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One of the crises that the world is going through today; TheCovid-19 pandemic has killed hundreds of thousands ofPeople around theWorld and caused manyEconomic losses. EpidemicDiseases, due toTheir negativeEffects, haveCalled countries toPrepare programs to eliminate these losses in all sectors. Therefore, theCurrent research was conducted with the aim of identifying different scenarios toDeal with crisesCaused byWidespread trends in the industry. In line with thisGoal, the presentStudy seeks toAnswer theQuestion "What scenariosAre there in order to deal with crises caused by the spread ofWidespread viruses, andHow can they be identified?" haveBeen. In terms of interpretive paradigm, inTerms of exploratory strategy, in terms of descriptive and prescriptive implementation, and based on the type of data collection, the current research was of the qualitative research type. The statistical population consisted of experts with experience and expertise in various industrial crises, especially in the field of widespread diseases. To select the experts, the snowball method was used and 26 experts were selected as sample members. To collect data, interviews and data available in the industry were used, and factor-based modeling was used to analyze information. The results of factor-based simulation were explained by implementing three scenarios (optimistic, probable and pessimistic). Based on the findings, what is common in all three scenarios is the confirmation of global economic recession, and the difference between the scenarios is often related to the duration of the recession and its waves. The most important consequence of the scenarios is the preparation to face different conditions and obtain appropriate solutions.

The purpose of this research is to design a model to evaluate the level of digitization of the innovation process centered on artificial intelligence in knowledge-based companies, so that the digital maturity of the innovation process in an organization can be measured. The results of 188 indicators were distributed in the form of a 5-point Likert questionnaire and by Delphi method in two times among 18 experts in this field. The result of the work was 5 components as input to the model, which was sent in the form of a questionnaire to 230 knowledge-based companies of Pardis Technology Park. 198 companies completed it and sent it back. From this number of samples, 150 data were separated for training data and 48 data as model test based on a random function. In the last stage, i.e. modeling, the adaptive neural-fuzzy inference method was used for the model. The method of grid separation or lookup table (PG) in MATLAB 2023 software was used to evaluate the performance of the model using root mean square, error (RMSE) and relative error (E). This research was able to provide an intelligent model with a very low error. As a result, it was able to achieve effective indicators in the degree of digitization of the innovation process.

Personalized Digital Marketing is one of the key and thriving trends in the world of digital marketing, developed with the aim of delivering unique and tailored experiences to each customer. In this approach, marketers strive to understand the needs, preferences, and behaviors of users by collecting and analyzing their data. Based on these insights, they design marketing content, advertisements, and offers that align specifically with each individual. The goal of this research is to identify the dimensions and components of personalized digital marketing through a recommendation engine using a thematic analysis approach. To achieve this, after reviewing the literature and research background, basic and organizing themes were identified. Then, through surveys and interviews with 25 experts and university professors familiar with the concepts of personalized digital marketing, a conceptual model of the research was introduced. In conclusion, practical suggestions were provided to IT and marketing managers in online stores, including Digikala.

The present study deals with the dynamic analysis of the performance of Iranian financial markets based on investment risk using a combined approach of social network analysis and system dynamics. For this purpose, first, critical risks in the financial markets identified and evaluated using social network analysis, and then the performance of each financial market based on investment risk examined and the system dynamics model based on critical risk interactions. Its effects on the performance of financial markets using the data of gold, land and construction financial markets, capital market, deposit in the design bank and after model validation, simulation in ten-year horizon (1408-1398) Done. According to the behavior of variables and model sensitivity analysis, financial market development policies including "capital market development", "liquidity reduction monetary policy", "land and building market organization" and "gold market organization" are identified and identified separately and A combination was applied to the model and the results were compared and the behavior was analyzed. According to the findings of the model, the combined policies of investment risk management are presented as the best policies for the development of productive financial markets. In general, considering the causal relationship between the risks of investment markets and identifying critical risks is provided by network analysis and testing financial and investment policies over time by system dynamics modeling.

