maintenance and repair scheduling. The model increases system reliability and minimizes total system costsover a defined operational horizon. It leverages Weibull reliability modeling to predict degradation and incorporates IoT-enabled data for dynamic updates. Decision variables, including preventive maintenance intervals and actions, are optimized while adhering to reliability thresholds. The proposed approach balances the trade-offs between frequent, costly preventive actions and higher risks of failure. A practical case study on a high-speed press demonstrates the framework's effectiveness, yielding a Pareto-optimal set of solutions that guide maintenance strategies. This research provides manufacturers with a flexible, data-driven tool to enhance uptime, reduce costs, and maintain operational excellence in competitive industrial environments.

Digital transformation is a key driver of growth and success in today’s competitive environment. This applied research follows a mixed-methods approach (qualitative-quantitative). In the qualitative phase, semi-structured interviews with experts were conducted, and data was coded using Max QDA, leading to the development of an initial model based on Strauss and Corbin’s framework. In the quantitative phase, the model’s relationships were evaluated using ISM methodology and analyzed with PLS software. The proposed model has five primary dimensions: human capital management, digital strategy development, sales and marketing, innovative production, and service development. These dimensions are linked to contextual elements such as regulations, culture, organizational structure, and value creation. The findings highlight that digital transformation, supported by advanced technologies, enhances output growth, productivity, cost reduction, and product quality in traditional industries, ensuring industrial development and sustainability.

Diabetes poses significant challenges due to its prevalence and the potential consequences of inaccurate or delayed diagnosis. This study focuses on enhancing prediction reliability to mitigate such risks. Initially, it identifies diabetes-related factors through correlation analysis with the target variable and implements models to address missing data. Subsequently, various imputation methods including CART, GMM, and RFR are employed to evaluate these factors. Results from each imputation scenario inform the selection of the most effective method. The study then employs ensemble algorithms like AdaBoost, Bagging, Gradient Boosting, and RF to enhance classification model accuracy. Further refinement is achieved by optimizing hyper-parameters through grid search. Evaluation involves comparing model predictions with those of medical professionals to assess accuracy. The findings reveal superior performance of optimized machine learning models over human predictions, indicating potential for improved diagnosis accuracy and reduced medical errors. This research contributes to advancing predictive modeling in diabetes diagnosis, offering prospects for enhanced community health and reduced socioeconomic burdens.

The present research aimed to design an agent-based simulation model of the service supply chain. In this respect, library studies were first conducted, and then the research gap was found. Suppliers were divided into suppliers of fast-moving consumer goods and slow-moving consumer goods, repairmen, and medicine suppliers, while services were divided into general, emergency, specialized, and nursing services, and patients were divided into people with insurance and those without insurance. Also, conditions of the service supply chain in this hospital were investigated and analyzed from different aspects. Next, this supply chain was implemented using NetLogo software, and the amount of unfulfilled demand and other cases were checked, and various weaknesses and gaps were shown. In the following, it was tried to decrease the existing gaps by developing different scenarios. Although the results of all the scenarios showed improved conditions, apparently, fast-moving consumer goods are the least affected by the decreased demand gap compared to the developed scenarios. In the maintenance section, the cost of preventive and in fact its increase can have the greatest effect. Regarding the increase of people covered by health insurance, the increase of insured people is a better scenario than reducing the treatment costs, and in fact, the reduction in treatment costs increase the number of people covered by health care services as it should.

Blockchain technology has emerged as a revolutionary tool for enhancing transparency, efficiency, and security across various industries, particularly in supply chain management. This study investigates the dimensions, components, and key indicators integral to a blockchain-based supply chain. Drawing on an extensive literature review and empirical analysis, the research highlights the transformative potential of blockchain in fostering trust, reducing costs, and ensuring traceability. The study employs qualitative and quantitative methods to explore the adoption barriers, implementation strategies, and critical success factors. Findings underscore the pivotal role of smart contracts, decentralized data sharing, and interoperability standards. This paper discusses implications for practitioners and policymakers, outlining future research avenues to optimize blockchain deployment in supply chains.