Today, one of the requirements and foremost policies in research organizations is preparation for commercialization of research achievements. Commercialization is a multi-dimensional activity that requires a systematic approach in which stakeholders, functions, environmental conditions and other influencing factors in the commercialization process are considered. In addition to producing, transferring and promoting knowledge, agricultural research institutes are responsible for the mission of converting and exploiting knowledge, or commercialization. According to the relatively new approach of universities and domestic research institutes to the commercialization category, in many cases there is no clear understanding of the importance and necessity of commercialization among those engaged in agricultural research, and so far the process, organizational and organizational structure, and coherent executive mechanisms in the form of a model The requirement for the commercialization of agricultural research has not been defined. Based on this, the current article identifies the indicators and requirements affecting the commercialization ecosystem of agricultural research achievements. This research has been conducted through Sandelowski and Barso's metacombination method with a systematic review of 36 articles related to the purpose of the research out of 325 primary articles. The validity of the research was confirmed according to the entry criteria of holding meetings with the members of the research team, using an expert and auditing the entire process for theoretical consensus. Reliability was also confirmed through the critical evaluation skills program. The agricultural research commercialization ecosystem can be classified into three categories (willingness and ability to commercialize, commercialization leadership, and innovation and marketing).

Today, the research activities of knowledge-based companies are considered important sources of transformation in the fields of technology and knowledge production. The more advanced the level of these activities and the higher their technological complexity are, the more innovative and competitive products grow. The present study was conducted with the aim of providing a model for determining the level of technological complexity of research and development in knowledge-based companies. In terms of its purpose, this research is part of applied research, and regarding methodology, it is in the category of mixed research. In the qualitative section, the data was collected from the literature review and semi-structured interviews with 20 experts in the field of research and development. First, seven categories were extracted for the complexity levels of research and development, and the Frascati category (including basic research, applied research, and experimental development) was selected based on the experts’ opinions. Then, during three stages of coding (open, central and selective), the influential factors in determining the level of complexity were identified and the research model was formed. In the quantitative part, the data was collected from a sample of 290 people from the target statistical population (Golestan Science & Technology Park) through a researcher-made questionnaire. Then, the data was analyzed and model fit test using structural equation modeling method, was conducted. The data analysis was done using smart pls3 software. The results indicated a suitable fit of the data with the final model, including complexity levels and 91 effective indicators in determining complexity levels. This model can be a suitable tool for research and development departments of knowledge-based companies, in order to improve the level of research and development based on global standards and create transformation in the fields of technology and knowledge production and increase added value.

In this research, the researchers are looking for a mechanism for future-proofing digital governance in the direction of urban smartness with a sustainability approach based on scenario writing in Tehran. The research method is mixed in terms of how to check the data, and for that purpose both quantitative research strategies (in expert data) and qualitative method strategy (in interview content analysis) are used. In terms of the nature of the data, the current research uses both quantitative and qualitative methods. This article is a basic-applied research and is of exploratory type and its main goal is to identify the environmental drivers related to the subject of the research, so the research is of a fundamental type. At the same time, the achievements of that measure are included in urban management, especially in relevant organizations including municipality. Therefore, it is also considered practical. The statistical community of the research includes elites, managers and senior experts, whose opinions can be used in the field of digital governance and urban smartness with a sustainable approach. Findings based on the obtained results show that the first scenarios in the areas of intelligence, participation-oriented, transparency, structural measures, integration, culturalization and stabilization are the best situations and the sixth scenario and to some extent the fifth scenario show the worst possible situations. The second to fourth scenarios are mostly based on the least changes in the main factors and shows progress in only one factor and regression in one factor. The results of the research showed that capacity building to create the right to access information, increase law enforcement, discipline urban management mechanisms, strengthen internal platforms in order to network and secure information in the direction of urban smartness through the implementation of digital governance requirements.

Due to the innovations in the IT industry in recent years, the dependence on cloud computing technology has increased. Cloud computing has grown significantly in recent years and its adoption is a current infrastructure challenge for the software industry, as it creates a fundamental change in this industry. Despite the extensive benefits of cloud computing services, the adoption of these services by organizations remains limited and many software companies are hesitant to adopt this technology for various reasons. This study examines the factors affecting organizational decisions to adopt cloud computing using the technology-organization-environment framework.

This research utilizes a combination of methods, including the systematic literature review approach proposed by Kitchenham and Charters (2007), content analysis, and thematic analysis. A total of 26 articles published between 2014 and 2024 were selected. Supplementary data were collected through interviews with nine senior decision-makers specializing in software and cloud computing from leading Iranian software companies.

The results indicate that technological factors such as security, complexity, compatibility, and relative advantage; organizational factors including top management support, organizational size, and cost savings; and environmental factors such as competitive pressure, supportive policies, and external expertise are the most critical components influencing cloud computing adoption in Iran’s software industry. Balancing these factors is essential to accelerating digital transformation within organizations.

This study highlights the significance of addressing all technological, organizational, and environmental aspects by identifying and categorizing these factors. The findings suggest that organizations focusing on these elements can facilitate cloud computing adoption and achieve digital transformation. Special attention to supportive policies and infrastructure development can play a pivotal role in enhancing the productivity and competitiveness of Iran's software industry.

Today, the development of research and development activities that have a high level of technological complexity is considered an important and influential factor in the development and growth of the knowledge-based economy of developed countries; Therefore, the current research was conducted with the aim of identifying the effective components on the level of technological complexity of research and development activities. This research is in the category of applied research and in terms of methodology, it is in the category of qualitative research. With the approach of data-based theory, data was collected from studying documents and reviewing literature and conducting semi-structured interviews with 20 experts in the field of research and development. By analyzing the data, first 149 variables were extracted and during three stages of open, central and selective coding, the components affecting the level of technological complexity of research and development activities were identified, then the factor of obtaining advanced technology was identified as a central category. was selected and the rest of the factors were placed within the research structures (causal, background, intervening factors, strategies and consequences) and the research paradigm model was formed and five hypotheses were proposed. Based on the results of the research, 97 components (including 19 cases of causal factors, 16 cases of contextual factors, 12 cases of intervening factors, 11 cases of central subcategories, 23 cases of strategy and 16 cases of consequences) on the level of technological complexity of research activities and Development is influential.

The fashion and apparel industry has always been considered as one of the weighty element of societies from economic, social, cultural and environmental point of view. Today, companies due to the drastic rate of changes in the customers’ needs and lifestyle, need to introduce more new collections and speed up the luanch process. Therefore, this article is designed with the aim of determining the key success factors in the new product development process in the fashion and apparel industry. The research method designed based on qualitative approach and literature review, expert survey and Delphi methods were used to identify, prioritize and allocate key success factors to different stages of the NPD model. The research results show that there are 14 KSFs that must be taken into account through the different stages of the NPD process in the fashion and apparel industry, and three are three KSFs including: senior management support, organizational and production capabilities and cross-functional teams are more important among them. Finally, the findings of the research make it possible for the managers to change the arrangement of the KSFs based on the necessity of change in the NPD model according to the conditions of given organization.

  Financial supply is one of the key factors for the growth and survival of small and medium-sized enterprises (SMEs). This research focuses on identifying and prioritizing the components of financial supply for SMEs. To achieve this, an artificial neural network (ANN) is used as an advanced method. The study consists of three main phases. Firstly, through a review of the literature on the subject, the components of financial supply for SMEs were identified. Then, by using surveys and gathering data from experts and managers of SMEs in Iran, the necessary data for training the neural network were collected. Finally, by analyzing the data using the neural network, the components of financial supply for SMEs were determined based on their priorities. The results showed that the most important components of financial supply for SMEs, in order of priority, are: investment, internal financial supply methods, and external financial supply. This research can help SME managers and decision-makers choose the best financial supply strategies for the growth and development of their businesses. Additionally, the results of this research can assist policymakers and economic planners in identifying and implementing appropriate support policies for SMEs.

The purpose of this research is to identify the influential components in the use of new technologies for the media industry with a focus on digital transformation.

To achieve the research objective, all studies conducted between the years 2000 and 2022 on the research topic were examined using the meta-synthesis method based on the seven-stage approach of Sandelowski and Barroso (2007). By searching for keywords in domestic and foreign scientific databases, a total of 1753 sources were identified. The identified sources underwent multiple stages of screening, including examining publication dates and assessing compatibility of titles, abstracts, and full texts with the study objectives. Finally, 42 articles were selected as the final articles for the meta-synthesis.

Finally, eight effective components of big data infrastructure, digital literacy of managers and employees, innovation in technology and content, technological capabilities of new media, media communication management, semantics in the media industry, audience participation, and strategy Technology-based businesses were identified.

Deliberating on these components and paying attention and applying concepts related to them, especially paying attention to the dimensions of big data infrastructure in the space of digital transformation and innovation in technologies and digital content can be a guide and instruction for any media that It plans to implement the approach of digital transformation in its media organization through new media technologies.