Portfolio optimization is a widely studied problem in financial engineering literature. Its objective is to effectively distribute capital among different assets to maximize returns and minimize the risk of losing capital. Although portfolio optimization has been extensively investigated, there has been limited focus on optimizing portfolios consisting of cryptocurrencies, which are rapidly growing and emerging markets. The cryptocurrency market has demonstrated significant growth over the past two decades, offering potential profits but also presenting heightened risks compared to traditional financial markets. This situation creates challenges in constructing portfolios, necessitating the development of new and improved risk management models for cryptocurrency funds. This paper utilizes a new risk measurement approach called Conditional Drawdown at Risk (CDaR) in constructing portfolios within high-risk financial markets. Traditionally, portfolio optimization has been approached under certain conditions, considering risk and profit as decision criteria. However, recent approaches have addressed uncertainty in the decision-making process. To contribute to the advancement of scientific knowledge in this field, this paper proposes a new mathematical formulation of CDaR based on a chance-constrained programming (CCP) approach for portfolio optimization. To demonstrate the effectiveness of the proposed model, a practical empirical case study is conducted using real-world market data from 10 months focused on cryptocurrencies. The results obtained from this model can provide valuable guidance in making investment decisions in high-risk financial markets.

In response to the dynamic requirements of contemporary businesses, this research delves into the imperative for organizations to optimize the efficiency, flexibility, and sustainability of their value chains, especially concerning energy consumption. The study introduces a multi-objective model meticulously designed to align efficiency, flexibility, and sustainability, aiming for a well-balanced and optimal energy consumption profile throughout the entire value chain. The optimization process employs multi-objective programming, with the overarching objective of maximizing minimum levels of flexibility, stability, and efficiency while minimizing the maximum energy consumption. Addressing the intricacies of large-scale multi-objective models, the research proposes a two-phase Multi-Objective Evolutionary Algorithm (MOEA), leveraging the strengths of NSGA-II and MOACO. The effectiveness of the proposed model is substantiated through a series of numerical experiments and sensitivity analyses, providing conclusive evidence of its capability to navigate the complexities of optimizing energy consumption in value chains. Furthermore, the performance of the proposed algorithm is affirmed through the examination of indicators such as generation gap (GD), high volume (HV), error ratio (ER), and non-dominant vector generation (ONVG). Hence, the presented model and solution algorithm are suitable for real-world problems.

The aim of this research is to present a model for valuing new industrial projects in Iran's automotive industry. In this regard, a mixed-methods approach (qualitative and quantitative) was utilized. The qualitative part of the research employed grounded theory and involved interviews with 15 professors, experts, and specialists in the fields of valuation and automotive industry, using purposeful sampling to design the conceptual model of the research. In the next phase (the quantitative phase of the research), a fuzzy Delphi approach was used to screen and validate or reject these categories. The results showed that all indicators received a score higher than 0.7, thus confirming all 37 identified sub-categories by the experts. Subsequently, the fuzzy TOPSIS approach was applied to prioritize each of the sub-categories within the framework of the main categories. The results of this section also indicated that in the causal factors section (idea generation), the highest scores were related to organizational business policies and customer needs assessment. In the contextual factors section, the highest scores were related to market structure and product structure, respectively. In the intervening factors section, prioritization was given, in order, to the company's capabilities, market and stakeholders, and laws and regulations. The main categories were prioritized as including product portfolio management and quality management. The actions and interactions of the model also included process screening indicators, product value engineering, financial screening, economic screening, and after-sales services. Finally, the outcomes prioritized included initial prototyping, initial market testing, and pilot production.

Blockchain is a technology that can be used in various organizations. Blockchain, through a decentralized computer network, leads to the facilitation of high-level transactions of organizations as well as their registration, in order to better respond to people's needs. In this research, a preliminary conceptual model consisting of the behavioral factors of blockchain technology acceptance in the banking industry, which is derived from theoretical literature and research background, is presented. Behavioral factors include facilitating conditions, attitude, literacy and skill, perceived risk, technology, perceived behavioral control, external motivation, internal motivation, competition and mental norm. Then, in order to check the fit of the model, structural equation modeling and smart pls software were used using a researcher-made questionnaire extracted from the research model. For this purpose, due to the unlimited size of the statistical population, 384 samples were randomly selected and the questionnaire was distributed among them. The result indicates that all the relationships are significant and the factors cause more than 80% of changes in technology adoption. In addition, agent-based modeling and Anylogic software have been used in order to predict changes in the adoption of blockchain technology over time, affected by the identified behavioral factors. The results showed that with the improvement of behavioral factors, the adoption of blockchain technology increases over time. In this study, insight is generated for key decision makers and relevant policy makers to propagate the adoption of blockchain technology in the banking industry.

One of the constant problems that people with mental health conditions are faced with now is that they cannot establish a good relationship with their therapist, or the client's disease type is not in the therapist's specialty. These clients may not receive adequate treatment and stop the therapy before feeling well. Therefore, the classification of mental patients based on their disorder types and allocating a therapist with the same expertise to them could lead to better treatment and improve the quality of the therapy sessions. This paper will compare several machine learning (ML) algorithms to classify patients with mental conditions. Moreover, benefiting from the best ML algorithm, patients will be categorized into different classes based on their disorder types. Finally, a mathematical model will be developed to determine the allocation policy of therapists to each group of patients to maximize the summation of the utilization between therapists and patients. To explore the implementation of the proposed method, we have conducted a real-life case study to assess the validation of the model.

Liquefaction of soil exposes buildings, bridges, and other vital infrastructure to structural failure, subsidence, and loss of bearing capacity during earthquakes. It can endanger safety and result in significant financial losses in seismically active areas. The objective of the present study is to identify the key factors influencing soil liquefaction is essential to improving the precision of risk assessment and mitigation plans in seismically active regions. An abrupt increase in pore water pressure leads to a significant fall in effective stress, which in turn causes a significant drop in shear strength. Soil liquefies, losing its ability to bear shear stresses and behaves as a fluid. To identify the key factors influencing soil liquefaction, an integrated Multi-Criteria Decision Making (MCDM) technique FUCOM-SVNN TOPSIS (Full Consistency Method (FUCOM), Single Valued Neutrosophic Number (SVNN), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)) has been developed. The study meticulously selects three key criteria and nine alternatives influenced by extensive literature review. Employing the FUCOM for establishing weights of criteria. FUCOM indicates that 'Seismic' is the most significant criteria. SVNN-TOPSIS applied for degerming the rank of alternatives. The results of SVNN-TOPSIS indicate that 'Peak Ground Acceleration' plays the crucial role in determining liquefaction potential. Validation of the proposed model by comparative study, statistical and sensitivity analysis, this methodology addresses the inherent uncertainties and interdependencies among various parameters, thereby enhancing the decision-making process related to seismic hazards.

Quality Management System (QMS) plays a crucial role in each company that aims to ‎gain a competitive advantage. ‎However, ‎the QMS implementation requires a ‎competent staff. Therefore, companies need a tool that will help them ‎select the ‎most ‎competent candidates for quality positions. To this aim, we have developed a ‎new fuzzy hybrid approach ‎based on the ‎integration of the 2-tuple linguistic ‎representation model with the Distance to the Ideal Alternative (DIA) ‎method, which ‎‎enables assessing candidates without distortion of the initial information. In this ‎article, we present an ‎illustrative example ‎of ranking candidates for a quality ‎coordinator position to demonstrate the validity and efficiency of ‎our approach. In ‎‎addition, we compare our approach with the most well-known and used ‎classification approach, ‎which is based on the use ‎of the TOPSIS method.