Steel Supply Chain (SSC) is a interpretative segment in Steel's value-creation process for ensuring on-time delivery of the right quality of raw materials, Work In Process (WIP), other goods and services to manufacturing sites, and finished products to the ultimate consumers. Graph theory provides a real-time, end-to-end view of the Steel supply chain, from suppliers to customers. By representing the supply chain as a network of interconnected nodes, graph theory provides a visual representation of the relationships between nodes including: suppliers, manufacturers, distributors, and customers. The main purpose of this study is to focus on Iran’s Steel Supply Chain (ISSC) based on complex adaptive network analysis and graph theory. Data collection performed by steel industry monitoring reports produced by relevant organizations. By mapping the Iranian SSC to the network in the Gephi environment (0.9.2), the graph theory applied to analyze node-level and network-level indices. This hypothesis tested that the corresponding network of the target SSC contained a Complex Adaptive System (CAS) which is an inception to combining social science insights to develop systems-level models and insights that allow for phase transitions and emergent behavior.

With the passage of four generations of theoretical and experimental development of the smart city, it was expected that by relying on technologies such as the Internet of Things, artificial intelligence, cloud computing, and the like, this theory would be the way to face the problem and solve the complexities of resilience planning. But countless recent researches show the opposite. The root of the problem is not in the application of technology, but in the theoretical integration of smart city management and urban resilience. In the meantime, the resilience approach is a new point of view that instead of focusing on reducing vulnerability and looking at confrontation, it emphasizes increasing the resilience and flexibility of cities against natural and human hazards. The purpose of this research is to evaluate the feasibility of smart urban management to improve the resilience of Urmia city. To achieve this goal, a descriptive-analytical research method was used based on the analysis of a questionnaire that was completed by 30 urban experts and specialists. The results of the statistical analysis show that Urmia city is relatively favorable in terms of social and infrastructural resilience, but it does not have a favorable situation in terms of institutional-management, economic and physical-environmental resilience. In general, the overall resilience of Urmia city is lower than the optimal level according to the experts, so that the calculated value of the overall resilience of Urmia city is 2.90, which is less than the average level (3).

Today, in order to increase the efficiency of rail transport lines, the use of advanced high-speed line technology is recommended as a superior option compared to conventional railway technology. One of the fundamental challenges in the application and development of rail transport with new and advanced technologies is the lack of a suitable model for assessing technological capability. The present research is aimed at identifying key dimensions affecting the evaluation of technological capabilities in high-tech rail transport industries. The statistical community includes 57 managers and experts from companies active in the field of rail transport. In this study, 39 influential variables were identified and categorized into 7 dimensions by experts. After collecting data from the statistical community, the data was analyzed by modeling structural equations and smart pls software. The results show that 3 variables with a factor of less than 0.7 were removed from the model and the corrective model was formed with 36 indicators. The results of the principle of software output show that the 7 dimensions are significant at the 99% confidence level and affect the evaluation of technological capability in high-tech rail transport industries.

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decisionmaking problem in financial management. The proposed approach (Genetic Algorithm and Fuzzy Synthetic Evaluation) solves the problem in two stages. In the first stage، by using genetic algorithm and fuzzy synthetic evaluation، weight of criteria will be calculated. In second stage، using Fuzzy Synthetic Evaluation، Portfolios will be prioritized. A multi objective genetic algorithm is used to determine return and risk in the efficient frontier in Tehran stock market. In this research, we have used of firms’ performance between 1396-1400 in civil engineering, construction, investment and construction materials and tools manufacturers in order to determine portfolio selection. The main advantage of proposed approach is helping an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge

In recent years, the world has been engulfed in a crisis caused by a contagious disease called the coronavirus. The impact of this crisis is so significant that some researchers have also paid special attention to the post-coronavirus world. Therefore, the aim of this research is to identify the factors of sustainable value in using information technology for managing acute viral diseases.

The research method is mixed. In the qualitative section, the thematic analysis method has been employed. The reliability assessment of this section is also conducted using the method of comparison by two coders, resulting in a coefficient of 83%. In this section, interviews continued until theoretical saturation was reached, and 23 interviews were collected. In the quantitative section, structural equation modeling with a partial least squares approach using SmartPLS_3 software was employed to analyze the data, and a semi-structured questionnaire with a five-option Likert spectrum was distributed among 241 individuals. The research population includes healthcare professionals in medical sciences, faculty members in management and technology fields, as well as managers of knowledge-based companies active in medical technology in Tehran province. Simple random sampling using the Cochran formula is also employed in this section.

The results showed that in the area of pandemic management and control, organizational factors (with a coefficient of 0.41), control and supervision (with a coefficient of 0.28), and planning (with a coefficient of 0.43) were confirmed. In the sustainable value section, environmental factors (with a coefficient of 0.29), social factors (with a coefficient of 0.37), and economic factors (with a coefficient of 0.47) were confirmed.

Thus, the sustainable use of information technology can not only control pandemics but also prevent their consequences for future generations.

Scientific and technological developments, especially in the field of information and communication technology, have caused significant changes in people's daily lives, and remote work has been promoted as a new approach in managing work and performing activities, especially in the conditions created by the Corona pandemic. This study examines the concept of remote work in a special framework that includes the Corona period and the fifth industrial revolution and emphasizes the need for comprehensive scientific attention and innovative methods. To solve these challenges, this research presents a comprehensive model by applying the soft systems methodology (SSM), which takes into account the perspectives and limitations of different stakeholders and aims to increase the implementation of remote work. Using systematic methods and qualitative data analysis, this study develops the model in a flexible and comprehensive way. In addition, it highlights the role of technology, organizational culture and management strategies in reducing social isolation and increasing telework efficiency. The findings emphasize the dynamic aspect of the remote work ecosystem and emphasize the importance of multifaceted solutions for organizational success in the era of the fifth industrial revolution.

Global competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%.

Today, research and development activities are considered important sources of transformation in the fields of technology and knowledge production, and research and development departments of knowledge-based companies play an important role in innovation and improvement of products and services. The more advanced and fundamental the level of research activities of knowledge-based companies and the higher the level of technological complexity of these activities, the more innovative and competitive products are available to the domestic and global markets. turns In this regard, the present research was conducted with the aim of investigating the levels of technological complexity of research and development activities in knowledge-based companies. This research is an applied-developmental research in terms of its purpose and descriptive-analytical in terms of its nature and method. The statistical population of the research was all CEOs of knowledge-based companies located in Golestan Science and Technology Park, numbering 85 people. The sample size was considered to be 80 people using Morgan's table and more cautiously, and available sampling method was used. The tool used to collect research data is a researcher-made questionnaire, whose validity was confirmed by measuring face and content validity and its reliability using Cronbach's alpha method. The results of the research indicate that 65% of research and development activities in knowledge-based companies based in Golestan Science and Technology Park are experimental development, 31.2% are applied research and only 3.8%. The percentage is the type of basic research.

In recent years, due to the withdrawal of foreign companies from Iran's fashion and apparel market, it has been possible for domestic producers to operate in more attractive segments of the market, leave the position of cost leadership in the fashion market, and move towards segments with higher profit margins in the fashion industry. The attempt to cover the absence of foreign companies has caused many efforts to find the optimal process of introducing new products, reducing the failure rate of new designs and continuity in the new product development process by market leader companies.In this research, the discovering of the new product development pattern in the fashion and apparel industry is done employing the multi -case study methodology. In this research, based on information obtained from observations, notes, and manuscripts of interviews with senior executives of 9 leader companies in the fashion and apparel market and import this information into the Quirkos software, the initial codes were extracted. Then, using Braun and Clarke thematic analysis and open and axial coding, as well as controlling the initial codes based on the Keywords in context (KWIC) method, have been achieved to 46 concepts and 7 categories that, after re-arrangement and examining the story line, the seven stages pattern of new product development in the fashion and apparel industry proposed, including: design, product engineering, procurement, production, marketing, launch and product success monitoring stage. The details of each step of the pattern and compare its characteristics with the findings mentioned in the literature have been approved by selected experts in the sample and the validity of the model has been confirmed.

The present research was conducted with the aim of presenting a sustainable innovation development model in information technology startups based on grounded theory approach. The method used in the research is qualitative and based on grounded theory strategy. An in-depth interview tool was used to collect data.The target population was the managers and senior experts of startups who participated in the sustainable innovation process and decisions of information technology startups, using targeted sampling after 11 in-depth interviews, theoretical saturation was achieved. In the foundation's data approach, in this research, during three stages of open, central and selective coding, 24 general categories have been expressed in a paradigmatic format, including causal, contextual, intervening conditions, central categories, strategies and consequences.The results showed that the sustainable innovation development in information technology startups by causal conditions (risk tolerance, analysis of the business situation of the startup's sustainable innovative capabilities and technology absorption capacity), contextual (organizational structure of startups, market orientation and organizational readiness), intervening (infrastructures legal, managerial factors, behavioral patterns, environmental factors and economic uncertainty), core categories (open sustainable innovation, technological capabilities and optimization of innovation processes), strategies (development of digital technologies, strategic planning for startups, value creation of startups and co-creation) and consequences (competitive advantage, strategic intelligence, business model improvement, digital transformation and organizational effectiveness) are realized.

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA.